From c48e68de271ba7168b5447b7d37bb4d5537931c6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 21:50:06 -0700 Subject: [PATCH 001/195] fixing markdown link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eb283fa..c4ada19 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ pip install ISLP ### Windows -See the [https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line](python-packaging-instructions) for a simple way to run `pip` within +See the [python-packaging-instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within Jupyter. Alternatively, within a python shell, the following commands should install `ISLP`: From 876717e1fb8f59f635f760afe8936edba817df4a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 21:55:52 -0700 Subject: [PATCH 002/195] some work on readme --- README.md | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index c4ada19..29b1c3f 100644 --- a/README.md +++ b/README.md @@ -5,18 +5,34 @@ for ISLP. ## Install instructions +We generally recommend creating a [conda](https://anaconda.org) environment to isolate any code +from other dependencies. The `ISLP` package does not have unusual dependencies, but this is still +good practice. To create a conda environment in a Mac OS X or Linux environment run: + +```{python} +conda create --name islp +``` + +To run python code in this environment, you must activate it: + +```{python} +conda activate islp +``` + ### Mac OS X +Running in the desired environment, we use `pip` to install the `ISLP` package: + ```{python} pip install ISLP ``` ### Windows -See the [python-packaging-instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within -Jupyter. +See the [python packaging instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within +Jupyter or IPython. -Alternatively, within a python shell, the following commands should install `ISLP`: +Alternatively, within a python shell in the environment you want to install `ISLP`, the following commands should install `ISLP`: ```{python} import os, sys @@ -37,7 +53,7 @@ os.system(cmd) ## Documentation -See the [read the docs](https://islp.readthedocs.io/en/latest/models.html) +See the [read the docs page](https://islp.readthedocs.io/en/latest/models.html) for the latest documentation. From b8062d60008c421566c6b4a77bfaef359c8cc1f9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 22:03:04 -0700 Subject: [PATCH 003/195] updating docs, and syncing sources --- .readthedocs.yaml | 1 + docs/jupyterbook/datasets/Auto.ipynb | 4 +- docs/jupyterbook/datasets/Auto.md | 6 +- docs/jupyterbook/datasets/Bikeshare.ipynb | 4 +- docs/jupyterbook/datasets/Bikeshare.md | 6 +- docs/jupyterbook/datasets/Boston.ipynb | 4 +- docs/jupyterbook/datasets/Boston.md | 6 +- docs/jupyterbook/datasets/BrainCancer.ipynb | 4 +- docs/jupyterbook/datasets/BrainCancer.md | 6 +- docs/jupyterbook/datasets/Caravan.ipynb | 4 +- docs/jupyterbook/datasets/Caravan.md | 6 +- docs/jupyterbook/datasets/Carseats.ipynb | 4 +- docs/jupyterbook/datasets/Carseats.md | 6 +- docs/jupyterbook/datasets/College.ipynb | 4 +- docs/jupyterbook/datasets/College.md | 6 +- docs/jupyterbook/datasets/Credit.ipynb | 4 +- docs/jupyterbook/datasets/Credit.md | 6 +- docs/jupyterbook/datasets/Default.ipynb | 4 +- docs/jupyterbook/datasets/Default.md | 6 +- docs/jupyterbook/datasets/Fund.ipynb | 4 +- docs/jupyterbook/datasets/Fund.md | 6 +- docs/jupyterbook/datasets/Hitters.ipynb | 4 +- docs/jupyterbook/datasets/Hitters.md | 6 +- docs/jupyterbook/datasets/Khan.ipynb | 4 +- docs/jupyterbook/datasets/Khan.md | 6 +- docs/jupyterbook/datasets/NCI60.ipynb | 4 +- docs/jupyterbook/datasets/NCI60.md | 6 +- docs/jupyterbook/datasets/NYSE.ipynb | 4 +- docs/jupyterbook/datasets/NYSE.md | 6 +- docs/jupyterbook/datasets/OJ.ipynb | 4 +- docs/jupyterbook/datasets/OJ.md | 6 +- docs/jupyterbook/datasets/Portfolio.ipynb | 4 +- docs/jupyterbook/datasets/Portfolio.md | 6 +- docs/jupyterbook/datasets/Publication.ipynb | 4 +- docs/jupyterbook/datasets/Publication.md | 6 +- docs/jupyterbook/datasets/Smarket.ipynb | 4 +- docs/jupyterbook/datasets/Smarket.md | 6 +- docs/jupyterbook/datasets/USArrests.ipynb | 4 +- docs/jupyterbook/datasets/USArrests.md | 6 +- docs/jupyterbook/datasets/Wage.ipynb | 4 +- docs/jupyterbook/datasets/Wage.md | 6 +- docs/jupyterbook/datasets/Weekly.ipynb | 4 +- docs/jupyterbook/datasets/Weekly.md | 6 +- docs/jupyterbook/helpers/cluster.ipynb | 4 +- docs/jupyterbook/helpers/cluster.md | 6 +- docs/jupyterbook/helpers/pygam.ipynb | 4 +- docs/jupyterbook/helpers/pygam.md | 6 +- docs/jupyterbook/helpers/survival.ipynb | 4 +- docs/jupyterbook/helpers/survival.md | 6 +- docs/jupyterbook/helpers/svm.ipynb | 4 +- docs/jupyterbook/helpers/svm.md | 6 +- docs/jupyterbook/imdb.ipynb | 4 +- docs/jupyterbook/imdb.md | 6 +- docs/jupyterbook/models/derived.ipynb | 4 +- docs/jupyterbook/models/derived.md | 6 +- docs/jupyterbook/models/selection.ipynb | 4 +- docs/jupyterbook/models/selection.md | 6 +- docs/jupyterbook/models/submodels.ipynb | 4 +- docs/jupyterbook/models/submodels.md | 6 +- docs/jupyterbook/transforms/PCA.ipynb | 4 +- docs/jupyterbook/transforms/PCA.md | 6 +- docs/jupyterbook/transforms/poly.ipynb | 4 +- docs/jupyterbook/transforms/poly.md | 6 +- docs/jupyterbook/transforms/splines.ipynb | 4 +- docs/jupyterbook/transforms/splines.md | 6 +- environment.yml | 240 -------------------- 66 files changed, 161 insertions(+), 400 deletions(-) delete mode 100644 environment.yml diff --git a/.readthedocs.yaml b/.readthedocs.yaml index aacca4b..871a974 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -25,5 +25,6 @@ python: - requirements: requirements.txt - requirements: docs/requirements.txt - requirements: torch_requirements.txt + - requirements: keras_requirements.txt - method: pip path: . diff --git a/docs/jupyterbook/datasets/Auto.ipynb b/docs/jupyterbook/datasets/Auto.ipynb index f84fbfc..b88ea02 100644 --- a/docs/jupyterbook/datasets/Auto.ipynb +++ b/docs/jupyterbook/datasets/Auto.ipynb @@ -88,9 +88,9 @@ "formats": "source/datasets///ipynb,jupyterbook/datasets///md:myst,jupyterbook/datasets///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Auto.md b/docs/jupyterbook/datasets/Auto.md index fe851ed..627d70b 100644 --- a/docs/jupyterbook/datasets/Auto.md +++ b/docs/jupyterbook/datasets/Auto.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Auto Data diff --git a/docs/jupyterbook/datasets/Bikeshare.ipynb b/docs/jupyterbook/datasets/Bikeshare.ipynb index b0edebc..ddb1053 100644 --- a/docs/jupyterbook/datasets/Bikeshare.ipynb +++ b/docs/jupyterbook/datasets/Bikeshare.ipynb @@ -102,9 +102,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Bikeshare.md b/docs/jupyterbook/datasets/Bikeshare.md index 90e1f7f..380bc1b 100644 --- a/docs/jupyterbook/datasets/Bikeshare.md +++ b/docs/jupyterbook/datasets/Bikeshare.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Bike sharing data diff --git a/docs/jupyterbook/datasets/Boston.ipynb b/docs/jupyterbook/datasets/Boston.ipynb index 1b5dce0..569f5b4 100644 --- a/docs/jupyterbook/datasets/Boston.ipynb +++ b/docs/jupyterbook/datasets/Boston.ipynb @@ -95,9 +95,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Boston.md b/docs/jupyterbook/datasets/Boston.md index 60b6f5e..1146a86 100644 --- a/docs/jupyterbook/datasets/Boston.md +++ b/docs/jupyterbook/datasets/Boston.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Boston Data diff --git a/docs/jupyterbook/datasets/BrainCancer.ipynb b/docs/jupyterbook/datasets/BrainCancer.ipynb index fd8e84e..cb75946 100644 --- a/docs/jupyterbook/datasets/BrainCancer.ipynb +++ b/docs/jupyterbook/datasets/BrainCancer.ipynb @@ -95,9 +95,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/BrainCancer.md b/docs/jupyterbook/datasets/BrainCancer.md index 3e1a2be..7307a69 100644 --- a/docs/jupyterbook/datasets/BrainCancer.md +++ b/docs/jupyterbook/datasets/BrainCancer.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Brain Cancer Data diff --git a/docs/jupyterbook/datasets/Caravan.ipynb b/docs/jupyterbook/datasets/Caravan.ipynb index ad1af58..f093422 100644 --- a/docs/jupyterbook/datasets/Caravan.ipynb +++ b/docs/jupyterbook/datasets/Caravan.ipynb @@ -63,9 +63,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Caravan.md b/docs/jupyterbook/datasets/Caravan.md index a42ddb1..24f8335 100644 --- a/docs/jupyterbook/datasets/Caravan.md +++ b/docs/jupyterbook/datasets/Caravan.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Caravan diff --git a/docs/jupyterbook/datasets/Carseats.ipynb b/docs/jupyterbook/datasets/Carseats.ipynb index 911e767..dfd36d4 100644 --- a/docs/jupyterbook/datasets/Carseats.ipynb +++ b/docs/jupyterbook/datasets/Carseats.ipynb @@ -83,9 +83,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Carseats.md b/docs/jupyterbook/datasets/Carseats.md index 3c74d37..76f56e4 100644 --- a/docs/jupyterbook/datasets/Carseats.md +++ b/docs/jupyterbook/datasets/Carseats.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Sales of Child Car Seats diff --git a/docs/jupyterbook/datasets/College.ipynb b/docs/jupyterbook/datasets/College.ipynb index ef2f53d..af1027d 100644 --- a/docs/jupyterbook/datasets/College.ipynb +++ b/docs/jupyterbook/datasets/College.ipynb @@ -104,9 +104,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/College.md b/docs/jupyterbook/datasets/College.md index 5e2e422..95b0bb3 100644 --- a/docs/jupyterbook/datasets/College.md +++ b/docs/jupyterbook/datasets/College.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # U.S. News and World Report's College Data diff --git a/docs/jupyterbook/datasets/Credit.ipynb b/docs/jupyterbook/datasets/Credit.ipynb index c4c79b5..f5e51a9 100644 --- a/docs/jupyterbook/datasets/Credit.ipynb +++ b/docs/jupyterbook/datasets/Credit.ipynb @@ -89,9 +89,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Credit.md b/docs/jupyterbook/datasets/Credit.md index 36d2502..51de59d 100644 --- a/docs/jupyterbook/datasets/Credit.md +++ b/docs/jupyterbook/datasets/Credit.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Credit Card Balance Data diff --git a/docs/jupyterbook/datasets/Default.ipynb b/docs/jupyterbook/datasets/Default.ipynb index 4799474..64357ef 100644 --- a/docs/jupyterbook/datasets/Default.ipynb +++ b/docs/jupyterbook/datasets/Default.ipynb @@ -83,9 +83,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Default.md b/docs/jupyterbook/datasets/Default.md index f1c9acc..5aeaed2 100644 --- a/docs/jupyterbook/datasets/Default.md +++ b/docs/jupyterbook/datasets/Default.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Credit Card Default Data diff --git a/docs/jupyterbook/datasets/Fund.ipynb b/docs/jupyterbook/datasets/Fund.ipynb index 905528d..fce1859 100644 --- a/docs/jupyterbook/datasets/Fund.ipynb +++ b/docs/jupyterbook/datasets/Fund.ipynb @@ -51,9 +51,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Fund.md b/docs/jupyterbook/datasets/Fund.md index 4e53d4f..89009c2 100644 --- a/docs/jupyterbook/datasets/Fund.md +++ b/docs/jupyterbook/datasets/Fund.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Fund Manager Data diff --git a/docs/jupyterbook/datasets/Hitters.ipynb b/docs/jupyterbook/datasets/Hitters.ipynb index 295f50b..6f261cd 100644 --- a/docs/jupyterbook/datasets/Hitters.ipynb +++ b/docs/jupyterbook/datasets/Hitters.ipynb @@ -110,9 +110,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Hitters.md b/docs/jupyterbook/datasets/Hitters.md index 7f8d6b7..2fdecf0 100644 --- a/docs/jupyterbook/datasets/Hitters.md +++ b/docs/jupyterbook/datasets/Hitters.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Baseball Data diff --git a/docs/jupyterbook/datasets/Khan.ipynb b/docs/jupyterbook/datasets/Khan.ipynb index a1f89a4..f12a5ca 100644 --- a/docs/jupyterbook/datasets/Khan.ipynb +++ b/docs/jupyterbook/datasets/Khan.ipynb @@ -81,9 +81,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Khan.md b/docs/jupyterbook/datasets/Khan.md index f943e99..6f0c303 100644 --- a/docs/jupyterbook/datasets/Khan.md +++ b/docs/jupyterbook/datasets/Khan.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Khan Gene Data diff --git a/docs/jupyterbook/datasets/NCI60.ipynb b/docs/jupyterbook/datasets/NCI60.ipynb index d8e2aec..bbb576f 100644 --- a/docs/jupyterbook/datasets/NCI60.ipynb +++ b/docs/jupyterbook/datasets/NCI60.ipynb @@ -62,9 +62,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/NCI60.md b/docs/jupyterbook/datasets/NCI60.md index 4cc96c6..621445e 100644 --- a/docs/jupyterbook/datasets/NCI60.md +++ b/docs/jupyterbook/datasets/NCI60.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # NCI 60 Data diff --git a/docs/jupyterbook/datasets/NYSE.ipynb b/docs/jupyterbook/datasets/NYSE.ipynb index d884201..5f9dbd5 100644 --- a/docs/jupyterbook/datasets/NYSE.ipynb +++ b/docs/jupyterbook/datasets/NYSE.ipynb @@ -79,9 +79,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/NYSE.md b/docs/jupyterbook/datasets/NYSE.md index a84a9d4..bdb9581 100644 --- a/docs/jupyterbook/datasets/NYSE.md +++ b/docs/jupyterbook/datasets/NYSE.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # New York Stock Exchange Data diff --git a/docs/jupyterbook/datasets/OJ.ipynb b/docs/jupyterbook/datasets/OJ.ipynb index 30046cb..e18a4de 100644 --- a/docs/jupyterbook/datasets/OJ.ipynb +++ b/docs/jupyterbook/datasets/OJ.ipynb @@ -107,9 +107,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/OJ.md b/docs/jupyterbook/datasets/OJ.md index 8681ea9..94fd7c6 100644 --- a/docs/jupyterbook/datasets/OJ.md +++ b/docs/jupyterbook/datasets/OJ.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Orange Juice Data diff --git a/docs/jupyterbook/datasets/Portfolio.ipynb b/docs/jupyterbook/datasets/Portfolio.ipynb index 0596162..6d6a60d 100644 --- a/docs/jupyterbook/datasets/Portfolio.ipynb +++ b/docs/jupyterbook/datasets/Portfolio.ipynb @@ -68,9 +68,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Portfolio.md b/docs/jupyterbook/datasets/Portfolio.md index e130b81..3a79d35 100644 --- a/docs/jupyterbook/datasets/Portfolio.md +++ b/docs/jupyterbook/datasets/Portfolio.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Portfolio Data diff --git a/docs/jupyterbook/datasets/Publication.ipynb b/docs/jupyterbook/datasets/Publication.ipynb index c97b201..a4a6dfa 100644 --- a/docs/jupyterbook/datasets/Publication.ipynb +++ b/docs/jupyterbook/datasets/Publication.ipynb @@ -91,9 +91,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Publication.md b/docs/jupyterbook/datasets/Publication.md index 94c18bd..78261af 100644 --- a/docs/jupyterbook/datasets/Publication.md +++ b/docs/jupyterbook/datasets/Publication.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Time-to-Publication Data diff --git a/docs/jupyterbook/datasets/Smarket.ipynb b/docs/jupyterbook/datasets/Smarket.ipynb index 35a1918..cced2a9 100644 --- a/docs/jupyterbook/datasets/Smarket.ipynb +++ b/docs/jupyterbook/datasets/Smarket.ipynb @@ -87,9 +87,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Smarket.md b/docs/jupyterbook/datasets/Smarket.md index a42c94e..2c0e120 100644 --- a/docs/jupyterbook/datasets/Smarket.md +++ b/docs/jupyterbook/datasets/Smarket.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # S&P Stock Market Data diff --git a/docs/jupyterbook/datasets/USArrests.ipynb b/docs/jupyterbook/datasets/USArrests.ipynb index 4a6a1c0..1107424 100644 --- a/docs/jupyterbook/datasets/USArrests.ipynb +++ b/docs/jupyterbook/datasets/USArrests.ipynb @@ -202,9 +202,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/datasets/USArrests.md b/docs/jupyterbook/datasets/USArrests.md index 7cbede1..ee3c962 100644 --- a/docs/jupyterbook/datasets/USArrests.md +++ b/docs/jupyterbook/datasets/USArrests.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Violent Crime Rates by US State diff --git a/docs/jupyterbook/datasets/Wage.ipynb b/docs/jupyterbook/datasets/Wage.ipynb index ad8f9b0..b95d853 100644 --- a/docs/jupyterbook/datasets/Wage.ipynb +++ b/docs/jupyterbook/datasets/Wage.ipynb @@ -99,9 +99,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Wage.md b/docs/jupyterbook/datasets/Wage.md index eeeb3c4..fd22e30 100644 --- a/docs/jupyterbook/datasets/Wage.md +++ b/docs/jupyterbook/datasets/Wage.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Mid-Atlantic Wage Data diff --git a/docs/jupyterbook/datasets/Weekly.ipynb b/docs/jupyterbook/datasets/Weekly.ipynb index cf08b80..69f26d6 100644 --- a/docs/jupyterbook/datasets/Weekly.ipynb +++ b/docs/jupyterbook/datasets/Weekly.ipynb @@ -95,9 +95,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/datasets/Weekly.md b/docs/jupyterbook/datasets/Weekly.md index c0639ea..c239c5e 100644 --- a/docs/jupyterbook/datasets/Weekly.md +++ b/docs/jupyterbook/datasets/Weekly.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Weekly S&P Stock Market Data diff --git a/docs/jupyterbook/helpers/cluster.ipynb b/docs/jupyterbook/helpers/cluster.ipynb index bf237a3..56cf3d8 100644 --- a/docs/jupyterbook/helpers/cluster.ipynb +++ b/docs/jupyterbook/helpers/cluster.ipynb @@ -101,9 +101,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/helpers/cluster.md b/docs/jupyterbook/helpers/cluster.md index ab31348..ab448dc 100644 --- a/docs/jupyterbook/helpers/cluster.md +++ b/docs/jupyterbook/helpers/cluster.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Clustering diff --git a/docs/jupyterbook/helpers/pygam.ipynb b/docs/jupyterbook/helpers/pygam.ipynb index 01a1e55..aab61d1 100644 --- a/docs/jupyterbook/helpers/pygam.ipynb +++ b/docs/jupyterbook/helpers/pygam.ipynb @@ -207,9 +207,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/helpers/pygam.md b/docs/jupyterbook/helpers/pygam.md index c91084c..56adc84 100644 --- a/docs/jupyterbook/helpers/pygam.md +++ b/docs/jupyterbook/helpers/pygam.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Generalized Additive Models diff --git a/docs/jupyterbook/helpers/survival.ipynb b/docs/jupyterbook/helpers/survival.ipynb index e6b9e3a..7cb30a3 100644 --- a/docs/jupyterbook/helpers/survival.ipynb +++ b/docs/jupyterbook/helpers/survival.ipynb @@ -108,9 +108,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/helpers/survival.md b/docs/jupyterbook/helpers/survival.md index 715f8bd..58b129d 100644 --- a/docs/jupyterbook/helpers/survival.md +++ b/docs/jupyterbook/helpers/survival.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Survival Analysis diff --git a/docs/jupyterbook/helpers/svm.ipynb b/docs/jupyterbook/helpers/svm.ipynb index dac6c39..593d840 100644 --- a/docs/jupyterbook/helpers/svm.ipynb +++ b/docs/jupyterbook/helpers/svm.ipynb @@ -103,9 +103,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/helpers/svm.md b/docs/jupyterbook/helpers/svm.md index 007eb7a..3025490 100644 --- a/docs/jupyterbook/helpers/svm.md +++ b/docs/jupyterbook/helpers/svm.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Support Vector Machines diff --git a/docs/jupyterbook/imdb.ipynb b/docs/jupyterbook/imdb.ipynb index d490921..1718a58 100644 --- a/docs/jupyterbook/imdb.ipynb +++ b/docs/jupyterbook/imdb.ipynb @@ -249,9 +249,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" } }, "nbformat": 4, diff --git a/docs/jupyterbook/imdb.md b/docs/jupyterbook/imdb.md index 313952f..ae67f68 100644 --- a/docs/jupyterbook/imdb.md +++ b/docs/jupyterbook/imdb.md @@ -7,11 +7,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Creating a clean IMDB dataset diff --git a/docs/jupyterbook/models/derived.ipynb b/docs/jupyterbook/models/derived.ipynb index 92fc096..cc1b0ac 100644 --- a/docs/jupyterbook/models/derived.ipynb +++ b/docs/jupyterbook/models/derived.ipynb @@ -2103,9 +2103,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/derived.md b/docs/jupyterbook/models/derived.md index 1d0f23b..9710500 100644 --- a/docs/jupyterbook/models/derived.md +++ b/docs/jupyterbook/models/derived.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Building design matrices with `ModelSpec` diff --git a/docs/jupyterbook/models/selection.ipynb b/docs/jupyterbook/models/selection.ipynb index b41cf6a..3a7d002 100644 --- a/docs/jupyterbook/models/selection.ipynb +++ b/docs/jupyterbook/models/selection.ipynb @@ -2726,9 +2726,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/selection.md b/docs/jupyterbook/models/selection.md index c868c75..1d92259 100644 --- a/docs/jupyterbook/models/selection.md +++ b/docs/jupyterbook/models/selection.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Model selection using `ModelSpec` diff --git a/docs/jupyterbook/models/submodels.ipynb b/docs/jupyterbook/models/submodels.ipynb index 777037a..825bedd 100644 --- a/docs/jupyterbook/models/submodels.ipynb +++ b/docs/jupyterbook/models/submodels.ipynb @@ -3105,9 +3105,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/submodels.md b/docs/jupyterbook/models/submodels.md index c2a97fd..6f339ed 100644 --- a/docs/jupyterbook/models/submodels.md +++ b/docs/jupyterbook/models/submodels.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Building design matrices with `ModelSpec` diff --git a/docs/jupyterbook/transforms/PCA.ipynb b/docs/jupyterbook/transforms/PCA.ipynb index d8b41f3..224992b 100644 --- a/docs/jupyterbook/transforms/PCA.ipynb +++ b/docs/jupyterbook/transforms/PCA.ipynb @@ -386,9 +386,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/transforms/PCA.md b/docs/jupyterbook/transforms/PCA.md index b9ba769..e3bc5d4 100644 --- a/docs/jupyterbook/transforms/PCA.md +++ b/docs/jupyterbook/transforms/PCA.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Derived features: using PCA on a subset of columns diff --git a/docs/jupyterbook/transforms/poly.ipynb b/docs/jupyterbook/transforms/poly.ipynb index 54d7b4e..c2b740b 100644 --- a/docs/jupyterbook/transforms/poly.ipynb +++ b/docs/jupyterbook/transforms/poly.ipynb @@ -319,9 +319,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/transforms/poly.md b/docs/jupyterbook/transforms/poly.md index 45e0e3d..4ef3a24 100644 --- a/docs/jupyterbook/transforms/poly.md +++ b/docs/jupyterbook/transforms/poly.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Polynomial features diff --git a/docs/jupyterbook/transforms/splines.ipynb b/docs/jupyterbook/transforms/splines.ipynb index f28d786..399b0be 100644 --- a/docs/jupyterbook/transforms/splines.ipynb +++ b/docs/jupyterbook/transforms/splines.ipynb @@ -310,9 +310,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/transforms/splines.md b/docs/jupyterbook/transforms/splines.md index f14bc17..de0ee3d 100644 --- a/docs/jupyterbook/transforms/splines.md +++ b/docs/jupyterbook/transforms/splines.md @@ -5,11 +5,11 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Spline features diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 3b4fd24..0000000 --- a/environment.yml +++ /dev/null @@ -1,240 +0,0 @@ -name: islp_test -channels: - - defaults -dependencies: - - ca-certificates=2022.07.19=hca03da5_0 - - certifi=2022.9.14=py39hca03da5_0 - - libcxx=14.0.6=h848a8c0_0 - - libffi=3.4.2=hc377ac9_4 - - ncurses=6.3=h1a28f6b_3 - - openssl=1.1.1q=h1a28f6b_0 - - python=3.9.13=hbdb9e5c_1 - - readline=8.1.2=h1a28f6b_1 - - sqlite=3.39.2=h1058600_0 - - tk=8.6.12=hb8d0fd4_0 - - wheel=0.37.1=pyhd3eb1b0_0 - - xz=5.2.5=h1a28f6b_1 - - zlib=1.2.12=h5a0b063_3 - - pip: - - absl-py==1.2.0 - - aiohttp==3.8.1 - - aiosignal==1.2.0 - - alabaster==0.7.12 - - ansiwrap==0.8.4 - - anyio==3.6.1 - - appnope==0.1.3 - - argon2-cffi==21.3.0 - - argon2-cffi-bindings==21.2.0 - - astor==0.8.1 - - asttokens==2.0.8 - - astunparse==1.6.3 - - async-timeout==4.0.2 - - attrs==22.1.0 - - autograd==1.4 - - autograd-gamma==0.5.0 - - babel==2.10.3 - - backcall==0.2.0 - - beautifulsoup4==4.11.1 - - bleach==5.0.1 - - build==0.8.0 - - cachetools==4.2.4 - - cffi==1.15.1 - - charset-normalizer==2.1.1 - - click==8.1.3 - - commonmark==0.9.1 - - contourpy==1.0.5 - - cycler==0.11.0 - - debugpy==1.6.3 - - decorator==5.1.1 - - defusedxml==0.7.1 - - docutils==0.17.1 - - entrypoints==0.4 - - exceptiongroup==1.1.0 - - executing==1.0.0 - - fastjsonschema==2.16.2 - - flatbuffers==2.0.7 - - fonttools==4.37.2 - - formulaic==0.5.2 - - frozenlist==1.3.1 - - fsspec==2022.8.2 - - future==0.18.2 - - gast==0.4.0 - - google-auth==1.35.0 - - google-auth-oauthlib==0.4.6 - - google-pasta==0.2.0 - - grpcio==1.48.1 - - h5py==3.7.0 - - html2text==2020.1.16 - - idna==3.4 - - imagesize==1.4.1 - - importlib-metadata==4.12.0 - - iniconfig==2.0.0 - - interface-meta==1.3.0 - - ipykernel==6.15.3 - - ipython==8.5.0 - - ipython-genutils==0.2.0 - - ipywidgets==8.0.2 - - jaraco-classes==3.2.2 - - jedi==0.18.1 - - jinja2==3.1.2 - - joblib==1.2.0 - - json5==0.9.10 - - jsonschema==4.16.0 - - jupyter==1.0.0 - - jupyter-cache==0.5.0 - - jupyter-client==7.3.5 - - jupyter-console==6.4.4 - - jupyter-core==4.11.1 - - jupyter-server==1.18.1 - - jupyterlab==3.4.7 - - jupyterlab-pygments==0.2.2 - - jupyterlab-server==2.15.1 - - jupyterlab-widgets==3.0.3 - - jupytext==1.14.5 - - keras==2.10.0 - - keras-preprocessing==1.1.2 - - keyring==23.9.3 - - kiwisolver==1.4.4 - - l0bnb==1.0.0 - - libclang==14.0.6 - - lifelines==0.27.2 - - llvmlite==0.39.1 - - lxml==4.9.1 - - markdown==3.4.1 - - markdown-it-py==2.1.0 - - markupsafe==2.1.1 - - matplotlib==3.6.0 - - matplotlib-inline==0.1.6 - - mdit-py-plugins==0.3.0 - - mdurl==0.1.2 - - mistune==2.0.4 - - more-itertools==8.14.0 - - multidict==6.0.2 - - myst==1.0.4 - - myst-nb==0.16.0 - - myst-parser==0.18.0 - - nbclassic==0.4.3 - - nbclient==0.5.13 - - nbconvert==7.0.0 - - nbformat==5.5.0 - - nbsphinx==0.8.11 - - nest-asyncio==1.5.5 - - notebook==6.4.12 - - notebook-shim==0.1.0 - - numba==0.56.2 - - numpy==1.23.3 - - numpydoc==1.4.0 - - oauthlib==3.2.1 - - opt-einsum==3.3.0 - - packaging==21.3 - - pandas==1.5.0 - - pandocfilters==1.5.0 - - papermill==2.4.0 - - parso==0.8.3 - - patsy==0.5.2 - - pep517==0.13.0 - - pexpect==4.8.0 - - pickleshare==0.7.5 - - pillow==9.2.0 - - pip==22.2.2 - - pkginfo==1.8.3 - - pluggy==1.0.0 - - portalocker==2.5.1 - - progressbar2==4.0.0 - - prometheus-client==0.14.1 - - prompt-toolkit==3.0.31 - - protobuf==3.19.5 - - psutil==5.9.2 - - ptyprocess==0.7.0 - - pure-eval==0.2.2 - - pyasn1==0.4.8 - - pyasn1-modules==0.2.8 - - pycparser==2.21 - - pydash==5.1.0 - - pydeprecate==0.3.2 - - pygam==0.8.0 - - pygments==2.13.0 - - pyparsing==3.0.9 - - pyrsistent==0.18.1 - - pytest==7.2.0 - - python-dateutil==2.8.2 - - python-utils==3.3.3 - - pytorch-lightning==1.7.6 - - pytz==2022.2.1 - - pytz-deprecation-shim==0.1.0.post0 - - pyyaml==6.0 - - pyzmq==24.0.0 - - qtconsole==5.3.2 - - qtpy==2.2.0 - - readme-renderer==37.1 - - requests==2.28.1 - - requests-oauthlib==1.3.1 - - requests-toolbelt==0.9.1 - - rfc3986==2.0.0 - - rich==12.5.1 - - rpy2==3.5.7 - - rsa==4.9 - - scikit-learn==1.1.2 - - scipy==1.9.1 - - send2trash==1.8.0 - - setuptools==59.8.0 - - six==1.16.0 - - sniffio==1.3.0 - - snowballstemmer==2.2.0 - - soupsieve==2.3.2.post1 - - sphinx==5.1.1 - - sphinx-markdown-builder==0.5.4 - - sphinx-rst-builder==0.0.3 - - sphinx-rtd-theme==1.1.1 - - sphinx-togglebutton==0.3.2 - - sphinxcontrib-applehelp==1.0.2 - - sphinxcontrib-devhelp==1.0.2 - - sphinxcontrib-htmlhelp==2.0.0 - - sphinxcontrib-jsmath==1.0.1 - - sphinxcontrib-qthelp==1.0.3 - - sphinxcontrib-serializinghtml==1.1.5 - - sqlalchemy==1.4.41 - - stack-data==0.5.0 - - statsmodels==0.13.2 - - tabulate==0.8.10 - - tenacity==6.3.1 - - tensorboard==2.10.0 - - tensorboard-data-server==0.6.1 - - tensorboard-plugin-wit==1.8.1 - - tensorflow-estimator==2.10.0 - - tensorflow-macos==2.10.0 - - tensorflow-metal==0.6.0 - - termcolor==2.0.1 - - terminado==0.15.0 - - texext==0.6.7 - - textwrap3==0.9.2 - - threadpoolctl==3.1.0 - - tinycss2==1.1.1 - - toml==0.10.2 - - tomli==2.0.1 - - torch==1.12.1 - - torchdata==0.4.1 - - torchinfo==1.7.0 - - torchmetrics==0.9.3 - - torchvision==0.13.1 - - tornado==6.2 - - tqdm==4.64.1 - - traitlets==5.4.0 - - twine==4.0.1 - - typing-extensions==4.3.0 - - tzdata==2022.7 - - tzlocal==4.2 - - unidecode==1.3.4 - - unify==0.5 - - untokenize==0.1.1 - - urllib3==1.26.12 - - wcwidth==0.2.5 - - webencodings==0.5.1 - - websocket-client==1.4.1 - - werkzeug==2.2.2 - - widgetsnbextension==4.0.3 - - wrapt==1.14.1 - - yapf==0.32.0 - - yarl==1.8.1 - - zipp==3.8.1 -prefix: /Users/jonathantaylor/miniconda3/envs/islp_test From cb7fc72d528181fc63a55bd8407aff578c476236 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 22:05:00 -0700 Subject: [PATCH 004/195] added note for torch requirements --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 29b1c3f..71c8065 100644 --- a/README.md +++ b/README.md @@ -43,10 +43,10 @@ os.system(cmd) ### Torch requirements The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](requirements.txt). Alternatively, you can install them directly using `pip` +can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` ```{python} -reqs = 'https://raw.githubusercontent.com/jonathan-taylor/ISLP/master/requirements.txt' +reqs = 'https://raw.githubusercontent.com/jonathan-taylor/ISLP/master/torch_requirements.txt' cmd = f'{sys.executable} -m pip install -r {reqs}' os.system(cmd) ``` From eb25cbd6e5e36ed80e651fe3ff9f206c7d09e10c Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 22:38:35 -0700 Subject: [PATCH 005/195] updated api pages --- docs/source/api/gen.rst | 1 - .../api/generated/ISLP.bart.tmpbart.rst | 42 ------------------- 2 files changed, 43 deletions(-) delete mode 100644 docs/source/api/generated/ISLP.bart.tmpbart.rst diff --git a/docs/source/api/gen.rst b/docs/source/api/gen.rst index 2539220..fb3bec5 100644 --- a/docs/source/api/gen.rst +++ b/docs/source/api/gen.rst @@ -6,7 +6,6 @@ generated/ISLP.bart.bart generated/ISLP.bart.likelihood generated/ISLP.bart.particle_tree - generated/ISLP.bart.tmpbart generated/ISLP.bart.tree generated/ISLP.cluster generated/ISLP.models diff --git a/docs/source/api/generated/ISLP.bart.tmpbart.rst b/docs/source/api/generated/ISLP.bart.tmpbart.rst deleted file mode 100644 index b72117a..0000000 --- a/docs/source/api/generated/ISLP.bart.tmpbart.rst +++ /dev/null @@ -1,42 +0,0 @@ -.. AUTO-GENERATED FILE -- DO NOT EDIT! - -bart.tmpbart -============ - -Module: :mod:`bart.tmpbart` ---------------------------- -Inheritance diagram for ``ISLP.bart.tmpbart``: - -.. inheritance-diagram:: ISLP.bart.tmpbart - :parts: 3 - -.. automodule:: ISLP.bart.tmpbart - -.. currentmodule:: ISLP.bart.tmpbart - -Classes -------- - -:class:`BART` -~~~~~~~~~~~~~ - - -.. autoclass:: BART - :members: - :undoc-members: - :show-inheritance: - :inherited-members: - - .. automethod:: __init__ - -:class:`SampleSplittingVariable` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - -.. autoclass:: SampleSplittingVariable - :members: - :undoc-members: - :show-inheritance: - :inherited-members: - - .. automethod:: __init__ From f1c08020a56265d185026ba6d2572c5f1dac6421 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 22:55:30 -0700 Subject: [PATCH 006/195] np.bool->bool --- ISLP/models/model_spec.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index c5be3f9..79502dc 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -98,7 +98,7 @@ def fit(self, X): if self.method == 'drop': self.columns_ = [column_names[j] for j in colmap] self.contrast_matrix_ = np.identity(len(cats)) - keep = np.ones(len(cats), np.bool) + keep = np.ones(len(cats), bool) keep[drop_idx] = 0 self.contrast_matrix_ = self.contrast_matrix_[:,keep] self.contrast_matrix_ = self.contrast_matrix_[:,colmap] @@ -203,7 +203,7 @@ def fit(self, X, y=None): X) self.columns_ = X.columns if self.is_categorical_ is None: - self.is_categorical_ = np.zeros(X.shape[1], np.bool) + self.is_categorical_ = np.zeros(X.shape[1], bool) self.is_ordinal_ = pd.Series(self.is_ordinal_, index=self.columns_) self.is_categorical_ = pd.Series(self.is_categorical_, @@ -214,9 +214,9 @@ def fit(self, X, y=None): self.known_categories_) = _check_categories(categorical_features, X) if self.is_categorical_ is None: - self.is_categorical_ = np.zeros(X.shape[1], np.bool) + self.is_categorical_ = np.zeros(X.shape[1], bool) self.is_ordinal_ = np.zeros(self.is_categorical_.shape, - np.bool) + bool) self.columns_ = np.arange(X.shape[1]) self.variables_ = {} From c7656dc990667db1bae7994d515e27992cfe5c67 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 23:08:38 -0700 Subject: [PATCH 007/195] using sphinx book theme --- docs/requirements.txt | 1 + docs/source/conf.py | 9 ++++++++- docs/source/index.rst | 3 --- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 68ef4bc..37938b9 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,4 @@ texext numpydoc myst_nb +sphinx-book-theme diff --git a/docs/source/conf.py b/docs/source/conf.py index 5da3dda..01e1044 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -28,6 +28,7 @@ graphviz_dot = '/opt/homebrew/bin/dot' numpydoc_class_members_toctree = False nb_execution_mode = "cache" +nb_kernel_rgx_aliases = {'python3': "islp_test"} intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), @@ -42,7 +43,13 @@ # -- Options for HTML output -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_book_theme" +html_theme_options = { + "repository_url": "https://github.com/intro-stat-learning/ISLP.git", + "use_repository_button": True, +} +html_title = "Introduction to Statistical Learning (Python)" +html_logo = "logo.png" # -- Options for EPUB output epub_show_urls = 'footnote' diff --git a/docs/source/index.rst b/docs/source/index.rst index 2c80bdc..7c23aa0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,9 +3,6 @@ Welcome to ISLP documentation! .. automodule:: ISLP -Check out the :doc:`installation` section for further information. - - Contents -------- From 30277fc3459d2174bc92b63ac7a3bb72b5243c11 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 23:12:33 -0700 Subject: [PATCH 008/195] BF: fit_encoder and warning about sparse for OneHot --- ISLP/models/model_spec.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 79502dc..4ed39fc 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -67,7 +67,7 @@ def fit(self, X): Xa = np.asarray(X).reshape((-1,1)) self.encoder_ = OneHotEncoder(drop=None, - sparse=False).fit(Xa) + sparse_output=False).fit(Xa) cats = self.encoder_.categories_[0] column_names = [str(n) for n in cats] @@ -503,8 +503,10 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): raise ValueError('encoder has already been fit previously') except NotFittedError as e: if fit: - fit_encoder(var, pd.DataFrame(np.asarray(cols), - columns=names)) + fit_encoder(encoders, + var, + pd.DataFrame(np.asarray(cols), + columns=names)) # known issue with Pipeline # https://github.com/scikit-learn/scikit-learn/issues/18648 elif 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b/docs/source/conf.py @@ -28,7 +28,7 @@ graphviz_dot = '/opt/homebrew/bin/dot' numpydoc_class_members_toctree = False nb_execution_mode = "cache" -nb_kernel_rgx_aliases = {'python3': "islp_test"} +#nb_kernel_rgx_aliases = {'python3': "islp_test"} intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), From cda040c65672a7f0eedbdd78597ced132bdd007d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 24 Apr 2023 23:53:07 -0700 Subject: [PATCH 011/195] trying to use rpy2 --- .readthedocs.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 871a974..c8ea271 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -28,3 +28,5 @@ python: - requirements: keras_requirements.txt - method: pip path: . + - method: pip + path: rpy2 From ac5c68c2ccd26830012c16fb1c6bb9446d786353 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:00:17 -0700 Subject: [PATCH 012/195] putting rpy2 in docs requirements --- .readthedocs.yaml | 2 -- docs/requirements.txt | 1 + 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index c8ea271..871a974 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -28,5 +28,3 @@ python: - requirements: keras_requirements.txt - method: pip path: . - - method: pip - path: rpy2 diff --git a/docs/requirements.txt b/docs/requirements.txt index 37938b9..b06b49b 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,3 +2,4 @@ texext numpydoc myst_nb sphinx-book-theme +rpy2 From e84eec5a040b674a14a56d2c01a62eb1f5ac07a8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:07:49 -0700 Subject: [PATCH 013/195] using r-base ubuntu package --- .readthedocs.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 871a974..9e65eb7 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,7 +10,9 @@ build: os: ubuntu-22.04 tools: python: "3.9" - + apt-packages: + - r-base + # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py From eed8f43471800e8f324a07ec833b136eae1e8931 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:08:33 -0700 Subject: [PATCH 014/195] fixing yaml --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 9e65eb7..141d660 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,7 +10,7 @@ build: os: ubuntu-22.04 tools: python: "3.9" - apt-packages: + apt_packages: - r-base # Build documentation in the docs/ directory with Sphinx From dd4884617c4eaf3650aaf2fafd600a7f8fd1d7e7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:11:46 -0700 Subject: [PATCH 015/195] removing jupyter requirement --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index bf393e1..bbe21a2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ numpy>=1.7.1 scipy>=0.9 -jupyter pandas>=0.20 lxml # pandas needs this for html scikit-learn>=1.0 From 0b0496d7e62277e18899e834e823c05a5b5c330f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:23:29 -0700 Subject: [PATCH 016/195] fixing PCA notebook --- docs/jupyterbook/transforms/PCA.ipynb | 199 ++++---------------------- docs/jupyterbook/transforms/PCA.md | 27 ++-- docs/source/transforms/PCA.ipynb | 199 ++++---------------------- 3 files changed, 73 insertions(+), 352 deletions(-) diff --git a/docs/jupyterbook/transforms/PCA.ipynb b/docs/jupyterbook/transforms/PCA.ipynb index 224992b..6f34c5a 100644 --- a/docs/jupyterbook/transforms/PCA.ipynb +++ b/docs/jupyterbook/transforms/PCA.ipynb @@ -19,9 +19,14 @@ "outputs": [], "source": [ "import numpy as np\n", + "from sklearn.decomposition import PCA\n", + "\n", "from ISLP import load_data\n", - "from ISLP.models import ModelSpec, pca, Variable, derived_variable\n", - "from sklearn.decomposition import PCA" + "from ISLP.models import (ModelSpec, \n", + " pca, \n", + " Variable, \n", + " derived_variable,\n", + " build_columns)" ] }, { @@ -105,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "6cfe8861-ad07-47b9-95d1-5d5513ff6fbe", "metadata": {}, "outputs": [ @@ -113,170 +118,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n" ] } ], "source": [ - "sklearn_pca.fit(design.build_columns(Carseats, grouped)[0]) \n", + "grouped_features = build_columns(design.column_info_,\n", + " Carseats,\n", + " grouped)[0]\n", + "sklearn_pca.fit(grouped_features) \n", "pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", " name='pca(grouped)', encoder=sklearn_pca)\n", - "derived_features, _ = design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "aeb47184-9e15-4a6e-b60a-916f5ff89063", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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"/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n" ] }, @@ -329,7 +186,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -350,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "4a8d9b28", "metadata": {}, "outputs": [], @@ -361,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "6efa6c67-86e1-4f51-86c2-25c838a90bf4", "metadata": {}, "outputs": [ @@ -371,7 +228,7 @@ "(4.073428490498941e-14, 0.0)" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -386,9 +243,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/transforms/PCA.md b/docs/jupyterbook/transforms/PCA.md index e3bc5d4..612e595 100644 --- a/docs/jupyterbook/transforms/PCA.md +++ b/docs/jupyterbook/transforms/PCA.md @@ -7,9 +7,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: python3 + display_name: islp_test language: python - name: python3 + name: islp_test --- # Derived features: using PCA on a subset of columns @@ -19,9 +19,14 @@ construction of transformers applied to features. ```{code-cell} ipython3 import numpy as np -from ISLP import load_data -from ISLP.models import ModelSpec, pca, Variable, derived_variable from sklearn.decomposition import PCA + +from ISLP import load_data +from ISLP.models import (ModelSpec, + pca, + Variable, + derived_variable, + build_columns) ``` ```{code-cell} ipython3 @@ -51,14 +56,16 @@ sklearn_pca = PCA(n_components=3, whiten=True) We can now fit `sklearn_pca` and create our new variable. ```{code-cell} ipython3 -sklearn_pca.fit(design.build_columns(Carseats, grouped)[0]) +grouped_features = build_columns(design.column_info_, + Carseats, + grouped)[0] +sklearn_pca.fit(grouped_features) pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'], name='pca(grouped)', encoder=sklearn_pca) -derived_features, _ = design.build_columns(Carseats, pca_var) -``` - -```{code-cell} ipython3 -design.build_columns(Carseats, grouped)[0] +derived_features, _ = build_columns(design.column_info_, + Carseats, + pca_var, + encoders=design.encoders_) ``` ## Helper function diff --git a/docs/source/transforms/PCA.ipynb b/docs/source/transforms/PCA.ipynb index 224992b..6f34c5a 100644 --- a/docs/source/transforms/PCA.ipynb +++ b/docs/source/transforms/PCA.ipynb @@ -19,9 +19,14 @@ "outputs": [], "source": [ "import numpy as np\n", + "from sklearn.decomposition import PCA\n", + "\n", "from ISLP import load_data\n", - "from ISLP.models import ModelSpec, pca, Variable, derived_variable\n", - "from sklearn.decomposition import PCA" + "from ISLP.models import (ModelSpec, \n", + " pca, \n", + " Variable, \n", + " derived_variable,\n", + " build_columns)" ] }, { @@ -105,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "6cfe8861-ad07-47b9-95d1-5d5513ff6fbe", "metadata": {}, "outputs": [ @@ -113,170 +118,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n" ] } ], "source": [ - "sklearn_pca.fit(design.build_columns(Carseats, grouped)[0]) \n", + "grouped_features = build_columns(design.column_info_,\n", + " Carseats,\n", + " grouped)[0]\n", + "sklearn_pca.fit(grouped_features) \n", "pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", " name='pca(grouped)', encoder=sklearn_pca)\n", - "derived_features, _ = design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "aeb47184-9e15-4a6e-b60a-916f5ff89063", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - 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" - ], - "text/plain": [ - " CompPrice Income Advertising Population Price\n", - "0 138 73 11 276 120\n", - "1 111 48 16 260 83\n", - "2 113 35 10 269 80\n", - "3 117 100 4 466 97\n", - "4 141 64 3 340 128\n", - ".. ... ... ... ... ...\n", - "395 138 108 17 203 128\n", - "396 139 23 3 37 120\n", - "397 162 26 12 368 159\n", - "398 100 79 7 284 95\n", - "399 134 37 0 27 120\n", - "\n", - "[400 rows x 5 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, grouped)[0]" + "derived_features, _ = build_columns(design.column_info_,\n", + " Carseats, \n", + " pca_var,\n", + " encoders=design.encoders_)" ] }, { @@ -291,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "9f4b0955", "metadata": {}, "outputs": [], @@ -304,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "6b382699-eb86-457f-8e91-09a63eb21d49", "metadata": {}, "outputs": [ @@ -312,11 +169,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", + "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", " warnings.warn(\n" ] }, @@ -329,7 +186,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -350,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "4a8d9b28", "metadata": {}, "outputs": [], @@ -361,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "6efa6c67-86e1-4f51-86c2-25c838a90bf4", "metadata": {}, "outputs": [ @@ -371,7 +228,7 @@ "(4.073428490498941e-14, 0.0)" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -386,9 +243,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" }, "language_info": { "codemirror_mode": { From f8b3ff2ad643aca5d15b2810f60b261411a60f66 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:27:20 -0700 Subject: [PATCH 017/195] fixing kernel in PCA notebook --- docs/jupyterbook/transforms/PCA.ipynb | 4 ++-- docs/jupyterbook/transforms/PCA.md | 4 ++-- docs/source/transforms/PCA.ipynb | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/jupyterbook/transforms/PCA.ipynb b/docs/jupyterbook/transforms/PCA.ipynb index 6f34c5a..047efde 100644 --- a/docs/jupyterbook/transforms/PCA.ipynb +++ b/docs/jupyterbook/transforms/PCA.ipynb @@ -243,9 +243,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/transforms/PCA.md b/docs/jupyterbook/transforms/PCA.md index 612e595..22aeea6 100644 --- a/docs/jupyterbook/transforms/PCA.md +++ b/docs/jupyterbook/transforms/PCA.md @@ -7,9 +7,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Derived features: using PCA on a subset of columns diff --git a/docs/source/transforms/PCA.ipynb b/docs/source/transforms/PCA.ipynb index 6f34c5a..047efde 100644 --- a/docs/source/transforms/PCA.ipynb +++ b/docs/source/transforms/PCA.ipynb @@ -243,9 +243,9 @@ "formats": "source/transforms///ipynb,jupyterbook/transforms///md:myst,jupyterbook/transforms///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { From 7d147841cbdc80cd761dddf35d6833f27aa4aad7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 00:33:18 -0700 Subject: [PATCH 018/195] consistent running of encoder on a dataframe --- ISLP/models/model_spec.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 4ed39fc..41893a8 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -495,8 +495,9 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): cols = np.column_stack(cols) if len(names) != cols.shape[1]: names = ['{0}[{1}]'.format(var.name, j) for j in range(cols.shape[1])] - if var.encoder: + df_cols = pd.DataFrame(np.asarray(cols), + columns=names) try: check_is_fitted(var.encoder) if fit and var not in encoders: @@ -505,8 +506,7 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): if fit: fit_encoder(encoders, var, - pd.DataFrame(np.asarray(cols), - columns=names)) + df_cols) # known issue with Pipeline # https://github.com/scikit-learn/scikit-learn/issues/18648 elif isinstance(var.encoder, Pipeline): @@ -516,9 +516,9 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): except Exception as e: # was not the NotFitted raise ValueError(e) if var.use_transform: - cols = var.encoder.transform(cols) + cols = var.encoder.transform(df_cols) else: - cols = var.encoder.predict(cols) + cols = var.encoder.predict(df_cols) if hasattr(var.encoder, 'columns_') and not var.override_encoder_colnames: names = var.encoder.columns_ else: From acd3d364af0c704a2e67dbe1dd5d6debec0e77a8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 01:10:33 -0700 Subject: [PATCH 019/195] fixup install page --- docs/jupyterbook/transforms/PCA.ipynb | 33 ++++-------------- docs/source/conf.py | 6 ++++ docs/source/installation.myst | 48 +++++++++++++++++++++++++++ docs/source/installation.rst | 18 ---------- docs/source/transforms/PCA.ipynb | 33 ++++-------------- 5 files changed, 66 insertions(+), 72 deletions(-) create mode 100644 docs/source/installation.myst delete mode 100644 docs/source/installation.rst diff --git a/docs/jupyterbook/transforms/PCA.ipynb b/docs/jupyterbook/transforms/PCA.ipynb index 047efde..04c50ad 100644 --- a/docs/jupyterbook/transforms/PCA.ipynb +++ b/docs/jupyterbook/transforms/PCA.ipynb @@ -110,19 +110,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "6cfe8861-ad07-47b9-95d1-5d5513ff6fbe", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "grouped_features = build_columns(design.column_info_,\n", " Carseats,\n", @@ -148,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "9f4b0955", "metadata": {}, "outputs": [], @@ -161,22 +152,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "6b382699-eb86-457f-8e91-09a63eb21d49", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ @@ -186,7 +165,7 @@ " dtype='object')" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "4a8d9b28", "metadata": {}, "outputs": [], diff --git a/docs/source/conf.py b/docs/source/conf.py index 9b650c4..b009ae7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -51,5 +51,11 @@ html_title = "Introduction to Statistical Learning (Python)" html_logo = "logo.png" +source_suffix = { + '.rst': 'restructuredtext', + '.ipynb': 'myst-nb', + '.myst': 'myst-nb', +} + # -- Options for EPUB output epub_show_urls = 'footnote' diff --git a/docs/source/installation.myst b/docs/source/installation.myst new file mode 100644 index 0000000..428c1b6 --- /dev/null +++ b/docs/source/installation.myst @@ -0,0 +1,48 @@ +# Installation instructions + +We generally recommend creating a [conda](https://anaconda.org) environment to isolate any code +from other dependencies. The `ISLP` package does not have unusual dependencies, but this is still +good practice. To create a conda environment in a Mac OS X or Linux environment run: + +```{python} +!conda create --name islp +``` + +To run python code in this environment, you must activate it: + +```{python} +!conda activate islp +``` + +## Mac OS X + +Running in the desired environment, we use `pip` to install the `ISLP` package: + +```{python} +!pip install ISLP +``` + +## Windows + +See the [python packaging instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within +Jupyter or IPython. + +Alternatively, within a python shell in the environment you want to install `ISLP`, the following commands should install `ISLP`: + +```{python} +import os, sys +cmd = f'{sys.executable} -m pip install ISLP' +os.system(cmd) +``` + +## Torch requirements + +The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements +can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` + +```{python} +reqs = 'https://raw.githubusercontent.com/jonathan-taylor/ISLP/master/torch_requirements.txt' +cmd = f'{sys.executable} -m pip install -r {reqs}' +os.system(cmd) +``` + diff --git a/docs/source/installation.rst b/docs/source/installation.rst deleted file mode 100644 index 981b1ae..0000000 --- a/docs/source/installation.rst +++ /dev/null @@ -1,18 +0,0 @@ -Usage -===== - -.. _installation: - -Installation ------------- - -To use ISLP, first install it using pip: - -.. code-block:: console - - (.venv) $ pip install ISLP - -Creating recipes ----------------- - -BLAH diff --git a/docs/source/transforms/PCA.ipynb b/docs/source/transforms/PCA.ipynb index 047efde..04c50ad 100644 --- a/docs/source/transforms/PCA.ipynb +++ b/docs/source/transforms/PCA.ipynb @@ -110,19 +110,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "6cfe8861-ad07-47b9-95d1-5d5513ff6fbe", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "grouped_features = build_columns(design.column_info_,\n", " Carseats,\n", @@ -148,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "9f4b0955", "metadata": {}, "outputs": [], @@ -161,22 +152,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "6b382699-eb86-457f-8e91-09a63eb21d49", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ @@ -186,7 +165,7 @@ " dtype='object')" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "4a8d9b28", "metadata": {}, "outputs": [], From 667ad84c2fd2aca3fe9d4a5455c83c27ff06bfc7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 09:41:11 -0700 Subject: [PATCH 020/195] moved build_submodel from method to function --- ISLP/models/model_spec.py | 176 ++++++++++++++++++++++------------ docs/source/models/spec.ipynb | 144 +++++++++------------------- 2 files changed, 162 insertions(+), 158 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 41893a8..2526ba6 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -52,18 +52,53 @@ class Variable(NamedTuple): #### contrast specific code class Contrast(TransformerMixin, BaseEstimator): - """ - Contrast encoding for categorical variables. - """ def __init__(self, method='drop', drop_level=None): + """ + Contrast encoding for categorical variables. + + Parameters + ---------- + method : ['drop', 'sum', None, callable] + If 'drop', then a column of the one-hot + encoding will be dropped. If 'sum', then the sum of + coefficients is constrained to sum to 1. + If `None`, the full one-hot encoding is returned. + Finally, if callable, then it should take the number of + levels of the category as a single argument and return + an appropriate contrast of the full one-hot encoding. + + drop_level : str (optional) + If not None, this level of the category + will be dropped if `method=='drop'`. + + """ self.method = method self.drop_level = drop_level - def fit(self, X): + def fit(self, X, y=None): + + """ + Construct contrast of categorical variable + for use in building a design matrix. + + Parameters + ---------- + X : array-like + X on which model matrix will be evaluated. + If a :py:class:`pd.DataFrame` or :py:class:`pd.Series`, variables that are of + categorical dtype will be treated as categorical. + + Returns + ------- + F : array-like + Columns of design matrix implied by the + categorical variable. + + """ Xa = np.asarray(X).reshape((-1,1)) self.encoder_ = OneHotEncoder(drop=None, @@ -310,65 +345,15 @@ def transform(self, X, y=None): Ignored. This parameter exists only for compatibility with :py:class:`sklearn.pipeline.Pipeline`. """ - return self.build_submodel(X, self.terms_) + check_is_fitted(self) + return build_model(self.column_info_, + X, + self.terms_, + intercept=self.intercept, + encoders=self.encoders_) # ModelSpec specific methods - def build_submodel(self, X, terms): - - """ - Construct design matrix on a - sequence of terms and X after - fitting. - - Parameters - ---------- - X : array-like - X on which model matrix will be evaluated. - - Returns - ------- - df : np.ndarray or pd.DataFrame - Design matrix. - """ - - check_is_fitted(self) - - dfs = [] - - col_cache = {} # avoid recomputing the same columns - - if self.intercept: - df = pd.DataFrame({'intercept':np.ones(X.shape[0])}) - if isinstance(X, (pd.Series, pd.DataFrame)): - df.index = X.index - dfs.append(df) - - for term_ in terms: - term_df = build_columns(self.column_info_, - X, - term_, - col_cache=col_cache, - encoders=self.encoders_, - fit=False)[0] - dfs.append(term_df) - - if len(dfs): - if isinstance(X, (pd.Series, pd.DataFrame)): - df = pd.concat(dfs, axis=1) - df.index = X.index - return df - else: - return np.column_stack(dfs) - else: # return a 0 design - zero = np.zeros(X.shape[0]) - if isinstance(X, (pd.Series, pd.DataFrame)): - df = pd.DataFrame({'zero': zero}) - df.index = X.index - return df - else: - return zero - def build_sequence(self, X, anova_type='sequential'): @@ -455,7 +440,10 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): var : Variable Variable whose columns will be built, typically a key in `column_info`. - col_cache: + encoders : dict + Dict that stores encoder of each Variable. + + col_cache: dict Dict where columns will be stored -- if `var.name` in `col_cache` then just returns those columns. @@ -539,6 +527,72 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): col_cache[joblib_hash([var.name, X])] = (val, names) return val, names +def build_model(column_info, + X, + terms, + intercept=True, + encoders={}): + + """ + Construct design matrix on a + sequence of terms and X after + fitting. + + Parameters + ---------- + column_info: dict + Dictionary with values specifying sets of columns to + be concatenated into a design matrix. + + X : array-like + X on which columns are evaluated. + + terms : [Variable] + Sequence of variables + + encoders : dict + Dict that stores encoder of each Variable. + + Returns + ------- + df : np.ndarray or pd.DataFrame + Design matrix. + """ + + dfs = [] + + col_cache = {} # avoid recomputing the same columns + + if intercept: + df = pd.DataFrame({'intercept':np.ones(X.shape[0])}) + if isinstance(X, (pd.Series, pd.DataFrame)): + df.index = X.index + dfs.append(df) + + for term_ in terms: + term_df = build_columns(column_info, + X, + term_, + col_cache=col_cache, + encoders=encoders, + fit=False)[0] + dfs.append(term_df) + + if len(dfs): + if isinstance(X, (pd.Series, pd.DataFrame)): + df = pd.concat(dfs, axis=1) + df.index = X.index + return df + else: + return np.column_stack(dfs) + else: # return a 0 design + zero = np.zeros(X.shape[0]) + if isinstance(X, (pd.Series, pd.DataFrame)): + df = pd.DataFrame({'zero': zero}) + df.index = X.index + return df + else: + return zero def derived_variable(variables, encoder=None, name=None, use_transform=True): """ diff --git a/docs/source/models/spec.ipynb b/docs/source/models/spec.ipynb index d6ba7b0..1ea45d2 100644 --- a/docs/source/models/spec.ipynb +++ b/docs/source/models/spec.ipynb @@ -597,14 +597,6 @@ "shelve.get_columns(Carseats)" ] }, - { - "cell_type": "markdown", - "id": "5d8b048f-3c31-47ac-8946-0662f5e57b63", - "metadata": {}, - "source": [ - "shelve.get_columns?" - ] - }, { "cell_type": "markdown", "id": "269e6d18-4ae4-4a77-8498-90281ae7c803", @@ -1292,14 +1284,6 @@ "id": "imported-measure", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/html": [ @@ -1433,14 +1417,6 @@ "id": "western-bloom", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/html": [ @@ -1583,25 +1559,25 @@ }, { "cell_type": "markdown", - "id": "absent-branch", + "id": "e289feba-e3f5-48e0-9e29-cdd88d7f9923", "metadata": {}, "source": [ - "## Predicting at new points\n", - "\n", - "As `ModelSpec` is a transformer, it can be evaluated at new feature values.\n", - "Constructing the design matrix at any values is carried out by the `transform` method." + "## Predicting at new points" ] }, { "cell_type": "code", "execution_count": 19, - "id": "naked-hollywood", + "id": "6efed2fa-9e5d-429c-a8d9-ac544cab2b41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 9.73389663, 26.06456997])" + "intercept 12.661546\n", + "Price -0.052213\n", + "Income 0.012829\n", + "dtype: float64" ] }, "execution_count": 19, @@ -1610,94 +1586,68 @@ } ], "source": [ - "new_data = pd.DataFrame({'Income':['Bad', 'Good'], 'Price':[40, 50]})\n", - "new_X = MS.transform(new_data)\n", - "M_ols.get_prediction(new_X).predicted_mean" + "MS = ModelSpec(['Price', 'Income']).fit(Carseats)\n", + "X = MS.transform(Carseats)\n", + "Y = Carseats['Sales']\n", + "M_ols = sm.OLS(Y, X).fit()\n", + "M_ols.params" ] }, { "cell_type": "markdown", - "id": "signal-yahoo", + "id": "e6b4609b-fcb2-4cc2-b630-509df4c87546", "metadata": {}, "source": [ - "## Using `np.ndarray`\n", - "\n", - "As the basic model is to concatenate columns extracted from a columnar data\n", - "representation, one *can* use `np.ndarray` as the column data. In this case,\n", - "columns will be selected by integer indices. \n", - "\n", - "### Caveats using `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "However,\n", - "unless all features are floats, `np.ndarray` will default to a dtype of `object`, complicating issues.\n", - "\n", - "However, if we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. \n", - "\n", - "We illustrate this below, where we build a model from `Price` and `Income` for `Sales` and want to find predictions at new\n", - "values of `Price` and `Location`. We first find the predicitions using `pd.DataFrame` and then illustrate the difficulties\n", - "in using `np.ndarray`." + "As `ModelSpec` is a transformer, it can be evaluated at new feature values.\n", + "Constructing the design matrix at any values is carried out by the `transform` method." ] }, { "cell_type": "code", - "execution_count": 42, - "id": "964ecc79-7303-410c-b258-2d58341c7dc0", + "execution_count": 20, + "id": "8784b0e8-ce53-4a90-aee6-b935834295c7", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " intercept Price Income\n", - "0 1.0 40 10\n", - "1 1.0 50 20\n" - ] - }, { "data": { "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" + "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 42, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "MS = ModelSpec(['Price', 'Income']).fit(Carseats)\n", - "M_ols = sm.OLS(Y, MS.transform(Carseats)).fit()\n", - "\n", "new_data = pd.DataFrame({'Price':[40, 50], 'Income':[10, 20]})\n", "new_X = MS.transform(new_data)\n", - "print(new_X)\n", - "M_ols.params" + "M_ols.get_prediction(new_X).predicted_mean" ] }, { - "cell_type": "code", - "execution_count": 25, - "id": "a42c239e-a5eb-4c5d-919e-16c4d58d1c8d", + "cell_type": "markdown", + "id": "signal-yahoo", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10.70130676, 10.307465 ])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "M_ols.get_prediction(new_X).predicted_mean" + "## Using `np.ndarray`\n", + "\n", + "As the basic model is to concatenate columns extracted from a columnar data\n", + "representation, one *can* use `np.ndarray` as the column data. In this case,\n", + "columns will be selected by integer indices. \n", + "\n", + "### Caveats using `np.ndarray`\n", + "\n", + "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", + "However,\n", + "unless all features are floats, `np.ndarray` will default to a dtype of `object`, complicating issues.\n", + "\n", + "However, if we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", + "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. \n", + "\n", + "We illustrate this below, where we build a model from `Price` and `Income` for `Sales` and want to find predictions at new\n", + "values of `Price` and `Location`. We first find the predicitions using `pd.DataFrame` and then illustrate the difficulties\n", + "in using `np.ndarray`." ] }, { @@ -1710,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "id": "4fec9030-7445-48be-a15f-2ac5a789e717", "metadata": {}, "outputs": [ @@ -1726,7 +1676,7 @@ " [ 1., 120., 37.]])" ] }, - "execution_count": 34, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1739,7 +1689,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 22, "id": "c864e365-2476-4ca6-9d27-625cac2b2271", "metadata": {}, "outputs": [ @@ -1752,7 +1702,7 @@ "dtype: float64" ] }, - "execution_count": 36, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1779,7 +1729,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 23, "id": "incredible-concert", "metadata": {}, "outputs": [ @@ -1813,7 +1763,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 24, "id": "stunning-container", "metadata": {}, "outputs": [ @@ -1831,7 +1781,7 @@ "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 45, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } From fa99dc3752a136dcb4eface999cbf1a8a76c4e57 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 12:51:48 -0700 Subject: [PATCH 021/195] cleanup some notebooks --- ISLP/models/__init__.py | 4 +- ISLP/models/model_spec.py | 189 +- ISLP/models/strategy.py | 20 +- ISLP/models/tests/test_model_matrix.py | 29 +- docs/jupyterbook/models/anova.ipynb | 633 ++++ docs/jupyterbook/models/anova.md | 111 + docs/jupyterbook/models/derived.ipynb | 2125 ----------- docs/jupyterbook/models/derived.md | 487 --- docs/jupyterbook/models/selection.ipynb | 2702 +------------- docs/jupyterbook/models/selection.md | 689 +--- docs/jupyterbook/models/spec.ipynb | 2672 +++++++------- docs/jupyterbook/models/spec.md | 524 +-- docs/jupyterbook/models/submodels.ipynb | 3127 ----------------- docs/jupyterbook/models/submodels.md | 652 ---- docs/jupyterbook/transforms/PCA.ipynb | 16 +- docs/jupyterbook/transforms/PCA.md | 16 +- docs/jupyterbook/transforms/poly.ipynb | 2 +- docs/jupyterbook/transforms/poly.md | 2 +- .../api/generated/ISLP.models.model_spec.rst | 17 +- docs/source/api/index.rst | 13 +- docs/source/index.rst | 4 +- docs/source/installation.myst | 36 +- docs/source/models.rst | 4 +- docs/source/models/anova.ipynb | 633 ++++ docs/source/models/derived.ipynb | 2125 ----------- docs/source/models/selection.ipynb | 2702 +------------- docs/source/models/spec.ipynb | 72 +- docs/source/models/submodels.ipynb | 3127 ----------------- docs/source/transforms/PCA.ipynb | 16 +- docs/source/transforms/poly.ipynb | 2 +- 30 files changed, 3315 insertions(+), 19436 deletions(-) create mode 100644 docs/jupyterbook/models/anova.ipynb create mode 100644 docs/jupyterbook/models/anova.md delete mode 100644 docs/jupyterbook/models/derived.ipynb delete mode 100644 docs/jupyterbook/models/derived.md delete mode 100644 docs/jupyterbook/models/submodels.ipynb delete mode 100644 docs/jupyterbook/models/submodels.md create mode 100644 docs/source/models/anova.ipynb delete mode 100644 docs/source/models/derived.ipynb delete mode 100644 docs/source/models/submodels.ipynb diff --git a/ISLP/models/__init__.py b/ISLP/models/__init__.py index bf9cd55..ee124d3 100644 --- a/ISLP/models/__init__.py +++ b/ISLP/models/__init__.py @@ -7,11 +7,11 @@ from .model_spec import (ModelSpec, Column, - Variable, + Feature, poly, ns, bs, - derived_variable, + derived_feature, pca, contrast, build_columns) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 2526ba6..dda82b2 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -35,7 +35,7 @@ DOCACHE = False -class Variable(NamedTuple): +class Feature(NamedTuple): """ An element in a model matrix that will build @@ -49,6 +49,7 @@ class Variable(NamedTuple): pure_columns: bool=False override_encoder_colnames: bool=False + #### contrast specific code class Contrast(TransformerMixin, BaseEstimator): @@ -254,7 +255,7 @@ def fit(self, X, y=None): bool) self.columns_ = np.arange(X.shape[1]) - self.variables_ = {} + self.features_ = {} self.encoders_ = {} self.column_info_ = _get_column_info(X, @@ -262,19 +263,19 @@ def fit(self, X, y=None): self.is_categorical_, self.is_ordinal_, default_encoders=self.default_encoders) - # include each column as a Variable + # include each column as a Feature # so that their columns are built if needed for col_ in self.columns_: - self.variables_[col_] = Variable((col_,), str(col_), None, pure_columns=True) + self.features_[col_] = Feature((col_,), str(col_), None, pure_columns=True) - # find possible interactions and other variables + # find possible interactions and other features tmp_cache = {} for term in self.terms: - if isinstance(term, Variable): - self.variables_[term] = term + if isinstance(term, Feature): + self.features_[term] = term build_columns(self.column_info_, X, term, @@ -282,18 +283,18 @@ def fit(self, X, y=None): col_cache=tmp_cache, fit=True) # these encoders won't have been fit yet for var in term.variables: - if var not in self.variables_ and isinstance(var, Variable): - self.variables_[var] = var + if var not in self.features_ and isinstance(var, Feature): + self.features_[var] = var elif term not in self.column_info_: - # a tuple of variables represents an interaction + # a tuple of features represents an interaction if type(term) == type((1,)): names = [] column_map = {} column_names = {} idx = 0 for var in term: - if var in self.variables_: - var = self.variables_[var] + if var in self.features_: + var = self.features_[var] cols, cur_names = build_columns(self.column_info_, X, var, @@ -305,17 +306,17 @@ def fit(self, X, y=None): idx += cols.shape[1] names.append(var.name) encoder_ = Interaction(names, column_map, column_names) - self.variables_[term] = Variable(term, ':'.join(n for n in names), encoder_) + self.features_[term] = Feature(term, ':'.join(n for n in names), encoder_) elif isinstance(term, Column): - self.variables_[term] = Variable((term,), term.name, None, pure_columns=True) + self.features_[term] = Feature((term,), term.name, None, pure_columns=True) else: - raise ValueError('each element in a term should be a Variable, Column or identify a column') + raise ValueError('each element in a term should be a Feature, Column or identify a column') # build the mapping of terms to columns and column names self.column_names_ = {} self.column_map_ = {} - self.terms_ = [self.variables_[t] for t in self.terms] + self.terms_ = [self.features_[t] for t in self.terms] idx = 0 if self.intercept: @@ -354,12 +355,61 @@ def transform(self, X, y=None): # ModelSpec specific methods + @property + def names(self, help='Name for each term in model specification.'): + names = [] + if self.intercept: + names = ['intercept'] + return names + [t.name for t in self.terms_] + + + def build_submodel(self, + X, + terms): + """ + Build design on X after fitting. + + Parameters + ---------- + X : array-like + X on which columns are evaluated. + + terms : [Feature] + Sequence of features + + Returns + ------- + D : array-like + Design matrix created with `terms` + """ + + return build_model(self.column_info_, + X, + terms, + intercept=self.intercept, + encoders=self.encoders_) + def build_sequence(self, X, anova_type='sequential'): """ Build implied sequence of submodels based on successively including more terms. + + Parameters + ---------- + X : array-like + X on which columns are evaluated. + + anova_type: str + One of "sequential" or "drop". + + Returns + ------- + + models : generator + Generator for sequence of models for ANOVA. + """ check_is_fitted(self) @@ -412,8 +462,11 @@ def fit_encoder(encoders, var, X): Parameters ---------- - var : Variable - Variable whose encoder will be fit. + encoders : dict + Dictionary of encoders for each feature. + + var : Feature + Feature whose encoder will be fit. X : array-like X on which encoder will be fit. @@ -425,7 +478,7 @@ def fit_encoder(encoders, var, X): def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): """ - Build columns for a Variable from X. + Build columns for a Feature from X. Parameters ---------- @@ -437,11 +490,11 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): X : array-like X on which columns are evaluated. - var : Variable - Variable whose columns will be built, typically a key in `column_info`. + var : Feature + Feature whose columns will be built, typically a key in `column_info`. encoders : dict - Dict that stores encoder of each Variable. + Dict that stores encoder of each Feature. col_cache: dict Dict where columns will be stored -- @@ -468,7 +521,7 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): cols, name = col_cache[joblib_hash([var, X])] else: cols, names = var.get_columns(X, fit=fit) - elif isinstance(var, Variable): + elif isinstance(var, Feature): cols = [] names = [] for v in var.variables: @@ -517,7 +570,7 @@ def build_columns(column_info, X, var, encoders={}, col_cache={}, fit=False): else: - raise ValueError('expecting either a column or a Variable') + raise ValueError('expecting either a column or a Feature') val = pd.DataFrame(np.asarray(cols), columns=names) if isinstance(X, (pd.DataFrame, pd.Series)): @@ -547,11 +600,11 @@ def build_model(column_info, X : array-like X on which columns are evaluated. - terms : [Variable] - Sequence of variables + terms : [Feature] + Sequence of features encoders : dict - Dict that stores encoder of each Variable. + Dict that stores encoder of each Feature. Returns ------- @@ -594,15 +647,15 @@ def build_model(column_info, else: return zero -def derived_variable(variables, encoder=None, name=None, use_transform=True): +def derived_feature(variables, encoder=None, name=None, use_transform=True): """ - Create a Variable, optionally + Create a Feature, optionally applying an encoder to the stacked columns. Parameters ---------- - variables : [column identifier, Column, Variable] + variables : [column identifier, Column, Feature] Variables to apply transform to. Could be column identifiers or variables: all columns will be stacked before encoding. @@ -616,12 +669,12 @@ def derived_variable(variables, encoder=None, name=None, use_transform=True): Returns ------- - var : Variable + var : Feature """ if name is None: name = str(encoder) - var = Variable(tuple([v for v in variables]), + var = Feature(tuple([v for v in variables]), name, encoder, use_transform=use_transform, @@ -646,7 +699,7 @@ def contrast(col, Returns ------- - var : Variable + var : Feature """ @@ -674,7 +727,7 @@ def ordinal(col, *args, **kwargs): Returns ------- - var : Variable + var : Feature """ @@ -693,7 +746,7 @@ def ordinal(col, *args, **kwargs): name = f'{shortname}({name})' - return derived_variable([col], + return derived_feature([col], name=name, encoder=encoder) @@ -704,7 +757,7 @@ def poly(col, name=None): """ - Create a polynomial Variable + Create a polynomial Feature for a given column. Additional `args` and `kwargs` @@ -732,7 +785,7 @@ def poly(col, Returns ------- - var : Variable + var : Feature """ shortname, klass = 'poly', Poly encoder = klass(degree=degree, @@ -757,13 +810,13 @@ def poly(col, name = f'{shortname}({name})' - return derived_variable([col], + return derived_feature([col], name=name, encoder=encoder) def ns(col, intercept=False, name=None, **spline_args): """ - Create a natural spline Variable + Create a natural spline Feature for a given column. Additional *spline_args* @@ -783,7 +836,7 @@ def ns(col, intercept=False, name=None, **spline_args): Returns ------- - var : Variable + var : Feature """ shortname, klass = 'ns', NaturalSpline @@ -800,13 +853,13 @@ def ns(col, intercept=False, name=None, **spline_args): name = f'{shortname}({name})' encoder = klass(intercept=intercept, **spline_args) - return derived_variable([col], + return derived_feature([col], name=name, encoder=encoder) def bs(col, intercept=False, name=None, **spline_args): """ - Create a B-spline Variable + Create a B-spline Feature for a given column. Additional args and *spline_args* @@ -827,7 +880,7 @@ def bs(col, intercept=False, name=None, **spline_args): Returns ------- - var : Variable + var : Feature """ shortname, klass = 'bs', BSpline @@ -844,7 +897,7 @@ def bs(col, intercept=False, name=None, **spline_args): name = f'{shortname}({name})' encoder = klass(intercept=intercept, **spline_args) - return derived_variable([col], + return derived_feature([col], name=name, encoder=encoder) @@ -859,13 +912,13 @@ def pca(variables, name, scale=False, **pca_args): Parameters ---------- - variables : [column identifier, Column or Variable] + variables : [column identifier, Column or Feature] Sequence whose columns will be encoded by PCA. Returns ------- - var : Variable + var : Feature """ shortname, klass = 'pca', PCA @@ -880,52 +933,10 @@ def pca(variables, name, scale=False, **pca_args): if _args: name = ', '.join([name, _args]) - return derived_variable(variables, + return derived_feature(variables, name=f'{shortname}({name})', encoder=encoder) -# def clusterer(variables, name, transform, scale=False): -# """ -# Create PCA encoding of features -# from a sequence of variables. - -# Additional `args` and `kwargs` -# are passed to `PCA`. - -# Parameters -# ---------- - -# variables : [column identifier, Column or Variable] -# Sequence whose columns will be encoded by PCA. - -# name: str -# name for the Variable - -# transform: Transformer -# A transform with a `predict` method. - -# Returns -# ------- - -# var : Variable - -# """ - -# if scale: -# scaler = StandardScaler(with_mean=True, -# with_std=True) -# encoder = make_pipeline(scaler, transform) -# else: -# encoder = transform - -# intermed = Variable((derived_variable(*variables, -# name='cluster_intermed', -# encoder=encoder, -# use_transform=False),), -# name=f'Cat({encoder}({name}))', -# encoder=Contrast(method='drop')) - -# return intermed def _argstring(*args, **kwargs): _args = ', '.join([str(a) for a in args]) diff --git a/ISLP/models/strategy.py b/ISLP/models/strategy.py index 028ac94..f237db3 100644 --- a/ISLP/models/strategy.py +++ b/ISLP/models/strategy.py @@ -74,9 +74,9 @@ def __init__(self, Minumum number of terms to select max_terms: int (default: 0) Maximum number of terms to select - lower_terms: [Variable] + lower_terms: [Feature] Subset of terms to keep: smallest model. - upper_terms: [Variable] + upper_terms: [Feature] Largest possible model. validator: callable Callable taking a single argument: state, @@ -216,9 +216,9 @@ class Stepwise(MinMaxCandidates): Minumum number of terms to select max_terms: int (default: 1) Maximum number of terms to select - lower_terms: [Variable] + lower_terms: [Feature] Subset of terms to keep: smallest model. - upper_terms: [Variable] + upper_terms: [Feature] Largest possible model. constraints: {array-like} (optional), shape [n_terms, n_terms] Boolean matrix decribing a dag with [i,j] nonzero implying that j is @@ -342,9 +342,9 @@ def first_peak(model_spec, Minumum number of terms to select max_terms: int (default: 1) Maximum number of terms to select - lower_terms: [Variable] + lower_terms: [Feature] Subset of terms to keep: smallest model. - upper_terms: [Variable] + upper_terms: [Feature] Largest possible model. initial_terms: column identifiers, default=[] Subset of terms to be used to initialize when direction @@ -441,9 +441,9 @@ def fixed_steps(model_spec, max_terms: int (default: None) Maximum number of terms to select. If None defaults to number of terms in *model_spec*. - lower_terms: [Variable] + lower_terms: [Feature] Subset of terms to keep: smallest model. - upper_terms: [Variable] + upper_terms: [Feature] Largest possible model. initial_terms: column identifiers, default=[] Subset of terms to be used to initialize. @@ -506,9 +506,9 @@ def min_max(model_spec, Minumum number of terms to select max_terms: int (default: 1) Maximum number of terms to select - lower_terms: [Variable] + lower_terms: [Feature] Subset of terms to keep: smallest model. - upper_terms: [Variable] + upper_terms: [Feature] Largest possible model. validator: callable Callable taking a single argument: state, diff --git a/ISLP/models/tests/test_model_matrix.py b/ISLP/models/tests/test_model_matrix.py index 51e079c..a2759f4 100644 --- a/ISLP/models/tests/test_model_matrix.py +++ b/ISLP/models/tests/test_model_matrix.py @@ -2,7 +2,7 @@ from sklearn.base import clone from ISLP.transforms import Poly, NaturalSpline, BSpline, Interaction -from ISLP.models.model_spec import ModelSpec, Variable, ns, bs, poly, pca, contrast, Contrast +from ISLP.models.model_spec import ModelSpec, Feature, ns, bs, poly, pca, contrast, Contrast, build_model from sklearn.preprocessing import (OneHotEncoder, OrdinalEncoder) @@ -119,7 +119,6 @@ def test_dataframe4(): np.testing.assert_allclose(MX, MX2) print(MX2.columns) - return M, D def test_dataframe5(): @@ -144,7 +143,7 @@ def test_dataframe6(): rng = np.random.default_rng(11) X = rng.standard_normal((50,5)) D = pd.DataFrame(X, columns=['A','B','C','D','E']) - W = Variable(('A','E'), 'AE', None) + W = Feature(('A','E'), 'AE', None) D['D'] = pd.Categorical(rng.choice(['a','b','c'], 50, replace=True)) D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) @@ -178,7 +177,7 @@ def test_dataframe8(): poly = Poly(degree=3) # raises a ValueError because poly will have been already fit -- need new instance of Poly - W = Variable(('A',), 'poly(A)', poly) + W = Feature(('A',), 'poly(A)', poly) M = ModelSpec(terms=list(D.columns.drop(['Y','C'])) + [(W,'E')], default_encoders=default_encoders) MX = M.fit_transform(D) @@ -196,8 +195,8 @@ def test_dataframe9(): poly = Poly(degree=3) # raises a ValueError because poly will have been already fit -- need new instance of Poly - W = Variable(('A',), 'poly(A)', poly) - U = Variable(('B',), 'poly(B)', clone(poly)) + W = Feature(('A',), 'poly(A)', poly) + U = Feature(('B',), 'poly(B)', clone(poly)) M = ModelSpec(terms=list(D.columns.drop(['Y','C'])) + [W,U], default_encoders=default_encoders) MX = M.fit_transform(D) @@ -210,8 +209,8 @@ def test_dataframe10(): rng = np.random.default_rng(15) X = rng.standard_normal((50,5)) D = pd.DataFrame(X, columns=['A','B','C','D','E']) - W = Variable(('A','E'), 'AE', None) - U = Variable((W, 'C'), 'WC', None) + W = Feature(('A','E'), 'AE', None) + U = Feature((W, 'C'), 'WC', None) D['D'] = pd.Categorical(rng.choice(['a','b','c'], 50, replace=True)) D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) @@ -258,7 +257,11 @@ def test_submodel(): M.fit(D) MX = M.transform(D) - MXsub = M.build_submodel(D, M.terms[:2]) + MXsub = build_model(M.column_info_, + D, + M.terms[:2], + intercept=M.intercept, + encoders=M.encoders_) print(MX.columns) print(MXsub.columns) @@ -275,7 +278,11 @@ def test_contrast(): M.fit(D) MX = M.transform(D) - MXsub = M.build_submodel(D, M.terms[:2]) + MXsub = build_model(M.column_info_, + D, + M.terms[:2], + intercept=M.intercept, + encoders=M.encoders_) print(method, MX.columns) print(MXsub.columns) @@ -309,7 +316,7 @@ def test_pca(): X = rng.standard_normal((50,8)) D = pd.DataFrame(X, columns=['A','B','C','D','E', 'F', 'G', 'H']) - pca_ = Variable(('A','B','C','D'), 'pca(ABCD)', PCA(n_components=2)) + pca_ = Feature(('A','B','C','D'), 'pca(ABCD)', PCA(n_components=2)) M = ModelSpec(terms=[poly('F', intercept=True, degree=3), pca_]) diff --git a/docs/jupyterbook/models/anova.ipynb b/docs/jupyterbook/models/anova.ipynb new file mode 100644 index 0000000..2bfe2ae --- /dev/null +++ b/docs/jupyterbook/models/anova.ipynb @@ -0,0 +1,633 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee33d364", + "metadata": {}, + "source": [ + "# ANOVA using `ModelSpec`\n", + "\n", + "\n", + "In this lab we illustrate how to run create specific ANOVA analyses\n", + "using `ModelSpec`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c70fbaa", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from statsmodels.api import OLS\n", + "from statsmodels.stats.anova import anova_lm\n", + "\n", + "from ISLP import load_data\n", + "from ISLP.models import (ModelSpec,\n", + " derived_feature,\n", + " summarize)" + ] + }, + { + "cell_type": "markdown", + "id": "333a49cf", + "metadata": {}, + "source": [ + "### Forward Selection\n", + " \n", + "We will apply the forward-selection approach to the `Hitters` \n", + "data. We wish to predict a baseball player’s `Salary` on the\n", + "basis of various statistics associated with performance in the\n", + "previous year." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8a708215", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "59" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hitters = load_data('Hitters')\n", + "np.isnan(Hitters['Salary']).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "dad5e991", + "metadata": {}, + "source": [ + " \n", + " We see that `Salary` is missing for 59 players. The\n", + "`dropna()` method of data frames removes all of the rows that have missing\n", + "values in any variable (by default --- see `Hitters.dropna?`)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac7086a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n", + " 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League', 'Division',\n", + " 'PutOuts', 'Assists', 'Errors', 'Salary', 'NewLeague'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hitters = Hitters.dropna()\n", + "Hitters.columns" + ] + }, + { + "cell_type": "markdown", + "id": "1a0a3521-be74-40df-a404-3895d80a11dc", + "metadata": {}, + "source": [ + "## Grouping variables\n", + "\n", + "A look at the [description](https://islp.readthedocs.io/en/latest/datasets/Hitters.html) of the data shows\n", + "that there are both career and 1986 offensive stats, as well as some defensive stats.\n", + "\n", + "Let's group the offensive into recent and career offensive stats, as well as a group of defensive variables." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a215e43b-7bc8-4bdd-91cf-40d717cd7978", + "metadata": {}, + "outputs": [], + "source": [ + "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", + " name='offense_1986')\n", + "offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'],\n", + " name='offense_career')\n", + "defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'],\n", + " name='defense_1986')\n", + "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", + " name='confounders')" + ] + }, + { + "cell_type": "markdown", + "id": "aa15fd0c-1e8a-431e-8425-c61da8439976", + "metadata": {}, + "source": [ + "We'll first do a sequential ANOVA where terms are added sequentially" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40cd6c28", + "metadata": {}, + "outputs": [], + "source": [ + "design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters)\n", + "Y = np.array(Hitters['Salary'])\n", + "X = design.transform(Hitters)" + ] + }, + { + "cell_type": "markdown", + "id": "074120b1", + "metadata": {}, + "source": [ + "Along with a score we need to specify the search strategy. This is done through the object\n", + "`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()`\n", + "runs forward stepwise until any further additions to the model do not result\n", + "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n", + "runs a fixed number of steps of stepwise search." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e65f5607", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept148.218773.5952.0140.045
Division[W]-116.040440.188-2.8870.004
League[N]63.750379.0060.8070.421
NewLeague[N]-24.398978.843-0.3090.757
AtBat-1.95090.624-3.1250.002
Hits7.43952.3633.1480.002
HmRun4.34496.1900.7020.483
Runs-2.33122.971-0.7850.433
RBI-1.06702.595-0.4110.681
Walks6.21961.8253.4090.001
PutOuts0.28270.0773.6610.000
Assists0.37550.2201.7050.089
Errors-3.29404.377-0.7530.452
CAtBat-0.18870.120-1.5720.117
CHits0.16360.6650.2460.806
CHmRun-0.15171.612-0.0940.925
CRuns1.47160.7471.9710.050
CRBI0.80210.6911.1610.247
CWalks-0.81240.327-2.4810.014
\n", + "
" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 148.2187 73.595 2.014 0.045\n", + "Division[W] -116.0404 40.188 -2.887 0.004\n", + "League[N] 63.7503 79.006 0.807 0.421\n", + "NewLeague[N] -24.3989 78.843 -0.309 0.757\n", + "AtBat -1.9509 0.624 -3.125 0.002\n", + "Hits 7.4395 2.363 3.148 0.002\n", + "HmRun 4.3449 6.190 0.702 0.483\n", + "Runs -2.3312 2.971 -0.785 0.433\n", + "RBI -1.0670 2.595 -0.411 0.681\n", + "Walks 6.2196 1.825 3.409 0.001\n", + "PutOuts 0.2827 0.077 3.661 0.000\n", + "Assists 0.3755 0.220 1.705 0.089\n", + "Errors -3.2940 4.377 -0.753 0.452\n", + "CAtBat -0.1887 0.120 -1.572 0.117\n", + "CHits 0.1636 0.665 0.246 0.806\n", + "CHmRun -0.1517 1.612 -0.094 0.925\n", + "CRuns 1.4716 0.747 1.971 0.050\n", + "CRBI 0.8021 0.691 1.161 0.247\n", + "CWalks -0.8124 0.327 -2.481 0.014" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = OLS(Y, X).fit()\n", + "summarize(M)" + ] + }, + { + "cell_type": "markdown", + "id": "29d9b55f", + "metadata": {}, + "source": [ + "We'll first produce the sequential, or Type I ANOVA results. This builds up a model sequentially and compares\n", + "two successive models." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cfbe5b92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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df_residssrdf_diffss_diffFPr(>F)
0262.05.331911e+070.0NaNNaNNaN
1259.05.131263e+073.02.006478e+066.7411472.144265e-04
2253.03.589842e+076.01.541422e+0725.8935106.063309e-24
3250.03.471882e+073.01.179602e+063.9630998.730527e-03
4244.02.420857e+076.01.051025e+0717.6555965.701196e-17
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" + ], + "text/plain": [ + " df_resid ssr df_diff ss_diff F Pr(>F)\n", + "0 262.0 5.331911e+07 0.0 NaN NaN NaN\n", + "1 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 2.144265e-04\n", + "2 253.0 3.589842e+07 6.0 1.541422e+07 25.893510 6.063309e-24\n", + "3 250.0 3.471882e+07 3.0 1.179602e+06 3.963099 8.730527e-03\n", + "4 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 5.701196e-17" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])" + ] + }, + { + "cell_type": "markdown", + "id": "7092f666", + "metadata": {}, + "source": [ + "We can similarly compute the Type II ANOVA results which drops each term and compares to the full model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e2d43844", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " df_resid ssr df_diff ss_diff F \\\n", + "intercept 244.0 2.420857e+07 1.0 4.024254e+05 4.056076 \n", + "confounders 244.0 2.420857e+07 3.0 9.661738e+05 3.246046 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", + "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", + "offense_career 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 \n", + "\n", + " Pr(>F) \n", + "intercept 4.511037e-02 \n", + "confounders 2.261572e-02 \n", + "offense_1986 4.648586e-05 \n", + "defense_1986 2.415732e-03 \n", + "offense_career 5.701196e-17 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D_full = design.transform(Hitters)\n", + "OLS_full = OLS(Y, D_full).fit()\n", + "dfs = []\n", + "for d in design.build_sequence(Hitters, anova_type='drop'):\n", + " dfs.append(anova_lm(OLS(Y,d).fit(), OLS_full).iloc[1:])\n", + "df = pd.concat(dfs)\n", + "df.index = design.names\n", + "df" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" + }, + "kernelspec": { + "display_name": "islp_test", + "language": "python", + "name": "islp_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/jupyterbook/models/anova.md b/docs/jupyterbook/models/anova.md new file mode 100644 index 0000000..054ce3e --- /dev/null +++ b/docs/jupyterbook/models/anova.md @@ -0,0 +1,111 @@ +--- +jupytext: + formats: source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.14.5 +kernelspec: + display_name: islp_test + language: python + name: islp_test +--- + +# ANOVA using `ModelSpec` + + +In this lab we illustrate how to run create specific ANOVA analyses +using `ModelSpec`. + +```{code-cell} ipython3 +import numpy as np +import pandas as pd + +from statsmodels.api import OLS +from statsmodels.stats.anova import anova_lm + +from ISLP import load_data +from ISLP.models import (ModelSpec, + derived_feature, + summarize) +``` + +### Forward Selection + +We will apply the forward-selection approach to the `Hitters` +data. We wish to predict a baseball player’s `Salary` on the +basis of various statistics associated with performance in the +previous year. + +```{code-cell} ipython3 +Hitters = load_data('Hitters') +np.isnan(Hitters['Salary']).sum() +``` + + + We see that `Salary` is missing for 59 players. The +`dropna()` method of data frames removes all of the rows that have missing +values in any variable (by default --- see `Hitters.dropna?`). + +```{code-cell} ipython3 +Hitters = Hitters.dropna() +Hitters.columns +``` + +## Grouping variables + +A look at the [description](https://islp.readthedocs.io/en/latest/datasets/Hitters.html) of the data shows +that there are both career and 1986 offensive stats, as well as some defensive stats. + +Let's group the offensive into recent and career offensive stats, as well as a group of defensive variables. + +```{code-cell} ipython3 +offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'], + name='offense_1986') +offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'], + name='offense_career') +defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'], + name='defense_1986') +confounders = derived_feature(['Division', 'League', 'NewLeague'], + name='confounders') +``` + +We'll first do a sequential ANOVA where terms are added sequentially + +```{code-cell} ipython3 +design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters) +Y = np.array(Hitters['Salary']) +X = design.transform(Hitters) +``` + +Along with a score we need to specify the search strategy. This is done through the object +`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()` +runs forward stepwise until any further additions to the model do not result +in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()` +runs a fixed number of steps of stepwise search. + +```{code-cell} ipython3 +M = OLS(Y, X).fit() +summarize(M) +``` + +We'll first produce the sequential, or Type I ANOVA results. This builds up a model sequentially and compares +two successive models. + +```{code-cell} ipython3 +anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')]) +``` + +We can similarly compute the Type II ANOVA results which drops each term and compares to the full model. + +```{code-cell} ipython3 +D_full = design.transform(Hitters) +OLS_full = OLS(Y, D_full).fit() +dfs = [] +for d in design.build_sequence(Hitters, anova_type='drop'): + dfs.append(anova_lm(OLS(Y,d).fit(), OLS_full).iloc[1:]) +df = pd.concat(dfs) +df.index = design.names +df +``` diff --git a/docs/jupyterbook/models/derived.ipynb b/docs/jupyterbook/models/derived.ipynb deleted file mode 100644 index cc1b0ac..0000000 --- a/docs/jupyterbook/models/derived.ipynb +++ /dev/null @@ -1,2125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "38217f02", - "metadata": {}, - "source": [ - "# Building design matrices with `ModelSpec`\n", - "\n", - "Force rebuild" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3107d1f9", - "metadata": {}, - "outputs": [], - "source": [ - "x=4\n", - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cdc46a4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "e0a2a83a", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "68b40caf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "35558d88", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e5e81a95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "4bbf9e13", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1ad729b3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d05e9ec8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "b4e9ee33", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "64ac65d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "620f0e01", - "metadata": {}, - "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "77b898e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "4580a6bf", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c2dab855", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['OIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5e7963d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "markdown", - "id": "6b689966", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ff3b96b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7e87da20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" - ] - }, - { - "cell_type": "markdown", - "id": "4f2030ac", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "27fc4fb3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "16316981", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "ef3f2bd0", - "metadata": {}, - "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "dd9c7fa6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" - ] - }, - { - "cell_type": "markdown", - "id": "5fc4cc45", - "metadata": {}, - "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "49d7fb46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" - ] - }, - { - "cell_type": "markdown", - "id": "bdfc0fe9", - "metadata": {}, - "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "cf6f3f4c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "1552d19a", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "12d955dd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "f5ea292d", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ae2af29b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "57305dbe", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "89656ec4", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "f6cb8167", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "547cb625", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ff5b41d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "932759cf", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e2190b00", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6545c5da", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "cd088b51", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "8f37ae20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "184aefc2", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e4134980", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "53808f3b", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "62059c57", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "ded12f69", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "fbb509d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "f01391e4", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "10df55ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "b43099fb", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "50bce64d", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "2eb2ff16", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "6686dff8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "0e0eafd7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "43cce209", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "99bf408e", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "11c19ebf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "4b48e5d2", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "81f641ba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "79f7eb4d", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "2afb3b5d", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "c44692ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "c0bfb2a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "d263056c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "edf0dc68", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "82071a54", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cd18a4a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "229fa32d", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "b8c52dbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "e7f93464", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "4094c01f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "d448c9ca", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "634e05c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "4c09c93f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "48c1989f", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "85a28d87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3)', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "e17c8a9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3)[0] 10.036373\n", - "poly(Income, 3)[1] -2.799156\n", - "poly(Income, 3)[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "944f56d6", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1889caca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "bd4dca31", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "70fae990", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca" - ] - }, - { - "cell_type": "markdown", - "id": "2d812694", - "metadata": {}, - "source": [ - "## Custom encoding\n", - "\n", - "Instead of PCA we might run some clustering on some features and then uses the clusters to\n", - "create new features. This can be done with `derived_variable`. Indeed, `pca`, `ns` and `bs` are all examples\n", - "of this." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "8e5d2305", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import derived_variable, Contrast" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "8a40c663", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 1, 2, 1, 0, 1, 0, 0, 0, 1, 2, 2, 0, 1, 2, 1, 0, 0, 0, 2,\n", - " 2, 2, 1, 2, 1, 0, 0, 1, 0, 1, 2, 1, 2, 0, 0, 2, 2, 2, 0, 2, 0, 2,\n", - " 0, 2, 0, 0, 2, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 2, 2, 0, 1, 2,\n", - " 0, 1, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 2, 0, 1, 0, 0, 2, 2, 0, 1, 2,\n", - " 2, 2, 2, 2, 0, 2, 0, 2, 2, 0, 1, 2, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0,\n", - " 0, 1, 0, 2, 0, 2, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0,\n", - " 0, 0, 2, 1, 0, 2, 1, 1, 1, 2, 0, 0, 2, 0, 2, 1, 0, 0, 0, 1, 2, 2,\n", - " 1, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 1, 0, 0, 1, 1, 1, 0, 0, 2, 0,\n", - " 1, 0, 0, 2, 1, 0, 2, 1, 2, 1, 0, 2, 2, 1, 1, 2, 2, 0, 1, 1, 2, 2,\n", - " 1, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 2, 2, 1, 1, 0, 0, 1, 2, 2, 1, 1,\n", - " 1, 2, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 1, 2, 1, 2, 2, 0, 0, 0,\n", - " 2, 2, 2, 2, 1, 0, 0, 0, 1, 0, 0, 2, 1, 0, 2, 1, 2, 1, 1, 2, 1, 2,\n", - " 2, 2, 1, 1, 0, 2, 2, 2, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 2,\n", - " 1, 2, 2, 1, 1, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 0, 1, 1, 1, 1, 2, 0,\n", - " 1, 0, 1, 1, 0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 2, 1, 0, 1,\n", - " 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 0, 1, 2, 0, 2, 0, 2, 1, 1, 1, 1,\n", - " 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 0, 2, 1, 2, 1, 0, 2, 1, 1, 0, 2, 2,\n", - " 2, 2, 1, 2, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 2, 0, 0, 1, 0, 1, 1,\n", - " 2, 2, 0, 2], dtype=int32)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.cluster import KMeans\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "cluster = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "group = Variable(('Income', 'Price', 'Advertising', 'Population'), 'group', None)\n", - "X = design.build_submodel(Carseats, [group]).drop('intercept', axis=1)\n", - "cluster.fit(X.values)\n", - "cluster.predict(X.values)" - ] - }, - { - "cell_type": "markdown", - "id": "9bc38836", - "metadata": {}, - "source": [ - "For clustering, we often want to use the `predict` method rather than the `transform` method. If the ultimate\n", - "features all use `transform` then the do not even need to use these two calls to `make_pipeline`." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "8ceab9b6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept myclus\n", - "0 1.0 1\n", - "1 1.0 1\n", - "2 1.0 2\n", - "3 1.0 1\n", - "4 1.0 2\n", - ".. ... ...\n", - "395 1.0 1\n", - "396 1.0 2\n", - "397 1.0 2\n", - "398 1.0 0\n", - "399 1.0 2\n", - "\n", - "[400 rows x 2 columns]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], \n", - " name='myclus', \n", - " encoder=cluster2,\n", - " use_transform=False)\n", - "design = ModelSpec([cluster_var]).fit(Carseats)\n", - "design.transform(Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "1f9b2630", - "metadata": {}, - "source": [ - "Somewhat clunkily, we can make this a categorical variable by creating a `Variable` with a\n", - "categorical encoder." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "ffde00a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=(Variable(variables=('Income', 'Price', 'Advertising', 'Population'), name='myclus', encoder=Pipeline(steps=[('standardscaler', StandardScaler()),\n", - " ('kmeans', KMeans(n_clusters=3, random_state=0))]), use_transform=False, pure_columns=False, override_encoder_colnames=True),), name='mynewcat', encoder=Contrast(), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], \n", - " name='myclus', \n", - " encoder=cluster2,\n", - " use_transform=False)\n", - "cat_cluster = Variable((cluster_var,), name='mynewcat', encoder=Contrast(method='drop'))\n", - "cat_cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "5afeab7c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept 1 2\n", - "0 1.0 1.0 0.0\n", - "1 1.0 1.0 0.0\n", - "2 1.0 0.0 1.0\n", - "3 1.0 1.0 0.0\n", - "4 1.0 0.0 1.0\n", - ".. ... ... ...\n", - "395 1.0 1.0 0.0\n", - "396 1.0 0.0 1.0\n", - "397 1.0 0.0 1.0\n", - "398 1.0 0.0 0.0\n", - "399 1.0 0.0 1.0\n", - "\n", - "[400 rows x 3 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([cat_cluster]).fit(Carseats)\n", - "\n", - "design.transform(Carseats)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e24d5637-80fb-49bf-ac10-8ff68cb8bd8f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" - }, - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/jupyterbook/models/derived.md b/docs/jupyterbook/models/derived.md deleted file mode 100644 index 9710500..0000000 --- a/docs/jupyterbook/models/derived.md +++ /dev/null @@ -1,487 +0,0 @@ ---- -jupytext: - formats: source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: python3 - language: python - name: python3 ---- - -# Building design matrices with `ModelSpec` - -Force rebuild - -```{code-cell} ipython3 -x=4 -import numpy as np, pandas as pd -%load_ext rpy2.ipython - -from ISLP import load_data -from ISLP.models import ModelSpec - -import statsmodels.api as sm -``` - -```{code-cell} ipython3 -Carseats = load_data('Carseats') -%R -i Carseats -Carseats.columns -``` - -## Let's break up income into groups - -```{code-cell} ipython3 -Carseats['OIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H']) -Carseats['OIncome'] -``` - -Let's also create an unordered version - -```{code-cell} ipython3 -Carseats['UIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H'], - ordered=False) -Carseats['UIncome'] -``` - -## A simple model - -```{code-cell} ipython3 -design = ModelSpec(['Price', 'Income']) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -Y = Carseats['Sales'] -M = sm.OLS(Y, X).fit() -M.params -``` - -## Basic procedure - -The design matrix is built by cobbling together a set of columns and possibly transforming them. -A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit` -is to inspect a dataframe for column info. The column `ShelveLoc` is categorical: - -```{code-cell} ipython3 -Carseats['ShelveLoc'] -``` - -This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods -`get_columns` and `fit_encoder`. - -```{code-cell} ipython3 -design.column_info_['ShelveLoc'] -``` - -It recognized ordinal columns as well. - -```{code-cell} ipython3 -design.column_info_['OIncome'] -``` - -```{code-cell} ipython3 -income = design.column_info_['Income'] -cols, names = income.get_columns(Carseats) -(cols[:4], names) -``` - -## Encoding a column - -In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical -variables usually are encoded by several columns, typically one less than the number of categories. -This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform -model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits -its encoder the first time data is passed to it. - -```{code-cell} ipython3 -shelve = design.column_info_['ShelveLoc'] -cols, names = shelve.get_columns(Carseats) -(cols[:4], names) -``` - -```{code-cell} ipython3 -oincome = design.column_info_['OIncome'] -oincome.get_columns(Carseats)[0][:4] -``` - -## The terms - -The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through -the `terms` argument which should be a sequence. The elements of `terms` are often -going to be strings (or tuples of strings for interactions, see below) but are converted to a -`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple. - -```{code-cell} ipython3 -design.terms -``` - -```{code-cell} ipython3 -design.terms_ -``` - -While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A -`Variable` can also create columns through the `build_columns` method of `ModelSpec` - -```{code-cell} ipython3 -price = design.terms_[0] -design.build_columns(Carseats, price) -``` - -Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The -tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then -is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during -the call to `ModelSpec.fit`. - -```{code-cell} ipython3 -from ISLP.models.model_spec import Variable - -new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None) -design.build_columns(Carseats, new_var) -``` - -Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the -arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`. - -```{code-cell} ipython3 -from sklearn.decomposition import PCA -pca = PCA(n_components=2) -pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit` -pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca) -design.build_columns(Carseats, pca_var) -``` - -The elements of the `variables` attribute may be column identifiers ( `"Price"`), `Column` instances (`price`) -or `Variable` instances (`pca_var`). - -```{code-cell} ipython3 -fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None) -design.build_columns(Carseats, fancy_var) -``` - -We can of course run PCA again on these features (if we wanted). - -```{code-cell} ipython3 -pca2 = PCA(n_components=2) -pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit` -pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2) -design.build_columns(Carseats, pca2_var) -``` - -## Building the design matrix - -With these notions in mind, the final design is essentially then - -```{code-cell} ipython3 -X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4] -``` - -An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is -a dataframe the index is adjusted accordingly. - -```{code-cell} ipython3 -design.intercept -``` - -```{code-cell} ipython3 -design.transform(Carseats)[:4] -``` - -## Predicting - -Constructing the design matrix at any values is carried out by the `transform` method. - -```{code-cell} ipython3 -new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]}) -new_X = design.transform(new_data) -M.get_prediction(new_X).predicted_mean -``` - -```{code-cell} ipython3 -%%R -i new_data,Carseats -predict(lm(Sales ~ Price + Income, data=Carseats), new_data) -``` - -### Difference between using `pd.DataFrame` and `np.ndarray` - -If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns. - -If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so, -in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. - -```{code-cell} ipython3 -Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']]) -design_np = ModelSpec([0,3]).fit(Carseats_np) -design_np.transform(Carseats_np)[:4] -``` - -The following will fail for hopefully obvious reasons - -```{code-cell} ipython3 -try: - new_D = np.zeros((2,2)) - new_D[:,0] = [10,20] - new_D[:,1] = [40,50] - M.get_prediction(new_D).predicted_mean -except ValueError as e: - print(e) -``` - -Ultimately, `M` expects 3 columns for new predictions because it was fit -with a matrix having 3 columns (the first representing an intercept). - -We might be tempted to try as with the `pd.DataFrame` and produce -an `np.ndarray` with only the necessary variables. - -```{code-cell} ipython3 -try: - new_X = np.zeros((2,2)) - new_X[:,0] = [10,20] - new_X[:,1] = [40,50] - new_D = design_np.transform(new_X) - M.get_prediction(new_D).predicted_mean -except IndexError as e: - print(e) -``` - -This fails because `design_np` is looking for column `3` from its `terms`: - -```{code-cell} ipython3 -design_np.terms_ -``` - -However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed -represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`: - -```{code-cell} ipython3 -new_X = np.zeros((2,4)) -new_X[:,0] = [10,20] -new_X[:,3] = [40,50] -new_D = design_np.transform(new_X) -M.get_prediction(new_D).predicted_mean -``` - -Given this subtlety about needing to supply arrays with identical column structure to `transform` when -using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case. - -+++ - -## A model with some categorical variables - -Categorical variables become `Column` instances with encoders. - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats) -design.column_info_['UIncome'] -``` - -```{code-cell} ipython3 -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Getting the encoding you want - -By default the level dropped by `ModelSpec` will be the first of the `categories_` values from -`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems -as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)` -where `new_encoder` would somehow drop the column we want dropped. - -However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`: - -```{code-cell} ipython3 -design.column_info_['UIncome'] -``` - -This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when -we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest -and we need a way to allow different encodings of the same column of `Carseats` - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - - We can create a new -`Column` with the encoder we want. For categorical variables, there is a convenience function to do so. - -```{code-cell} ipython3 -from ISLP.models.model_spec import contrast -pref_encoding = contrast('UIncome', 'drop', 'L') -``` - -```{code-cell} ipython3 -design.build_columns(Carseats, pref_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Interactions - -We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument -to `ModelSpec`. - -```{code-cell} ipython3 -design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with -`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`. - -```{code-cell} ipython3 -design.terms_[0] -``` - -Comparing this to the previous `R` model. - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - -We note a few important things: - -1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these -columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!** - -2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.** - -A few notes: - -- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.** - -- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily. - -```{code-cell} ipython3 -full_encoding = contrast('UIncome', None) -design.build_columns(Carseats, full_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -## Special encodings - -For flexible models, we may want to consider transformations of features, i.e. polynomial -or spline transformations. Given transforms that follow the `fit/transform` paradigm -we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms` -package includes a `Poly` transform - -```{code-cell} ipython3 -from ISLP.models.model_spec import poly -poly('Income', 3) -``` - -```{code-cell} ipython3 -design = ModelSpec([poly('Income', 3), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -Compare: - -```{code-cell} ipython3 -%%R -lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef -``` - -## Splines - -Support for natural and B-splines is also included - -```{code-cell} ipython3 -from ISLP.models.model_spec import ns, bs, pca -``` - -## Custom encoding - -Instead of PCA we might run some clustering on some features and then uses the clusters to -create new features. This can be done with `derived_variable`. Indeed, `pca`, `ns` and `bs` are all examples -of this. - -```{code-cell} ipython3 -from ISLP.models.model_spec import derived_variable, Contrast -``` - -```{code-cell} ipython3 -from sklearn.cluster import KMeans -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import StandardScaler -cluster = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0)) -group = Variable(('Income', 'Price', 'Advertising', 'Population'), 'group', None) -X = design.build_submodel(Carseats, [group]).drop('intercept', axis=1) -cluster.fit(X.values) -cluster.predict(X.values) -``` - -For clustering, we often want to use the `predict` method rather than the `transform` method. If the ultimate -features all use `transform` then the do not even need to use these two calls to `make_pipeline`. - -```{code-cell} ipython3 -cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0)) -cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], - name='myclus', - encoder=cluster2, - use_transform=False) -design = ModelSpec([cluster_var]).fit(Carseats) -design.transform(Carseats) -``` - -Somewhat clunkily, we can make this a categorical variable by creating a `Variable` with a -categorical encoder. - -```{code-cell} ipython3 -cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0)) -cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], - name='myclus', - encoder=cluster2, - use_transform=False) -cat_cluster = Variable((cluster_var,), name='mynewcat', encoder=Contrast(method='drop')) -cat_cluster -``` - -```{code-cell} ipython3 -design = ModelSpec([cat_cluster]).fit(Carseats) - -design.transform(Carseats) -``` - -```{code-cell} ipython3 - -``` diff --git a/docs/jupyterbook/models/selection.ipynb b/docs/jupyterbook/models/selection.ipynb index 3a7d002..0f597af 100644 --- a/docs/jupyterbook/models/selection.ipynb +++ b/docs/jupyterbook/models/selection.ipynb @@ -2,2723 +2,259 @@ "cells": [ { "cell_type": "markdown", - "id": "72bae06a", + "id": "247387ec-1477-42e6-9e69-cad1cacb5721", "metadata": {}, "source": [ - "# Model selection using `ModelSpec`" + "# Model selection using `ModelSpec`\n", + "\n", + "\n", + "In this lab we illustrate how to run forward stepwise model selection\n", + "using the model specification capability of `ModelSpec`." ] }, { "cell_type": "code", "execution_count": 1, - "id": "ae6bd850", + "id": "4720bb2a-6bec-4e91-a57e-9689aa4f0532", "metadata": {}, "outputs": [], "source": [ - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from statsmodels.api import OLS\n", "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5ac10e72", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "80a586d9", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "850356ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "e24def3a", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "edf83080", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "aa22bb9c", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "38d92522", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cfc2056f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "4674c345", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5688f0ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" + "from ISLP.models import (ModelSpec,\n", + " Stepwise,\n", + " sklearn_selected)" ] }, { "cell_type": "markdown", - "id": "4ae28ffa", + "id": "1c224240-ce8b-47f3-a85a-052c43038b26", "metadata": {}, "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." + "### Forward Selection\n", + " \n", + "We will apply the forward-selection approach to the `Hitters` \n", + "data. We wish to predict a baseball player’s `Salary` on the\n", + "basis of various statistics associated with performance in the\n", + "previous year." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "5f8926fd", + "execution_count": 2, + "id": "2adc66cc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" + "59" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design.column_info_['ShelveLoc']" + "Hitters = load_data('Hitters')\n", + "np.isnan(Hitters['Salary']).sum()" ] }, { "cell_type": "markdown", - "id": "966f53a5", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a137fa1e", + "id": "40c9a484", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "design.column_info_['OIncome']" + " \n", + " We see that `Salary` is missing for 59 players. The\n", + "`dropna()` method of data frames removes all of the rows that have missing\n", + "values in any variable (by default --- see `Hitters.dropna?`)." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "3390dcb0", + "execution_count": 3, + "id": "1869fdab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" + "(263, 20)" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" + "Hitters = Hitters.dropna()\n", + "Hitters.shape" ] }, { "cell_type": "markdown", - "id": "b6667415", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a1b42dbd", + "id": "0a1fe9e6", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" + "We first choose the best model using forward selection based on AIC. This score\n", + "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n", + "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n", + " our scoring function computes the negative AIC statistic." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "31367988", + "execution_count": 4, + "id": "76bd8110", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" + "def negAIC(estimator, X, Y):\n", + " \"Negative AIC\"\n", + " n, p = X.shape\n", + " Yhat = estimator.predict(X)\n", + " MSE = np.mean((Y - Yhat)**2)\n", + " return n + n * np.log(MSE) + 2 * (p + 1)\n", + " " ] }, { "cell_type": "markdown", - "id": "751c1487", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6e2b6155", + "id": "14ba6f49", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "design.terms" + "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n", + "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "d3e669da", + "execution_count": 5, + "id": "94e10f35", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "design.terms_" + "design = ModelSpec(Hitters.columns.drop('Salary')).fit(Hitters)\n", + "Y = np.array(Hitters['Salary'])\n", + "X = design.transform(Hitters)" ] }, { "cell_type": "markdown", - "id": "fb0a45c9", + "id": "afdda5f2", "metadata": {}, "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" + "Along with a score we need to specify the search strategy. This is done through the object\n", + "`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()`\n", + "runs forward stepwise until any further additions to the model do not result\n", + "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n", + "runs a fixed number of steps of stepwise search." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "554c67cb", + "execution_count": 6, + "id": "048c8500", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" + "strategy = Stepwise.first_peak(design,\n", + " direction='forward',\n", + " max_terms=len(design.terms))" ] }, { "cell_type": "markdown", - "id": "06956a6f", + "id": "e0c0af0e", "metadata": {}, "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." + " \n", + "We now fit a linear regression model with `Salary` as outcome using forward\n", + "selection. To do so, we use the function `sklearn_selected()` from the `ISLP.models` package. This takes\n", + "a model from `statsmodels` along with a search strategy and selects a model with its\n", + "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n", + "selected." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "dd434884", + "execution_count": 7, + "id": "26f09fe9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" + "('Assists',\n", + " 'AtBat',\n", + " 'CAtBat',\n", + " 'CHits',\n", + " 'CHmRun',\n", + " 'CRBI',\n", + " 'CRuns',\n", + " 'CWalks',\n", + " 'Division',\n", + " 'Errors',\n", + " 'Hits',\n", + " 'HmRun',\n", + " 'League',\n", + " 'NewLeague',\n", + " 'PutOuts',\n", + " 'RBI',\n", + " 'Runs',\n", + " 'Walks',\n", + " 'Years')" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" + "hitters_MSE = sklearn_selected(OLS,\n", + " strategy)\n", + "hitters_MSE.fit(Hitters, Y)\n", + "hitters_MSE.selected_state_" ] }, { "cell_type": "markdown", - "id": "5cdb088c", + "id": "4acf4792", "metadata": {}, "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." + " Using `neg_Cp` results in a smaller model, as expected, with just 4variables selected." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "519a642e", + "execution_count": 8, + "id": "a825f4d8", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" + "('Assists', 'Errors', 'League', 'NewLeague')" ] }, - "execution_count": 17, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "403921a2", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." + "hitters_Cp = sklearn_selected(OLS,\n", + " strategy,\n", + " scoring=negAIC)\n", + "hitters_Cp.fit(Hitters, Y)\n", + "hitters_Cp.selected_state_" ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b422cde1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "53e38f57", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6347acb6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "08b5ddb0", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a8eb3e33", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "97912337", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "72b5e629", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - 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" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "8624ab8c", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6052765e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9158de59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "9608bed3", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f0b8120f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "270a02a6", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4ffbce7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "bc5ff62b", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "34dae1e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "7e9da262", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "938b9430", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "083e9529", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d413a9fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "0f4b508b", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "8bcbd973", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "cf13f72e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c1fa0a90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b28aa313", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "aa764acc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "31876a29", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "bac2643c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "1485735d", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "3987c5d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "7a6631c9", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "83a9b94e", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "f0ffabea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "4a5fdc64", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ae7e3bd2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "c12ac3df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "53bf8aef", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "47723bce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "86060622", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "d7a2ab9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "2a5e7f6b", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "bbb02036", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "89106a85", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "151f3fee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "945ce7bc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "450b94dd", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "18d5c1c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3, )', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "46c7d911", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3, )[0] 10.036373\n", - "poly(Income, 3, )[1] -2.799156\n", - "poly(Income, 3, )[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "99bf13a1", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7606facd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "a4931031", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "1c1bf5f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 4.240421\n", - "ns(Income, , df=5)[0] 1.468196\n", - "ns(Income, , df=5)[1] 1.499471\n", - "ns(Income, , df=5)[2] 1.152070\n", - "ns(Income, , df=5)[3] 2.418398\n", - "ns(Income, , df=5)[4] 1.804460\n", - "ShelveLoc[Good] 4.810449\n", - "ShelveLoc[Medium] 1.881095\n", - "dtype: float64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca\n", - "design = ModelSpec([ns('Income', df=5), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "8c24254b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) ns(Income, df = 5)1 ns(Income, df = 5)2 ns(Income, df = 5)3 \n", - " 4.240421 1.468196 1.499471 1.152070 \n", - "ns(Income, df = 5)4 ns(Income, df = 5)5 ShelveLocGood ShelveLocMedium \n", - " 2.418398 1.804460 4.810449 1.881095 \n" - ] - } - ], - "source": [ - "%%R\n", - "library(splines)\n", - "lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "f9d6c4a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 3.495085\n", - "bs(Income, , df=7, degree=2)[0] 1.813118\n", - "bs(Income, , df=7, degree=2)[1] 0.961852\n", - "bs(Income, , df=7, degree=2)[2] 2.471545\n", - "bs(Income, , df=7, degree=2)[3] 2.158891\n", - "bs(Income, , df=7, degree=2)[4] 2.091625\n", - "bs(Income, , df=7, degree=2)[5] 2.600669\n", - "bs(Income, , df=7, degree=2)[6] 2.843108\n", - "ShelveLoc[Good] 4.804919\n", - "ShelveLoc[Medium] 1.880337\n", - "dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "0bf1726a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) bs(Income, df = 7, degree = 2)1 \n", - " 3.4950851 1.8131176 \n", - "bs(Income, df = 7, degree = 2)2 bs(Income, df = 7, degree = 2)3 \n", - " 0.9618523 2.4715450 \n", - "bs(Income, df = 7, degree = 2)4 bs(Income, df = 7, degree = 2)5 \n", - " 2.1588908 2.0916252 \n", - "bs(Income, df = 7, degree = 2)6 bs(Income, df = 7, degree = 2)7 \n", - " 2.6006694 2.8431084 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.8049190 1.8803375 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "914df4cf", - "metadata": {}, - "source": [ - "## PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "cc22e780", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.419405\n", - "pca(myvars, , n_components=2)[0] -0.001131\n", - "pca(myvars, , n_components=2)[1] -0.024217\n", - "ShelveLoc[Good] 4.816253\n", - "ShelveLoc[Medium] 1.924139\n", - "dtype: float64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars'), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "de571e61", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population))$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.419405 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC1 \n", - " 0.001131 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC2 \n", - " -0.024217 \n", - " ShelveLocGood \n", - " 4.816253 \n", - " ShelveLocMedium \n", - " 1.924139 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "0a103b5a", - "metadata": {}, - "source": [ - "It is of course common to scale before running PCA." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "95ca42f5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.352159\n", - "pca(myvars, , n_components=2)[0] 0.446383\n", - "pca(myvars, , n_components=2)[1] -1.219788\n", - "ShelveLoc[Good] 4.922780\n", - "ShelveLoc[Medium] 2.005617\n", - "dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars',\n", - " scale=True), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "0dc22e35", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population), scale = TRUE)$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.3522 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC1 \n", - " 0.4469 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC2 \n", - " -1.2213 \n", - " ShelveLocGood \n", - " 4.9228 \n", - " ShelveLocMedium \n", - " 2.0056 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "70347ee9", - "metadata": {}, - "source": [ - "There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead\n", - "of `np.std(ddof=1)`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "aa0c2f2e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.44694166, -1.22131519])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1))" - ] - }, - { - "cell_type": "markdown", - "id": "ab05c497", - "metadata": {}, - "source": [ - "## Model selection\n", - "\n", - "Another task requiring different design matrices is model selection. Manipulating\n", - "the `terms` attribute of a `ModelSpec` (or more precisely its more uniform version `terms_`)\n", - "can clearly allow for both exhaustive and stepwise model selection." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "9505c178", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.strategy import (Stepwise, \n", - " min_max)\n", - "from ISLP.models.generic_selector import FeatureSelector" - ] - }, - { - "cell_type": "markdown", - "id": "020c2532", - "metadata": {}, - "source": [ - "### Best subsets" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "f9aba6db", - "metadata": {}, - "outputs": [], - "source": [ - "design = ModelSpec(['Price', \n", - " 'UIncome', \n", - " 'Advertising', \n", - " 'US', \n", - " 'Income',\n", - " 'ShelveLoc',\n", - " 'Education',\n", - " 'Urban']).fit(Carseats)\n", - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "91144a3d", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error')" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "ae3cb2eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.fit(Carseats, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "e63b2744", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'ShelveLoc')" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "0a774b48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('UIncome',), ('Advertising',), ('US',), ('Income',), ('ShelveLoc',), ('Education',), ('Urban',), ('Price', 'UIncome'), ('Price', 'Advertising'), ('Price', 'US'), ('Price', 'Income'), ('Price', 'ShelveLoc'), ('Price', 'Education'), ('Price', 'Urban'), ('UIncome', 'Advertising'), ('UIncome', 'US'), ('UIncome', 'Income'), ('UIncome', 'ShelveLoc'), ('UIncome', 'Education'), ('UIncome', 'Urban'), ('Advertising', 'US'), ('Advertising', 'Income'), ('Advertising', 'ShelveLoc'), ('Advertising', 'Education'), ('Advertising', 'Urban'), ('US', 'Income'), ('US', 'ShelveLoc'), ('US', 'Education'), ('US', 'Urban'), ('Income', 'ShelveLoc'), ('Income', 'Education'), ('Income', 'Urban'), ('ShelveLoc', 'Education'), ('ShelveLoc', 'Urban'), ('Education', 'Urban'), ('Price', 'UIncome', 'Advertising'), ('Price', 'UIncome', 'US'), ('Price', 'UIncome', 'Income'), ('Price', 'UIncome', 'ShelveLoc'), ('Price', 'UIncome', 'Education'), ('Price', 'UIncome', 'Urban'), ('Price', 'Advertising', 'US'), ('Price', 'Advertising', 'Income'), ('Price', 'Advertising', 'ShelveLoc'), ('Price', 'Advertising', 'Education'), ('Price', 'Advertising', 'Urban'), ('Price', 'US', 'Income'), ('Price', 'US', 'ShelveLoc'), ('Price', 'US', 'Education'), ('Price', 'US', 'Urban'), ('Price', 'Income', 'ShelveLoc'), ('Price', 'Income', 'Education'), ('Price', 'Income', 'Urban'), ('Price', 'ShelveLoc', 'Education'), ('Price', 'ShelveLoc', 'Urban'), ('Price', 'Education', 'Urban'), ('UIncome', 'Advertising', 'US'), ('UIncome', 'Advertising', 'Income'), ('UIncome', 'Advertising', 'ShelveLoc'), ('UIncome', 'Advertising', 'Education'), ('UIncome', 'Advertising', 'Urban'), ('UIncome', 'US', 'Income'), ('UIncome', 'US', 'ShelveLoc'), ('UIncome', 'US', 'Education'), ('UIncome', 'US', 'Urban'), ('UIncome', 'Income', 'ShelveLoc'), ('UIncome', 'Income', 'Education'), ('UIncome', 'Income', 'Urban'), ('UIncome', 'ShelveLoc', 'Education'), ('UIncome', 'ShelveLoc', 'Urban'), ('UIncome', 'Education', 'Urban'), ('Advertising', 'US', 'Income'), ('Advertising', 'US', 'ShelveLoc'), ('Advertising', 'US', 'Education'), ('Advertising', 'US', 'Urban'), ('Advertising', 'Income', 'ShelveLoc'), ('Advertising', 'Income', 'Education'), ('Advertising', 'Income', 'Urban'), ('Advertising', 'ShelveLoc', 'Education'), ('Advertising', 'ShelveLoc', 'Urban'), ('Advertising', 'Education', 'Urban'), ('US', 'Income', 'ShelveLoc'), ('US', 'Income', 'Education'), ('US', 'Income', 'Urban'), ('US', 'ShelveLoc', 'Education'), ('US', 'ShelveLoc', 'Urban'), ('US', 'Education', 'Urban'), ('Income', 'ShelveLoc', 'Education'), ('Income', 'ShelveLoc', 'Urban'), ('Income', 'Education', 'Urban'), ('ShelveLoc', 'Education', 'Urban')])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "0ca1f28c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'Income')" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=3,\n", - " lower_terms=['Price'],\n", - " upper_terms=['Price', 'Income', 'Advertising'])\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error')\n", - "selector.fit(Carseats, Y)\n", - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "5c6732fa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([('Price',), ('Price', 'Advertising'), ('Price', 'Income'), ('Price', 'Advertising', 'Income')])" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "markdown", - "id": "7bb6fcc3", - "metadata": {}, - "source": [ - "### Stepwise selection" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "9985d0fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Advertising', 'Income', 'Price', 'ShelveLoc')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy = Stepwise.first_peak(design,\n", - " min_terms=0,\n", - " max_terms=6,\n", - " lower_terms=['Price'],\n", - " upper_terms=['Price', 'Income', 'Advertising', 'ShelveLoc', 'UIncome', 'US'\n", - " 'Education', 'Urban'])\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error',\n", - " cv=3)\n", - "selector.fit(Carseats, Y)\n", - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "d3cf3e9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('Price', 'UIncome'), ('Advertising', 'Price'), ('Income', 'Price'), ('Price', 'ShelveLoc'), ('Price', 'Urban'), ('Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Price', 'ShelveLoc'), ('Income', 'Price', 'ShelveLoc'), ('Price', 'ShelveLoc', 'Urban'), ('Advertising', 'Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Income', 'Price', 'ShelveLoc'), ('Advertising', 'Price', 'ShelveLoc', 'Urban'), ('Advertising', 'Income', 'Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Income', 'Price', 'ShelveLoc', 'Urban')])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "dd43ea7c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(): -8.055847677297269,\n", - " ('Price',): -6.514630258019962,\n", - " ('Price', 'UIncome'): -6.621654905418576,\n", - " ('Advertising', 'Price'): -5.825225309857156,\n", - " ('Income', 'Price'): -6.455432795910743,\n", - " ('Price', 'ShelveLoc'): -3.780183168075897,\n", - " ('Price', 'Urban'): -6.5430157266926114,\n", - " ('Price', 'ShelveLoc', 'UIncome'): -3.6938729706475004,\n", - " ('Advertising', 'Price', 'ShelveLoc'): -3.2067316025050645,\n", - " ('Income', 'Price', 'ShelveLoc'): -3.634698914456587,\n", - " ('Price', 'ShelveLoc', 'Urban'): -3.776148947585277,\n", - " ('Advertising', 'Price', 'ShelveLoc', 'UIncome'): -3.1240961493998642,\n", - " ('Advertising', 'Income', 'Price', 'ShelveLoc'): -3.0801704971796244,\n", - " ('Advertising', 'Price', 'ShelveLoc', 'Urban'): -3.207569489139369,\n", - " ('Advertising',\n", - " 'Income',\n", - " 'Price',\n", - " 'ShelveLoc',\n", - " 'UIncome'): -3.1048826894036115,\n", - " ('Advertising', 'Income', 'Price', 'ShelveLoc', 'Urban'): -3.0867130108677423}" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "7c026f0a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Advertising', 'Income', 'Price', 'ShelveLoc')" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "markdown", - "id": "b4b89d04", - "metadata": {}, - "source": [ - "### Enforcing constraints\n", - "\n", - "In models with interactions, we may often want to impose constraints on interactions and main effects.\n", - "This can be achieved here by use of a `validator` that checks whether a given model is valid.\n", - "\n", - "Suppose we want to have the following constraint: `ShelveLoc` may not be in the model unless\n", - "`Price` is in the following model." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "1c1e31d0", - "metadata": {}, - "outputs": [], - "source": [ - "design = ModelSpec(['Price', \n", - " 'Advertising', \n", - " 'Income',\n", - " 'ShelveLoc']).fit(Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "be929807", - "metadata": {}, - "source": [ - "The constraints are described with a boolean matrix with `(i,j)` as `j` is a child of `i`: so `j` should not\n", - "be in the model when `i` is not and enforced with a callable `validator` that evaluates each candidate state.\n", - "\n", - "Both `min_max_strategy` and `step_strategy` accept a `validator` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "c075b1b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('Advertising',), ('Income',), ('Price', 'Advertising'), ('Price', 'Income'), ('Price', 'ShelveLoc'), ('Advertising', 'Income'), ('Price', 'Advertising', 'Income'), ('Price', 'Advertising', 'ShelveLoc'), ('Price', 'Income', 'ShelveLoc'), ('Price', 'Advertising', 'Income', 'ShelveLoc')])" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.strategy import validator_from_constraints\n", - "constraints = np.zeros((4, 4))\n", - "constraints[0,3] = 1\n", - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=4,\n", - " validator=validator_from_constraints(design,\n", - " constraints))\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error',\n", - " cv=3)\n", - "selector.fit(Carseats, Y)\n", - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "3472d47c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'Income', 'ShelveLoc')" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "5d2c82b9", - "metadata": {}, - "outputs": [], - "source": [ - "Hitters=load_data('Hitters')" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4b2ac2c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n", - " 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League', 'Division',\n", - " 'PutOuts', 'Assists', 'Errors', 'Salary', 'NewLeague'],\n", - " dtype='object')" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Hitters.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "bd2ad0dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('AtBat',), ('Hits',), ('HmRun',), ('Runs',), ('RBI',), ('Walks',), ('Years',), ('CAtBat',), ('CHits',), ('CHmRun',), ('CRuns',), ('CRBI',), ('CWalks',), ('League',), ('Division',), ('PutOuts',), ('Assists',), ('Errors',), ('NewLeague',), ('AtBat', 'CRBI'), ('CRBI', 'Hits'), ('CRBI', 'HmRun'), ('CRBI', 'Runs'), ('CRBI', 'RBI'), ('CRBI', 'Walks'), ('CRBI', 'Years'), ('CAtBat', 'CRBI'), ('CHits', 'CRBI'), ('CHmRun', 'CRBI'), ('CRBI', 'CRuns'), ('CRBI', 'CWalks'), ('CRBI', 'League'), ('CRBI', 'Division'), ('CRBI', 'PutOuts'), ('Assists', 'CRBI'), ('CRBI', 'Errors'), ('CRBI', 'NewLeague'), ('AtBat', 'CRBI', 'Hits'), ('CRBI', 'Hits', 'HmRun'), ('CRBI', 'Hits', 'Runs'), ('CRBI', 'Hits', 'RBI'), ('CRBI', 'Hits', 'Walks'), ('CRBI', 'Hits', 'Years'), ('CAtBat', 'CRBI', 'Hits'), ('CHits', 'CRBI', 'Hits'), ('CHmRun', 'CRBI', 'Hits'), ('CRBI', 'CRuns', 'Hits'), ('CRBI', 'CWalks', 'Hits'), ('CRBI', 'Hits', 'League'), ('CRBI', 'Division', 'Hits'), ('CRBI', 'Hits', 'PutOuts'), ('Assists', 'CRBI', 'Hits'), ('CRBI', 'Errors', 'Hits'), ('CRBI', 'Hits', 'NewLeague'), ('AtBat', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'HmRun', 'PutOuts'), ('CRBI', 'Hits', 'PutOuts', 'Runs'), ('CRBI', 'Hits', 'PutOuts', 'RBI'), ('CRBI', 'Hits', 'PutOuts', 'Walks'), ('CRBI', 'Hits', 'PutOuts', 'Years'), ('CAtBat', 'CRBI', 'Hits', 'PutOuts'), ('CHits', 'CRBI', 'Hits', 'PutOuts'), ('CHmRun', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'CRuns', 'Hits', 'PutOuts'), ('CRBI', 'CWalks', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'League', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'PutOuts'), ('Assists', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'Errors', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Runs'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'RBI'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Years'), ('CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CHits', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'League', 'PutOuts'), ('Assists', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Errors', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Runs'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'RBI'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CHits', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'League', 'PutOuts'), ('Assists', 'AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Errors', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'RBI', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years')])" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Hitters = Hitters.dropna()\n", - "Y=Hitters['Salary']\n", - "X=Hitters.drop('Salary', axis=1)\n", - "design = ModelSpec(X.columns).fit(X)\n", - "strategy = Stepwise.first_peak(design,\n", - " direction='forward',\n", - " min_terms=0,\n", - " max_terms=19)\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error', cv=None)\n", - "selector.fit(X, Y)\n", - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "31788748", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(selector.selected_state_)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "e97d80c3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(X.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a71f0332", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start: AIC=3215.77\n", - "Salary ~ 1\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CRBI 1 17139434 36179679 3115.8\n", - "+ CRuns 1 16881162 36437951 3117.6\n", - "+ CHits 1 16065140 37253973 3123.5\n", - "+ CAtBat 1 14759710 38559403 3132.5\n", - "+ CHmRun 1 14692193 38626920 3133.0\n", - "+ CWalks 1 12792622 40526491 3145.6\n", - "+ RBI 1 10771083 42548030 3158.4\n", - "+ Walks 1 10504833 42814280 3160.1\n", - "+ Hits 1 10260491 43058621 3161.6\n", - "+ Runs 1 9399158 43919955 3166.8\n", - "+ Years 1 8559105 44760007 3171.7\n", - "+ AtBat 1 8309469 45009644 3173.2\n", - "+ HmRun 1 6273967 47045145 3184.8\n", - "+ PutOuts 1 4814100 48505013 3192.9\n", - "+ Division 1 1976102 51343011 3207.8\n", - " 53319113 3215.8\n", - "+ Assists 1 34497 53284615 3217.6\n", - "+ League 1 10876 53308237 3217.7\n", - "+ Errors 1 1555 53317558 3217.8\n", - "+ NewLeague 1 428 53318684 3217.8\n", - "\n", - "Step: AIC=3115.78\n", - "Salary ~ CRBI\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Hits 1 5533119 30646560 3074.1\n", - "+ Runs 1 5176532 31003147 3077.2\n", - "+ Walks 1 4199733 31979946 3085.3\n", - "+ AtBat 1 4064585 32115095 3086.4\n", - "+ RBI 1 3308272 32871407 3092.6\n", - "+ PutOuts 1 3267035 32912644 3092.9\n", - "+ Division 1 1733887 34445793 3104.9\n", - "+ Years 1 1667339 34512340 3105.4\n", - "+ HmRun 1 1271587 34908092 3108.4\n", - "+ CRuns 1 354561 35825119 3115.2\n", - "+ Assists 1 346020 35833659 3115.2\n", - " 36179679 3115.8\n", - "+ Errors 1 194403 35985276 3116.4\n", - "+ CAtBat 1 92261 36087418 3117.1\n", - "+ CHits 1 75469 36104210 3117.2\n", - "+ CWalks 1 51974 36127705 3117.4\n", - "+ NewLeague 1 17778 36161901 3117.7\n", - "+ League 1 11825 36167855 3117.7\n", - "+ CHmRun 1 515 36179165 3117.8\n", - "\n", - "Step: AIC=3074.13\n", - "Salary ~ CRBI + Hits\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ PutOuts 1 1397263 29249297 3063.8\n", - "+ Division 1 1279275 29367285 3064.9\n", - "+ AtBat 1 821767 29824793 3069.0\n", - "+ Walks 1 781767 29864793 3069.3\n", - "+ Years 1 254910 30391650 3073.9\n", - " 30646560 3074.1\n", - "+ League 1 208880 30437680 3074.3\n", - "+ CRuns 1 132614 30513946 3075.0\n", - "+ NewLeague 1 118474 30528086 3075.1\n", - "+ Runs 1 114198 30532362 3075.1\n", - "+ Errors 1 99776 30546784 3075.3\n", - "+ CAtBat 1 83517 30563043 3075.4\n", - "+ Assists 1 44781 30601779 3075.7\n", - "+ CWalks 1 23668 30622892 3075.9\n", - "+ CHmRun 1 4790 30641769 3076.1\n", - "+ CHits 1 4358 30642202 3076.1\n", - "+ HmRun 1 2173 30644387 3076.1\n", - "+ RBI 1 1137 30645423 3076.1\n", - "\n", - "Step: AIC=3063.85\n", - "Salary ~ CRBI + Hits + PutOuts\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Division 1 1278445 27970852 3054.1\n", - "+ AtBat 1 1009933 28239364 3056.6\n", - "+ Walks 1 539490 28709807 3061.0\n", - "+ CRuns 1 273649 28975648 3063.4\n", - " 29249297 3063.8\n", - "+ Years 1 136906 29112391 3064.6\n", - "+ League 1 122841 29126456 3064.8\n", - "+ Runs 1 117930 29131367 3064.8\n", - "+ Errors 1 97244 29152053 3065.0\n", - "+ NewLeague 1 57839 29191458 3065.3\n", - "+ CHits 1 35096 29214201 3065.5\n", - "+ RBI 1 33965 29215331 3065.6\n", - "+ HmRun 1 31227 29218070 3065.6\n", - "+ CWalks 1 28572 29220725 3065.6\n", - "+ CAtBat 1 20518 29228779 3065.7\n", - "+ Assists 1 1681 29247616 3065.8\n", - "+ CHmRun 1 1419 29247878 3065.8\n", - "\n", - "Step: AIC=3054.1\n", - "Salary ~ CRBI + Hits + PutOuts + Division\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ AtBat 1 820952 27149899 3048.3\n", - "+ Walks 1 491584 27479268 3051.4\n", - " 27970852 3054.1\n", - "+ CRuns 1 193604 27777248 3054.3\n", - "+ Years 1 166845 27804007 3054.5\n", - "+ League 1 110628 27860224 3055.1\n", - "+ Errors 1 81385 27889467 3055.3\n", - "+ Runs 1 65921 27904931 3055.5\n", - "+ RBI 1 53719 27917133 3055.6\n", - "+ NewLeague 1 52275 27918577 3055.6\n", - "+ CHits 1 33863 27936989 3055.8\n", - "+ HmRun 1 26390 27944462 3055.8\n", - "+ CAtBat 1 18751 27952101 3055.9\n", - "+ CWalks 1 5723 27965129 3056.0\n", - "+ Assists 1 1036 27969816 3056.1\n", - "+ CHmRun 1 165 27970687 3056.1\n", - "\n", - "Step: AIC=3048.26\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Walks 1 954996 26194904 3040.8\n", - "+ Years 1 253362 26896537 3047.8\n", - "+ Runs 1 208743 26941157 3048.2\n", - " 27149899 3048.3\n", - "+ CRuns 1 185825 26964075 3048.5\n", - "+ League 1 95986 27053913 3049.3\n", - "+ NewLeague 1 52693 27097206 3049.8\n", - "+ CHmRun 1 43173 27106726 3049.8\n", - "+ Assists 1 28898 27121001 3050.0\n", - "+ CAtBat 1 20989 27128910 3050.1\n", - "+ CWalks 1 15599 27134301 3050.1\n", - "+ Errors 1 6265 27143634 3050.2\n", - "+ CHits 1 5305 27144594 3050.2\n", - "+ RBI 1 1236 27148663 3050.2\n", - "+ HmRun 1 11 27149888 3050.3\n", - "\n", - "Step: AIC=3040.85\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat + Walks\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CWalks 1 240687 25954217 3040.4\n", - " 26194904 3040.8\n", - "+ Years 1 184508 26010396 3041.0\n", - "+ CRuns 1 110695 26084209 3041.7\n", - "+ League 1 77974 26116930 3042.1\n", - "+ Assists 1 75782 26119122 3042.1\n", - "+ NewLeague 1 40909 26153995 3042.4\n", - "+ CHits 1 37304 26157599 3042.5\n", - "+ RBI 1 11728 26183176 3042.7\n", - "+ HmRun 1 4747 26190157 3042.8\n", - "+ Errors 1 2727 26192177 3042.8\n", - "+ CAtBat 1 2630 26192274 3042.8\n", - "+ CHmRun 1 943 26193961 3042.8\n", - "+ Runs 1 37 26194867 3042.8\n", - "\n", - "Step: AIC=3040.42\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat + Walks + CWalks\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CRuns 1 794983 25159234 3034.2\n", - "+ CHits 1 273728 25680489 3039.6\n", - " 25954217 3040.4\n", - "+ Assists 1 138506 25815711 3041.0\n", - "+ CAtBat 1 89289 25864929 3041.5\n", - "+ RBI 1 86941 25867276 3041.5\n", - "+ League 1 77159 25877058 3041.6\n", - "+ Years 1 70126 25884091 3041.7\n", - "+ NewLeague 1 37807 25916410 3042.0\n", - "+ HmRun 1 33601 25920616 3042.1\n", - "+ CHmRun 1 9034 25945183 3042.3\n", - "+ Errors 1 6928" - ] - } - ], - "source": [ - "%%R -i Hitters\n", - "step(lm(Salary ~ 1, data=Hitters), scope=list(upper=lm(Salary ~ ., data=Hitters)), direction='forward', trace=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6117f650", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "536a8bc3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bddc13c5", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -2726,9 +262,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/selection.md b/docs/jupyterbook/models/selection.md index 1d92259..5793852 100644 --- a/docs/jupyterbook/models/selection.md +++ b/docs/jupyterbook/models/selection.md @@ -7,668 +7,105 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: python3 + display_name: islp_test language: python - name: python3 + name: islp_test --- # Model selection using `ModelSpec` -```{code-cell} ipython3 -import numpy as np, pandas as pd -%load_ext rpy2.ipython - -from ISLP import load_data -from ISLP.models import ModelSpec - -import statsmodels.api as sm -``` - -```{code-cell} ipython3 -Carseats = load_data('Carseats') -%R -i Carseats -Carseats.columns -``` - -## Let's break up income into groups - -```{code-cell} ipython3 -Carseats['OIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H']) -Carseats['OIncome'] -``` - -Let's also create an unordered version - -```{code-cell} ipython3 -Carseats['UIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H'], - ordered=False) -Carseats['UIncome'] -``` - -## A simple model - -```{code-cell} ipython3 -design = ModelSpec(['Price', 'Income']) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -Y = Carseats['Sales'] -M = sm.OLS(Y, X).fit() -M.params -``` - -## Basic procedure - -The design matrix is built by cobbling together a set of columns and possibly transforming them. -A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit` -is to inspect a dataframe for column info. The column `ShelveLoc` is categorical: - -```{code-cell} ipython3 -Carseats['ShelveLoc'] -``` - -This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods -`get_columns` and `fit_encoder`. - -```{code-cell} ipython3 -design.column_info_['ShelveLoc'] -``` - -It recognized ordinal columns as well. - -```{code-cell} ipython3 -design.column_info_['OIncome'] -``` - -```{code-cell} ipython3 -income = design.column_info_['Income'] -cols, names = income.get_columns(Carseats) -(cols[:4], names) -``` - -## Encoding a column - -In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical -variables usually are encoded by several columns, typically one less than the number of categories. -This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform -model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits -its encoder the first time data is passed to it. - -```{code-cell} ipython3 -shelve = design.column_info_['ShelveLoc'] -cols, names = shelve.get_columns(Carseats) -(cols[:4], names) -``` - -```{code-cell} ipython3 -oincome = design.column_info_['OIncome'] -oincome.get_columns(Carseats)[0][:4] -``` - -## The terms - -The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through -the `terms` argument which should be a sequence. The elements of `terms` are often -going to be strings (or tuples of strings for interactions, see below) but are converted to a -`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple. - -```{code-cell} ipython3 -design.terms -``` - -```{code-cell} ipython3 -design.terms_ -``` - -While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A -`Variable` can also create columns through the `build_columns` method of `ModelSpec` - -```{code-cell} ipython3 -price = design.terms_[0] -design.build_columns(Carseats, price) -``` - -Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The -tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then -is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during -the call to `ModelSpec.fit`. - -```{code-cell} ipython3 -from ISLP.models.model_spec import Variable - -new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None) -design.build_columns(Carseats, new_var) -``` - -Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the -arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`. - -```{code-cell} ipython3 -from sklearn.decomposition import PCA -pca = PCA(n_components=2) -pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit` -pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca) -design.build_columns(Carseats, pca_var) -``` - -The elements of the `variables` attribute may be column identifiers ( `"Price"`), `Column` instances (`price`) -or `Variable` instances (`pca_var`). - -```{code-cell} ipython3 -fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None) -design.build_columns(Carseats, fancy_var) -``` - -We can of course run PCA again on these features (if we wanted). - -```{code-cell} ipython3 -pca2 = PCA(n_components=2) -pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit` -pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2) -design.build_columns(Carseats, pca2_var) -``` - -## Building the design matrix - -With these notions in mind, the final design is essentially then - -```{code-cell} ipython3 -X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4] -``` - -An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is -a dataframe the index is adjusted accordingly. - -```{code-cell} ipython3 -design.intercept -``` - -```{code-cell} ipython3 -design.transform(Carseats)[:4] -``` - -## Predicting - -Constructing the design matrix at any values is carried out by the `transform` method. - -```{code-cell} ipython3 -new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]}) -new_X = design.transform(new_data) -M.get_prediction(new_X).predicted_mean -``` - -```{code-cell} ipython3 -%%R -i new_data,Carseats -predict(lm(Sales ~ Price + Income, data=Carseats), new_data) -``` - -### Difference between using `pd.DataFrame` and `np.ndarray` - -If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns. - -If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so, -in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. - -```{code-cell} ipython3 -Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']]) -design_np = ModelSpec([0,3]).fit(Carseats_np) -design_np.transform(Carseats_np)[:4] -``` - -The following will fail for hopefully obvious reasons - -```{code-cell} ipython3 -try: - new_D = np.zeros((2,2)) - new_D[:,0] = [10,20] - new_D[:,1] = [40,50] - M.get_prediction(new_D).predicted_mean -except ValueError as e: - print(e) -``` - -Ultimately, `M` expects 3 columns for new predictions because it was fit -with a matrix having 3 columns (the first representing an intercept). - -We might be tempted to try as with the `pd.DataFrame` and produce -an `np.ndarray` with only the necessary variables. - -```{code-cell} ipython3 -try: - new_X = np.zeros((2,2)) - new_X[:,0] = [10,20] - new_X[:,1] = [40,50] - new_D = design_np.transform(new_X) - M.get_prediction(new_D).predicted_mean -except IndexError as e: - print(e) -``` - -This fails because `design_np` is looking for column `3` from its `terms`: - -```{code-cell} ipython3 -design_np.terms_ -``` - -However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed -represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`: - -```{code-cell} ipython3 -new_X = np.zeros((2,4)) -new_X[:,0] = [10,20] -new_X[:,3] = [40,50] -new_D = design_np.transform(new_X) -M.get_prediction(new_D).predicted_mean -``` - -Given this subtlety about needing to supply arrays with identical column structure to `transform` when -using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case. - -+++ - -## A model with some categorical variables - -Categorical variables become `Column` instances with encoders. - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats) -design.column_info_['UIncome'] -``` - -```{code-cell} ipython3 -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Getting the encoding you want - -By default the level dropped by `ModelSpec` will be the first of the `categories_` values from -`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems -as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)` -where `new_encoder` would somehow drop the column we want dropped. - -However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`: - -```{code-cell} ipython3 -design.column_info_['UIncome'] -``` - -This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when -we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest -and we need a way to allow different encodings of the same column of `Carseats` - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - - We can create a new -`Column` with the encoder we want. For categorical variables, there is a convenience function to do so. - -```{code-cell} ipython3 -from ISLP.models.model_spec import contrast -pref_encoding = contrast('UIncome', 'drop', 'L') -``` - -```{code-cell} ipython3 -design.build_columns(Carseats, pref_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Interactions - -We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument -to `ModelSpec`. - -```{code-cell} ipython3 -design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with -`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`. - -```{code-cell} ipython3 -design.terms_[0] -``` - -Comparing this to the previous `R` model. - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - -We note a few important things: - -1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these -columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!** - -2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.** - -A few notes: - -- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.** -- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily. - -```{code-cell} ipython3 -full_encoding = contrast('UIncome', None) -design.build_columns(Carseats, full_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -## Special encodings - -For flexible models, we may want to consider transformations of features, i.e. polynomial -or spline transformations. Given transforms that follow the `fit/transform` paradigm -we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms` -package includes a `Poly` transform - -```{code-cell} ipython3 -from ISLP.models.model_spec import poly -poly('Income', 3) -``` - -```{code-cell} ipython3 -design = ModelSpec([poly('Income', 3), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -Compare: - -```{code-cell} ipython3 -%%R -lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef -``` - -## Splines - -Support for natural and B-splines is also included - -```{code-cell} ipython3 -from ISLP.models.model_spec import ns, bs, pca -design = ModelSpec([ns('Income', df=5), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -library(splines) -lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef -``` - -```{code-cell} ipython3 -design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef -``` - -## PCA - -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars'), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` +In this lab we illustrate how to run forward stepwise model selection +using the model specification capability of `ModelSpec`. ```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats) -``` - -It is of course common to scale before running PCA. - -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars', - scale=True), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats) -``` - -There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead -of `np.std(ddof=1)`. - -```{code-cell} ipython3 -np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1)) -``` - -## Model selection - -Another task requiring different design matrices is model selection. Manipulating -the `terms` attribute of a `ModelSpec` (or more precisely its more uniform version `terms_`) -can clearly allow for both exhaustive and stepwise model selection. - -```{code-cell} ipython3 -from ISLP.models.strategy import (Stepwise, - min_max) -from ISLP.models.generic_selector import FeatureSelector -``` - -### Best subsets - -```{code-cell} ipython3 -design = ModelSpec(['Price', - 'UIncome', - 'Advertising', - 'US', - 'Income', - 'ShelveLoc', - 'Education', - 'Urban']).fit(Carseats) -strategy = min_max(design, - min_terms=0, - max_terms=3) -``` - -```{code-cell} ipython3 -from sklearn.linear_model import LinearRegression -selector = FeatureSelector(LinearRegression(fit_intercept=False), - strategy, - scoring='neg_mean_squared_error') -``` - -```{code-cell} ipython3 -selector.fit(Carseats, Y) -``` - -```{code-cell} ipython3 -selector.selected_state_ -``` - -```{code-cell} ipython3 -selector.results_.keys() -``` - -```{code-cell} ipython3 -strategy = min_max(design, - min_terms=0, - max_terms=3, - lower_terms=['Price'], - upper_terms=['Price', 'Income', 'Advertising']) -selector = FeatureSelector(LinearRegression(fit_intercept=False), - strategy, - scoring='neg_mean_squared_error') -selector.fit(Carseats, Y) -selector.selected_state_ -``` - -```{code-cell} ipython3 -selector.results_.keys() +import numpy as np +import pandas as pd +from statsmodels.api import OLS +from ISLP import load_data +from ISLP.models import (ModelSpec, + Stepwise, + sklearn_selected) ``` -### Stepwise selection +### Forward Selection + +We will apply the forward-selection approach to the `Hitters` +data. We wish to predict a baseball player’s `Salary` on the +basis of various statistics associated with performance in the +previous year. ```{code-cell} ipython3 -strategy = Stepwise.first_peak(design, - min_terms=0, - max_terms=6, - lower_terms=['Price'], - upper_terms=['Price', 'Income', 'Advertising', 'ShelveLoc', 'UIncome', 'US' - 'Education', 'Urban']) -selector = FeatureSelector(LinearRegression(fit_intercept=False), - strategy, - scoring='neg_mean_squared_error', - cv=3) -selector.fit(Carseats, Y) -selector.selected_state_ +Hitters = load_data('Hitters') +np.isnan(Hitters['Salary']).sum() ``` -```{code-cell} ipython3 -selector.results_.keys() -``` - -```{code-cell} ipython3 -selector.results_ -``` + + We see that `Salary` is missing for 59 players. The +`dropna()` method of data frames removes all of the rows that have missing +values in any variable (by default --- see `Hitters.dropna?`). ```{code-cell} ipython3 -selector.selected_state_ +Hitters = Hitters.dropna() +Hitters.shape ``` -### Enforcing constraints - -In models with interactions, we may often want to impose constraints on interactions and main effects. -This can be achieved here by use of a `validator` that checks whether a given model is valid. - -Suppose we want to have the following constraint: `ShelveLoc` may not be in the model unless -`Price` is in the following model. +We first choose the best model using forward selection based on AIC. This score +is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use +it as a scorer. By default, `sklearn` tries to maximize a score, hence + our scoring function computes the negative AIC statistic. ```{code-cell} ipython3 -design = ModelSpec(['Price', - 'Advertising', - 'Income', - 'ShelveLoc']).fit(Carseats) +def negAIC(estimator, X, Y): + "Negative AIC" + n, p = X.shape + Yhat = estimator.predict(X) + MSE = np.mean((Y - Yhat)**2) + return n + n * np.log(MSE) + 2 * (p + 1) + ``` -The constraints are described with a boolean matrix with `(i,j)` as `j` is a child of `i`: so `j` should not -be in the model when `i` is not and enforced with a callable `validator` that evaluates each candidate state. - -Both `min_max_strategy` and `step_strategy` accept a `validator` argument. +We need to estimate the residual variance $\sigma^2$, which is the first argument in our scoring function above. +We will fit the biggest model, using all the variables, and estimate $\sigma^2$ based on its MSE. ```{code-cell} ipython3 -from ISLP.models.strategy import validator_from_constraints -constraints = np.zeros((4, 4)) -constraints[0,3] = 1 -strategy = min_max(design, - min_terms=0, - max_terms=4, - validator=validator_from_constraints(design, - constraints)) -selector = FeatureSelector(LinearRegression(fit_intercept=False), - strategy, - scoring='neg_mean_squared_error', - cv=3) -selector.fit(Carseats, Y) -selector.results_.keys() +design = ModelSpec(Hitters.columns.drop('Salary')).fit(Hitters) +Y = np.array(Hitters['Salary']) +X = design.transform(Hitters) ``` -```{code-cell} ipython3 -selector.selected_state_ -``` - -```{code-cell} ipython3 -Hitters=load_data('Hitters') -``` +Along with a score we need to specify the search strategy. This is done through the object +`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()` +runs forward stepwise until any further additions to the model do not result +in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()` +runs a fixed number of steps of stepwise search. ```{code-cell} ipython3 -Hitters.columns -``` - -```{code-cell} ipython3 -Hitters = Hitters.dropna() -Y=Hitters['Salary'] -X=Hitters.drop('Salary', axis=1) -design = ModelSpec(X.columns).fit(X) strategy = Stepwise.first_peak(design, direction='forward', - min_terms=0, - max_terms=19) -selector = FeatureSelector(LinearRegression(fit_intercept=False), - strategy, - scoring='neg_mean_squared_error', cv=None) -selector.fit(X, Y) -selector.results_.keys() + max_terms=len(design.terms)) ``` -```{code-cell} ipython3 -len(selector.selected_state_) -``` + +We now fit a linear regression model with `Salary` as outcome using forward +selection. To do so, we use the function `sklearn_selected()` from the `ISLP.models` package. This takes +a model from `statsmodels` along with a search strategy and selects a model with its +`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be +selected. ```{code-cell} ipython3 -len(X.columns) +hitters_MSE = sklearn_selected(OLS, + strategy) +hitters_MSE.fit(Hitters, Y) +hitters_MSE.selected_state_ ``` -```{code-cell} ipython3 -%%R -i Hitters -step(lm(Salary ~ 1, data=Hitters), scope=list(upper=lm(Salary ~ ., data=Hitters)), direction='forward', trace=TRUE) -``` + Using `neg_Cp` results in a smaller model, as expected, with just 4variables selected. ```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - +hitters_Cp = sklearn_selected(OLS, + strategy, + scoring=negAIC) +hitters_Cp.fit(Hitters, Y) +hitters_Cp.selected_state_ ``` diff --git a/docs/jupyterbook/models/spec.ipynb b/docs/jupyterbook/models/spec.ipynb index b60e402..fce6b32 100644 --- a/docs/jupyterbook/models/spec.ipynb +++ b/docs/jupyterbook/models/spec.ipynb @@ -7,7 +7,14 @@ "source": [ "# Building design matrices with `ModelSpec`\n", "\n", - "Force rebuild" + "The `ISLP` package provides a facility to build design\n", + "matrices for regression and classification tasks. It provides similar functionality to the formula\n", + "notation of `R` though uses python objects rather than specification through the special formula syntax.\n", + "\n", + "Related tools include `patsy` and `ColumnTransformer` from `sklearn.compose`. \n", + "\n", + "Perhaps the most common use is to extract some columns from a `pd.DataFrame` and \n", + "produce a design matrix, optionally with an intercept." ] }, { @@ -17,12 +24,15 @@ "metadata": {}, "outputs": [], "source": [ - "x=4\n", - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", + "import pandas as pd\n", + "import numpy as np\n", "\n", "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", + "from ISLP.models import (ModelSpec,\n", + " summarize,\n", + " Column,\n", + " Feature,\n", + " build_columns)\n", "\n", "import statsmodels.api as sm" ] @@ -48,40 +58,42 @@ ], "source": [ "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", "Carseats.columns" ] }, { "cell_type": "markdown", - "id": "excellent-hamilton", + "id": "b7a2e6ab-491d-4a57-8184-a9fcccb2047b", "metadata": {}, "source": [ - "## Let's break up income into groups" + "We'll first build a design matrix that we can use to model `Sales`\n", + "in terms of the categorical variable `ShelveLoc` and `Price`.\n", + "\n", + "We see first that `ShelveLoc` is a categorical variable:" ] }, { "cell_type": "code", "execution_count": 3, - "id": "going-administrator", + "id": "7d3642a6-90c6-48ad-8d35-88231b4991f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" + "0 Bad\n", + "1 Good\n", + "2 Medium\n", + "3 Medium\n", + "4 Bad\n", + " ... \n", + "395 Good\n", + "396 Medium\n", + "397 Medium\n", + "398 Bad\n", + "399 Good\n", + "Name: ShelveLoc, Length: 400, dtype: category\n", + "Categories (3, object): ['Bad', 'Good', 'Medium']" ] }, "execution_count": 3, @@ -90,42 +102,142 @@ } ], "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" + "Carseats['ShelveLoc']" ] }, { "cell_type": "markdown", - "id": "warming-mobile", + "id": "4afa201d-4b19-4d85-9e1b-1392a54d027b", "metadata": {}, "source": [ - "Let's also create an unordered version" + "This is recognized by `ModelSpec` and only 2 columns are added for the three levels. The\n", + "default behavior is to drop the first level of the categories. Later, \n", + "we will show other contrasts of the 3 columns can be produced. \n", + "\n", + "This simple example below illustrates how the first argument (its `terms`) is\n", + "used to construct a design matrix." ] }, { "cell_type": "code", "execution_count": 4, - "id": "varying-fourth", + "id": "fd5528fe-11da-4e10-8996-06085896c1a0", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" + " intercept ShelveLoc[Good] ShelveLoc[Medium] Price\n", + "0 1.0 0.0 0.0 120\n", + "1 1.0 1.0 0.0 83\n", + "2 1.0 0.0 1.0 80\n", + "3 1.0 0.0 1.0 97\n", + "4 1.0 0.0 0.0 128\n", + "5 1.0 0.0 0.0 72\n", + "6 1.0 0.0 1.0 108\n", + "7 1.0 1.0 0.0 120\n", + "8 1.0 0.0 1.0 124\n", + "9 1.0 0.0 1.0 124" ] }, "execution_count": 4, @@ -134,31 +246,129 @@ } ], "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" + "MS = ModelSpec(['ShelveLoc', 'Price'])\n", + "X = MS.fit_transform(Carseats)\n", + "X.iloc[:10]" ] }, { "cell_type": "markdown", - "id": "utility-viking", + "id": "6948e1ef-3685-4840-a4f2-ef15a1bcfb69", "metadata": {}, "source": [ - "## A simple model" + "We note that a column has been added for the intercept by default. This can be changed using the\n", + "`intercept` argument." ] }, { "cell_type": "code", "execution_count": 5, - "id": "unlikely-begin", + "id": "682d4c81-eba9-467d-a176-911a0269a21d", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" + " ShelveLoc[Good] ShelveLoc[Medium] Price\n", + "0 0.0 0.0 120\n", + "1 1.0 0.0 83\n", + "2 0.0 1.0 80\n", + "3 0.0 1.0 97\n", + "4 0.0 0.0 128\n", + "5 0.0 0.0 72\n", + "6 0.0 1.0 108\n", + "7 1.0 0.0 120\n", + "8 0.0 1.0 124\n", + "9 0.0 1.0 124" ] }, "execution_count": 5, @@ -167,24 +377,143 @@ } ], "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" + "MS_no1 = ModelSpec(['ShelveLoc', 'Price'], intercept=False)\n", + "MS_no1.fit_transform(Carseats)[:10]" + ] + }, + { + "cell_type": "markdown", + "id": "54d8fd20-d8f5-44d6-9965-83e745680798", + "metadata": {}, + "source": [ + "We see that `ShelveLoc` still only contributes\n", + "two columns to the design. The `ModelSpec` object does no introspection of its arguments to effectively include an intercept term\n", + "in the column space of the design matrix.\n", + "\n", + "To include this intercept via `ShelveLoc` we can use 3 columns to encode this categorical variable. Following the nomenclature of\n", + "`R`, we call this a `Contrast` of the categorical variable." ] }, { "cell_type": "code", "execution_count": 6, - "id": "driven-employee", + "id": "555734bb-2682-4721-a1cd-6fb207394b0e", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" + " ShelveLoc[Bad] ShelveLoc[Good] ShelveLoc[Medium] Price\n", + "0 1.0 0.0 0.0 120\n", + "1 0.0 1.0 0.0 83\n", + "2 0.0 0.0 1.0 80\n", + "3 0.0 0.0 1.0 97\n", + "4 1.0 0.0 0.0 128\n", + "5 1.0 0.0 0.0 72\n", + "6 0.0 0.0 1.0 108\n", + "7 0.0 1.0 0.0 120\n", + "8 0.0 0.0 1.0 124\n", + "9 0.0 0.0 1.0 124" ] }, "execution_count": 6, @@ -193,45 +522,32 @@ } ], "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" + "from ISLP.models import contrast\n", + "shelve = contrast('ShelveLoc', None)\n", + "MS_contr = ModelSpec([shelve, 'Price'], intercept=False)\n", + "MS_contr.fit_transform(Carseats)[:10]" ] }, { "cell_type": "markdown", - "id": "secondary-winner", + "id": "66db03cf-489c-40b6-8fac-762d66cf9932", "metadata": {}, "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" + "This example above illustrates that columns need not be identified by name in `terms`. The basic\n", + "role of an item in the `terms` sequence is a description of how to extract a column\n", + "from a columnar data object, usually a `pd.DataFrame`." ] }, { "cell_type": "code", "execution_count": 7, - "id": "bored-making", + "id": "852ee40e-05d2-4785-ab7d-968fb087f3c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" + "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=(), encoder=Contrast(method=None))" ] }, "execution_count": 7, @@ -240,28 +556,36 @@ } ], "source": [ - "Carseats['ShelveLoc']" + "shelve" ] }, { "cell_type": "markdown", - "id": "phantom-assurance", + "id": "b3be8808-1dbf-4154-882b-f61656a2ed4e", "metadata": {}, "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." + "The `Column` object can be used to directly extract relevant columns from a `pd.DataFrame`. If the `encoder` field is not\n", + "`None`, then the extracted columns will be passed through `encoder`.\n", + "The `get_columns` method produces these columns as well as names for the columns." ] }, { "cell_type": "code", "execution_count": 8, - "id": "blind-harvest", + "id": "0ebadfc0-0ea2-4abc-aac6-ef78be227ce1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" + "(array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.],\n", + " ...,\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [0., 1., 0.]]),\n", + " ['ShelveLoc[Bad]', 'ShelveLoc[Good]', 'ShelveLoc[Medium]'])" ] }, "execution_count": 8, @@ -270,27 +594,89 @@ } ], "source": [ - "design.column_info_['ShelveLoc']" + "shelve.get_columns(Carseats)" ] }, { "cell_type": "markdown", - "id": "suspended-affairs", + "id": "269e6d18-4ae4-4a77-8498-90281ae7c803", "metadata": {}, "source": [ - "It recognized ordinal columns as well." + "Let's now fit a simple OLS model with this design." ] }, { "cell_type": "code", "execution_count": 9, - "id": "military-locking", + "id": "411238d0-dd36-4878-a869-e8ce0ada099c", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" + "text/html": [ + "
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "ShelveLoc[Bad] 12.0018 0.503 23.839 0.0\n", + "ShelveLoc[Good] 16.8976 0.522 32.386 0.0\n", + "ShelveLoc[Medium] 13.8638 0.487 28.467 0.0\n", + "Price -0.0567 0.004 -13.967 0.0" ] }, "execution_count": 9, @@ -299,19 +685,166 @@ } ], "source": [ - "design.column_info_['OIncome']" + "X = MS_contr.transform(Carseats)\n", + "Y = Carseats['Sales']\n", + "M_ols = sm.OLS(Y, X).fit()\n", + "summarize(M_ols)" + ] + }, + { + "cell_type": "markdown", + "id": "40ddf68e-7d58-4e30-93a8-5b7fe840d37a", + "metadata": {}, + "source": [ + "## Interactions\n", + "\n", + "One of the common uses of formulae in `R` is to specify interactions between variables.\n", + "This is done in `ModelSpec` by including a tuple in the `terms` argument." ] }, { "cell_type": "code", "execution_count": 10, - "id": "italic-shakespeare", + "id": "3f5e314c-7a7f-4e8d-bb07-295beb42c728", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" + " intercept ShelveLoc[Bad]:Price ShelveLoc[Good]:Price \\\n", + "0 1.0 120.0 0.0 \n", + "1 1.0 0.0 83.0 \n", + "2 1.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 1.0 128.0 0.0 \n", + "5 1.0 72.0 0.0 \n", + "6 1.0 0.0 0.0 \n", + "7 1.0 0.0 120.0 \n", + "8 1.0 0.0 0.0 \n", + "9 1.0 0.0 0.0 \n", + "\n", + " ShelveLoc[Medium]:Price Price \n", + "0 0.0 120 \n", + "1 0.0 83 \n", + "2 80.0 80 \n", + "3 97.0 97 \n", + "4 0.0 128 \n", + "5 0.0 72 \n", + "6 108.0 108 \n", + "7 0.0 120 \n", + "8 124.0 124 \n", + "9 124.0 124 " ] }, "execution_count": 10, @@ -320,65 +853,71 @@ } ], "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" + "ModelSpec([(shelve, 'Price'), 'Price']).fit_transform(Carseats).iloc[:10]" + ] + }, + { + "cell_type": "markdown", + "id": "3f85fcb2-f0ef-4c1b-a89f-fcf083937274", + "metadata": {}, + "source": [ + "The above design matrix is clearly rank deficient, as `ModelSpec` has not inspected the formula\n", + "and attempted to produce a corresponding matrix that may or may not match a user's intent." ] }, { "cell_type": "markdown", - "id": "medieval-speed", + "id": "excellent-hamilton", "metadata": {}, "source": [ - "## Encoding a column\n", + "## Ordinal variables\n", "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." + "Ordinal variables are handled by a corresponding encoder)" ] }, { "cell_type": "code", "execution_count": 11, - "id": "public-basket", + "id": "going-administrator", + "metadata": {}, + "outputs": [], + "source": [ + "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", + " [0,50,90,200], \n", + " labels=['L','M','H'])\n", + "MS_order = ModelSpec(['OIncome']).fit(Carseats)" + ] + }, + { + "cell_type": "markdown", + "id": "5e1defb1-071b-4751-9358-b8d2f0b3412e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" + "Part of the `fit` method of `ModelSpec` involves inspection of the columns of `Carseats`. \n", + "The results of that inspection can be found in the `column_info_` attribute:" ] }, { "cell_type": "code", "execution_count": 12, - "id": "improved-alloy", + "id": "050fb4ae-648d-429d-9cb2-8423ad9707d7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" + "{'Sales': Column(idx='Sales', name='Sales', is_categorical=False, is_ordinal=False, columns=('Sales',), encoder=None),\n", + " 'CompPrice': Column(idx='CompPrice', name='CompPrice', is_categorical=False, is_ordinal=False, columns=('CompPrice',), encoder=None),\n", + " 'Income': Column(idx='Income', name='Income', is_categorical=False, is_ordinal=False, columns=('Income',), encoder=None),\n", + " 'Advertising': Column(idx='Advertising', name='Advertising', is_categorical=False, is_ordinal=False, columns=('Advertising',), encoder=None),\n", + " 'Population': Column(idx='Population', name='Population', is_categorical=False, is_ordinal=False, columns=('Population',), encoder=None),\n", + " 'Price': Column(idx='Price', name='Price', is_categorical=False, is_ordinal=False, columns=('Price',), encoder=None),\n", + " 'ShelveLoc': Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast()),\n", + " 'Age': Column(idx='Age', name='Age', is_categorical=False, is_ordinal=False, columns=('Age',), encoder=None),\n", + " 'Education': Column(idx='Education', name='Education', is_categorical=False, is_ordinal=False, columns=('Education',), encoder=None),\n", + " 'Urban': Column(idx='Urban', name='Urban', is_categorical=True, is_ordinal=False, columns=('Urban[Yes]',), encoder=Contrast()),\n", + " 'US': Column(idx='US', name='US', is_categorical=True, is_ordinal=False, columns=('US[Yes]',), encoder=Contrast()),\n", + " 'OIncome': Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())}" ] }, "execution_count": 12, @@ -387,33 +926,32 @@ } ], "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" + "MS_order.column_info_" ] }, { "cell_type": "markdown", - "id": "frank-mathematics", + "id": "debf7e2e-0a9d-451b-866c-66c0df9f43e5", "metadata": {}, "source": [ - "## The terms\n", + "## Structure of a `ModelSpec`\n", "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." + "The first argument to `ModelSpec` is stored as the `terms` attribute. Under the hood,\n", + "this sequence is inspected to produce the `terms_` attribute which specify the objects\n", + "that will ultimately create the design matrix." ] }, { "cell_type": "code", "execution_count": 13, - "id": "together-north", + "id": "ea51e988-0857-4d49-9987-d7531b34a233", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Price', 'Income']" + "[Feature(variables=('ShelveLoc',), name='ShelveLoc', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", + " Feature(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" ] }, "execution_count": 13, @@ -422,64 +960,145 @@ } ], "source": [ - "design.terms" + "MS = ModelSpec(['ShelveLoc', 'Price'])\n", + "MS.fit(Carseats)\n", + "MS.terms_" + ] + }, + { + "cell_type": "markdown", + "id": "warming-mobile", + "metadata": {}, + "source": [ + "Each element of `terms_` should be a `Feature` which describes a set of columns to be extracted from\n", + "a columnar data form as well as possible a possible encoder." ] }, { "cell_type": "code", "execution_count": 14, - "id": "chinese-necessity", + "id": "59214a70-1e6b-41c4-9f44-a92d340723c9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "design.terms_" + "shelve_var = MS.terms_[0]" ] }, { "cell_type": "markdown", - "id": "simplified-chinese", + "id": "5fed3ea2-ff50-4e5d-819d-a948f121f9d3", "metadata": {}, "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" + "We can find the columns associated to each term using the `build_columns` method of `ModelSpec`:" ] }, { "cell_type": "code", "execution_count": 15, - "id": "automotive-hobby", + "id": "5e25ef64-497d-4f42-9f20-3d4a320cda23", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "floral-liabilities", + "id": "63edf7a2-e776-45b0-b434-d676d7e13dbd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" + "The design matrix is constructed by running through `terms_` and concatenating the corresponding columns." ] }, { "cell_type": "markdown", - "id": "reasonable-canadian", + "id": "former-spring", "metadata": {}, "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." + "### `Feature` objects\n", + "\n", + "Note that `Feature` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", + "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", + "is run through `encoder.transform`. The `encoder.fit` method of each `Feature` is run once during \n", + "the call to `ModelSpec.fit`." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "imported-measure", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "institutional-burden", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "western-bloom", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "ordinary-newman", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "modern-negotiation", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "private-shepherd", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "hollywood-union", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "suffering-lover", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "successful-express", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "banner-metadata", + "execution_count": 18, + "id": "floral-liabilities", "metadata": {}, "outputs": [ { @@ -785,1227 +1161,643 @@ " \n", " \n", " \n", - " intercept\n", " Price\n", " Income\n", + " OIncome\n", " \n", " \n", " \n", " \n", " 0\n", - " 1.0\n", - " 120\n", - " 73\n", + " 120.0\n", + " 73.0\n", + " 2.0\n", " \n", " \n", " 1\n", + " 83.0\n", + " 48.0\n", " 1.0\n", - " 83\n", - " 48\n", " \n", " \n", " 2\n", + " 80.0\n", + " 35.0\n", " 1.0\n", - " 80\n", - " 35\n", " \n", " \n", " 3\n", + " 97.0\n", + " 100.0\n", + " 0.0\n", + " \n", + " \n", + " 4\n", + " 128.0\n", + " 64.0\n", + " 2.0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 395\n", + " 128.0\n", + " 108.0\n", + " 0.0\n", + " \n", + " \n", + " 396\n", + " 120.0\n", + " 23.0\n", + " 1.0\n", + " \n", + " \n", + " 397\n", + " 159.0\n", + " 26.0\n", + " 1.0\n", + " \n", + " \n", + " 398\n", + " 95.0\n", + " 79.0\n", + " 2.0\n", + " \n", + " \n", + " 399\n", + " 120.0\n", + " 37.0\n", " 1.0\n", - " 97\n", - " 100\n", " \n", " \n", "\n", + "

400 rows × 3 columns

\n", "" ], "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "absent-branch", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "naked-hollywood", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" + " Price Income OIncome\n", + "0 120.0 73.0 2.0\n", + "1 83.0 48.0 1.0\n", + "2 80.0 35.0 1.0\n", + "3 97.0 100.0 0.0\n", + "4 128.0 64.0 2.0\n", + ".. ... ... ...\n", + "395 128.0 108.0 0.0\n", + "396 120.0 23.0 1.0\n", + "397 159.0 26.0 1.0\n", + "398 95.0 79.0 2.0\n", + "399 120.0 37.0 1.0\n", + "\n", + "[400 rows x 3 columns]" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "iraqi-divorce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" + "new_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=None)\n", + "build_columns(MS.column_info_,\n", + " Carseats, \n", + " new_var)[0]" ] }, { "cell_type": "markdown", - "id": "signal-yahoo", + "id": "reasonable-canadian", "metadata": {}, "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." + "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", + "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." ] }, { "cell_type": "code", - "execution_count": 25, - "id": "completed-surveillance", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "undefined-sacrifice", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "incredible-concert", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "allied-botswana", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "stunning-container", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "specific-tobacco", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "latin-publisher", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "rocky-franchise", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "returning-matthew", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "sapphire-adelaide", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "standing-involvement", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "taken-university", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "rural-cycling", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "former-trick", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "specialized-processing", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "verified-administrator", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "limited-johns", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "saving-remainder", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "satisfied-harbor", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "silver-wesley", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "crazy-bikini", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "accredited-barrier", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "smaller-execution", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "limited-center", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "combined-relaxation", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "focal-determination", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "earned-ready", + "execution_count": 20, + "id": "imported-measure", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" + " mynewvar[0] mynewvar[1]\n", + "0 -3.595740 -4.850530\n", + "1 15.070401 35.706773\n", + "2 27.412228 40.772377\n", + "3 -33.983048 13.468087\n", + "4 6.580644 -11.287452\n", + ".. ... ...\n", + "395 -36.856308 -18.418138\n", + "396 45.731520 3.243768\n", + "397 49.087659 -35.727136\n", + "398 -13.565178 18.847760\n", + "399 31.917072 0.976615\n", + "\n", + "[400 rows x 2 columns]" ] }, - "execution_count": 41, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "pca.fit(build_columns(MS.column_info_, Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", + "pca_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=pca)\n", + "build_columns(MS.column_info_,\n", + " Carseats, \n", + " pca_var)[0]" ] }, { "cell_type": "markdown", - "id": "prescribed-accessory", + "id": "institutional-burden", "metadata": {}, "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." + "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", + "or `Feature` instances (`pca_var`)." ] }, { "cell_type": "code", - "execution_count": 42, - "id": "pacific-animal", + "execution_count": 21, + "id": "western-bloom", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" + " Income Price mynewvar[0] mynewvar[1]\n", + "0 73.0 120.0 -3.595740 -4.850530\n", + "1 48.0 83.0 15.070401 35.706773\n", + "2 35.0 80.0 27.412228 40.772377\n", + "3 100.0 97.0 -33.983048 13.468087\n", + "4 64.0 128.0 6.580644 -11.287452\n", + ".. ... ... ... ...\n", + "395 108.0 128.0 -36.856308 -18.418138\n", + "396 23.0 120.0 45.731520 3.243768\n", + "397 26.0 159.0 49.087659 -35.727136\n", + "398 79.0 95.0 -13.565178 18.847760\n", + "399 37.0 120.0 31.917072 0.976615\n", + "\n", + "[400 rows x 4 columns]" ] }, - "execution_count": 42, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design.terms_[0]" + "price = MS.column_info_['Price']\n", + "fancy_var = Feature(('Income', price, pca_var), name='fancy', encoder=None)\n", + "build_columns(MS.column_info_,\n", + " Carseats, \n", + " fancy_var)[0]" ] }, { "cell_type": "markdown", - "id": "planned-wrestling", + "id": "e289feba-e3f5-48e0-9e29-cdd88d7f9923", "metadata": {}, "source": [ - "Comparing this to the previous `R` model." + "## Predicting at new points" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "given-testimony", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "external-barrier", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "authentic-meditation", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "lucky-success", + "execution_count": 22, + "id": "6efed2fa-9e5d-429c-a8d9-ac544cab2b41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", + "intercept 12.661546\n", + "Price -0.052213\n", + "Income 0.012829\n", "dtype: float64" ] }, - "execution_count": 45, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" + "MS = ModelSpec(['Price', 'Income']).fit(Carseats)\n", + "X = MS.transform(Carseats)\n", + "Y = Carseats['Sales']\n", + "M_ols = sm.OLS(Y, X).fit()\n", + "M_ols.params" ] }, { "cell_type": "markdown", - "id": "laden-beach", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "copyrighted-luther", + "id": "e6b4609b-fcb2-4cc2-b630-509df4c87546", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3)', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" + "As `ModelSpec` is a transformer, it can be evaluated at new feature values.\n", + "Constructing the design matrix at any values is carried out by the `transform` method." ] }, { "cell_type": "code", - "execution_count": 47, - "id": "threatened-marine", + "execution_count": 23, + "id": "8784b0e8-ce53-4a90-aee6-b935834295c7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3)[0] 10.036373\n", - "poly(Income, 3)[1] -2.799156\n", - "poly(Income, 3)[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" + "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 47, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" + "new_data = pd.DataFrame({'Price':[40, 50], 'Income':[10, 20]})\n", + "new_X = MS.transform(new_data)\n", + "M_ols.get_prediction(new_X).predicted_mean" ] }, { "cell_type": "markdown", - "id": "senior-spokesman", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "prompt-fifteen", + "id": "signal-yahoo", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" + "## Using `np.ndarray`\n", + "\n", + "As the basic model is to concatenate columns extracted from a columnar data\n", + "representation, one *can* use `np.ndarray` as the column data. In this case,\n", + "columns will be selected by integer indices. \n", + "\n", + "### Caveats using `np.ndarray`\n", + "\n", + "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", + "However,\n", + "unless all features are floats, `np.ndarray` will default to a dtype of `object`, complicating issues.\n", + "\n", + "However, if we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", + "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. \n", + "\n", + "We illustrate this below, where we build a model from `Price` and `Income` for `Sales` and want to find predictions at new\n", + "values of `Price` and `Location`. We first find the predicitions using `pd.DataFrame` and then illustrate the difficulties\n", + "in using `np.ndarray`." ] }, { "cell_type": "markdown", - "id": "better-christianity", + "id": "e7ffdd07-4d6b-4a4c-ab38-ad1270e85de6", "metadata": {}, "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" + "We will refit this model, using `ModelSpec` with an `np.ndarray` instead" ] }, { "cell_type": "code", - "execution_count": 49, - "id": "outstanding-performer", + "execution_count": 24, + "id": "4fec9030-7445-48be-a15f-2ac5a789e717", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "intercept 4.240421\n", - "ns(Income, df=5)[0] 1.468196\n", - "ns(Income, df=5)[1] 1.499471\n", - "ns(Income, df=5)[2] 1.152070\n", - "ns(Income, df=5)[3] 2.418398\n", - "ns(Income, df=5)[4] 1.804460\n", - "ShelveLoc[Good] 4.810449\n", - "ShelveLoc[Medium] 1.881095\n", - "dtype: float64" + "array([[ 1., 120., 73.],\n", + " [ 1., 83., 48.],\n", + " [ 1., 80., 35.],\n", + " ...,\n", + " [ 1., 159., 26.],\n", + " [ 1., 95., 79.],\n", + " [ 1., 120., 37.]])" ] }, - "execution_count": 49, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from ISLP.models.model_spec import ns, bs, pca\n", - "design = ModelSpec([ns('Income', df=5), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "informative-spirituality", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) ns(Income, df = 5)1 ns(Income, df = 5)2 ns(Income, df = 5)3 \n", - " 4.240421 1.468196 1.499471 1.152070 \n", - "ns(Income, df = 5)4 ns(Income, df = 5)5 ShelveLocGood ShelveLocMedium \n", - " 2.418398 1.804460 4.810449 1.881095 \n" - ] - } - ], - "source": [ - "%%R\n", - "library(splines)\n", - "lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef" + "Carseats_np = np.asarray(Carseats[['Price', 'Education', 'Income']])\n", + "MS_np = ModelSpec([0,2]).fit(Carseats_np)\n", + "MS_np.transform(Carseats_np)" ] }, { "cell_type": "code", - "execution_count": 51, - "id": "destroyed-complexity", + "execution_count": 25, + "id": "c864e365-2476-4ca6-9d27-625cac2b2271", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "intercept 3.495085\n", - "bs(Income, df=7, degree=2)[0] 1.813118\n", - "bs(Income, df=7, degree=2)[1] 0.961852\n", - "bs(Income, df=7, degree=2)[2] 2.471545\n", - "bs(Income, df=7, degree=2)[3] 2.158891\n", - "bs(Income, df=7, degree=2)[4] 2.091625\n", - "bs(Income, df=7, degree=2)[5] 2.600669\n", - "bs(Income, df=7, degree=2)[6] 2.843108\n", - "ShelveLoc[Good] 4.804919\n", - "ShelveLoc[Medium] 1.880337\n", + "const 12.661546\n", + "x1 -0.052213\n", + "x2 0.012829\n", "dtype: float64" ] }, - "execution_count": 51, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "incident-nicaragua", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) bs(Income, df = 7, degree = 2)1 \n", - " 3.4950851 1.8131176 \n", - "bs(Income, df = 7, degree = 2)2 bs(Income, df = 7, degree = 2)3 \n", - " 0.9618523 2.4715450 \n", - "bs(Income, df = 7, degree = 2)4 bs(Income, df = 7, degree = 2)5 \n", - " 2.1588908 2.0916252 \n", - "bs(Income, df = 7, degree = 2)6 bs(Income, df = 7, degree = 2)7 \n", - " 2.6006694 2.8431084 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.8049190 1.8803375 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef" + "M_ols_np = sm.OLS(Y, MS_np.transform(Carseats_np)).fit()\n", + "M_ols_np.params" ] }, { "cell_type": "markdown", - "id": "formal-medline", - "metadata": {}, - "source": [ - "## PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "general-joshua", + "id": "undefined-sacrifice", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.419405\n", - "pca(myvars, n_components=2)[0] -0.001131\n", - "pca(myvars, n_components=2)[1] -0.024217\n", - "ShelveLoc[Good] 4.816253\n", - "ShelveLoc[Medium] 1.924139\n", - "dtype: float64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars'), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" + "Now, let's consider finding the design matrix at new points. \n", + "When using `pd.DataFrame` we only need to supply the `transform` method\n", + "a data frame with columns implicated in the `terms` argument (in this case, `Price` and `Income`). \n", + "\n", + "However, when using `np.ndarray` with integers as indices, `Price` was column 0 and `Income` was column 2. The only\n", + "sensible way to produce a return for predict is to extract its 0th and 2nd columns. Note this means\n", + "that the meaning of columns in an `np.ndarray` provided to `transform` essentially must be identical to those\n", + "passed to `fit`." ] }, { "cell_type": "code", - "execution_count": 54, - "id": "coordinate-calcium", + "execution_count": 26, + "id": "incredible-concert", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population))$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.419405 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC1 \n", - " 0.001131 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC2 \n", - " -0.024217 \n", - " ShelveLocGood \n", - " 4.816253 \n", - " ShelveLocMedium \n", - " 1.924139 \n", - "\n" + "index 2 is out of bounds for axis 1 with size 2\n" ] } ], "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats)" + "try:\n", + " new_D = np.array([[40,50], [10,20]]).T\n", + " new_X = MS_np.transform(new_D)\n", + "except IndexError as e:\n", + " print(e)" ] }, { "cell_type": "markdown", - "id": "foster-canvas", + "id": "allied-botswana", "metadata": {}, "source": [ - "It is of course common to scale before running PCA." + "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", + "with a matrix having 3 columns (the first representing an intercept).\n", + "\n", + "We might be tempted to try as with the `pd.DataFrame` and produce\n", + "an `np.ndarray` with only the necessary variables." ] }, { "cell_type": "code", - "execution_count": 55, - "id": "geographic-founder", + "execution_count": 27, + "id": "stunning-container", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" + "[[ 1. 40. 10.]\n", + " [ 1. 50. 20.]]\n" ] }, { "data": { "text/plain": [ - "intercept 5.352159\n", - "pca(myvars, n_components=2)[0] 0.446383\n", - "pca(myvars, n_components=2)[1] -1.219788\n", - "ShelveLoc[Good] 4.922780\n", - "ShelveLoc[Medium] 2.005617\n", - "dtype: float64" + "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 55, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars',\n", - " scale=True), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "floral-packaging", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population), scale = TRUE)$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.3522 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC1 \n", - " 0.4469 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC2 \n", - " -1.2213 \n", - " ShelveLocGood \n", - " 4.9228 \n", - " ShelveLocMedium \n", - " 2.0056 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats)" + "new_D = np.array([[40,50], [np.nan, np.nan], [10,20]]).T\n", + "new_X = MS_np.transform(new_D)\n", + "print(new_X)\n", + "M_ols.get_prediction(new_X).predicted_mean" ] }, { "cell_type": "markdown", - "id": "social-cherry", - "metadata": {}, - "source": [ - "There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead\n", - "of `np.std(ddof=1)`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "another-glory", + "id": "specific-tobacco", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.44694166, -1.22131519])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1))" + "For more complicated design contructions ensuring the columns of `new_D` match that of the original data will be more cumbersome. We expect\n", + "then that `pd.DataFrame` (or a columnar data representation with similar API) will likely be easier to use with `ModelSpec`." ] } ], @@ -2014,9 +1806,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2028,7 +1820,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/docs/jupyterbook/models/spec.md b/docs/jupyterbook/models/spec.md index fdf8c60..27bb3a4 100644 --- a/docs/jupyterbook/models/spec.md +++ b/docs/jupyterbook/models/spec.md @@ -5,490 +5,296 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.14.1 + jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: Python 3 (ipykernel) language: python - name: islp_test + name: python3 --- # Building design matrices with `ModelSpec` -Force rebuild +The `ISLP` package provides a facility to build design +matrices for regression and classification tasks. It provides similar functionality to the formula +notation of `R` though uses python objects rather than specification through the special formula syntax. + +Related tools include `patsy` and `ColumnTransformer` from `sklearn.compose`. + +Perhaps the most common use is to extract some columns from a `pd.DataFrame` and +produce a design matrix, optionally with an intercept. ```{code-cell} ipython3 -x=4 -import numpy as np, pandas as pd -%load_ext rpy2.ipython +import pandas as pd +import numpy as np from ISLP import load_data -from ISLP.models import ModelSpec +from ISLP.models import (ModelSpec, + summarize, + Column, + Feature, + build_columns) import statsmodels.api as sm ``` ```{code-cell} ipython3 Carseats = load_data('Carseats') -%R -i Carseats Carseats.columns ``` -## Let's break up income into groups +We'll first build a design matrix that we can use to model `Sales` +in terms of the categorical variable `ShelveLoc` and `Price`. -```{code-cell} ipython3 -Carseats['OIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H']) -Carseats['OIncome'] -``` - -Let's also create an unordered version - -```{code-cell} ipython3 -Carseats['UIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H'], - ordered=False) -Carseats['UIncome'] -``` - -## A simple model - -```{code-cell} ipython3 -design = ModelSpec(['Price', 'Income']) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -Y = Carseats['Sales'] -M = sm.OLS(Y, X).fit() -M.params -``` - -## Basic procedure - -The design matrix is built by cobbling together a set of columns and possibly transforming them. -A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit` -is to inspect a dataframe for column info. The column `ShelveLoc` is categorical: +We see first that `ShelveLoc` is a categorical variable: ```{code-cell} ipython3 Carseats['ShelveLoc'] ``` -This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods -`get_columns` and `fit_encoder`. - -```{code-cell} ipython3 -design.column_info_['ShelveLoc'] -``` - -It recognized ordinal columns as well. - -```{code-cell} ipython3 -design.column_info_['OIncome'] -``` - -```{code-cell} ipython3 -income = design.column_info_['Income'] -cols, names = income.get_columns(Carseats) -(cols[:4], names) -``` - -## Encoding a column - -In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical -variables usually are encoded by several columns, typically one less than the number of categories. -This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform -model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits -its encoder the first time data is passed to it. - -```{code-cell} ipython3 -shelve = design.column_info_['ShelveLoc'] -cols, names = shelve.get_columns(Carseats) -(cols[:4], names) -``` - -```{code-cell} ipython3 -oincome = design.column_info_['OIncome'] -oincome.get_columns(Carseats)[0][:4] -``` - -## The terms +This is recognized by `ModelSpec` and only 2 columns are added for the three levels. The +default behavior is to drop the first level of the categories. Later, +we will show other contrasts of the 3 columns can be produced. -The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through -the `terms` argument which should be a sequence. The elements of `terms` are often -going to be strings (or tuples of strings for interactions, see below) but are converted to a -`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple. +This simple example below illustrates how the first argument (its `terms`) is +used to construct a design matrix. ```{code-cell} ipython3 -design.terms +MS = ModelSpec(['ShelveLoc', 'Price']) +X = MS.fit_transform(Carseats) +X.iloc[:10] ``` -```{code-cell} ipython3 -design.terms_ -``` - -While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A -`Variable` can also create columns through the `build_columns` method of `ModelSpec` +We note that a column has been added for the intercept by default. This can be changed using the +`intercept` argument. ```{code-cell} ipython3 -price = design.terms_[0] -design.build_columns(Carseats, price) +MS_no1 = ModelSpec(['ShelveLoc', 'Price'], intercept=False) +MS_no1.fit_transform(Carseats)[:10] ``` -Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The -tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then -is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during -the call to `ModelSpec.fit`. +We see that `ShelveLoc` still only contributes +two columns to the design. The `ModelSpec` object does no introspection of its arguments to effectively include an intercept term +in the column space of the design matrix. -```{code-cell} ipython3 -from ISLP.models.model_spec import Variable - -new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None) -design.build_columns(Carseats, new_var) -``` - -Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the -arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`. +To include this intercept via `ShelveLoc` we can use 3 columns to encode this categorical variable. Following the nomenclature of +`R`, we call this a `Contrast` of the categorical variable. ```{code-cell} ipython3 -from sklearn.decomposition import PCA -pca = PCA(n_components=2) -pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit` -pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca) -design.build_columns(Carseats, pca_var) -``` - -The elements of the `variables` attribute may be column identifiers ( `"Price"`), `Column` instances (`price`) -or `Variable` instances (`pca_var`). - -```{code-cell} ipython3 -fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None) -design.build_columns(Carseats, fancy_var) +from ISLP.models import contrast +shelve = contrast('ShelveLoc', None) +MS_contr = ModelSpec([shelve, 'Price'], intercept=False) +MS_contr.fit_transform(Carseats)[:10] ``` -We can of course run PCA again on these features (if we wanted). +This example above illustrates that columns need not be identified by name in `terms`. The basic +role of an item in the `terms` sequence is a description of how to extract a column +from a columnar data object, usually a `pd.DataFrame`. ```{code-cell} ipython3 -pca2 = PCA(n_components=2) -pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit` -pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2) -design.build_columns(Carseats, pca2_var) +shelve ``` -## Building the design matrix - -With these notions in mind, the final design is essentially then +The `Column` object can be used to directly extract relevant columns from a `pd.DataFrame`. If the `encoder` field is not +`None`, then the extracted columns will be passed through `encoder`. +The `get_columns` method produces these columns as well as names for the columns. ```{code-cell} ipython3 -X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4] +shelve.get_columns(Carseats) ``` -An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is -a dataframe the index is adjusted accordingly. +Let's now fit a simple OLS model with this design. ```{code-cell} ipython3 -design.intercept -``` - -```{code-cell} ipython3 -design.transform(Carseats)[:4] +X = MS_contr.transform(Carseats) +Y = Carseats['Sales'] +M_ols = sm.OLS(Y, X).fit() +summarize(M_ols) ``` -## Predicting +## Interactions -Constructing the design matrix at any values is carried out by the `transform` method. +One of the common uses of formulae in `R` is to specify interactions between variables. +This is done in `ModelSpec` by including a tuple in the `terms` argument. ```{code-cell} ipython3 -new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]}) -new_X = design.transform(new_data) -M.get_prediction(new_X).predicted_mean +ModelSpec([(shelve, 'Price'), 'Price']).fit_transform(Carseats).iloc[:10] ``` -```{code-cell} ipython3 -%%R -i new_data,Carseats -predict(lm(Sales ~ Price + Income, data=Carseats), new_data) -``` +The above design matrix is clearly rank deficient, as `ModelSpec` has not inspected the formula +and attempted to produce a corresponding matrix that may or may not match a user's intent. -### Difference between using `pd.DataFrame` and `np.ndarray` ++++ -If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns. +## Ordinal variables -If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so, -in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. +Ordinal variables are handled by a corresponding encoder) ```{code-cell} ipython3 -Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']]) -design_np = ModelSpec([0,3]).fit(Carseats_np) -design_np.transform(Carseats_np)[:4] +Carseats['OIncome'] = pd.cut(Carseats['Income'], + [0,50,90,200], + labels=['L','M','H']) +MS_order = ModelSpec(['OIncome']).fit(Carseats) ``` -The following will fail for hopefully obvious reasons +Part of the `fit` method of `ModelSpec` involves inspection of the columns of `Carseats`. +The results of that inspection can be found in the `column_info_` attribute: ```{code-cell} ipython3 -try: - new_D = np.zeros((2,2)) - new_D[:,0] = [10,20] - new_D[:,1] = [40,50] - M.get_prediction(new_D).predicted_mean -except ValueError as e: - print(e) +MS_order.column_info_ ``` -Ultimately, `M` expects 3 columns for new predictions because it was fit -with a matrix having 3 columns (the first representing an intercept). +## Structure of a `ModelSpec` -We might be tempted to try as with the `pd.DataFrame` and produce -an `np.ndarray` with only the necessary variables. +The first argument to `ModelSpec` is stored as the `terms` attribute. Under the hood, +this sequence is inspected to produce the `terms_` attribute which specify the objects +that will ultimately create the design matrix. ```{code-cell} ipython3 -try: - new_X = np.zeros((2,2)) - new_X[:,0] = [10,20] - new_X[:,1] = [40,50] - new_D = design_np.transform(new_X) - M.get_prediction(new_D).predicted_mean -except IndexError as e: - print(e) +MS = ModelSpec(['ShelveLoc', 'Price']) +MS.fit(Carseats) +MS.terms_ ``` -This fails because `design_np` is looking for column `3` from its `terms`: +Each element of `terms_` should be a `Feature` which describes a set of columns to be extracted from +a columnar data form as well as possible a possible encoder. ```{code-cell} ipython3 -design_np.terms_ +shelve_var = MS.terms_[0] ``` -However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed -represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`: +We can find the columns associated to each term using the `build_columns` method of `ModelSpec`: ```{code-cell} ipython3 -new_X = np.zeros((2,4)) -new_X[:,0] = [10,20] -new_X[:,3] = [40,50] -new_D = design_np.transform(new_X) -M.get_prediction(new_D).predicted_mean +df, names = build_columns(MS.column_info_, + Carseats, + shelve_var) +df ``` -Given this subtlety about needing to supply arrays with identical column structure to `transform` when -using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case. +The design matrix is constructed by running through `terms_` and concatenating the corresponding columns. +++ -## A model with some categorical variables +### `Feature` objects -Categorical variables become `Column` instances with encoders. - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats) -design.column_info_['UIncome'] -``` - -```{code-cell} ipython3 -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Getting the encoding you want - -By default the level dropped by `ModelSpec` will be the first of the `categories_` values from -`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems -as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)` -where `new_encoder` would somehow drop the column we want dropped. - -However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`: - -```{code-cell} ipython3 -design.column_info_['UIncome'] -``` - -This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when -we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest -and we need a way to allow different encodings of the same column of `Carseats` - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - - We can create a new -`Column` with the encoder we want. For categorical variables, there is a convenience function to do so. - -```{code-cell} ipython3 -from ISLP.models.model_spec import contrast -pref_encoding = contrast('UIncome', 'drop', 'L') -``` - -```{code-cell} ipython3 -design.build_columns(Carseats, pref_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` +Note that `Feature` objects have a tuple of `variables` as well as an `encoder` attribute. The +tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then +is run through `encoder.transform`. The `encoder.fit` method of each `Feature` is run once during +the call to `ModelSpec.fit`. ```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef +new_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=None) +build_columns(MS.column_info_, + Carseats, + new_var)[0] ``` -## Interactions - -We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument -to `ModelSpec`. +Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the +arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`. ```{code-cell} ipython3 -design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params +from sklearn.decomposition import PCA +pca = PCA(n_components=2) +pca.fit(build_columns(MS.column_info_, Carseats, new_var)[0]) # this is done within `ModelSpec.fit` +pca_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=pca) +build_columns(MS.column_info_, + Carseats, + pca_var)[0] ``` -The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with -`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`. +The elements of the `variables` attribute may be column identifiers ( `"Price"`), `Column` instances (`price`) +or `Feature` instances (`pca_var`). ```{code-cell} ipython3 -design.terms_[0] +price = MS.column_info_['Price'] +fancy_var = Feature(('Income', price, pca_var), name='fancy', encoder=None) +build_columns(MS.column_info_, + Carseats, + fancy_var)[0] ``` -Comparing this to the previous `R` model. +## Predicting at new points ```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) +MS = ModelSpec(['Price', 'Income']).fit(Carseats) +X = MS.transform(Carseats) +Y = Carseats['Sales'] +M_ols = sm.OLS(Y, X).fit() +M_ols.params ``` -We note a few important things: - -1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these -columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!** - -2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.** - -A few notes: - -- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.** - -- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily. - -```{code-cell} ipython3 -full_encoding = contrast('UIncome', None) -design.build_columns(Carseats, full_encoding) -``` +As `ModelSpec` is a transformer, it can be evaluated at new feature values. +Constructing the design matrix at any values is carried out by the `transform` method. ```{code-cell} ipython3 -design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params +new_data = pd.DataFrame({'Price':[40, 50], 'Income':[10, 20]}) +new_X = MS.transform(new_data) +M_ols.get_prediction(new_X).predicted_mean ``` -## Special encodings - -For flexible models, we may want to consider transformations of features, i.e. polynomial -or spline transformations. Given transforms that follow the `fit/transform` paradigm -we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms` -package includes a `Poly` transform - -```{code-cell} ipython3 -from ISLP.models.model_spec import poly -poly('Income', 3) -``` +## Using `np.ndarray` -```{code-cell} ipython3 -design = ModelSpec([poly('Income', 3), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` +As the basic model is to concatenate columns extracted from a columnar data +representation, one *can* use `np.ndarray` as the column data. In this case, +columns will be selected by integer indices. -Compare: +### Caveats using `np.ndarray` -```{code-cell} ipython3 -%%R -lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef -``` +If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns. +However, +unless all features are floats, `np.ndarray` will default to a dtype of `object`, complicating issues. -## Splines +However, if we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so, +in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. -Support for natural and B-splines is also included +We illustrate this below, where we build a model from `Price` and `Income` for `Sales` and want to find predictions at new +values of `Price` and `Location`. We first find the predicitions using `pd.DataFrame` and then illustrate the difficulties +in using `np.ndarray`. -```{code-cell} ipython3 -from ISLP.models.model_spec import ns, bs, pca -design = ModelSpec([ns('Income', df=5), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` ++++ -```{code-cell} ipython3 -%%R -library(splines) -lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef -``` +We will refit this model, using `ModelSpec` with an `np.ndarray` instead ```{code-cell} ipython3 -design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params +Carseats_np = np.asarray(Carseats[['Price', 'Education', 'Income']]) +MS_np = ModelSpec([0,2]).fit(Carseats_np) +MS_np.transform(Carseats_np) ``` ```{code-cell} ipython3 -%%R -lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef +M_ols_np = sm.OLS(Y, MS_np.transform(Carseats_np)).fit() +M_ols_np.params ``` -## PCA +Now, let's consider finding the design matrix at new points. +When using `pd.DataFrame` we only need to supply the `transform` method +a data frame with columns implicated in the `terms` argument (in this case, `Price` and `Income`). -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars'), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` +However, when using `np.ndarray` with integers as indices, `Price` was column 0 and `Income` was column 2. The only +sensible way to produce a return for predict is to extract its 0th and 2nd columns. Note this means +that the meaning of columns in an `np.ndarray` provided to `transform` essentially must be identical to those +passed to `fit`. ```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats) +try: + new_D = np.array([[40,50], [10,20]]).T + new_X = MS_np.transform(new_D) +except IndexError as e: + print(e) ``` -It is of course common to scale before running PCA. +Ultimately, `M` expects 3 columns for new predictions because it was fit +with a matrix having 3 columns (the first representing an intercept). -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars', - scale=True), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` +We might be tempted to try as with the `pd.DataFrame` and produce +an `np.ndarray` with only the necessary variables. ```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats) +new_D = np.array([[40,50], [np.nan, np.nan], [10,20]]).T +new_X = MS_np.transform(new_D) +print(new_X) +M_ols.get_prediction(new_X).predicted_mean ``` -There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead -of `np.std(ddof=1)`. - -```{code-cell} ipython3 -np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1)) -``` +For more complicated design contructions ensuring the columns of `new_D` match that of the original data will be more cumbersome. We expect +then that `pd.DataFrame` (or a columnar data representation with similar API) will likely be easier to use with `ModelSpec`. diff --git a/docs/jupyterbook/models/submodels.ipynb b/docs/jupyterbook/models/submodels.ipynb deleted file mode 100644 index 825bedd..0000000 --- a/docs/jupyterbook/models/submodels.ipynb +++ /dev/null @@ -1,3127 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ee33d364", - "metadata": {}, - "source": [ - "# Building design matrices with `ModelSpec`\n", - "\n", - "Force rebuild" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4c70fbaa", - "metadata": {}, - "outputs": [], - "source": [ - "x=4\n", - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8a708215", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "dad5e991", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ac7086a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "261446c8", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "674bb806", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "8f030039", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "40cd6c28", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e65f5607", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "29d9b55f", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cfbe5b92", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "7092f666", - "metadata": {}, - "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2d43844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "46a01612", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "465a9326", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['OIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "76f8480d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "markdown", - "id": "25fcc1de", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "dfe6cc35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8fc9779a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" - ] - }, - { - "cell_type": "markdown", - "id": "8e04da60", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c579dbce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4587b8bd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "2595f0fa", - "metadata": {}, - "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "03bd9366", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" - ] - }, - { - "cell_type": "markdown", - "id": "de04ca48", - "metadata": {}, - "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a42af4c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" - ] - }, - { - "cell_type": "markdown", - "id": "b146d0c0", - "metadata": {}, - "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b6c394a6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "3bb30a3f", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ea7770ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "b2b4a01a", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "21ad8b44", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "2262377d", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1654ca47", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "1db0e0a9", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d20e8ea8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "450fe910", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "0705ba6f", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "866c2863", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f2021166", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "20e1a31a", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "a5926ec9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "997a63cb", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "40410c48", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "920203e9", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "1061da77", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "c6bfe001", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "5ae6d25f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "edd7ebeb", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "9455e532", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "fd726791", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "967d9ebc", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d0429b56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "415e3fd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8a99c3a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "9250a28a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "fe90c12c", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "0546ec84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "6ec4fe65", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "61e7f56e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "802ed854", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "82d7a01d", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e26849a1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2fc4cd8c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "49e33d41", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "ce018fdf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "2d42b822", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "fbb3e3ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "f9a7d4ad", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "5a6f8e69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "98eef5c8", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "58c99601", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "9c979d7e", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "0cb3b63a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "272098d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "fe05c471", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "67062299", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3, )', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "df5e5b4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3, )[0] 10.036373\n", - "poly(Income, 3, )[1] -2.799156\n", - "poly(Income, 3, )[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "01be9c13", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "3244d6f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "8ad5bb1d", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "6a6f4358", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 4.240421\n", - "ns(Income, , df=5)[0] 1.468196\n", - "ns(Income, , df=5)[1] 1.499471\n", - "ns(Income, , df=5)[2] 1.152070\n", - "ns(Income, , df=5)[3] 2.418398\n", - "ns(Income, , df=5)[4] 1.804460\n", - "ShelveLoc[Good] 4.810449\n", - "ShelveLoc[Medium] 1.881095\n", - "dtype: float64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca\n", - "design = ModelSpec([ns('Income', df=5), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "fb740953", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) ns(Income, df = 5)1 ns(Income, df = 5)2 ns(Income, df = 5)3 \n", - " 4.240421 1.468196 1.499471 1.152070 \n", - "ns(Income, df = 5)4 ns(Income, df = 5)5 ShelveLocGood ShelveLocMedium \n", - " 2.418398 1.804460 4.810449 1.881095 \n" - ] - } - ], - "source": [ - "%%R\n", - "library(splines)\n", - "lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "fe1bf7fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 3.495085\n", - "bs(Income, , df=7, degree=2)[0] 1.813118\n", - "bs(Income, , df=7, degree=2)[1] 0.961852\n", - "bs(Income, , df=7, degree=2)[2] 2.471545\n", - "bs(Income, , df=7, degree=2)[3] 2.158891\n", - "bs(Income, , df=7, degree=2)[4] 2.091625\n", - "bs(Income, , df=7, degree=2)[5] 2.600669\n", - "bs(Income, , df=7, degree=2)[6] 2.843108\n", - "ShelveLoc[Good] 4.804919\n", - "ShelveLoc[Medium] 1.880337\n", - "dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "86e966e0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) bs(Income, df = 7, degree = 2)1 \n", - " 3.4950851 1.8131176 \n", - "bs(Income, df = 7, degree = 2)2 bs(Income, df = 7, degree = 2)3 \n", - " 0.9618523 2.4715450 \n", - "bs(Income, df = 7, degree = 2)4 bs(Income, df = 7, degree = 2)5 \n", - " 2.1588908 2.0916252 \n", - "bs(Income, df = 7, degree = 2)6 bs(Income, df = 7, degree = 2)7 \n", - " 2.6006694 2.8431084 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.8049190 1.8803375 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "877d4784", - "metadata": {}, - "source": [ - "## PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "8ba6cb20", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.419405\n", - "pca(myvars, , n_components=2)[0] -0.001131\n", - "pca(myvars, , n_components=2)[1] -0.024217\n", - "ShelveLoc[Good] 4.816253\n", - "ShelveLoc[Medium] 1.924139\n", - "dtype: float64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars'), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "f0319e51", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population))$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.419405 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC1 \n", - " 0.001131 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC2 \n", - " -0.024217 \n", - " ShelveLocGood \n", - " 4.816253 \n", - " ShelveLocMedium \n", - " 1.924139 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "1f55086a", - "metadata": {}, - "source": [ - "It is of course common to scale before running PCA." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "bbe9e004", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.352159\n", - "pca(myvars, , n_components=2)[0] 0.446383\n", - "pca(myvars, , n_components=2)[1] -1.219788\n", - "ShelveLoc[Good] 4.922780\n", - "ShelveLoc[Medium] 2.005617\n", - "dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars',\n", - " scale=True), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "d78c02e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population), scale = TRUE)$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.3522 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC1 \n", - " 0.4469 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC2 \n", - " -1.2213 \n", - " ShelveLocGood \n", - " 4.9228 \n", - " ShelveLocMedium \n", - " 2.0056 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "8a03c603", - "metadata": {}, - "source": [ - "There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead\n", - "of `np.std(ddof=1)`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "f8215cef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.44694166, -1.22131519])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1))" - ] - }, - { - "cell_type": "markdown", - "id": "a15d0ead", - "metadata": {}, - "source": [ - "## Submodels\n", - "\n", - "We can build submodels as well, even if the terms do not appear in the original `terms` argument.\n", - "Fundamentally, the terms just need to be able to have the `design.build_columns` work for us to be\n", - "able to build a design matrix. The initial inspection of the columns of `Carseats` has created\n", - "a column for `US`, hence we can build this submodel." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "d58c6244", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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interceptUS[Yes]
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" - ], - "text/plain": [ - " intercept US[Yes]\n", - "0 1.0 1.0\n", - "1 1.0 1.0\n", - "2 1.0 1.0\n", - "3 1.0 1.0\n", - "4 1.0 0.0\n", - ".. ... ...\n", - "395 1.0 1.0\n", - "396 1.0 1.0\n", - "397 1.0 1.0\n", - "398 1.0 1.0\n", - "399 1.0 1.0\n", - "\n", - "[400 rows x 2 columns]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['UIncome', 'ShelveLoc', 'Price']).fit(Carseats)\n", - "design.build_submodel(Carseats, ['US'])" - ] - }, - { - "cell_type": "markdown", - "id": "9365ba27", - "metadata": {}, - "source": [ - "## ANOVA \n", - "\n", - "For a given `terms` argument, there as a natural sequence of models, namely those specified by `[terms[:i] for i in range(len(terms)+1]`." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "332ab454", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['intercept'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price',\n", - " 'UIncome[L]', 'UIncome[M]'],\n", - " dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price',\n", - " 'UIncome[L]', 'UIncome[M]', 'US[Yes]'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "design = ModelSpec(['ShelveLoc', 'Price', 'UIncome', 'US']).fit(Carseats)\n", - "for D in design.build_sequence(Carseats):\n", - " print(D.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "f6cfd031", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
0399.03182.2746980.0NaNNaNNaN
1397.02172.7435552.01009.531143153.0108585.452815e-50
2396.01455.6407021.0717.102853217.3771921.583751e-39
3394.01378.9159382.076.72476411.6288851.239031e-05
4393.01296.4627001.082.45323824.9942578.678832e-07
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 2172.743555 2.0 1009.531143 153.010858 5.452815e-50\n", - "2 396.0 1455.640702 1.0 717.102853 217.377192 1.583751e-39\n", - "3 394.0 1378.915938 2.0 76.724764 11.628885 1.239031e-05\n", - "4 393.0 1296.462700 1.0 82.453238 24.994257 8.678832e-07" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) ))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "11c4aee8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "ShelveLoc 2 1009.53 504.77 153.011 < 2.2e-16 ***\n", - "Price 1 717.10 717.10 217.377 < 2.2e-16 ***\n", - "UIncome 2 76.72 38.36 11.629 1.240e-05 ***\n", - "US 1 82.45 82.45 24.994 8.679e-07 ***\n", - "Residuals 393 1296.46 3.30 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ ShelveLoc + Price + UIncome + US, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "9a4e6e63", - "metadata": {}, - "source": [ - "Recall that `ModelSpec` does not inspect `terms` to reorder based on degree of \n", - "interaction as `R` does:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "6e7bf361", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
0399.03182.2746980.0NaNNaNNaN
1393.02059.3764136.01122.89828435.9400471.175738e-34
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 393.0 2059.376413 6.0 1122.898284 35.940047 1.175738e-34\n", - "2 391.0 2036.044596 2.0 23.331817 2.240310 1.077900e-01" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([(full_encoding, 'ShelveLoc'), pref_encoding]).fit(Carseats)\n", - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) ))" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "ed7d4bfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "UIncome 2 61.92 30.962 5.9458 0.002859 ** \n", - "UIncome:ShelveLoc 6 1084.31 180.718 34.7049 < 2.2e-16 ***\n", - "Residuals 391 2036.04 5.207 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "0350da34", - "metadata": {}, - "source": [ - "To agree with `R` we must order `terms` as `R` will." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "5ddaf87c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
0399.03182.2746980.0NaNNaNNaN
1397.03120.3513822.061.9233165.9458462.855424e-03
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 3120.351382 2.0 61.923316 5.945846 2.855424e-03\n", - "2 391.0 2036.044596 6.0 1084.306785 34.704868 1.346561e-33" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]).fit(Carseats)\n", - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "1ef70ce3", - "metadata": {}, - "source": [ - "## More complicated interactions\n", - "\n", - "Can we have an interaction of a polynomial effect with a categorical? Absolutely" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "a1a14742", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "UIncome 2 61.92 30.9617 4.0310 0.01851 *\n", - "UIncome:poly(Income, 3) 9 79.72 8.8581 1.1533 0.32408 \n", - "UIncome:US 3 83.51 27.8367 3.6242 0.01324 *\n", - "Residuals 385 2957.12 7.6808 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ UIncome + poly(Income, 3):UIncome + UIncome:US, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "a909be1a", - "metadata": {}, - "source": [ - "To match `R` we note that it has used its inspection rules to encode `UIncome` with 3 levels\n", - "for the two interactions." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "ae286cf3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 65.978856\n", - "UIncome[M] -60.159607\n", - "UIncome[H] -147.276154\n", - "poly(Income, 3, )[0]:UIncome[H] 1957.694387\n", - "poly(Income, 3, )[0]:UIncome[L] 1462.060650\n", - "poly(Income, 3, )[0]:UIncome[M] 83.035153\n", - "poly(Income, 3, )[1]:UIncome[H] -984.494570\n", - "poly(Income, 3, )[1]:UIncome[L] 881.537647\n", - "poly(Income, 3, )[1]:UIncome[M] -18.006234\n", - "poly(Income, 3, )[2]:UIncome[H] 207.614692\n", - "poly(Income, 3, )[2]:UIncome[L] 217.190749\n", - "poly(Income, 3, )[2]:UIncome[M] 34.065434\n", - "UIncome[H]:US 0.903404\n", - "UIncome[L]:US 0.895538\n", - "UIncome[M]:US 1.048728\n", - "dtype: float64" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p3 = poly('Income', 3)\n", - "design = ModelSpec([pref_encoding, (p3, full_encoding), (full_encoding, 'US')]).fit(Carseats)\n", - "X = design.transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "236ab2d2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
0399.03182.2746980.0NaNNaNNaN
1397.03120.3513822.061.9233164.0310320.018488
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 3120.351382 2.0 61.923316 4.031032 0.018488\n", - "2 388.0 3040.628559 9.0 79.722823 1.153273 0.324049\n", - "3 385.0 2957.118444 3.0 83.510115 3.624181 0.013244" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "0a45c720", - "metadata": {}, - "source": [ - "## Grouping columns for ANOVA\n", - "\n", - "The `Variable` construct can be used to group\n", - "variables together to get custom sequences of models for `anova_lm`." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "f36c1b3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['intercept'], dtype='object')\n", - "Index(['intercept', 'Price', 'UIncome[M]', 'UIncome[H]'], dtype='object')\n", - "Index(['intercept', 'Price', 'UIncome[M]', 'UIncome[H]', 'US[Yes]',\n", - " 'Advertising'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "group1 = Variable(('Price', pref_encoding), 'group1', None)\n", - "group2 = Variable(('US', 'Advertising'), 'group2', None)\n", - "design = ModelSpec([group1, group2]).fit(Carseats)\n", - "for D in design.build_sequence(Carseats):\n", - " print(D.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "3daf7638", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 396.0 2508.187788 3.0 674.086910 39.304841 2.970412e-22\n", - "2 394.0 2252.396343 2.0 255.791445 22.372135 6.267562e-10" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "46c1ace8", - "metadata": {}, - "source": [ - "It is not clear this is simple to do in `R` as the formula object expands all parentheses." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "0b87e430", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "Price 1 630.03 630.03 110.2079 < 2.2e-16 ***\n", - "UIncome 2 44.06 22.03 3.8533 0.02201 * \n", - "US 1 121.88 121.88 21.3196 5.270e-06 ***\n", - "Advertising 1 133.91 133.91 23.4247 1.868e-06 ***\n", - "Residuals 394 2252.40 5.72 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ (Price + UIncome) + (US + Advertising), data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "7c137360", - "metadata": {}, - "source": [ - "It can be done by building up the models\n", - "by hand and likely is possible to be done programmatically but it seems not obvious." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "b678d323", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ 1\n", - "Model 2: Sales ~ Price + UIncome\n", - "Model 3: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 399 3182.3 \n", - "2 396 2508.2 3 674.09 39.305 < 2.2e-16 ***\n", - "3 394 2252.4 2 255.79 22.372 6.268e-10 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ 1, data=Carseats)\n", - "M2 = lm(Sales ~ Price + UIncome, data=Carseats)\n", - "M3 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "anova(M1, M2, M3)" - ] - }, - { - "cell_type": "markdown", - "id": "b0388949", - "metadata": {}, - "source": [ - "## Alternative anova\n", - "\n", - "Another common ANOVA table involves dropping each term in succession from the model and comparing\n", - "to the full model." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "ac5b916a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'intercept'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 395.0 4417.273517 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 1.0 2164.877175 378.690726 1.359177e-59\n", - "{'Price', 'UIncome[H]', 'UIncome[M]'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 397.0 2950.808154 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 3.0 698.411811 40.723184 6.077848e-23\n", - "{'US[Yes]', 'Advertising'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 396.0 2508.187788 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 2.0 255.791445 22.372135 6.267562e-10\n" - ] - } - ], - "source": [ - "Dfull = design.transform(Carseats)\n", - "Mfull = sm.OLS(Y, Dfull).fit()\n", - "for i, D in enumerate(design.build_sequence(Carseats, anova_type='drop')):\n", - " if i == 0:\n", - " D0 = D\n", - " print(set(D.columns) ^ set(Dfull.columns))\n", - " print(sm.stats.anova_lm(sm.OLS(Y, D).fit(), Mfull))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "a0c71948", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ US + Advertising\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 397 2950.8 \n", - "2 394 2252.4 3 698.41 40.723 < 2.2e-16 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ Price + UIncome\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 396 2508.2 \n", - "2 394 2252.4 2 255.79 22.372 6.268e-10 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "M2 = lm(Sales ~ US + Advertising, data=Carseats)\n", - "print(anova(M2, M1))\n", - "M3 = lm(Sales ~ Price + UIncome, data=Carseats)\n", - "print(anova(M3, M1))" - ] - }, - { - "cell_type": "markdown", - "id": "a5e4880d", - "metadata": {}, - "source": [ - "The comparison without the intercept here is actually very hard to achieve in `R` with `anova` due to its inspection\n", - "of the formula." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4b383401", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ Price + UIncome + US + Advertising - 1\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F)\n", - "1 394 2252.4 \n", - "2 394 2252.4 0 9.0949e-13 \n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "M4 = lm(Sales ~ Price + UIncome + US + Advertising - 1, data=Carseats)\n", - "print(anova(M4, M1))" - ] - }, - { - "cell_type": "markdown", - "id": "72d7c83b", - "metadata": {}, - "source": [ - "It can be found with `summary`." - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "4d5ce789", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ Price + UIncome + US + Advertising, data = Carseats)\n", - "\n", - "Residuals:\n", - " Min 1Q Median 3Q Max \n", - "-7.4437 -1.6351 -0.0932 1.4920 6.8076 \n", - "\n", - "Coefficients:\n", - " Estimate Std. Error t value Pr(>|t|) \n", - "(Intercept) 12.520356 0.643390 19.460 < 2e-16 ***\n", - "Price -0.054000 0.005072 -10.647 < 2e-16 ***\n", - "UIncomeM 0.548906 0.281693 1.949 0.0521 . \n", - "UIncomeH 0.708219 0.322028 2.199 0.0284 * \n", - "USYes 0.024181 0.343246 0.070 0.9439 \n", - "Advertising 0.119509 0.024692 4.840 1.87e-06 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "\n", - "Residual standard error: 2.391 on 394 degrees of freedom\n", - "Multiple R-squared: 0.2922,\tAdjusted R-squared: 0.2832 \n", - "F-statistic: 32.53 on 5 and 394 DF, p-value: < 2.2e-16\n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "summary(M1)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "56b82d02", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(378.690726, 378.69160000000005)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "378.690726, 19.46**2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "872f645c-1d6f-4d08-9eec-2b80276bc82c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" - }, - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/jupyterbook/models/submodels.md b/docs/jupyterbook/models/submodels.md deleted file mode 100644 index 6f339ed..0000000 --- a/docs/jupyterbook/models/submodels.md +++ /dev/null @@ -1,652 +0,0 @@ ---- -jupytext: - formats: source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.14.5 -kernelspec: - display_name: python3 - language: python - name: python3 ---- - -# Building design matrices with `ModelSpec` - -Force rebuild - -```{code-cell} ipython3 -x=4 -import numpy as np, pandas as pd -%load_ext rpy2.ipython - -from ISLP import load_data -from ISLP.models import ModelSpec - -import statsmodels.api as sm -``` - -```{code-cell} ipython3 -Carseats = load_data('Carseats') -%R -i Carseats -Carseats.columns -``` - -## Let's break up income into groups - -```{code-cell} ipython3 -Carseats['OIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H']) -Carseats['OIncome'] -``` - -Let's also create an unordered version - -```{code-cell} ipython3 -Carseats['UIncome'] = pd.cut(Carseats['Income'], - [0,50,90,200], - labels=['L','M','H'], - ordered=False) -Carseats['UIncome'] -``` - -## A simple model - -```{code-cell} ipython3 -design = ModelSpec(['Price', 'Income']) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -Y = Carseats['Sales'] -M = sm.OLS(Y, X).fit() -M.params -``` - -## Basic procedure - -The design matrix is built by cobbling together a set of columns and possibly transforming them. -A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit` -is to inspect a dataframe for column info. The column `ShelveLoc` is categorical: - -```{code-cell} ipython3 -Carseats['ShelveLoc'] -``` - -This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods -`get_columns` and `fit_encoder`. - -```{code-cell} ipython3 -design.column_info_['ShelveLoc'] -``` - -It recognized ordinal columns as well. - -```{code-cell} ipython3 -design.column_info_['OIncome'] -``` - -```{code-cell} ipython3 -income = design.column_info_['Income'] -cols, names = income.get_columns(Carseats) -(cols[:4], names) -``` - -## Encoding a column - -In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical -variables usually are encoded by several columns, typically one less than the number of categories. -This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform -model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits -its encoder the first time data is passed to it. - -```{code-cell} ipython3 -shelve = design.column_info_['ShelveLoc'] -cols, names = shelve.get_columns(Carseats) -(cols[:4], names) -``` - -```{code-cell} ipython3 -oincome = design.column_info_['OIncome'] -oincome.get_columns(Carseats)[0][:4] -``` - -## The terms - -The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through -the `terms` argument which should be a sequence. The elements of `terms` are often -going to be strings (or tuples of strings for interactions, see below) but are converted to a -`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple. - -```{code-cell} ipython3 -design.terms -``` - -```{code-cell} ipython3 -design.terms_ -``` - -While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A -`Variable` can also create columns through the `build_columns` method of `ModelSpec` - -```{code-cell} ipython3 -price = design.terms_[0] -design.build_columns(Carseats, price) -``` - -Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The -tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then -is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during -the call to `ModelSpec.fit`. - -```{code-cell} ipython3 -from ISLP.models.model_spec import Variable - -new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None) -design.build_columns(Carseats, new_var) -``` - -Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the -arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`. - -```{code-cell} ipython3 -from sklearn.decomposition import PCA -pca = PCA(n_components=2) -pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit` -pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca) -design.build_columns(Carseats, pca_var) -``` - -The elements of the `variables` attribute may be column identifiers ( `"Price"`), `Column` instances (`price`) -or `Variable` instances (`pca_var`). - -```{code-cell} ipython3 -fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None) -design.build_columns(Carseats, fancy_var) -``` - -We can of course run PCA again on these features (if we wanted). - -```{code-cell} ipython3 -pca2 = PCA(n_components=2) -pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit` -pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2) -design.build_columns(Carseats, pca2_var) -``` - -## Building the design matrix - -With these notions in mind, the final design is essentially then - -```{code-cell} ipython3 -X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4] -``` - -An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is -a dataframe the index is adjusted accordingly. - -```{code-cell} ipython3 -design.intercept -``` - -```{code-cell} ipython3 -design.transform(Carseats)[:4] -``` - -## Predicting - -Constructing the design matrix at any values is carried out by the `transform` method. - -```{code-cell} ipython3 -new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]}) -new_X = design.transform(new_data) -M.get_prediction(new_X).predicted_mean -``` - -```{code-cell} ipython3 -%%R -i new_data,Carseats -predict(lm(Sales ~ Price + Income, data=Carseats), new_data) -``` - -### Difference between using `pd.DataFrame` and `np.ndarray` - -If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns. - -If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so, -in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning. - -```{code-cell} ipython3 -Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']]) -design_np = ModelSpec([0,3]).fit(Carseats_np) -design_np.transform(Carseats_np)[:4] -``` - -The following will fail for hopefully obvious reasons - -```{code-cell} ipython3 -try: - new_D = np.zeros((2,2)) - new_D[:,0] = [10,20] - new_D[:,1] = [40,50] - M.get_prediction(new_D).predicted_mean -except ValueError as e: - print(e) -``` - -Ultimately, `M` expects 3 columns for new predictions because it was fit -with a matrix having 3 columns (the first representing an intercept). - -We might be tempted to try as with the `pd.DataFrame` and produce -an `np.ndarray` with only the necessary variables. - -```{code-cell} ipython3 -try: - new_X = np.zeros((2,2)) - new_X[:,0] = [10,20] - new_X[:,1] = [40,50] - new_D = design_np.transform(new_X) - M.get_prediction(new_D).predicted_mean -except IndexError as e: - print(e) -``` - -This fails because `design_np` is looking for column `3` from its `terms`: - -```{code-cell} ipython3 -design_np.terms_ -``` - -However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed -represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`: - -```{code-cell} ipython3 -new_X = np.zeros((2,4)) -new_X[:,0] = [10,20] -new_X[:,3] = [40,50] -new_D = design_np.transform(new_X) -M.get_prediction(new_D).predicted_mean -``` - -Given this subtlety about needing to supply arrays with identical column structure to `transform` when -using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case. - -+++ - -## A model with some categorical variables - -Categorical variables become `Column` instances with encoders. - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats) -design.column_info_['UIncome'] -``` - -```{code-cell} ipython3 -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Getting the encoding you want - -By default the level dropped by `ModelSpec` will be the first of the `categories_` values from -`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems -as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)` -where `new_encoder` would somehow drop the column we want dropped. - -However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`: - -```{code-cell} ipython3 -design.column_info_['UIncome'] -``` - -This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when -we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest -and we need a way to allow different encodings of the same column of `Carseats` - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - - We can create a new -`Column` with the encoder we want. For categorical variables, there is a convenience function to do so. - -```{code-cell} ipython3 -from ISLP.models.model_spec import contrast -pref_encoding = contrast('UIncome', 'drop', 'L') -``` - -```{code-cell} ipython3 -design.build_columns(Carseats, pref_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats) -X = design.fit_transform(Carseats) -X.columns -``` - -```{code-cell} ipython3 -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef -``` - -## Interactions - -We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument -to `ModelSpec`. - -```{code-cell} ipython3 -design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with -`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`. - -```{code-cell} ipython3 -design.terms_[0] -``` - -Comparing this to the previous `R` model. - -```{code-cell} ipython3 -%%R -lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats) -``` - -We note a few important things: - -1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these -columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!** - -2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.** - -A few notes: - -- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.** - -- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily. - -```{code-cell} ipython3 -full_encoding = contrast('UIncome', None) -design.build_columns(Carseats, full_encoding) -``` - -```{code-cell} ipython3 -design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -## Special encodings - -For flexible models, we may want to consider transformations of features, i.e. polynomial -or spline transformations. Given transforms that follow the `fit/transform` paradigm -we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms` -package includes a `Poly` transform - -```{code-cell} ipython3 -from ISLP.models.model_spec import poly -poly('Income', 3) -``` - -```{code-cell} ipython3 -design = ModelSpec([poly('Income', 3), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -Compare: - -```{code-cell} ipython3 -%%R -lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef -``` - -## Splines - -Support for natural and B-splines is also included - -```{code-cell} ipython3 -from ISLP.models.model_spec import ns, bs, pca -design = ModelSpec([ns('Income', df=5), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -library(splines) -lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef -``` - -```{code-cell} ipython3 -design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef -``` - -## PCA - -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars'), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats) -``` - -It is of course common to scale before running PCA. - -```{code-cell} ipython3 -design = ModelSpec([pca(['Income', - 'Price', - 'Advertising', - 'Population'], - n_components=2, - name='myvars', - scale=True), 'ShelveLoc']) -X = design.fit_transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -%%R -lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats) -``` - -There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead -of `np.std(ddof=1)`. - -```{code-cell} ipython3 -np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1)) -``` - -## Submodels - -We can build submodels as well, even if the terms do not appear in the original `terms` argument. -Fundamentally, the terms just need to be able to have the `design.build_columns` work for us to be -able to build a design matrix. The initial inspection of the columns of `Carseats` has created -a column for `US`, hence we can build this submodel. - -```{code-cell} ipython3 -design = ModelSpec(['UIncome', 'ShelveLoc', 'Price']).fit(Carseats) -design.build_submodel(Carseats, ['US']) -``` - -## ANOVA - -For a given `terms` argument, there as a natural sequence of models, namely those specified by `[terms[:i] for i in range(len(terms)+1]`. - -```{code-cell} ipython3 -design = ModelSpec(['ShelveLoc', 'Price', 'UIncome', 'US']).fit(Carseats) -for D in design.build_sequence(Carseats): - print(D.columns) -``` - -```{code-cell} ipython3 -sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) )) -``` - -```{code-cell} ipython3 -%%R -anova(lm(Sales ~ ShelveLoc + Price + UIncome + US, data=Carseats)) -``` - -Recall that `ModelSpec` does not inspect `terms` to reorder based on degree of -interaction as `R` does: - -```{code-cell} ipython3 -design = ModelSpec([(full_encoding, 'ShelveLoc'), pref_encoding]).fit(Carseats) -sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) )) -``` - -```{code-cell} ipython3 -%%R -anova(lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)) -``` - -To agree with `R` we must order `terms` as `R` will. - -```{code-cell} ipython3 -design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]).fit(Carseats) -sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats))) -``` - -## More complicated interactions - -Can we have an interaction of a polynomial effect with a categorical? Absolutely - -```{code-cell} ipython3 -%%R -anova(lm(Sales ~ UIncome + poly(Income, 3):UIncome + UIncome:US, data=Carseats)) -``` - -To match `R` we note that it has used its inspection rules to encode `UIncome` with 3 levels -for the two interactions. - -```{code-cell} ipython3 -p3 = poly('Income', 3) -design = ModelSpec([pref_encoding, (p3, full_encoding), (full_encoding, 'US')]).fit(Carseats) -X = design.transform(Carseats) -sm.OLS(Y, X).fit().params -``` - -```{code-cell} ipython3 -sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats))) -``` - -## Grouping columns for ANOVA - -The `Variable` construct can be used to group -variables together to get custom sequences of models for `anova_lm`. - -```{code-cell} ipython3 -group1 = Variable(('Price', pref_encoding), 'group1', None) -group2 = Variable(('US', 'Advertising'), 'group2', None) -design = ModelSpec([group1, group2]).fit(Carseats) -for D in design.build_sequence(Carseats): - print(D.columns) -``` - -```{code-cell} ipython3 -sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats))) -``` - -It is not clear this is simple to do in `R` as the formula object expands all parentheses. - -```{code-cell} ipython3 -%%R -anova(lm(Sales ~ (Price + UIncome) + (US + Advertising), data=Carseats)) -``` - -It can be done by building up the models -by hand and likely is possible to be done programmatically but it seems not obvious. - -```{code-cell} ipython3 -%%R -M1 = lm(Sales ~ 1, data=Carseats) -M2 = lm(Sales ~ Price + UIncome, data=Carseats) -M3 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats) -anova(M1, M2, M3) -``` - -## Alternative anova - -Another common ANOVA table involves dropping each term in succession from the model and comparing -to the full model. - -```{code-cell} ipython3 -Dfull = design.transform(Carseats) -Mfull = sm.OLS(Y, Dfull).fit() -for i, D in enumerate(design.build_sequence(Carseats, anova_type='drop')): - if i == 0: - D0 = D - print(set(D.columns) ^ set(Dfull.columns)) - print(sm.stats.anova_lm(sm.OLS(Y, D).fit(), Mfull)) -``` - -```{code-cell} ipython3 -%%R -M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats) -M2 = lm(Sales ~ US + Advertising, data=Carseats) -print(anova(M2, M1)) -M3 = lm(Sales ~ Price + UIncome, data=Carseats) -print(anova(M3, M1)) -``` - -The comparison without the intercept here is actually very hard to achieve in `R` with `anova` due to its inspection -of the formula. - -```{code-cell} ipython3 -%%R -M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats) -M4 = lm(Sales ~ Price + UIncome + US + Advertising - 1, data=Carseats) -print(anova(M4, M1)) -``` - -It can be found with `summary`. - -```{code-cell} ipython3 -%%R -summary(M1) -``` - -```{code-cell} ipython3 -378.690726, 19.46**2 -``` - -```{code-cell} ipython3 - -``` diff --git a/docs/jupyterbook/transforms/PCA.ipynb b/docs/jupyterbook/transforms/PCA.ipynb index 04c50ad..ec1e0ae 100644 --- a/docs/jupyterbook/transforms/PCA.ipynb +++ b/docs/jupyterbook/transforms/PCA.ipynb @@ -24,8 +24,8 @@ "from ISLP import load_data\n", "from ISLP.models import (ModelSpec, \n", " pca, \n", - " Variable, \n", - " derived_variable,\n", + " Feature, \n", + " derived_feature,\n", " build_columns)" ] }, @@ -76,7 +76,7 @@ "id": "fff603bf", "metadata": {}, "source": [ - "Suppose we want to make a `Variable` representing the first 3 principal components of the\n", + "Suppose we want to make a `Feature` representing the first 3 principal components of the\n", " features `['CompPrice', 'Income', 'Advertising', 'Population', 'Price']`." ] }, @@ -85,8 +85,8 @@ "id": "eab49ad1-3957-478f-8a76-28a8f58551e9", "metadata": {}, "source": [ - "We first make a `Variable` that represents these five features columns, then `pca`\n", - "can be used to compute a new `Variable` that returns the first three principal components." + "We first make a `Feature` that represents these five features columns, then `pca`\n", + "can be used to compute a new `Feature` that returns the first three principal components." ] }, { @@ -96,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "grouped = Variable(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None)\n", + "grouped = Feature(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None)\n", "sklearn_pca = PCA(n_components=3, whiten=True)" ] }, @@ -105,7 +105,7 @@ "id": "b45655a3-393d-4b4c-b754-cda61ed0e014", "metadata": {}, "source": [ - "We can now fit `sklearn_pca` and create our new variable." + "We can now fit `sklearn_pca` and create our new feature." ] }, { @@ -119,7 +119,7 @@ " Carseats,\n", " grouped)[0]\n", "sklearn_pca.fit(grouped_features) \n", - "pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", + "pca_var = derived_feature(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", " name='pca(grouped)', encoder=sklearn_pca)\n", "derived_features, _ = build_columns(design.column_info_,\n", " Carseats, \n", diff --git a/docs/jupyterbook/transforms/PCA.md b/docs/jupyterbook/transforms/PCA.md index 22aeea6..6b1a77f 100644 --- a/docs/jupyterbook/transforms/PCA.md +++ b/docs/jupyterbook/transforms/PCA.md @@ -24,8 +24,8 @@ from sklearn.decomposition import PCA from ISLP import load_data from ISLP.models import (ModelSpec, pca, - Variable, - derived_variable, + Feature, + derived_feature, build_columns) ``` @@ -40,27 +40,27 @@ Let's create a `ModelSpec` that is aware of all of the relevant columns. design = ModelSpec(Carseats.columns.drop(['Sales'])).fit(Carseats) ``` -Suppose we want to make a `Variable` representing the first 3 principal components of the +Suppose we want to make a `Feature` representing the first 3 principal components of the features `['CompPrice', 'Income', 'Advertising', 'Population', 'Price']`. +++ -We first make a `Variable` that represents these five features columns, then `pca` -can be used to compute a new `Variable` that returns the first three principal components. +We first make a `Feature` that represents these five features columns, then `pca` +can be used to compute a new `Feature` that returns the first three principal components. ```{code-cell} ipython3 -grouped = Variable(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None) +grouped = Feature(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None) sklearn_pca = PCA(n_components=3, whiten=True) ``` -We can now fit `sklearn_pca` and create our new variable. +We can now fit `sklearn_pca` and create our new feature. ```{code-cell} ipython3 grouped_features = build_columns(design.column_info_, Carseats, grouped)[0] sklearn_pca.fit(grouped_features) -pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'], +pca_var = derived_feature(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'], name='pca(grouped)', encoder=sklearn_pca) derived_features, _ = build_columns(design.column_info_, Carseats, diff --git a/docs/jupyterbook/transforms/poly.ipynb b/docs/jupyterbook/transforms/poly.ipynb index c2b740b..45c862e 100644 --- a/docs/jupyterbook/transforms/poly.ipynb +++ b/docs/jupyterbook/transforms/poly.ipynb @@ -168,7 +168,7 @@ "source": [ "## Underlying model\n", "\n", - "If we look at `quartic`, we see it is a `Variable`, i.e. it can be used to produce a set of columns\n", + "If we look at `quartic`, we see it is a `Feature`, i.e. it can be used to produce a set of columns\n", "in a design matrix when it is a term used in creating the `ModelSpec`.\n", "\n", "Its encoder is `Poly(degree=4)`. This is a special `sklearn` transform that expects a single column\n", diff --git a/docs/jupyterbook/transforms/poly.md b/docs/jupyterbook/transforms/poly.md index 4ef3a24..e5aef11 100644 --- a/docs/jupyterbook/transforms/poly.md +++ b/docs/jupyterbook/transforms/poly.md @@ -66,7 +66,7 @@ np.linalg.norm(ISLP_features - R_features) ## Underlying model -If we look at `quartic`, we see it is a `Variable`, i.e. it can be used to produce a set of columns +If we look at `quartic`, we see it is a `Feature`, i.e. it can be used to produce a set of columns in a design matrix when it is a term used in creating the `ModelSpec`. Its encoder is `Poly(degree=4)`. This is a special `sklearn` transform that expects a single column diff --git a/docs/source/api/generated/ISLP.models.model_spec.rst b/docs/source/api/generated/ISLP.models.model_spec.rst index c379253..d457e3a 100644 --- a/docs/source/api/generated/ISLP.models.model_spec.rst +++ b/docs/source/api/generated/ISLP.models.model_spec.rst @@ -29,11 +29,11 @@ Classes .. automethod:: __init__ -:class:`ModelSpec` -~~~~~~~~~~~~~~~~~~ +:class:`Feature` +~~~~~~~~~~~~~~~~ -.. autoclass:: ModelSpec +.. autoclass:: Feature :members: :undoc-members: :show-inheritance: @@ -41,11 +41,11 @@ Classes .. automethod:: __init__ -:class:`Variable` -~~~~~~~~~~~~~~~~~ +:class:`ModelSpec` +~~~~~~~~~~~~~~~~~~ -.. autoclass:: Variable +.. autoclass:: ModelSpec :members: :undoc-members: :show-inheritance: @@ -63,10 +63,13 @@ Functions .. autofunction:: ISLP.models.model_spec.build_columns +.. autofunction:: ISLP.models.model_spec.build_model + + .. autofunction:: ISLP.models.model_spec.contrast -.. autofunction:: ISLP.models.model_spec.derived_variable +.. autofunction:: ISLP.models.model_spec.derived_feature .. autofunction:: ISLP.models.model_spec.fit_encoder diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst index 4734cda..8aededd 100644 --- a/docs/source/api/index.rst +++ b/docs/source/api/index.rst @@ -1,12 +1,7 @@ -.. _api-index: +ISLP reference +-------------- -##### - API -##### -.. only:: html +.. toctree:: - :Release: |version| - :Date: |today| - -.. include:: gen.rst + gen diff --git a/docs/source/index.rst b/docs/source/index.rst index 7c23aa0..f4c260a 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,6 +3,8 @@ Welcome to ISLP documentation! .. automodule:: ISLP +See the :doc:`api/index` + Contents -------- @@ -14,4 +16,4 @@ Contents models helpers imdb - api/index + diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 428c1b6..d039ca9 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -1,16 +1,29 @@ +--- +file_format: mystnb +kernelspec: + name: python3 + display_name: python3 +--- + # Installation instructions We generally recommend creating a [conda](https://anaconda.org) environment to isolate any code from other dependencies. The `ISLP` package does not have unusual dependencies, but this is still good practice. To create a conda environment in a Mac OS X or Linux environment run: -```{python} +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- !conda create --name islp ``` To run python code in this environment, you must activate it: -```{python} +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- !conda activate islp ``` @@ -18,7 +31,10 @@ To run python code in this environment, you must activate it: Running in the desired environment, we use `pip` to install the `ISLP` package: -```{python} +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- !pip install ISLP ``` @@ -29,7 +45,10 @@ Jupyter or IPython. Alternatively, within a python shell in the environment you want to install `ISLP`, the following commands should install `ISLP`: -```{python} +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- import os, sys cmd = f'{sys.executable} -m pip install ISLP' os.system(cmd) @@ -38,10 +57,13 @@ os.system(cmd) ## Torch requirements The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` +can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them directly using `pip` -```{python} -reqs = 'https://raw.githubusercontent.com/jonathan-taylor/ISLP/master/torch_requirements.txt' +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- +reqs = 'https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt' cmd = f'{sys.executable} -m pip install -r {reqs}' os.system(cmd) ``` diff --git a/docs/source/models.rst b/docs/source/models.rst index b34581f..5f9e5c8 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -4,8 +4,8 @@ Tools for regression models .. toctree:: models/spec - models/derived - models/submodels models/selection + models/anova + diff --git a/docs/source/models/anova.ipynb b/docs/source/models/anova.ipynb new file mode 100644 index 0000000..2bfe2ae --- /dev/null +++ b/docs/source/models/anova.ipynb @@ -0,0 +1,633 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ee33d364", + "metadata": {}, + "source": [ + "# ANOVA using `ModelSpec`\n", + "\n", + "\n", + "In this lab we illustrate how to run create specific ANOVA analyses\n", + "using `ModelSpec`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c70fbaa", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from statsmodels.api import OLS\n", + "from statsmodels.stats.anova import anova_lm\n", + "\n", + "from ISLP import load_data\n", + "from ISLP.models import (ModelSpec,\n", + " derived_feature,\n", + " summarize)" + ] + }, + { + "cell_type": "markdown", + "id": "333a49cf", + "metadata": {}, + "source": [ + "### Forward Selection\n", + " \n", + "We will apply the forward-selection approach to the `Hitters` \n", + "data. We wish to predict a baseball player’s `Salary` on the\n", + "basis of various statistics associated with performance in the\n", + "previous year." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8a708215", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "59" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hitters = load_data('Hitters')\n", + "np.isnan(Hitters['Salary']).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "dad5e991", + "metadata": {}, + "source": [ + " \n", + " We see that `Salary` is missing for 59 players. The\n", + "`dropna()` method of data frames removes all of the rows that have missing\n", + "values in any variable (by default --- see `Hitters.dropna?`)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac7086a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n", + " 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League', 'Division',\n", + " 'PutOuts', 'Assists', 'Errors', 'Salary', 'NewLeague'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Hitters = Hitters.dropna()\n", + "Hitters.columns" + ] + }, + { + "cell_type": "markdown", + "id": "1a0a3521-be74-40df-a404-3895d80a11dc", + "metadata": {}, + "source": [ + "## Grouping variables\n", + "\n", + "A look at the [description](https://islp.readthedocs.io/en/latest/datasets/Hitters.html) of the data shows\n", + "that there are both career and 1986 offensive stats, as well as some defensive stats.\n", + "\n", + "Let's group the offensive into recent and career offensive stats, as well as a group of defensive variables." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a215e43b-7bc8-4bdd-91cf-40d717cd7978", + "metadata": {}, + "outputs": [], + "source": [ + "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", + " name='offense_1986')\n", + "offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'],\n", + " name='offense_career')\n", + "defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'],\n", + " name='defense_1986')\n", + "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", + " name='confounders')" + ] + }, + { + "cell_type": "markdown", + "id": "aa15fd0c-1e8a-431e-8425-c61da8439976", + "metadata": {}, + "source": [ + "We'll first do a sequential ANOVA where terms are added sequentially" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40cd6c28", + "metadata": {}, + "outputs": [], + "source": [ + "design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters)\n", + "Y = np.array(Hitters['Salary'])\n", + "X = design.transform(Hitters)" + ] + }, + { + "cell_type": "markdown", + "id": "074120b1", + "metadata": {}, + "source": [ + "Along with a score we need to specify the search strategy. This is done through the object\n", + "`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()`\n", + "runs forward stepwise until any further additions to the model do not result\n", + "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n", + "runs a fixed number of steps of stepwise search." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e65f5607", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept148.218773.5952.0140.045
Division[W]-116.040440.188-2.8870.004
League[N]63.750379.0060.8070.421
NewLeague[N]-24.398978.843-0.3090.757
AtBat-1.95090.624-3.1250.002
Hits7.43952.3633.1480.002
HmRun4.34496.1900.7020.483
Runs-2.33122.971-0.7850.433
RBI-1.06702.595-0.4110.681
Walks6.21961.8253.4090.001
PutOuts0.28270.0773.6610.000
Assists0.37550.2201.7050.089
Errors-3.29404.377-0.7530.452
CAtBat-0.18870.120-1.5720.117
CHits0.16360.6650.2460.806
CHmRun-0.15171.612-0.0940.925
CRuns1.47160.7471.9710.050
CRBI0.80210.6911.1610.247
CWalks-0.81240.327-2.4810.014
\n", + "
" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 148.2187 73.595 2.014 0.045\n", + "Division[W] -116.0404 40.188 -2.887 0.004\n", + "League[N] 63.7503 79.006 0.807 0.421\n", + "NewLeague[N] -24.3989 78.843 -0.309 0.757\n", + "AtBat -1.9509 0.624 -3.125 0.002\n", + "Hits 7.4395 2.363 3.148 0.002\n", + "HmRun 4.3449 6.190 0.702 0.483\n", + "Runs -2.3312 2.971 -0.785 0.433\n", + "RBI -1.0670 2.595 -0.411 0.681\n", + "Walks 6.2196 1.825 3.409 0.001\n", + "PutOuts 0.2827 0.077 3.661 0.000\n", + "Assists 0.3755 0.220 1.705 0.089\n", + "Errors -3.2940 4.377 -0.753 0.452\n", + "CAtBat -0.1887 0.120 -1.572 0.117\n", + "CHits 0.1636 0.665 0.246 0.806\n", + "CHmRun -0.1517 1.612 -0.094 0.925\n", + "CRuns 1.4716 0.747 1.971 0.050\n", + "CRBI 0.8021 0.691 1.161 0.247\n", + "CWalks -0.8124 0.327 -2.481 0.014" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = OLS(Y, X).fit()\n", + "summarize(M)" + ] + }, + { + "cell_type": "markdown", + "id": "29d9b55f", + "metadata": {}, + "source": [ + "We'll first produce the sequential, or Type I ANOVA results. This builds up a model sequentially and compares\n", + "two successive models." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cfbe5b92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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df_residssrdf_diffss_diffFPr(>F)
0262.05.331911e+070.0NaNNaNNaN
1259.05.131263e+073.02.006478e+066.7411472.144265e-04
2253.03.589842e+076.01.541422e+0725.8935106.063309e-24
3250.03.471882e+073.01.179602e+063.9630998.730527e-03
4244.02.420857e+076.01.051025e+0717.6555965.701196e-17
\n", + "
" + ], + "text/plain": [ + " df_resid ssr df_diff ss_diff F Pr(>F)\n", + "0 262.0 5.331911e+07 0.0 NaN NaN NaN\n", + "1 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 2.144265e-04\n", + "2 253.0 3.589842e+07 6.0 1.541422e+07 25.893510 6.063309e-24\n", + "3 250.0 3.471882e+07 3.0 1.179602e+06 3.963099 8.730527e-03\n", + "4 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 5.701196e-17" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])" + ] + }, + { + "cell_type": "markdown", + "id": "7092f666", + "metadata": {}, + "source": [ + "We can similarly compute the Type II ANOVA results which drops each term and compares to the full model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e2d43844", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
df_residssrdf_diffss_diffFPr(>F)
intercept244.02.420857e+071.04.024254e+054.0560764.511037e-02
confounders244.02.420857e+073.09.661738e+053.2460462.261572e-02
offense_1986244.02.420857e+076.03.097572e+065.2034444.648586e-05
defense_1986244.02.420857e+073.01.467933e+064.9318032.415732e-03
offense_career244.02.420857e+076.01.051025e+0717.6555965.701196e-17
\n", + "
" + ], + "text/plain": [ + " df_resid ssr df_diff ss_diff F \\\n", + "intercept 244.0 2.420857e+07 1.0 4.024254e+05 4.056076 \n", + "confounders 244.0 2.420857e+07 3.0 9.661738e+05 3.246046 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", + "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", + "offense_career 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 \n", + "\n", + " Pr(>F) \n", + "intercept 4.511037e-02 \n", + "confounders 2.261572e-02 \n", + "offense_1986 4.648586e-05 \n", + "defense_1986 2.415732e-03 \n", + "offense_career 5.701196e-17 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D_full = design.transform(Hitters)\n", + "OLS_full = OLS(Y, D_full).fit()\n", + "dfs = []\n", + "for d in design.build_sequence(Hitters, anova_type='drop'):\n", + " dfs.append(anova_lm(OLS(Y,d).fit(), OLS_full).iloc[1:])\n", + "df = pd.concat(dfs)\n", + "df.index = design.names\n", + "df" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" + }, + "kernelspec": { + "display_name": "islp_test", + "language": "python", + "name": "islp_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/models/derived.ipynb b/docs/source/models/derived.ipynb deleted file mode 100644 index cc1b0ac..0000000 --- a/docs/source/models/derived.ipynb +++ /dev/null @@ -1,2125 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "38217f02", - "metadata": {}, - "source": [ - "# Building design matrices with `ModelSpec`\n", - "\n", - "Force rebuild" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3107d1f9", - "metadata": {}, - "outputs": [], - "source": [ - "x=4\n", - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cdc46a4e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "e0a2a83a", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "68b40caf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "35558d88", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e5e81a95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "4bbf9e13", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1ad729b3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d05e9ec8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "b4e9ee33", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "64ac65d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "620f0e01", - "metadata": {}, - "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "77b898e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "4580a6bf", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c2dab855", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['OIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5e7963d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "markdown", - "id": "6b689966", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ff3b96b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7e87da20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" - ] - }, - { - "cell_type": "markdown", - "id": "4f2030ac", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "27fc4fb3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "16316981", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "ef3f2bd0", - "metadata": {}, - "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "dd9c7fa6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" - ] - }, - { - "cell_type": "markdown", - "id": "5fc4cc45", - "metadata": {}, - "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "49d7fb46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" - ] - }, - { - "cell_type": "markdown", - "id": "bdfc0fe9", - "metadata": {}, - "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "cf6f3f4c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "1552d19a", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "12d955dd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "f5ea292d", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ae2af29b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "57305dbe", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "89656ec4", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "f6cb8167", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "547cb625", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ff5b41d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "932759cf", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e2190b00", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6545c5da", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "cd088b51", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "8f37ae20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "184aefc2", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e4134980", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "53808f3b", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "62059c57", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "ded12f69", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "fbb509d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "f01391e4", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "10df55ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "b43099fb", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "50bce64d", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "2eb2ff16", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "6686dff8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "0e0eafd7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "43cce209", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "99bf408e", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "11c19ebf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "4b48e5d2", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "81f641ba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "79f7eb4d", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "2afb3b5d", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "c44692ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "c0bfb2a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "d263056c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "edf0dc68", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "82071a54", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "cd18a4a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "229fa32d", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "b8c52dbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "e7f93464", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "4094c01f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "d448c9ca", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "634e05c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "4c09c93f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "48c1989f", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "85a28d87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3)', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "e17c8a9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3)[0] 10.036373\n", - "poly(Income, 3)[1] -2.799156\n", - "poly(Income, 3)[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "944f56d6", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1889caca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "bd4dca31", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "70fae990", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca" - ] - }, - { - "cell_type": "markdown", - "id": "2d812694", - "metadata": {}, - "source": [ - "## Custom encoding\n", - "\n", - "Instead of PCA we might run some clustering on some features and then uses the clusters to\n", - "create new features. This can be done with `derived_variable`. Indeed, `pca`, `ns` and `bs` are all examples\n", - "of this." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "8e5d2305", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import derived_variable, Contrast" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "8a40c663", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 2, 1, 2, 1, 0, 1, 0, 0, 0, 1, 2, 2, 0, 1, 2, 1, 0, 0, 0, 2,\n", - " 2, 2, 1, 2, 1, 0, 0, 1, 0, 1, 2, 1, 2, 0, 0, 2, 2, 2, 0, 2, 0, 2,\n", - " 0, 2, 0, 0, 2, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 2, 2, 0, 1, 2,\n", - " 0, 1, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 2, 0, 1, 0, 0, 2, 2, 0, 1, 2,\n", - " 2, 2, 2, 2, 0, 2, 0, 2, 2, 0, 1, 2, 0, 0, 2, 0, 0, 1, 2, 0, 1, 0,\n", - " 0, 1, 0, 2, 0, 2, 0, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0,\n", - " 0, 0, 2, 1, 0, 2, 1, 1, 1, 2, 0, 0, 2, 0, 2, 1, 0, 0, 0, 1, 2, 2,\n", - " 1, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 1, 0, 0, 1, 1, 1, 0, 0, 2, 0,\n", - " 1, 0, 0, 2, 1, 0, 2, 1, 2, 1, 0, 2, 2, 1, 1, 2, 2, 0, 1, 1, 2, 2,\n", - " 1, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 2, 2, 1, 1, 0, 0, 1, 2, 2, 1, 1,\n", - " 1, 2, 0, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 1, 2, 1, 2, 2, 0, 0, 0,\n", - " 2, 2, 2, 2, 1, 0, 0, 0, 1, 0, 0, 2, 1, 0, 2, 1, 2, 1, 1, 2, 1, 2,\n", - " 2, 2, 1, 1, 0, 2, 2, 2, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 2,\n", - " 1, 2, 2, 1, 1, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 0, 1, 1, 1, 1, 2, 0,\n", - " 1, 0, 1, 1, 0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 2, 1, 0, 1,\n", - " 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 0, 1, 2, 0, 2, 0, 2, 1, 1, 1, 1,\n", - " 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 0, 2, 1, 2, 1, 0, 2, 1, 1, 0, 2, 2,\n", - " 2, 2, 1, 2, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 2, 0, 0, 1, 0, 1, 1,\n", - " 2, 2, 0, 2], dtype=int32)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.cluster import KMeans\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "cluster = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "group = Variable(('Income', 'Price', 'Advertising', 'Population'), 'group', None)\n", - "X = design.build_submodel(Carseats, [group]).drop('intercept', axis=1)\n", - "cluster.fit(X.values)\n", - "cluster.predict(X.values)" - ] - }, - { - "cell_type": "markdown", - "id": "9bc38836", - "metadata": {}, - "source": [ - "For clustering, we often want to use the `predict` method rather than the `transform` method. If the ultimate\n", - "features all use `transform` then the do not even need to use these two calls to `make_pipeline`." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "8ceab9b6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept myclus\n", - "0 1.0 1\n", - "1 1.0 1\n", - "2 1.0 2\n", - "3 1.0 1\n", - "4 1.0 2\n", - ".. ... ...\n", - "395 1.0 1\n", - "396 1.0 2\n", - "397 1.0 2\n", - "398 1.0 0\n", - "399 1.0 2\n", - "\n", - "[400 rows x 2 columns]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], \n", - " name='myclus', \n", - " encoder=cluster2,\n", - " use_transform=False)\n", - "design = ModelSpec([cluster_var]).fit(Carseats)\n", - "design.transform(Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "1f9b2630", - "metadata": {}, - "source": [ - "Somewhat clunkily, we can make this a categorical variable by creating a `Variable` with a\n", - "categorical encoder." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "ffde00a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=(Variable(variables=('Income', 'Price', 'Advertising', 'Population'), name='myclus', encoder=Pipeline(steps=[('standardscaler', StandardScaler()),\n", - " ('kmeans', KMeans(n_clusters=3, random_state=0))]), use_transform=False, pure_columns=False, override_encoder_colnames=True),), name='mynewcat', encoder=Contrast(), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cluster2 = make_pipeline(StandardScaler(), KMeans(n_clusters=3, random_state=0))\n", - "cluster_var = derived_variable(['Income', 'Price', 'Advertising', 'Population'], \n", - " name='myclus', \n", - " encoder=cluster2,\n", - " use_transform=False)\n", - "cat_cluster = Variable((cluster_var,), name='mynewcat', encoder=Contrast(method='drop'))\n", - "cat_cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "5afeab7c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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400 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " intercept 1 2\n", - "0 1.0 1.0 0.0\n", - "1 1.0 1.0 0.0\n", - "2 1.0 0.0 1.0\n", - "3 1.0 1.0 0.0\n", - "4 1.0 0.0 1.0\n", - ".. ... ... ...\n", - "395 1.0 1.0 0.0\n", - "396 1.0 0.0 1.0\n", - "397 1.0 0.0 1.0\n", - "398 1.0 0.0 0.0\n", - "399 1.0 0.0 1.0\n", - "\n", - "[400 rows x 3 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([cat_cluster]).fit(Carseats)\n", - "\n", - "design.transform(Carseats)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e24d5637-80fb-49bf-ac10-8ff68cb8bd8f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" - }, - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/models/selection.ipynb b/docs/source/models/selection.ipynb index 3a7d002..0f597af 100644 --- a/docs/source/models/selection.ipynb +++ b/docs/source/models/selection.ipynb @@ -2,2723 +2,259 @@ "cells": [ { "cell_type": "markdown", - "id": "72bae06a", + "id": "247387ec-1477-42e6-9e69-cad1cacb5721", "metadata": {}, "source": [ - "# Model selection using `ModelSpec`" + "# Model selection using `ModelSpec`\n", + "\n", + "\n", + "In this lab we illustrate how to run forward stepwise model selection\n", + "using the model specification capability of `ModelSpec`." ] }, { "cell_type": "code", "execution_count": 1, - "id": "ae6bd850", + "id": "4720bb2a-6bec-4e91-a57e-9689aa4f0532", "metadata": {}, "outputs": [], "source": [ - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from statsmodels.api import OLS\n", "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5ac10e72", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "80a586d9", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "850356ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "e24def3a", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "edf83080", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "aa22bb9c", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "38d92522", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cfc2056f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "4674c345", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5688f0ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" + "from ISLP.models import (ModelSpec,\n", + " Stepwise,\n", + " sklearn_selected)" ] }, { "cell_type": "markdown", - "id": "4ae28ffa", + "id": "1c224240-ce8b-47f3-a85a-052c43038b26", "metadata": {}, "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." + "### Forward Selection\n", + " \n", + "We will apply the forward-selection approach to the `Hitters` \n", + "data. We wish to predict a baseball player’s `Salary` on the\n", + "basis of various statistics associated with performance in the\n", + "previous year." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "5f8926fd", + "execution_count": 2, + "id": "2adc66cc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" + "59" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "design.column_info_['ShelveLoc']" + "Hitters = load_data('Hitters')\n", + "np.isnan(Hitters['Salary']).sum()" ] }, { "cell_type": "markdown", - "id": "966f53a5", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a137fa1e", + "id": "40c9a484", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "design.column_info_['OIncome']" + " \n", + " We see that `Salary` is missing for 59 players. The\n", + "`dropna()` method of data frames removes all of the rows that have missing\n", + "values in any variable (by default --- see `Hitters.dropna?`)." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "3390dcb0", + "execution_count": 3, + "id": "1869fdab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" + "(263, 20)" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" + "Hitters = Hitters.dropna()\n", + "Hitters.shape" ] }, { "cell_type": "markdown", - "id": "b6667415", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a1b42dbd", + "id": "0a1fe9e6", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" + "We first choose the best model using forward selection based on AIC. This score\n", + "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n", + "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n", + " our scoring function computes the negative AIC statistic." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "31367988", + "execution_count": 4, + "id": "76bd8110", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" + "def negAIC(estimator, X, Y):\n", + " \"Negative AIC\"\n", + " n, p = X.shape\n", + " Yhat = estimator.predict(X)\n", + " MSE = np.mean((Y - Yhat)**2)\n", + " return n + n * np.log(MSE) + 2 * (p + 1)\n", + " " ] }, { "cell_type": "markdown", - "id": "751c1487", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6e2b6155", + "id": "14ba6f49", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "design.terms" + "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n", + "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "d3e669da", + "execution_count": 5, + "id": "94e10f35", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "design.terms_" + "design = ModelSpec(Hitters.columns.drop('Salary')).fit(Hitters)\n", + "Y = np.array(Hitters['Salary'])\n", + "X = design.transform(Hitters)" ] }, { "cell_type": "markdown", - "id": "fb0a45c9", + "id": "afdda5f2", "metadata": {}, "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" + "Along with a score we need to specify the search strategy. This is done through the object\n", + "`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()`\n", + "runs forward stepwise until any further additions to the model do not result\n", + "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n", + "runs a fixed number of steps of stepwise search." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "554c67cb", + "execution_count": 6, + "id": "048c8500", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" + "strategy = Stepwise.first_peak(design,\n", + " direction='forward',\n", + " max_terms=len(design.terms))" ] }, { "cell_type": "markdown", - "id": "06956a6f", + "id": "e0c0af0e", "metadata": {}, "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." + " \n", + "We now fit a linear regression model with `Salary` as outcome using forward\n", + "selection. To do so, we use the function `sklearn_selected()` from the `ISLP.models` package. This takes\n", + "a model from `statsmodels` along with a search strategy and selects a model with its\n", + "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n", + "selected." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "dd434884", + "execution_count": 7, + "id": "26f09fe9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" + "('Assists',\n", + " 'AtBat',\n", + " 'CAtBat',\n", + " 'CHits',\n", + " 'CHmRun',\n", + " 'CRBI',\n", + " 'CRuns',\n", + " 'CWalks',\n", + " 'Division',\n", + " 'Errors',\n", + " 'Hits',\n", + " 'HmRun',\n", + " 'League',\n", + " 'NewLeague',\n", + " 'PutOuts',\n", + " 'RBI',\n", + " 'Runs',\n", + " 'Walks',\n", + " 'Years')" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" + "hitters_MSE = sklearn_selected(OLS,\n", + " strategy)\n", + "hitters_MSE.fit(Hitters, Y)\n", + "hitters_MSE.selected_state_" ] }, { "cell_type": "markdown", - "id": "5cdb088c", + "id": "4acf4792", "metadata": {}, "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." + " Using `neg_Cp` results in a smaller model, as expected, with just 4variables selected." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "519a642e", + "execution_count": 8, + "id": "a825f4d8", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" + "('Assists', 'Errors', 'League', 'NewLeague')" ] }, - "execution_count": 17, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "403921a2", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." + "hitters_Cp = sklearn_selected(OLS,\n", + " strategy,\n", + " scoring=negAIC)\n", + "hitters_Cp.fit(Hitters, Y)\n", + "hitters_Cp.selected_state_" ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b422cde1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "53e38f57", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6347acb6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "08b5ddb0", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a8eb3e33", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "97912337", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "72b5e629", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - 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" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "8624ab8c", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6052765e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9158de59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "9608bed3", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f0b8120f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "270a02a6", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "4ffbce7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "bc5ff62b", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "34dae1e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "7e9da262", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "938b9430", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "083e9529", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d413a9fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "0f4b508b", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "8bcbd973", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "cf13f72e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c1fa0a90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b28aa313", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "aa764acc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "31876a29", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "bac2643c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "1485735d", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "3987c5d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "7a6631c9", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "83a9b94e", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "f0ffabea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "4a5fdc64", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ae7e3bd2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "c12ac3df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "53bf8aef", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "47723bce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "86060622", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "d7a2ab9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "2a5e7f6b", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "bbb02036", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "89106a85", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "151f3fee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "945ce7bc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "450b94dd", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "18d5c1c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3, )', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "46c7d911", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3, )[0] 10.036373\n", - "poly(Income, 3, )[1] -2.799156\n", - "poly(Income, 3, )[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "99bf13a1", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7606facd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "a4931031", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "1c1bf5f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 4.240421\n", - "ns(Income, , df=5)[0] 1.468196\n", - "ns(Income, , df=5)[1] 1.499471\n", - "ns(Income, , df=5)[2] 1.152070\n", - "ns(Income, , df=5)[3] 2.418398\n", - "ns(Income, , df=5)[4] 1.804460\n", - "ShelveLoc[Good] 4.810449\n", - "ShelveLoc[Medium] 1.881095\n", - "dtype: float64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca\n", - "design = ModelSpec([ns('Income', df=5), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "8c24254b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) ns(Income, df = 5)1 ns(Income, df = 5)2 ns(Income, df = 5)3 \n", - " 4.240421 1.468196 1.499471 1.152070 \n", - "ns(Income, df = 5)4 ns(Income, df = 5)5 ShelveLocGood ShelveLocMedium \n", - " 2.418398 1.804460 4.810449 1.881095 \n" - ] - } - ], - "source": [ - "%%R\n", - "library(splines)\n", - "lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "f9d6c4a7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 3.495085\n", - "bs(Income, , df=7, degree=2)[0] 1.813118\n", - "bs(Income, , df=7, degree=2)[1] 0.961852\n", - "bs(Income, , df=7, degree=2)[2] 2.471545\n", - "bs(Income, , df=7, degree=2)[3] 2.158891\n", - "bs(Income, , df=7, degree=2)[4] 2.091625\n", - "bs(Income, , df=7, degree=2)[5] 2.600669\n", - "bs(Income, , df=7, degree=2)[6] 2.843108\n", - "ShelveLoc[Good] 4.804919\n", - "ShelveLoc[Medium] 1.880337\n", - "dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "0bf1726a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) bs(Income, df = 7, degree = 2)1 \n", - " 3.4950851 1.8131176 \n", - "bs(Income, df = 7, degree = 2)2 bs(Income, df = 7, degree = 2)3 \n", - " 0.9618523 2.4715450 \n", - "bs(Income, df = 7, degree = 2)4 bs(Income, df = 7, degree = 2)5 \n", - " 2.1588908 2.0916252 \n", - "bs(Income, df = 7, degree = 2)6 bs(Income, df = 7, degree = 2)7 \n", - " 2.6006694 2.8431084 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.8049190 1.8803375 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "914df4cf", - "metadata": {}, - "source": [ - "## PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "cc22e780", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.419405\n", - "pca(myvars, , n_components=2)[0] -0.001131\n", - "pca(myvars, , n_components=2)[1] -0.024217\n", - "ShelveLoc[Good] 4.816253\n", - "ShelveLoc[Medium] 1.924139\n", - "dtype: float64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars'), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "de571e61", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population))$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.419405 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC1 \n", - " 0.001131 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC2 \n", - " -0.024217 \n", - " ShelveLocGood \n", - " 4.816253 \n", - " ShelveLocMedium \n", - " 1.924139 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "0a103b5a", - "metadata": {}, - "source": [ - "It is of course common to scale before running PCA." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "95ca42f5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.352159\n", - "pca(myvars, , n_components=2)[0] 0.446383\n", - "pca(myvars, , n_components=2)[1] -1.219788\n", - "ShelveLoc[Good] 4.922780\n", - "ShelveLoc[Medium] 2.005617\n", - "dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars',\n", - " scale=True), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "0dc22e35", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population), scale = TRUE)$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.3522 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC1 \n", - " 0.4469 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC2 \n", - " -1.2213 \n", - " ShelveLocGood \n", - " 4.9228 \n", - " ShelveLocMedium \n", - " 2.0056 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "70347ee9", - "metadata": {}, - "source": [ - "There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead\n", - "of `np.std(ddof=1)`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "aa0c2f2e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.44694166, -1.22131519])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1))" - ] - }, - { - "cell_type": "markdown", - "id": "ab05c497", - "metadata": {}, - "source": [ - "## Model selection\n", - "\n", - "Another task requiring different design matrices is model selection. Manipulating\n", - "the `terms` attribute of a `ModelSpec` (or more precisely its more uniform version `terms_`)\n", - "can clearly allow for both exhaustive and stepwise model selection." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "9505c178", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.strategy import (Stepwise, \n", - " min_max)\n", - "from ISLP.models.generic_selector import FeatureSelector" - ] - }, - { - "cell_type": "markdown", - "id": "020c2532", - "metadata": {}, - "source": [ - "### Best subsets" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "f9aba6db", - "metadata": {}, - "outputs": [], - "source": [ - "design = ModelSpec(['Price', \n", - " 'UIncome', \n", - " 'Advertising', \n", - " 'US', \n", - " 'Income',\n", - " 'ShelveLoc',\n", - " 'Education',\n", - " 'Urban']).fit(Carseats)\n", - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "91144a3d", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error')" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "ae3cb2eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.fit(Carseats, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "e63b2744", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'ShelveLoc')" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "0a774b48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('UIncome',), ('Advertising',), ('US',), ('Income',), ('ShelveLoc',), ('Education',), ('Urban',), ('Price', 'UIncome'), ('Price', 'Advertising'), ('Price', 'US'), ('Price', 'Income'), ('Price', 'ShelveLoc'), ('Price', 'Education'), ('Price', 'Urban'), ('UIncome', 'Advertising'), ('UIncome', 'US'), ('UIncome', 'Income'), ('UIncome', 'ShelveLoc'), ('UIncome', 'Education'), ('UIncome', 'Urban'), ('Advertising', 'US'), ('Advertising', 'Income'), ('Advertising', 'ShelveLoc'), ('Advertising', 'Education'), ('Advertising', 'Urban'), ('US', 'Income'), ('US', 'ShelveLoc'), ('US', 'Education'), ('US', 'Urban'), ('Income', 'ShelveLoc'), ('Income', 'Education'), ('Income', 'Urban'), ('ShelveLoc', 'Education'), ('ShelveLoc', 'Urban'), ('Education', 'Urban'), ('Price', 'UIncome', 'Advertising'), ('Price', 'UIncome', 'US'), ('Price', 'UIncome', 'Income'), ('Price', 'UIncome', 'ShelveLoc'), ('Price', 'UIncome', 'Education'), ('Price', 'UIncome', 'Urban'), ('Price', 'Advertising', 'US'), ('Price', 'Advertising', 'Income'), ('Price', 'Advertising', 'ShelveLoc'), ('Price', 'Advertising', 'Education'), ('Price', 'Advertising', 'Urban'), ('Price', 'US', 'Income'), ('Price', 'US', 'ShelveLoc'), ('Price', 'US', 'Education'), ('Price', 'US', 'Urban'), ('Price', 'Income', 'ShelveLoc'), ('Price', 'Income', 'Education'), ('Price', 'Income', 'Urban'), ('Price', 'ShelveLoc', 'Education'), ('Price', 'ShelveLoc', 'Urban'), ('Price', 'Education', 'Urban'), ('UIncome', 'Advertising', 'US'), ('UIncome', 'Advertising', 'Income'), ('UIncome', 'Advertising', 'ShelveLoc'), ('UIncome', 'Advertising', 'Education'), ('UIncome', 'Advertising', 'Urban'), ('UIncome', 'US', 'Income'), ('UIncome', 'US', 'ShelveLoc'), ('UIncome', 'US', 'Education'), ('UIncome', 'US', 'Urban'), ('UIncome', 'Income', 'ShelveLoc'), ('UIncome', 'Income', 'Education'), ('UIncome', 'Income', 'Urban'), ('UIncome', 'ShelveLoc', 'Education'), ('UIncome', 'ShelveLoc', 'Urban'), ('UIncome', 'Education', 'Urban'), ('Advertising', 'US', 'Income'), ('Advertising', 'US', 'ShelveLoc'), ('Advertising', 'US', 'Education'), ('Advertising', 'US', 'Urban'), ('Advertising', 'Income', 'ShelveLoc'), ('Advertising', 'Income', 'Education'), ('Advertising', 'Income', 'Urban'), ('Advertising', 'ShelveLoc', 'Education'), ('Advertising', 'ShelveLoc', 'Urban'), ('Advertising', 'Education', 'Urban'), ('US', 'Income', 'ShelveLoc'), ('US', 'Income', 'Education'), ('US', 'Income', 'Urban'), ('US', 'ShelveLoc', 'Education'), ('US', 'ShelveLoc', 'Urban'), ('US', 'Education', 'Urban'), ('Income', 'ShelveLoc', 'Education'), ('Income', 'ShelveLoc', 'Urban'), ('Income', 'Education', 'Urban'), ('ShelveLoc', 'Education', 'Urban')])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "0ca1f28c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'Income')" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=3,\n", - " lower_terms=['Price'],\n", - " upper_terms=['Price', 'Income', 'Advertising'])\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error')\n", - "selector.fit(Carseats, Y)\n", - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "5c6732fa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([('Price',), ('Price', 'Advertising'), ('Price', 'Income'), ('Price', 'Advertising', 'Income')])" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "markdown", - "id": "7bb6fcc3", - "metadata": {}, - "source": [ - "### Stepwise selection" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "9985d0fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Advertising', 'Income', 'Price', 'ShelveLoc')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy = Stepwise.first_peak(design,\n", - " min_terms=0,\n", - " max_terms=6,\n", - " lower_terms=['Price'],\n", - " upper_terms=['Price', 'Income', 'Advertising', 'ShelveLoc', 'UIncome', 'US'\n", - " 'Education', 'Urban'])\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error',\n", - " cv=3)\n", - "selector.fit(Carseats, Y)\n", - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "d3cf3e9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('Price', 'UIncome'), ('Advertising', 'Price'), ('Income', 'Price'), ('Price', 'ShelveLoc'), ('Price', 'Urban'), ('Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Price', 'ShelveLoc'), ('Income', 'Price', 'ShelveLoc'), ('Price', 'ShelveLoc', 'Urban'), ('Advertising', 'Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Income', 'Price', 'ShelveLoc'), ('Advertising', 'Price', 'ShelveLoc', 'Urban'), ('Advertising', 'Income', 'Price', 'ShelveLoc', 'UIncome'), ('Advertising', 'Income', 'Price', 'ShelveLoc', 'Urban')])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "dd43ea7c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(): -8.055847677297269,\n", - " ('Price',): -6.514630258019962,\n", - " ('Price', 'UIncome'): -6.621654905418576,\n", - " ('Advertising', 'Price'): -5.825225309857156,\n", - " ('Income', 'Price'): -6.455432795910743,\n", - " ('Price', 'ShelveLoc'): -3.780183168075897,\n", - " ('Price', 'Urban'): -6.5430157266926114,\n", - " ('Price', 'ShelveLoc', 'UIncome'): -3.6938729706475004,\n", - " ('Advertising', 'Price', 'ShelveLoc'): -3.2067316025050645,\n", - " ('Income', 'Price', 'ShelveLoc'): -3.634698914456587,\n", - " ('Price', 'ShelveLoc', 'Urban'): -3.776148947585277,\n", - " ('Advertising', 'Price', 'ShelveLoc', 'UIncome'): -3.1240961493998642,\n", - " ('Advertising', 'Income', 'Price', 'ShelveLoc'): -3.0801704971796244,\n", - " ('Advertising', 'Price', 'ShelveLoc', 'Urban'): -3.207569489139369,\n", - " ('Advertising',\n", - " 'Income',\n", - " 'Price',\n", - " 'ShelveLoc',\n", - " 'UIncome'): -3.1048826894036115,\n", - " ('Advertising', 'Income', 'Price', 'ShelveLoc', 'Urban'): -3.0867130108677423}" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.results_" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "7c026f0a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Advertising', 'Income', 'Price', 'ShelveLoc')" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "markdown", - "id": "b4b89d04", - "metadata": {}, - "source": [ - "### Enforcing constraints\n", - "\n", - "In models with interactions, we may often want to impose constraints on interactions and main effects.\n", - "This can be achieved here by use of a `validator` that checks whether a given model is valid.\n", - "\n", - "Suppose we want to have the following constraint: `ShelveLoc` may not be in the model unless\n", - "`Price` is in the following model." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "1c1e31d0", - "metadata": {}, - "outputs": [], - "source": [ - "design = ModelSpec(['Price', \n", - " 'Advertising', \n", - " 'Income',\n", - " 'ShelveLoc']).fit(Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "be929807", - "metadata": {}, - "source": [ - "The constraints are described with a boolean matrix with `(i,j)` as `j` is a child of `i`: so `j` should not\n", - "be in the model when `i` is not and enforced with a callable `validator` that evaluates each candidate state.\n", - "\n", - "Both `min_max_strategy` and `step_strategy` accept a `validator` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "c075b1b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('Price',), ('Advertising',), ('Income',), ('Price', 'Advertising'), ('Price', 'Income'), ('Price', 'ShelveLoc'), ('Advertising', 'Income'), ('Price', 'Advertising', 'Income'), ('Price', 'Advertising', 'ShelveLoc'), ('Price', 'Income', 'ShelveLoc'), ('Price', 'Advertising', 'Income', 'ShelveLoc')])" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.strategy import validator_from_constraints\n", - "constraints = np.zeros((4, 4))\n", - "constraints[0,3] = 1\n", - "strategy = min_max(design,\n", - " min_terms=0,\n", - " max_terms=4,\n", - " validator=validator_from_constraints(design,\n", - " constraints))\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error',\n", - " cv=3)\n", - "selector.fit(Carseats, Y)\n", - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "3472d47c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Price', 'Advertising', 'Income', 'ShelveLoc')" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selector.selected_state_" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "5d2c82b9", - "metadata": {}, - "outputs": [], - "source": [ - "Hitters=load_data('Hitters')" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4b2ac2c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'CAtBat',\n", - " 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League', 'Division',\n", - " 'PutOuts', 'Assists', 'Errors', 'Salary', 'NewLeague'],\n", - " dtype='object')" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Hitters.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "bd2ad0dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys([(), ('AtBat',), ('Hits',), ('HmRun',), ('Runs',), ('RBI',), ('Walks',), ('Years',), ('CAtBat',), ('CHits',), ('CHmRun',), ('CRuns',), ('CRBI',), ('CWalks',), ('League',), ('Division',), ('PutOuts',), ('Assists',), ('Errors',), ('NewLeague',), ('AtBat', 'CRBI'), ('CRBI', 'Hits'), ('CRBI', 'HmRun'), ('CRBI', 'Runs'), ('CRBI', 'RBI'), ('CRBI', 'Walks'), ('CRBI', 'Years'), ('CAtBat', 'CRBI'), ('CHits', 'CRBI'), ('CHmRun', 'CRBI'), ('CRBI', 'CRuns'), ('CRBI', 'CWalks'), ('CRBI', 'League'), ('CRBI', 'Division'), ('CRBI', 'PutOuts'), ('Assists', 'CRBI'), ('CRBI', 'Errors'), ('CRBI', 'NewLeague'), ('AtBat', 'CRBI', 'Hits'), ('CRBI', 'Hits', 'HmRun'), ('CRBI', 'Hits', 'Runs'), ('CRBI', 'Hits', 'RBI'), ('CRBI', 'Hits', 'Walks'), ('CRBI', 'Hits', 'Years'), ('CAtBat', 'CRBI', 'Hits'), ('CHits', 'CRBI', 'Hits'), ('CHmRun', 'CRBI', 'Hits'), ('CRBI', 'CRuns', 'Hits'), ('CRBI', 'CWalks', 'Hits'), ('CRBI', 'Hits', 'League'), ('CRBI', 'Division', 'Hits'), ('CRBI', 'Hits', 'PutOuts'), ('Assists', 'CRBI', 'Hits'), ('CRBI', 'Errors', 'Hits'), ('CRBI', 'Hits', 'NewLeague'), ('AtBat', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'HmRun', 'PutOuts'), ('CRBI', 'Hits', 'PutOuts', 'Runs'), ('CRBI', 'Hits', 'PutOuts', 'RBI'), ('CRBI', 'Hits', 'PutOuts', 'Walks'), ('CRBI', 'Hits', 'PutOuts', 'Years'), ('CAtBat', 'CRBI', 'Hits', 'PutOuts'), ('CHits', 'CRBI', 'Hits', 'PutOuts'), ('CHmRun', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'CRuns', 'Hits', 'PutOuts'), ('CRBI', 'CWalks', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'League', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'PutOuts'), ('Assists', 'CRBI', 'Hits', 'PutOuts'), ('CRBI', 'Errors', 'Hits', 'PutOuts'), ('CRBI', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Runs'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'RBI'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('CRBI', 'Division', 'Hits', 'PutOuts', 'Years'), ('CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CHits', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'League', 'PutOuts'), ('Assists', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Errors', 'Hits', 'PutOuts'), ('CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Runs'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'RBI'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CHits', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'League', 'PutOuts'), ('Assists', 'AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Errors', 'Hits', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts'), ('AtBat', 'CRBI', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'RBI', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'RBI', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks'), ('Assists', 'AtBat', 'CAtBat', 'CHits', 'CHmRun', 'CRBI', 'CRuns', 'CWalks', 'Division', 'Errors', 'Hits', 'HmRun', 'League', 'NewLeague', 'PutOuts', 'RBI', 'Runs', 'Walks', 'Years')])" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Hitters = Hitters.dropna()\n", - "Y=Hitters['Salary']\n", - "X=Hitters.drop('Salary', axis=1)\n", - "design = ModelSpec(X.columns).fit(X)\n", - "strategy = Stepwise.first_peak(design,\n", - " direction='forward',\n", - " min_terms=0,\n", - " max_terms=19)\n", - "selector = FeatureSelector(LinearRegression(fit_intercept=False),\n", - " strategy,\n", - " scoring='neg_mean_squared_error', cv=None)\n", - "selector.fit(X, Y)\n", - "selector.results_.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "31788748", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(selector.selected_state_)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "e97d80c3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(X.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a71f0332", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start: AIC=3215.77\n", - "Salary ~ 1\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CRBI 1 17139434 36179679 3115.8\n", - "+ CRuns 1 16881162 36437951 3117.6\n", - "+ CHits 1 16065140 37253973 3123.5\n", - "+ CAtBat 1 14759710 38559403 3132.5\n", - "+ CHmRun 1 14692193 38626920 3133.0\n", - "+ CWalks 1 12792622 40526491 3145.6\n", - "+ RBI 1 10771083 42548030 3158.4\n", - "+ Walks 1 10504833 42814280 3160.1\n", - "+ Hits 1 10260491 43058621 3161.6\n", - "+ Runs 1 9399158 43919955 3166.8\n", - "+ Years 1 8559105 44760007 3171.7\n", - "+ AtBat 1 8309469 45009644 3173.2\n", - "+ HmRun 1 6273967 47045145 3184.8\n", - "+ PutOuts 1 4814100 48505013 3192.9\n", - "+ Division 1 1976102 51343011 3207.8\n", - " 53319113 3215.8\n", - "+ Assists 1 34497 53284615 3217.6\n", - "+ League 1 10876 53308237 3217.7\n", - "+ Errors 1 1555 53317558 3217.8\n", - "+ NewLeague 1 428 53318684 3217.8\n", - "\n", - "Step: AIC=3115.78\n", - "Salary ~ CRBI\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Hits 1 5533119 30646560 3074.1\n", - "+ Runs 1 5176532 31003147 3077.2\n", - "+ Walks 1 4199733 31979946 3085.3\n", - "+ AtBat 1 4064585 32115095 3086.4\n", - "+ RBI 1 3308272 32871407 3092.6\n", - "+ PutOuts 1 3267035 32912644 3092.9\n", - "+ Division 1 1733887 34445793 3104.9\n", - "+ Years 1 1667339 34512340 3105.4\n", - "+ HmRun 1 1271587 34908092 3108.4\n", - "+ CRuns 1 354561 35825119 3115.2\n", - "+ Assists 1 346020 35833659 3115.2\n", - " 36179679 3115.8\n", - "+ Errors 1 194403 35985276 3116.4\n", - "+ CAtBat 1 92261 36087418 3117.1\n", - "+ CHits 1 75469 36104210 3117.2\n", - "+ CWalks 1 51974 36127705 3117.4\n", - "+ NewLeague 1 17778 36161901 3117.7\n", - "+ League 1 11825 36167855 3117.7\n", - "+ CHmRun 1 515 36179165 3117.8\n", - "\n", - "Step: AIC=3074.13\n", - "Salary ~ CRBI + Hits\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ PutOuts 1 1397263 29249297 3063.8\n", - "+ Division 1 1279275 29367285 3064.9\n", - "+ AtBat 1 821767 29824793 3069.0\n", - "+ Walks 1 781767 29864793 3069.3\n", - "+ Years 1 254910 30391650 3073.9\n", - " 30646560 3074.1\n", - "+ League 1 208880 30437680 3074.3\n", - "+ CRuns 1 132614 30513946 3075.0\n", - "+ NewLeague 1 118474 30528086 3075.1\n", - "+ Runs 1 114198 30532362 3075.1\n", - "+ Errors 1 99776 30546784 3075.3\n", - "+ CAtBat 1 83517 30563043 3075.4\n", - "+ Assists 1 44781 30601779 3075.7\n", - "+ CWalks 1 23668 30622892 3075.9\n", - "+ CHmRun 1 4790 30641769 3076.1\n", - "+ CHits 1 4358 30642202 3076.1\n", - "+ HmRun 1 2173 30644387 3076.1\n", - "+ RBI 1 1137 30645423 3076.1\n", - "\n", - "Step: AIC=3063.85\n", - "Salary ~ CRBI + Hits + PutOuts\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Division 1 1278445 27970852 3054.1\n", - "+ AtBat 1 1009933 28239364 3056.6\n", - "+ Walks 1 539490 28709807 3061.0\n", - "+ CRuns 1 273649 28975648 3063.4\n", - " 29249297 3063.8\n", - "+ Years 1 136906 29112391 3064.6\n", - "+ League 1 122841 29126456 3064.8\n", - "+ Runs 1 117930 29131367 3064.8\n", - "+ Errors 1 97244 29152053 3065.0\n", - "+ NewLeague 1 57839 29191458 3065.3\n", - "+ CHits 1 35096 29214201 3065.5\n", - "+ RBI 1 33965 29215331 3065.6\n", - "+ HmRun 1 31227 29218070 3065.6\n", - "+ CWalks 1 28572 29220725 3065.6\n", - "+ CAtBat 1 20518 29228779 3065.7\n", - "+ Assists 1 1681 29247616 3065.8\n", - "+ CHmRun 1 1419 29247878 3065.8\n", - "\n", - "Step: AIC=3054.1\n", - "Salary ~ CRBI + Hits + PutOuts + Division\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ AtBat 1 820952 27149899 3048.3\n", - "+ Walks 1 491584 27479268 3051.4\n", - " 27970852 3054.1\n", - "+ CRuns 1 193604 27777248 3054.3\n", - "+ Years 1 166845 27804007 3054.5\n", - "+ League 1 110628 27860224 3055.1\n", - "+ Errors 1 81385 27889467 3055.3\n", - "+ Runs 1 65921 27904931 3055.5\n", - "+ RBI 1 53719 27917133 3055.6\n", - "+ NewLeague 1 52275 27918577 3055.6\n", - "+ CHits 1 33863 27936989 3055.8\n", - "+ HmRun 1 26390 27944462 3055.8\n", - "+ CAtBat 1 18751 27952101 3055.9\n", - "+ CWalks 1 5723 27965129 3056.0\n", - "+ Assists 1 1036 27969816 3056.1\n", - "+ CHmRun 1 165 27970687 3056.1\n", - "\n", - "Step: AIC=3048.26\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ Walks 1 954996 26194904 3040.8\n", - "+ Years 1 253362 26896537 3047.8\n", - "+ Runs 1 208743 26941157 3048.2\n", - " 27149899 3048.3\n", - "+ CRuns 1 185825 26964075 3048.5\n", - "+ League 1 95986 27053913 3049.3\n", - "+ NewLeague 1 52693 27097206 3049.8\n", - "+ CHmRun 1 43173 27106726 3049.8\n", - "+ Assists 1 28898 27121001 3050.0\n", - "+ CAtBat 1 20989 27128910 3050.1\n", - "+ CWalks 1 15599 27134301 3050.1\n", - "+ Errors 1 6265 27143634 3050.2\n", - "+ CHits 1 5305 27144594 3050.2\n", - "+ RBI 1 1236 27148663 3050.2\n", - "+ HmRun 1 11 27149888 3050.3\n", - "\n", - "Step: AIC=3040.85\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat + Walks\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CWalks 1 240687 25954217 3040.4\n", - " 26194904 3040.8\n", - "+ Years 1 184508 26010396 3041.0\n", - "+ CRuns 1 110695 26084209 3041.7\n", - "+ League 1 77974 26116930 3042.1\n", - "+ Assists 1 75782 26119122 3042.1\n", - "+ NewLeague 1 40909 26153995 3042.4\n", - "+ CHits 1 37304 26157599 3042.5\n", - "+ RBI 1 11728 26183176 3042.7\n", - "+ HmRun 1 4747 26190157 3042.8\n", - "+ Errors 1 2727 26192177 3042.8\n", - "+ CAtBat 1 2630 26192274 3042.8\n", - "+ CHmRun 1 943 26193961 3042.8\n", - "+ Runs 1 37 26194867 3042.8\n", - "\n", - "Step: AIC=3040.42\n", - "Salary ~ CRBI + Hits + PutOuts + Division + AtBat + Walks + CWalks\n", - "\n", - " Df Sum of Sq RSS AIC\n", - "+ CRuns 1 794983 25159234 3034.2\n", - "+ CHits 1 273728 25680489 3039.6\n", - " 25954217 3040.4\n", - "+ Assists 1 138506 25815711 3041.0\n", - "+ CAtBat 1 89289 25864929 3041.5\n", - "+ RBI 1 86941 25867276 3041.5\n", - "+ League 1 77159 25877058 3041.6\n", - "+ Years 1 70126 25884091 3041.7\n", - "+ NewLeague 1 37807 25916410 3042.0\n", - "+ HmRun 1 33601 25920616 3042.1\n", - "+ CHmRun 1 9034 25945183 3042.3\n", - "+ Errors 1 6928" - ] - } - ], - "source": [ - "%%R -i Hitters\n", - "step(lm(Salary ~ 1, data=Hitters), scope=list(upper=lm(Salary ~ ., data=Hitters)), direction='forward', trace=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6117f650", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "536a8bc3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bddc13c5", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -2726,9 +262,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" }, "language_info": { "codemirror_mode": { diff --git a/docs/source/models/spec.ipynb b/docs/source/models/spec.ipynb index 1ea45d2..fce6b32 100644 --- a/docs/source/models/spec.ipynb +++ b/docs/source/models/spec.ipynb @@ -31,7 +31,7 @@ "from ISLP.models import (ModelSpec,\n", " summarize,\n", " Column,\n", - " Variable,\n", + " Feature,\n", " build_columns)\n", "\n", "import statsmodels.api as sm" @@ -257,7 +257,7 @@ "metadata": {}, "source": [ "We note that a column has been added for the intercept by default. This can be changed using the\n", - "`intercept` argument. " + "`intercept` argument." ] }, { @@ -391,7 +391,7 @@ "in the column space of the design matrix.\n", "\n", "To include this intercept via `ShelveLoc` we can use 3 columns to encode this categorical variable. Following the nomenclature of\n", - "`R`, we call this a `Contrast` of the categorical variable. " + "`R`, we call this a `Contrast` of the categorical variable." ] }, { @@ -938,7 +938,7 @@ "\n", "The first argument to `ModelSpec` is stored as the `terms` attribute. Under the hood,\n", "this sequence is inspected to produce the `terms_` attribute which specify the objects\n", - "that will ultimately create the design matrix. " + "that will ultimately create the design matrix." ] }, { @@ -950,8 +950,8 @@ { "data": { "text/plain": [ - "[Variable(variables=('ShelveLoc',), name='ShelveLoc', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" + "[Feature(variables=('ShelveLoc',), name='ShelveLoc', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", + " Feature(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" ] }, "execution_count": 13, @@ -970,7 +970,7 @@ "id": "warming-mobile", "metadata": {}, "source": [ - "Each element of `terms_` should be a `Variable` which describes a set of columns to be extracted from\n", + "Each element of `terms_` should be a `Feature` which describes a set of columns to be extracted from\n", "a columnar data form as well as possible a possible encoder." ] }, @@ -1126,17 +1126,17 @@ "id": "former-spring", "metadata": {}, "source": [ - "### `Variable` objects\n", + "### `Feature` objects\n", "\n", - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", + "Note that `Feature` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", + "is run through `encoder.transform`. The `encoder.fit` method of each `Feature` is run once during \n", "the call to `ModelSpec.fit`." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "floral-liabilities", "metadata": {}, "outputs": [ @@ -1255,15 +1255,13 @@ "[400 rows x 3 columns]" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=None)\n", + "new_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=None)\n", "build_columns(MS.column_info_,\n", " Carseats, \n", " new_var)[0]" @@ -1280,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "id": "imported-measure", "metadata": {}, "outputs": [ @@ -1387,7 +1385,7 @@ "[400 rows x 2 columns]" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1396,7 +1394,7 @@ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=2)\n", "pca.fit(build_columns(MS.column_info_, Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=pca)\n", + "pca_var = Feature(('Price', 'Income', 'OIncome'), name='mynewvar', encoder=pca)\n", "build_columns(MS.column_info_,\n", " Carseats, \n", " pca_var)[0]" @@ -1408,12 +1406,12 @@ "metadata": {}, "source": [ "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." + "or `Feature` instances (`pca_var`)." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "western-bloom", "metadata": {}, "outputs": [ @@ -1544,14 +1542,14 @@ "[400 rows x 4 columns]" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "price = MS.column_info_['Price']\n", - "fancy_var = Variable(('Income', price, pca_var), name='fancy', encoder=None)\n", + "fancy_var = Feature(('Income', price, pca_var), name='fancy', encoder=None)\n", "build_columns(MS.column_info_,\n", " Carseats, \n", " fancy_var)[0]" @@ -1567,7 +1565,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "6efed2fa-9e5d-429c-a8d9-ac544cab2b41", "metadata": {}, "outputs": [ @@ -1580,7 +1578,7 @@ "dtype: float64" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1604,7 +1602,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "8784b0e8-ce53-4a90-aee6-b935834295c7", "metadata": {}, "outputs": [ @@ -1614,7 +1612,7 @@ "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1660,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "4fec9030-7445-48be-a15f-2ac5a789e717", "metadata": {}, "outputs": [ @@ -1676,7 +1674,7 @@ " [ 1., 120., 37.]])" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1689,7 +1687,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "c864e365-2476-4ca6-9d27-625cac2b2271", "metadata": {}, "outputs": [ @@ -1702,7 +1700,7 @@ "dtype: float64" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1729,7 +1727,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "incredible-concert", "metadata": {}, "outputs": [ @@ -1763,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "id": "stunning-container", "metadata": {}, "outputs": [ @@ -1781,7 +1779,7 @@ "array([10.70130676, 10.307465 ])" ] }, - "execution_count": 24, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1790,7 +1788,7 @@ "new_D = np.array([[40,50], [np.nan, np.nan], [10,20]]).T\n", "new_X = MS_np.transform(new_D)\n", "print(new_X)\n", - "M_ols.get_prediction(new_X).predicted_mean\n" + "M_ols.get_prediction(new_X).predicted_mean" ] }, { @@ -1805,10 +1803,10 @@ ], "metadata": { "jupytext": { - "formats": "ipynb" + "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1822,7 +1820,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/docs/source/models/submodels.ipynb b/docs/source/models/submodels.ipynb deleted file mode 100644 index 825bedd..0000000 --- a/docs/source/models/submodels.ipynb +++ /dev/null @@ -1,3127 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ee33d364", - "metadata": {}, - "source": [ - "# Building design matrices with `ModelSpec`\n", - "\n", - "Force rebuild" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4c70fbaa", - "metadata": {}, - "outputs": [], - "source": [ - "x=4\n", - "import numpy as np, pandas as pd\n", - "%load_ext rpy2.ipython\n", - "\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec\n", - "\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8a708215", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',\n", - " 'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],\n", - " dtype='object')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats = load_data('Carseats')\n", - "%R -i Carseats\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "dad5e991", - "metadata": {}, - "source": [ - "## Let's break up income into groups" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ac7086a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: OIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L' < 'M' < 'H']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['OIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'])\n", - "Carseats['OIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "261446c8", - "metadata": {}, - "source": [ - "Let's also create an unordered version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "674bb806", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 M\n", - "1 L\n", - "2 L\n", - "3 H\n", - "4 M\n", - " ..\n", - "395 H\n", - "396 L\n", - "397 L\n", - "398 M\n", - "399 L\n", - "Name: UIncome, Length: 400, dtype: category\n", - "Categories (3, object): ['L', 'M', 'H']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['UIncome'] = pd.cut(Carseats['Income'], \n", - " [0,50,90,200], \n", - " labels=['L','M','H'],\n", - " ordered=False)\n", - "Carseats['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "8f030039", - "metadata": {}, - "source": [ - "## A simple model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "40cd6c28", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Price', 'Income'], dtype='object')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Price', 'Income'])\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e65f5607", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 12.661546\n", - "Price -0.052213\n", - "Income 0.012829\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y = Carseats['Sales']\n", - "M = sm.OLS(Y, X).fit()\n", - "M.params" - ] - }, - { - "cell_type": "markdown", - "id": "29d9b55f", - "metadata": {}, - "source": [ - "## Basic procedure\n", - "\n", - "The design matrix is built by cobbling together a set of columns and possibly transforming them.\n", - "A `pd.DataFrame` is essentially a list of columns. One of the first tasks done in `ModelSpec.fit`\n", - "is to inspect a dataframe for column info. The column `ShelveLoc` is categorical:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cfbe5b92", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Bad\n", - "1 Good\n", - "2 Medium\n", - "3 Medium\n", - "4 Bad\n", - " ... \n", - "395 Good\n", - "396 Medium\n", - "397 Medium\n", - "398 Bad\n", - "399 Good\n", - "Name: ShelveLoc, Length: 400, dtype: category\n", - "Categories (3, object): ['Bad', 'Good', 'Medium']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "7092f666", - "metadata": {}, - "source": [ - "This is recognized by `ModelSpec` in the form of `Column` objects which are just named tuples with two methods\n", - "`get_columns` and `fit_encoder`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2d43844", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='ShelveLoc', name='ShelveLoc', is_categorical=True, is_ordinal=False, columns=('ShelveLoc[Good]', 'ShelveLoc[Medium]'), encoder=Contrast())" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['ShelveLoc']" - ] - }, - { - "cell_type": "markdown", - "id": "46a01612", - "metadata": {}, - "source": [ - "It recognized ordinal columns as well." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "465a9326", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='OIncome', name='OIncome', is_categorical=True, is_ordinal=True, columns=('OIncome',), encoder=OrdinalEncoder())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['OIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "76f8480d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 73, 48, 35, 100]), ('Income',))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income = design.column_info_['Income']\n", - "cols, names = income.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "markdown", - "id": "25fcc1de", - "metadata": {}, - "source": [ - "## Encoding a column\n", - "\n", - "In building a design matrix we must extract columns from our dataframe (or `np.ndarray`). Categorical\n", - "variables usually are encoded by several columns, typically one less than the number of categories.\n", - "This task is handled by the `encoder` of the `Column`. The encoder must satisfy the `sklearn` transform\n", - "model, i.e. `fit` on some array and `transform` on future arrays. The `fit_encoder` method of `Column` fits\n", - "its encoder the first time data is passed to it." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "dfe6cc35", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[0., 0.],\n", - " [1., 0.],\n", - " [0., 1.],\n", - " [0., 1.]]),\n", - " ['ShelveLoc[Good]', 'ShelveLoc[Medium]'])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shelve = design.column_info_['ShelveLoc']\n", - "cols, names = shelve.get_columns(Carseats)\n", - "(cols[:4], names)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8fc9779a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oincome = design.column_info_['OIncome']\n", - "oincome.get_columns(Carseats)[0][:4]" - ] - }, - { - "cell_type": "markdown", - "id": "8e04da60", - "metadata": {}, - "source": [ - "## The terms\n", - "\n", - "The design matrix consists of several sets of columns. This is managed by the `ModelSpec` through\n", - "the `terms` argument which should be a sequence. The elements of `terms` are often\n", - "going to be strings (or tuples of strings for interactions, see below) but are converted to a\n", - "`Variable` object and stored in the `terms_` of the fitted `ModelSpec`. A `Variable` is just a named tuple." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c579dbce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Price', 'Income']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4587b8bd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=('Price',), name='Price', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=('Income',), name='Income', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "2595f0fa", - "metadata": {}, - "source": [ - "While each `Column` can itself extract data, they are all promoted to `Variable` to be of a uniform type. A\n", - "`Variable` can also create columns through the `build_columns` method of `ModelSpec`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "03bd9366", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price\n", - " 0 120\n", - " 1 83\n", - " 2 80\n", - " 3 97\n", - " 4 128\n", - " .. ...\n", - " 395 128\n", - " 396 120\n", - " 397 159\n", - " 398 95\n", - " 399 120\n", - " \n", - " [400 rows x 1 columns],\n", - " ['Price'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "price = design.terms_[0]\n", - "design.build_columns(Carseats, price)" - ] - }, - { - "cell_type": "markdown", - "id": "de04ca48", - "metadata": {}, - "source": [ - "Note that `Variable` objects have a tuple of `variables` as well as an `encoder` attribute. The\n", - "tuple of `variables` first creates a concatenated dataframe from all corresponding variables and then\n", - "is run through `encoder.transform`. The `encoder.fit` method of each `Variable` is run once during \n", - "the call to `ModelSpec.fit`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a42af4c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Price Income UIncome[L] UIncome[M]\n", - " 0 120.0 73.0 0.0 1.0\n", - " 1 83.0 48.0 1.0 0.0\n", - " 2 80.0 35.0 1.0 0.0\n", - " 3 97.0 100.0 0.0 0.0\n", - " 4 128.0 64.0 0.0 1.0\n", - " .. ... ... ... ...\n", - " 395 128.0 108.0 0.0 0.0\n", - " 396 120.0 23.0 1.0 0.0\n", - " 397 159.0 26.0 1.0 0.0\n", - " 398 95.0 79.0 0.0 1.0\n", - " 399 120.0 37.0 1.0 0.0\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Income', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import Variable\n", - "\n", - "new_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=None)\n", - "design.build_columns(Carseats, new_var)" - ] - }, - { - "cell_type": "markdown", - "id": "b146d0c0", - "metadata": {}, - "source": [ - "Let's now transform these columns with an encoder. Within `ModelSpec` we will first build the\n", - "arrays above and then call `pca.fit` and finally `pca.transform` within `design.build_columns`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b6c394a6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( mynewvar[0] mynewvar[1]\n", - " 0 -3.608693 -4.853177\n", - " 1 15.081506 35.708630\n", - " 2 27.422871 40.774250\n", - " 3 -33.973209 13.470489\n", - " 4 6.567316 -11.290100\n", - " .. ... ...\n", - " 395 -36.846346 -18.415783\n", - " 396 45.741500 3.245602\n", - " 397 49.097533 -35.725355\n", - " 398 -13.577772 18.845139\n", - " 399 31.927566 0.978436\n", - " \n", - " [400 rows x 2 columns],\n", - " ['mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "pca.fit(design.build_columns(Carseats, new_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca_var = Variable(('Price', 'Income', 'UIncome'), name='mynewvar', encoder=pca)\n", - "design.build_columns(Carseats, pca_var)" - ] - }, - { - "cell_type": "markdown", - "id": "3bb30a3f", - "metadata": {}, - "source": [ - "The elements of the `variables` attribute may be column identifiers ( `\"Price\"`), `Column` instances (`price`)\n", - "or `Variable` instances (`pca_var`)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ea7770ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( Price Price mynewvar[0] mynewvar[1]\n", - " 0 120.0 120.0 -3.608693 -4.853177\n", - " 1 83.0 83.0 15.081506 35.708630\n", - " 2 80.0 80.0 27.422871 40.774250\n", - " 3 97.0 97.0 -33.973209 13.470489\n", - " 4 128.0 128.0 6.567316 -11.290100\n", - " .. ... ... ... ...\n", - " 395 128.0 128.0 -36.846346 -18.415783\n", - " 396 120.0 120.0 45.741500 3.245602\n", - " 397 159.0 159.0 49.097533 -35.725355\n", - " 398 95.0 95.0 -13.577772 18.845139\n", - " 399 120.0 120.0 31.927566 0.978436\n", - " \n", - " [400 rows x 4 columns],\n", - " ['Price', 'Price', 'mynewvar[0]', 'mynewvar[1]'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fancy_var = Variable(('Price', price, pca_var), name='fancy', encoder=None)\n", - "design.build_columns(Carseats, fancy_var)" - ] - }, - { - "cell_type": "markdown", - "id": "b2b4a01a", - "metadata": {}, - "source": [ - "We can of course run PCA again on these features (if we wanted)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "21ad8b44", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "( fancy_pca[0] fancy_pca[1]\n", - " 0 -6.951792 4.859283\n", - " 1 55.170148 -24.694875\n", - " 2 59.418556 -38.033572\n", - " 3 34.722389 28.922184\n", - " 4 -21.419184 -3.120673\n", - " .. ... ...\n", - " 395 -18.257348 40.760122\n", - " 396 -10.546709 -45.021658\n", - " 397 -77.706359 -37.174379\n", - " 398 36.668694 7.730851\n", - " 399 -9.540535 -31.059122\n", - " \n", - " [400 rows x 2 columns],\n", - " ['fancy_pca[0]', 'fancy_pca[1]'])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca2 = PCA(n_components=2)\n", - "pca2.fit(design.build_columns(Carseats, fancy_var)[0]) # this is done within `ModelSpec.fit`\n", - "pca2_var = Variable(('Price', price, pca_var), name='fancy_pca', encoder=pca2)\n", - "design.build_columns(Carseats, pca2_var)" - ] - }, - { - "cell_type": "markdown", - "id": "2262377d", - "metadata": {}, - "source": [ - "## Building the design matrix\n", - "\n", - "With these notions in mind, the final design is essentially then" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1654ca47", - "metadata": {}, - "outputs": [], - "source": [ - "X_hand = np.column_stack([design.build_columns(Carseats, v)[0] for v in design.terms_])[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "1db0e0a9", - "metadata": {}, - "source": [ - "An intercept column is added if `design.intercept` is `True` and if the original argument to `transform` is\n", - "a dataframe the index is adjusted accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d20e8ea8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "450fe910", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " intercept Price Income\n", - "0 1.0 120 73\n", - "1 1.0 83 48\n", - "2 1.0 80 35\n", - "3 1.0 97 100" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.transform(Carseats)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "0705ba6f", - "metadata": {}, - "source": [ - "## Predicting\n", - "\n", - "Constructing the design matrix at any values is carried out by the `transform` method." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "866c2863", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_data = pd.DataFrame({'Price':[10,20], 'Income':[40, 50]})\n", - "new_X = design.transform(new_data)\n", - "M.get_prediction(new_X).predicted_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f2021166", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 \n", - "12.65258 12.25873 \n" - ] - } - ], - "source": [ - "%%R -i new_data,Carseats\n", - "predict(lm(Sales ~ Price + Income, data=Carseats), new_data)" - ] - }, - { - "cell_type": "markdown", - "id": "20e1a31a", - "metadata": {}, - "source": [ - "### Difference between using `pd.DataFrame` and `np.ndarray`\n", - "\n", - "If the `terms` only refer to a few columns of the data frame, the `transform` method only needs a dataframe with those columns.\n", - "\n", - "If we had used an `np.ndarray`, the column identifiers would be integers identifying specific columns so,\n", - "in order to work correctly, `transform` would need another `np.ndarray` where the columns have the same meaning." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "a5926ec9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.0, 120, 73],\n", - " [1.0, 83, 48],\n", - " [1.0, 80, 35],\n", - " [1.0, 97, 100]], dtype=object)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Carseats_np = np.asarray(Carseats[['Price', 'ShelveLoc', 'US', 'Income']])\n", - "design_np = ModelSpec([0,3]).fit(Carseats_np)\n", - "design_np.transform(Carseats_np)[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "997a63cb", - "metadata": {}, - "source": [ - "The following will fail for hopefully obvious reasons" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "40410c48", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shapes (2,2) and (3,) not aligned: 2 (dim 1) != 3 (dim 0)\n" - ] - } - ], - "source": [ - "try:\n", - " new_D = np.zeros((2,2))\n", - " new_D[:,0] = [10,20]\n", - " new_D[:,1] = [40,50]\n", - " M.get_prediction(new_D).predicted_mean\n", - "except ValueError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "920203e9", - "metadata": {}, - "source": [ - "Ultimately, `M` expects 3 columns for new predictions because it was fit\n", - "with a matrix having 3 columns (the first representing an intercept).\n", - "\n", - "We might be tempted to try as with the `pd.DataFrame` and produce\n", - "an `np.ndarray` with only the necessary variables." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "1061da77", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 3 is out of bounds for axis 1 with size 2\n" - ] - } - ], - "source": [ - "try:\n", - " new_X = np.zeros((2,2))\n", - " new_X[:,0] = [10,20]\n", - " new_X[:,1] = [40,50]\n", - " new_D = design_np.transform(new_X)\n", - " M.get_prediction(new_D).predicted_mean\n", - "except IndexError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "c6bfe001", - "metadata": {}, - "source": [ - "This fails because `design_np` is looking for column `3` from its `terms`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "5ae6d25f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Variable(variables=(0,), name='0', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False),\n", - " Variable(variables=(3,), name='3', encoder=None, use_transform=True, pure_columns=True, override_encoder_colnames=False)]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design_np.terms_" - ] - }, - { - "cell_type": "markdown", - "id": "edd7ebeb", - "metadata": {}, - "source": [ - "However, if we have an `np.ndarray` in which the first column indeed represents `Price` and the fourth indeed\n", - "represents `Income` then we can arrive at the correct answer by supplying such the array to `design_np.transform`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "9455e532", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.65257604, 12.25873428])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_X = np.zeros((2,4))\n", - "new_X[:,0] = [10,20]\n", - "new_X[:,3] = [40,50]\n", - "new_D = design_np.transform(new_X)\n", - "M.get_prediction(new_D).predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "fd726791", - "metadata": {}, - "source": [ - "Given this subtlety about needing to supply arrays with identical column structure to `transform` when\n", - "using `np.ndarray` we presume that using a `pd.DataFrame` will be the more popular use case." - ] - }, - { - "cell_type": "markdown", - "id": "967d9ebc", - "metadata": {}, - "source": [ - "## A model with some categorical variables\n", - "\n", - "Categorical variables become `Column` instances with encoders." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d0429b56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', 'UIncome', 'ShelveLoc']).fit(Carseats)\n", - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "415e3fd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[L]', 'UIncome[M]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8a99c3a5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 11.876012\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[L] -1.042297\n", - "UIncome[M] -0.119123\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "9250a28a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "fe90c12c", - "metadata": {}, - "source": [ - "## Getting the encoding you want\n", - "\n", - "By default the level dropped by `ModelSpec` will be the first of the `categories_` values from \n", - "`sklearn.preprocessing.OneHotEncoder()`. We might wish to change this. It seems\n", - "as if the correct way to do this would be something like `Variable(('UIncome',), 'mynewencoding', new_encoder)`\n", - "where `new_encoder` would somehow drop the column we want dropped. \n", - "\n", - "However, when using the convenient identifier `UIncome` in the `variables` argument, this maps to the `Column` associated to `UIncome` within `design.column_info_`:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "0546ec84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Column(idx='UIncome', name='UIncome', is_categorical=True, is_ordinal=False, columns=('UIncome[L]', 'UIncome[M]'), encoder=Contrast())" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.column_info_['UIncome']" - ] - }, - { - "cell_type": "markdown", - "id": "6ec4fe65", - "metadata": {}, - "source": [ - "This column already has an encoder and `Column` instances are immutable as named tuples. Further, there are times when \n", - "we may want to encode `UIncome` differently within the same model. In the model below the main effect of `UIncome` is encoded with two columns while in the interaction `UIncome` (see below) has three columns. This is a design of interest\n", - "and we need a way to allow different encodings of the same column of `Carseats`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "61e7f56e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "802ed854", - "metadata": {}, - "source": [ - " We can create a new \n", - "`Column` with the encoder we want. For categorical variables, there is a convenience function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "82d7a01d", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.models.model_spec import contrast\n", - "pref_encoding = contrast('UIncome', 'drop', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e26849a1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[M] UIncome[H]\n", - " 0 1.0 0.0\n", - " 1 0.0 0.0\n", - " 2 0.0 0.0\n", - " 3 0.0 1.0\n", - " 4 1.0 0.0\n", - " .. ... ...\n", - " 395 0.0 1.0\n", - " 396 0.0 0.0\n", - " 397 0.0 0.0\n", - " 398 1.0 0.0\n", - " 399 0.0 0.0\n", - " \n", - " [400 rows x 2 columns],\n", - " ['UIncome[M]', 'UIncome[H]'])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.build_columns(Carseats, pref_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2fc4cd8c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['intercept', 'Population', 'Price', 'UIncome[M]', 'UIncome[H]',\n", - " 'ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['Population', 'Price', pref_encoding, 'ShelveLoc']).fit(Carseats)\n", - "X = design.fit_transform(Carseats)\n", - "X.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "49e33d41", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 10.833715\n", - "Population 0.001163\n", - "Price -0.055725\n", - "UIncome[M] 0.923174\n", - "UIncome[H] 1.042297\n", - "ShelveLoc[Good] 4.999623\n", - "ShelveLoc[Medium] 1.964278\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "ce018fdf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) Population Price UIncomeM UIncomeH \n", - " 10.83371503 0.00116301 -0.05572469 0.92317388 1.04229679 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.99962319 1.96427771 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ Population + Price + UIncome + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "2d42b822", - "metadata": {}, - "source": [ - "## Interactions\n", - "\n", - "We've referred to interactions above. These are specified (by convenience) as tuples in the `terms` argument\n", - "to `ModelSpec`." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "fbb3e3ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 7.866634\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "UIncome[L] -2.734895\n", - "UIncome[M] -2.619745\n", - "dtype: float64" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([('UIncome', 'ShelveLoc'), 'UIncome'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "f9a7d4ad", - "metadata": {}, - "source": [ - "The tuples in `terms` are converted to `Variable` in the formalized `terms_` attribute by creating a `Variable` with\n", - "`variables` set to the tuple and the encoder an `Interaction` encoder which (unsurprisingly) creates the interaction columns from the concatenated data frames of `UIncome` and `ShelveLoc`." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "5a6f8e69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('UIncome', 'ShelveLoc'), name='UIncome:ShelveLoc', encoder=Interaction(column_names={'ShelveLoc': ['ShelveLoc[Good]', 'ShelveLoc[Medium]'],\n", - " 'UIncome': ['UIncome[L]', 'UIncome[M]']},\n", - " columns={'ShelveLoc': range(2, 4), 'UIncome': range(0, 2)},\n", - " variables=['UIncome', 'ShelveLoc']), use_transform=True, pure_columns=False, override_encoder_colnames=False)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design.terms_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "98eef5c8", - "metadata": {}, - "source": [ - "Comparing this to the previous `R` model." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "58c99601", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ UIncome:ShelveLoc + UIncome, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) UIncomeM UIncomeH \n", - " 5.1317 0.1151 1.1561 \n", - " UIncomeL:ShelveLocGood UIncomeM:ShelveLocGood UIncomeH:ShelveLocGood \n", - " 4.5121 5.5752 3.7381 \n", - "UIncomeL:ShelveLocMedium UIncomeM:ShelveLocMedium UIncomeH:ShelveLocMedium \n", - " 1.2473 2.4782 1.5141 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "9c979d7e", - "metadata": {}, - "source": [ - "We note a few important things:\n", - "\n", - "1. `R` has reorganized the columns of the design from the formula: although we wrote `UIncome:ShelveLoc` first these\n", - "columns have been built later. **`ModelSpec` builds columns in the order determined by `terms`!**\n", - "\n", - "2. As noted above, `R` has encoded `UIncome` differently in the main effect and in the interaction. For `ModelSpec`, the reference to `UIncome` always refers to the column in `design.column_info_` and will always build only the columns for `L` and `M`. **`ModelSpec` does no inspection of terms to decide how to encode categorical variables.**\n", - "\n", - "A few notes:\n", - "\n", - "- **Why not try to inspect the terms?** For any nontrivial formula in `R` with several categorical variables and interactions, predicting what columns will be produced from a given formula is not simple. **`ModelSpec` errs on the side of being explicit.**\n", - "\n", - "- **Is it impossible to build the design as `R` has?** No. An advanced user who *knows* they want the columns built as `R` has can do so (fairly) easily." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "0cb3b63a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( UIncome[H] UIncome[L] UIncome[M]\n", - " 0 0.0 0.0 1.0\n", - " 1 0.0 1.0 0.0\n", - " 2 0.0 1.0 0.0\n", - " 3 1.0 0.0 0.0\n", - " 4 0.0 0.0 1.0\n", - " .. ... ... ...\n", - " 395 1.0 0.0 0.0\n", - " 396 0.0 1.0 0.0\n", - " 397 0.0 1.0 0.0\n", - " 398 0.0 0.0 1.0\n", - " 399 0.0 1.0 0.0\n", - " \n", - " [400 rows x 3 columns],\n", - " ['UIncome[H]', 'UIncome[L]', 'UIncome[M]'])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "full_encoding = contrast('UIncome', None)\n", - "design.build_columns(Carseats, full_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "272098d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.131739\n", - "UIncome[M] 0.115150\n", - "UIncome[H] 1.156118\n", - "UIncome[H]:ShelveLoc[Good] 3.738052\n", - "UIncome[H]:ShelveLoc[Medium] 1.514104\n", - "UIncome[L]:ShelveLoc[Good] 4.512054\n", - "UIncome[L]:ShelveLoc[Medium] 1.247275\n", - "UIncome[M]:ShelveLoc[Good] 5.575170\n", - "UIncome[M]:ShelveLoc[Medium] 2.478163\n", - "dtype: float64" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "fe05c471", - "metadata": {}, - "source": [ - "## Special encodings\n", - "\n", - "For flexible models, we may want to consider transformations of features, i.e. polynomial\n", - "or spline transformations. Given transforms that follow the `fit/transform` paradigm\n", - "we can of course achieve this with a `Column` and an `encoder`. The `ISLP.transforms`\n", - "package includes a `Poly` transform" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "67062299", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable(variables=('Income',), name='poly(Income, 3, )', encoder=Poly(degree=3), use_transform=True, pure_columns=False, override_encoder_colnames=True)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import poly\n", - "poly('Income', 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "df5e5b4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 5.440077\n", - "poly(Income, 3, )[0] 10.036373\n", - "poly(Income, 3, )[1] -2.799156\n", - "poly(Income, 3, )[2] 2.399601\n", - "ShelveLoc[Good] 4.808133\n", - "ShelveLoc[Medium] 1.889533\n", - "dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([poly('Income', 3), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "markdown", - "id": "01be9c13", - "metadata": {}, - "source": [ - "Compare:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "3244d6f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) poly(Income, 3)1 poly(Income, 3)2 poly(Income, 3)3 \n", - " 5.440077 10.036373 -2.799156 2.399601 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.808133 1.889533 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ poly(Income, 3) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "8ad5bb1d", - "metadata": {}, - "source": [ - "## Splines\n", - "\n", - "Support for natural and B-splines is also included" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "6a6f4358", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 4.240421\n", - "ns(Income, , df=5)[0] 1.468196\n", - "ns(Income, , df=5)[1] 1.499471\n", - "ns(Income, , df=5)[2] 1.152070\n", - "ns(Income, , df=5)[3] 2.418398\n", - "ns(Income, , df=5)[4] 1.804460\n", - "ShelveLoc[Good] 4.810449\n", - "ShelveLoc[Medium] 1.881095\n", - "dtype: float64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from ISLP.models.model_spec import ns, bs, pca\n", - "design = ModelSpec([ns('Income', df=5), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "fb740953", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) ns(Income, df = 5)1 ns(Income, df = 5)2 ns(Income, df = 5)3 \n", - " 4.240421 1.468196 1.499471 1.152070 \n", - "ns(Income, df = 5)4 ns(Income, df = 5)5 ShelveLocGood ShelveLocMedium \n", - " 2.418398 1.804460 4.810449 1.881095 \n" - ] - } - ], - "source": [ - "%%R\n", - "library(splines)\n", - "lm(Sales ~ ns(Income, df=5) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "fe1bf7fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 3.495085\n", - "bs(Income, , df=7, degree=2)[0] 1.813118\n", - "bs(Income, , df=7, degree=2)[1] 0.961852\n", - "bs(Income, , df=7, degree=2)[2] 2.471545\n", - "bs(Income, , df=7, degree=2)[3] 2.158891\n", - "bs(Income, , df=7, degree=2)[4] 2.091625\n", - "bs(Income, , df=7, degree=2)[5] 2.600669\n", - "bs(Income, , df=7, degree=2)[6] 2.843108\n", - "ShelveLoc[Good] 4.804919\n", - "ShelveLoc[Medium] 1.880337\n", - "dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([bs('Income', df=7, degree=2), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "86e966e0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (Intercept) bs(Income, df = 7, degree = 2)1 \n", - " 3.4950851 1.8131176 \n", - "bs(Income, df = 7, degree = 2)2 bs(Income, df = 7, degree = 2)3 \n", - " 0.9618523 2.4715450 \n", - "bs(Income, df = 7, degree = 2)4 bs(Income, df = 7, degree = 2)5 \n", - " 2.1588908 2.0916252 \n", - "bs(Income, df = 7, degree = 2)6 bs(Income, df = 7, degree = 2)7 \n", - " 2.6006694 2.8431084 \n", - " ShelveLocGood ShelveLocMedium \n", - " 4.8049190 1.8803375 \n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ bs(Income, df=7, degree=2) + ShelveLoc, data=Carseats)$coef" - ] - }, - { - "cell_type": "markdown", - "id": "877d4784", - "metadata": {}, - "source": [ - "## PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "8ba6cb20", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but PCA was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.419405\n", - "pca(myvars, , n_components=2)[0] -0.001131\n", - "pca(myvars, , n_components=2)[1] -0.024217\n", - "ShelveLoc[Good] 4.816253\n", - "ShelveLoc[Medium] 1.924139\n", - "dtype: float64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars'), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "f0319e51", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population))$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.419405 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC1 \n", - " 0.001131 \n", - "prcomp(cbind(Income, Price, Advertising, Population))$x[, 1:2]PC2 \n", - " -0.024217 \n", - " ShelveLocGood \n", - " 4.816253 \n", - " ShelveLocMedium \n", - " 1.924139 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population))$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "1f55086a", - "metadata": {}, - "source": [ - "It is of course common to scale before running PCA." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "bbe9e004", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n", - "/Users/jonathantaylor/miniconda3/envs/islp_test/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "intercept 5.352159\n", - "pca(myvars, , n_components=2)[0] 0.446383\n", - "pca(myvars, , n_components=2)[1] -1.219788\n", - "ShelveLoc[Good] 4.922780\n", - "ShelveLoc[Medium] 2.005617\n", - "dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pca(['Income', \n", - " 'Price', \n", - " 'Advertising', \n", - " 'Population'], \n", - " n_components=2, \n", - " name='myvars',\n", - " scale=True), 'ShelveLoc'])\n", - "X = design.fit_transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "d78c02e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ prcomp(cbind(Income, Price, Advertising, \n", - " Population), scale = TRUE)$x[, 1:2] + ShelveLoc, data = Carseats)\n", - "\n", - "Coefficients:\n", - " (Intercept) \n", - " 5.3522 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC1 \n", - " 0.4469 \n", - "prcomp(cbind(Income, Price, Advertising, Population), scale = TRUE)$x[, 1:2]PC2 \n", - " -1.2213 \n", - " ShelveLocGood \n", - " 4.9228 \n", - " ShelveLocMedium \n", - " 2.0056 \n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "lm(Sales ~ prcomp(cbind(Income, Price, Advertising, Population), scale=TRUE)$x[,1:2] + ShelveLoc, data=Carseats)" - ] - }, - { - "cell_type": "markdown", - "id": "8a03c603", - "metadata": {}, - "source": [ - "There will be some small differences in the coefficients due to `sklearn` use of `np.std(ddof=0)` instead\n", - "of `np.std(ddof=1)`." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "f8215cef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.44694166, -1.22131519])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(sm.OLS(Y, X).fit().params)[1:3] * np.sqrt(X.shape[0] / (X.shape[0]-1))" - ] - }, - { - "cell_type": "markdown", - "id": "a15d0ead", - "metadata": {}, - "source": [ - "## Submodels\n", - "\n", - "We can build submodels as well, even if the terms do not appear in the original `terms` argument.\n", - "Fundamentally, the terms just need to be able to have the `design.build_columns` work for us to be\n", - "able to build a design matrix. The initial inspection of the columns of `Carseats` has created\n", - "a column for `US`, hence we can build this submodel." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "d58c6244", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " intercept US[Yes]\n", - "0 1.0 1.0\n", - "1 1.0 1.0\n", - "2 1.0 1.0\n", - "3 1.0 1.0\n", - "4 1.0 0.0\n", - ".. ... ...\n", - "395 1.0 1.0\n", - "396 1.0 1.0\n", - "397 1.0 1.0\n", - "398 1.0 1.0\n", - "399 1.0 1.0\n", - "\n", - "[400 rows x 2 columns]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec(['UIncome', 'ShelveLoc', 'Price']).fit(Carseats)\n", - "design.build_submodel(Carseats, ['US'])" - ] - }, - { - "cell_type": "markdown", - "id": "9365ba27", - "metadata": {}, - "source": [ - "## ANOVA \n", - "\n", - "For a given `terms` argument, there as a natural sequence of models, namely those specified by `[terms[:i] for i in range(len(terms)+1]`." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "332ab454", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['intercept'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price'], dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price',\n", - " 'UIncome[L]', 'UIncome[M]'],\n", - " dtype='object')\n", - "Index(['intercept', 'ShelveLoc[Good]', 'ShelveLoc[Medium]', 'Price',\n", - " 'UIncome[L]', 'UIncome[M]', 'US[Yes]'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "design = ModelSpec(['ShelveLoc', 'Price', 'UIncome', 'US']).fit(Carseats)\n", - "for D in design.build_sequence(Carseats):\n", - " print(D.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "f6cfd031", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 2172.743555 2.0 1009.531143 153.010858 5.452815e-50\n", - "2 396.0 1455.640702 1.0 717.102853 217.377192 1.583751e-39\n", - "3 394.0 1378.915938 2.0 76.724764 11.628885 1.239031e-05\n", - "4 393.0 1296.462700 1.0 82.453238 24.994257 8.678832e-07" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) ))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "11c4aee8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "ShelveLoc 2 1009.53 504.77 153.011 < 2.2e-16 ***\n", - "Price 1 717.10 717.10 217.377 < 2.2e-16 ***\n", - "UIncome 2 76.72 38.36 11.629 1.240e-05 ***\n", - "US 1 82.45 82.45 24.994 8.679e-07 ***\n", - "Residuals 393 1296.46 3.30 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ ShelveLoc + Price + UIncome + US, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "9a4e6e63", - "metadata": {}, - "source": [ - "Recall that `ModelSpec` does not inspect `terms` to reorder based on degree of \n", - "interaction as `R` does:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "6e7bf361", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 393.0 2059.376413 6.0 1122.898284 35.940047 1.175738e-34\n", - "2 391.0 2036.044596 2.0 23.331817 2.240310 1.077900e-01" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([(full_encoding, 'ShelveLoc'), pref_encoding]).fit(Carseats)\n", - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats) ))" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "ed7d4bfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "UIncome 2 61.92 30.962 5.9458 0.002859 ** \n", - "UIncome:ShelveLoc 6 1084.31 180.718 34.7049 < 2.2e-16 ***\n", - "Residuals 391 2036.04 5.207 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ UIncome:ShelveLoc + UIncome, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "0350da34", - "metadata": {}, - "source": [ - "To agree with `R` we must order `terms` as `R` will." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "5ddaf87c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 3120.351382 2.0 61.923316 5.945846 2.855424e-03\n", - "2 391.0 2036.044596 6.0 1084.306785 34.704868 1.346561e-33" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "design = ModelSpec([pref_encoding, (full_encoding, 'ShelveLoc')]).fit(Carseats)\n", - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "1ef70ce3", - "metadata": {}, - "source": [ - "## More complicated interactions\n", - "\n", - "Can we have an interaction of a polynomial effect with a categorical? Absolutely" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "a1a14742", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "UIncome 2 61.92 30.9617 4.0310 0.01851 *\n", - "UIncome:poly(Income, 3) 9 79.72 8.8581 1.1533 0.32408 \n", - "UIncome:US 3 83.51 27.8367 3.6242 0.01324 *\n", - "Residuals 385 2957.12 7.6808 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ UIncome + poly(Income, 3):UIncome + UIncome:US, data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "a909be1a", - "metadata": {}, - "source": [ - "To match `R` we note that it has used its inspection rules to encode `UIncome` with 3 levels\n", - "for the two interactions." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "ae286cf3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 65.978856\n", - "UIncome[M] -60.159607\n", - "UIncome[H] -147.276154\n", - "poly(Income, 3, )[0]:UIncome[H] 1957.694387\n", - "poly(Income, 3, )[0]:UIncome[L] 1462.060650\n", - "poly(Income, 3, )[0]:UIncome[M] 83.035153\n", - "poly(Income, 3, )[1]:UIncome[H] -984.494570\n", - "poly(Income, 3, )[1]:UIncome[L] 881.537647\n", - "poly(Income, 3, )[1]:UIncome[M] -18.006234\n", - "poly(Income, 3, )[2]:UIncome[H] 207.614692\n", - "poly(Income, 3, )[2]:UIncome[L] 217.190749\n", - "poly(Income, 3, )[2]:UIncome[M] 34.065434\n", - "UIncome[H]:US 0.903404\n", - "UIncome[L]:US 0.895538\n", - "UIncome[M]:US 1.048728\n", - "dtype: float64" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p3 = poly('Income', 3)\n", - "design = ModelSpec([pref_encoding, (p3, full_encoding), (full_encoding, 'US')]).fit(Carseats)\n", - "X = design.transform(Carseats)\n", - "sm.OLS(Y, X).fit().params" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "236ab2d2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 397.0 3120.351382 2.0 61.923316 4.031032 0.018488\n", - "2 388.0 3040.628559 9.0 79.722823 1.153273 0.324049\n", - "3 385.0 2957.118444 3.0 83.510115 3.624181 0.013244" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "0a45c720", - "metadata": {}, - "source": [ - "## Grouping columns for ANOVA\n", - "\n", - "The `Variable` construct can be used to group\n", - "variables together to get custom sequences of models for `anova_lm`." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "f36c1b3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['intercept'], dtype='object')\n", - "Index(['intercept', 'Price', 'UIncome[M]', 'UIncome[H]'], dtype='object')\n", - "Index(['intercept', 'Price', 'UIncome[M]', 'UIncome[H]', 'US[Yes]',\n", - " 'Advertising'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "group1 = Variable(('Price', pref_encoding), 'group1', None)\n", - "group2 = Variable(('US', 'Advertising'), 'group2', None)\n", - "design = ModelSpec([group1, group2]).fit(Carseats)\n", - "for D in design.build_sequence(Carseats):\n", - " print(D.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "3daf7638", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 399.0 3182.274698 0.0 NaN NaN NaN\n", - "1 396.0 2508.187788 3.0 674.086910 39.304841 2.970412e-22\n", - "2 394.0 2252.396343 2.0 255.791445 22.372135 6.267562e-10" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sm.stats.anova_lm(*(sm.OLS(Y, D).fit() for D in design.build_sequence(Carseats)))" - ] - }, - { - "cell_type": "markdown", - "id": "46c1ace8", - "metadata": {}, - "source": [ - "It is not clear this is simple to do in `R` as the formula object expands all parentheses." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "0b87e430", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Response: Sales\n", - " Df Sum Sq Mean Sq F value Pr(>F) \n", - "Price 1 630.03 630.03 110.2079 < 2.2e-16 ***\n", - "UIncome 2 44.06 22.03 3.8533 0.02201 * \n", - "US 1 121.88 121.88 21.3196 5.270e-06 ***\n", - "Advertising 1 133.91 133.91 23.4247 1.868e-06 ***\n", - "Residuals 394 2252.40 5.72 \n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "anova(lm(Sales ~ (Price + UIncome) + (US + Advertising), data=Carseats))" - ] - }, - { - "cell_type": "markdown", - "id": "7c137360", - "metadata": {}, - "source": [ - "It can be done by building up the models\n", - "by hand and likely is possible to be done programmatically but it seems not obvious." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "b678d323", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ 1\n", - "Model 2: Sales ~ Price + UIncome\n", - "Model 3: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 399 3182.3 \n", - "2 396 2508.2 3 674.09 39.305 < 2.2e-16 ***\n", - "3 394 2252.4 2 255.79 22.372 6.268e-10 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ 1, data=Carseats)\n", - "M2 = lm(Sales ~ Price + UIncome, data=Carseats)\n", - "M3 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "anova(M1, M2, M3)" - ] - }, - { - "cell_type": "markdown", - "id": "b0388949", - "metadata": {}, - "source": [ - "## Alternative anova\n", - "\n", - "Another common ANOVA table involves dropping each term in succession from the model and comparing\n", - "to the full model." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "ac5b916a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'intercept'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 395.0 4417.273517 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 1.0 2164.877175 378.690726 1.359177e-59\n", - "{'Price', 'UIncome[H]', 'UIncome[M]'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 397.0 2950.808154 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 3.0 698.411811 40.723184 6.077848e-23\n", - "{'US[Yes]', 'Advertising'}\n", - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 396.0 2508.187788 0.0 NaN NaN NaN\n", - "1 394.0 2252.396343 2.0 255.791445 22.372135 6.267562e-10\n" - ] - } - ], - "source": [ - "Dfull = design.transform(Carseats)\n", - "Mfull = sm.OLS(Y, Dfull).fit()\n", - "for i, D in enumerate(design.build_sequence(Carseats, anova_type='drop')):\n", - " if i == 0:\n", - " D0 = D\n", - " print(set(D.columns) ^ set(Dfull.columns))\n", - " print(sm.stats.anova_lm(sm.OLS(Y, D).fit(), Mfull))" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "a0c71948", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ US + Advertising\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 397 2950.8 \n", - "2 394 2252.4 3 698.41 40.723 < 2.2e-16 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ Price + UIncome\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F) \n", - "1 396 2508.2 \n", - "2 394 2252.4 2 255.79 22.372 6.268e-10 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "M2 = lm(Sales ~ US + Advertising, data=Carseats)\n", - "print(anova(M2, M1))\n", - "M3 = lm(Sales ~ Price + UIncome, data=Carseats)\n", - "print(anova(M3, M1))" - ] - }, - { - "cell_type": "markdown", - "id": "a5e4880d", - "metadata": {}, - "source": [ - "The comparison without the intercept here is actually very hard to achieve in `R` with `anova` due to its inspection\n", - "of the formula." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "4b383401", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis of Variance Table\n", - "\n", - "Model 1: Sales ~ Price + UIncome + US + Advertising - 1\n", - "Model 2: Sales ~ Price + UIncome + US + Advertising\n", - " Res.Df RSS Df Sum of Sq F Pr(>F)\n", - "1 394 2252.4 \n", - "2 394 2252.4 0 9.0949e-13 \n" - ] - } - ], - "source": [ - "%%R\n", - "M1 = lm(Sales ~ Price + UIncome + US + Advertising, data=Carseats)\n", - "M4 = lm(Sales ~ Price + UIncome + US + Advertising - 1, data=Carseats)\n", - "print(anova(M4, M1))" - ] - }, - { - "cell_type": "markdown", - "id": "72d7c83b", - "metadata": {}, - "source": [ - "It can be found with `summary`." - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "4d5ce789", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call:\n", - "lm(formula = Sales ~ Price + UIncome + US + Advertising, data = Carseats)\n", - "\n", - "Residuals:\n", - " Min 1Q Median 3Q Max \n", - "-7.4437 -1.6351 -0.0932 1.4920 6.8076 \n", - "\n", - "Coefficients:\n", - " Estimate Std. Error t value Pr(>|t|) \n", - "(Intercept) 12.520356 0.643390 19.460 < 2e-16 ***\n", - "Price -0.054000 0.005072 -10.647 < 2e-16 ***\n", - "UIncomeM 0.548906 0.281693 1.949 0.0521 . \n", - "UIncomeH 0.708219 0.322028 2.199 0.0284 * \n", - "USYes 0.024181 0.343246 0.070 0.9439 \n", - "Advertising 0.119509 0.024692 4.840 1.87e-06 ***\n", - "---\n", - "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", - "\n", - "Residual standard error: 2.391 on 394 degrees of freedom\n", - "Multiple R-squared: 0.2922,\tAdjusted R-squared: 0.2832 \n", - "F-statistic: 32.53 on 5 and 394 DF, p-value: < 2.2e-16\n", - "\n" - ] - } - ], - "source": [ - "%%R\n", - "summary(M1)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "56b82d02", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(378.690726, 378.69160000000005)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "378.690726, 19.46**2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "872f645c-1d6f-4d08-9eec-2b80276bc82c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" - }, - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/transforms/PCA.ipynb b/docs/source/transforms/PCA.ipynb index 04c50ad..ec1e0ae 100644 --- a/docs/source/transforms/PCA.ipynb +++ b/docs/source/transforms/PCA.ipynb @@ -24,8 +24,8 @@ "from ISLP import load_data\n", "from ISLP.models import (ModelSpec, \n", " pca, \n", - " Variable, \n", - " derived_variable,\n", + " Feature, \n", + " derived_feature,\n", " build_columns)" ] }, @@ -76,7 +76,7 @@ "id": "fff603bf", "metadata": {}, "source": [ - "Suppose we want to make a `Variable` representing the first 3 principal components of the\n", + "Suppose we want to make a `Feature` representing the first 3 principal components of the\n", " features `['CompPrice', 'Income', 'Advertising', 'Population', 'Price']`." ] }, @@ -85,8 +85,8 @@ "id": "eab49ad1-3957-478f-8a76-28a8f58551e9", "metadata": {}, "source": [ - "We first make a `Variable` that represents these five features columns, then `pca`\n", - "can be used to compute a new `Variable` that returns the first three principal components." + "We first make a `Feature` that represents these five features columns, then `pca`\n", + "can be used to compute a new `Feature` that returns the first three principal components." ] }, { @@ -96,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "grouped = Variable(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None)\n", + "grouped = Feature(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None)\n", "sklearn_pca = PCA(n_components=3, whiten=True)" ] }, @@ -105,7 +105,7 @@ "id": "b45655a3-393d-4b4c-b754-cda61ed0e014", "metadata": {}, "source": [ - "We can now fit `sklearn_pca` and create our new variable." + "We can now fit `sklearn_pca` and create our new feature." ] }, { @@ -119,7 +119,7 @@ " Carseats,\n", " grouped)[0]\n", "sklearn_pca.fit(grouped_features) \n", - "pca_var = derived_variable(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", + "pca_var = derived_feature(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],\n", " name='pca(grouped)', encoder=sklearn_pca)\n", "derived_features, _ = build_columns(design.column_info_,\n", " Carseats, \n", diff --git a/docs/source/transforms/poly.ipynb b/docs/source/transforms/poly.ipynb index c2b740b..45c862e 100644 --- a/docs/source/transforms/poly.ipynb +++ b/docs/source/transforms/poly.ipynb @@ -168,7 +168,7 @@ "source": [ "## Underlying model\n", "\n", - "If we look at `quartic`, we see it is a `Variable`, i.e. it can be used to produce a set of columns\n", + "If we look at `quartic`, we see it is a `Feature`, i.e. it can be used to produce a set of columns\n", "in a design matrix when it is a term used in creating the `ModelSpec`.\n", "\n", "Its encoder is `Poly(degree=4)`. This is a special `sklearn` transform that expects a single column\n", From 92d5e0c43a19bec7ea477b09d882172955b288b6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 12:52:13 -0700 Subject: [PATCH 022/195] update link in readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 71c8065..19757ab 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ The `ISLP` labs use `torch` and various related packages for the lab on deep lea can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` ```{python} -reqs = 'https://raw.githubusercontent.com/jonathan-taylor/ISLP/master/torch_requirements.txt' +reqs = 'https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt' cmd = f'{sys.executable} -m pip install -r {reqs}' os.system(cmd) ``` From 950e2dbeba29d4b8abd0c9431803beab3dc21a75 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 13:25:16 -0700 Subject: [PATCH 023/195] using default_rng if None supplied --- ISLP/survival.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ISLP/survival.py b/ISLP/survival.py index b11967b..c352942 100644 --- a/ISLP/survival.py +++ b/ISLP/survival.py @@ -14,7 +14,7 @@ def sim_time(linpred, cum_hazard, - rng): + rng=None): """ Simulate a survival time for a cumulative hazard function $H$ with cumulative hazard @@ -39,6 +39,9 @@ def sim_time(linpred, Used to generate survival times. """ + if rng is None: + rng = np.random.default_rng() + U = rng.uniform() B = - np.log(U) / np.exp(linpred) lower, upper = 1, 2 From 52983536293a189cd659392d4cff76f45c0a6aed Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 13:29:34 -0700 Subject: [PATCH 024/195] moving tests out of source tree --- ISLP/models/tests/__init__.py | 0 {ISLP/models/tests => tests/models}/test_columns.py | 0 {ISLP/models/tests => tests/models}/test_model_matrix.py | 0 {ISLP/models/tests => tests/models}/test_selection.py | 0 4 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ISLP/models/tests/__init__.py rename {ISLP/models/tests => tests/models}/test_columns.py (100%) rename {ISLP/models/tests => tests/models}/test_model_matrix.py (100%) rename {ISLP/models/tests => tests/models}/test_selection.py (100%) diff --git a/ISLP/models/tests/__init__.py b/ISLP/models/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/ISLP/models/tests/test_columns.py b/tests/models/test_columns.py similarity index 100% rename from ISLP/models/tests/test_columns.py rename to tests/models/test_columns.py diff --git a/ISLP/models/tests/test_model_matrix.py b/tests/models/test_model_matrix.py similarity index 100% rename from ISLP/models/tests/test_model_matrix.py rename to tests/models/test_model_matrix.py diff --git a/ISLP/models/tests/test_selection.py b/tests/models/test_selection.py similarity index 100% rename from ISLP/models/tests/test_selection.py rename to tests/models/test_selection.py From 720189f23c336c8efc84f9256d245dd202dd5bba Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 13:35:37 -0700 Subject: [PATCH 025/195] cleanup some packaging issues --- setup.py | 4 +++- {ISLP/bart/tests => tests/bart}/test_bart.py | 0 2 files changed, 3 insertions(+), 1 deletion(-) rename {ISLP/bart/tests => tests/bart}/test_bart.py (100%) diff --git a/setup.py b/setup.py index 95fca7d..568200d 100755 --- a/setup.py +++ b/setup.py @@ -234,9 +234,11 @@ def main(**extra_args): requires=info.REQUIRES, provides=info.PROVIDES, packages = ['ISLP', + 'ISLP.models', 'ISLP.models', 'ISLP.bart', - 'ISLP.torch' + 'ISLP.torch', + 'ISLP.data' ], ext_modules = EXTS, package_data = {"ISLP":["data/*csv", "data/*npy", "data/*data"]}, diff --git a/ISLP/bart/tests/test_bart.py b/tests/bart/test_bart.py similarity index 100% rename from ISLP/bart/tests/test_bart.py rename to tests/bart/test_bart.py From 68a248e35c7f74b46ed6c1d20d34528e3fa1f40e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 13:47:44 -0700 Subject: [PATCH 026/195] fixing kernel_name --- docs/jupyterbook/models/anova.ipynb | 4 ++-- docs/jupyterbook/models/anova.md | 4 ++-- docs/jupyterbook/models/selection.ipynb | 4 ++-- docs/jupyterbook/models/selection.md | 4 ++-- docs/source/models/anova.ipynb | 4 ++-- docs/source/models/selection.ipynb | 4 ++-- 6 files changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/jupyterbook/models/anova.ipynb b/docs/jupyterbook/models/anova.ipynb index 2bfe2ae..bedd405 100644 --- a/docs/jupyterbook/models/anova.ipynb +++ b/docs/jupyterbook/models/anova.ipynb @@ -611,9 +611,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/anova.md b/docs/jupyterbook/models/anova.md index 054ce3e..0e3882b 100644 --- a/docs/jupyterbook/models/anova.md +++ b/docs/jupyterbook/models/anova.md @@ -7,9 +7,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # ANOVA using `ModelSpec` diff --git a/docs/jupyterbook/models/selection.ipynb b/docs/jupyterbook/models/selection.ipynb index 0f597af..fd66d95 100644 --- a/docs/jupyterbook/models/selection.ipynb +++ b/docs/jupyterbook/models/selection.ipynb @@ -262,9 +262,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/models/selection.md b/docs/jupyterbook/models/selection.md index 5793852..949ccc1 100644 --- a/docs/jupyterbook/models/selection.md +++ b/docs/jupyterbook/models/selection.md @@ -7,9 +7,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Model selection using `ModelSpec` diff --git a/docs/source/models/anova.ipynb b/docs/source/models/anova.ipynb index 2bfe2ae..bedd405 100644 --- a/docs/source/models/anova.ipynb +++ b/docs/source/models/anova.ipynb @@ -611,9 +611,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/source/models/selection.ipynb b/docs/source/models/selection.ipynb index 0f597af..fd66d95 100644 --- a/docs/source/models/selection.ipynb +++ b/docs/source/models/selection.ipynb @@ -262,9 +262,9 @@ "formats": "source/models///ipynb,jupyterbook/models///md:myst,jupyterbook/models///ipynb" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { From 663450e433e0c2a295086bee59a183cbdf4798e2 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 14:45:30 -0700 Subject: [PATCH 027/195] reference to plotting dendrogram example --- docs/jupyterbook/helpers/cluster.ipynb | 34 ++++++++++++++++++++++---- docs/jupyterbook/helpers/cluster.md | 14 +++++------ docs/source/helpers/cluster.ipynb | 34 ++++++++++++++++++++++---- 3 files changed, 65 insertions(+), 17 deletions(-) diff --git a/docs/jupyterbook/helpers/cluster.ipynb b/docs/jupyterbook/helpers/cluster.ipynb index 56cf3d8..31798a0 100644 --- a/docs/jupyterbook/helpers/cluster.ipynb +++ b/docs/jupyterbook/helpers/cluster.ipynb @@ -8,15 +8,27 @@ "# Clustering\n", "\n", "This module has a single function, used to help visualize a dendrogram from a\n", - "hierarchical clustering." + "hierarchical clustering. The function is based on this example from [sklearn.cluster](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d5df152d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'sklearn'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AgglomerativeClustering\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhierarchy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dendrogram\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mISLP\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compute_linkage\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" + ] + } + ], "source": [ "import numpy as np\n", "from sklearn.cluster import AgglomerativeClustering\n", @@ -34,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "0135c1fb", "metadata": {}, "outputs": [], @@ -101,9 +113,21 @@ "main_language": "python" }, "kernelspec": { - "display_name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" } }, "nbformat": 4, diff --git a/docs/jupyterbook/helpers/cluster.md b/docs/jupyterbook/helpers/cluster.md index ab448dc..b951b18 100644 --- a/docs/jupyterbook/helpers/cluster.md +++ b/docs/jupyterbook/helpers/cluster.md @@ -9,7 +9,7 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: python3 + display_name: Python 3 (ipykernel) language: python name: python3 --- @@ -17,9 +17,9 @@ kernelspec: # Clustering This module has a single function, used to help visualize a dendrogram from a -hierarchical clustering. +hierarchical clustering. The function is based on this example from [sklearn.cluster](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html). -```{code-cell} +```{code-cell} ipython3 import numpy as np from sklearn.cluster import AgglomerativeClustering from scipy.cluster.hierarchy import dendrogram @@ -28,7 +28,7 @@ from ISLP.cluster import compute_linkage ## Make a toy dataset -```{code-cell} +```{code-cell} ipython3 rng = np.random.default_rng(1) X = rng.normal(size=(30, 5)) X[:10] += 1 @@ -36,19 +36,19 @@ X[:10] += 1 ## Cluster it -```{code-cell} +```{code-cell} ipython3 clust = AgglomerativeClustering(distance_threshold=0, n_clusters=None, linkage='complete') ``` -```{code-cell} +```{code-cell} ipython3 clust.fit(X) ``` ## Plot the dendrogram -```{code-cell} +```{code-cell} ipython3 linkage = compute_linkage(clust) dendrogram(linkage); ``` diff --git a/docs/source/helpers/cluster.ipynb b/docs/source/helpers/cluster.ipynb index 56cf3d8..31798a0 100644 --- a/docs/source/helpers/cluster.ipynb +++ b/docs/source/helpers/cluster.ipynb @@ -8,15 +8,27 @@ "# Clustering\n", "\n", "This module has a single function, used to help visualize a dendrogram from a\n", - "hierarchical clustering." + "hierarchical clustering. The function is based on this example from [sklearn.cluster](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d5df152d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'sklearn'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AgglomerativeClustering\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhierarchy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dendrogram\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mISLP\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compute_linkage\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" + ] + } + ], "source": [ "import numpy as np\n", "from sklearn.cluster import AgglomerativeClustering\n", @@ -34,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "0135c1fb", "metadata": {}, "outputs": [], @@ -101,9 +113,21 @@ "main_language": "python" }, "kernelspec": { - "display_name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" } }, "nbformat": 4, From e1708a0a96b4ad4b357dda5f143c2f9977d88eae Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 15:05:47 -0700 Subject: [PATCH 028/195] cleanup imdb example --- docs/jupyterbook/imdb.ipynb | 198 ++++++++++++++++++++++++++++-------- docs/jupyterbook/imdb.md | 96 +++++++++++------ docs/source/imdb.ipynb | 198 ++++++++++++++++++++++++++++-------- 3 files changed, 373 insertions(+), 119 deletions(-) diff --git a/docs/jupyterbook/imdb.ipynb b/docs/jupyterbook/imdb.ipynb index 1718a58..bddd7a8 100644 --- a/docs/jupyterbook/imdb.ipynb +++ b/docs/jupyterbook/imdb.ipynb @@ -5,71 +5,109 @@ "id": "50f2b809", "metadata": {}, "source": [ - "# Creating a clean IMDB dataset\n", + "# Creating IMDB dataset from `keras` version\n", + "\n", + "This script details how the `IMDB` data in `ISLP` was constructed.\n", "\n", "Running this example requires `keras`. Use `pip install keras` to install if necessary." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d920bb2e", "metadata": {}, "outputs": [], "source": [ - "import pickle" + "import pickle\n", + "import numpy as np\n", + "from scipy.sparse import coo_matrix, save_npz\n", + "import torch\n", + "from keras.datasets import imdb\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "e507f1fb", + "cell_type": "markdown", + "id": "eaf27f0c-0cb0-4ad5-8775-d138e3f20933", "metadata": {}, - "outputs": [], "source": [ - "import numpy as np\n", - "from scipy.sparse import coo_matrix, save_npz\n", - "import torch" + "We first load the data using `keras`, limiting focus to the 10000 most commmon words." ] }, { "cell_type": "code", - "execution_count": null, - "id": "b94d3f35", + "execution_count": 2, + "id": "29f0e01e", "metadata": {}, "outputs": [], "source": [ - "from keras.datasets import imdb\n", - "from tensorflow.keras.preprocessing.sequence import pad_sequences" + "# the 3 is for three terms: \n", + "num_words = 10000+3\n", + "((S_train, L_train), \n", + " (S_test, L_test)) = imdb.load_data(num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "id": "9020ab27-cc62-4b86-85ba-80a94ff692de", + "metadata": {}, + "source": [ + "The object `S_train` is effectively a list in which each document has been encoded into a sequence of\n", + "values from 0 to 10002." ] }, { "cell_type": "code", - "execution_count": null, - "id": "29f0e01e", + "execution_count": 3, + "id": "e27564c4-320f-42b6-9f2e-2a2afdebefcf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# the 3 is for three terms: \n", - "num_words = 10000+3\n", - "((S_train, Y_train), \n", - " (S_test, Y_test)) = imdb.load_data(num_words=num_words)" + "S_train[0][:10]" + ] + }, + { + "cell_type": "markdown", + "id": "15f039fe-faed-4884-a725-1c51d6c8d4d4", + "metadata": {}, + "source": [ + "We'll use `np.float32` as that is the common precision used in `torch`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "6cc3c3cb", "metadata": {}, "outputs": [], "source": [ - "Y_train = Y_train.astype(np.float32)\n", - "Y_test = Y_test.astype(np.float32)" + "L_train = L_train.astype(np.float32)\n", + "L_test = L_test.astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "005679bc-4337-4757-831e-f9a6ea50f6aa", + "metadata": {}, + "source": [ + "We will use a one-hot encoding that captures whether or not a given word appears in a given review." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7b6d1098", "metadata": {}, "outputs": [], @@ -88,18 +126,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "afcdc8b2", "metadata": {}, "outputs": [], "source": [ - "X_train, L_train = one_hot(S_train, num_words), Y_train\n", + "X_train = one_hot(S_train, num_words)\n", "X_test = one_hot(S_test, num_words)" ] }, + { + "cell_type": "markdown", + "id": "a67e299d-8774-4758-8953-77afdce775ab", + "metadata": {}, + "source": [ + "## Store as sparse tensors\n", + "\n", + "We see later in the lab that the dense representation is faster. Nevertheless,\n", + "let's store the one-hot representation as sparse `torch` tensors \n", + "as well as sparse `scipy` matrices." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "b19366ea", "metadata": {}, "outputs": [], @@ -115,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b45ae6d1", "metadata": {}, "outputs": [], @@ -126,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a47d6eb6", "metadata": {}, "outputs": [], @@ -137,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "d1b37b37", "metadata": {}, "outputs": [], @@ -151,12 +201,12 @@ "id": "1119823a", "metadata": {}, "source": [ - "save the sparse matrices" + "### Save as sparse `scipy` matrices" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6cb6bfdf", "metadata": {}, "outputs": [], @@ -167,12 +217,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "eac1c2ae", "metadata": {}, "outputs": [], "source": [ - "np.save('IMDB_Y_test.npy', Y_test)\n", + "np.save('IMDB_Y_test.npy', L_test)\n", "np.save('IMDB_Y_train.npy', L_train)" ] }, @@ -181,12 +231,14 @@ "id": "25c128e3", "metadata": {}, "source": [ - "save and pickle the word index" + "## Save and pickle the word index\n", + "\n", + "We'll also want to store a lookup table to convert representations such as `S_train[0]` into words" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8458bf67", "metadata": {}, "outputs": [], @@ -199,9 +251,46 @@ "lookup[4] = \"\"" ] }, + { + "cell_type": "markdown", + "id": "5e62ebff-2575-4d35-b46c-51c6f7598efc", + "metadata": {}, + "source": [ + "Let's look at our first training document:" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "2aaefdf8-0a49-4bdb-8b40-55665283c8a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" this film was just brilliant casting location scenery story direction everyone's really suited part they played and you\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join([lookup[i] for i in S_train[0][:20]])" + ] + }, + { + "cell_type": "markdown", + "id": "0e985a73-bfd9-42bd-a523-3dc6e223d602", + "metadata": {}, + "source": [ + "We save this lookup table so it can be loaded later " + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "d95252de", "metadata": {}, "outputs": [], @@ -214,12 +303,15 @@ "id": "b3d900b9", "metadata": {}, "source": [ - "create the padded representations" + "## Padded representations\n", + "\n", + "For some of the recurrent models, we'll need sequences of common lengths, padded if necessary.\n", + "Here, we pad up to a maximum length of 500, filling the remaining entries with 0." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "637b3c5e", "metadata": {}, "outputs": [], @@ -230,9 +322,17 @@ " S_test]]" ] }, + { + "cell_type": "markdown", + "id": "a6218300-b355-44cc-b7fb-4bff81211aa6", + "metadata": {}, + "source": [ + "Finally, we save these for later use in the deep learning lab." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "bac69f88", "metadata": {}, "outputs": [], @@ -249,9 +349,21 @@ "main_language": "python" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" } }, "nbformat": 4, diff --git a/docs/jupyterbook/imdb.md b/docs/jupyterbook/imdb.md index ae67f68..0a3ddf1 100644 --- a/docs/jupyterbook/imdb.md +++ b/docs/jupyterbook/imdb.md @@ -9,43 +9,52 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: python3 + display_name: islp_test language: python - name: python3 + name: islp_test --- -# Creating a clean IMDB dataset +# Creating IMDB dataset from `keras` version + +This script details how the `IMDB` data in `ISLP` was constructed. Running this example requires `keras`. Use `pip install keras` to install if necessary. -```{code-cell} +```{code-cell} ipython3 import pickle -``` - -```{code-cell} import numpy as np from scipy.sparse import coo_matrix, save_npz import torch -``` - -```{code-cell} from keras.datasets import imdb from tensorflow.keras.preprocessing.sequence import pad_sequences ``` -```{code-cell} +We first load the data using `keras`, limiting focus to the 10000 most commmon words. + +```{code-cell} ipython3 # the 3 is for three terms: num_words = 10000+3 -((S_train, Y_train), - (S_test, Y_test)) = imdb.load_data(num_words=num_words) +((S_train, L_train), + (S_test, L_test)) = imdb.load_data(num_words=num_words) ``` -```{code-cell} -Y_train = Y_train.astype(np.float32) -Y_test = Y_test.astype(np.float32) +The object `S_train` is effectively a list in which each document has been encoded into a sequence of +values from 0 to 10002. + +```{code-cell} ipython3 +S_train[0][:10] +``` + +We'll use `np.float32` as that is the common precision used in `torch`. + +```{code-cell} ipython3 +L_train = L_train.astype(np.float32) +L_test = L_test.astype(np.float32) ``` -```{code-cell} +We will use a one-hot encoding that captures whether or not a given word appears in a given review. + +```{code-cell} ipython3 def one_hot(sequences, ncol): idx, vals = [], [] for i, s in enumerate(sequences): @@ -58,12 +67,18 @@ def one_hot(sequences, ncol): return tens.coalesce() ``` -```{code-cell} -X_train, L_train = one_hot(S_train, num_words), Y_train +```{code-cell} ipython3 +X_train = one_hot(S_train, num_words) X_test = one_hot(S_test, num_words) ``` -```{code-cell} +## Store as sparse tensors + +We see later in the lab that the dense representation is faster. Nevertheless, +let's store the one-hot representation as sparse `torch` tensors +as well as sparse `scipy` matrices. + +```{code-cell} ipython3 def convert_sparse_tensor(X): idx = np.asarray(X.indices()) vals = np.asarray(X.values()) @@ -73,36 +88,38 @@ def convert_sparse_tensor(X): shape=X.shape).tocsr() ``` -```{code-cell} +```{code-cell} ipython3 X_train_s = convert_sparse_tensor(X_train) X_test_s = convert_sparse_tensor(X_test) ``` -```{code-cell} +```{code-cell} ipython3 X_train_d = torch.tensor(X_train_s.todense()) X_test_d = torch.tensor(X_test_s.todense()) ``` -```{code-cell} +```{code-cell} ipython3 torch.save(X_train_d, 'IMDB_X_train.tensor') torch.save(X_test_d, 'IMDB_X_test.tensor') ``` -save the sparse matrices +### Save as sparse `scipy` matrices -```{code-cell} +```{code-cell} ipython3 save_npz('IMDB_X_test.npz', X_test_s) save_npz('IMDB_X_train.npz', X_train_s) ``` -```{code-cell} -np.save('IMDB_Y_test.npy', Y_test) +```{code-cell} ipython3 +np.save('IMDB_Y_test.npy', L_test) np.save('IMDB_Y_train.npy', L_train) ``` -save and pickle the word index +## Save and pickle the word index -```{code-cell} +We'll also want to store a lookup table to convert representations such as `S_train[0]` into words + +```{code-cell} ipython3 word_index = imdb.get_word_index() lookup = {(i+3):w for w, i in word_index.items()} lookup[0] = "" @@ -111,20 +128,33 @@ lookup[2] = "" lookup[4] = "" ``` -```{code-cell} +Let's look at our first training document: + +```{code-cell} ipython3 +' '.join([lookup[i] for i in S_train[0][:20]]) +``` + +We save this lookup table so it can be loaded later + +```{code-cell} ipython3 pickle.dump(lookup, open('IMDB_word_index.pkl', 'bw')) ``` -create the padded representations +## Padded representations -```{code-cell} +For some of the recurrent models, we'll need sequences of common lengths, padded if necessary. +Here, we pad up to a maximum length of 500, filling the remaining entries with 0. + +```{code-cell} ipython3 (S_train, S_test) = [torch.tensor(pad_sequences(S, maxlen=500, value=0)) for S in [S_train, S_test]] ``` -```{code-cell} +Finally, we save these for later use in the deep learning lab. + +```{code-cell} ipython3 torch.save(S_train, 'IMDB_S_train.tensor') torch.save(S_test, 'IMDB_S_test.tensor') ``` diff --git a/docs/source/imdb.ipynb b/docs/source/imdb.ipynb index 1718a58..bddd7a8 100644 --- a/docs/source/imdb.ipynb +++ b/docs/source/imdb.ipynb @@ -5,71 +5,109 @@ "id": "50f2b809", "metadata": {}, "source": [ - "# Creating a clean IMDB dataset\n", + "# Creating IMDB dataset from `keras` version\n", + "\n", + "This script details how the `IMDB` data in `ISLP` was constructed.\n", "\n", "Running this example requires `keras`. Use `pip install keras` to install if necessary." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d920bb2e", "metadata": {}, "outputs": [], "source": [ - "import pickle" + "import pickle\n", + "import numpy as np\n", + "from scipy.sparse import coo_matrix, save_npz\n", + "import torch\n", + "from keras.datasets import imdb\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "e507f1fb", + "cell_type": "markdown", + "id": "eaf27f0c-0cb0-4ad5-8775-d138e3f20933", "metadata": {}, - "outputs": [], "source": [ - "import numpy as np\n", - "from scipy.sparse import coo_matrix, save_npz\n", - "import torch" + "We first load the data using `keras`, limiting focus to the 10000 most commmon words." ] }, { "cell_type": "code", - "execution_count": null, - "id": "b94d3f35", + "execution_count": 2, + "id": "29f0e01e", "metadata": {}, "outputs": [], "source": [ - "from keras.datasets import imdb\n", - "from tensorflow.keras.preprocessing.sequence import pad_sequences" + "# the 3 is for three terms: \n", + "num_words = 10000+3\n", + "((S_train, L_train), \n", + " (S_test, L_test)) = imdb.load_data(num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "id": "9020ab27-cc62-4b86-85ba-80a94ff692de", + "metadata": {}, + "source": [ + "The object `S_train` is effectively a list in which each document has been encoded into a sequence of\n", + "values from 0 to 10002." ] }, { "cell_type": "code", - "execution_count": null, - "id": "29f0e01e", + "execution_count": 3, + "id": "e27564c4-320f-42b6-9f2e-2a2afdebefcf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# the 3 is for three terms: \n", - "num_words = 10000+3\n", - "((S_train, Y_train), \n", - " (S_test, Y_test)) = imdb.load_data(num_words=num_words)" + "S_train[0][:10]" + ] + }, + { + "cell_type": "markdown", + "id": "15f039fe-faed-4884-a725-1c51d6c8d4d4", + "metadata": {}, + "source": [ + "We'll use `np.float32` as that is the common precision used in `torch`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "6cc3c3cb", "metadata": {}, "outputs": [], "source": [ - "Y_train = Y_train.astype(np.float32)\n", - "Y_test = Y_test.astype(np.float32)" + "L_train = L_train.astype(np.float32)\n", + "L_test = L_test.astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "005679bc-4337-4757-831e-f9a6ea50f6aa", + "metadata": {}, + "source": [ + "We will use a one-hot encoding that captures whether or not a given word appears in a given review." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7b6d1098", "metadata": {}, "outputs": [], @@ -88,18 +126,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "afcdc8b2", "metadata": {}, "outputs": [], "source": [ - "X_train, L_train = one_hot(S_train, num_words), Y_train\n", + "X_train = one_hot(S_train, num_words)\n", "X_test = one_hot(S_test, num_words)" ] }, + { + "cell_type": "markdown", + "id": "a67e299d-8774-4758-8953-77afdce775ab", + "metadata": {}, + "source": [ + "## Store as sparse tensors\n", + "\n", + "We see later in the lab that the dense representation is faster. Nevertheless,\n", + "let's store the one-hot representation as sparse `torch` tensors \n", + "as well as sparse `scipy` matrices." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "b19366ea", "metadata": {}, "outputs": [], @@ -115,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b45ae6d1", "metadata": {}, "outputs": [], @@ -126,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a47d6eb6", "metadata": {}, "outputs": [], @@ -137,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "d1b37b37", "metadata": {}, "outputs": [], @@ -151,12 +201,12 @@ "id": "1119823a", "metadata": {}, "source": [ - "save the sparse matrices" + "### Save as sparse `scipy` matrices" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6cb6bfdf", "metadata": {}, "outputs": [], @@ -167,12 +217,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "eac1c2ae", "metadata": {}, "outputs": [], "source": [ - "np.save('IMDB_Y_test.npy', Y_test)\n", + "np.save('IMDB_Y_test.npy', L_test)\n", "np.save('IMDB_Y_train.npy', L_train)" ] }, @@ -181,12 +231,14 @@ "id": "25c128e3", "metadata": {}, "source": [ - "save and pickle the word index" + "## Save and pickle the word index\n", + "\n", + "We'll also want to store a lookup table to convert representations such as `S_train[0]` into words" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8458bf67", "metadata": {}, "outputs": [], @@ -199,9 +251,46 @@ "lookup[4] = \"\"" ] }, + { + "cell_type": "markdown", + "id": "5e62ebff-2575-4d35-b46c-51c6f7598efc", + "metadata": {}, + "source": [ + "Let's look at our first training document:" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "2aaefdf8-0a49-4bdb-8b40-55665283c8a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" this film was just brilliant casting location scenery story direction everyone's really suited part they played and you\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join([lookup[i] for i in S_train[0][:20]])" + ] + }, + { + "cell_type": "markdown", + "id": "0e985a73-bfd9-42bd-a523-3dc6e223d602", + "metadata": {}, + "source": [ + "We save this lookup table so it can be loaded later " + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "d95252de", "metadata": {}, "outputs": [], @@ -214,12 +303,15 @@ "id": "b3d900b9", "metadata": {}, "source": [ - "create the padded representations" + "## Padded representations\n", + "\n", + "For some of the recurrent models, we'll need sequences of common lengths, padded if necessary.\n", + "Here, we pad up to a maximum length of 500, filling the remaining entries with 0." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "637b3c5e", "metadata": {}, "outputs": [], @@ -230,9 +322,17 @@ " S_test]]" ] }, + { + "cell_type": "markdown", + "id": "a6218300-b355-44cc-b7fb-4bff81211aa6", + "metadata": {}, + "source": [ + "Finally, we save these for later use in the deep learning lab." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "bac69f88", "metadata": {}, "outputs": [], @@ -249,9 +349,21 @@ "main_language": "python" }, "kernelspec": { - "display_name": "python3", + "display_name": "islp_test", "language": "python", - "name": "python3" + "name": "islp_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" } }, "nbformat": 4, From ff42bd81795b4c0004cefde402b81b8aae992bd0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 15:07:28 -0700 Subject: [PATCH 029/195] index of anova df --- docs/jupyterbook/imdb.ipynb | 4 +- docs/jupyterbook/imdb.md | 4 +- docs/jupyterbook/models/anova.ipynb | 253 +++++++++++++++------------- docs/jupyterbook/models/anova.md | 24 +-- docs/source/imdb.ipynb | 4 +- docs/source/models/anova.ipynb | 253 +++++++++++++++------------- 6 files changed, 288 insertions(+), 254 deletions(-) diff --git a/docs/jupyterbook/imdb.ipynb b/docs/jupyterbook/imdb.ipynb index bddd7a8..ae0d7dd 100644 --- a/docs/jupyterbook/imdb.ipynb +++ b/docs/jupyterbook/imdb.ipynb @@ -349,9 +349,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/jupyterbook/imdb.md b/docs/jupyterbook/imdb.md index 0a3ddf1..0b87bae 100644 --- a/docs/jupyterbook/imdb.md +++ b/docs/jupyterbook/imdb.md @@ -9,9 +9,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.14.5 kernelspec: - display_name: islp_test + display_name: python3 language: python - name: islp_test + name: python3 --- # Creating IMDB dataset from `keras` version diff --git a/docs/jupyterbook/models/anova.ipynb b/docs/jupyterbook/models/anova.ipynb index bedd405..41e8bcb 100644 --- a/docs/jupyterbook/models/anova.ipynb +++ b/docs/jupyterbook/models/anova.ipynb @@ -36,10 +36,8 @@ "id": "333a49cf", "metadata": {}, "source": [ - "### Forward Selection\n", - " \n", - "We will apply the forward-selection approach to the `Hitters` \n", - "data. We wish to predict a baseball player’s `Salary` on the\n", + "We will carry out two simple ANOVA analyses of the `Hitters` data.\n", + "We wish to predict a baseball player’s `Salary` on the\n", "basis of various statistics associated with performance in the\n", "previous year." ] @@ -122,14 +120,14 @@ "metadata": {}, "outputs": [], "source": [ - "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", - " name='offense_1986')\n", + "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", + " name='confounders')\n", "offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'],\n", " name='offense_career')\n", + "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", + " name='offense_1986')\n", "defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'],\n", - " name='defense_1986')\n", - "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", - " name='confounders')" + " name='defense_1986')" ] }, { @@ -147,7 +145,7 @@ "metadata": {}, "outputs": [], "source": [ - "design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters)\n", + "design = ModelSpec([confounders, offense_career, defense_1986, offense_1986]).fit(Hitters)\n", "Y = np.array(Hitters['Salary'])\n", "X = design.transform(Hitters)" ] @@ -227,46 +225,46 @@ " 0.757\n", " \n", " \n", - " AtBat\n", - " -1.9509\n", - " 0.624\n", - " -3.125\n", - " 0.002\n", + " CAtBat\n", + " -0.1887\n", + " 0.120\n", + " -1.572\n", + " 0.117\n", " \n", " \n", - " Hits\n", - " 7.4395\n", - " 2.363\n", - " 3.148\n", - " 0.002\n", + " CHits\n", + " 0.1636\n", + " 0.665\n", + " 0.246\n", + " 0.806\n", " \n", " \n", - " HmRun\n", - " 4.3449\n", - " 6.190\n", - " 0.702\n", - " 0.483\n", + " CHmRun\n", + " -0.1517\n", + " 1.612\n", + " -0.094\n", + " 0.925\n", " \n", " \n", - " Runs\n", - " -2.3312\n", - " 2.971\n", - " -0.785\n", - " 0.433\n", + " CRuns\n", + " 1.4716\n", + " 0.747\n", + " 1.971\n", + " 0.050\n", " \n", " \n", - " RBI\n", - " -1.0670\n", - " 2.595\n", - " -0.411\n", - " 0.681\n", + " CRBI\n", + " 0.8021\n", + " 0.691\n", + " 1.161\n", + " 0.247\n", " \n", " \n", - " Walks\n", - " 6.2196\n", - " 1.825\n", - " 3.409\n", - " 0.001\n", + " CWalks\n", + " -0.8124\n", + " 0.327\n", + " -2.481\n", + " 0.014\n", " \n", " \n", " PutOuts\n", @@ -290,46 +288,46 @@ " 0.452\n", " \n", " \n", - " CAtBat\n", - " -0.1887\n", - " 0.120\n", - " -1.572\n", - " 0.117\n", + " AtBat\n", + " -1.9509\n", + " 0.624\n", + " -3.125\n", + " 0.002\n", " \n", " \n", - " CHits\n", - " 0.1636\n", - " 0.665\n", - " 0.246\n", - " 0.806\n", + " Hits\n", + " 7.4395\n", + " 2.363\n", + " 3.148\n", + " 0.002\n", " \n", " \n", - " CHmRun\n", - " -0.1517\n", - " 1.612\n", - " -0.094\n", - " 0.925\n", + " HmRun\n", + " 4.3449\n", + " 6.190\n", + " 0.702\n", + " 0.483\n", " \n", " \n", - " CRuns\n", - " 1.4716\n", - " 0.747\n", - " 1.971\n", - " 0.050\n", + " Runs\n", + " -2.3312\n", + " 2.971\n", + " -0.785\n", + " 0.433\n", " \n", " \n", - " CRBI\n", - " 0.8021\n", - " 0.691\n", - " 1.161\n", - " 0.247\n", + " RBI\n", + " -1.0670\n", + " 2.595\n", + " -0.411\n", + " 0.681\n", " \n", " \n", - " CWalks\n", - " -0.8124\n", - " 0.327\n", - " -2.481\n", - " 0.014\n", + " Walks\n", + " 6.2196\n", + " 1.825\n", + " 3.409\n", + " 0.001\n", " \n", " \n", "\n", @@ -341,21 +339,21 @@ "Division[W] -116.0404 40.188 -2.887 0.004\n", "League[N] 63.7503 79.006 0.807 0.421\n", "NewLeague[N] -24.3989 78.843 -0.309 0.757\n", - "AtBat -1.9509 0.624 -3.125 0.002\n", - "Hits 7.4395 2.363 3.148 0.002\n", - "HmRun 4.3449 6.190 0.702 0.483\n", - "Runs -2.3312 2.971 -0.785 0.433\n", - "RBI -1.0670 2.595 -0.411 0.681\n", - "Walks 6.2196 1.825 3.409 0.001\n", - "PutOuts 0.2827 0.077 3.661 0.000\n", - "Assists 0.3755 0.220 1.705 0.089\n", - "Errors -3.2940 4.377 -0.753 0.452\n", "CAtBat -0.1887 0.120 -1.572 0.117\n", "CHits 0.1636 0.665 0.246 0.806\n", "CHmRun -0.1517 1.612 -0.094 0.925\n", "CRuns 1.4716 0.747 1.971 0.050\n", "CRBI 0.8021 0.691 1.161 0.247\n", - "CWalks -0.8124 0.327 -2.481 0.014" + "CWalks -0.8124 0.327 -2.481 0.014\n", + "PutOuts 0.2827 0.077 3.661 0.000\n", + "Assists 0.3755 0.220 1.705 0.089\n", + "Errors -3.2940 4.377 -0.753 0.452\n", + "AtBat -1.9509 0.624 -3.125 0.002\n", + "Hits 7.4395 2.363 3.148 0.002\n", + "HmRun 4.3449 6.190 0.702 0.483\n", + "Runs -2.3312 2.971 -0.785 0.433\n", + "RBI -1.0670 2.595 -0.411 0.681\n", + "Walks 6.2196 1.825 3.409 0.001" ] }, "execution_count": 6, @@ -379,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "cfbe5b92", "metadata": {}, "outputs": [ @@ -414,7 +412,7 @@ " \n", " \n", " \n", - " 0\n", + " intercept\n", " 262.0\n", " 5.331911e+07\n", " 0.0\n", @@ -423,7 +421,7 @@ " NaN\n", " \n", " \n", - " 1\n", + " confounders\n", " 259.0\n", " 5.131263e+07\n", " 3.0\n", @@ -432,52 +430,61 @@ " 2.144265e-04\n", " \n", " \n", - " 2\n", + " offense_career\n", " 253.0\n", - " 3.589842e+07\n", + " 3.059130e+07\n", " 6.0\n", - " 1.541422e+07\n", - " 25.893510\n", - " 6.063309e-24\n", + " 2.072134e+07\n", + " 34.808656\n", + " 1.470455e-30\n", " \n", " \n", - " 3\n", + " defense_1986\n", " 250.0\n", - " 3.471882e+07\n", + " 2.730614e+07\n", " 3.0\n", - " 1.179602e+06\n", - " 3.963099\n", - " 8.730527e-03\n", + " 3.285156e+06\n", + " 11.037111\n", + " 7.880207e-07\n", " \n", " \n", - " 4\n", + " offense_1986\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 1.051025e+07\n", - " 17.655596\n", - " 5.701196e-17\n", + " 3.097572e+06\n", + " 5.203444\n", + " 4.648586e-05\n", " \n", " \n", "\n", "" ], "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 262.0 5.331911e+07 0.0 NaN NaN NaN\n", - "1 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 2.144265e-04\n", - "2 253.0 3.589842e+07 6.0 1.541422e+07 25.893510 6.063309e-24\n", - "3 250.0 3.471882e+07 3.0 1.179602e+06 3.963099 8.730527e-03\n", - "4 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 5.701196e-17" + " df_resid ssr df_diff ss_diff F \\\n", + "intercept 262.0 5.331911e+07 0.0 NaN NaN \n", + "confounders 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 \n", + "offense_career 253.0 3.059130e+07 6.0 2.072134e+07 34.808656 \n", + "defense_1986 250.0 2.730614e+07 3.0 3.285156e+06 11.037111 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", + "\n", + " Pr(>F) \n", + "intercept NaN \n", + "confounders 2.144265e-04 \n", + "offense_career 1.470455e-30 \n", + "defense_1986 7.880207e-07 \n", + "offense_1986 4.648586e-05 " ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])" + "df = anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])\n", + "df.index = design.names\n", + "df" ] }, { @@ -490,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "e2d43844", "metadata": {}, "outputs": [ @@ -543,13 +550,13 @@ " 2.261572e-02\n", " \n", " \n", - " offense_1986\n", + " offense_career\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 3.097572e+06\n", - " 5.203444\n", - " 4.648586e-05\n", + " 1.051025e+07\n", + " 17.655596\n", + " 5.701196e-17\n", " \n", " \n", " defense_1986\n", @@ -561,13 +568,13 @@ " 2.415732e-03\n", " \n", " \n", - " offense_career\n", + " offense_1986\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 1.051025e+07\n", - " 17.655596\n", - " 5.701196e-17\n", + " 3.097572e+06\n", + " 5.203444\n", + " 4.648586e-05\n", " \n", " \n", "\n", @@ -577,19 +584,19 @@ " df_resid ssr df_diff ss_diff F \\\n", "intercept 244.0 2.420857e+07 1.0 4.024254e+05 4.056076 \n", "confounders 244.0 2.420857e+07 3.0 9.661738e+05 3.246046 \n", - "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", - "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", "offense_career 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 \n", + "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", "\n", " Pr(>F) \n", "intercept 4.511037e-02 \n", "confounders 2.261572e-02 \n", - "offense_1986 4.648586e-05 \n", + "offense_career 5.701196e-17 \n", "defense_1986 2.415732e-03 \n", - "offense_career 5.701196e-17 " + "offense_1986 4.648586e-05 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -604,6 +611,14 @@ "df.index = design.names\n", "df" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "362709ae-9558-4c4c-8f5e-f8388caf631d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/jupyterbook/models/anova.md b/docs/jupyterbook/models/anova.md index 0e3882b..574f9eb 100644 --- a/docs/jupyterbook/models/anova.md +++ b/docs/jupyterbook/models/anova.md @@ -31,10 +31,8 @@ from ISLP.models import (ModelSpec, summarize) ``` -### Forward Selection - -We will apply the forward-selection approach to the `Hitters` -data. We wish to predict a baseball player’s `Salary` on the +We will carry out two simple ANOVA analyses of the `Hitters` data. +We wish to predict a baseball player’s `Salary` on the basis of various statistics associated with performance in the previous year. @@ -61,20 +59,20 @@ that there are both career and 1986 offensive stats, as well as some defensive s Let's group the offensive into recent and career offensive stats, as well as a group of defensive variables. ```{code-cell} ipython3 -offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'], - name='offense_1986') +confounders = derived_feature(['Division', 'League', 'NewLeague'], + name='confounders') offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'], name='offense_career') +offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'], + name='offense_1986') defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'], name='defense_1986') -confounders = derived_feature(['Division', 'League', 'NewLeague'], - name='confounders') ``` We'll first do a sequential ANOVA where terms are added sequentially ```{code-cell} ipython3 -design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters) +design = ModelSpec([confounders, offense_career, defense_1986, offense_1986]).fit(Hitters) Y = np.array(Hitters['Salary']) X = design.transform(Hitters) ``` @@ -94,7 +92,9 @@ We'll first produce the sequential, or Type I ANOVA results. This builds up a mo two successive models. ```{code-cell} ipython3 -anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')]) +df = anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')]) +df.index = design.names +df ``` We can similarly compute the Type II ANOVA results which drops each term and compares to the full model. @@ -109,3 +109,7 @@ df = pd.concat(dfs) df.index = design.names df ``` + +```{code-cell} ipython3 + +``` diff --git a/docs/source/imdb.ipynb b/docs/source/imdb.ipynb index bddd7a8..ae0d7dd 100644 --- a/docs/source/imdb.ipynb +++ b/docs/source/imdb.ipynb @@ -349,9 +349,9 @@ "main_language": "python" }, "kernelspec": { - "display_name": "islp_test", + "display_name": "python3", "language": "python", - "name": "islp_test" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/source/models/anova.ipynb b/docs/source/models/anova.ipynb index bedd405..41e8bcb 100644 --- a/docs/source/models/anova.ipynb +++ b/docs/source/models/anova.ipynb @@ -36,10 +36,8 @@ "id": "333a49cf", "metadata": {}, "source": [ - "### Forward Selection\n", - " \n", - "We will apply the forward-selection approach to the `Hitters` \n", - "data. We wish to predict a baseball player’s `Salary` on the\n", + "We will carry out two simple ANOVA analyses of the `Hitters` data.\n", + "We wish to predict a baseball player’s `Salary` on the\n", "basis of various statistics associated with performance in the\n", "previous year." ] @@ -122,14 +120,14 @@ "metadata": {}, "outputs": [], "source": [ - "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", - " name='offense_1986')\n", + "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", + " name='confounders')\n", "offense_career = derived_feature(['CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks'],\n", " name='offense_career')\n", + "offense_1986 = derived_feature(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks'],\n", + " name='offense_1986')\n", "defense_1986 = derived_feature(['PutOuts', 'Assists', 'Errors'],\n", - " name='defense_1986')\n", - "confounders = derived_feature(['Division', 'League', 'NewLeague'],\n", - " name='confounders')" + " name='defense_1986')" ] }, { @@ -147,7 +145,7 @@ "metadata": {}, "outputs": [], "source": [ - "design = ModelSpec([confounders, offense_1986, defense_1986, offense_career]).fit(Hitters)\n", + "design = ModelSpec([confounders, offense_career, defense_1986, offense_1986]).fit(Hitters)\n", "Y = np.array(Hitters['Salary'])\n", "X = design.transform(Hitters)" ] @@ -227,46 +225,46 @@ " 0.757\n", " \n", " \n", - " AtBat\n", - " -1.9509\n", - " 0.624\n", - " -3.125\n", - " 0.002\n", + " CAtBat\n", + " -0.1887\n", + " 0.120\n", + " -1.572\n", + " 0.117\n", " \n", " \n", - " Hits\n", - " 7.4395\n", - " 2.363\n", - " 3.148\n", - " 0.002\n", + " CHits\n", + " 0.1636\n", + " 0.665\n", + " 0.246\n", + " 0.806\n", " \n", " \n", - " HmRun\n", - " 4.3449\n", - " 6.190\n", - " 0.702\n", - " 0.483\n", + " CHmRun\n", + " -0.1517\n", + " 1.612\n", + " -0.094\n", + " 0.925\n", " \n", " \n", - " Runs\n", - " -2.3312\n", - " 2.971\n", - " -0.785\n", - " 0.433\n", + " CRuns\n", + " 1.4716\n", + " 0.747\n", + " 1.971\n", + " 0.050\n", " \n", " \n", - " RBI\n", - " -1.0670\n", - " 2.595\n", - " -0.411\n", - " 0.681\n", + " CRBI\n", + " 0.8021\n", + " 0.691\n", + " 1.161\n", + " 0.247\n", " \n", " \n", - " Walks\n", - " 6.2196\n", - " 1.825\n", - " 3.409\n", - " 0.001\n", + " CWalks\n", + " -0.8124\n", + " 0.327\n", + " -2.481\n", + " 0.014\n", " \n", " \n", " PutOuts\n", @@ -290,46 +288,46 @@ " 0.452\n", " \n", " \n", - " CAtBat\n", - " -0.1887\n", - " 0.120\n", - " -1.572\n", - " 0.117\n", + " AtBat\n", + " -1.9509\n", + " 0.624\n", + " -3.125\n", + " 0.002\n", " \n", " \n", - " CHits\n", - " 0.1636\n", - " 0.665\n", - " 0.246\n", - " 0.806\n", + " Hits\n", + " 7.4395\n", + " 2.363\n", + " 3.148\n", + " 0.002\n", " \n", " \n", - " CHmRun\n", - " -0.1517\n", - " 1.612\n", - " -0.094\n", - " 0.925\n", + " HmRun\n", + " 4.3449\n", + " 6.190\n", + " 0.702\n", + " 0.483\n", " \n", " \n", - " CRuns\n", - " 1.4716\n", - " 0.747\n", - " 1.971\n", - " 0.050\n", + " Runs\n", + " -2.3312\n", + " 2.971\n", + " -0.785\n", + " 0.433\n", " \n", " \n", - " CRBI\n", - " 0.8021\n", - " 0.691\n", - " 1.161\n", - " 0.247\n", + " RBI\n", + " -1.0670\n", + " 2.595\n", + " -0.411\n", + " 0.681\n", " \n", " \n", - " CWalks\n", - " -0.8124\n", - " 0.327\n", - " -2.481\n", - " 0.014\n", + " Walks\n", + " 6.2196\n", + " 1.825\n", + " 3.409\n", + " 0.001\n", " \n", " \n", "\n", @@ -341,21 +339,21 @@ "Division[W] -116.0404 40.188 -2.887 0.004\n", "League[N] 63.7503 79.006 0.807 0.421\n", "NewLeague[N] -24.3989 78.843 -0.309 0.757\n", - "AtBat -1.9509 0.624 -3.125 0.002\n", - "Hits 7.4395 2.363 3.148 0.002\n", - "HmRun 4.3449 6.190 0.702 0.483\n", - "Runs -2.3312 2.971 -0.785 0.433\n", - "RBI -1.0670 2.595 -0.411 0.681\n", - "Walks 6.2196 1.825 3.409 0.001\n", - "PutOuts 0.2827 0.077 3.661 0.000\n", - "Assists 0.3755 0.220 1.705 0.089\n", - "Errors -3.2940 4.377 -0.753 0.452\n", "CAtBat -0.1887 0.120 -1.572 0.117\n", "CHits 0.1636 0.665 0.246 0.806\n", "CHmRun -0.1517 1.612 -0.094 0.925\n", "CRuns 1.4716 0.747 1.971 0.050\n", "CRBI 0.8021 0.691 1.161 0.247\n", - "CWalks -0.8124 0.327 -2.481 0.014" + "CWalks -0.8124 0.327 -2.481 0.014\n", + "PutOuts 0.2827 0.077 3.661 0.000\n", + "Assists 0.3755 0.220 1.705 0.089\n", + "Errors -3.2940 4.377 -0.753 0.452\n", + "AtBat -1.9509 0.624 -3.125 0.002\n", + "Hits 7.4395 2.363 3.148 0.002\n", + "HmRun 4.3449 6.190 0.702 0.483\n", + "Runs -2.3312 2.971 -0.785 0.433\n", + "RBI -1.0670 2.595 -0.411 0.681\n", + "Walks 6.2196 1.825 3.409 0.001" ] }, "execution_count": 6, @@ -379,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "cfbe5b92", "metadata": {}, "outputs": [ @@ -414,7 +412,7 @@ " \n", " \n", " \n", - " 0\n", + " intercept\n", " 262.0\n", " 5.331911e+07\n", " 0.0\n", @@ -423,7 +421,7 @@ " NaN\n", " \n", " \n", - " 1\n", + " confounders\n", " 259.0\n", " 5.131263e+07\n", " 3.0\n", @@ -432,52 +430,61 @@ " 2.144265e-04\n", " \n", " \n", - " 2\n", + " offense_career\n", " 253.0\n", - " 3.589842e+07\n", + " 3.059130e+07\n", " 6.0\n", - " 1.541422e+07\n", - " 25.893510\n", - " 6.063309e-24\n", + " 2.072134e+07\n", + " 34.808656\n", + " 1.470455e-30\n", " \n", " \n", - " 3\n", + " defense_1986\n", " 250.0\n", - " 3.471882e+07\n", + " 2.730614e+07\n", " 3.0\n", - " 1.179602e+06\n", - " 3.963099\n", - " 8.730527e-03\n", + " 3.285156e+06\n", + " 11.037111\n", + " 7.880207e-07\n", " \n", " \n", - " 4\n", + " offense_1986\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 1.051025e+07\n", - " 17.655596\n", - " 5.701196e-17\n", + " 3.097572e+06\n", + " 5.203444\n", + " 4.648586e-05\n", " \n", " \n", "\n", "" ], "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 262.0 5.331911e+07 0.0 NaN NaN NaN\n", - "1 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 2.144265e-04\n", - "2 253.0 3.589842e+07 6.0 1.541422e+07 25.893510 6.063309e-24\n", - "3 250.0 3.471882e+07 3.0 1.179602e+06 3.963099 8.730527e-03\n", - "4 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 5.701196e-17" + " df_resid ssr df_diff ss_diff F \\\n", + "intercept 262.0 5.331911e+07 0.0 NaN NaN \n", + "confounders 259.0 5.131263e+07 3.0 2.006478e+06 6.741147 \n", + "offense_career 253.0 3.059130e+07 6.0 2.072134e+07 34.808656 \n", + "defense_1986 250.0 2.730614e+07 3.0 3.285156e+06 11.037111 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", + "\n", + " Pr(>F) \n", + "intercept NaN \n", + "confounders 2.144265e-04 \n", + "offense_career 1.470455e-30 \n", + "defense_1986 7.880207e-07 \n", + "offense_1986 4.648586e-05 " ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])" + "df = anova_lm(*[OLS(Y, D).fit() for D in design.build_sequence(Hitters, anova_type='sequential')])\n", + "df.index = design.names\n", + "df" ] }, { @@ -490,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "e2d43844", "metadata": {}, "outputs": [ @@ -543,13 +550,13 @@ " 2.261572e-02\n", " \n", " \n", - " offense_1986\n", + " offense_career\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 3.097572e+06\n", - " 5.203444\n", - " 4.648586e-05\n", + " 1.051025e+07\n", + " 17.655596\n", + " 5.701196e-17\n", " \n", " \n", " defense_1986\n", @@ -561,13 +568,13 @@ " 2.415732e-03\n", " \n", " \n", - " offense_career\n", + " offense_1986\n", " 244.0\n", " 2.420857e+07\n", " 6.0\n", - " 1.051025e+07\n", - " 17.655596\n", - " 5.701196e-17\n", + " 3.097572e+06\n", + " 5.203444\n", + " 4.648586e-05\n", " \n", " \n", "\n", @@ -577,19 +584,19 @@ " df_resid ssr df_diff ss_diff F \\\n", "intercept 244.0 2.420857e+07 1.0 4.024254e+05 4.056076 \n", "confounders 244.0 2.420857e+07 3.0 9.661738e+05 3.246046 \n", - "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", - "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", "offense_career 244.0 2.420857e+07 6.0 1.051025e+07 17.655596 \n", + "defense_1986 244.0 2.420857e+07 3.0 1.467933e+06 4.931803 \n", + "offense_1986 244.0 2.420857e+07 6.0 3.097572e+06 5.203444 \n", "\n", " Pr(>F) \n", "intercept 4.511037e-02 \n", "confounders 2.261572e-02 \n", - "offense_1986 4.648586e-05 \n", + "offense_career 5.701196e-17 \n", "defense_1986 2.415732e-03 \n", - "offense_career 5.701196e-17 " + "offense_1986 4.648586e-05 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -604,6 +611,14 @@ "df.index = design.names\n", "df" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "362709ae-9558-4c4c-8f5e-f8388caf631d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 3b79c74d66758f0ff642d24d28e7100c43899e99 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 15:25:36 -0700 Subject: [PATCH 030/195] updating install torch requirements --- README.md | 4 +--- docs/source/installation.myst | 4 +--- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 19757ab..6b4ca4a 100644 --- a/README.md +++ b/README.md @@ -46,9 +46,7 @@ The `ISLP` labs use `torch` and various related packages for the lab on deep lea can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` ```{python} -reqs = 'https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt' -cmd = f'{sys.executable} -m pip install -r {reqs}' -os.system(cmd) +!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt ``` ## Documentation diff --git a/docs/source/installation.myst b/docs/source/installation.myst index d039ca9..665b7c1 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -63,8 +63,6 @@ can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_ --- tags: [skip-execution] --- -reqs = 'https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt' -cmd = f'{sys.executable} -m pip install -r {reqs}' -os.system(cmd) +!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt ``` From cb8cee2b17748d8082bb3f53650dc45ea6c48e19 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 15:26:18 -0700 Subject: [PATCH 031/195] master->main --- README.md | 2 +- docs/source/installation.myst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6b4ca4a..313a4e5 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ The `ISLP` labs use `torch` and various related packages for the lab on deep lea can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` ```{python} -!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt +!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt ``` ## Documentation diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 665b7c1..af549a9 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -63,6 +63,6 @@ can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_ --- tags: [skip-execution] --- -!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/master/torch_requirements.txt +!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt ``` From ac505c01031c533d02ed37ab8e1655d75b36c4cb Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 17:41:51 -0700 Subject: [PATCH 032/195] bumping sklearn minimum for onehotencoder sparse_output --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index bbe21a2..797c729 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy>=1.7.1 scipy>=0.9 pandas>=0.20 lxml # pandas needs this for html -scikit-learn>=1.0 +scikit-learn>=1.2 joblib statsmodels>=0.13 lifelines From 167928004bdfaf3a7605dd13bf7b44e55052b763 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 17:42:40 -0700 Subject: [PATCH 033/195] removing ipynb_checkpoints --- .../.ipynb_checkpoints/imdb-checkpoint.ipynb | 271 ------------------ 1 file changed, 271 deletions(-) delete mode 100644 docs/source/.ipynb_checkpoints/imdb-checkpoint.ipynb diff --git a/docs/source/.ipynb_checkpoints/imdb-checkpoint.ipynb b/docs/source/.ipynb_checkpoints/imdb-checkpoint.ipynb deleted file mode 100644 index c78ca44..0000000 --- a/docs/source/.ipynb_checkpoints/imdb-checkpoint.ipynb +++ /dev/null @@ -1,271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3eff5ba8", - "metadata": {}, - "source": [ - "# Creating a clean IMDB dataset\n", - "\n", - "Running this example requires `keras`. Use `pip install keras` to install if necessary." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "53925437", - "metadata": {}, - "outputs": [], - "source": [ - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a855c7c0", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy.sparse import coo_matrix, save_npz\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fe16fa84", - "metadata": {}, - "outputs": [], - "source": [ - "from keras.datasets import imdb\n", - "from tensorflow.keras.preprocessing.sequence import pad_sequences\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0369a36a", - "metadata": {}, - "outputs": [], - "source": [ - "# the 3 is for three terms: \n", - "num_words = 10000+3\n", - "((S_train, Y_train), \n", - " (S_test, Y_test)) = imdb.load_data(num_words=num_words)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9e84d7e3", - "metadata": {}, - "outputs": [], - "source": [ - "Y_train = Y_train.astype(np.float32)\n", - "Y_test = Y_test.astype(np.float32)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1a737737", - "metadata": {}, - "outputs": [], - "source": [ - "def one_hot(sequences, ncol):\n", - " idx, vals = [], []\n", - " for i, s in enumerate(sequences):\n", - " idx.extend({(i,v):1 for v in s}.keys())\n", - " idx = np.array(idx).T\n", - " vals = np.ones(idx.shape[1], dtype=np.float32)\n", - " tens = torch.sparse_coo_tensor(indices=idx,\n", - " values=vals,\n", - " size=(len(sequences), ncol))\n", - " return tens.coalesce()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f08ad327", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, L_train = one_hot(S_train, num_words), Y_train\n", - "X_test = one_hot(S_test, num_words)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "98481bbb", - "metadata": {}, - "outputs": [], - "source": [ - "def convert_sparse_tensor(X):\n", - " idx = np.asarray(X.indices())\n", - " vals = np.asarray(X.values())\n", - " return coo_matrix((vals,\n", - " (idx[0],\n", - " idx[1])),\n", - " shape=X.shape).tocsr()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5a17bd62", - "metadata": {}, - "outputs": [], - "source": [ - "X_train_s = convert_sparse_tensor(X_train)\n", - "X_test_s = convert_sparse_tensor(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ca57aea4", - "metadata": {}, - "outputs": [], - "source": [ - "X_train_d = torch.tensor(X_train_s.todense())\n", - "X_test_d = torch.tensor(X_test_s.todense())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3d017780", - "metadata": {}, - "outputs": [], - "source": [ - "torch.save(X_train_d, 'IMDB_X_train.tensor')\n", - "torch.save(X_test_d, 'IMDB_X_test.tensor')" - ] - }, - { - "cell_type": "markdown", - "id": "f9bb0163", - "metadata": {}, - "source": [ - "save the sparse matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "23afd3e5", - "metadata": {}, - "outputs": [], - "source": [ - "save_npz('IMDB_X_test.npz', X_test_s)\n", - "save_npz('IMDB_X_train.npz', X_train_s)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d33568d1", - "metadata": {}, - "outputs": [], - "source": [ - "np.save('IMDB_Y_test.npy', Y_test)\n", - "np.save('IMDB_Y_train.npy', L_train)" - ] - }, - { - "cell_type": "markdown", - "id": "f9110984", - "metadata": {}, - "source": [ - "save and pickle the word index" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ff44a0b4", - "metadata": {}, - "outputs": [], - "source": [ - "word_index = imdb.get_word_index()\n", - "lookup = {(i+3):w for w, i in word_index.items()}\n", - "lookup[0] = \"\"\n", - "lookup[1] = \"\"\n", - "lookup[2] = \"\"\n", - "lookup[4] = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1486c640", - "metadata": {}, - "outputs": [], - "source": [ - "pickle.dump(lookup, open('IMDB_word_index.pkl', 'bw'))" - ] - }, - { - "cell_type": "markdown", - "id": "57e606c5", - "metadata": {}, - "source": [ - "create the padded representations" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "3ab7a4ac", - "metadata": {}, - "outputs": [], - "source": [ - "(S_train,\n", - " S_test) = [torch.tensor(pad_sequences(S, maxlen=500, value=0))\n", - " for S in [S_train,\n", - " S_test]]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "55cb2d49", - "metadata": {}, - "outputs": [], - "source": [ - "torch.save(S_train, 'IMDB_S_train.tensor')\n", - "torch.save(S_test, 'IMDB_S_test.tensor')" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "py:percent,ipynb,md:myst", - "main_language": "python" - }, - "kernelspec": { - "display_name": "islp_test", - "language": "python", - "name": "islp_test" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From ed6abd037220a479ee21202aab2f539610ee2301 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 17:50:56 -0700 Subject: [PATCH 034/195] minimum sklearn version --- ISLP/info.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ISLP/info.py b/ISLP/info.py index 3a1fecd..56bf839 100644 --- a/ISLP/info.py +++ b/ISLP/info.py @@ -44,7 +44,7 @@ NUMPY_MIN_VERSION='1.7.1' SCIPY_MIN_VERSION = '0.9' PANDAS_MIN_VERSION = "0.20" -SKLEARN_MIN_VERSION = '1.0' +SKLEARN_MIN_VERSION = '1.2' STATSMODELS_MIN_VERSION = '0.13' MATPLOTLIB_MIN_VERSION = '3.3.3' From db6158f3cf4aca4982454c5b73186536633465ae Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Apr 2023 18:14:36 -0700 Subject: [PATCH 035/195] putting a max pandas version due to bug in lifelines --- ISLP/info.py | 2 ++ requirements.txt | 1 + 2 files changed, 3 insertions(+) diff --git a/ISLP/info.py b/ISLP/info.py index 56bf839..9dba591 100644 --- a/ISLP/info.py +++ b/ISLP/info.py @@ -44,6 +44,7 @@ NUMPY_MIN_VERSION='1.7.1' SCIPY_MIN_VERSION = '0.9' PANDAS_MIN_VERSION = "0.20" +PANDAS_MAX_VERSION = "1.9" SKLEARN_MIN_VERSION = '1.2' STATSMODELS_MIN_VERSION = '0.13' MATPLOTLIB_MIN_VERSION = '3.3.3' @@ -71,6 +72,7 @@ "scipy (>=%s)" % SCIPY_MIN_VERSION, "statsmodels (>=%s)" % STATSMODELS_MIN_VERSION, "pandas (>=%s)" % PANDAS_MIN_VERSION, + "pandas (<=%s)" % PANDAS_MAX_VERSION, "sklearn (>=%s)" % SKLEARN_MIN_VERSION, "lifelines", "joblib", diff --git a/requirements.txt b/requirements.txt index 797c729..1503cf7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ numpy>=1.7.1 scipy>=0.9 pandas>=0.20 +pandas<=1.9 lxml # pandas needs this for html scikit-learn>=1.2 joblib From d477e45fa6ac20bd8346317190147fa3636c2b7c Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 2 May 2023 11:46:45 -0700 Subject: [PATCH 036/195] updated install instructions --- README.md | 24 ++++++++---------------- 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 313a4e5..75c3aae 100644 --- a/README.md +++ b/README.md @@ -19,34 +19,26 @@ To run python code in this environment, you must activate it: conda activate islp ``` -### Mac OS X +On windows, create a `Python` environment called `islp` in the Anaconda app. This can be done by selecting `Environments` on the left hand side of the app's screen. After creating the environment, open a terminal within that environment by clicking on the Play button. -Running in the desired environment, we use `pip` to install the `ISLP` package: - -```{python} -pip install ISLP -``` - -### Windows -See the [python packaging instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within -Jupyter or IPython. +## Installing `ISLP` -Alternatively, within a python shell in the environment you want to install `ISLP`, the following commands should install `ISLP`: +Running in the desired environment, we use `pip` to install the `ISLP` package: ```{python} -import os, sys -cmd = f'{sys.executable} -m pip install ISLP' -os.system(cmd) +pip install ISLP ``` ### Torch requirements The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` +can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` within a terminal. As above, ensure +that you have activated that conda environment (Mac OS or Linux) or +started a terminal within that environment from the Anaconda app (Windows). ```{python} -!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt ``` ## Documentation From b36eef70b112277af90689c248403038c6bafa9b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 2 May 2023 11:48:45 -0700 Subject: [PATCH 037/195] update README --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 75c3aae..816267b 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ for ISLP. ## Install instructions +### Mac OS X / Linux + We generally recommend creating a [conda](https://anaconda.org) environment to isolate any code from other dependencies. The `ISLP` package does not have unusual dependencies, but this is still good practice. To create a conda environment in a Mac OS X or Linux environment run: @@ -19,7 +21,9 @@ To run python code in this environment, you must activate it: conda activate islp ``` -On windows, create a `Python` environment called `islp` in the Anaconda app. This can be done by selecting `Environments` on the left hand side of the app's screen. After creating the environment, open a terminal within that environment by clicking on the Play button. +### Windows + +On windows, create a `Python` environment called `islp` in the Anaconda app. This can be done by selecting `Environments` on the left hand side of the app's screen. After creating the environment, open a terminal within that environment by clicking on the "Play" button. ## Installing `ISLP` From 13b8191ef93649b6aa7805f75b7b9970529756ce Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 2 May 2023 11:52:06 -0700 Subject: [PATCH 038/195] more updates to windows --- README.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 816267b..8dd8bcf 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ On windows, create a `Python` environment called `islp` in the Anaconda app. Thi ## Installing `ISLP` -Running in the desired environment, we use `pip` to install the `ISLP` package: +Having completed the steps above, we use `pip` to install the `ISLP` package: ```{python} pip install ISLP @@ -45,6 +45,22 @@ started a terminal within that environment from the Anaconda app (Windows). pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt ``` +## Jupyter + +### Mac OS X + +If JupyterLab is not already installed, run the following after having activated your `islp` environment: + +```{python} +pip install jupyterlab +``` + +### Windows + +Either use the same `pip` command above or install JupyterLab from the `Home` tab. Ensure that the environment +is your `islp` environment. This information appears near the top left in the Anaconda `Home` page. + + ## Documentation See the [read the docs page](https://islp.readthedocs.io/en/latest/models.html) for the latest documentation. From 62e336605df95bb63d1f1c3d8948b2511f5b8b78 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 2 May 2023 11:52:45 -0700 Subject: [PATCH 039/195] updating doc link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8dd8bcf..a5aac69 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ is your `islp` environment. This information appears near the top left in the An ## Documentation -See the [read the docs page](https://islp.readthedocs.io/en/latest/models.html) for the latest documentation. +See the [read the docs page](https://islp.readthedocs.io/en/latest) for the latest documentation. From b74d5b872c6f70d2b3e1af95dd37c12bd08d1fbc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 2 May 2023 11:56:47 -0700 Subject: [PATCH 040/195] updating installation docs --- docs/source/installation.myst | 53 ++++++++++++++++++++++------------- 1 file changed, 34 insertions(+), 19 deletions(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index af549a9..5fa2f70 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -5,17 +5,22 @@ kernelspec: display_name: python3 --- -# Installation instructions + +# Install instructions We generally recommend creating a [conda](https://anaconda.org) environment to isolate any code from other dependencies. The `ISLP` package does not have unusual dependencies, but this is still -good practice. To create a conda environment in a Mac OS X or Linux environment run: +good practice. + +## Mac OS X / Linux + +To create a conda environment in a Mac OS X or Linux environment run: ```{code-cell} ipython3 --- tags: [skip-execution] --- -!conda create --name islp +conda create --name islp ``` To run python code in this environment, you must activate it: @@ -24,45 +29,55 @@ To run python code in this environment, you must activate it: --- tags: [skip-execution] --- -!conda activate islp +conda activate islp ``` -## Mac OS X +## Windows + +On windows, create a `Python` environment called `islp` in the Anaconda app. This can be done by selecting `Environments` on the left hand side of the app's screen. After creating the environment, open a terminal within that environment by clicking on the "Play" button. -Running in the desired environment, we use `pip` to install the `ISLP` package: + +# Installing `ISLP` + +Having completed the steps above, we use `pip` to install the `ISLP` package: ```{code-cell} ipython3 --- tags: [skip-execution] --- -!pip install ISLP +pip install ISLP ``` -## Windows - -See the [python packaging instructions](https://packaging.python.org/en/latest/tutorials/installing-packages/#ensure-you-can-run-pip-from-the-command-line) for a simple way to run `pip` within -Jupyter or IPython. +## Torch requirements -Alternatively, within a python shell in the environment you want to install `ISLP`, the following commands should install `ISLP`: +The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements +can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them directly using `pip` within a terminal. As above, ensure +that you have activated that conda environment (Mac OS or Linux) or +started a terminal within that environment from the Anaconda app (Windows). ```{code-cell} ipython3 --- tags: [skip-execution] --- -import os, sys -cmd = f'{sys.executable} -m pip install ISLP' -os.system(cmd) +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt ``` -## Torch requirements +## Jupyter -The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them directly using `pip` +### Mac OS X + +If JupyterLab is not already installed, run the following after having activated your `islp` environment: ```{code-cell} ipython3 --- tags: [skip-execution] --- -!pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt +pip install jupyterlab ``` +### Windows + +Either use the same `pip` command above or install JupyterLab from the `Home` tab. Ensure that the environment +is your `islp` environment. This information appears near the top left in the Anaconda `Home` page. + + From 506b695016df7c8385ac705067251005bda0ac5b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 18 Jul 2023 11:09:08 -0700 Subject: [PATCH 041/195] BF: rnp.float -> float --- ISLP/bart/bart.py | 4 ++-- ISLP/bart/tree.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ISLP/bart/bart.py b/ISLP/bart/bart.py index 2c33aba..27a7f01 100644 --- a/ISLP/bart/bart.py +++ b/ISLP/bart/bart.py @@ -101,7 +101,7 @@ def predict(self, check_is_fitted(self) nsample = len(self.trees_sample_) - output = np.zeros(X.shape[0], np.float) + output = np.zeros(X.shape[0], float) for trees in self.trees_sample_: for tree in trees: @@ -118,7 +118,7 @@ def staged_predict(self, trees_sample_ = self.trees_sample_[start_idx:] nsample = len(trees_sample_) - output = np.zeros((nsample, X.shape[0]), np.float) + output = np.zeros((nsample, X.shape[0]), float) for nstep, trees in enumerate(trees_sample_): for tree in trees: diff --git a/ISLP/bart/tree.py b/ISLP/bart/tree.py index 8726929..49b4789 100644 --- a/ISLP/bart/tree.py +++ b/ISLP/bart/tree.py @@ -96,7 +96,7 @@ def predict_output(self): current_node = self.get_node(node_index) output[current_node.idx_data_points] = current_node.value - return output.astype(np.float) + return output.astype(float) def predict_out_of_sample(self, X): """ From b55ddae9252d555bfb61f320a702987d62a4d4ab Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 16:09:46 -0700 Subject: [PATCH 042/195] adding notebooks to package docs --- .readthedocs.yaml | 2 +- docs/notebooks/requirements.txt | 160 + docs/source/conf.py | 2 + docs/source/index.rst | 1 + docs/source/installation.myst | 34 +- docs/source/labs.rst | 36 + docs/source/labs/Ch10-deeplearning-lab.ipynb | 3260 +++++++++++++++++ docs/source/labs/Ch11-surv-lab.ipynb | 959 +++++ docs/source/labs/Ch12-unsup-lab.ipynb | 1634 +++++++++ docs/source/labs/Ch13-multiple-lab.ipynb | 961 +++++ docs/source/labs/Ch2-statlearn-lab.ipynb | 3036 +++++++++++++++ docs/source/labs/Ch3-linreg-lab.ipynb | 1123 ++++++ docs/source/labs/Ch4-classification-lab.ipynb | 2181 +++++++++++ docs/source/labs/Ch5-resample-lab.ipynb | 901 +++++ docs/source/labs/Ch6-varselect-lab.ipynb | 1645 +++++++++ docs/source/labs/Ch7-nonlin-lab.ipynb | 1510 ++++++++ docs/source/labs/Ch8-baggboost-lab.ipynb | 1052 ++++++ docs/source/labs/Ch9-svm-lab.ipynb | 977 +++++ torch_requirements.txt | 11 +- 19 files changed, 19474 insertions(+), 11 deletions(-) create mode 100644 docs/notebooks/requirements.txt create mode 100644 docs/source/labs.rst create mode 100644 docs/source/labs/Ch10-deeplearning-lab.ipynb create mode 100644 docs/source/labs/Ch11-surv-lab.ipynb create mode 100644 docs/source/labs/Ch12-unsup-lab.ipynb create mode 100644 docs/source/labs/Ch13-multiple-lab.ipynb create mode 100644 docs/source/labs/Ch2-statlearn-lab.ipynb create mode 100644 docs/source/labs/Ch3-linreg-lab.ipynb create mode 100644 docs/source/labs/Ch4-classification-lab.ipynb create mode 100644 docs/source/labs/Ch5-resample-lab.ipynb create mode 100644 docs/source/labs/Ch6-varselect-lab.ipynb create mode 100644 docs/source/labs/Ch7-nonlin-lab.ipynb create mode 100644 docs/source/labs/Ch8-baggboost-lab.ipynb create mode 100644 docs/source/labs/Ch9-svm-lab.ipynb diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 141d660..3655b23 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -24,7 +24,7 @@ sphinx: # Optionally declare the Python requirements required to build your docs python: install: - - requirements: requirements.txt + - requirements: frozen_requirements.txt - requirements: docs/requirements.txt - requirements: torch_requirements.txt - requirements: keras_requirements.txt diff --git a/docs/notebooks/requirements.txt b/docs/notebooks/requirements.txt new file mode 100644 index 0000000..8da6115 --- /dev/null +++ b/docs/notebooks/requirements.txt @@ -0,0 +1,160 @@ +anyio==3.6.2 +appnope==0.1.3 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 +arrow==1.2.3 +astor==0.8.1 +asttokens==2.2.1 +attrs==22.2.0 +autograd==1.5 +autograd-gamma==0.5.0 +backcall==0.2.0 +beautifulsoup4==4.12.1 +bleach==6.0.0 +build==0.10.0 +CacheControl==0.12.11 +certifi==2023.5.7 +cffi==1.15.1 +charset-normalizer==3.1.0 +cleo==2.0.1 +comm==0.1.3 +contourpy==1.0.7 +crashtest==0.4.1 +cycler==0.11.0 +debugpy==1.6.7 +decorator==5.1.1 +defusedxml==0.7.1 +distlib==0.3.6 +dulwich==0.21.5 +executing==1.2.0 +fastjsonschema==2.16.3 +filelock==3.11.0 +fonttools==4.39.4 +formulaic==0.6.1 +fqdn==1.5.1 +future==0.18.3 +html5lib==1.1 +idna==3.4 +importlib-metadata==6.1.0 +importlib-resources==5.12.0 +installer==0.7.0 +interface-meta==1.3.0 +ipykernel==6.22.0 +ipython==8.12.0 +ipython-genutils==0.2.0 +ipywidgets==8.0.6 +ISLP==0.3.16 +isoduration==20.11.0 +jaraco.classes==3.2.3 +jedi==0.18.2 +Jinja2==3.1.2 +joblib==1.2.0 +jsonpointer==2.3 +jsonschema==4.17.3 +jupyter==1.0.0 +jupyter-console==6.6.3 +jupyter-events==0.6.3 +jupyter_client==8.1.0 +jupyter_core==5.3.0 +jupyter_server==2.5.0 +jupyter_server_terminals==0.4.4 +jupyterlab-pygments==0.2.2 +jupyterlab-widgets==3.0.7 +keyring==23.13.1 +kiwisolver==1.4.4 +lab==7.3 +lifelines==0.27.7 +lockfile==0.12.2 +lxml==4.9.2 +markdown-it-py==2.2.0 +MarkupSafe==2.1.2 +matplotlib==3.7.1 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +mistune==2.0.5 +more-itertools==9.1.0 +msgpack==1.0.5 +nbclassic==0.5.5 +nbclient==0.7.3 +nbconvert==7.3.0 +nbformat==5.8.0 +nest-asyncio==1.5.6 +notebook==6.5.4 +notebook_shim==0.2.2 +numpy==1.24.2 +packaging==23.0 +pandas==2.0.1 +pandocfilters==1.5.0 +parso==0.8.3 +patsy==0.5.3 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==9.5.0 +pkginfo==1.9.6 +platformdirs==3.2.0 +poetry==1.5.0 +poetry-core==1.6.0 +poetry-plugin-export==1.3.1 +progressbar2==4.2.0 +prometheus-client==0.16.0 +prompt-toolkit==3.0.38 +psutil==5.9.4 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pycparser==2.21 +pygam==0.9.0 +Pygments==2.14.0 +pyparsing==3.0.9 +pyproject_hooks==1.0.0 +pyrsistent==0.19.3 +python-dateutil==2.8.2 +python-json-logger==2.0.7 +python-utils==3.5.2 +pytz==2023.3 +pytz-deprecation-shim==0.1.0.post0 +PyYAML==6.0 +pyzmq==25.0.2 +qtconsole==5.4.3 +QtPy==2.3.1 +rapidfuzz==2.15.1 +requests==2.30.0 +requests-toolbelt==1.0.0 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +scikit-learn==1.2.2 +scipy==1.10.1 +Send2Trash==1.8.0 +shellingham==1.5.0.post1 +simplejson==3.19.1 +six==1.16.0 +sniffio==1.3.0 +soupsieve==2.4 +stack-data==0.6.2 +statsmodels==0.14.0 +terminado==0.17.1 +threadpoolctl==3.1.0 +tinycss2==1.2.1 +tomli==2.0.1 +tomlkit==0.11.8 +tornado==6.2 +traitlets==5.9.0 +trove-classifiers==2023.5.2 +txt2tags==3.8 +typing_extensions==4.5.0 +tzdata==2023.3 +uri-template==1.2.0 +urllib3==1.26.15 +virtualenv==20.23.0 +wcwidth==0.2.6 +webcolors==1.13 +webencodings==0.5.1 +websocket-client==1.5.1 +widgetsnbextension==4.0.7 +wrapt==1.15.0 +xattr==0.10.1 +zipp==3.15.0 +torch==2.0.0 +torchvision==0.15.1 +pytorch_lightning==2.0.2 +torchinfo==1.7.2 +torchmetrics==0.11.4 diff --git a/docs/source/conf.py b/docs/source/conf.py index b009ae7..654522f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -28,6 +28,8 @@ graphviz_dot = '/opt/homebrew/bin/dot' numpydoc_class_members_toctree = False nb_execution_mode = "cache" +nb_execution_timeout = 60*3 #*100 + #nb_kernel_rgx_aliases = {'python3': "islp_test"} intersphinx_mapping = { diff --git a/docs/source/index.rst b/docs/source/index.rst index f4c260a..44b40fc 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -15,5 +15,6 @@ Contents transforms models helpers + labs imdb diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 5fa2f70..d1c30aa 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -48,12 +48,38 @@ tags: [skip-execution] pip install ISLP ``` +## Frozen environment + +```{attention} + +Python packages change frequently. The labs here are built +with specific versions of the various packages. +``` + +To ensure you have the same package versions as those built here, run: + +```{code-cell} ipython3 +--- +tags: [skip-execution] +--- +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/frozen_requirements.txt +``` + ## Torch requirements -The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them directly using `pip` within a terminal. As above, ensure -that you have activated that conda environment (Mac OS or Linux) or -started a terminal within that environment from the Anaconda app (Windows). +The `ISLP` labs use `torch` and various related packages for the lab +on deep learning. The requirements can be found +[here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them +directly using `pip` within a terminal. As above, ensure that you have +activated that conda environment (Mac OS or Linux) or started a +terminal within that environment from the Anaconda app (Windows). + +```{attention} +Because +`torch` and related libraries change frequently, you will note that we +have pinned the versions at specific versions that were used to make +current verisons of the labs. +``` ```{code-cell} ipython3 --- diff --git a/docs/source/labs.rst b/docs/source/labs.rst new file mode 100644 index 0000000..074a07e --- /dev/null +++ b/docs/source/labs.rst @@ -0,0 +1,36 @@ +Labs +==== + +The current version of the labs for `ISLP` are included here. + + +Package versions +---------------- + +.. attention:: + + Python packages change frequently. The labs here are built + with specific versions of the various packages. + + +To ensure you have the same package versions as those built here, run: + + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/frozen_requirements.txt + +.. toctree:: + :maxdepth: 1 + :numbered: + + labs/Ch2-statlearn-lab + labs/Ch3-linreg-lab + labs/Ch4-classification-lab + labs/Ch5-resample-lab + labs/Ch6-varselect-lab + labs/Ch7-nonlin-lab + labs/Ch8-baggboost-lab + labs/Ch9-svm-lab + labs/Ch11-surv-lab + labs/Ch12-unsup-lab + labs/Ch13-multiple-lab + + diff --git a/docs/source/labs/Ch10-deeplearning-lab.ipynb b/docs/source/labs/Ch10-deeplearning-lab.ipynb new file mode 100644 index 0000000..6693e01 --- /dev/null +++ b/docs/source/labs/Ch10-deeplearning-lab.ipynb @@ -0,0 +1,3260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8140eb0d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "1334dfc7", + "metadata": {}, + "source": [ + "# Deep Learning\n", + "In this section we demonstrate how to fit the examples discussed\n", + "in the text. We use the `Python` `torch` package, along with the\n", + "`pytorch_lightning` package which provides utilities to simplify\n", + "fitting and evaluating models. This code can be impressively fast\n", + "with certain special processors, such as Apple’s new M1 chip. The package is well-structured, flexible, and will feel comfortable\n", + "to `Python` users. A good companion is the site\n", + "[pytorch.org/tutorials](https://pytorch.org/tutorials/beginner/basics/intro.html).\n", + "Much of our code is adapted from there, as well as the `pytorch_lightning` documentation. {The precise URLs at the time of writing are and .}\n", + "\n", + "We start with several standard imports that we have seen in other contexts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aeb913e2", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import numpy as np, pandas as pd\n", + "from matplotlib.pyplot import subplots\n", + "from sklearn.linear_model import \\\n", + " (LinearRegression,\n", + " LogisticRegression,\n", + " Lasso)\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.pipeline import Pipeline\n", + "from ISLP import load_data\n", + "from ISLP.models import ModelSpec as MS\n", + "from sklearn.model_selection import \\\n", + " (train_test_split,\n", + " GridSearchCV)" + ] + }, + { + "cell_type": "markdown", + "id": "36a4ac8d", + "metadata": {}, + "source": [ + "### Torch-Specific Imports\n", + "There are a number of imports for `torch`. (These are not\n", + "included with `ISLP`, so must be installed separately.)\n", + "First we import the main library\n", + "and essential tools used to specify sequentially-structured networks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a3e91e7", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch.optim import RMSprop\n", + "from torch.utils.data import TensorDataset\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e89c676", + "metadata": {}, + "source": [ + "There are several other helper packages for `torch`. For instance,\n", + "the `torchmetrics` package has utilities to compute\n", + "various metrics to evaluate performance when fitting\n", + "a model. The `torchinfo` package provides a useful\n", + "summary of the layers of a model. We use the `read_image()`\n", + "function when loading test images in Section 10.9.4." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f4fdb8c", + "metadata": {}, + "outputs": [], + "source": [ + "from torchmetrics import (MeanAbsoluteError,\n", + " R2Score)\n", + "from torchinfo import summary\n", + "from torchvision.io import read_image\n" + ] + }, + { + "cell_type": "markdown", + "id": "b7b12e5c", + "metadata": {}, + "source": [ + "The package `pytorch_lightning` is a somewhat higher-level\n", + "interface to `torch` that simplifies the specification and\n", + "fitting of\n", + "models by reducing the amount of boilerplate code needed\n", + "(compared to using `torch` alone)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71cc6f03", + "metadata": {}, + "outputs": [], + "source": [ + "from pytorch_lightning import Trainer\n", + "from pytorch_lightning.loggers import CSVLogger\n" + ] + }, + { + "cell_type": "markdown", + "id": "8394fc0a", + "metadata": {}, + "source": [ + "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n", + "where possible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd1290f1", + "metadata": {}, + "outputs": [], + "source": [ + "from pytorch_lightning.utilities.seed import seed_everything\n", + "seed_everything(0, workers=True)\n", + "torch.use_deterministic_algorithms(True, warn_only=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f29a6c10", + "metadata": {}, + "source": [ + "We will use several datasets shipped with `torchvision` for our\n", + "examples: a pretrained network for image classification,\n", + "as well as some transforms used for preprocessing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c85308d1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "from torchvision.datasets import MNIST, CIFAR100\n", + "from torchvision.models import (resnet50,\n", + " ResNet50_Weights)\n", + "from torchvision.transforms import (Resize,\n", + " Normalize,\n", + " CenterCrop,\n", + " ToTensor)" + ] + }, + { + "cell_type": "markdown", + "id": "6ca24f46", + "metadata": {}, + "source": [ + "We have provided a few utilities in `ISLP` specifically for this lab.\n", + "The `SimpleDataModule` and `SimpleModule` are simple\n", + "versions of objects used in `pytorch_lightning`, the\n", + "high-level module for fitting `torch` models. Although more advanced\n", + "uses such as computing on graphical processing units (GPUs) and parallel data processing\n", + "are possible in this module, we will not be focusing much on these\n", + "in this lab. The `ErrorTracker` handles\n", + "collections of targets and predictions over each mini-batch\n", + "in the validation or test stage, allowing computation\n", + "of the metric over the entire validation or test data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4de9f03c", + "metadata": {}, + "outputs": [], + "source": [ + "from ISLP.torch import (SimpleDataModule,\n", + " SimpleModule,\n", + " ErrorTracker,\n", + " rec_num_workers)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d81a775a", + "metadata": {}, + "source": [ + "In addition we have included some helper\n", + "functions to load the\n", + "`IMDb` database, as well as a lookup that maps integers\n", + "to particular keys in the database. We’ve included\n", + "a slightly modified copy of the preprocessed\n", + "`IMDb` data from `keras`, a separate package\n", + "for fitting deep learning models. This saves us significant\n", + "preprocessing and allows us to focus on specifying and fitting\n", + "the models themselves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ef95fe3", + "metadata": {}, + "outputs": [], + "source": [ + "from ISLP.torch.imdb import (load_lookup,\n", + " load_tensor,\n", + " load_sparse,\n", + " load_sequential)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8d760ad", + "metadata": {}, + "source": [ + "Finally, we introduce some utility imports not directly related to\n", + "`torch`.\n", + "The `glob()` function from the `glob` module is used\n", + "to find all files matching wildcard characters, which we will use\n", + "in our example applying the `ResNet50` model\n", + "to some of our own images.\n", + "The `json` module will be used to load\n", + "a JSON file for looking up classes to identify the labels of the\n", + "pictures in the `ResNet50` example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "493821a9", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from glob import glob\n", + "import json\n" + ] + }, + { + "cell_type": "markdown", + "id": "c415e4e5", + "metadata": {}, + "source": [ + "## Single Layer Network on Hitters Data\n", + "We start by fitting the models in Section 10.6 on the `Hitters` data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a427e224", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Hitters = load_data('Hitters').dropna()\n", + "n = Hitters.shape[0]\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1ca2542", + "metadata": {}, + "source": [ + " We will fit two linear models (least squares and lasso) and compare their performance\n", + "to that of a neural network. For this comparison we will use mean absolute error on a validation dataset.\n", + "\\begin{equation*}\n", + "\\begin{split}\n", + "\\mbox{MAE}(y,\\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n |y_i-\\hat{y}_i|.\n", + "\\end{split}\n", + "\\end{equation*}\n", + "We set up the model matrix and the response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feac4b15", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "model = MS(Hitters.columns.drop('Salary'), intercept=False)\n", + "X = model.fit_transform(Hitters).to_numpy()\n", + "Y = Hitters['Salary'].to_numpy()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ad0d90b5", + "metadata": {}, + "source": [ + "The `to_numpy()` method above converts `pandas`\n", + "data frames or series to `numpy` arrays.\n", + "We do this because we will need to use `sklearn` to fit the lasso model,\n", + "and it requires this conversion. \n", + "We also use a linear regression method from `sklearn`, rather than the method\n", + "in Chapter~3 from `statsmodels`, to facilitate the comparisons." + ] + }, + { + "cell_type": "markdown", + "id": "35acacc2", + "metadata": {}, + "source": [ + "We now split the data into test and training, fixing the random\n", + "state used by `sklearn` to do the split." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a75a640", + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, \n", + " X_test,\n", + " Y_train,\n", + " Y_test) = train_test_split(X,\n", + " Y,\n", + " test_size=1/3,\n", + " random_state=1)" + ] + }, + { + "cell_type": "markdown", + "id": "edc24bfb", + "metadata": {}, + "source": [ + "### Linear Models\n", + "We fit the linear model and evaluate the test error directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32d8de64", + "metadata": {}, + "outputs": [], + "source": [ + "hit_lm = LinearRegression().fit(X_train, Y_train)\n", + "Yhat_test = hit_lm.predict(X_test)\n", + "np.abs(Yhat_test - Y_test).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "d60340ef", + "metadata": {}, + "source": [ + "Next we fit the lasso using `sklearn`. We are using\n", + "mean absolute error to select and evaluate a model, rather than mean squared error.\n", + "The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work, we create a cross-validation grid and perform the cross-validation directly. \n", + "\n", + "We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,\n", + "and then fit the lasso without further normalization. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46786db6", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler(with_mean=True, with_std=True)\n", + "lasso = Lasso(warm_start=True, max_iter=30000)\n", + "standard_lasso = Pipeline(steps=[('scaler', scaler),\n", + " ('lasso', lasso)])" + ] + }, + { + "cell_type": "markdown", + "id": "e7ff854a", + "metadata": {}, + "source": [ + "We need to create a grid of values for $\\lambda$. As is common practice, \n", + "we choose a grid of 100 values of $\\lambda$, uniform on the log scale from `lam_max` down to `0.01*lam_max`. Here `lam_max` is the smallest value of\n", + "$\\lambda$ with an all-zero solution. This value equals the largest absolute inner-product between any predictor and the (centered) response. {The derivation of this result is beyond the scope of this book.}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b55b38b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_s = scaler.fit_transform(X_train)\n", + "n = X_s.shape[0]\n", + "lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n\n", + "param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), 100))\n", + " * lam_max}" + ] + }, + { + "cell_type": "markdown", + "id": "977dea00", + "metadata": {}, + "source": [ + "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n", + "We now perform cross-validation using this sequence of $\\lambda$ values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc44c559", + "metadata": {}, + "outputs": [], + "source": [ + "cv = KFold(10,\n", + " shuffle=True,\n", + " random_state=1)\n", + "grid = GridSearchCV(lasso,\n", + " param_grid,\n", + " cv=cv,\n", + " scoring='neg_mean_absolute_error')\n", + "grid.fit(X_train, Y_train);" + ] + }, + { + "cell_type": "markdown", + "id": "fca88ebb", + "metadata": {}, + "source": [ + "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n", + "performance on `X_test` and `Y_test`, which were not used in\n", + "cross-validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bd1fd13", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "trained_lasso = grid.best_estimator_\n", + "Yhat_test = trained_lasso.predict(X_test)\n", + "np.fabs(Yhat_test - Y_test).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "f19cd1f2", + "metadata": {}, + "source": [ + "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n", + "\n", + "### Specifying a Network: Classes and Inheritance\n", + "To fit the neural network, we first set up a model structure\n", + "that describes the network.\n", + "Doing so requires us to define new classes specific to the model we wish to fit.\n", + "Typically this is done in `pytorch` by sub-classing a generic\n", + "representation of a network, which is the approach we take here.\n", + "Although this example is simple, we will go through the steps in some detail, since it will serve us well\n", + "for the more complex examples to follow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b2ccc5a", + "metadata": {}, + "outputs": [], + "source": [ + "class HittersModel(nn.Module):\n", + "\n", + " def __init__(self, input_size):\n", + " super(HittersModel, self).__init__()\n", + " self.flatten = nn.Flatten()\n", + " self.sequential = nn.Sequential(\n", + " nn.Linear(input_size, 50),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.4),\n", + " nn.Linear(50, 1))\n", + "\n", + " def forward(self, x):\n", + " x = self.flatten(x)\n", + " return torch.flatten(self.sequential(x))\n" + ] + }, + { + "cell_type": "markdown", + "id": "5da217b2", + "metadata": {}, + "source": [ + "The `class` statement identifies the code chunk as a\n", + "declaration for a class `HittersModel`\n", + "that inherits from the base class `nn.Module`. This base\n", + "class is ubiquitous in `torch` and represents the\n", + "mappings in the neural networks.\n", + "\n", + "Indented beneath the `class` statement are the methods of this class:\n", + "in this case `__init__` and `forward`. The `__init__` method is\n", + "called when an instance of the class is created as in the cell\n", + "below. In the methods, `self` always refers to an instance of the\n", + "class. In the `__init__` method, we have attached two objects to\n", + "`self` as attributes: `flatten` and `sequential`. These are used in\n", + "the `forward` method to describe the map that this module implements.\n", + "\n", + "There is one additional line in the `__init__` method, which\n", + "is a call to\n", + "`super()`. This function allows subclasses (i.e. `HittersModel`)\n", + "to access methods of the class they inherit from. For example,\n", + "the class `nn.Module` has its own `__init__` method, which is different from\n", + "the `HittersModel.__init__()` method we’ve written above.\n", + "Using `super()` allows us to call the method of the base class. For\n", + "`torch` models, we will always be making this `super()` call as it is necessary\n", + "for the model to be properly interpreted by `torch`.\n", + "\n", + "The object `nn.Module` has more methods than simply `__init__` and `forward`. These\n", + "methods are directly accessible to `HittersModel` instances because of this inheritance.\n", + "One such method we will see shortly is the `eval()` method, used\n", + "to disable dropout for when we want to evaluate the model on test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108ad1f1", + "metadata": {}, + "outputs": [], + "source": [ + "hit_model = HittersModel(X.shape[1])\n" + ] + }, + { + "cell_type": "markdown", + "id": "46d750dd", + "metadata": {}, + "source": [ + "The object `self.sequential` is a composition of four maps. The\n", + "first maps the 19 features of `Hitters` to 50 dimensions, introducing $50\\times 19+50$ parameters\n", + "for the weights and *intercept* of the map (often called the *bias*). This layer\n", + "is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a\n", + "linear map down to 1 dimension, again with a bias. The total number of\n", + "trainable parameters is therefore $50\\times 19+50+50+1=1051$.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "e0c250c5", + "metadata": {}, + "source": [ + "The package `torchinfo` provides a `summary()` function that neatly summarizes\n", + "this information. We specify the size of the input and see the size\n", + "of each tensor as it passes through layers of the network. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f20059e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "summary(hit_model, \n", + " input_size=X_train.shape,\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "05a0eb81", + "metadata": {}, + "source": [ + "We have truncated the end of the output slightly, here and in subsequent uses.\n", + "\n", + "We now need to transform our training data into a form accessible to `torch`.\n", + "The basic\n", + "datatype in `torch` is a `tensor`, which is very similar\n", + "to an `ndarray` from early chapters.\n", + "We also note here that `torch` typically\n", + "works with 32-bit (*single precision*)\n", + "rather than 64-bit (*double precision*) floating point numbers.\n", + "We therefore convert our data to `np.float32` before\n", + "forming the tensor.\n", + "The $X$ and $Y$ tensors are then arranged into a `Dataset`\n", + "recognized by `torch`\n", + "using `TensorDataset()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ca3030d", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_train_t = torch.tensor(X_train.astype(np.float32))\n", + "Y_train_t = torch.tensor(Y_train.astype(np.float32))\n", + "hit_train = TensorDataset(X_train_t, Y_train_t)" + ] + }, + { + "cell_type": "markdown", + "id": "7924f53d", + "metadata": {}, + "source": [ + "We do the same for the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86723b3e", + "metadata": {}, + "outputs": [], + "source": [ + "X_test_t = torch.tensor(X_test.astype(np.float32))\n", + "Y_test_t = torch.tensor(Y_test.astype(np.float32))\n", + "hit_test = TensorDataset(X_test_t, Y_test_t)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d055846", + "metadata": {}, + "source": [ + "Finally, this dataset is passed to a `DataLoader()` which ultimately\n", + "passes data into our network. While this may seem\n", + "like a lot of overhead, this structure is helpful for more\n", + "complex tasks where data may live on different machines,\n", + "or where data must be passed to a GPU.\n", + "We provide a helper function `SimpleDataModule()` in `ISLP` to make this task easier for\n", + "standard usage.\n", + "One of its arguments is `num_workers`, which indicates\n", + "how many processes we will use\n", + "for loading the data. For small\n", + "data like `Hitters` this will have little effect, but\n", + "it does provide an advantage for the `MNIST` and `CIFAR100` examples below.\n", + "The `torch` package will inspect the process running and determine a\n", + "maximum number of workers. {This depends on the computing hardware and the number of cores available.} We’ve included a function\n", + "`rec_num_workers()` to compute this so we know how many\n", + "workers might be reasonable (here the max was 16)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "999279fd", + "metadata": {}, + "outputs": [], + "source": [ + "max_num_workers = rec_num_workers()" + ] + }, + { + "cell_type": "markdown", + "id": "9fe60b31", + "metadata": {}, + "source": [ + "The general training setup in `pytorch_lightning` involves\n", + "training, validation and test data. These are each\n", + "represented by different data loaders. During each epoch,\n", + "we run a training step to learn the model and a validation\n", + "step to track the error. The test data is typically\n", + "used at the end of training to evaluate the model.\n", + "\n", + "In this case, as we had split only into test and training,\n", + "we’ll use the test data as validation data with the\n", + "argument `validation=hit_test`. The\n", + "`validation` argument can be a float between 0 and 1, an\n", + "integer, or a\n", + "`Dataset`. If a float (respectively, integer), it is interpreted\n", + "as a percentage (respectively number) of the *training* observations to be used for validation.\n", + "If it is a `Dataset`, it is passed directly to a data loader." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bd9cc5c", + "metadata": {}, + "outputs": [], + "source": [ + "hit_dm = SimpleDataModule(hit_train,\n", + " hit_test,\n", + " batch_size=32,\n", + " num_workers=min(4, max_num_workers),\n", + " validation=hit_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9be1f578", + "metadata": {}, + "source": [ + "Next we must provide a `pytorch_lightning` module that controls\n", + "the steps performed during the training process. We provide methods for our\n", + "`SimpleModule()` that simply record the value\n", + "of the loss function and any additional\n", + "metrics at the end of each epoch. These operations\n", + "are controlled by the methods `SimpleModule.[training/test/validation]_step()`, though\n", + "we will not be modifying these in our examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5be2f822", + "metadata": {}, + "outputs": [], + "source": [ + "hit_module = SimpleModule.regression(hit_model,\n", + " metrics={'mae':MeanAbsoluteError()})\n" + ] + }, + { + "cell_type": "markdown", + "id": "78e707d8", + "metadata": {}, + "source": [ + " By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in\n", + "(10.23).\n", + "We have also asked for mean absolute error to be tracked as well\n", + "in the metrics that are logged.\n", + "\n", + "We log our results via `CSVLogger()`, which in this case stores the results in a CSV file within a directory `logs/hitters`. After the fitting is complete, this allows us to load the\n", + "results as a `pd.DataFrame()` and visualize them below. There are\n", + "several ways to log the results within `pytorch_lightning`, though\n", + "we will not cover those here in detail." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87334d33", + "metadata": {}, + "outputs": [], + "source": [ + "hit_logger = CSVLogger('logs', name='hitters')" + ] + }, + { + "cell_type": "markdown", + "id": "07663639", + "metadata": {}, + "source": [ + "Finally we are ready to train our model and log the results. We\n", + "use the `Trainer()` object from `pytorch_lightning`\n", + "to do this work. The argument `datamodule=hit_dm` tells the trainer\n", + "how training/validation/test logs are produced,\n", + "while the first argument `hit_module`\n", + "specifies the network architecture\n", + "as well as the training/validation/test steps.\n", + "The `callbacks` argument allows for\n", + "several tasks to be carried out at various\n", + "points while training a model. Here\n", + "our `ErrorTracker()` callback will enable\n", + "us to compute validation error while training\n", + "and, finally, the test error.\n", + "We now fit the model for 50 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a474999", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "hit_trainer = Trainer(deterministic=True,\n", + " max_epochs=50,\n", + " log_every_n_steps=5,\n", + " logger=hit_logger,\n", + " callbacks=[ErrorTracker()])\n", + "hit_trainer.fit(hit_module, datamodule=hit_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "5e154e47", + "metadata": {}, + "source": [ + "At each step of SGD, the algorithm randomly selects 32 training observations for\n", + "the computation of the gradient. Recall from Section 10.7\n", + "that an epoch amounts to the number of SGD steps required to process $n$\n", + "observations. Since the training set has\n", + "$n=175$, and we specified a `batch_size` of 32 in the construction of `hit_dm`, an epoch is $175/32=5.5$ SGD steps.\n", + "\n", + "After having fit the model, we can evaluate performance on our test\n", + "data using the `test()` method of our trainer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b24643", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "hit_trainer.test(hit_module, datamodule=hit_dm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0220713b", + "metadata": {}, + "source": [ + "The results of the fit have been logged into a CSV file. We can find the\n", + "results specific to this run in the `experiment.metrics_file_path`\n", + "attribute of our logger. Note that each time the model is fit, the logger will output\n", + "results into a new subdirectory of our directory `logs/hitters`.\n", + "\n", + "We now create a plot of the MAE (mean absolute error) as a function of\n", + "the number of epochs.\n", + "First we retrieve the logged summaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9b266e7", + "metadata": {}, + "outputs": [], + "source": [ + "hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path)" + ] + }, + { + "cell_type": "markdown", + "id": "b7ff26d7", + "metadata": {}, + "source": [ + "Since we will produce similar plots in later examples, we write a\n", + "simple generic function to produce this plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5752a0b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "def summary_plot(results,\n", + " ax,\n", + " col='loss',\n", + " valid_legend='Validation',\n", + " training_legend='Training',\n", + " ylabel='Loss',\n", + " fontsize=20):\n", + " for (column,\n", + " color,\n", + " label) in zip([f'train_{col}_epoch',\n", + " f'valid_{col}'],\n", + " ['black',\n", + " 'red'],\n", + " [training_legend,\n", + " valid_legend]):\n", + " results.plot(x='epoch',\n", + " y=column,\n", + " label=label,\n", + " marker='o',\n", + " color=color,\n", + " ax=ax)\n", + " ax.set_xlabel('Epoch')\n", + " ax.set_ylabel(ylabel)\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "id": "874c2611", + "metadata": {}, + "source": [ + "We now set up our axes, and use our function to produce the MAE plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff0c9fa0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(1, 1, figsize=(6, 6))\n", + "ax = summary_plot(hit_results,\n", + " ax,\n", + " col='mae',\n", + " ylabel='MAE',\n", + " valid_legend='Validation (=Test)')\n", + "ax.set_ylim([0, 400])\n", + "ax.set_xticks(np.linspace(0, 50, 11).astype(int));" + ] + }, + { + "cell_type": "markdown", + "id": "212c64a3", + "metadata": {}, + "source": [ + "We can predict directly from the final model, and\n", + "evaluate its performance on the test data.\n", + "Before fitting, we call the `eval()` method\n", + "of `hit_model`.\n", + "This tells\n", + "`torch` to effectively consider this model to be fitted, so that\n", + "we can use it to predict on new data. For our model here,\n", + "the biggest change is that the dropout layers will\n", + "be turned off, i.e. no weights will be randomly\n", + "dropped in predicting on new data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ee2b61b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "hit_model.eval() \n", + "preds = hit_module(X_test_t)\n", + "torch.abs(Y_test_t - preds).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "fd356e46", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "64fa7609", + "metadata": {}, + "source": [ + "### Cleanup\n", + "In setting up our data module, we had initiated\n", + "several worker processes that will remain running.\n", + "We delete all references to the torch objects to ensure these processes\n", + "will be killed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ca069a3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "del(Hitters,\n", + " hit_model, hit_dm,\n", + " hit_logger,\n", + " hit_test, hit_train,\n", + " X, Y,\n", + " X_test, X_train,\n", + " Y_test, Y_train,\n", + " X_test_t, Y_test_t,\n", + " hit_trainer, hit_module)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac321b7f", + "metadata": {}, + "source": [ + "## Multilayer Network on the MNIST Digit Data\n", + "The `torchvision` package comes with a number of example datasets,\n", + "including the `MNIST` digit data. Our first step is to retrieve\n", + "the training and test data sets; the `MNIST()` function within\n", + "`torchvision.datasets` is provided for this purpose. The\n", + "data will be downloaded the first time this function is executed, and stored in the directory `data/MNIST`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af6a0a33", + "metadata": {}, + "outputs": [], + "source": [ + "(mnist_train, \n", + " mnist_test) = [MNIST(root='data',\n", + " train=train,\n", + " download=True,\n", + " transform=ToTensor())\n", + " for train in [True, False]]\n", + "mnist_train\n" + ] + }, + { + "cell_type": "markdown", + "id": "6c8aa040", + "metadata": {}, + "source": [ + "There are 60,000 images in the training data and 10,000 in the test\n", + "data. The images are $28\\times 28$, and stored as a matrix of pixels. We\n", + "need to transform each one into a vector.\n", + "\n", + "Neural networks are somewhat sensitive to the scale of the inputs, much as ridge and\n", + "lasso regularization are affected by scaling. Here the inputs are eight-bit\n", + "grayscale values between 0 and 255, so we rescale to the unit\n", + "interval. {Note: eight bits means $2^8$, which equals 256. Since the convention\n", + "is to start at $0$, the possible values range from $0$ to $255$.}\n", + "This transformation, along with some reordering\n", + "of the axes, is performed by the `ToTensor()` transform\n", + "from the `torchvision.transforms` package.\n", + "\n", + "As in our `Hitters` example, we form a data module\n", + "from the training and test datasets, setting aside 20%\n", + "of the training images for validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df8a0c8", + "metadata": {}, + "outputs": [], + "source": [ + "mnist_dm = SimpleDataModule(mnist_train,\n", + " mnist_test,\n", + " validation=0.2,\n", + " num_workers=max_num_workers,\n", + " batch_size=256)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3dc50b18", + "metadata": {}, + "source": [ + "Let’s take a look at the data that will get fed into our network. We loop through the first few\n", + "chunks of the test dataset, breaking after 2 batches:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c7dd6ee", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", + " print('X: ', X_.shape)\n", + " print('Y: ', Y_.shape)\n", + " if idx >= 1:\n", + " break\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc80f21e", + "metadata": {}, + "source": [ + "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n", + "Here the `1` indicates a single channel (greyscale). For RGB images such as `CIFAR100` below,\n", + "we will see that the `1` in the size will be replaced by `3` for the three RGB channels.\n", + "\n", + "Now we are ready to specify our neural network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63e48c70", + "metadata": {}, + "outputs": [], + "source": [ + "class MNISTModel(nn.Module):\n", + " def __init__(self):\n", + " super(MNISTModel, self).__init__()\n", + " self.layer1 = nn.Sequential(\n", + " nn.Flatten(),\n", + " nn.Linear(28*28, 256),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.4))\n", + " self.layer2 = nn.Sequential(\n", + " nn.Linear(256, 128),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.3))\n", + " self._forward = nn.Sequential(\n", + " self.layer1,\n", + " self.layer2,\n", + " nn.Linear(128, 10))\n", + " def forward(self, x):\n", + " return self._forward(x)" + ] + }, + { + "cell_type": "markdown", + "id": "d5a5ffef", + "metadata": {}, + "source": [ + "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n", + "256 dimensions where we apply a ReLU activation with 40% dropout.\n", + "A second layer maps the first layer’s output down to\n", + "128 dimensions, applying a ReLU activation with 30% dropout. Finally,\n", + "the 128 dimensions are mapped down to 10, the number of classes in the\n", + "`MNIST` data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92405826", + "metadata": {}, + "outputs": [], + "source": [ + "mnist_model = MNISTModel()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b16bd23a", + "metadata": {}, + "source": [ + "We can check that the model produces output of expected size based\n", + "on our existing batch `X_` above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c8f7a48", + "metadata": {}, + "outputs": [], + "source": [ + "mnist_model(X_).size()" + ] + }, + { + "cell_type": "markdown", + "id": "da0de828", + "metadata": {}, + "source": [ + "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n", + "a tensor of correct shape. In this case, we pass through the final\n", + "batched `X_` from above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f502df8", + "metadata": {}, + "outputs": [], + "source": [ + "summary(mnist_model,\n", + " input_data=X_,\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])" + ] + }, + { + "cell_type": "markdown", + "id": "f98b70bb", + "metadata": {}, + "source": [ + "Having set up both the model and the data module, fitting this model is\n", + "now almost identical to the `Hitters` example. In contrast to our regression model, here we will use the\n", + "`SimpleModule.classification()` method which\n", + "uses the cross-entropy loss function instead of mean squared error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1a7590f", + "metadata": {}, + "outputs": [], + "source": [ + "mnist_module = SimpleModule.classification(mnist_model)\n", + "mnist_logger = CSVLogger('logs', name='MNIST')\n" + ] + }, + { + "cell_type": "markdown", + "id": "ca665912", + "metadata": {}, + "source": [ + "Now we are ready to go. The final step is to supply training data, and fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2fe6de3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "mnist_trainer = Trainer(deterministic=True,\n", + " max_epochs=30,\n", + " logger=mnist_logger,\n", + " callbacks=[ErrorTracker()])\n", + "mnist_trainer.fit(mnist_module,\n", + " datamodule=mnist_dm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6b4a4e0c", + "metadata": {}, + "source": [ + "We have suppressed the output here, which is a progress report on the\n", + "fitting of the model, grouped by epoch. This is very useful, since on\n", + "large datasets fitting can take time. Fitting this model took 245\n", + "seconds on a MacBook Pro with an Apple M1 Pro chip with 10 cores and 16 GB of RAM.\n", + "Here we specified a\n", + "validation split of 20%, so training is actually performed on\n", + "80% of the 60,000 observations in the training set. This is an\n", + "alternative to actually supplying validation data, like we did for the `Hitters` data.\n", + "SGD uses batches\n", + "of 256 observations in computing the gradient, and doing the\n", + "arithmetic, we see that an epoch corresponds to 188 gradient steps." + ] + }, + { + "cell_type": "markdown", + "id": "25528d72", + "metadata": {}, + "source": [ + "`SimpleModule.classification()` includes\n", + "an accuracy metric by default. Other\n", + "classification metrics can be added from `torchmetrics`.\n", + "We will use our `summary_plot()` function to display \n", + "accuracy across epochs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73755987", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", + "fig, ax = subplots(1, 1, figsize=(6, 6))\n", + "summary_plot(mnist_results,\n", + " ax,\n", + " col='accuracy',\n", + " ylabel='Accuracy')\n", + "ax.set_ylim([0.5, 1])\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_xticks(np.linspace(0, 30, 7).astype(int));\n" + ] + }, + { + "cell_type": "markdown", + "id": "ba63a8da", + "metadata": {}, + "source": [ + "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n", + "97% accuracy on the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5269c40", + "metadata": {}, + "outputs": [], + "source": [ + "mnist_trainer.test(mnist_module,\n", + " datamodule=mnist_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "bca75042", + "metadata": {}, + "source": [ + "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n", + "regression. For LDA we refer the reader to Section 4.7.3.\n", + "Although we could use the `sklearn` function `LogisticRegression()` to fit \n", + "multiclass logistic regression, we are set up here to fit such a model\n", + "with `torch`.\n", + "We just have an input layer and an output layer, and omit the hidden layers!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97a0b304", + "metadata": {}, + "outputs": [], + "source": [ + "class MNIST_MLR(nn.Module):\n", + " def __init__(self):\n", + " super(MNIST_MLR, self).__init__()\n", + " self.linear = nn.Sequential(nn.Flatten(),\n", + " nn.Linear(784, 10))\n", + " def forward(self, x):\n", + " return self.linear(x)\n", + "\n", + "mlr_model = MNIST_MLR()\n", + "mlr_module = SimpleModule.classification(mlr_model)\n", + "mlr_logger = CSVLogger('logs', name='MNIST_MLR')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea685183", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "mlr_trainer = Trainer(deterministic=True,\n", + " max_epochs=30,\n", + " callbacks=[ErrorTracker()])\n", + "mlr_trainer.fit(mlr_module, datamodule=mnist_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "c376e402", + "metadata": {}, + "source": [ + "We fit the model just as before and compute the test results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0bd63e3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "mlr_trainer.test(mlr_module,\n", + " datamodule=mnist_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "e0e99a86", + "metadata": {}, + "source": [ + "The accuracy is above 90% even for this pretty simple model.\n", + "\n", + "As in the `Hitters` example, we delete some of\n", + "the objects we created above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b0d3daa", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "del(mnist_test,\n", + " mnist_train,\n", + " mnist_model,\n", + " mnist_dm,\n", + " mnist_trainer,\n", + " mnist_module,\n", + " mnist_results,\n", + " mlr_model,\n", + " mlr_module,\n", + " mlr_trainer)" + ] + }, + { + "cell_type": "markdown", + "id": "1e370989", + "metadata": {}, + "source": [ + "## Convolutional Neural Networks\n", + "In this section we fit a CNN to the `CIFAR100` data, which is available in the `torchvision`\n", + "package. It is arranged in a similar fashion as the `MNIST` data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67517b11", + "metadata": {}, + "outputs": [], + "source": [ + "(cifar_train, \n", + " cifar_test) = [CIFAR100(root=\"data\",\n", + " train=train,\n", + " download=True)\n", + " for train in [True, False]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee7b040e", + "metadata": {}, + "outputs": [], + "source": [ + "transform = ToTensor()\n", + "cifar_train_X = torch.stack([transform(x) for x in\n", + " cifar_train.data])\n", + "cifar_test_X = torch.stack([transform(x) for x in\n", + " cifar_test.data])\n", + "cifar_train = TensorDataset(cifar_train_X,\n", + " torch.tensor(cifar_train.targets))\n", + "cifar_test = TensorDataset(cifar_test_X,\n", + " torch.tensor(cifar_test.targets))" + ] + }, + { + "cell_type": "markdown", + "id": "2bba84d8", + "metadata": {}, + "source": [ + "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n", + "each three-color image is represented as a set of three channels, each of which consists of\n", + "$32\\times 32$ eight-bit pixels. We standardize as we did for the\n", + "digits, but keep the array structure. This is accomplished with the `ToTensor()` transform.\n", + "\n", + "Creating the data module is similar to the `MNIST` example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd9e74ad", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "cifar_dm = SimpleDataModule(cifar_train,\n", + " cifar_test,\n", + " validation=0.2,\n", + " num_workers=max_num_workers,\n", + " batch_size=128)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ee8883b", + "metadata": {}, + "source": [ + "We again look at the shape of typical batches in our data loaders." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a553b926", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", + " print('X: ', X_.shape)\n", + " print('Y: ', Y_.shape)\n", + " if idx >= 1:\n", + " break\n" + ] + }, + { + "cell_type": "markdown", + "id": "2b25a138", + "metadata": {}, + "source": [ + "Before we start, we look at some of the training images; similar code produced\n", + "Figure 10.5 on page 445. The example below also illustrates\n", + "that `TensorDataset` objects can be indexed with integers --- we are choosing\n", + "random images from the training data by indexing `cifar_train`. In order to display correctly,\n", + "we must reorder the dimensions by a call to `np.transpose()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94885e68", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, axes = subplots(5, 5, figsize=(10,10))\n", + "rng = np.random.default_rng(4)\n", + "indices = rng.choice(np.arange(len(cifar_train)), 25,\n", + " replace=False).reshape((5,5))\n", + "for i in range(5):\n", + " for j in range(5):\n", + " idx = indices[i,j]\n", + " axes[i,j].imshow(np.transpose(cifar_train[idx][0],\n", + " [1,2,0]),\n", + " interpolation=None)\n", + " axes[i,j].set_xticks([])\n", + " axes[i,j].set_yticks([])\n" + ] + }, + { + "cell_type": "markdown", + "id": "04fda6b8", + "metadata": {}, + "source": [ + "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n", + "\n", + "We specify a moderately-sized CNN for\n", + "demonstration purposes, similar in structure to Figure 10.8.\n", + "We use several layers, each consisting of convolution, ReLU, and max-pooling steps.\n", + "We first define a module that defines one of these layers. As in our\n", + "previous examples, we overwrite the `__init__()` and `forward()` methods\n", + "of `nn.Module`. This user-defined module can now be used in ways just like\n", + "`nn.Linear()` or `nn.Dropout()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdc2528a", + "metadata": {}, + "outputs": [], + "source": [ + "class BuildingBlock(nn.Module):\n", + "\n", + " def __init__(self,\n", + " in_channels,\n", + " out_channels):\n", + "\n", + " super(BuildingBlock, self).__init__()\n", + " self.conv = nn.Conv2d(in_channels=in_channels,\n", + " out_channels=out_channels,\n", + " kernel_size=(3,3),\n", + " padding='same')\n", + " self.activation = nn.ReLU()\n", + " self.pool = nn.MaxPool2d(kernel_size=(2,2))\n", + "\n", + " def forward(self, x):\n", + " return self.pool(self.activation(self.conv(x)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "31891282", + "metadata": {}, + "source": [ + "Notice that we used the `padding = \"same\"` argument to\n", + "`nn.Conv2d()`, which ensures that the output channels have the\n", + "same dimension as the input channels. There are 32 channels in the first\n", + "hidden layer, in contrast to the three channels in the input layer. We\n", + "use a $3\\times 3$ convolution filter for each channel in all the layers. Each\n", + "convolution is followed by a max-pooling layer over $2\\times2$\n", + "blocks.\n", + "\n", + "In forming our deep learning model for the `CIFAR100` data, we use several of our `BuildingBlock()`\n", + "modules sequentially. This simple example\n", + "illustrates some of the power of `torch`. Users can\n", + "define modules of their own, which can be combined in other\n", + "modules. Ultimately, everything is fit by a generic trainer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "845be1ae", + "metadata": {}, + "outputs": [], + "source": [ + "class CIFARModel(nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(CIFARModel, self).__init__()\n", + " sizes = [(3,32),\n", + " (32,64),\n", + " (64,128),\n", + " (128,256)]\n", + " self.conv = nn.Sequential(*[BuildingBlock(in_, out_)\n", + " for in_, out_ in sizes])\n", + "\n", + " self.output = nn.Sequential(nn.Dropout(0.5),\n", + " nn.Linear(2*2*256, 512),\n", + " nn.ReLU(),\n", + " nn.Linear(512, 100))\n", + " def forward(self, x):\n", + " val = self.conv(x)\n", + " val = torch.flatten(val, start_dim=1)\n", + " return self.output(val)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1ed2c036", + "metadata": {}, + "source": [ + "We build the model and look at the summary. (We had created examples of `X_` earlier.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be768cbb", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "cifar_model = CIFARModel()\n", + "summary(cifar_model,\n", + " input_data=X_,\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])" + ] + }, + { + "cell_type": "markdown", + "id": "6320d294", + "metadata": {}, + "source": [ + "The total number of trainable parameters is 964,516.\n", + "By studying the size of the parameters, we can see that the channels halve in both\n", + "dimensions\n", + "after each of these max-pooling operations. After the last of these we\n", + "have a layer with 256 channels of dimension $2\\times 2$. These are then\n", + "flattened to a dense layer of size 1,024;\n", + "in other words, each of the $2\\times 2$ matrices is turned into a\n", + "$4$-vector, and put side-by-side in one layer. This is followed by a\n", + "dropout regularization layer, then\n", + "another dense layer of size 512, and finally, the\n", + "output layer.\n", + "\n", + "Up to now, we have been using a default\n", + "optimizer in `SimpleModule()`. For these data,\n", + "experiments show that a smaller learning rate performs\n", + "better than the default 0.01. We use a\n", + "custom optimizer here with a learning rate of 0.001.\n", + "Besides this, the logging and training\n", + "follow a similar pattern to our previous examples. The optimizer\n", + "takes an argument `params` that informs\n", + "the optimizer which parameters are involved in SGD (stochastic gradient descent).\n", + "\n", + "We saw earlier that entries of a module’s parameters are tensors. In passing\n", + "the parameters to the optimizer we are doing more than\n", + "simply passing arrays; part of the structure of the graph\n", + "is encoded in the tensors themselves.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67e3e2d9", + "metadata": {}, + "outputs": [], + "source": [ + "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n", + "cifar_module = SimpleModule.classification(cifar_model,\n", + " optimizer=cifar_optimizer)\n", + "cifar_logger = CSVLogger('logs', name='CIFAR100')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ea339ff", + "metadata": {}, + "outputs": [], + "source": [ + "cifar_trainer = Trainer(deterministic=True,\n", + " max_epochs=30,\n", + " logger=cifar_logger,\n", + " callbacks=[ErrorTracker()])\n", + "cifar_trainer.fit(cifar_module,\n", + " datamodule=cifar_dm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "977e27d9", + "metadata": {}, + "source": [ + "This model takes 10 minutes or more to run and achieves about 42% accuracy on the test\n", + "data. Although this is not terrible for 100-class data (a random\n", + "classifier gets 1% accuracy), searching the web we see results around\n", + "75%. Typically it takes a lot of architecture carpentry,\n", + "fiddling with regularization, and time, to achieve such results.\n", + "\n", + "Let’s take a look at the validation and training accuracy\n", + "across epochs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a9068d9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "log_path = cifar_logger.experiment.metrics_file_path\n", + "cifar_results = pd.read_csv(log_path)\n", + "fig, ax = subplots(1, 1, figsize=(6, 6))\n", + "summary_plot(cifar_results,\n", + " ax,\n", + " col='accuracy',\n", + " ylabel='Accuracy')\n", + "ax.set_xticks(np.linspace(0, 10, 6).astype(int))\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_ylim([0, 1]);" + ] + }, + { + "cell_type": "markdown", + "id": "9c48006a", + "metadata": {}, + "source": [ + "Finally, we evaluate our model on our test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e71eff9b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "cifar_trainer.test(cifar_module,\n", + " datamodule=cifar_dm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2d03b891", + "metadata": {}, + "source": [ + "### Hardware Acceleration\n", + "As deep learning has become ubiquitous in machine learning, hardware\n", + "manufacturers have produced special libraries that can\n", + "often speed up the gradient-descent steps.\n", + "\n", + "For instance, Mac OS devices with the M1 chip may have the *Metal* programming framework\n", + "enabled, which can speed up the `torch` \n", + "computations. We present an example of how to use this acceleration.\n", + "\n", + "The main changes are to the `Trainer()` call as well as to the metrics\n", + "that will be evaluated on the data. These metrics must be told where\n", + "the data will be located at evaluation time. This is\n", + "accomplished with a call to the `to()` method of the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58eae430", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "try:\n", + " for name, metric in cifar_module.metrics.items():\n", + " cifar_module.metrics[name] = metric.to('mps')\n", + " cifar_trainer_mps = Trainer(accelerator='mps',\n", + " deterministic=True,\n", + " max_epochs=30)\n", + " cifar_trainer_mps.fit(cifar_module,\n", + " datamodule=cifar_dm)\n", + " cifar_trainer_mps.test(cifar_module,\n", + " datamodule=cifar_dm)\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "0c7227ef", + "metadata": {}, + "source": [ + "This yields approximately two- or three-fold acceleration for each epoch.\n", + "We have protected this code block using `try:` and `except:`\n", + "clauses; if it works, we get the speedup, if it fails, nothing happens." + ] + }, + { + "cell_type": "markdown", + "id": "4328a190", + "metadata": {}, + "source": [ + "## Using Pretrained CNN Models\n", + "We now show how to use a CNN pretrained on the `imagenet` database to classify natural\n", + "images, and demonstrate how we produced Figure 10.10.\n", + "We copied six JPEG images from a digital photo album into the\n", + "directory `book_images`. These images are available\n", + "from the data section of , the ISLP book website. Download `book_images.zip`; when\n", + "clicked it creates the `book_images` directory. \n", + "\n", + "The pretrained network we use is called `resnet50`; specification details can be found on the web.\n", + "We will read in the images, and\n", + "convert them into the array format expected by the `torch`\n", + "software to match the specifications in `resnet50`. \n", + "The conversion involves a resize, a crop and then a predefined standardization for each of the three channels.\n", + "We now read in the images and preprocess them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "638a1c8c", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "resize = Resize((232,232))\n", + "crop = CenterCrop(224)\n", + "normalize = Normalize([0.485,0.456,0.406],\n", + " [0.229,0.224,0.225])\n", + "imgfiles = sorted([f for f in glob('book_images/*')])\n", + "imgs = torch.stack([torch.div(crop(resize(read_image(f))), 255)\n", + " for f in imgfiles])\n", + "imgs = normalize(imgs)\n", + "imgs.size()" + ] + }, + { + "cell_type": "markdown", + "id": "a2c5aec2", + "metadata": {}, + "source": [ + "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7776ed1e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", + "summary(resnet_model,\n", + " input_data=imgs,\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d29ab45", + "metadata": {}, + "source": [ + "We set the mode to `eval()` to ensure that the model is ready to predict on new data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c8e359d", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "resnet_model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "228a6411", + "metadata": {}, + "source": [ + "Inspecting the output above, we see that when setting up the\n", + "`resnet_model`, the authors defined a `Bottleneck`, much like our\n", + "`BuildingBlock` module.\n", + "\n", + "We now feed our six images through the fitted network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80f19869", + "metadata": {}, + "outputs": [], + "source": [ + "img_preds = resnet_model(imgs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7bd653f", + "metadata": {}, + "source": [ + "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n", + "the probabilities by applying the softmax to the logits in `img_preds`. Note that\n", + "we have had to call the `detach()` method on the tensor `img_preds` in order to convert\n", + "it to our a more familiar `ndarray`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6892597d", + "metadata": {}, + "outputs": [], + "source": [ + "img_probs = np.exp(np.asarray(img_preds.detach()))\n", + "img_probs /= img_probs.sum(1)[:,None]\n" + ] + }, + { + "cell_type": "markdown", + "id": "b0f07763", + "metadata": {}, + "source": [ + "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eb1e1b0", + "metadata": {}, + "outputs": [], + "source": [ + "labs = json.load(open('imagenet_class_index.json'))\n", + "class_labels = pd.DataFrame([(int(k), v[1]) for k, v in \n", + " labs.items()],\n", + " columns=['idx', 'label'])\n", + "class_labels = class_labels.set_index('idx')\n", + "class_labels = class_labels.sort_index()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7779e914", + "metadata": {}, + "source": [ + "We’ll now construct a data frame for each image file\n", + "with the labels with the three highest probabilities as\n", + "estimated by the model above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e89d251", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "for i, imgfile in enumerate(imgfiles):\n", + " img_df = class_labels.copy()\n", + " img_df['prob'] = img_probs[i]\n", + " img_df = img_df.sort_values(by='prob', ascending=False)[:3]\n", + " print(f'Image: {imgfile}')\n", + " print(img_df.reset_index().drop(columns=['idx']))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f9d3843", + "metadata": {}, + "source": [ + "We see that the model\n", + "is quite confident about `Flamingo.jpg`, but a little less so for the\n", + "other images.\n", + "\n", + "We end this section with our usual cleanup." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69456669", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "del(cifar_test,\n", + " cifar_train,\n", + " cifar_dm,\n", + " cifar_module,\n", + " cifar_logger,\n", + " cifar_optimizer,\n", + " cifar_trainer)" + ] + }, + { + "cell_type": "markdown", + "id": "64b33c4d", + "metadata": {}, + "source": [ + "## IMDB Document Classification\n", + "We now implement models for sentiment classification (Section 10.4) on the `IMDB`\n", + "dataset. As mentioned above code block~8, we are using\n", + "a preprocessed version of the `IMDB` dataset found in the\n", + "`keras` package. As `keras` uses `tensorflow`, a different\n", + "tensor and deep learning library, we have\n", + "converted the data to be suitable for `torch`. The\n", + "code used to convert from `keras` is\n", + "available in the module `ISLP.torch._make_imdb`. It\n", + "requires some of the `keras` packages to run. These data use a dictionary of size 10,000.\n", + "\n", + "We have stored three different representations of the review data for this lab:\n", + "\n", + "* `load_tensor()`, a sparse tensor version usable by `torch`;\n", + "* `load_sparse()`, a sparse matrix version usable by `sklearn`, since we will compare with a lasso fit;\n", + "* `load_sequential()`, a padded\n", + "version of the original sequence representation, limited to the last\n", + "500 words of each review.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70b3ece2", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "(imdb_seq_train,\n", + " imdb_seq_test) = load_sequential(root='data/IMDB')\n", + "padded_sample = np.asarray(imdb_seq_train.tensors[0][0])\n", + "sample_review = padded_sample[padded_sample > 0][:12]\n", + "sample_review[:12]\n" + ] + }, + { + "cell_type": "markdown", + "id": "d6b73c18", + "metadata": {}, + "source": [ + "The datasets `imdb_seq_train` and `imdb_seq_test` are\n", + "both instances of the class `TensorDataset`. The\n", + "tensors used to construct them can be found in the `tensors` attribute, with\n", + "the first tensor the features `X` and the second the outcome `Y`.\n", + "We have taken the first row of features and stored it as `padded_sample`. In the preprocessing\n", + "used to form these data, sequences were padded with 0s in the beginning if they were\n", + "not long enough, hence we remove this padding by restricting to entries where\n", + "`padded_sample > 0`. We then provide the first 12 words of the sample review.\n", + "\n", + "We can find these words in the `lookup` dictionary from the `ISLP.torch.imdb` module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "349b2af7", + "metadata": {}, + "outputs": [], + "source": [ + "lookup = load_lookup(root='data/IMDB')\n", + "' '.join(lookup[i] for i in sample_review)" + ] + }, + { + "cell_type": "markdown", + "id": "99d5a108", + "metadata": {}, + "source": [ + "For our first model, we have created a binary feature for each\n", + "of the 10,000 possible words in the dataset, with an entry of one\n", + "in the $i,j$ entry if word $j$ appears in review $i$. As most reviews\n", + "are quite short, such a feature matrix has over 98% zeros. These data\n", + "are accessed using `load_tensor()` from the `ISLP` library." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffa8da37", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "max_num_workers=10\n", + "(imdb_train,\n", + " imdb_test) = load_tensor(root='data/IMDB')\n", + "imdb_dm = SimpleDataModule(imdb_train,\n", + " imdb_test,\n", + " validation=2000,\n", + " num_workers=min(6, max_num_workers),\n", + " batch_size=512)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e53eea99", + "metadata": {}, + "source": [ + "We’ll use a two-layer model for our first model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11eeeabb", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "class IMDBModel(nn.Module):\n", + "\n", + " def __init__(self, input_size):\n", + " super(IMDBModel, self).__init__()\n", + " self.dense1 = nn.Linear(input_size, 16)\n", + " self.activation = nn.ReLU()\n", + " self.dense2 = nn.Linear(16, 16)\n", + " self.output = nn.Linear(16, 1)\n", + "\n", + " def forward(self, x):\n", + " val = x\n", + " for _map in [self.dense1,\n", + " self.activation,\n", + " self.dense2,\n", + " self.activation,\n", + " self.output]:\n", + " val = _map(val)\n", + " return torch.flatten(val)\n" + ] + }, + { + "cell_type": "markdown", + "id": "943bf8d4", + "metadata": {}, + "source": [ + "We now instantiate our model and look at a summary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fe55a29", + "metadata": {}, + "outputs": [], + "source": [ + "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", + "summary(imdb_model,\n", + " input_size=imdb_test.tensors[0].size(),\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a49a0145", + "metadata": {}, + "source": [ + "We’ll again use\n", + "a smaller learning rate for these data,\n", + "hence we pass an `optimizer` to the\n", + "`SimpleModule`. \n", + "Since the reviews are classified into\n", + "positive or negative sentiment, we use\n", + "`SimpleModule.binary_classification()`. {Our use of\n", + " `binary_classification()` instead of `classification()` is\n", + " due to some subtlety in how `torchmetrics.Accuracy()` works,\n", + "as well as the data type of the targets.}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1ebbc70", + "metadata": {}, + "outputs": [], + "source": [ + "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n", + "imdb_module = SimpleModule.binary_classification(\n", + " imdb_model,\n", + " optimizer=imdb_optimizer)\n" + ] + }, + { + "cell_type": "markdown", + "id": "18076d4c", + "metadata": {}, + "source": [ + "Having loaded the datasets into a data module\n", + "and created a `SimpleModule`, the remaining steps\n", + "are familiar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9feddeb2", + "metadata": {}, + "outputs": [], + "source": [ + "imdb_logger = CSVLogger('logs', name='IMDB')\n", + "imdb_trainer = Trainer(deterministic=True,\n", + " max_epochs=30,\n", + " logger=imdb_logger,\n", + " callbacks=[ErrorTracker()])\n", + "imdb_trainer.fit(imdb_module,\n", + " datamodule=imdb_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "f28e6658", + "metadata": {}, + "source": [ + "Evaluating the test error yields roughly 86% accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "887d7dad", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", + "test_results" + ] + }, + { + "cell_type": "markdown", + "id": "d2f87545", + "metadata": {}, + "source": [ + "### Comparison to Lasso\n", + "We now fit a lasso logistic regression model\n", + " using `LogisticRegression()` from `sklearn`. Since `sklearn` does not recognize\n", + "the sparse tensors of `torch`, we use a sparse\n", + "matrix that is recognized by `sklearn.`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "055a9aeb", + "metadata": {}, + "outputs": [], + "source": [ + "((X_train, Y_train),\n", + " (X_valid, Y_valid),\n", + " (X_test, Y_test)) = load_sparse(validation=2000,\n", + " random_state=0,\n", + " root='data/IMDB')\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8324d03", + "metadata": {}, + "source": [ + "Similar to what we did in\n", + "Section 10.9.1,\n", + "we construct a series of 50 values for the lasso reguralization parameter $\\lambda$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e1e181", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "lam_max = np.abs(X_train.T * (Y_train - Y_train.mean())).max()\n", + "lam_val = lam_max * np.exp(np.linspace(np.log(1),\n", + " np.log(1e-4), 50))\n" + ] + }, + { + "cell_type": "markdown", + "id": "73e7d53e", + "metadata": {}, + "source": [ + "With `LogisticRegression()` the regularization parameter\n", + "$C$ is specified as the inverse of $\\lambda$. There are several\n", + "solvers for logistic regression; here we use `liblinear` which\n", + "works well with the sparse input format. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd64350c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "logit = LogisticRegression(penalty='l1', \n", + " C=1/lam_max,\n", + " solver='liblinear',\n", + " warm_start=True,\n", + " fit_intercept=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f0e9739a", + "metadata": {}, + "source": [ + "The path of 50 values takes approximately 40 seconds to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bf8b868", + "metadata": {}, + "outputs": [], + "source": [ + "coefs = []\n", + "intercepts = []\n", + "\n", + "for l in lam_val:\n", + " logit.C = 1/l\n", + " logit.fit(X_train, Y_train)\n", + " coefs.append(logit.coef_.copy())\n", + " intercepts.append(logit.intercept_)\n" + ] + }, + { + "cell_type": "markdown", + "id": "95f4fbff", + "metadata": {}, + "source": [ + "The coefficient and intercepts have an extraneous dimension which can be removed\n", + "by the `np.squeeze()` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28256828", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "coefs = np.squeeze(coefs)\n", + "intercepts = np.squeeze(intercepts)\n" + ] + }, + { + "cell_type": "markdown", + "id": "94e746f6", + "metadata": {}, + "source": [ + "We’ll now make a plot to compare our neural network results with the\n", + "lasso." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5945618", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "%%capture\n", + "fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True)\n", + "for ((X_, Y_),\n", + " data_,\n", + " color) in zip([(X_train, Y_train),\n", + " (X_valid, Y_valid),\n", + " (X_test, Y_test)],\n", + " ['Training', 'Validation', 'Test'],\n", + " ['black', 'red', 'blue']):\n", + " linpred_ = X_ * coefs.T + intercepts[None,:]\n", + " label_ = np.array(linpred_ > 0)\n", + " accuracy_ = np.array([np.mean(Y_ == l) for l in label_.T])\n", + " axes[0].plot(-np.log(lam_val / X_train.shape[0]),\n", + " accuracy_,\n", + " '.--',\n", + " color=color,\n", + " markersize=13,\n", + " linewidth=2,\n", + " label=data_)\n", + "axes[0].legend()\n", + "axes[0].set_xlabel(r'$-\\log(\\lambda)$', fontsize=20)\n", + "axes[0].set_ylabel('Accuracy', fontsize=20)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dc7e494", + "metadata": {}, + "source": [ + "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n", + "when making a complex figure, since the steps can be spread across two or more cells.\n", + "We now add a plot of the lasso accuracy, and display the composed figure by simply entering its name at the end of the cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74704884", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", + "summary_plot(imdb_results,\n", + " axes[1],\n", + " col='accuracy',\n", + " ylabel='Accuracy')\n", + "axes[1].set_xticks(np.linspace(0, 30, 7).astype(int))\n", + "axes[1].set_ylabel('Accuracy', fontsize=20)\n", + "axes[1].set_xlabel('Epoch', fontsize=20)\n", + "axes[1].set_ylim([0.5, 1]);\n", + "axes[1].axhline(test_results[0]['test_accuracy'],\n", + " color='blue',\n", + " linestyle='--',\n", + " linewidth=3)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "7898334d", + "metadata": {}, + "source": [ + "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$, as it does for the neural network.\n", + "\n", + "Once again, we end with a cleanup." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1ecfca8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "del(imdb_model,\n", + " imdb_trainer,\n", + " imdb_logger,\n", + " imdb_dm,\n", + " imdb_train,\n", + " imdb_test)" + ] + }, + { + "cell_type": "markdown", + "id": "0169cd1b", + "metadata": {}, + "source": [ + "## Recurrent Neural Networks\n", + "In this lab we fit the models illustrated in\n", + "Section 10.5." + ] + }, + { + "cell_type": "markdown", + "id": "6e7f96cc", + "metadata": {}, + "source": [ + "### Sequential Models for Document Classification\n", + "Here we fit a simple LSTM RNN for sentiment prediction to\n", + "the `IMDb` movie-review data, as discussed in Section 10.5.1.\n", + "For an RNN we use the sequence of words in a document, taking their\n", + "order into account. We loaded the preprocessed \n", + "data at the beginning of\n", + "Section 10.9.5.\n", + "A script that details the preprocessing can be found in the\n", + "`ISLP` library. Notably, since more than 90% of the documents\n", + "had fewer than 500 words, we set the document length to 500. For\n", + "longer documents, we used the last 500 words, and for shorter\n", + "documents, we padded the front with blanks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5503cb3f", + "metadata": {}, + "outputs": [], + "source": [ + "imdb_seq_dm = SimpleDataModule(imdb_seq_train,\n", + " imdb_seq_test,\n", + " validation=2000,\n", + " batch_size=300,\n", + " num_workers=min(6, max_num_workers)\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "356b76f2", + "metadata": {}, + "source": [ + "The first layer of the RNN is an embedding layer of size 32, which will be\n", + "learned during training. This layer one-hot encodes each document\n", + "as a matrix of dimension $500 \\times 10,003$, and then maps these\n", + "$10,003$ dimensions down to $32$. {The extra 3 dimensions\n", + "correspond to commonly occurring non-word entries in the reviews.}\n", + " Since each word is represented by an\n", + "integer, this is effectively achieved by the creation of an embedding\n", + "matrix of size $10,003\\times 32$; each of the 500 integers in the\n", + "document are then mapped to the appropriate 32 real numbers by\n", + "indexing the appropriate rows of this matrix." + ] + }, + { + "cell_type": "markdown", + "id": "14ddd9a9", + "metadata": {}, + "source": [ + "The second layer is an LSTM with 32 units, and the output\n", + "layer is a single logit for the binary classification task.\n", + "In the last line of the `forward()` method below,\n", + "we take the last 32-dimensional output of the LSTM and map it to our response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b683641", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "class LSTMModel(nn.Module):\n", + " def __init__(self, input_size):\n", + " super(LSTMModel, self).__init__()\n", + " self.embedding = nn.Embedding(input_size, 32)\n", + " self.lstm = nn.LSTM(input_size=32,\n", + " hidden_size=32,\n", + " batch_first=True)\n", + " self.dense = nn.Linear(32, 1)\n", + " def forward(self, x):\n", + " val, (h_n, c_n) = self.lstm(self.embedding(x))\n", + " return torch.flatten(self.dense(val[:,-1]))" + ] + }, + { + "cell_type": "markdown", + "id": "b7903383", + "metadata": {}, + "source": [ + "We instantiate and take a look at the summary of the model, using the\n", + "first 10 documents in the corpus." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "702aa8de", + "metadata": {}, + "outputs": [], + "source": [ + "lstm_model = LSTMModel(X_test.shape[-1])\n", + "summary(lstm_model,\n", + " input_data=imdb_seq_train.tensors[0][:10],\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "f663b95c", + "metadata": {}, + "source": [ + "The 10,003 is suppressed in the summary, but we see it in the\n", + "parameter count, since $10,003\\times 32=320,096$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e48aa272", + "metadata": {}, + "outputs": [], + "source": [ + "lstm_module = SimpleModule.binary_classification(lstm_model)\n", + "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "143be7e8", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "lstm_trainer = Trainer(deterministic=True,\n", + " max_epochs=20,\n", + " logger=lstm_logger,\n", + " callbacks=[ErrorTracker()])\n", + "lstm_trainer.fit(lstm_module,\n", + " datamodule=imdb_seq_dm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "55dc2b66", + "metadata": {}, + "source": [ + "The rest is now similar to other networks we have fit. We\n", + "track the test performance as the network is fit, and see that it attains 85% accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9272e2ba", + "metadata": {}, + "outputs": [], + "source": [ + "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "eee55195", + "metadata": {}, + "source": [ + "We once again show the learning progress, followed by cleanup." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d9d387e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", + "fig, ax = subplots(1, 1, figsize=(6, 6))\n", + "summary_plot(lstm_results,\n", + " ax,\n", + " col='accuracy',\n", + " ylabel='Accuracy')\n", + "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_ylim([0.5, 1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c6d1072", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "del(lstm_model,\n", + " lstm_trainer,\n", + " lstm_logger,\n", + " imdb_seq_dm,\n", + " imdb_seq_train,\n", + " imdb_seq_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7d179385", + "metadata": {}, + "source": [ + "### Time Series Prediction\n", + "We now show how to fit the models in Section 10.5.2\n", + "for time series prediction.\n", + "We first load and standardize the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e2d1d5c", + "metadata": {}, + "outputs": [], + "source": [ + "NYSE = load_data('NYSE')\n", + "cols = ['DJ_return', 'log_volume', 'log_volatility']\n", + "X = pd.DataFrame(StandardScaler(\n", + " with_mean=True,\n", + " with_std=True).fit_transform(NYSE[cols]),\n", + " columns=NYSE[cols].columns,\n", + " index=NYSE.index)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ad4a748", + "metadata": {}, + "source": [ + "Next we set up the lagged versions of the data, dropping\n", + "any rows with missing values using the `dropna()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6d0e12", + "metadata": {}, + "outputs": [], + "source": [ + "for lag in range(1, 6):\n", + " for col in cols:\n", + " newcol = np.zeros(X.shape[0]) * np.nan\n", + " newcol[lag:] = X[col].values[:-lag]\n", + " X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n", + "X.insert(len(X.columns), 'train', NYSE['train'])\n", + "X = X.dropna()\n" + ] + }, + { + "cell_type": "markdown", + "id": "91a924d2", + "metadata": {}, + "source": [ + "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n", + "`log_volatility` to predict only from previous day’s data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3c961ec", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Y, train = X['log_volume'], X['train']\n", + "X = X.drop(columns=['train'] + cols)\n", + "X.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "1c5f8d2f", + "metadata": {}, + "source": [ + "We first fit a simple linear model and compute the $R^2$ on the test data using\n", + "the `score()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb89da88", + "metadata": {}, + "outputs": [], + "source": [ + "M = LinearRegression()\n", + "M.fit(X[train], Y[train])\n", + "M.score(X[~train], Y[~train])" + ] + }, + { + "cell_type": "markdown", + "id": "b92425ab", + "metadata": {}, + "source": [ + "We refit this model, including the factor variable `day_of_week`.\n", + "For a categorical series in `pandas`, we can form the indicators\n", + "using the `get_dummies()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55d921d7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_day = pd.merge(X, \n", + " pd.get_dummies(NYSE['day_of_week']),\n", + " on='date')" + ] + }, + { + "cell_type": "markdown", + "id": "afab736a", + "metadata": {}, + "source": [ + " Note that we do not have\n", + "to reinstantiate the linear regression model\n", + "as its `fit()` method accepts a design matrix and a response directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e87eeb9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "M.fit(X_day[train], Y[train])\n", + "M.score(X_day[~train], Y[~train])" + ] + }, + { + "cell_type": "markdown", + "id": "43309e3b", + "metadata": {}, + "source": [ + "This model achieves an $R^2$ of about 46%." + ] + }, + { + "cell_type": "markdown", + "id": "93c61d80", + "metadata": {}, + "source": [ + "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n", + "versions of each feature as indicated by the `input_shape` argument\n", + "to the layer `nn.RNN()` below. We first\n", + "ensure the columns of our data frame are such that a reshaped\n", + "matrix will have the variables correctly lagged. We use the\n", + "`reindex()` method to do this.\n", + "\n", + "For an input shape `(5,3)`, each row represents a lagged version of the three variables.\n", + "The `nn.RNN()` layer also expects the first row of each\n", + "observation to be earliest in time, so we must reverse the current order. Hence we loop over\n", + "`range(5,0,-1)` below, which is\n", + "an example of using a `slice()` to index\n", + "iterable objects. The general notation is `start:end:step`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3689b318", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ordered_cols = []\n", + "for lag in range(5,0,-1):\n", + " for col in cols:\n", + " ordered_cols.append('{0}_{1}'.format(col, lag))\n", + "X = X.reindex(columns=ordered_cols)\n", + "X.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "9dbcf9f7", + "metadata": {}, + "source": [ + "We now reshape the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5633e521", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_rnn = X.to_numpy().reshape((-1,5,3))\n", + "X_rnn.shape" + ] + }, + { + "cell_type": "markdown", + "id": "bd3ae3ff", + "metadata": {}, + "source": [ + "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n", + "\n", + "Now we are ready to proceed with the RNN, which uses 12 hidden units, and 10%\n", + "dropout.\n", + "After passing through the RNN, we extract the final time point as `val[:,-1]`\n", + "in `forward()` below. This gets passed through a 10% dropout and then flattened through\n", + "a linear layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "979a6066", + "metadata": {}, + "outputs": [], + "source": [ + "class NYSEModel(nn.Module):\n", + " def __init__(self):\n", + " super(NYSEModel, self).__init__()\n", + " self.rnn = nn.RNN(3,\n", + " 12,\n", + " batch_first=True)\n", + " self.dense = nn.Linear(12, 1)\n", + " self.dropout = nn.Dropout(0.1)\n", + " def forward(self, x):\n", + " val, h_n = self.rnn(x)\n", + " val = self.dense(self.dropout(val[:,-1]))\n", + " return torch.flatten(val)\n", + "nyse_model = NYSEModel()" + ] + }, + { + "cell_type": "markdown", + "id": "14671ab2", + "metadata": {}, + "source": [ + "We fit the model in a similar fashion to previous networks. We\n", + "supply the `fit` function with test data as validation data, so that when\n", + "we monitor its progress and plot the history function we can see the\n", + "progress on the test data. Of course we should not use this as a basis for\n", + "early stopping, since then the test performance would be biased.\n", + "\n", + "We form the training dataset similar to\n", + "our `Hitters` example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3528f4e7", + "metadata": {}, + "outputs": [], + "source": [ + "datasets = []\n", + "for mask in [train, ~train]:\n", + " X_rnn_t = torch.tensor(X_rnn[mask].astype(np.float32))\n", + " Y_t = torch.tensor(Y[mask].astype(np.float32))\n", + " datasets.append(TensorDataset(X_rnn_t, Y_t))\n", + "nyse_train, nyse_test = datasets\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a731099", + "metadata": {}, + "source": [ + "Following our usual pattern, we inspect the summary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "795c283e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "summary(nyse_model,\n", + " input_data=X_rnn_t,\n", + " col_names=['input_size',\n", + " 'output_size',\n", + " 'num_params'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "650e0ec7", + "metadata": {}, + "source": [ + "\\newpage\n", + "We again put the two datasets into a data module, with a\n", + "batch size of 64." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31640121", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "nyse_dm = SimpleDataModule(nyse_train,\n", + " nyse_test,\n", + " num_workers=min(4, max_num_workers),\n", + " validation=nyse_test,\n", + " batch_size=64)" + ] + }, + { + "cell_type": "markdown", + "id": "1de7ae89", + "metadata": {}, + "source": [ + "We run some data through our model to be sure the sizes match up correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af6795f5", + "metadata": {}, + "outputs": [], + "source": [ + "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", + " out = nyse_model(x)\n", + " print(y.size(), out.size())\n", + " if idx >= 2:\n", + " break\n" + ] + }, + { + "cell_type": "markdown", + "id": "eecb6d9c", + "metadata": {}, + "source": [ + "We follow our previous example for setting up a trainer for a\n", + "regression problem, requesting the $R^2$ metric\n", + "to be be computed at each epoch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d04344c", + "metadata": {}, + "outputs": [], + "source": [ + "nyse_optimizer = RMSprop(nyse_model.parameters(),\n", + " lr=0.001)\n", + "nyse_module = SimpleModule.regression(nyse_model,\n", + " optimizer=nyse_optimizer,\n", + " metrics={'r2':R2Score()})\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a10444d", + "metadata": {}, + "source": [ + "Fitting the model should by now be familiar.\n", + "The results on the test data are very similar to the linear AR model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "485d7bb1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "nyse_trainer = Trainer(deterministic=True,\n", + " max_epochs=200,\n", + " callbacks=[ErrorTracker()])\n", + "nyse_trainer.fit(nyse_module,\n", + " datamodule=nyse_dm)\n", + "nyse_trainer.test(nyse_module,\n", + " datamodule=nyse_dm)" + ] + }, + { + "cell_type": "markdown", + "id": "dbd13ee3", + "metadata": {}, + "source": [ + "We could also fit a model without the `nn.RNN()` layer by just\n", + "using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the \n", + "hidden layer, this would be equivalent to our earlier linear AR model. \n", + "\n", + "Instead we will fit a nonlinear AR model using the feature set `X_day` that includes the `day_of_week` indicators.\n", + "To do so, we\n", + "must first create our test and training datasets and a corresponding\n", + "data module. This may seem a little burdensome, but is part of the\n", + "general pipeline for `torch`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91d6633a", + "metadata": {}, + "outputs": [], + "source": [ + "datasets = []\n", + "for mask in [train, ~train]:\n", + " X_day_t = torch.tensor(\n", + " np.asarray(X_day[mask]).astype(np.float32))\n", + " Y_t = torch.tensor(np.asarray(Y[mask]).astype(np.float32))\n", + " datasets.append(TensorDataset(X_day_t, Y_t))\n", + "day_train, day_test = datasets" + ] + }, + { + "cell_type": "markdown", + "id": "b5cf941c", + "metadata": {}, + "source": [ + "Creating a data module follows a familiar pattern." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87137503", + "metadata": {}, + "outputs": [], + "source": [ + "day_dm = SimpleDataModule(day_train,\n", + " day_test,\n", + " num_workers=min(4, max_num_workers),\n", + " validation=day_test,\n", + " batch_size=64)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2575f374", + "metadata": {}, + "source": [ + "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer with 32 units. The remaining steps are familiar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cf09a55", + "metadata": {}, + "outputs": [], + "source": [ + "class NonLinearARModel(nn.Module):\n", + " def __init__(self):\n", + " super(NonLinearARModel, self).__init__()\n", + " self._forward = nn.Sequential(nn.Flatten(),\n", + " nn.Linear(20, 32),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.5),\n", + " nn.Linear(32, 1))\n", + " def forward(self, x):\n", + " return torch.flatten(self._forward(x))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "930576f3", + "metadata": {}, + "outputs": [], + "source": [ + "nl_model = NonLinearARModel()\n", + "nl_optimizer = RMSprop(nl_model.parameters(),\n", + " lr=0.001)\n", + "nl_module = SimpleModule.regression(nl_model,\n", + " optimizer=nl_optimizer,\n", + " metrics={'r2':R2Score()})\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0e3043c", + "metadata": {}, + "source": [ + "We continue with the usual training steps, fit the model,\n", + "and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9ae8d0e", + "metadata": {}, + "outputs": [], + "source": [ + "nl_trainer = Trainer(deterministic=True,\n", + " max_epochs=20,\n", + " callbacks=[ErrorTracker()])\n", + "nl_trainer.fit(nl_module, datamodule=day_dm)\n", + "nl_trainer.test(nl_module, datamodule=day_dm) " + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch11-surv-lab.ipynb b/docs/source/labs/Ch11-surv-lab.ipynb new file mode 100644 index 0000000..9e0eda6 --- /dev/null +++ b/docs/source/labs/Ch11-surv-lab.ipynb @@ -0,0 +1,959 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "709be2a4", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7f4eb5a", + "metadata": {}, + "source": [ + "# Survival Analysis\n", + " In this lab, we perform survival analyses on three separate data\n", + "sets. In Section 11.8.1 we analyze the `BrainCancer` \n", + "data that was first described in Section 11.3. In Section 11.8.2, we examine the `Publication` \n", + "data from Section 11.5.4. Finally, Section 11.8.3 explores\n", + "a simulated call-center data set.\n", + "\n", + "We begin by importing some of our libraries at this top\n", + "level. This makes the code more readable, as scanning the first few\n", + "lines of the notebook tell us what libraries are used in this\n", + "notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91ac40fd", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.pyplot import subplots\n", + "import numpy as np\n", + "import pandas as pd\n", + "from ISLP.models import ModelSpec as MS\n", + "from ISLP import load_data\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3dbcbbf", + "metadata": {}, + "source": [ + "We also collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99782418", + "metadata": {}, + "outputs": [], + "source": [ + "from lifelines import \\\n", + " (KaplanMeierFitter,\n", + " CoxPHFitter)\n", + "from lifelines.statistics import \\\n", + " (logrank_test,\n", + " multivariate_logrank_test)\n", + "from ISLP.survival import sim_time\n" + ] + }, + { + "cell_type": "markdown", + "id": "2c538d28", + "metadata": {}, + "source": [ + "## Brain Cancer Data\n", + "\n", + "We begin with the `BrainCancer` data set, contained in the `ISLP` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3137149a", + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer = load_data('BrainCancer')\n", + "BrainCancer.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "e798f172", + "metadata": {}, + "source": [ + "The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n", + "We first briefly examine the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45963c92", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "BrainCancer['sex'].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73be61f6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "BrainCancer['diagnosis'].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "572f0b9e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "BrainCancer['status'].value_counts()\n" + ] + }, + { + "cell_type": "markdown", + "id": "fbd132de", + "metadata": {}, + "source": [ + "Before beginning an analysis, it is important to know how the\n", + "`status` variable has been coded. Most software\n", + "uses the convention that a `status` of 1 indicates an\n", + "uncensored observation (often death), and a `status` of 0 indicates a censored\n", + "observation. But some scientists might use the opposite coding. For\n", + "the `BrainCancer` data set 35 patients died before the end of\n", + "the study, so we are using the conventional coding.\n", + "\n", + "To begin the analysis, we re-create the Kaplan-Meier survival curve shown in Figure 11.2. The main\n", + "package we will use for survival analysis\n", + "is `lifelines`.\n", + "The variable `time` corresponds to $y_i$, the time to the $i$th event (either censoring or\n", + "death). The first argument to `km.fit` is the event time, and the\n", + "second argument is the censoring variable, with a 1 indicating an observed\n", + "failure time. The `plot()` method produces a survival curve with pointwise confidence\n", + "intervals. By default, these are 90% confidence intervals, but this can be changed\n", + "by setting the `alpha` argument to one minus the desired\n", + "confidence level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92c39707", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "km = KaplanMeierFitter()\n", + "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n", + "km_brain.plot(label='Kaplan Meier estimate', ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f037665b", + "metadata": {}, + "source": [ + "Next we create Kaplan-Meier survival curves that are stratified by\n", + "`sex`, in order to reproduce Figure 11.3.\n", + "We do this using the `groupby()` method of a dataframe.\n", + "This method returns a generator that can\n", + "be iterated over in the `for` loop. In this case,\n", + "the items in the `for` loop are 2-tuples representing\n", + "the groups: the first entry is the value\n", + "of the grouping column `sex` while the second value\n", + "is the dataframe consisting of all rows in the\n", + "dataframe matching that value of `sex`.\n", + "We will want to use this data below\n", + "in the log-rank test, hence we store this\n", + "information in the dictionary `by_sex`. Finally,\n", + "we have also used the notion of\n", + " *string interpolation* to automatically\n", + "label the different lines in the plot. String\n", + "interpolation is a powerful technique to format strings ---\n", + "`Python` has many ways to facilitate such operations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fc7848c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "by_sex = {}\n", + "for sex, df in BrainCancer.groupby('sex'):\n", + " by_sex[sex] = df\n", + " km_sex = km.fit(df['time'], df['status'])\n", + " km_sex.plot(label='Sex=%s' % sex, ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0c1a16a", + "metadata": {}, + "source": [ + "As discussed in Section 11.4, we can perform a\n", + "log-rank test to compare the survival of males to females. We use\n", + "the `logrank_test()` function from the `lifelines.statistics` module.\n", + "The first two arguments are the event times, with the second\n", + "denoting the corresponding (optional) censoring indicators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf30d26f", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "logrank_test(by_sex['Male']['time'],\n", + " by_sex['Female']['time'],\n", + " by_sex['Male']['status'],\n", + " by_sex['Female']['status'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "e270649c", + "metadata": {}, + "source": [ + "The resulting $p$-value is $0.23$, indicating no evidence of a\n", + "difference in survival between the two sexes.\n", + "\n", + "Next, we use the `CoxPHFitter()` estimator\n", + "from `lifelines` to fit Cox proportional hazards models.\n", + "To begin, we consider a model that uses `sex` as the only predictor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab78e07", + "metadata": {}, + "outputs": [], + "source": [ + "coxph = CoxPHFitter # shorthand\n", + "sex_df = BrainCancer[['time', 'status', 'sex']]\n", + "model_df = MS(['time', 'status', 'sex'],\n", + " intercept=False).fit_transform(sex_df)\n", + "cox_fit = coxph().fit(model_df,\n", + " 'time',\n", + " 'status')\n", + "cox_fit.summary[['coef', 'se(coef)', 'p']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "b58b93ae", + "metadata": {}, + "source": [ + "The first argument to `fit` should be a data frame containing\n", + "at least the event time (the second argument `time` in this case),\n", + "as well as an optional censoring variable (the argument `status` in this case).\n", + "Note also that the Cox model does not include an intercept, which is why\n", + "we used the `intercept=False` argument to `ModelSpec` above.\n", + "The `summary()` method delivers many columns; we chose to abbreviate its output here.\n", + "It is possible to obtain the likelihood ratio test comparing this model to the one\n", + "with no features as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4716b7b0", + "metadata": {}, + "outputs": [], + "source": [ + "cox_fit.log_likelihood_ratio_test()\n" + ] + }, + { + "cell_type": "markdown", + "id": "2820f486", + "metadata": {}, + "source": [ + "Regardless of which test we use, we see that there is no clear\n", + "evidence for a difference in survival between males and females. As\n", + "we learned in this chapter, the score test from the Cox model is\n", + "exactly equal to the log rank test statistic!\n", + "\n", + "Now we fit a model that makes use of additional predictors. We first note\n", + "that one of our `diagnosis` values is missing, hence\n", + "we drop that observation before continuing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2767d88", + "metadata": {}, + "outputs": [], + "source": [ + "cleaned = BrainCancer.dropna()\n", + "all_MS = MS(cleaned.columns, intercept=False)\n", + "all_df = all_MS.fit_transform(cleaned)\n", + "fit_all = coxph().fit(all_df,\n", + " 'time',\n", + " 'status')\n", + "fit_all.summary[['coef', 'se(coef)', 'p']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "eee4ab1f", + "metadata": {}, + "source": [ + " The `diagnosis` variable has been coded so that the baseline\n", + "corresponds to HG glioma. The results indicate that the risk associated with HG glioma\n", + "is more than eight times (i.e. $e^{2.15}=8.62$) the risk associated\n", + "with meningioma. In other words, after adjusting for the other\n", + "predictors, patients with HG glioma have much worse survival compared\n", + "to those with meningioma. In addition, larger values of the Karnofsky\n", + "index, `ki`, are associated with lower risk, i.e. longer survival.\n", + "\n", + "Finally, we plot estimated survival curves for each diagnosis category,\n", + "adjusting for the other predictors. To make these plots, we set the\n", + "values of the other predictors equal to the mean for quantitative variables\n", + "and equal to the mode for categorical. To do this, we use the\n", + "`apply()` method across rows (i.e. `axis=0`) with a function\n", + "`representative` that checks if a column is categorical\n", + "or not." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede1d219", + "metadata": {}, + "outputs": [], + "source": [ + "levels = cleaned['diagnosis'].unique()\n", + "def representative(series):\n", + " if hasattr(series.dtype, 'categories'):\n", + " return pd.Series.mode(series)\n", + " else:\n", + " return series.mean()\n", + "modal_data = cleaned.apply(representative, axis=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1c307ae", + "metadata": {}, + "source": [ + "We make four\n", + "copies of the column means and assign the `diagnosis` column to be the four different\n", + "diagnoses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc032a71", + "metadata": {}, + "outputs": [], + "source": [ + "modal_df = pd.DataFrame(\n", + " [modal_data.iloc[0] for _ in range(len(levels))])\n", + "modal_df['diagnosis'] = levels\n", + "modal_df\n" + ] + }, + { + "cell_type": "markdown", + "id": "84da2586", + "metadata": {}, + "source": [ + "We then construct the model matrix based on the model specification `all_MS` used to fit\n", + "the model, and name the rows according to the levels of `diagnosis`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7c1fe43", + "metadata": {}, + "outputs": [], + "source": [ + "modal_X = all_MS.transform(modal_df)\n", + "modal_X.index = levels\n", + "modal_X\n" + ] + }, + { + "cell_type": "markdown", + "id": "3cfe1ec4", + "metadata": {}, + "source": [ + "We can use the `predict_survival_function()` method to obtain the estimated survival function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f89fbed7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "predicted_survival = fit_all.predict_survival_function(modal_X)\n", + "predicted_survival\n" + ] + }, + { + "cell_type": "markdown", + "id": "29afd641", + "metadata": {}, + "source": [ + "This returns a data frame,\n", + "whose plot methods yields the different survival curves. To avoid clutter in\n", + "the plots, we do not display confidence intervals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f0329b4", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "predicted_survival.plot(ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "12723ce5", + "metadata": {}, + "source": [ + "## Publication Data\n", + "The `Publication` data presented in Section 11.5.4 can be\n", + "found in the `ISLP` package.\n", + "We first reproduce Figure 11.5 by plotting the Kaplan-Meier curves\n", + "stratified on the `posres` variable, which records whether the\n", + "study had a positive or negative result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3045bfc0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "Publication = load_data('Publication')\n", + "by_result = {}\n", + "for result, df in Publication.groupby('posres'):\n", + " by_result[result] = df\n", + " km_result = km.fit(df['time'], df['status'])\n", + " km_result.plot(label='Result=%d' % result, ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6fcb22f7", + "metadata": {}, + "source": [ + "As discussed previously, the $p$-values from fitting Cox’s\n", + "proportional hazards model to the `posres` variable are quite\n", + "large, providing no evidence of a difference in time-to-publication\n", + "between studies with positive versus negative results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d070f716", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "posres_df = MS(['posres',\n", + " 'time',\n", + " 'status'],\n", + " intercept=False).fit_transform(Publication)\n", + "posres_fit = coxph().fit(posres_df,\n", + " 'time',\n", + " 'status')\n", + "posres_fit.summary[['coef', 'se(coef)', 'p']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "513a55b1", + "metadata": {}, + "source": [ + "However, the results change dramatically when we include other\n", + "predictors in the model. Here we exclude the funding mechanism\n", + "variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bbcdd0c", + "metadata": {}, + "outputs": [], + "source": [ + "model = MS(Publication.columns.drop('mech'),\n", + " intercept=False)\n", + "coxph().fit(model.fit_transform(Publication),\n", + " 'time',\n", + " 'status').summary[['coef', 'se(coef)', 'p']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "75bb8aa6", + "metadata": {}, + "source": [ + "We see that there are a number of statistically significant variables,\n", + "including whether the trial focused on a clinical endpoint, the impact\n", + "of the study, and whether the study had positive or negative results." + ] + }, + { + "cell_type": "markdown", + "id": "bfe236e5", + "metadata": {}, + "source": [ + "## Call Center Data\n", + "\n", + "In this section, we will simulate survival data using the relationship\n", + "between cumulative hazard and\n", + "the survival function explored in Exercise 8.\n", + "Our simulated data will represent the observed\n", + "wait times (in seconds) for 2,000 customers who have phoned a call\n", + "center. In this context, censoring occurs if a customer hangs up\n", + "before his or her call is answered.\n", + "\n", + "There are three covariates: `Operators` (the number of call\n", + "center operators available at the time of the call, which can range\n", + "from $5$ to $15$), `Center` (either A, B, or C), and\n", + "`Time` of day (Morning, Afternoon, or Evening). We generate data\n", + "for these covariates so that all possibilities are equally likely: for\n", + "instance, morning, afternoon and evening calls are equally likely, and\n", + "any number of operators from $5$ to $15$ is equally likely. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8ece43a", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(10)\n", + "N = 2000\n", + "Operators = rng.choice(np.arange(5, 16),\n", + " N,\n", + " replace=True)\n", + "Center = rng.choice(['A', 'B', 'C'],\n", + " N,\n", + " replace=True)\n", + "Time = rng.choice(['Morn.', 'After.', 'Even.'],\n", + " N,\n", + " replace=True)\n", + "D = pd.DataFrame({'Operators': Operators,\n", + " 'Center': pd.Categorical(Center),\n", + " 'Time': pd.Categorical(Time)})" + ] + }, + { + "cell_type": "markdown", + "id": "c93e44f3", + "metadata": {}, + "source": [ + "We then build a model matrix (omitting the intercept)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e4f766f", + "metadata": {}, + "outputs": [], + "source": [ + "model = MS(['Operators',\n", + " 'Center',\n", + " 'Time'],\n", + " intercept=False)\n", + "X = model.fit_transform(D)" + ] + }, + { + "cell_type": "markdown", + "id": "cad1ed19", + "metadata": {}, + "source": [ + "It is worthwhile to take a peek at the model matrix `X`, so\n", + "that we can be sure that we understand how the variables have been coded. By default,\n", + "the levels of categorical variables are sorted and, as usual, the first column of the one-hot encoding\n", + "of the variable is dropped." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72f42d14", + "metadata": {}, + "outputs": [], + "source": [ + "X[:5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "38c40ae1", + "metadata": {}, + "source": [ + "Next, we specify the coefficients and the hazard function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b921536", + "metadata": {}, + "outputs": [], + "source": [ + "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n", + "true_linpred = X.dot(true_beta)\n", + "hazard = lambda t: 1e-5 * t\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0698ffd", + "metadata": {}, + "source": [ + "Here, we have set the coefficient associated with `Operators` to\n", + "equal $0.04$; in other words, each additional operator leads to a\n", + "$e^{0.04}=1.041$-fold increase in the “risk” that the call will be\n", + "answered, given the `Center` and `Time` covariates. This\n", + "makes sense: the greater the number of operators at hand, the shorter\n", + "the wait time! The coefficient associated with `Center == B` is\n", + "$-0.3$, and `Center == A` is treated as the baseline. This means\n", + "that the risk of a call being answered at Center B is 0.74 times the\n", + "risk that it will be answered at Center A; in other words, the wait\n", + "times are a bit longer at Center B.\n", + "\n", + "Recall from Section 2.3.7 the use of `lambda`\n", + "for creating short functions on the fly.\n", + "We use the function\n", + "`sim_time()` from the `ISLP.survival` package. This function\n", + "uses the relationship between the survival function\n", + "and cumulative hazard $S(t) = \\exp(-H(t))$ and the specific\n", + "form of the cumulative hazard function in the Cox model\n", + "to simulate data based on values of the linear predictor\n", + "`true_linpred` and the cumulative hazard. \n", + " We need to provide the cumulative hazard function, which we do here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96ce0f99", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "cum_hazard = lambda t: 1e-5 * t**2 / 2\n" + ] + }, + { + "cell_type": "markdown", + "id": "1956e4c2", + "metadata": {}, + "source": [ + "We are now ready to generate data under the Cox proportional hazards\n", + "model. We truncate the maximum time to 1000 seconds to keep\n", + "simulated wait times reasonable. The function\n", + "`sim_time()` takes a linear predictor,\n", + "a cumulative hazard function and a\n", + "random number generator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63d78ff9", + "metadata": {}, + "outputs": [], + "source": [ + "W = np.array([sim_time(l, cum_hazard, rng)\n", + " for l in true_linpred])\n", + "D['Wait time'] = np.clip(W, 0, 1000)\n" + ] + }, + { + "cell_type": "markdown", + "id": "035e4ecf", + "metadata": {}, + "source": [ + "We now simulate our censoring variable, for which we assume\n", + "90% of calls were answered (`Failed==1`) before the\n", + "customer hung up (`Failed==0`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe008dbf", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "D['Failed'] = rng.choice([1, 0],\n", + " N,\n", + " p=[0.9, 0.1])\n", + "D[:5]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3a2bec7", + "metadata": {}, + "outputs": [], + "source": [ + "D['Failed'].mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "207937e5", + "metadata": {}, + "source": [ + "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b27af56", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "by_center = {}\n", + "for center, df in D.groupby('Center'):\n", + " by_center[center] = df\n", + " km_center = km.fit(df['Wait time'], df['Failed'])\n", + " km_center.plot(label='Center=%s' % center, ax=ax)\n", + "ax.set_title(\"Probability of Still Being on Hold\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "be6d37f7", + "metadata": {}, + "source": [ + "Next, we stratify by `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9625598d", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "by_time = {}\n", + "for time, df in D.groupby('Time'):\n", + " by_time[time] = df\n", + " km_time = km.fit(df['Wait time'], df['Failed'])\n", + " km_time.plot(label='Time=%s' % time, ax=ax)\n", + "ax.set_title(\"Probability of Still Being on Hold\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1408ebc0", + "metadata": {}, + "source": [ + "It seems that calls at Call Center B take longer to be answered than\n", + "calls at Centers A and C. Similarly, it appears that wait times are\n", + "longest in the morning and shortest in the evening hours. We can use a\n", + "log-rank test to determine whether these differences are statistically\n", + "significant using the function `multivariate_logrank_test()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75a744ef", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "multivariate_logrank_test(D['Wait time'],\n", + " D['Center'],\n", + " D['Failed'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "be5055e4", + "metadata": {}, + "source": [ + "Next, we consider the effect of `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9badb3e3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "multivariate_logrank_test(D['Wait time'],\n", + " D['Time'],\n", + " D['Failed'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "64b2bc33", + "metadata": {}, + "source": [ + "As in the case of a categorical variable with 2 levels, these\n", + "results are similar to the likelihood ratio test\n", + "from the Cox proportional hazards model. First, we\n", + "look at the results for `Center`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "026e9ff8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X = MS(['Wait time',\n", + " 'Failed',\n", + " 'Center'],\n", + " intercept=False).fit_transform(D)\n", + "F = coxph().fit(X, 'Wait time', 'Failed')\n", + "F.log_likelihood_ratio_test()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4ed54fe0", + "metadata": {}, + "source": [ + "Next, we look at the results for `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cab3789", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X = MS(['Wait time',\n", + " 'Failed',\n", + " 'Time'],\n", + " intercept=False).fit_transform(D)\n", + "F = coxph().fit(X, 'Wait time', 'Failed')\n", + "F.log_likelihood_ratio_test()\n" + ] + }, + { + "cell_type": "markdown", + "id": "2d250dc9", + "metadata": {}, + "source": [ + "We find that differences between centers are highly significant, as\n", + "are differences between times of day.\n", + "\n", + "Finally, we fit Cox's proportional hazards model to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cc4b898", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X = MS(D.columns,\n", + " intercept=False).fit_transform(D)\n", + "fit_queuing = coxph().fit(\n", + " X,\n", + " 'Wait time',\n", + " 'Failed')\n", + "fit_queuing.summary[['coef', 'se(coef)', 'p']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "bec9d61d", + "metadata": {}, + "source": [ + "The $p$-values for Center B and evening time\n", + "are very small. It is also clear that the\n", + "hazard --- that is, the instantaneous risk that a call will be\n", + "answered --- increases with the number of operators. Since we\n", + "generated the data ourselves, we know that the true coefficients for\n", + " `Operators`, `Center = B`, `Center = C`, \n", + "`Time = Even.` and `Time = Morn.` are $0.04$, $-0.3$,\n", + "$0$, $0.2$, and $-0.2$, respectively. The coefficient estimates\n", + "from the fitted Cox model are fairly accurate." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch12-unsup-lab.ipynb b/docs/source/labs/Ch12-unsup-lab.ipynb new file mode 100644 index 0000000..165ca03 --- /dev/null +++ b/docs/source/labs/Ch12-unsup-lab.ipynb @@ -0,0 +1,1634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e1733b5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "7dc7070a", + "metadata": {}, + "source": [ + " # Unsupervised Learning\n", + "In this lab we demonstrate PCA and clustering on several datasets.\n", + "As in other labs, we import some of our libraries at this top\n", + "level. This makes the code more readable, as scanning the first few\n", + "lines of the notebook tell us what libraries are used in this\n", + "notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24559be0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from statsmodels.datasets import get_rdataset\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler\n", + "from ISLP import load_data\n" + ] + }, + { + "cell_type": "markdown", + "id": "59b24a4b", + "metadata": {}, + "source": [ + "We also collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06fff57d", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import \\\n", + " (KMeans,\n", + " AgglomerativeClustering)\n", + "from scipy.cluster.hierarchy import \\\n", + " (dendrogram,\n", + " cut_tree)\n", + "from ISLP.cluster import compute_linkage\n" + ] + }, + { + "cell_type": "markdown", + "id": "f091de06", + "metadata": {}, + "source": [ + "## Principal Components Analysis\n", + "In this lab, we perform PCA on `USArrests`, a data set in the\n", + "`R` computing environment.\n", + "We retrieve the data using `get_rdataset()`, which can fetch data from\n", + "many standard `R` packages.\n", + "\n", + "The rows of the data set contain the 50 states, in alphabetical order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f425e07e", + "metadata": {}, + "outputs": [], + "source": [ + "USArrests = get_rdataset('USArrests').data\n", + "USArrests\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2942890", + "metadata": {}, + "source": [ + "The columns of the data set contain the four variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b127d014", + "metadata": {}, + "outputs": [], + "source": [ + "USArrests.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "68718331", + "metadata": {}, + "source": [ + "We first briefly examine the data. We notice that the variables have vastly different means." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7343f72", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "USArrests.mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "171a1ee0", + "metadata": {}, + "source": [ + "Dataframes have several useful methods for computing\n", + "column-wise summaries. We can also examine the\n", + "variance of the four variables using the `var()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34501140", + "metadata": {}, + "outputs": [], + "source": [ + "USArrests.var()\n" + ] + }, + { + "cell_type": "markdown", + "id": "5634db88", + "metadata": {}, + "source": [ + "Not surprisingly, the variables also have vastly different variances.\n", + "The `UrbanPop` variable measures the percentage of the population\n", + "in each state living in an urban area, which is not a comparable\n", + "number to the number of rapes in each state per 100,000 individuals.\n", + "PCA looks for derived variables that account for most of the variance in the data set.\n", + "If we do not scale the variables before performing PCA, then the principal components\n", + "would mostly be driven by the\n", + "`Assault` variable, since it has by far the largest\n", + "variance. So if the variables are measured in different units or vary widely in scale, it is recommended to standardize the variables to have standard deviation one before performing PCA.\n", + "Typically we set the means to zero as well.\n", + "\n", + "This scaling can be done via the `StandardScaler()` transform imported above. We first `fit` the\n", + "scaler, which computes the necessary means and standard\n", + "deviations and then apply it to our data using the\n", + "`transform` method. As before, we combine these steps using the `fit_transform()` method.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "daf119e8", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "scaler = StandardScaler(with_std=True,\n", + " with_mean=True)\n", + "USArrests_scaled = scaler.fit_transform(USArrests)\n" + ] + }, + { + "cell_type": "markdown", + "id": "dd6e5f5b", + "metadata": {}, + "source": [ + "Having scaled the data, we can then\n", + "perform principal components analysis using the `PCA()` transform\n", + "from the `sklearn.decomposition` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0eda7c9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pcaUS = PCA()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e5758ee5", + "metadata": {}, + "source": [ + "(By default, the `PCA()` transform centers the variables to have\n", + "mean zero though it does not scale them.) The transform `pcaUS`\n", + "can be used to find the PCA\n", + "`scores` returned by `fit()`. Once the `fit` method has been called, the `pcaUS` object also contains a number of useful quantities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1430fb3c", + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.fit(USArrests_scaled)\n" + ] + }, + { + "cell_type": "markdown", + "id": "56890b11", + "metadata": {}, + "source": [ + "After fitting, the `mean_` attribute corresponds to the means\n", + "of the variables. In this case, since we centered and scaled the data with\n", + "`scaler()` the means will all be 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6131d8d1", + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.mean_\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c1c34e7", + "metadata": {}, + "source": [ + "The scores can be computed using the `transform()` method\n", + "of `pcaUS` after it has been fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08246aad", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "scores = pcaUS.transform(USArrests_scaled)\n" + ] + }, + { + "cell_type": "markdown", + "id": "574ed47f", + "metadata": {}, + "source": [ + "We will plot these scores a bit further down.\n", + "The `components_` attribute provides the principal component loadings:\n", + "each row of `pcaUS.components_` contains the corresponding\n", + "principal component loading vector.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b682b632", + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.components_ \n" + ] + }, + { + "cell_type": "markdown", + "id": "fc8bfc99", + "metadata": {}, + "source": [ + "The `biplot` is a common visualization method used with\n", + "PCA. It is not built in as a standard\n", + "part of `sklearn`, though there are python\n", + "packages that do produce such plots. Here we\n", + "make a simple biplot manually." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c165e990", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "i, j = 0, 1 # which components\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "ax.scatter(scores[:,0], scores[:,1])\n", + "ax.set_xlabel('PC%d' % (i+1))\n", + "ax.set_ylabel('PC%d' % (j+1))\n", + "for k in range(pcaUS.components_.shape[1]):\n", + " ax.arrow(0, 0, pcaUS.components_[i,k], pcaUS.components_[j,k])\n", + " ax.text(pcaUS.components_[i,k],\n", + " pcaUS.components_[j,k],\n", + " USArrests.columns[k])\n" + ] + }, + { + "cell_type": "markdown", + "id": "eaef3e98", + "metadata": {}, + "source": [ + "Notice that this figure is a reflection of Figure 12.1 through the $y$-axis. Recall that the\n", + "principal components are only unique up to a sign change, so we can\n", + "reproduce that figure by flipping the\n", + "signs of the second set of scores and loadings.\n", + "We also increase the length of the arrows to emphasize the loadings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "848c9f35", + "metadata": {}, + "outputs": [], + "source": [ + "scale_arrow = s_ = 2\n", + "scores[:,1] *= -1\n", + "pcaUS.components_[1] *= -1 # flip the y-axis\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "ax.scatter(scores[:,0], scores[:,1])\n", + "ax.set_xlabel('PC%d' % (i+1))\n", + "ax.set_ylabel('PC%d' % (j+1))\n", + "for k in range(pcaUS.components_.shape[1]):\n", + " ax.arrow(0, 0, s_*pcaUS.components_[i,k], s_*pcaUS.components_[j,k])\n", + " ax.text(s_*pcaUS.components_[i,k],\n", + " s_*pcaUS.components_[j,k],\n", + " USArrests.columns[k])\n" + ] + }, + { + "cell_type": "markdown", + "id": "e380e98d", + "metadata": {}, + "source": [ + "The standard deviations of the principal component scores are as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34fdfe21", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "scores.std(0, ddof=1)" + ] + }, + { + "cell_type": "markdown", + "id": "c24b8ca2", + "metadata": {}, + "source": [ + "The variance of each score can be extracted directly from the `pcaUS` object via\n", + "the `explained_variance_` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31b43c57", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pcaUS.explained_variance_\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2dcf543", + "metadata": {}, + "source": [ + "The proportion of variance explained by each principal \n", + "component (PVE) is stored as `explained_variance_ratio_`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68e47d3a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pcaUS.explained_variance_ratio_\n" + ] + }, + { + "cell_type": "markdown", + "id": "831b19b7", + "metadata": {}, + "source": [ + "We see that the first principal component explains 62.0% of the\n", + "variance in the data, the next principal component explains 24.7%\n", + "of the variance, and so forth.\n", + "We can plot the PVE explained by each component, as well as the cumulative PVE. We first\n", + "plot the proportion of variance explained." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e87fe198", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "%%capture\n", + "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", + "ticks = np.arange(pcaUS.n_components_)+1\n", + "ax = axes[0]\n", + "ax.plot(ticks,\n", + " pcaUS.explained_variance_ratio_,\n", + " marker='o')\n", + "ax.set_xlabel('Principal Component');\n", + "ax.set_ylabel('Proportion of Variance Explained')\n", + "ax.set_ylim([0,1])\n", + "ax.set_xticks(ticks)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ed96395", + "metadata": {}, + "source": [ + "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "409fb0c6", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = axes[1]\n", + "ax.plot(ticks,\n", + " pcaUS.explained_variance_ratio_.cumsum(),\n", + " marker='o')\n", + "ax.set_xlabel('Principal Component')\n", + "ax.set_ylabel('Cumulative Proportion of Variance Explained')\n", + "ax.set_ylim([0, 1])\n", + "ax.set_xticks(ticks)\n", + "fig\n" + ] + }, + { + "cell_type": "markdown", + "id": "496fb6be", + "metadata": {}, + "source": [ + "The result is similar to that shown in Figure 12.3. Note\n", + "that the method `cumsum()` computes the cumulative sum of\n", + "the elements of a numeric vector. For instance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e563e41b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "a = np.array([1,2,8,-3])\n", + "np.cumsum(a)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6794b1a3", + "metadata": {}, + "source": [ + "## Matrix Completion\n", + "\n", + "We now re-create the analysis carried out on the `USArrests` data in\n", + "Section 12.3.\n", + "\n", + "We saw in Section 12.2.2 that solving the optimization\n", + "problem (12.6) on a centered data matrix $\\bf X$ is\n", + "equivalent to computing the first $M$ principal\n", + "components of the data. We use our scaled\n", + "and centered `USArrests` data as $\\bf X$ below. The *singular value decomposition* \n", + "(SVD) is a general algorithm for solving\n", + "(12.6). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f83ad0bc", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X = USArrests_scaled\n", + "U, D, V = np.linalg.svd(X, full_matrices=False)\n", + "U.shape, D.shape, V.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9c71e57", + "metadata": {}, + "source": [ + "The `np.linalg.svd()` function returns three components, `U`, `D` and `V`. The matrix `V` is equivalent to the\n", + "loading matrix from principal components (up to an unimportant sign flip). Using the `full_matrices=False` option ensures that\n", + "for a tall matrix the shape of `U` is the same as the shape of `X`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb9bdc46", + "metadata": {}, + "outputs": [], + "source": [ + "V\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f23c101e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pcaUS.components_\n" + ] + }, + { + "cell_type": "markdown", + "id": "a5d9e0ae", + "metadata": {}, + "source": [ + "The matrix `U` corresponds to a *standardized* version of the PCA score matrix (each column standardized to have sum-of-squares one). If we multiply each column of `U` by the corresponding element of `D`, we recover the PCA scores exactly (up to a meaningless sign flip)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cc49622", + "metadata": {}, + "outputs": [], + "source": [ + "(U * D[None,:])[:3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c96c9fe1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "scores[:3]\n" + ] + }, + { + "cell_type": "markdown", + "id": "6b7002cb", + "metadata": {}, + "source": [ + "While it would be possible to carry out this lab using the `PCA()` estimator,\n", + "here we use the `np.linalg.svd()` function in order to illustrate its use.\n", + "\n", + "We now omit 20 entries in the $50\\times 4$ data matrix at random. We do so\n", + "by first selecting 20 rows (states) at random, and then selecting one\n", + "of the four entries in each row at random. This ensures that every row has\n", + "at least three observed values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "574409d6", + "metadata": {}, + "outputs": [], + "source": [ + "n_omit = 20\n", + "np.random.seed(15)\n", + "r_idx = np.random.choice(np.arange(X.shape[0]),\n", + " n_omit,\n", + " replace=False)\n", + "c_idx = np.random.choice(np.arange(X.shape[1]),\n", + " n_omit,\n", + " replace=True)\n", + "Xna = X.copy()\n", + "Xna[r_idx, c_idx] = np.nan\n" + ] + }, + { + "cell_type": "markdown", + "id": "fd4a5fdf", + "metadata": {}, + "source": [ + "Here the array `r_idx`\n", + "contains 20 integers from 0 to 49; this represents the states (rows of `X`) that are selected to contain missing values. And `c_idx` contains\n", + "20 integers from 0 to 3, representing the features (columns in `X`) that contain the missing values for each of the selected states.\n", + "\n", + "We now write some code to implement Algorithm 12.1. \n", + "We first write a function that takes in a matrix, and returns an approximation to the matrix using the `svd()` function.\n", + "This will be needed in Step 2 of Algorithm 12.1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89f190ae", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "def low_rank(X, M=1):\n", + " U, D, V = np.linalg.svd(X)\n", + " L = U[:,:M] * D[None,:M]\n", + " return L.dot(V[:M])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a04129d0", + "metadata": {}, + "source": [ + "To conduct Step 1 of the algorithm, we initialize `Xhat` --- this is $\\tilde{\\bf X}$ in Algorithm 12.1 --- by replacing\n", + "the missing values with the column means of the non-missing entries. These are stored in\n", + "`Xbar` below after running `np.nanmean()` over the row axis.\n", + "We make a copy so that when we assign values to `Xhat` below we do not also overwrite the\n", + "values in `Xna`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "322f339c", + "metadata": {}, + "outputs": [], + "source": [ + "Xhat = Xna.copy()\n", + "Xbar = np.nanmean(Xhat, axis=0)\n", + "Xhat[r_idx, c_idx] = Xbar[c_idx]\n" + ] + }, + { + "cell_type": "markdown", + "id": "357041e8", + "metadata": {}, + "source": [ + "Before we begin Step 2, we set ourselves up to measure the progress of our\n", + "iterations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e106d1a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "thresh = 1e-7\n", + "rel_err = 1\n", + "count = 0\n", + "ismiss = np.isnan(Xna)\n", + "mssold = np.mean(Xhat[~ismiss]**2)\n", + "mss0 = np.mean(Xna[~ismiss]**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "105ac73f", + "metadata": {}, + "source": [ + "Here `ismiss` is a logical matrix with the same dimensions as `Xna`;\n", + "a given element is `True` if the corresponding matrix element is missing. The notation `~ismiss` negates this boolean vector. This is useful\n", + "because it allows us to access both the missing and non-missing entries. We store the mean of the squared non-missing elements in `mss0`.\n", + "We store the mean squared error of the non-missing elements of the old version of `Xhat` in `mssold` (which currently\n", + "agrees with `mss0`). We plan to store the mean squared error of the non-missing elements of the current version of `Xhat` in `mss`, and will then\n", + "iterate Step 2 of Algorithm 12.1 until the *relative error*, defined as\n", + "`(mssold - mss) / mss0`, falls below `thresh = 1e-7`.\n", + " {Algorithm 12.1 tells us to iterate Step 2 until (12.14) is no longer decreasing. Determining whether (12.14) is decreasing requires us only to keep track of `mssold - mss`. However, in practice, we keep track of `(mssold - mss) / mss0` instead: this makes it so that the number of iterations required for Algorithm 12.1 to converge does not depend on whether we multiplied the raw data $\\bf X$ by a constant factor.}\n", + "\n", + "In Step 2(a) of Algorithm 12.1, we approximate `Xhat` using `low_rank()`; we call this `Xapp`. In Step 2(b), we use `Xapp` to update the estimates for elements in `Xhat` that are missing in `Xna`. Finally, in Step 2(c), we compute the relative error. These three steps are contained in the following `while` loop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cb05ce4", + "metadata": {}, + "outputs": [], + "source": [ + "while rel_err > thresh:\n", + " count += 1\n", + " # Step 2(a)\n", + " Xapp = low_rank(Xhat, M=1)\n", + " # Step 2(b)\n", + " Xhat[ismiss] = Xapp[ismiss]\n", + " # Step 2(c)\n", + " mss = np.mean(((Xna - Xapp)[~ismiss])**2)\n", + " rel_err = (mssold - mss) / mss0\n", + " mssold = mss\n", + " print(\"Iteration: {0}, MSS:{1:.3f}, Rel.Err {2:.2e}\"\n", + " .format(count, mss, rel_err))\n" + ] + }, + { + "cell_type": "markdown", + "id": "5cd8edf2", + "metadata": {}, + "source": [ + "We see that after eight iterations, the relative error has fallen below `thresh = 1e-7`, and so the algorithm terminates. When this happens, the mean squared error of the non-missing elements equals 0.381.\n", + "\n", + "Finally, we compute the correlation between the 20 imputed values\n", + "and the actual values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f245188", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]\n" + ] + }, + { + "cell_type": "markdown", + "id": "f22444ff", + "metadata": {}, + "source": [ + "In this lab, we implemented Algorithm 12.1 ourselves for didactic purposes. However, a reader who wishes to apply matrix completion to their data might look to more specialized `Python` implementations." + ] + }, + { + "cell_type": "markdown", + "id": "7da441a1", + "metadata": {}, + "source": [ + "## Clustering" + ] + }, + { + "cell_type": "markdown", + "id": "ef4903d6", + "metadata": {}, + "source": [ + "### $K$-Means Clustering\n", + "\n", + "The estimator `sklearn.cluster.KMeans()` performs $K$-means clustering in\n", + "`Python`. We begin with a simple simulated example in which there\n", + "truly are two clusters in the data: the first 25 observations have a\n", + "mean shift relative to the next 25 observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "345fb41e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "np.random.seed(0);\n", + "X = np.random.standard_normal((50,2));\n", + "X[:25,0] += 3;\n", + "X[:25,1] -= 4;\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ed3587f", + "metadata": {}, + "source": [ + "We now perform $K$-means clustering with $K=2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a8c21a2", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "kmeans = KMeans(n_clusters=2,\n", + " random_state=2,\n", + " n_init=20).fit(X)\n" + ] + }, + { + "cell_type": "markdown", + "id": "df4fb0eb", + "metadata": {}, + "source": [ + "We specify `random_state` to make the results reproducible. The cluster assignments of the 50 observations are contained in `kmeans.labels_`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e35b5d", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "kmeans.labels_\n" + ] + }, + { + "cell_type": "markdown", + "id": "6632fa4c", + "metadata": {}, + "source": [ + "The $K$-means clustering perfectly separated the observations into two\n", + "clusters even though we did not supply any group information to\n", + "`KMeans()`. We can plot the data, with each observation\n", + "colored according to its cluster assignment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d928650a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n", + "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", + "ax.set_title(\"K-Means Clustering Results with K=2\");\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f6a6d01", + "metadata": {}, + "source": [ + "Here the observations can be easily plotted because they are\n", + "two-dimensional. If there were more than two variables then we could\n", + "instead perform PCA and plot the first two principal component score\n", + "vectors to represent the clusters.\n", + "\n", + "In this example, we knew that there really\n", + "were two clusters because we generated the data. However, for real\n", + "data, we do not know the true number of clusters, nor whether they exist in any precise way. We could\n", + "instead have performed $K$-means clustering on this example with\n", + "$K=3$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92e5175c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "kmeans = KMeans(n_clusters=3,\n", + " random_state=3,\n", + " n_init=20).fit(X)\n", + "fig, ax = plt.subplots(figsize=(8,8))\n", + "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", + "ax.set_title(\"K-Means Clustering Results with K=3\");\n" + ] + }, + { + "cell_type": "markdown", + "id": "82317a30", + "metadata": {}, + "source": [ + "When $K=3$, $K$-means clustering splits up the two clusters.\n", + "We have used the `n_init` argument to run the $K$-means with 20 \n", + "initial cluster assignments (the default is 10). If a\n", + "value of `n_init` greater than one is used, then $K$-means\n", + "clustering will be performed using multiple random assignments in\n", + "Step 1 of Algorithm 12.2, and the `KMeans()` \n", + "function will report only the best results. Here we compare using\n", + "`n_init=1` to `n_init=20`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4911ecc7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "kmeans1 = KMeans(n_clusters=3,\n", + " random_state=3,\n", + " n_init=1).fit(X)\n", + "kmeans20 = KMeans(n_clusters=3,\n", + " random_state=3,\n", + " n_init=20).fit(X);\n", + "kmeans1.inertia_, kmeans20.inertia_\n" + ] + }, + { + "cell_type": "markdown", + "id": "96e6df1f", + "metadata": {}, + "source": [ + "Note that `kmeans.inertia_` is the total within-cluster sum\n", + "of squares, which we seek to minimize by performing $K$-means\n", + "clustering (12.17). \n", + "\n", + "We *strongly* recommend always running $K$-means clustering with\n", + "a large value of `n_init`, such as 20 or 50, since otherwise an\n", + "undesirable local optimum may be obtained.\n", + "\n", + "When performing $K$-means clustering, in addition to using multiple\n", + "initial cluster assignments, it is also important to set a random seed\n", + "using the `random_state` argument to `KMeans()`. This way, the initial\n", + "cluster assignments in Step 1 can be replicated, and the $K$-means\n", + "output will be fully reproducible." + ] + }, + { + "cell_type": "markdown", + "id": "904ea858", + "metadata": {}, + "source": [ + "### Hierarchical Clustering\n", + "\n", + "The `AgglomerativeClustering()` class from\n", + "the `sklearn.clustering` package implements hierarchical clustering.\n", + "As its\n", + "name is long, we use the short hand `HClust` for *hierarchical clustering*. Note that this will not change the return type\n", + "when using this method, so instances will still be of class `AgglomerativeClustering`.\n", + "In the following example we use the data from the previous lab to plot the hierarchical clustering\n", + "dendrogram using complete, single, and average linkage clustering\n", + "with Euclidean distance as the dissimilarity measure. We begin by\n", + "clustering observations using complete linkage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b42a700", + "metadata": {}, + "outputs": [], + "source": [ + "HClust = AgglomerativeClustering\n", + "hc_comp = HClust(distance_threshold=0,\n", + " n_clusters=None,\n", + " linkage='complete')\n", + "hc_comp.fit(X)\n" + ] + }, + { + "cell_type": "markdown", + "id": "22199f4d", + "metadata": {}, + "source": [ + "This computes the entire dendrogram.\n", + "We could just as easily perform hierarchical clustering with average or single linkage instead:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50ef7eea", + "metadata": {}, + "outputs": [], + "source": [ + "hc_avg = HClust(distance_threshold=0,\n", + " n_clusters=None,\n", + " linkage='average');\n", + "hc_avg.fit(X)\n", + "hc_sing = HClust(distance_threshold=0,\n", + " n_clusters=None,\n", + " linkage='single');\n", + "hc_sing.fit(X);\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e8db256", + "metadata": {}, + "source": [ + "To use a precomputed distance matrix, we provide an additional\n", + "argument `metric=\"precomputed\"`. In the code below, the first four lines computes the $50\\times 50$ pairwise-distance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf7a2408", + "metadata": {}, + "outputs": [], + "source": [ + "D = np.zeros((X.shape[0], X.shape[0]));\n", + "for i in range(X.shape[0]):\n", + " x_ = np.multiply.outer(np.ones(X.shape[0]), X[i])\n", + " D[i] = np.sqrt(np.sum((X - x_)**2, 1));\n", + "hc_sing_pre = HClust(distance_threshold=0,\n", + " n_clusters=None,\n", + " metric='precomputed',\n", + " linkage='single')\n", + "hc_sing_pre.fit(D)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8cd7fb7c", + "metadata": {}, + "source": [ + "We use\n", + "`dendrogram()` from `scipy.cluster.hierarchy` to plot the dendrogram. However,\n", + "`dendrogram()` expects a so-called *linkage-matrix representation*\n", + "of the clustering, which is not provided by `AgglomerativeClustering()`,\n", + "but can be computed. The function `compute_linkage()` in the\n", + "`ISLP.cluster` package is provided for this purpose.\n", + "\n", + "We can now plot the dendrograms. The numbers at the bottom of the plot\n", + "identify each observation. The `dendrogram()` function has a default method to\n", + "color different branches of the tree that suggests a pre-defined cut of the tree at a particular depth.\n", + "We prefer to overwrite this default by setting this threshold to be infinite. Since we want this behavior for many dendrograms, we store these values in a dictionary `cargs` and pass this as keyword arguments using the notation `**cargs`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a118c0ab", + "metadata": {}, + "outputs": [], + "source": [ + "cargs = {'color_threshold':-np.inf,\n", + " 'above_threshold_color':'black'}\n", + "linkage_comp = compute_linkage(hc_comp)\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "dendrogram(linkage_comp,\n", + " ax=ax,\n", + " **cargs);\n" + ] + }, + { + "cell_type": "markdown", + "id": "a325a001", + "metadata": {}, + "source": [ + "We may want to color branches of the tree above\n", + "and below a cut-threshold differently. This can be achieved\n", + "by changing the `color_threshold`. Let’s cut the tree at a height of 4,\n", + "coloring links that merge above 4 in black." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1ff41c0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "dendrogram(linkage_comp,\n", + " ax=ax,\n", + " color_threshold=4,\n", + " above_threshold_color='black');\n" + ] + }, + { + "cell_type": "markdown", + "id": "e774708c", + "metadata": {}, + "source": [ + "To determine the cluster labels for each observation associated with a\n", + "given cut of the dendrogram, we can use the `cut_tree()` \n", + "function from `scipy.cluster.hierarchy`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2752a96", + "metadata": {}, + "outputs": [], + "source": [ + "cut_tree(linkage_comp, n_clusters=4).T\n" + ] + }, + { + "cell_type": "markdown", + "id": "d501d220", + "metadata": {}, + "source": [ + "This can also be achieved by providing an argument `n_clusters`\n", + "to `HClust()`; however each cut would require recomputing\n", + "the clustering. Similarly, trees may be cut by distance threshold\n", + "with an argument of `distance_threshold` to `HClust()`\n", + "or `height` to `cut_tree()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1407f7a4", + "metadata": {}, + "outputs": [], + "source": [ + "cut_tree(linkage_comp, height=5)\n" + ] + }, + { + "cell_type": "markdown", + "id": "469bbad6", + "metadata": {}, + "source": [ + "To scale the variables before performing hierarchical clustering of\n", + "the observations, we use `StandardScaler()` as in our PCA example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d74f224", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "X_scale = scaler.fit_transform(X)\n", + "hc_comp_scale = HClust(distance_threshold=0,\n", + " n_clusters=None,\n", + " linkage='complete').fit(X_scale)\n", + "linkage_comp_scale = compute_linkage(hc_comp_scale)\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "dendrogram(linkage_comp_scale, ax=ax, **cargs)\n", + "ax.set_title(\"Hierarchical Clustering with Scaled Features\");\n" + ] + }, + { + "cell_type": "markdown", + "id": "3caaa1a5", + "metadata": {}, + "source": [ + "Correlation-based distances between observations can be used for\n", + "clustering. The correlation between two observations measures the\n", + "similarity of their feature values. {Suppose each observation has\n", + " $p$ features, each a single numerical value. We measure the\n", + " similarity of two such observations by computing the\n", + " correlation of these $p$ pairs of numbers.}\n", + "With $n$ observations, the $n\\times n$ correlation matrix can then be used as a similarity (or affinity) matrix, i.e. so that one minus the correlation matrix is the dissimilarity matrix used for clustering.\n", + "\n", + "Note that using correlation only makes sense for\n", + "data with at least three features since the absolute correlation\n", + "between any two observations with measurements on two features is\n", + "always one. Hence, we will cluster a three-dimensional data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7f7da12", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X = np.random.standard_normal((30, 3))\n", + "corD = 1 - np.corrcoef(X)\n", + "hc_cor = HClust(linkage='complete',\n", + " distance_threshold=0,\n", + " n_clusters=None,\n", + " metric='precomputed')\n", + "hc_cor.fit(corD)\n", + "linkage_cor = compute_linkage(hc_cor)\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", + "dendrogram(linkage_cor, ax=ax, **cargs)\n", + "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b00804e", + "metadata": {}, + "source": [ + "## NCI60 Data Example\n", + "Unsupervised techniques are often used in the analysis of genomic\n", + "data. In particular, PCA and hierarchical clustering are popular\n", + "tools. We illustrate these techniques on the `NCI60` cancer cell line\n", + "microarray data, which consists of 6830 gene expression\n", + "measurements on 64 cancer cell lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b94424fc", + "metadata": {}, + "outputs": [], + "source": [ + "NCI60 = load_data('NCI60')\n", + "nci_labs = NCI60['labels']\n", + "nci_data = NCI60['data']\n" + ] + }, + { + "cell_type": "markdown", + "id": "7313fea6", + "metadata": {}, + "source": [ + "Each cell line is labeled with a cancer type. We do not make use of\n", + "the cancer types in performing PCA and clustering, as these are\n", + "unsupervised techniques. But after performing PCA and clustering, we\n", + "will check to see the extent to which these cancer types agree with\n", + "the results of these unsupervised techniques.\n", + "\n", + "The data has 64 rows and 6830 columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cea54566", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "nci_data.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ebf27f1", + "metadata": {}, + "source": [ + "We begin by examining the cancer types for the cell lines.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dac41bb", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "nci_labs.value_counts()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f16f96a", + "metadata": {}, + "source": [ + "### PCA on the NCI60 Data\n", + "\n", + "We first perform PCA on the data after scaling the variables (genes)\n", + "to have standard deviation one, although here one could reasonably argue\n", + "that it is better not to scale the genes as they are measured in the same units." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8ebadd6", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "nci_scaled = scaler.fit_transform(nci_data)\n", + "nci_pca = PCA()\n", + "nci_scores = nci_pca.fit_transform(nci_scaled)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a8c9932", + "metadata": {}, + "source": [ + "We now plot the first few principal component score vectors, in order\n", + "to visualize the data. The observations (cell lines) corresponding to\n", + "a given cancer type will be plotted in the same color, so that we can\n", + "see to what extent the observations within a cancer type are similar\n", + "to each other. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63b5efe3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "cancer_types = list(np.unique(nci_labs))\n", + "nci_groups = np.array([cancer_types.index(lab)\n", + " for lab in nci_labs.values])\n", + "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", + "ax = axes[0]\n", + "ax.scatter(nci_scores[:,0],\n", + " nci_scores[:,1],\n", + " c=nci_groups,\n", + " marker='o',\n", + " s=50)\n", + "ax.set_xlabel('PC1'); ax.set_ylabel('PC2')\n", + "ax = axes[1]\n", + "ax.scatter(nci_scores[:,0],\n", + " nci_scores[:,2],\n", + " c=nci_groups,\n", + " marker='o',\n", + " s=50)\n", + "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');\n" + ] + }, + { + "cell_type": "markdown", + "id": "87b33a9c", + "metadata": {}, + "source": [ + "On the whole, cell lines corresponding to a single cancer type do tend to\n", + "have similar values on the first few principal component score\n", + "vectors. This indicates that cell lines from the same cancer type tend\n", + "to have pretty similar gene expression levels.\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "890fdb54", + "metadata": {}, + "source": [ + "We can also plot the percent variance\n", + "explained by the principal components as well as the cumulative percent variance explained.\n", + "This is similar to the plots we made earlier for the `USArrests` data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e20c3cc1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", + "ax = axes[0]\n", + "ticks = np.arange(nci_pca.n_components_)+1\n", + "ax.plot(ticks,\n", + " nci_pca.explained_variance_ratio_,\n", + " marker='o')\n", + "ax.set_xlabel('Principal Component');\n", + "ax.set_ylabel('PVE')\n", + "ax = axes[1]\n", + "ax.plot(ticks,\n", + " nci_pca.explained_variance_ratio_.cumsum(),\n", + " marker='o');\n", + "ax.set_xlabel('Principal Component')\n", + "ax.set_ylabel('Cumulative PVE');\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb6eb4f8", + "metadata": {}, + "source": [ + "We see that together, the first seven principal components explain\n", + "around 40% of the variance in the data. This is not a huge amount\n", + "of the variance. However, looking at the scree plot, we see that while\n", + "each of the first seven principal components explain a substantial\n", + "amount of variance, there is a marked decrease in the variance\n", + "explained by further principal components. That is, there is an\n", + "*elbow* in the plot after approximately the seventh\n", + "principal component. This suggests that there may be little benefit\n", + "to examining more than seven or so principal components (though even\n", + "examining seven principal components may be difficult)." + ] + }, + { + "cell_type": "markdown", + "id": "8962791f", + "metadata": {}, + "source": [ + "### Clustering the Observations of the NCI60 Data\n", + "\n", + "We now perform hierarchical clustering of the cell lines in the `NCI60` data using\n", + "complete, single, and average linkage. Once again, the goal is to find out whether or not the observations cluster into distinct types of cancer. Euclidean\n", + "distance is used as the dissimilarity measure. We first write a short\n", + "function to produce\n", + "the three dendrograms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "622de805", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_nci(linkage, ax, cut=-np.inf):\n", + " cargs = {'above_threshold_color':'black',\n", + " 'color_threshold':cut}\n", + " hc = HClust(n_clusters=None,\n", + " distance_threshold=0,\n", + " linkage=linkage.lower()).fit(nci_scaled)\n", + " linkage_ = compute_linkage(hc)\n", + " dendrogram(linkage_,\n", + " ax=ax,\n", + " labels=np.asarray(nci_labs),\n", + " leaf_font_size=10,\n", + " **cargs)\n", + " ax.set_title('%s Linkage' % linkage)\n", + " return hc\n" + ] + }, + { + "cell_type": "markdown", + "id": "9cf5f836", + "metadata": {}, + "source": [ + "Let’s plot our results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54d40449", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(15,30)) \n", + "ax = axes[0]; hc_comp = plot_nci('Complete', ax)\n", + "ax = axes[1]; hc_avg = plot_nci('Average', ax)\n", + "ax = axes[2]; hc_sing = plot_nci('Single', ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1703afbb", + "metadata": {}, + "source": [ + "We see that the\n", + "choice of linkage certainly does affect the results\n", + "obtained. Typically, single linkage will tend to yield *trailing*\n", + "clusters: very large clusters onto which individual observations\n", + "attach one-by-one. On the other hand, complete and average linkage\n", + "tend to yield more balanced, attractive clusters. For this reason,\n", + "complete and average linkage are generally preferred to single\n", + "linkage. Clearly cell lines within a single cancer type do tend to\n", + "cluster together, although the clustering is not perfect. We will use\n", + "complete linkage hierarchical clustering for the analysis that\n", + "follows.\n", + "\n", + "We can cut the dendrogram at the height that will yield a particular\n", + "number of clusters, say four:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc80afc8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "linkage_comp = compute_linkage(hc_comp)\n", + "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n", + "pd.crosstab(nci_labs['label'],\n", + " pd.Series(comp_cut.reshape(-1), name='Complete'))\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8ecae69", + "metadata": {}, + "source": [ + "There are some clear patterns. All the leukemia cell lines fall in\n", + "one cluster, while the breast cancer cell lines are spread out over\n", + "three different clusters.\n", + "\n", + "We can plot a cut on the dendrogram that produces these four clusters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40ff59f9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "plot_nci('Complete', ax, cut=140)\n", + "ax.axhline(140, c='r', linewidth=4);\n" + ] + }, + { + "cell_type": "markdown", + "id": "1c03368c", + "metadata": {}, + "source": [ + "The `axhline()` function draws a horizontal line line on top of any\n", + "existing set of axes. The argument `140` plots a horizontal\n", + "line at height 140 on the dendrogram; this is a height that\n", + "results in four distinct clusters. It is easy to verify that the\n", + "resulting clusters are the same as the ones we obtained in\n", + "`comp_cut`.\n", + "\n", + "We claimed earlier in Section 12.4.2 that\n", + "$K$-means clustering and hierarchical clustering with the dendrogram\n", + "cut to obtain the same number of clusters can yield very different\n", + "results. How do these `NCI60` hierarchical clustering results compare\n", + "to what we get if we perform $K$-means clustering with $K=4$?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1587e83b", + "metadata": {}, + "outputs": [], + "source": [ + "nci_kmeans = KMeans(n_clusters=4, \n", + " random_state=0,\n", + " n_init=20).fit(nci_scaled)\n", + "pd.crosstab(pd.Series(comp_cut, name='HClust'),\n", + " pd.Series(nci_kmeans.labels_, name='K-means'))\n" + ] + }, + { + "cell_type": "markdown", + "id": "b49c4a73", + "metadata": {}, + "source": [ + "We see that the four clusters obtained using hierarchical clustering\n", + "and $K$-means clustering are somewhat different. First we note\n", + "that the labels in the two clusterings are arbitrary. That is, swapping\n", + "the identifier of the cluster does not\n", + "change the clustering. We see here Cluster 3 in\n", + "$K$-means clustering is identical to cluster 2 in hierarchical\n", + "clustering. However, the other clusters differ: for instance,\n", + "cluster 0 in $K$-means clustering contains a portion of the\n", + "observations assigned to cluster 0 by hierarchical clustering, as well\n", + "as all of the observations assigned to cluster 1 by hierarchical\n", + "clustering.\n", + "\n", + "Rather than performing hierarchical clustering on the entire data\n", + "matrix, we can also perform hierarchical clustering on the first few\n", + "principal component score vectors, regarding these first few components\n", + "as a less noisy version of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b09ceeab", + "metadata": {}, + "outputs": [], + "source": [ + "hc_pca = HClust(n_clusters=None,\n", + " distance_threshold=0,\n", + " linkage='complete'\n", + " ).fit(nci_scores[:,:5])\n", + "linkage_pca = compute_linkage(hc_pca)\n", + "fig, ax = plt.subplots(figsize=(8,8))\n", + "dendrogram(linkage_pca,\n", + " labels=np.asarray(nci_labs),\n", + " leaf_font_size=10,\n", + " ax=ax,\n", + " **cargs)\n", + "ax.set_title(\"Hier. Clust. on First Five Score Vectors\")\n", + "pca_labels = pd.Series(cut_tree(linkage_pca,\n", + " n_clusters=4).reshape(-1),\n", + " name='Complete-PCA')\n", + "pd.crosstab(nci_labs['label'], pca_labels)\n" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch13-multiple-lab.ipynb b/docs/source/labs/Ch13-multiple-lab.ipynb new file mode 100644 index 0000000..a7c1cdc --- /dev/null +++ b/docs/source/labs/Ch13-multiple-lab.ipynb @@ -0,0 +1,961 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bb70c597", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "e21562bb", + "metadata": {}, + "source": [ + "# Multiple Testing\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "34e410a6", + "metadata": {}, + "source": [ + "We include our usual imports seen in earlier labs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f928b2d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from ISLP import load_data\n" + ] + }, + { + "cell_type": "markdown", + "id": "12319e0a", + "metadata": {}, + "source": [ + "We also collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb4b32aa", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from scipy.stats import \\\n", + " (ttest_1samp,\n", + " ttest_rel,\n", + " ttest_ind,\n", + " t as t_dbn)\n", + "from statsmodels.stats.multicomp import \\\n", + " pairwise_tukeyhsd\n", + "from statsmodels.stats.multitest import \\\n", + " multipletests as mult_test\n" + ] + }, + { + "cell_type": "markdown", + "id": "a2747e58", + "metadata": {}, + "source": [ + "## Review of Hypothesis Tests\n", + "We begin by performing some one-sample $t$-tests.\n", + "\n", + "First we create 100 variables, each consisting of 10 observations. The\n", + "first 50 variables have mean $0.5$ and variance $1$, while the others\n", + "have mean $0$ and variance $1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e12ac0cd", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(12)\n", + "X = rng.standard_normal((10, 100))\n", + "true_mean = np.array([0.5]*50 + [0]*50)\n", + "X += true_mean[None,:]\n" + ] + }, + { + "cell_type": "markdown", + "id": "70d37233", + "metadata": {}, + "source": [ + "To begin, we use `ttest_1samp()` from the\n", + "`scipy.stats` module to test $H_{0}: \\mu_1=0$, the null\n", + "hypothesis that the first variable has mean zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04d0f49e", + "metadata": {}, + "outputs": [], + "source": [ + "result = ttest_1samp(X[:,0], 0)\n", + "result.pvalue\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf83426f", + "metadata": {}, + "source": [ + "The $p$-value comes out to 0.931, which is not low enough to\n", + "reject the null hypothesis at level $\\alpha=0.05$. In this case,\n", + "$\\mu_1=0.5$, so the null hypothesis is false. Therefore, we have made\n", + "a Type II error by failing to reject the null hypothesis when the null\n", + "hypothesis is false. \n", + "\n", + "We now test $H_{0,j}: \\mu_j=0$ for $j=1,\\ldots,100$. We compute the\n", + "100 $p$-values, and then construct a vector recording whether the\n", + "$j$th $p$-value is less than or equal to 0.05, in which case we reject\n", + "$H_{0j}$, or greater than 0.05, in which case we do not reject\n", + "$H_{0j}$, for $j=1,\\ldots,100$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1f0c695", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "p_values = np.empty(100)\n", + "for i in range(100):\n", + " p_values[i] = ttest_1samp(X[:,i], 0).pvalue\n", + "decision = pd.cut(p_values,\n", + " [0, 0.05, 1],\n", + " labels=['Reject H0',\n", + " 'Do not reject H0'])\n", + "truth = pd.Categorical(true_mean == 0,\n", + " categories=[True, False],\n", + " ordered=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d8e0d96", + "metadata": {}, + "source": [ + "Since this is a simulated data set, we can create a $2 \\times 2$ table\n", + "similar to Table 13.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a9594a0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pd.crosstab(decision,\n", + " truth,\n", + " rownames=['Decision'],\n", + " colnames=['H0'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "9610c817", + "metadata": {}, + "source": [ + "Therefore, at level $\\alpha=0.05$, we reject 15 of the 50 false\n", + "null hypotheses, and we incorrectly reject 5 of the true null\n", + "hypotheses. Using the notation from Section 13.3, we have\n", + "$V=5$, $S=15$, $U=45$ and $W=35$.\n", + "We have set $\\alpha=0.05$, which means that we expect to reject around\n", + "5% of the true null hypotheses. This is in line with the $2 \\times 2$\n", + "table above, which indicates that we rejected $V=5$ of the $50$ true\n", + "null hypotheses.\n", + "\n", + "In the simulation above, for the false null hypotheses, the ratio of\n", + "the mean to the standard deviation was only $0.5/1 = 0.5$. This\n", + "amounts to quite a weak signal, and it resulted in a high number of\n", + "Type II errors. Let’s instead simulate data with a stronger signal,\n", + "so that the ratio of the mean to the standard deviation for the false\n", + "null hypotheses equals $1$. We make only 10 Type II errors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25f7fc5d", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "true_mean = np.array([1]*50 + [0]*50)\n", + "X = rng.standard_normal((10, 100))\n", + "X += true_mean[None,:]\n", + "for i in range(100):\n", + " p_values[i] = ttest_1samp(X[:,i], 0).pvalue\n", + "decision = pd.cut(p_values,\n", + " [0, 0.05, 1],\n", + " labels=['Reject H0',\n", + " 'Do not reject H0'])\n", + "truth = pd.Categorical(true_mean == 0,\n", + " categories=[True, False],\n", + " ordered=True)\n", + "pd.crosstab(decision,\n", + " truth,\n", + " rownames=['Decision'],\n", + " colnames=['H0'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4b5d621", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "f6953d33", + "metadata": {}, + "source": [ + "## Family-Wise Error Rate\n", + "Recall from (13.5) that if the null hypothesis is true\n", + "for each of $m$ independent hypothesis tests, then the FWER is equal\n", + "to $1-(1-\\alpha)^m$. We can use this expression to compute the FWER\n", + "for $m=1,\\ldots, 500$ and $\\alpha=0.05$, $0.01$, and $0.001$.\n", + "We plot the FWER for these values of $\\alpha$ in order to\n", + "reproduce Figure 13.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "369b5bd3", + "metadata": {}, + "outputs": [], + "source": [ + "m = np.linspace(1, 501)\n", + "fig, ax = plt.subplots()\n", + "[ax.plot(m,\n", + " 1 - (1 - alpha)**m,\n", + " label=r'$\\alpha=%s$' % str(alpha))\n", + " for alpha in [0.05, 0.01, 0.001]]\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel('Number of Hypotheses')\n", + "ax.set_ylabel('Family-Wise Error Rate')\n", + "ax.legend()\n", + "ax.axhline(0.05, c='k', ls='--');\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a81479e", + "metadata": {}, + "source": [ + "As discussed previously, even for moderate values of $m$ such as $50$,\n", + "the FWER exceeds $0.05$ unless $\\alpha$ is set to a very low value,\n", + "such as $0.001$. Of course, the problem with setting $\\alpha$ to such\n", + "a low value is that we are likely to make a number of Type II errors:\n", + "in other words, our power is very low.\n", + "\n", + "We now conduct a one-sample $t$-test for each of the first five\n", + "managers in the \n", + "`Fund` dataset, in order to test the null\n", + "hypothesis that the $j$th fund manager’s mean return equals zero,\n", + "$H_{0,j}: \\mu_j=0$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ce7a19f", + "metadata": {}, + "outputs": [], + "source": [ + "Fund = load_data('Fund')\n", + "fund_mini = Fund.iloc[:,:5]\n", + "fund_mini_pvals = np.empty(5)\n", + "for i in range(5):\n", + " fund_mini_pvals[i] = ttest_1samp(fund_mini.iloc[:,i], 0).pvalue\n", + "fund_mini_pvals\n" + ] + }, + { + "cell_type": "markdown", + "id": "7561e3a3", + "metadata": {}, + "source": [ + "The $p$-values are low for Managers One and Three, and high for the\n", + "other three managers. However, we cannot simply reject $H_{0,1}$ and\n", + "$H_{0,3}$, since this would fail to account for the multiple testing\n", + "that we have performed. Instead, we will conduct Bonferroni’s method\n", + "and Holm’s method to control the FWER.\n", + "\n", + "To do this, we use the `multipletests()` function from the\n", + "`statsmodels` module (abbreviated to `mult_test()`). Given the $p$-values,\n", + "for methods like Holm and Bonferroni the function outputs\n", + "adjusted $p$-values, which\n", + "can be thought of as a new set of $p$-values that have been corrected\n", + "for multiple testing. If the adjusted $p$-value for a given hypothesis\n", + "is less than or equal to $\\alpha$, then that hypothesis can be\n", + "rejected while maintaining a FWER of no more than $\\alpha$. In other\n", + "words, for such methods, the adjusted $p$-values resulting from the `multipletests()`\n", + "function can simply be compared to the desired FWER in order to\n", + "determine whether or not to reject each hypothesis. We will later\n", + "see that we can use the same function to control FDR as well." + ] + }, + { + "cell_type": "markdown", + "id": "5b608e46", + "metadata": {}, + "source": [ + "The `mult_test()` function takes $p$-values and a `method` argument, as well as an optional\n", + "`alpha` argument. It returns the decisions (`reject` below)\n", + "as well as the adjusted $p$-values (`bonf`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de6cffed", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n", + "reject\n" + ] + }, + { + "cell_type": "markdown", + "id": "5135c6b9", + "metadata": {}, + "source": [ + "The $p$-values `bonf` are simply the `fund_mini_pvalues` multiplied by 5 and truncated to be less than\n", + "or equal to 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0de71500", + "metadata": {}, + "outputs": [], + "source": [ + "bonf, np.minimum(fund_mini_pvals * 5, 1)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f0bc112", + "metadata": {}, + "source": [ + "Therefore, using Bonferroni’s method, we are able to reject the null hypothesis only for Manager\n", + "One while controlling FWER at $0.05$.\n", + "\n", + "By contrast, using Holm’s method, the adjusted $p$-values indicate\n", + "that we can reject the null\n", + "hypotheses for Managers One and Three at a FWER of $0.05$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7e87bdb", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]\n" + ] + }, + { + "cell_type": "markdown", + "id": "f762fecd", + "metadata": {}, + "source": [ + "As discussed previously, Manager One seems to perform particularly\n", + "well, whereas Manager Two has poor performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e88be376", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fund_mini.mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "88dbf0a6", + "metadata": {}, + "source": [ + "Is there evidence of a meaningful difference in performance between\n", + "these two managers? We can check this by performing a paired $t$-test using the `ttest_rel()` function\n", + "from `scipy.stats`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41149af6", + "metadata": {}, + "outputs": [], + "source": [ + "ttest_rel(fund_mini['Manager1'],\n", + " fund_mini['Manager2']).pvalue\n" + ] + }, + { + "cell_type": "markdown", + "id": "1aca6122", + "metadata": {}, + "source": [ + "The test results in a $p$-value of 0.038,\n", + "suggesting a statistically significant difference.\n", + "\n", + "However, we decided to perform this test only after examining the data\n", + "and noting that Managers One and Two had the highest and lowest mean\n", + "performances. In a sense, this means that we have implicitly\n", + "performed ${5 \\choose 2} = 5(5-1)/2=10$ hypothesis tests, rather than\n", + "just one, as discussed in Section 13.3.2. Hence, we use the\n", + "`pairwise_tukeyhsd()` function from\n", + "`statsmodels.stats.multicomp` to apply Tukey’s method\n", + " in order to adjust for multiple testing. This function takes\n", + "as input a fitted *ANOVA* regression model, which is\n", + "essentially just a linear regression in which all of the predictors\n", + "are qualitative. In this case, the response consists of the monthly\n", + "excess returns achieved by each manager, and the predictor indicates\n", + "the manager to which each return corresponds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61aabda7", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n", + "managers = np.hstack([[i+1]*50 for i in range(5)])\n", + "tukey = pairwise_tukeyhsd(returns, managers)\n", + "print(tukey.summary())\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0084fc5", + "metadata": {}, + "source": [ + "The `pairwise_tukeyhsd()` function provides confidence intervals\n", + "for the difference between each pair of managers (`lower` and\n", + "`upper`), as well as a $p$-value. All of these quantities have\n", + "been adjusted for multiple testing. Notice that the $p$-value for the\n", + "difference between Managers One and Two has increased from $0.038$ to\n", + "$0.186$, so there is no longer clear evidence of a difference between\n", + "the managers’ performances. We can plot the confidence intervals for\n", + "the pairwise comparisons using the `plot_simultaneous()` method\n", + "of `tukey`. Any pair of intervals that don’t overlap indicates a significant difference at the nominal level of 0.05. In this case,\n", + "no differences are considered significant as reported in the table above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbcad4de", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "tukey.plot_simultaneous(ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "6278d13c", + "metadata": {}, + "source": [ + "## False Discovery Rate\n", + "Now we perform hypothesis tests for all 2,000 fund managers in the\n", + "`Fund` dataset. We perform a one-sample $t$-test\n", + "of $H_{0,j}: \\mu_j=0$, which states that the\n", + "$j$th fund manager’s mean return is zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5842190", + "metadata": {}, + "outputs": [], + "source": [ + "fund_pvalues = np.empty(2000)\n", + "for i, manager in enumerate(Fund.columns):\n", + " fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue\n" + ] + }, + { + "cell_type": "markdown", + "id": "80fc2fcc", + "metadata": {}, + "source": [ + "There are far too many managers to consider trying to control the FWER.\n", + "Instead, we focus on controlling the FDR: that is, the expected fraction of rejected null hypotheses that are actually false positives.\n", + "The `multipletests()` function (abbreviated `mult_test()`) can be used to carry out the Benjamini--Hochberg procedure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c9d8bed", + "metadata": {}, + "outputs": [], + "source": [ + "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n", + "fund_qvalues[:10]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f73096d", + "metadata": {}, + "source": [ + "The *q-values* output by the\n", + "Benjamini--Hochberg procedure can be interpreted as the smallest FDR\n", + "threshold at which we would reject a particular null hypothesis. For\n", + "instance, a $q$-value of $0.1$ indicates that we can reject the\n", + "corresponding null hypothesis at an FDR of 10% or greater, but that\n", + "we cannot reject the null hypothesis at an FDR below 10%.\n", + "\n", + "If we control the FDR at 10%, then for how many of the fund managers can we reject $H_{0,j}: \\mu_j=0$?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfa39f7c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "(fund_qvalues <= 0.1).sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ccb44c8d", + "metadata": {}, + "source": [ + "We find that 146 of the 2,000 fund managers have a $q$-value below\n", + "0.1; therefore, we are able to conclude that 146 of the fund managers\n", + "beat the market at an FDR of 10%. Only about 15 (10% of 146) of\n", + "these fund managers are likely to be false discoveries.\n", + "\n", + "By contrast, if we had instead used Bonferroni’s method to control the\n", + "FWER at level $\\alpha=0.1$, then we would have failed to reject any\n", + "null hypotheses!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70b69b47", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "(fund_pvalues <= 0.1 / 2000).sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8a969f4", + "metadata": {}, + "source": [ + "Figure 13.6 displays the ordered\n", + "$p$-values, $p_{(1)} \\leq p_{(2)} \\leq \\cdots \\leq p_{(2000)}$, for\n", + "the `Fund` dataset, as well as the threshold for rejection by the\n", + "Benjamini--Hochberg procedure. Recall that the Benjamini--Hochberg\n", + "procedure identifies the largest $p$-value such that $p_{(j)} 0:\n", + " selected_ = fund_pvalues < sorted_[sorted_set_].max()\n", + " sorted_set_ = np.arange(sorted_set_.max())\n", + "else:\n", + " selected_ = []\n", + " sorted_set_ = []\n" + ] + }, + { + "cell_type": "markdown", + "id": "ddeb3900", + "metadata": {}, + "source": [ + "We now reproduce the middle panel of Figure 13.6." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0314eac9", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(np.arange(0, sorted_.shape[0]) + 1,\n", + " sorted_, s=10)\n", + "ax.set_yscale('log')\n", + "ax.set_xscale('log')\n", + "ax.set_ylabel('P-Value')\n", + "ax.set_xlabel('Index')\n", + "ax.scatter(sorted_set_+1, sorted_[sorted_set_], c='r', s=20)\n", + "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);\n" + ] + }, + { + "cell_type": "markdown", + "id": "83416f4a", + "metadata": {}, + "source": [ + "## A Re-Sampling Approach\n", + "Here, we implement the re-sampling approach to hypothesis testing\n", + "using the `Khan` dataset, which we investigated in\n", + "Section 13.5. First, we merge the training and\n", + "testing data, which results in observations on 83 patients for\n", + "2,308 genes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b59b8137", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Khan = load_data('Khan') \n", + "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n", + "D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n", + "D['Y'].value_counts()\n" + ] + }, + { + "cell_type": "markdown", + "id": "5534c8d4", + "metadata": {}, + "source": [ + "There are four classes of cancer. For each gene, we compare the mean\n", + "expression in the second class (rhabdomyosarcoma) to the mean\n", + "expression in the fourth class (Burkitt’s lymphoma). Performing a\n", + "standard two-sample $t$-test \n", + "using `ttest_ind()` from `scipy.stats` on the $11$th\n", + "gene produces a test-statistic of -2.09 and an associated $p$-value\n", + "of 0.0412, suggesting modest evidence of a difference in mean\n", + "expression levels between the two cancer types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96fb2f61", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "D2 = D[lambda df:df['Y'] == 2]\n", + "D4 = D[lambda df:df['Y'] == 4]\n", + "gene_11 = 'G0011'\n", + "observedT, pvalue = ttest_ind(D2[gene_11],\n", + " D4[gene_11],\n", + " equal_var=True)\n", + "observedT, pvalue\n" + ] + }, + { + "cell_type": "markdown", + "id": "3131124e", + "metadata": {}, + "source": [ + "However, this $p$-value relies on the assumption that under the null\n", + "hypothesis of no difference between the two groups, the test statistic\n", + "follows a $t$-distribution with $29+25-2=52$ degrees of freedom.\n", + "Instead of using this theoretical null distribution, we can randomly\n", + "split the 54 patients into two groups of 29 and 25, and compute a new\n", + "test statistic. Under the null hypothesis of no difference between\n", + "the groups, this new test statistic should have the same distribution\n", + "as our original one. Repeating this process 10,000 times allows us to\n", + "approximate the null distribution of the test statistic. We compute\n", + "the fraction of the time that our observed test statistic exceeds the\n", + "test statistics obtained via re-sampling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdc229fa", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "B = 10000\n", + "Tnull = np.empty(B)\n", + "D_ = np.hstack([D2[gene_11], D4[gene_11]])\n", + "n_ = D2[gene_11].shape[0]\n", + "D_null = D_.copy()\n", + "for b in range(B):\n", + " rng.shuffle(D_null)\n", + " ttest_ = ttest_ind(D_null[:n_],\n", + " D_null[n_:],\n", + " equal_var=True)\n", + " Tnull[b] = ttest_.statistic\n", + "(np.abs(Tnull) > np.abs(observedT)).mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7fc4557", + "metadata": {}, + "source": [ + "This fraction, 0.0398,\n", + "is our re-sampling-based $p$-value.\n", + "It is almost identical to the $p$-value of 0.0412 obtained using the theoretical null distribution.\n", + "We can plot a histogram of the re-sampling-based test statistics in order to reproduce Figure 13.7." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3894695", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "ax.hist(Tnull,\n", + " bins=100,\n", + " density=True,\n", + " facecolor='y',\n", + " label='Null')\n", + "xval = np.linspace(-4.2, 4.2, 1001)\n", + "ax.plot(xval,\n", + " t_dbn.pdf(xval, D_.shape[0]-2),\n", + " c='r')\n", + "ax.axvline(observedT,\n", + " c='b',\n", + " label='Observed')\n", + "ax.legend()\n", + "ax.set_xlabel(\"Null Distribution of Test Statistic\");\n" + ] + }, + { + "cell_type": "markdown", + "id": "3bd21158", + "metadata": {}, + "source": [ + "The re-sampling-based null distribution is almost identical to the theoretical null distribution, which is displayed in red.\n", + "\n", + "Finally, we implement the plug-in re-sampling FDR approach outlined in\n", + "Algorithm 13.4. Depending on the speed of your\n", + "computer, calculating the FDR for all 2,308 genes in the `Khan`\n", + "dataset may take a while. Hence, we will illustrate the approach on a\n", + "random subset of 100 genes. For each gene, we first compute the\n", + "observed test statistic, and then produce 10,000 re-sampled test\n", + "statistics. This may take a few minutes to run. If you are in a rush,\n", + "then you could set `B` equal to a smaller value (e.g. `B=500`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b7392cb", + "metadata": {}, + "outputs": [], + "source": [ + "m, B = 100, 10000\n", + "idx = rng.choice(Khan['xtest'].columns, m, replace=False)\n", + "T_vals = np.empty(m)\n", + "Tnull_vals = np.empty((m, B))\n", + "\n", + "for j in range(m):\n", + " col = idx[j]\n", + " T_vals[j] = ttest_ind(D2[col],\n", + " D4[col],\n", + " equal_var=True).statistic\n", + " D_ = np.hstack([D2[col], D4[col]])\n", + " D_null = D_.copy()\n", + " for b in range(B):\n", + " rng.shuffle(D_null)\n", + " ttest_ = ttest_ind(D_null[:n_],\n", + " D_null[n_:],\n", + " equal_var=True)\n", + " Tnull_vals[j,b] = ttest_.statistic\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b92df1b", + "metadata": {}, + "source": [ + "Next, we compute the number of rejected null hypotheses $R$, the\n", + "estimated number of false positives $\\widehat{V}$, and the estimated\n", + "FDR, for a range of threshold values $c$ in\n", + "Algorithm 13.4. The threshold values are chosen\n", + "using the absolute values of the test statistics from the 100 genes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cac15616", + "metadata": {}, + "outputs": [], + "source": [ + "cutoffs = np.sort(np.abs(T_vals))\n", + "FDRs, Rs, Vs = np.empty((3, m))\n", + "for j in range(m):\n", + " R = np.sum(np.abs(T_vals) >= cutoffs[j])\n", + " V = np.sum(np.abs(Tnull_vals) >= cutoffs[j]) / B\n", + " Rs[j] = R\n", + " Vs[j] = V\n", + " FDRs[j] = V / R\n" + ] + }, + { + "cell_type": "markdown", + "id": "f6779ea0", + "metadata": {}, + "source": [ + "Now, for any given FDR, we can find the genes that will be\n", + "rejected. For example, with FDR controlled at 0.1, we reject 15 of the\n", + "100 null hypotheses. On average, we would expect about one or two of\n", + "these genes (i.e. 10% of 15) to be false discoveries. At an FDR of\n", + "0.2, we can reject the null hypothesis for 28 genes, of which we\n", + "expect around six to be false discoveries.\n", + "\n", + "The variable `idx` stores which\n", + "genes were included in our 100 randomly-selected genes. Let’s look at\n", + "the genes whose estimated FDR is less than 0.1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9661eb10", + "metadata": {}, + "outputs": [], + "source": [ + "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])\n" + ] + }, + { + "cell_type": "markdown", + "id": "001e3fc1", + "metadata": {}, + "source": [ + "At an FDR threshold of 0.2, more genes are selected, at the cost of having a higher expected\n", + "proportion of false discoveries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18ad4900", + "metadata": {}, + "outputs": [], + "source": [ + "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])\n" + ] + }, + { + "cell_type": "markdown", + "id": "8767f70c", + "metadata": {}, + "source": [ + "The next line generates Figure 13.11, which is similar\n", + "to Figure 13.9,\n", + "except that it is based on only a subset of the genes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28c276b6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(Rs, FDRs, 'b', linewidth=3)\n", + "ax.set_xlabel(\"Number of Rejections\")\n", + "ax.set_ylabel(\"False Discovery Rate\");\n" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch2-statlearn-lab.ipynb b/docs/source/labs/Ch2-statlearn-lab.ipynb new file mode 100644 index 0000000..9b826df --- /dev/null +++ b/docs/source/labs/Ch2-statlearn-lab.ipynb @@ -0,0 +1,3036 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "06a14452", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "0025f21f", + "metadata": {}, + "source": [ + "# Introduction to Python\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e9222139", + "metadata": {}, + "source": [ + "## Getting Started" + ] + }, + { + "cell_type": "markdown", + "id": "a84726ab", + "metadata": {}, + "source": [ + "To run the labs in this book, you will need two things:\n", + "\n", + "* An installation of `Python3`, which is the specific version of `Python` used in the labs. \n", + "* Access to `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*. " + ] + }, + { + "cell_type": "markdown", + "id": "f922cc9e", + "metadata": {}, + "source": [ + "You can download and install `Python3` by following the instructions available at [anaconda.com](http://anaconda.com). " + ] + }, + { + "cell_type": "markdown", + "id": "baaeada6", + "metadata": {}, + "source": [ + " There are a number of ways to get access to `Jupyter`. Here are just a few:\n", + " \n", + " * Using Google's `Colaboratory` service: [colab.research.google.com/](https://colab.research.google.com/). \n", + " * Using `JupyterHub`, available at [jupyter.org/hub](https://jupyter.org/hub). \n", + " * Using your own `jupyter` installation. Installation instructions are available at [jupyter.org/install](https://jupyter.org/install). \n", + " \n", + "Please see the `Python` resources page on the book website [statlearning.com](https://www.statlearning.com) for up-to-date information about getting `Python` and `Jupyter` working on your computer. \n", + "\n", + "You will need to install the `ISLP` package, which provides access to the datasets and custom-built functions that we provide.\n", + "Inside a macOS or Linux terminal type `pip install ISLP`; this also installs most other packages needed in the labs. The `Python` resources page has a link to the `ISLP` documentation website.\n", + "\n", + "To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. \n", + "Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.\n", + "\n", + "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell.\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a44fbd9", + "metadata": {}, + "source": [ + "## Basic Commands\n" + ] + }, + { + "cell_type": "markdown", + "id": "3831ff0d", + "metadata": {}, + "source": [ + "In this lab, we will introduce some simple `Python` commands. \n", + " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/). \n", + "\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "b5460488", + "metadata": {}, + "source": [ + "Like most programming languages, `Python` uses *functions*\n", + "to perform operations. To run a\n", + "function called `fun`, we type\n", + "`fun(input1,input2)`, where the inputs (or *arguments*)\n", + "`input1` and `input2` tell\n", + "`Python` how to run the function. A function can have any number of\n", + "inputs. For example, the\n", + "`print()` function outputs a text representation of all of its arguments to the console." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88d0bfff", + "metadata": {}, + "outputs": [], + "source": [ + "print('fit a model with', 11, 'variables')\n" + ] + }, + { + "cell_type": "markdown", + "id": "9c07629a", + "metadata": {}, + "source": [ + " The following command will provide information about the `print()` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebe7aca8", + "metadata": {}, + "outputs": [], + "source": [ + "print?\n" + ] + }, + { + "cell_type": "markdown", + "id": "b0c41d18", + "metadata": {}, + "source": [ + "Adding two integers in `Python` is pretty intuitive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df2f8168", + "metadata": {}, + "outputs": [], + "source": [ + "3 + 5\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e7b4195", + "metadata": {}, + "source": [ + "In `Python`, textual data is handled using\n", + "*strings*. For instance, `\"hello\"` and\n", + "`'hello'`\n", + "are strings. \n", + "We can concatenate them using the addition `+` symbol." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cc5c2c1", + "metadata": {}, + "outputs": [], + "source": [ + "\"hello\" + \" \" + \"world\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "cfda314a", + "metadata": {}, + "source": [ + " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n", + " The three most important types of sequences are lists, tuples, and strings. \n", + "We introduce lists now. " + ] + }, + { + "cell_type": "markdown", + "id": "764ca7ea", + "metadata": {}, + "source": [ + "The following command instructs `Python` to join together\n", + "the numbers 3, 4, and 5, and to save them as a\n", + "*list* named `x`. When we\n", + "type `x`, it gives us back the list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5d81180", + "metadata": {}, + "outputs": [], + "source": [ + "x = [3, 4, 5]\n", + "x\n" + ] + }, + { + "cell_type": "markdown", + "id": "a2e75dce", + "metadata": {}, + "source": [ + "Note that we used the brackets\n", + "`[]` to construct this list. \n", + "\n", + "We will often want to add two sets of numbers together. It is reasonable to try the following code,\n", + "though it will not produce the desired results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "425a714c", + "metadata": {}, + "outputs": [], + "source": [ + "y = [4, 9, 7]\n", + "x + y\n" + ] + }, + { + "cell_type": "markdown", + "id": "245f069e", + "metadata": {}, + "source": [ + "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n", + "element-by-element? \n", + " In `Python`, lists hold *arbitrary* objects, and are added using *concatenation*. \n", + " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`. \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "071caddd", + "metadata": {}, + "source": [ + "This example reflects the fact that \n", + " `Python` is a general-purpose programming language. Much of `Python`'s data-specific\n", + "functionality comes from other packages, notably `numpy`\n", + "and `pandas`. \n", + "In the next section, we will introduce the `numpy` package. More information about `numpy` can be found at \n", + "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html).\n" + ] + }, + { + "cell_type": "markdown", + "id": "25581a8e", + "metadata": {}, + "source": [ + "## Introduction to Numerical Python\n", + "\n", + "As mentioned earlier, this book makes use of functionality that is contained in the `numpy` \n", + " *library*, or *package*. A package is a collection of modules that are not necessarily included in \n", + " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*. " + ] + }, + { + "cell_type": "markdown", + "id": "851a40d6", + "metadata": {}, + "source": [ + " To access `numpy`, we must first `import` it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f88669a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "import numpy as np " + ] + }, + { + "cell_type": "markdown", + "id": "5bdc55d1", + "metadata": {}, + "source": [ + "In the previous line, we named the `numpy` *module* `np`; an abbreviation for easier referencing." + ] + }, + { + "cell_type": "markdown", + "id": "7b2cbe8b", + "metadata": {}, + "source": [ + "In `numpy`, an *array* is a generic term for a multidimensional\n", + "set of numbers.\n", + "We use the `np.array()` function to define `x` and `y`, which are one-dimensional arrays, i.e. vectors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8ec382a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x = np.array([3, 4, 5])\n", + "y = np.array([4, 9, 7])" + ] + }, + { + "cell_type": "markdown", + "id": "3a5ce193", + "metadata": {}, + "source": [ + "Note that if you forgot to run the `import numpy as np` command earlier, then\n", + "you will encounter an error in calling the `np.array()` function in the previous line. \n", + " The syntax `np.array()` indicates that the function being called\n", + "is part of the `numpy` package, which we have abbreviated as `np`. " + ] + }, + { + "cell_type": "markdown", + "id": "b21e6ebd", + "metadata": {}, + "source": [ + "Since `x` and `y` have been defined using `np.array()`, we get a sensible result when we add them together. Compare this to our results in the previous section,\n", + " when we tried to add two lists without using `numpy`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04de39ad", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x + y" + ] + }, + { + "cell_type": "markdown", + "id": "c04d19a8", + "metadata": {}, + "source": [ + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "33df3775", + "metadata": {}, + "source": [ + "In `numpy`, matrices are typically represented as two-dimensional arrays, and vectors as one-dimensional arrays. {While it is also possible to create matrices using `np.matrix()`, we will use `np.array()` throughout the labs in this book.}\n", + "We can create a two-dimensional array as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fee5d798", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x = np.array([[1, 2], [3, 4]])\n", + "x" + ] + }, + { + "cell_type": "markdown", + "id": "477c992d", + "metadata": {}, + "source": [ + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "86de84a5", + "metadata": {}, + "source": [ + "The object `x` has several \n", + "*attributes*, or associated objects. To access an attribute of `x`, we type `x.attribute`, where we replace `attribute`\n", + "with the name of the attribute. \n", + "For instance, we can access the `ndim` attribute of `x` as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5871ca00", + "metadata": {}, + "outputs": [], + "source": [ + "x.ndim" + ] + }, + { + "cell_type": "markdown", + "id": "71a44f8e", + "metadata": {}, + "source": [ + "The output indicates that `x` is a two-dimensional array. \n", + "Similarly, `x.dtype` is the *data type* attribute of the object `x`. This indicates that `x` is \n", + "comprised of 64-bit integers:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a32759c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "e770da51", + "metadata": {}, + "source": [ + "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n", + " If\n", + "we had passed in any decimals, then we would have obtained an array of\n", + "*floating point numbers* (i.e. real-valued numbers). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55f01b16", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.array([[1, 2], [3.0, 4]]).dtype\n" + ] + }, + { + "cell_type": "markdown", + "id": "4595300f", + "metadata": {}, + "source": [ + "Typing `fun?` will cause `Python` to display \n", + "documentation associated with the function `fun`, if it exists.\n", + "We can try this for `np.array()`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a9de618", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "np.array?\n" + ] + }, + { + "cell_type": "markdown", + "id": "9640f3fe", + "metadata": {}, + "source": [ + "This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33e3e6ea", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.array([[1, 2], [3, 4]], float).dtype\n" + ] + }, + { + "cell_type": "markdown", + "id": "fbf59c0e", + "metadata": {}, + "source": [ + "The array `x` is two-dimensional. We can find out the number of rows and columns by looking\n", + "at its `shape` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "623f8434", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "x.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "81f0bbae", + "metadata": {}, + "source": [ + "A *method* is a function that is associated with an\n", + "object. \n", + "For instance, given an array `x`, the expression\n", + "`x.sum()` sums all of its elements, using the `sum()`\n", + "method for arrays. \n", + "The call `x.sum()` automatically provides `x` as the\n", + "first argument to its `sum()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "235738a7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x = np.array([1, 2, 3, 4])\n", + "x.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "72373ccf", + "metadata": {}, + "source": [ + "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f3494e1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x = np.array([1, 2, 3, 4])\n", + "np.sum(x)" + ] + }, + { + "cell_type": "markdown", + "id": "69967e23", + "metadata": {}, + "source": [ + " As another example, the\n", + "`reshape()` method returns a new array with the same elements as\n", + "`x`, but a different shape.\n", + " We do this by passing in a `tuple` in our call to\n", + " `reshape()`, in this case `(2, 3)`. This tuple specifies that we would like to create a two-dimensional array with \n", + "$2$ rows and $3$ columns. {Like lists, tuples represent a sequence of objects. Why do we need more than one way to create a sequence? There are a few differences between tuples and lists, but perhaps the most important is that elements of a tuple cannot be modified, whereas elements of a list can be.}\n", + " \n", + "In what follows, the\n", + "`\\n` character creates a *new line*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ca8f936", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array([1, 2, 3, 4, 5, 6])\n", + "print('beginning x:\\n', x)\n", + "x_reshape = x.reshape((2, 3))\n", + "print('reshaped x:\\n', x_reshape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c1e36ab", + "metadata": {}, + "source": [ + "The previous output reveals that `numpy` arrays are specified as a sequence\n", + "of *rows*. This is called *row-major ordering*, as opposed to *column-major ordering*. " + ] + }, + { + "cell_type": "markdown", + "id": "45263db2", + "metadata": {}, + "source": [ + "`Python` (and hence `numpy`) uses 0-based\n", + "indexing. This means that to access the top left element of `x_reshape`, \n", + "we type in `x_reshape[0,0]`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23fba624", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x_reshape[0, 0] " + ] + }, + { + "cell_type": "markdown", + "id": "26f1717c", + "metadata": {}, + "source": [ + "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n", + "of `x_reshape`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a1df926", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "x_reshape[1, 2] " + ] + }, + { + "cell_type": "markdown", + "id": "c6d6d359", + "metadata": {}, + "source": [ + "Similarly, `x[2]` yields the\n", + "third entry of `x`. \n", + "\n", + "Now, let's modify the top left element of `x_reshape`. To our surprise, we discover that the first element of `x` has been modified as well!\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bfcc1e1", + "metadata": {}, + "outputs": [], + "source": [ + "print('x before we modify x_reshape:\\n', x)\n", + "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n", + "x_reshape[0, 0] = 5\n", + "print('x_reshape after we modify its top left element:\\n', x_reshape)\n", + "print('x after we modify top left element of x_reshape:\\n', x)\n" + ] + }, + { + "cell_type": "markdown", + "id": "10ff8c21", + "metadata": {}, + "source": [ + "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory.\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "ff267ecb", + "metadata": {}, + "source": [ + "We just saw that we can modify an element of an array. Can we also modify a tuple? It turns out that we cannot --- and trying to do so introduces\n", + "an *exception*, or error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973c562a", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "my_tuple = (3, 4, 5)\n", + "my_tuple[0] = 2\n" + ] + }, + { + "cell_type": "markdown", + "id": "56393fe3", + "metadata": {}, + "source": [ + "We now briefly mention some attributes of arrays that will come in handy. An array's `shape` attribute contains its dimension; this is always a tuple.\n", + "The `ndim` attribute yields the number of dimensions, and `T` provides its transpose. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56b5c382", + "metadata": {}, + "outputs": [], + "source": [ + "x_reshape.shape, x_reshape.ndim, x_reshape.T\n" + ] + }, + { + "cell_type": "markdown", + "id": "22408350", + "metadata": {}, + "source": [ + "Notice that the three individual outputs `(2,3)`, `2`, and `array([[5, 4],[2, 5], [3,6]])` are themselves output as a tuple. \n", + " \n", + "We will often want to apply functions to arrays. \n", + "For instance, we can compute the\n", + "square root of the entries using the `np.sqrt()` function: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e624622", + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(x)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6c3e074d", + "metadata": {}, + "source": [ + "We can also square the elements:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f45bd25", + "metadata": {}, + "outputs": [], + "source": [ + "x**2\n" + ] + }, + { + "cell_type": "markdown", + "id": "481ca03c", + "metadata": {}, + "source": [ + "We can compute the square roots using the same notation, raising to the power of $1/2$ instead of 2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6980f628", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "x**0.5\n" + ] + }, + { + "cell_type": "markdown", + "id": "bc357745", + "metadata": {}, + "source": [ + "Throughout this book, we will often want to generate random data. \n", + "The `np.random.normal()` function generates a vector of random\n", + "normal variables. We can learn more about this function by looking at the help page, via a call to `np.random.normal?`.\n", + "The first line of the help page reads `normal(loc=0.0, scale=1.0, size=None)`. \n", + " This *signature* line tells us that the function's arguments are `loc`, `scale`, and `size`. These are *keyword* arguments, which means that when they are passed into\n", + " the function, they can be referred to by name (in any order). {`Python` also uses *positional* arguments. Positional arguments do not need to use a keyword. To see an example, type in `np.sum?`. We see that `a` is a positional argument, i.e. this function assumes that the first unnamed argument that it receives is the array to be summed. By contrast, `axis` and `dtype` are keyword arguments: the position in which these arguments are entered into `np.sum()` does not matter.}\n", + " By default, this function will generate random normal variable(s) with mean (`loc`) $0$ and standard deviation (`scale`) $1$; furthermore, \n", + " a single random variable will be generated unless the argument to `size` is changed. \n", + "\n", + "We now generate 50 independent random variables from a $N(0,1)$ distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e32100", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.random.normal(size=50)\n", + "x\n" + ] + }, + { + "cell_type": "markdown", + "id": "80939b41", + "metadata": {}, + "source": [ + "We create an array `y` by adding an independent $N(50,1)$ random variable to each element of `x`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3428155", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "y = x + np.random.normal(loc=50, scale=1, size=50)" + ] + }, + { + "cell_type": "markdown", + "id": "17f93000", + "metadata": {}, + "source": [ + "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n", + "correlation between `x` and `y`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df70172e", + "metadata": {}, + "outputs": [], + "source": [ + "np.corrcoef(x, y)" + ] + }, + { + "cell_type": "markdown", + "id": "8fb44ec4", + "metadata": {}, + "source": [ + "If you're following along in your own `Jupyter` notebook, then you probably noticed that you got a different set of results when you ran the past few \n", + "commands. In particular, \n", + " each\n", + "time we call `np.random.normal()`, we will get a different answer, as shown in the following example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d996956", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "print(np.random.normal(scale=5, size=2))\n", + "print(np.random.normal(scale=5, size=2)) \n" + ] + }, + { + "cell_type": "markdown", + "id": "d3d0e0fb", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "988ca34a", + "metadata": {}, + "source": [ + "In order to ensure that our code provides exactly the same results\n", + "each time it is run, we can set a *random seed* \n", + "using the \n", + "`np.random.default_rng()` function.\n", + "This function takes an arbitrary, user-specified integer argument. If we set a random seed before \n", + "generating random data, then re-running our code will yield the same results. The\n", + "object `rng` has essentially all the random number generating methods found in `np.random`. Hence, to\n", + "generate normal data we use `rng.normal()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37b3a30b", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(1303)\n", + "print(rng.normal(scale=5, size=2))\n", + "rng2 = np.random.default_rng(1303)\n", + "print(rng2.normal(scale=5, size=2)) " + ] + }, + { + "cell_type": "markdown", + "id": "039e7f65", + "metadata": {}, + "source": [ + "Throughout the labs in this book, we use `np.random.default_rng()` whenever we\n", + "perform calculations involving random quantities within `numpy`. In principle, this\n", + "should enable the reader to exactly reproduce the stated results. However, as new versions of `numpy` become available, it is possible\n", + "that some small discrepancies may occur between the output\n", + "in the labs and the output\n", + "from `numpy`.\n", + "\n", + "The `np.mean()`, `np.var()`, and `np.std()` functions can be used\n", + "to compute the mean, variance, and standard deviation of arrays. These functions are also\n", + "available as methods on the arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4f99e0e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(3)\n", + "y = rng.standard_normal(10)\n", + "np.mean(y), y.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "3b42d9c6", + "metadata": {}, + "source": [ + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbd70319", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.var(y), y.var(), np.mean((y - y.mean())**2)" + ] + }, + { + "cell_type": "markdown", + "id": "ba51f245", + "metadata": {}, + "source": [ + "Notice that by default `np.var()` divides by the sample size $n$ rather\n", + "than $n-1$; see the `ddof` argument in `np.var?`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf281844", + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(np.var(y)), np.std(y)" + ] + }, + { + "cell_type": "markdown", + "id": "877ca8e0", + "metadata": {}, + "source": [ + "The `np.mean()`, `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n", + "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f751b1dd", + "metadata": {}, + "outputs": [], + "source": [ + "X = rng.standard_normal((10, 3))\n", + "X" + ] + }, + { + "cell_type": "markdown", + "id": "f9028e1a", + "metadata": {}, + "source": [ + "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbf92de5", + "metadata": {}, + "outputs": [], + "source": [ + "X.mean(axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "009195d4", + "metadata": {}, + "source": [ + "The following yields the same result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efacc213", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X.mean(0)" + ] + }, + { + "cell_type": "markdown", + "id": "4b70cd2e", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "e33fa3fc", + "metadata": {}, + "source": [ + "## Graphics\n", + "In `Python`, common practice is to use the library\n", + "`matplotlib` for graphics.\n", + "However, since `Python` was not written with data analysis in mind,\n", + " the notion of plotting is not intrinsic to the language. \n", + "We will use the `subplots()` function\n", + "from `matplotlib.pyplot` to create a figure and the\n", + "axes onto which we plot our data.\n", + "For many more examples of how to make plots in `Python`,\n", + "readers are encouraged to visit [matplotlib.org/stable/gallery/](https://matplotlib.org/stable/gallery/index.html).\n", + "\n", + "In `matplotlib`, a plot consists of a *figure* and one or more *axes*. You can think of the figure as the blank canvas upon which \n", + "one or more plots will be displayed: it is the entire plotting window. \n", + "The *axes* contain important information about each plot, such as its $x$- and $y$-axis labels,\n", + "title, and more. (Note that in `matplotlib`, the word *axes* is not the plural of *axis*: a plot's *axes* contains much more information \n", + "than just the $x$-axis and the $y$-axis.)\n", + "\n", + "We begin by importing the `subplots()` function\n", + "from `matplotlib`. We use this function\n", + "throughout when creating figures.\n", + "The function returns a tuple of length two: a figure\n", + "object as well as the relevant axes object. We will typically\n", + "pass `figsize` as a keyword argument.\n", + "Having created our axes, we attempt our first plot using its `plot()` method.\n", + "To learn more about it, \n", + "type `ax.plot?`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "637fb67d", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.pyplot import subplots\n", + "fig, ax = subplots(figsize=(8, 8))\n", + "x = rng.standard_normal(100)\n", + "y = rng.standard_normal(100)\n", + "ax.plot(x, y);\n" + ] + }, + { + "cell_type": "markdown", + "id": "ca04f15f", + "metadata": {}, + "source": [ + "We pause here to note that we have *unpacked* the tuple of length two returned by `subplots()` into the two distinct\n", + "variables `fig` and `ax`. Unpacking\n", + "is typically preferred to the following equivalent but slightly more verbose code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "109d681d", + "metadata": {}, + "outputs": [], + "source": [ + "output = subplots(figsize=(8, 8))\n", + "fig = output[0]\n", + "ax = output[1]" + ] + }, + { + "cell_type": "markdown", + "id": "5509508c", + "metadata": {}, + "source": [ + "We see that our earlier cell produced a line plot, which is the default. To create a scatterplot, we provide an additional argument to `ax.plot()`, indicating that circles should be displayed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e905982c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.plot(x, y, 'o');" + ] + }, + { + "cell_type": "markdown", + "id": "42166016", + "metadata": {}, + "source": [ + "Different values\n", + "of this additional argument can be used to produce different colored lines\n", + "as well as different linestyles. \n" + ] + }, + { + "cell_type": "markdown", + "id": "a99dac78", + "metadata": {}, + "source": [ + "As an alternative, we could use the `ax.scatter()` function to create a scatterplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27133a4e", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.scatter(x, y, marker='o');" + ] + }, + { + "cell_type": "markdown", + "id": "000c125e", + "metadata": {}, + "source": [ + "Notice that in the code blocks above, we have ended\n", + "the last line with a semicolon. This prevents `ax.plot(x, y)` from printing\n", + "text to the notebook. However, it does not prevent a plot from being produced. \n", + " If we omit the trailing semi-colon, then we obtain the following output: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef41f90d", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.scatter(x, y, marker='o')\n" + ] + }, + { + "cell_type": "markdown", + "id": "014f66e5", + "metadata": {}, + "source": [ + "In what follows, we will use\n", + " trailing semicolons whenever the text that would be output is not\n", + "germane to the discussion at hand.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "98a1db4a", + "metadata": {}, + "source": [ + "To label our plot, we make use of the `set_xlabel()`, `set_ylabel()`, and `set_title()` methods\n", + "of `ax`.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16a9cb99", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.scatter(x, y, marker='o')\n", + "ax.set_xlabel(\"this is the x-axis\")\n", + "ax.set_ylabel(\"this is the y-axis\")\n", + "ax.set_title(\"Plot of X vs Y\");" + ] + }, + { + "cell_type": "markdown", + "id": "7f0af2d9", + "metadata": {}, + "source": [ + " Having access to the figure object `fig` itself means that we can go in and change some aspects and then redisplay it. Here, we change\n", + " the size from `(8, 8)` to `(12, 3)`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6707420e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig.set_size_inches(12,3)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "00c8816e", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "3cf4ed3f", + "metadata": {}, + "source": [ + "Occasionally we will want to create several plots within a figure. This can be\n", + "achieved by passing additional arguments to `subplots()`. \n", + "Below, we create a $2 \\times 3$ grid of plots\n", + "in a figure of size determined by the `figsize` argument. In such\n", + "situations, there is often a relationship between the axes in the plots. For example,\n", + "all plots may have a common $x$-axis. The `subplots()` function can automatically handle\n", + "this situation when passed the keyword argument `sharex=True`.\n", + "The `axes` object below is an array pointing to different plots in the figure. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c78ef8b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, axes = subplots(nrows=2,\n", + " ncols=3,\n", + " figsize=(15, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "40d89b7c", + "metadata": {}, + "source": [ + "We now produce a scatter plot with `'o'` in the second column of the first row and\n", + "a scatter plot with `'+'` in the third column of the second row." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a18402fb", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "axes[0,1].plot(x, y, 'o')\n", + "axes[1,2].scatter(x, y, marker='+')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "d29648ec", + "metadata": {}, + "source": [ + "Type `subplots?` to learn more about \n", + "`subplots()`. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2a6a0c9e", + "metadata": {}, + "source": [ + "To save the output of `fig`, we call its `savefig()`\n", + "method. The argument `dpi` is the dots per inch, used\n", + "to determine how large the figure will be in pixels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80e18950", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig.savefig(\"Figure.png\", dpi=400)\n", + "fig.savefig(\"Figure.pdf\", dpi=200);\n" + ] + }, + { + "cell_type": "markdown", + "id": "b8e17a71", + "metadata": {}, + "source": [ + "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe8c404d", + "metadata": {}, + "outputs": [], + "source": [ + "axes[0,1].set_xlim([-1,1])\n", + "fig.savefig(\"Figure_updated.jpg\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "55bff84e", + "metadata": {}, + "source": [ + "We now create some more sophisticated plots. The \n", + "`ax.contour()` method produces a *contour plot* \n", + "in order to represent three-dimensional data, similar to a\n", + "topographical map. It takes three arguments:\n", + "\n", + "* A vector of `x` values (the first dimension),\n", + "* A vector of `y` values (the second dimension), and\n", + "* A matrix whose elements correspond to the `z` value (the third\n", + "dimension) for each pair of `(x,y)` coordinates.\n", + "\n", + "To create `x` and `y`, we’ll use the function `np.linspace()`. The command `np.linspace(a, b, n)` \n", + "returns a vector of `n` numbers starting at `a` and ending at `b`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a99034cb", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "x = np.linspace(-np.pi, np.pi, 50)\n", + "y = x\n", + "f = np.multiply.outer(np.cos(y), 1 / (1 + x**2))\n", + "ax.contour(x, y, f);\n" + ] + }, + { + "cell_type": "markdown", + "id": "fd6d00e6", + "metadata": {}, + "source": [ + "We can increase the resolution by adding more levels to the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c613a5df", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.contour(x, y, f, levels=45);" + ] + }, + { + "cell_type": "markdown", + "id": "c1f2c4bd", + "metadata": {}, + "source": [ + "To fine-tune the output of the\n", + "`ax.contour()` function, take a\n", + "look at the help file by typing `?plt.contour`.\n", + " \n", + "The `ax.imshow()` method is similar to \n", + "`ax.contour()`, except that it produces a color-coded plot\n", + "whose colors depend on the `z` value. This is known as a\n", + "*heatmap*, and is sometimes used to plot temperature in\n", + "weather forecasts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bef21527", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.imshow(f);\n" + ] + }, + { + "cell_type": "markdown", + "id": "35ed7b29", + "metadata": {}, + "source": [ + "## Sequences and Slice Notation" + ] + }, + { + "cell_type": "markdown", + "id": "bb0cee76", + "metadata": {}, + "source": [ + "As seen above, the\n", + "function `np.linspace()` can be used to create a sequence\n", + "of numbers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4435ed50", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "seq1 = np.linspace(0, 10, 11)\n", + "seq1\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1495e1b", + "metadata": {}, + "source": [ + "The function `np.arange()`\n", + " returns a sequence of numbers spaced out by `step`. If `step` is not specified, then a default value of $1$ is used. Let's create a sequence\n", + " that starts at $0$ and ends at $10$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a2c664a", + "metadata": {}, + "outputs": [], + "source": [ + "seq2 = np.arange(0, 10)\n", + "seq2\n" + ] + }, + { + "cell_type": "markdown", + "id": "7b0e1b2f", + "metadata": {}, + "source": [ + "Why isn't $10$ output above? This has to do with *slice* notation in `Python`. \n", + "Slice notation \n", + "is used to index sequences such as lists, tuples and arrays.\n", + "Suppose we want to retrieve the fourth through sixth (inclusive) entries\n", + "of a string. We obtain a slice of the string using the indexing notation `[3:6]`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "810eafdf", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "\"hello world\"[3:6]" + ] + }, + { + "cell_type": "markdown", + "id": "d73d0b54", + "metadata": {}, + "source": [ + "In the code block above, the notation `3:6` is shorthand for `slice(3,6)` when used inside\n", + "`[]`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7a9f652", + "metadata": {}, + "outputs": [], + "source": [ + "\"hello world\"[slice(3,6)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "be578cf4", + "metadata": {}, + "source": [ + "You might have expected `slice(3,6)` to output the fourth through seventh characters in the text string (recalling that `Python` begins its indexing at zero), but instead it output the fourth through sixth. \n", + " This also explains why the earlier `np.arange(0, 10)` command output only the integers from $0$ to $9$. \n", + "See the documentation `slice?` for useful options in creating slices. \n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "7a9c183a", + "metadata": {}, + "source": [ + "## Indexing Data\n", + "To begin, we create a two-dimensional `numpy` array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24427538", + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array(np.arange(16)).reshape((4, 4))\n", + "A\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2c583a0", + "metadata": {}, + "source": [ + "Typing `A[1,2]` retrieves the element corresponding to the second row and third\n", + "column. (As usual, `Python` indexes from $0.$)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18684ed5", + "metadata": {}, + "outputs": [], + "source": [ + "A[1,2]\n" + ] + }, + { + "cell_type": "markdown", + "id": "b25734ec", + "metadata": {}, + "source": [ + "The first number after the open-bracket symbol `[`\n", + " refers to the row, and the second number refers to the column. \n", + "\n", + "### Indexing Rows, Columns, and Submatrices\n", + " To select multiple rows at a time, we can pass in a list\n", + " specifying our selection. For instance, `[1,3]` will retrieve the second and fourth rows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34d27b73", + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "97149703", + "metadata": {}, + "source": [ + "To select the first and third columns, we pass in `[0,2]` as the second argument in the square brackets.\n", + "In this case we need to supply the first argument `:` \n", + "which selects all rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bb799c5", + "metadata": {}, + "outputs": [], + "source": [ + "A[:,[0,2]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "2b9ce4a3", + "metadata": {}, + "source": [ + "Now, suppose that we want to select the submatrix made up of the second and fourth \n", + "rows as well as the first and third columns. This is where\n", + "indexing gets slightly tricky. It is natural to try to use lists to retrieve the rows and columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de853e7c", + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3],[0,2]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "dd57a0bf", + "metadata": {}, + "source": [ + " Oops --- what happened? We got a one-dimensional array of length 2 identical to" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e71bf672", + "metadata": {}, + "outputs": [], + "source": [ + "np.array([A[1,0],A[3,2]])\n" + ] + }, + { + "cell_type": "markdown", + "id": "9cd50d44", + "metadata": {}, + "source": [ + " Similarly, the following code fails to extract the submatrix comprised of the second and fourth rows and the first, third, and fourth columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "458fe5c9", + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3],[0,2,3]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "67657309", + "metadata": {}, + "source": [ + "We can see what has gone wrong here. When supplied with two indexing lists, the `numpy` interpretation is that these provide pairs of $i,j$ indices for a series of entries. That is why the pair of lists must have the same length. However, that was not our intent, since we are looking for a submatrix.\n", + "\n", + "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703832ac", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "A[[1,3]][:,[0,2]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "63980dff", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "25c3a988", + "metadata": {}, + "source": [ + "There are more efficient ways of achieving the same result.\n", + "\n", + "The *convenience function* `np.ix_()` allows us to extract a submatrix\n", + "using lists, by creating an intermediate *mesh* object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbf328b7", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "idx = np.ix_([1,3],[0,2,3])\n", + "A[idx]\n" + ] + }, + { + "cell_type": "markdown", + "id": "13f73d32", + "metadata": {}, + "source": [ + "Alternatively, we can subset matrices efficiently using slices.\n", + " \n", + "The slice\n", + "`1:4:2` captures the second and fourth items of a sequence, while the slice `0:3:2` captures\n", + "the first and third items (the third element in a slice sequence is the step size)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65784eb0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "A[1:4:2,0:3:2]\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c679e7b", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "481bc57a", + "metadata": {}, + "source": [ + "Why are we able to retrieve a submatrix directly using slices but not using lists?\n", + "Its because they are different `Python` types, and\n", + "are treated differently by `numpy`.\n", + "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited.\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "f1c55b8a", + "metadata": {}, + "source": [ + "### Boolean Indexing\n", + "In `numpy`, a *Boolean* is a type that equals either `True` or `False` (also represented as $1$ and $0$, respectively).\n", + "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ddb0d83", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "keep_rows = np.zeros(A.shape[0], bool)\n", + "keep_rows" + ] + }, + { + "cell_type": "markdown", + "id": "f3007c8d", + "metadata": {}, + "source": [ + "We now set two of the elements to `True`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90e72009", + "metadata": {}, + "outputs": [], + "source": [ + "keep_rows[[1,3]] = True\n", + "keep_rows\n" + ] + }, + { + "cell_type": "markdown", + "id": "932dac22", + "metadata": {}, + "source": [ + "Note that the elements of `keep_rows`, when viewed as integers, are the same as the\n", + "values of `np.array([0,1,0,1])`. Below, we use `==` to verify their equality. When\n", + "applied to two arrays, the `==` operation is applied elementwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb3b6534", + "metadata": {}, + "outputs": [], + "source": [ + "np.all(keep_rows == np.array([0,1,0,1]))\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8cb48c0", + "metadata": {}, + "source": [ + "(Here, the function `np.all()` has checked whether\n", + "all entries of an array are `True`. A similar function, `np.any()`, can be used to check whether any entries of an array are `True`.)" + ] + }, + { + "cell_type": "markdown", + "id": "ac824821", + "metadata": {}, + "source": [ + " However, even though `np.array([0,1,0,1])` and `keep_rows` are equal according to `==`, they index different sets of rows!\n", + "The former retrieves the first, second, first, and second rows of `A`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b146db22", + "metadata": {}, + "outputs": [], + "source": [ + "A[np.array([0,1,0,1])]\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0b18644", + "metadata": {}, + "source": [ + " By contrast, `keep_rows` retrieves only the second and fourth rows of `A` --- i.e. the rows for which the Boolean equals `TRUE`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bf7b4a3", + "metadata": {}, + "outputs": [], + "source": [ + "A[keep_rows]\n" + ] + }, + { + "cell_type": "markdown", + "id": "80686b74", + "metadata": {}, + "source": [ + "This example shows that Booleans and integers are treated differently by `numpy`." + ] + }, + { + "cell_type": "markdown", + "id": "d7905166", + "metadata": {}, + "source": [ + "We again make use of the `np.ix_()` function\n", + " to create a mesh containing the second and fourth rows, and the first, third, and fourth columns. This time, we apply the function to Booleans,\n", + " rather than lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "288011d9", + "metadata": {}, + "outputs": [], + "source": [ + "keep_cols = np.zeros(A.shape[1], bool)\n", + "keep_cols[[0, 2, 3]] = True\n", + "idx_bool = np.ix_(keep_rows, keep_cols)\n", + "A[idx_bool]\n" + ] + }, + { + "cell_type": "markdown", + "id": "37900313", + "metadata": {}, + "source": [ + "We can also mix a list with an array of Booleans in the arguments to `np.ix_()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e3b1954", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "idx_mixed = np.ix_([1,3], keep_cols)\n", + "A[idx_mixed]\n" + ] + }, + { + "cell_type": "markdown", + "id": "76ff57a7", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "2f4d70f3", + "metadata": {}, + "source": [ + "For more details on indexing in `numpy`, readers are referred\n", + "to the `numpy` tutorial mentioned earlier.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c59c8fb", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "Data sets often contain different types of data, and may have names associated with the rows or columns. \n", + "For these reasons, they typically are best accommodated using a\n", + " *data frame*. \n", + " We can think of a data frame as a sequence\n", + "of arrays of identical length; these are the columns. Entries in the\n", + "different arrays can be combined to form a row.\n", + " The `pandas`\n", + "library can be used to create and work with data frame objects." + ] + }, + { + "cell_type": "markdown", + "id": "95297c29", + "metadata": {}, + "source": [ + "### Reading in a Data Set\n", + "\n", + "The first step of most analyses involves importing a data set into\n", + "`Python`. \n", + " Before attempting to load\n", + "a data set, we must make sure that `Python` knows where to find the file containing it. \n", + "If the\n", + "file is in the same location\n", + "as this notebook file, then we are all set. \n", + "Otherwise, \n", + "the command\n", + "`os.chdir()` can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.) " + ] + }, + { + "cell_type": "markdown", + "id": "42760180", + "metadata": {}, + "source": [ + "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d02bfb28", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "Auto = pd.read_csv('Auto.csv')\n", + "Auto\n" + ] + }, + { + "cell_type": "markdown", + "id": "5acc3596", + "metadata": {}, + "source": [ + "The book website also has a whitespace-delimited version of this data, called `Auto.data`. This can be read in as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca6090af", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "350f1f42", + "metadata": {}, + "source": [ + " Both `Auto.csv` and `Auto.data` are simply text\n", + "files. Before loading data into `Python`, it is a good idea to view it using\n", + "a text editor or other software, such as Microsoft Excel.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1cae3ad2", + "metadata": {}, + "source": [ + "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7220aa87", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto['horsepower']\n" + ] + }, + { + "cell_type": "markdown", + "id": "c0adabf1", + "metadata": {}, + "source": [ + "We see that the `dtype` of this column is `object`. \n", + "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n", + "in the data. \n", + "We can find out why by looking at the unique values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca1939f0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "np.unique(Auto['horsepower'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "c35e48dc", + "metadata": {}, + "source": [ + "We see the culprit is the value `?`, which is being used to encode missing values.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ccf8e5de", + "metadata": {}, + "source": [ + "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n", + "Now, each instance of `?` in the file is replaced with the\n", + "value `np.nan`, which means *not a number*:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfb084e7", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Auto = pd.read_csv('Auto.data',\n", + " na_values=['?'],\n", + " delim_whitespace=True)\n", + "Auto['horsepower'].sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "2e8e504f", + "metadata": {}, + "source": [ + "The `Auto.shape` attribute tells us that the data has 397\n", + "observations, or rows, and nine variables, or columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e02b6a5", + "metadata": {}, + "outputs": [], + "source": [ + "Auto.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "df109192", + "metadata": {}, + "source": [ + "There are\n", + "various ways to deal with missing data. \n", + "In this case, since only five of the rows contain missing\n", + "observations, we choose to use the `Auto.dropna()` method to simply remove these rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d391f8d0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Auto_new = Auto.dropna()\n", + "Auto_new.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1ddfb53", + "metadata": {}, + "source": [ + "### Basics of Selecting Rows and Columns\n", + " \n", + "We can use `Auto.columns` to check the variable names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a222ca60", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Auto = Auto_new # overwrite the previous value\n", + "Auto.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "be6f2daf", + "metadata": {}, + "source": [ + "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n", + "Recall that the first argument to the `[]` method\n", + "is always applied to the rows of the array. \n", + "Similarly, \n", + "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a75921ac", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto[:3]\n" + ] + }, + { + "cell_type": "markdown", + "id": "17c02ad7", + "metadata": {}, + "source": [ + "Similarly, an array of Booleans can be used to subset the rows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7469ff2b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "idx_80 = Auto['year'] > 80\n", + "Auto[idx_80]\n" + ] + }, + { + "cell_type": "markdown", + "id": "27eb1780", + "metadata": {}, + "source": [ + "However, if we pass in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac455404", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto[['mpg', 'horsepower']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "1629d302", + "metadata": {}, + "source": [ + "Since we did not specify an *index* column when we loaded our data frame, the rows are labeled using integers\n", + "0 to 396." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f41235d4", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto.index\n" + ] + }, + { + "cell_type": "markdown", + "id": "79d3cebb", + "metadata": {}, + "source": [ + "We can use the\n", + "`set_index()` method to re-name the rows using the contents of `Auto['name']`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31caba24", + "metadata": {}, + "outputs": [], + "source": [ + "Auto_re = Auto.set_index('name')\n", + "Auto_re\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "594bedfa", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "184977f2", + "metadata": {}, + "source": [ + "We see that the column `'name'` is no longer there.\n", + " \n", + "Now that the index has been set to `name`, we can access rows of the data \n", + "frame by `name` using the `{loc[]`} method of\n", + "`Auto`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a59b083", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "rows = ['amc rebel sst', 'ford torino']\n", + "Auto_re.loc[rows]\n" + ] + }, + { + "cell_type": "markdown", + "id": "066af73b", + "metadata": {}, + "source": [ + "As an alternative to using the index name, we could retrieve the 4th and 5th rows of `Auto` using the `{iloc[]`} method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54457a11", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.iloc[[3,4]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "069ca71e", + "metadata": {}, + "source": [ + "We can also use it to retrieve the 1st, 3rd and and 4th columns of `Auto_re`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a4f3faf", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.iloc[:,[0,2,3]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6f4f9a2", + "metadata": {}, + "source": [ + "We can extract the 4th and 5th rows, as well as the 1st, 3rd and 4th columns, using\n", + "a single call to `iloc[]`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f1e6e56", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.iloc[[3,4],[0,2,3]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e4b778c", + "metadata": {}, + "source": [ + "Index entries need not be unique: there are several cars in the data frame named `ford galaxie 500`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1d0e795", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "b10eabb7", + "metadata": {}, + "source": [ + "### More on Selecting Rows and Columns\n", + "Suppose now that we want to create a data frame consisting of the `weight` and `origin` of the subset of cars with \n", + "`year` greater than 80 --- i.e. those built after 1980.\n", + "To do this, we first create a Boolean array that indexes the rows.\n", + "The `loc[]` method allows for Boolean entries as well as strings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "392f10a1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "idx_80 = Auto_re['year'] > 80\n", + "Auto_re.loc[idx_80, ['weight', 'origin']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3f85975", + "metadata": {}, + "source": [ + "To do this more concisely, we can use an anonymous function called a `lambda`: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f5b7906", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc67849e", + "metadata": {}, + "source": [ + "The `lambda` call creates a function that takes a single\n", + "argument, here `df`, and returns `df['year']>80`.\n", + "Since it is created inside the `loc[]` method for the\n", + "dataframe `Auto_re`, that dataframe will be the argument supplied.\n", + "As another example of using a `lambda`, suppose that\n", + "we want all cars built after 1980 that achieve greater than 30 miles per gallon:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b13c5728", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.loc[lambda df: (df['year'] > 80) & (df['mpg'] > 30),\n", + " ['weight', 'origin']\n", + " ]\n" + ] + }, + { + "cell_type": "markdown", + "id": "7885bba9", + "metadata": {}, + "source": [ + "The symbol `&` computes an element-wise *and* operation.\n", + "As another example, suppose that we want to retrieve all `Ford` and `Datsun`\n", + "cars with `displacement` less than 300. We check whether each `name` entry contains either the string `ford` or `datsun` using the `str.contains()` method of the `index` attribute of \n", + "of the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5a2c3be", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto_re.loc[lambda df: (df['displacement'] < 300)\n", + " & (df.index.str.contains('ford')\n", + " | df.index.str.contains('datsun')),\n", + " ['weight', 'origin']\n", + " ]\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a7a6c62", + "metadata": {}, + "source": [ + "Here, the symbol `|` computes an element-wise *or* operation.\n", + " \n", + "In summary, a powerful set of operations is available to index the rows and columns of data frames. For integer based queries, use the `iloc[]` method. For string and Boolean\n", + "selections, use the `loc[]` method. For functional queries that filter rows, use the `loc[]` method\n", + "with a function (typically a `lambda`) in the rows argument.\n", + "\n", + "## For Loops\n", + "A `for` loop is a standard tool in many languages that\n", + "repeatedly evaluates some chunk of code while\n", + "varying different values inside the code.\n", + "For example, suppose we loop over elements of a list and compute their sum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6278441", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "total = 0\n", + "for value in [3,2,19]:\n", + " total += value\n", + "print('Total is: {0}'.format(total))\n" + ] + }, + { + "cell_type": "markdown", + "id": "92948288", + "metadata": {}, + "source": [ + "The indented code beneath the line with the `for` statement is run\n", + "for each value in the sequence\n", + "specified in the `for` statement. The loop ends either\n", + "when the cell ends or when code is indented at the same level\n", + "as the original `for` statement.\n", + "We see that the final line above which prints the total is executed\n", + "only once after the for loop has terminated. Loops\n", + "can be nested by additional indentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa065a8", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "total = 0\n", + "for value in [2,3,19]:\n", + " for weight in [3, 2, 1]:\n", + " total += value * weight\n", + "print('Total is: {0}'.format(total))" + ] + }, + { + "cell_type": "markdown", + "id": "aaeb60c5", + "metadata": {}, + "source": [ + "Above, we summed over each combination of `value` and `weight`.\n", + "We also took advantage of the *increment* notation\n", + "in `Python`: the expression `a += b` is equivalent\n", + "to `a = a + b`. Besides\n", + "being a convenient notation, this can save time in computationally\n", + "heavy tasks in which the intermediate value of `a+b` need not\n", + "be explicitly created.\n", + "\n", + "Perhaps a more\n", + "common task would be to sum over `(value, weight)` pairs. For instance,\n", + "to compute the average value of a random variable that takes on\n", + "possible values 2, 3 or 19 with probability 0.2, 0.3, 0.5 respectively\n", + "we would compute the weighted sum. Tasks such as this\n", + "can often be accomplished using the `zip()` function that\n", + "loops over a sequence of tuples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "832c1b7d", + "metadata": {}, + "outputs": [], + "source": [ + "total = 0\n", + "for value, weight in zip([2,3,19],\n", + " [0.2,0.3,0.5]):\n", + " total += weight * value\n", + "print('Weighted average is: {0}'.format(total))\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac163d59", + "metadata": {}, + "source": [ + "### String Formatting\n", + "In the code chunk above we also printed a string\n", + "displaying the total. However, the object `total`\n", + "is an integer and not a string.\n", + "Inserting the value of something into\n", + "a string is a common task, made\n", + "simple using\n", + "some of the powerful string formatting\n", + "tools in `Python`.\n", + "Many data cleaning tasks involve\n", + "manipulating and programmatically\n", + "producing strings.\n", + "\n", + "For example we may want to loop over the columns of a data frame and\n", + "print the percent missing in each column.\n", + "Let’s create a data frame `D` with columns in which 20% of the entries are missing i.e. set\n", + "to `np.nan`. We’ll create the\n", + "values in `D` from a normal distribution with mean 0 and variance 1 using `rng.standard_normal()`\n", + "and then overwrite some random entries using `rng.choice()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca2e7cd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(1)\n", + "A = rng.standard_normal((127, 5))\n", + "M = rng.choice([0, np.nan], p=[0.8,0.2], size=A.shape)\n", + "A += M\n", + "D = pd.DataFrame(A, columns=['food',\n", + " 'bar',\n", + " 'pickle',\n", + " 'snack',\n", + " 'popcorn'])\n", + "D[:3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8ae1dcc", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "for col in D.columns:\n", + " template = 'Column \"{0}\" has {1:.2%} missing values'\n", + " print(template.format(col,\n", + " np.isnan(D[col]).mean()))\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4687a4a", + "metadata": {}, + "source": [ + "We see that the `template.format()` method expects two arguments `{0}`\n", + "and `{1:.2%}`, and the latter includes some formatting\n", + "information. In particular, it specifies that the second argument should be expressed as a percent with two decimal digits.\n", + "\n", + "The reference\n", + "[docs.python.org/3/library/string.html](https://docs.python.org/3/library/string.html)\n", + "includes many helpful and more complex examples." + ] + }, + { + "cell_type": "markdown", + "id": "22226024", + "metadata": {}, + "source": [ + "## Additional Graphical and Numerical Summaries\n", + "We can use the `ax.plot()` or `ax.scatter()` functions to display the quantitative variables. However, simply typing the variable names will produce an error message,\n", + "because `Python` does not know to look in the `Auto` data set for those variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30a8a663", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.plot(horsepower, mpg, 'o');" + ] + }, + { + "cell_type": "markdown", + "id": "f09f4e10", + "metadata": {}, + "source": [ + "We can address this by accessing the columns directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81fdedb1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');\n" + ] + }, + { + "cell_type": "markdown", + "id": "a64455fa", + "metadata": {}, + "source": [ + "Alternatively, we can use the `plot()` method with the call `Auto.plot()`.\n", + "Using this method,\n", + "the variables can be accessed by name.\n", + "The plot methods of a data frame return a familiar object:\n", + "an axes. We can use it to update the plot as we did previously: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61850515", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = Auto.plot.scatter('horsepower', 'mpg');\n", + "ax.set_title('Horsepower vs. MPG')" + ] + }, + { + "cell_type": "markdown", + "id": "7c73790f", + "metadata": {}, + "source": [ + "If we want to save\n", + "the figure that contains a given axes, we can find the relevant figure\n", + "by accessing the `figure` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e0c165", + "metadata": {}, + "outputs": [], + "source": [ + "fig = ax.figure\n", + "fig.savefig('horsepower_mpg.png');" + ] + }, + { + "cell_type": "markdown", + "id": "2378a5bb", + "metadata": {}, + "source": [ + "We can further instruct the data frame to plot to a particular axes object. In this\n", + "case the corresponding `plot()` method will return the\n", + "modified axes we passed in as an argument. Note that\n", + "when we request a one-dimensional grid of plots, the object `axes` is similarly\n", + "one-dimensional. We place our scatter plot in the middle plot of a row of three plots\n", + "within a figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35c72e77", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = subplots(ncols=3, figsize=(15, 5))\n", + "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n" + ] + }, + { + "cell_type": "markdown", + "id": "95b0f3de", + "metadata": {}, + "source": [ + "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`. " + ] + }, + { + "cell_type": "markdown", + "id": "5c6c4fa2", + "metadata": {}, + "source": [ + "We now consider the `cylinders` variable. Typing in `Auto.cylinders.dtype` reveals that it is being treated as a quantitative variable. \n", + "However, since there is only a small number of possible values for this variable, we may wish to treat it as \n", + " qualitative. Below, we replace\n", + "the `cylinders` column with a categorical version of `Auto.cylinders`. The function `pd.Series()` owes its name to the fact that `pandas` is often used in time series applications." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b6fb1ad", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n", + "Auto.cylinders.dtype\n" + ] + }, + { + "cell_type": "markdown", + "id": "2038c16d", + "metadata": {}, + "source": [ + " Now that `cylinders` is qualitative, we can display it using\n", + " the `boxplot()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f19f48c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "Auto.boxplot('mpg', by='cylinders', ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "4ae72e1b", + "metadata": {}, + "source": [ + "The `hist()` method can be used to plot a *histogram*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "929b6901", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "Auto.hist('mpg', ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "db7f6e7c", + "metadata": {}, + "source": [ + "The color of the bars and the number of bins can be changed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d48f1e9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "Auto.hist('mpg', color='red', bins=12, ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e53b8b6", + "metadata": {}, + "source": [ + " See `Auto.hist?` for more plotting\n", + "options.\n", + " \n", + "We can use the `pd.plotting.scatter_matrix()` function to create a *scatterplot matrix* to visualize all of the pairwise relationships between the columns in\n", + "a data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "400690e6", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pd.plotting.scatter_matrix(Auto);\n" + ] + }, + { + "cell_type": "markdown", + "id": "48a9a4f5", + "metadata": {}, + "source": [ + " We can also produce scatterplots\n", + "for a subset of the variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bbeca8b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pd.plotting.scatter_matrix(Auto[['mpg',\n", + " 'displacement',\n", + " 'weight']]);\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3bb4736", + "metadata": {}, + "source": [ + "The `describe()` method produces a numerical summary of each column in a data frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1727f604", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto[['mpg', 'weight']].describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b508635", + "metadata": {}, + "source": [ + "We can also produce a summary of just a single column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39bf4091", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Auto['cylinders'].describe()\n", + "Auto['mpg'].describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "22ecf5c3", + "metadata": {}, + "source": [ + "To exit `Jupyter`, select `File / Close and Halt`." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch3-linreg-lab.ipynb b/docs/source/labs/Ch3-linreg-lab.ipynb new file mode 100644 index 0000000..db9b4c2 --- /dev/null +++ b/docs/source/labs/Ch3-linreg-lab.ipynb @@ -0,0 +1,1123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3b9db985", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d8e6029", + "metadata": {}, + "source": [ + "# Linear Regression\n", + "\n", + "## Importing packages\n", + "We import our standard libraries at this top\n", + "level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca5277a6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib.pyplot import subplots\n" + ] + }, + { + "cell_type": "markdown", + "id": "95b0ae8c", + "metadata": {}, + "source": [ + "### New imports\n", + "Throughout this lab we will introduce new functions and libraries. However,\n", + "we will import them here to emphasize these are the new\n", + "code objects in this lab. Keeping imports near the top\n", + "of a notebook makes the code more readable, since scanning the first few\n", + "lines tells us what libraries are used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "675f24e6", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "import statsmodels.api as sm\n" + ] + }, + { + "cell_type": "markdown", + "id": "1fb0799f", + "metadata": {}, + "source": [ + " We will provide relevant details about the\n", + "functions below as they are needed.\n", + "\n", + "Besides importing whole modules, it is also possible\n", + "to import only a few items from a given module. This\n", + "will help keep the *namespace* clean.\n", + "We will use a few specific objects from the `statsmodels` package\n", + "which we import here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0ee23c2", + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.stats.outliers_influence \\\n", + " import variance_inflation_factor as VIF\n", + "from statsmodels.stats.anova import anova_lm\n" + ] + }, + { + "cell_type": "markdown", + "id": "dbfd346e", + "metadata": {}, + "source": [ + "As one of the import statements above is quite a long line, we inserted a line break `\\` to\n", + "ease readability.\n", + "\n", + "We will also use some functions written for the labs in this book in the `ISLP`\n", + "package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b35eb887", + "metadata": {}, + "outputs": [], + "source": [ + "from ISLP import load_data\n", + "from ISLP.models import (ModelSpec as MS,\n", + " summarize,\n", + " poly)\n" + ] + }, + { + "cell_type": "markdown", + "id": "84a8177e", + "metadata": {}, + "source": [ + "### Inspecting Objects and Namespaces\n", + "The\n", + "function `dir()`\n", + "provides a list of\n", + "objects in a namespace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "961908f7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "dir()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3efc6d99", + "metadata": {}, + "source": [ + " This shows you everything that `Python` can find at the top level.\n", + "There are certain objects like `__builtins__` that contain references to built-in\n", + "functions like `print()`.\n", + "\n", + "Every python object has its own notion of\n", + "namespace, also accessible with `dir()`. This will include\n", + "both the attributes of the object\n", + "as well as any methods associated with it. For instance, we see `'sum'` in the listing for an\n", + "array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "662caa15", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "A = np.array([3,5,11])\n", + "dir(A)\n" + ] + }, + { + "cell_type": "markdown", + "id": "49bdd416", + "metadata": {}, + "source": [ + " This indicates that the object `A.sum` exists. In this case it is a method\n", + "that can be used to compute the sum of the array `A` as can be seen by typing `A.sum?`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb7d126", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "A.sum()\n" + ] + }, + { + "cell_type": "markdown", + "id": "2e42056b", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "9fbed3f3", + "metadata": {}, + "source": [ + "## Simple Linear Regression\n", + "In this section we will construct model \n", + "matrices (also called design matrices) using the `ModelSpec()` transform from `ISLP.models`.\n", + "\n", + "We will use the `Boston` housing data set, which is contained in the `ISLP` package. The `Boston` dataset records `medv` (median house value) for $506$ neighborhoods\n", + "around Boston. We will build a regression model to predict `medv` using $13$\n", + "predictors such as `rmvar` (average number of rooms per house),\n", + " `age` (proportion of owner-occupied units built prior to 1940), and `lstat` (percent of\n", + "households with low socioeconomic status). We will use `statsmodels` for this\n", + "task, a `Python` package that implements several commonly used\n", + "regression methods.\n", + "\n", + "We have included a simple loading function `load_data()` in the\n", + "`ISLP` package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ea46cee", + "metadata": {}, + "outputs": [], + "source": [ + "Boston = load_data(\"Boston\")\n", + "Boston.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8cceee6", + "metadata": {}, + "source": [ + "Type `Boston?` to find out more about these data.\n", + "\n", + "We start by using the `sm.OLS()` function to fit a\n", + "simple linear regression model. Our response will be\n", + " `medv` and `lstat` will be the single predictor.\n", + "For this model, we can create the model matrix by hand.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c0ba88", + "metadata": {}, + "outputs": [], + "source": [ + "X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),\n", + " 'lstat': Boston['lstat']})\n", + "X[:4]\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ac7f183", + "metadata": {}, + "source": [ + "We extract the response, and fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4dd511b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "y = Boston['medv']\n", + "model = sm.OLS(y, X)\n", + "results = model.fit()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ddf44723", + "metadata": {}, + "source": [ + "Note that `sm.OLS()` does\n", + "not fit the model; it specifies the model, and then `model.fit()` does the actual fitting. \n", + "\n", + "Our `ISLP` function `summarize()` produces a simple table of the parameter estimates,\n", + "their standard errors, t-statistics and p-values.\n", + "The function takes a single argument, such as the object `results` \n", + "returned here by the `fit`\n", + "method, and returns such a summary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eef9f8e3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "summarize(results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "db916e19", + "metadata": {}, + "source": [ + "Before we describe other methods for working with fitted models, we outline a more useful and general framework for constructing a model matrix~`X`.\n", + "### Using Transformations: Fit and Transform\n", + "Our model above has a single predictor, and constructing `X` was straightforward. \n", + "In practice we often fit models with more than one predictor, typically selected from an array or data frame.\n", + "We may wish to introduce transformations to the variables before fitting the model, specify interactions between variables, and expand some particular variables into sets of variables (e.g. polynomials).\n", + "The `sklearn` package has a particular notion\n", + "for this type of task: a *transform*. A transform is an object\n", + "that is created with some parameters as arguments. The\n", + "object has two main methods: `fit()` and `transform()`.\n", + "\n", + "We provide a general approach for specifying models and constructing\n", + "the model matrix through the transform `ModelSpec()` in the `ISLP` library.\n", + "`ModelSpec()`\n", + "(renamed `MS()` in the preamble) creates a\n", + "transform object, and then a pair of methods\n", + "`transform()` and `fit()` are used to construct a\n", + "corresponding model matrix.\n", + "\n", + "We first describe this process for our simple regression model using a single predictor `lstat` in\n", + "the `Boston` data frame, but will use it repeatedly in more\n", + "complex tasks in this and other labs in this book.\n", + "In our case the transform is created by the expression\n", + "`design = MS(['lstat'])`.\n", + "\n", + "The `fit()` method takes the original array and may do some\n", + "initial computations on it, as specified in the transform object.\n", + "For example, it may compute means and standard deviations for centering and scaling.\n", + "The `transform()` \n", + "method applies the fitted transformation to the array of data, and produces the model matrix.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "557170d4", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "design = MS(['lstat'])\n", + "design = design.fit(Boston)\n", + "X = design.transform(Boston)\n", + "X[:4]" + ] + }, + { + "cell_type": "markdown", + "id": "86615813", + "metadata": {}, + "source": [ + "In this simple case, the `fit()` method does very little; it simply checks that the variable `'lstat'` specified in `design` exists in `Boston`. Then `transform()` constructs the model matrix with two columns: an `intercept` and the variable `lstat`.\n", + "\n", + "These two operations can be combined with the\n", + "`fit_transform()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b83ec097", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "design = MS(['lstat'])\n", + "X = design.fit_transform(Boston)\n", + "X[:4]" + ] + }, + { + "cell_type": "markdown", + "id": "c6becbcb", + "metadata": {}, + "source": [ + "Note that, as in the previous code chunk when the two steps were done separately, the `design` object is changed as a result of the `fit()` operation. The power of this pipeline will become clearer when we fit more complex models that involve interactions and transformations." + ] + }, + { + "cell_type": "markdown", + "id": "e097120f", + "metadata": {}, + "source": [ + "Let's return to our fitted regression model.\n", + "The object\n", + "`results` has several methods that can be used for inference.\n", + "We already presented a function `summarize()` for showing the essentials of the fit.\n", + "For a full and somewhat exhaustive summary of the fit, we can use the `summary()` \n", + "method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4dce5f6", + "metadata": {}, + "outputs": [], + "source": [ + "results.summary()\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a367738", + "metadata": {}, + "source": [ + "The fitted coefficients can also be retrieved as the\n", + "`params` attribute of `results`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0edf555", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "results.params\n" + ] + }, + { + "cell_type": "markdown", + "id": "4472913f", + "metadata": {}, + "source": [ + "The `get_prediction()` method can be used to obtain predictions, and produce confidence intervals and\n", + "prediction intervals for the prediction of `medv` for given values of `lstat`.\n", + "\n", + "We first create a new data frame, in this case containing only the variable `lstat`, with the values for this variable at which we wish to make predictions.\n", + "We then use the `transform()` method of `design` to create the corresponding model matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdc5a3f3", + "metadata": {}, + "outputs": [], + "source": [ + "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n", + "newX = design.transform(new_df)\n", + "newX\n" + ] + }, + { + "cell_type": "markdown", + "id": "590d6b7d", + "metadata": {}, + "source": [ + "Next we compute the predictions at `newX`, and view them by extracting the `predicted_mean` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c6acbf0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "new_predictions = results.get_prediction(newX);\n", + "new_predictions.predicted_mean\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2d7e037", + "metadata": {}, + "source": [ + "We can produce confidence intervals for the predicted values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c472ef33", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "new_predictions.conf_int(alpha=0.05)\n" + ] + }, + { + "cell_type": "markdown", + "id": "dfabf8a7", + "metadata": {}, + "source": [ + "Prediction intervals are computing by setting `obs=True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e2ffc7a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "new_predictions.conf_int(obs=True, alpha=0.05)\n" + ] + }, + { + "cell_type": "markdown", + "id": "65463c75", + "metadata": {}, + "source": [ + " For instance, the 95% confidence interval associated with an\n", + " `lstat` value of 10 is (24.47, 25.63), and the 95% prediction\n", + "interval is (12.82, 37.28). As expected, the confidence and\n", + "prediction intervals are centered around the same point (a predicted\n", + "value of 25.05 for `medv` when `lstat` equals\n", + "10), but the latter are substantially wider.\n", + "\n", + "Next we will plot `medv` and `lstat` \n", + "using `DataFrame.plot.scatter()`, \n", + "and wish to\n", + "add the regression line to the resulting plot." + ] + }, + { + "cell_type": "markdown", + "id": "f699e856", + "metadata": {}, + "source": [ + "### Defining Functions\n", + "While there is a function\n", + "within the `ISLP` package that adds a line to an existing plot, we take this opportunity\n", + "to define our first function to do so." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e56a1d3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "def abline(ax, b, m):\n", + " \"Add a line with slope m and intercept b to ax\"\n", + " xlim = ax.get_xlim()\n", + " ylim = [m * xlim[0] + b, m * xlim[1] + b]\n", + " ax.plot(xlim, ylim)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0a0dce5", + "metadata": {}, + "source": [ + " A few things are illustrated above. First we see the syntax for defining a function:\n", + "`def funcname(...)`. The function has arguments `ax, b, m`\n", + "where `ax` is an axis object for an exisiting plot, `b` is the intercept and\n", + "`m` is the slope of the desired line. Other plotting options can be passed on to\n", + "`ax.plot` by including additional optional arguments as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f43ffe7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "def abline(ax, b, m, *args, **kwargs):\n", + " \"Add a line with slope m and intercept b to ax\"\n", + " xlim = ax.get_xlim()\n", + " ylim = [m * xlim[0] + b, m * xlim[1] + b]\n", + " ax.plot(xlim, ylim, *args, **kwargs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f4981fa", + "metadata": {}, + "source": [ + "The addition of `*args` allows any number of\n", + "non-named arguments to `abline`, while `*kwargs` allows any\n", + "number of named arguments (such as `linewidth=3`) to `abline`.\n", + "In our function, we pass\n", + "these arguments verbatim to `ax.plot` above. Readers\n", + "interested in learning more about\n", + "functions are referred to the section on\n", + "defining functions in [docs.python.org/tutorial](https://docs.python.org/3/tutorial/controlflow.html#defining-functions).\n", + "\n", + "Let’s use our new function to add this regression line to a plot of\n", + "`medv` vs. `lstat`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f7b67c9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = Boston.plot.scatter('lstat', 'medv')\n", + "abline(ax,\n", + " results.params[0],\n", + " results.params[1],\n", + " 'r--',\n", + " linewidth=3)\n" + ] + }, + { + "cell_type": "markdown", + "id": "50d7066f", + "metadata": {}, + "source": [ + "Thus, the final call to `ax.plot()` is `ax.plot(xlim, ylim, 'r--', linewidth=3)`.\n", + "We have used the argument `'r--'` to produce a red dashed line, and added\n", + "an argument to make it of width 3.\n", + "There is some evidence for non-linearity in the relationship between `lstat` and `medv`. We will explore this issue later in this lab.\n", + "\n", + "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "82ab1913", + "metadata": {}, + "source": [ + "Next we examine some diagnostic plots, several of which were discussed\n", + "in Section 3.3.3.\n", + "We can find the fitted values and residuals\n", + "of the fit as attributes of the `results` object.\n", + "Various influence measures describing the regression model\n", + "are computed with the `get_influence()` method.\n", + "As we will not use the `fig` component returned\n", + "as the first value from `subplots()`, we simply\n", + "capture the second returned value in `ax` below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b35a2fd3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = subplots(figsize=(8,8))[1]\n", + "ax.scatter(results.fittedvalues, results.resid)\n", + "ax.set_xlabel('Fitted value')\n", + "ax.set_ylabel('Residual')\n", + "ax.axhline(0, c='k', ls='--');\n" + ] + }, + { + "cell_type": "markdown", + "id": "4deb547b", + "metadata": {}, + "source": [ + " We add a horizontal line at 0 for reference using the\n", + " `ax.axhline()` method, indicating\n", + "it should be black (`c='k'`) and have a dashed linestyle (`ls='--'`).\n", + "\n", + "On the basis of the residual plot, there is some evidence of non-linearity.\n", + "Leverage statistics can be computed for any number of predictors using the\n", + "`hat_matrix_diag` attribute of the value returned by the\n", + "`get_influence()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82673b80", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "infl = results.get_influence()\n", + "ax = subplots(figsize=(8,8))[1]\n", + "ax.scatter(np.arange(X.shape[0]), infl.hat_matrix_diag)\n", + "ax.set_xlabel('Index')\n", + "ax.set_ylabel('Leverage')\n", + "np.argmax(infl.hat_matrix_diag)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7eb69d75", + "metadata": {}, + "source": [ + " The `np.argmax()` function identifies the index of the largest element of an array, optionally computed over an axis of the array.\n", + "In this case, we maximized over the entire array\n", + "to determine which observation has the largest leverage statistic." + ] + }, + { + "cell_type": "markdown", + "id": "94c0a8c1", + "metadata": {}, + "source": [ + "## Multiple Linear Regression\n", + "In order to fit a multiple linear regression model using least squares, we again use\n", + "the `ModelSpec()` transform to construct the required\n", + "model matrix and response. The arguments\n", + "to `ModelSpec()` can be quite general, but in this case\n", + "a list of column names suffice. We consider a fit here with\n", + "the two variables `lstat` and `age`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54596dc4", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X = MS(['lstat', 'age']).fit_transform(Boston)\n", + "model1 = sm.OLS(y, X)\n", + "results1 = model1.fit()\n", + "summarize(results1)" + ] + }, + { + "cell_type": "markdown", + "id": "88450596", + "metadata": {}, + "source": [ + "Notice how we have compacted the first line into a succinct expression describing the construction of `X`.\n", + "\n", + "The `Boston` data set contains 12 variables, and so it would be cumbersome\n", + "to have to type all of these in order to perform a regression using all of the predictors.\n", + "Instead, we can use the following short-hand:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75c78238", + "metadata": {}, + "outputs": [], + "source": [ + "terms = Boston.columns.drop('medv')\n", + "terms\n" + ] + }, + { + "cell_type": "markdown", + "id": "653715b5", + "metadata": {}, + "source": [ + "We can now fit the model with all the variables in `terms` using\n", + "the same model matrix builder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f14b9e1a", + "metadata": {}, + "outputs": [], + "source": [ + "X = MS(terms).fit_transform(Boston)\n", + "model = sm.OLS(y, X)\n", + "results = model.fit()\n", + "summarize(results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "26acd5ab", + "metadata": {}, + "source": [ + "What if we would like to perform a regression using all of the variables but one? For\n", + "example, in the above regression output, `age` has a high $p$-value.\n", + "So we may wish to run a regression excluding this predictor.\n", + "The following syntax results in a regression using all predictors except `age`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2714b1", + "metadata": {}, + "outputs": [], + "source": [ + "minus_age = Boston.columns.drop(['medv', 'age']) \n", + "Xma = MS(minus_age).fit_transform(Boston)\n", + "model1 = sm.OLS(y, Xma)\n", + "summarize(model1.fit())\n" + ] + }, + { + "cell_type": "markdown", + "id": "28538920", + "metadata": {}, + "source": [ + "## Multivariate Goodness of Fit\n", + "We can access the individual components of `results` by name\n", + "(`dir(results)` shows us what is available). Hence\n", + "`results.rsquared` gives us the $R^2$,\n", + "and\n", + "`np.sqrt(results.scale)` gives us the RSE.\n", + "\n", + "Variance inflation factors (section 3.3.3) are sometimes useful\n", + "to assess the effect of collinearity in the model matrix of a regression model.\n", + "We will compute the VIFs in our multiple regression fit, and use the opportunity to introduce the idea of *list comprehension*.\n", + "\n", + "### List Comprehension\n", + "Often we encounter a sequence of objects which we would like to transform\n", + "for some other task. Below, we compute the VIF for each\n", + "feature in our `X` matrix and produce a data frame\n", + "whose index agrees with the columns of `X`.\n", + "The notion of list comprehension can often make such\n", + "a task easier.\n", + "\n", + "List comprehensions are simple and powerful ways to form\n", + "lists of `Python` objects. The language also supports\n", + "dictionary and *generator* comprehension, though these are\n", + "beyond our scope here. Let's look at an example. We compute the VIF for each of the variables\n", + "in the model matrix `X`, using the function `variance_inflation_factor()`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "961c9128", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "vals = [VIF(X, i)\n", + " for i in range(1, X.shape[1])]\n", + "vif = pd.DataFrame({'vif':vals},\n", + " index=X.columns[1:])\n", + "vif\n" + ] + }, + { + "cell_type": "markdown", + "id": "d052f0ab", + "metadata": {}, + "source": [ + "The function `VIF()` takes two arguments: a dataframe or array,\n", + "and a variable column index. In the code above we call `VIF()` on the fly for all columns in `X`. \n", + "We have excluded column 0 above (the intercept), which is not of interest. In this case the VIFs are not that exciting.\n", + "\n", + "The object `vals` above could have been constructed with the following for loop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4886f9e9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "vals = []\n", + "for i in range(1, X.values.shape[1]):\n", + " vals.append(VIF(X.values, i))\n" + ] + }, + { + "cell_type": "markdown", + "id": "fd3732d5", + "metadata": {}, + "source": [ + "List comprehension allows us to perform such repetitive operations in a more straightforward way.\n", + "## Interaction Terms\n", + "It is easy to include interaction terms in a linear model using `ModelSpec()`.\n", + "Including a tuple `(\"lstat\",\"age\")` tells the model\n", + "matrix builder to include an interaction term between\n", + " `lstat` and `age`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b54d2da1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X = MS(['lstat',\n", + " 'age',\n", + " ('lstat', 'age')]).fit_transform(Boston)\n", + "model2 = sm.OLS(y, X)\n", + "summarize(model2.fit())\n" + ] + }, + { + "cell_type": "markdown", + "id": "c72c4846", + "metadata": {}, + "source": [ + "## Non-linear Transformations of the Predictors\n", + "The model matrix builder can include terms beyond\n", + "just column names and interactions. For instance,\n", + "the `poly()` function supplied in `ISLP` specifies that\n", + "columns representing polynomial functions\n", + "of its first argument are added to the model matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b71633a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n", + "model3 = sm.OLS(y, X)\n", + "results3 = model3.fit()\n", + "summarize(results3)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f32c813", + "metadata": {}, + "source": [ + "The effectively zero *p*-value associated with the quadratic term\n", + "(i.e. the third row above) suggests that it leads to an improved model.\n", + "\n", + "By default, `poly()` creates a basis matrix for inclusion in the\n", + "model matrix whose\n", + "columns are *orthogonal polynomials*, which are designed for stable\n", + "least squares computations. {Actually, `poly()` is a wrapper for the workhorse and standalone function `Poly()` that does the work in building the model matrix.}\n", + "Alternatively, had we included an argument\n", + "`raw=True` in the above call to `poly()`, the basis matrix would consist simply of\n", + "`lstat` and `lstat**2`. Since either of these bases\n", + "represent quadratic polynomials, the fitted values would not\n", + "change in this case, just the polynomial coefficients. Also by default, the columns\n", + "created by `poly()` do not include an intercept column as\n", + "that is automatically added by `MS()`.\n", + "\n", + "We use the `anova_lm()` function to further quantify the extent to which the quadratic fit is\n", + "superior to the linear fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d30a306", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "anova_lm(results1, results3)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd0155d4", + "metadata": {}, + "source": [ + "Here `results1` represents the linear submodel containing\n", + "predictors `lstat` and `age`,\n", + "while `results3` corresponds to the larger model above with a quadratic\n", + "term in `lstat`.\n", + "The `anova_lm()` function performs a hypothesis test\n", + "comparing the two models. The null hypothesis is that the quadratic\n", + "term in the bigger model is not needed, and the alternative hypothesis is that the\n", + "bigger model is superior. Here the *F*-statistic is 177.28 and\n", + "the associated *p*-value is zero.\n", + "In this case the *F*-statistic is the square of the\n", + "*t*-statistic for the quadratic term in the linear model summary\n", + "for `results3` --- a consequence of the fact that these nested\n", + "models differ by one degree of freedom.\n", + "This provides very clear evidence that the quadratic polynomial in\n", + "`lstat` improves the linear model.\n", + "This is not surprising, since earlier we saw evidence for non-linearity in the relationship between `medv`\n", + "and `lstat`.\n", + "\n", + "The function `anova_lm()` can take more than two nested models\n", + "as input, in which case it compares every successive pair of models.\n", + "That also explains why their are `NaN`s in the first row above, since\n", + "there is no previous model with which to compare the first.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a5ec13f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = subplots(figsize=(8,8))[1]\n", + "ax.scatter(results3.fittedvalues, results3.resid)\n", + "ax.set_xlabel('Fitted value')\n", + "ax.set_ylabel('Residual')\n", + "ax.axhline(0, c='k', ls='--')\n" + ] + }, + { + "cell_type": "markdown", + "id": "88272f6f", + "metadata": {}, + "source": [ + "We see that when the quadratic term is included in the model,\n", + "there is little discernible pattern in the residuals.\n", + "In order to create a cubic or higher-degree polynomial fit, we can simply change the degree argument\n", + "to `poly()`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1a34084", + "metadata": {}, + "source": [ + "## Qualitative Predictors\n", + "Here we use the `Carseats` data, which is included in the\n", + "`ISLP` package. We will attempt to predict `Sales`\n", + "(child car seat sales) in 400 locations based on a number of\n", + "predictors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09bbc0c6", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Carseats = load_data('Carseats')\n", + "Carseats.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a882e65", + "metadata": {}, + "source": [ + "The `Carseats` \n", + " data includes qualitative predictors such as\n", + " `ShelveLoc`, an indicator of the quality of the shelving\n", + " location --- that is,\n", + "the space within a store in which the car seat is displayed. The predictor\n", + " `ShelveLoc` takes on three possible values, `Bad`, `Medium`, and `Good`.\n", + "Given a qualitative variable such as `ShelveLoc`, `ModelSpec()` generates dummy\n", + "variables automatically.\n", + "These variables are often referred to as a *one-hot encoding* of the categorical\n", + "feature. Their columns sum to one, so to avoid collinearity with an intercept, the first column is dropped. Below we see\n", + "the column `ShelveLoc[Bad]` has been dropped, since `Bad` is the first level of `ShelveLoc`.\n", + "Below we fit a multiple regression model that includes some interaction terms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e1da1fa", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "allvars = list(Carseats.columns.drop('Sales'))\n", + "y = Carseats['Sales']\n", + "final = allvars + [('Income', 'Advertising'),\n", + " ('Price', 'Age')]\n", + "X = MS(final).fit_transform(Carseats)\n", + "model = sm.OLS(y, X)\n", + "summarize(model.fit())\n" + ] + }, + { + "cell_type": "markdown", + "id": "79d9127c", + "metadata": {}, + "source": [ + "In the first line above, we made `allvars` a list, so that we\n", + "could add the interaction terms two lines down. \n", + "Our model-matrix builder has created a `ShelveLoc[Good]`\n", + "dummy variable that takes on a value of 1 if the\n", + "shelving location is good, and 0 otherwise. It has also created a `ShelveLoc[Medium]`\n", + "dummy variable that equals 1 if the shelving location is medium, and 0 otherwise.\n", + "A bad shelving location corresponds to a zero for each of the two dummy variables.\n", + "The fact that the coefficient for `ShelveLoc[Good]` in the regression output is\n", + "positive indicates that a good shelving location is associated with high sales (relative to a bad location).\n", + "And `ShelveLoc[Medium]` has a smaller positive coefficient,\n", + "indicating that a medium shelving location leads to higher sales than a bad\n", + "shelving location, but lower sales than a good shelving location." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch4-classification-lab.ipynb b/docs/source/labs/Ch4-classification-lab.ipynb new file mode 100644 index 0000000..0161f78 --- /dev/null +++ b/docs/source/labs/Ch4-classification-lab.ipynb @@ -0,0 +1,2181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2b1e8bf1", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "84b9c86a", + "metadata": {}, + "source": [ + "# Logistic Regression, LDA, QDA, and KNN\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4e58fe1", + "metadata": {}, + "source": [ + "## The Stock Market Data\n", + "\n", + "In this lab we will examine the `Smarket` \n", + "data, which is part of the `ISLP`\n", + "library. This data set consists of percentage returns for the S&P 500\n", + "stock index over 1,250 days, from the beginning of 2001 until the end\n", + "of 2005. For each date, we have recorded the percentage returns for\n", + "each of the five previous trading days, `Lag1` through\n", + " `Lag5`. We have also recorded `Volume` (the number of\n", + "shares traded on the previous day, in billions), `Today` (the\n", + "percentage return on the date in question) and `Direction`\n", + "(whether the market was `Up` or `Down` on this date).\n", + "\n", + "We start by importing our libraries at this top level; these are all imports we have seen in previous labs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71523a7f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib.pyplot import subplots\n", + "import statsmodels.api as sm\n", + "from ISLP import load_data\n", + "from ISLP.models import (ModelSpec as MS,\n", + " summarize)" + ] + }, + { + "cell_type": "markdown", + "id": "4759a767", + "metadata": {}, + "source": [ + "We also collect together the new imports needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "901ed7c5", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from ISLP import confusion_table\n", + "from ISLP.models import contrast\n", + "from sklearn.discriminant_analysis import \\\n", + " (LinearDiscriminantAnalysis as LDA,\n", + " QuadraticDiscriminantAnalysis as QDA)\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n" + ] + }, + { + "cell_type": "markdown", + "id": "dd5a4d61", + "metadata": {}, + "source": [ + "Now we are ready to load the `Smarket` data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b56ee0f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Smarket = load_data('Smarket')\n", + "Smarket" + ] + }, + { + "cell_type": "markdown", + "id": "400eb0f9", + "metadata": {}, + "source": [ + "This gives a truncated listing of the data.\n", + "We can see what the variable names are." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62d49b47", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Smarket.columns" + ] + }, + { + "cell_type": "markdown", + "id": "82e38285", + "metadata": {}, + "source": [ + "We compute the correlation matrix using the `corr()` method\n", + "for data frames, which produces a matrix that contains all of\n", + "the pairwise correlations among the variables.\n", + " \n", + "The `pandas` library does not report a correlation for the `Direction` variable because it is\n", + "qualitative." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc34e7b7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Smarket.corr()\n" + ] + }, + { + "cell_type": "markdown", + "id": "53a52be5", + "metadata": {}, + "source": [ + "As one would expect, the correlations between the lagged return variables and\n", + "today’s return are close to zero. The only substantial correlation is between `Year` and\n", + " `Volume`. By plotting the data we see that `Volume`\n", + "is increasing over time. In other words, the average number of shares traded\n", + "daily increased from 2001 to 2005.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f075144", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Smarket.plot(y='Volume');\n" + ] + }, + { + "cell_type": "markdown", + "id": "b8428c9e", + "metadata": {}, + "source": [ + "## Logistic Regression\n", + "Next, we will fit a logistic regression model in order to predict\n", + " `Direction` using `Lag1` through `Lag5` and\n", + " `Volume`. The `sm.GLM()` function fits *generalized linear models*, a class of\n", + "models that includes logistic regression. Alternatively,\n", + "the function `sm.Logit()` fits a logistic regression\n", + "model directly. The syntax of\n", + "`sm.GLM()` is similar to that of `sm.OLS()`, except\n", + "that we must pass in the argument `family=sm.families.Binomial()`\n", + "in order to tell `statsmodels` to run a logistic regression rather than some other\n", + "type of generalized linear model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab15aa3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "allvars = Smarket.columns.drop(['Today', 'Direction', 'Year'])\n", + "design = MS(allvars)\n", + "X = design.fit_transform(Smarket)\n", + "y = Smarket.Direction == 'Up'\n", + "glm = sm.GLM(y,\n", + " X,\n", + " family=sm.families.Binomial())\n", + "results = glm.fit()\n", + "summarize(results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ce1f682c", + "metadata": {}, + "source": [ + "The smallest *p*-value here is associated with `Lag1`. The\n", + "negative coefficient for this predictor suggests that if the market\n", + "had a positive return yesterday, then it is less likely to go up\n", + "today. However, at a value of 0.15, the *p*-value is still\n", + "relatively large, and so there is no clear evidence of a real\n", + "association between `Lag1` and `Direction`.\n", + "\n", + "We use the `params` attribute of `results`\n", + "in order to access just the\n", + "coefficients for this fitted model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0faa6a6c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "results.params\n" + ] + }, + { + "cell_type": "markdown", + "id": "b989df1d", + "metadata": {}, + "source": [ + "Likewise we can use the\n", + "`pvalues` attribute to access the *p*-values for the coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108aff98", + "metadata": {}, + "outputs": [], + "source": [ + "results.pvalues\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c5c1f8e", + "metadata": {}, + "source": [ + "The `predict()` method of `results` can be used to predict the\n", + "probability that the market will go up, given values of the\n", + "predictors. This method returns predictions\n", + "on the probability scale. If no data set is supplied to the `predict()`\n", + "function, then the probabilities are computed for the training data\n", + "that was used to fit the logistic regression model.\n", + "As with linear regression, one can pass an optional `exog` argument consistent\n", + "with a design matrix if desired. Here we have\n", + "printed only the first ten probabilities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54b4b96c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "probs = results.predict()\n", + "probs[:10]\n" + ] + }, + { + "cell_type": "markdown", + "id": "3b8f3c42", + "metadata": {}, + "source": [ + "In order to make a prediction as to whether the market will go up or\n", + "down on a particular day, we must convert these predicted\n", + "probabilities into class labels, `Up` or `Down`. The\n", + "following two commands create a vector of class predictions based on\n", + "whether the predicted probability of a market increase is greater than\n", + "or less than 0.5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46c2887c", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "labels = np.array(['Down']*1250)\n", + "labels[probs>0.5] = \"Up\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "47ac8c81", + "metadata": {}, + "source": [ + "The `confusion_table()`\n", + "function from the `ISLP` package summarizes these predictions, showing how\n", + "many observations were correctly or incorrectly classified. Our function, which is adapted from a similar function\n", + "in the module `sklearn.metrics`, transposes the resulting\n", + "matrix and includes row and column labels.\n", + "The `confusion_table()` function takes as first argument the\n", + "predicted labels, and second argument the true labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f816c4d", + "metadata": {}, + "outputs": [], + "source": [ + "confusion_table(labels, Smarket.Direction)\n" + ] + }, + { + "cell_type": "markdown", + "id": "327a9525", + "metadata": {}, + "source": [ + "The diagonal elements of the confusion matrix indicate correct\n", + "predictions, while the off-diagonals represent incorrect\n", + "predictions. Hence our model correctly predicted that the market would\n", + "go up on 507 days and that it would go down on 145 days, for a\n", + "total of 507 + 145 = 652 correct predictions. The `np.mean()`\n", + "function can be used to compute the fraction of days for which the\n", + "prediction was correct. In this case, logistic regression correctly\n", + "predicted the movement of the market 52.2% of the time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23668517", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "(507+145)/1250, np.mean(labels == Smarket.Direction)\n" + ] + }, + { + "cell_type": "markdown", + "id": "dafd9800", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1143b9c3", + "metadata": {}, + "source": [ + "At first glance, it appears that the logistic regression model is\n", + "working a little better than random guessing. However, this result is\n", + "misleading because we trained and tested the model on the same set of\n", + "1,250 observations. In other words, $100-52.2=47.8%$ is the\n", + "*training* error rate. As we have seen\n", + "previously, the training error rate is often overly optimistic --- it\n", + "tends to underestimate the test error rate. In\n", + "order to better assess the accuracy of the logistic regression model\n", + "in this setting, we can fit the model using part of the data, and\n", + "then examine how well it predicts the *held out* data. This\n", + "will yield a more realistic error rate, in the sense that in practice\n", + "we will be interested in our model’s performance not on the data that\n", + "we used to fit the model, but rather on days in the future for which\n", + "the market’s movements are unknown.\n", + "\n", + "To implement this strategy, we first create a Boolean vector\n", + "corresponding to the observations from 2001 through 2004. We then\n", + "use this vector to create a held out data set of observations from\n", + "2005." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86a02d7c", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "train = (Smarket.Year < 2005)\n", + "Smarket_train = Smarket.loc[train]\n", + "Smarket_test = Smarket.loc[~train]\n", + "Smarket_test.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "26b3a6d2", + "metadata": {}, + "source": [ + "The object `train` is a vector of 1,250 elements, corresponding\n", + "to the observations in our data set. The elements of the vector that\n", + "correspond to observations that occurred before 2005 are set to\n", + "`True`, whereas those that correspond to observations in 2005 are\n", + "set to `False`. Hence `train` is a\n", + "*boolean* array, since its\n", + "elements are `True` and `False`. Boolean arrays can be used\n", + "to obtain a subset of the rows or columns of a data frame\n", + "using the `loc` method. For instance,\n", + "the command `Smarket.loc[train]` would pick out a submatrix of the\n", + "stock market data set, corresponding only to the dates before 2005,\n", + "since those are the ones for which the elements of `train` are\n", + "`True`. The `~` symbol can be used to negate all of the\n", + "elements of a Boolean vector. That is, `~train` is a vector\n", + "similar to `train`, except that the elements that are `True`\n", + "in `train` get swapped to `False` in `~train`, and vice versa.\n", + "Therefore, `Smarket.loc[~train]` yields a\n", + "subset of the rows of the data frame\n", + "of the stock market data containing only the observations for which\n", + "`train` is `False`.\n", + "The output above indicates that there are 252 such\n", + "observations.\n", + "\n", + "We now fit a logistic regression model using only the subset of the\n", + "observations that correspond to dates before 2005. We then obtain predicted probabilities of the\n", + "stock market going up for each of the days in our test set --- that is,\n", + "for the days in 2005." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f23b1c7b", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test = X.loc[train], X.loc[~train]\n", + "y_train, y_test = y.loc[train], y.loc[~train]\n", + "glm_train = sm.GLM(y_train,\n", + " X_train,\n", + " family=sm.families.Binomial())\n", + "results = glm_train.fit()\n", + "probs = results.predict(exog=X_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ebe1900", + "metadata": {}, + "source": [ + "Notice that we have trained and tested our model on two completely\n", + "separate data sets: training was performed using only the dates before\n", + "2005, and testing was performed using only the dates in 2005.\n", + "\n", + "Finally, we compare the predictions for 2005 to the\n", + "actual movements of the market over that time period.\n", + "We will first store the test and training labels (recall `y_test` is binary)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82f849b9", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "D = Smarket.Direction\n", + "L_train, L_test = D.loc[train], D.loc[~train]\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac89c253", + "metadata": {}, + "source": [ + "Now we threshold the\n", + "fitted probability at 50% to form\n", + "our predicted labels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e216583a", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "labels = np.array(['Down']*252)\n", + "labels[probs>0.5] = 'Up'\n", + "confusion_table(labels, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f43e1451", + "metadata": {}, + "source": [ + "The test accuracy is about 48% while the error rate is about 52%" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5538dc17", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.mean(labels == L_test), np.mean(labels != L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ddf7670", + "metadata": {}, + "source": [ + "The `!=` notation means *not equal to*, and so the last command\n", + "computes the test set error rate. The results are rather\n", + "disappointing: the test error rate is 52%, which is worse than\n", + "random guessing! Of course this result is not all that surprising,\n", + "given that one would not generally expect to be able to use previous\n", + "days’ returns to predict future market performance. (After all, if it\n", + "were possible to do so, then the authors of this book would be out\n", + "striking it rich rather than writing a statistics textbook.)\n", + "\n", + "We recall that the logistic regression model had very underwhelming\n", + "*p*-values associated with all of the predictors, and that the\n", + "smallest *p*-value, though not very small, corresponded to\n", + " `Lag1`. Perhaps by removing the variables that appear not to be\n", + "helpful in predicting `Direction`, we can obtain a more\n", + "effective model. After all, using predictors that have no relationship\n", + "with the response tends to cause a deterioration in the test error\n", + "rate (since such predictors cause an increase in variance without a\n", + "corresponding decrease in bias), and so removing such predictors may\n", + "in turn yield an improvement. Below we refit the logistic\n", + "regression using just `Lag1` and `Lag2`, which seemed to\n", + "have the highest predictive power in the original logistic regression\n", + "model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "016110f9", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "model = MS(['Lag1', 'Lag2']).fit(Smarket)\n", + "X = model.transform(Smarket)\n", + "X_train, X_test = X.loc[train], X.loc[~train]\n", + "glm_train = sm.GLM(y_train,\n", + " X_train,\n", + " family=sm.families.Binomial())\n", + "results = glm_train.fit()\n", + "probs = results.predict(exog=X_test)\n", + "labels = np.array(['Down']*252)\n", + "labels[probs>0.5] = 'Up'\n", + "confusion_table(labels, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7468a74", + "metadata": {}, + "source": [ + "Let’s evaluate the overall accuracy as well as the accuracy within the days when\n", + "logistic regression predicts an increase." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70984cd6", + "metadata": {}, + "outputs": [], + "source": [ + "(35+106)/252,106/(106+76)\n" + ] + }, + { + "cell_type": "markdown", + "id": "393c0a26", + "metadata": {}, + "source": [ + "Now the results appear to be a little better: 56% of the daily\n", + "movements have been correctly predicted. It is worth noting that in\n", + "this case, a much simpler strategy of predicting that the market will\n", + "increase every day will also be correct 56% of the time! Hence, in\n", + "terms of overall error rate, the logistic regression method is no\n", + "better than the naive approach. However, the confusion matrix\n", + "shows that on days when logistic regression predicts an increase in\n", + "the market, it has a 58% accuracy rate. This suggests a possible\n", + "trading strategy of buying on days when the model predicts an\n", + "increasing market, and avoiding trades on days when a decrease is\n", + "predicted. Of course one would need to investigate more carefully\n", + "whether this small improvement was real or just due to random chance.\n", + "\n", + "Suppose that we want to predict the returns associated with particular\n", + "values of `Lag1` and `Lag2`. In particular, we want to\n", + "predict `Direction` on a day when `Lag1` and\n", + " `Lag2` equal $1.2$ and $1.1$, respectively, and on a day when they\n", + "equal $1.5$ and $-0.8$. We do this using the `predict()`\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b745fd8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "newdata = pd.DataFrame({'Lag1':[1.2, 1.5],\n", + " 'Lag2':[1.1, -0.8]});\n", + "newX = model.transform(newdata)\n", + "results.predict(newX)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae928a3e", + "metadata": {}, + "source": [ + "## Linear Discriminant Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "aab265c4", + "metadata": {}, + "source": [ + "We begin by performing LDA on the `Smarket` data, using the function\n", + "`LinearDiscriminantAnalysis()`, which we have abbreviated `LDA()`. We \n", + "fit the model using only the observations before 2005." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd533313", + "metadata": {}, + "outputs": [], + "source": [ + "lda = LDA(store_covariance=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c84aa056", + "metadata": {}, + "source": [ + "Since the `LDA` estimator automatically \n", + "adds an intercept, we should remove the column corresponding to the\n", + "intercept in both `X_train` and `X_test`. We can also directly\n", + "use the labels rather than the Boolean vectors `y_train`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953dc6ac", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_train, X_test = [M.drop(columns=['intercept'])\n", + " for M in [X_train, X_test]]\n", + "lda.fit(X_train, L_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0a51b5cd", + "metadata": {}, + "source": [ + "Here we have used the list comprehensions introduced\n", + "in Section 3.6.4. Looking at our first line above, we see that the right-hand side is a list\n", + "of length two. This is because the code `for M in [X_train, X_test]` iterates over a list\n", + "of length two. While here we loop over a list,\n", + "the list comprehension method works when looping over any iterable object.\n", + "We then apply the `drop()` method to each element in the iteration, collecting\n", + "the result in a list. The left-hand side tells `Python` to unpack this list\n", + "of length two, assigning its elements to the variables `X_train` and `X_test`. Of course,\n", + "this overwrites the previous values of `X_train` and `X_test`.\n", + "\n", + "Having fit the model, we can extract the means in the two classes with the `means_` attribute. These are the average of each predictor within each class, and\n", + "are used by LDA as estimates of $\\mu_k$. These suggest that there is\n", + "a tendency for the previous 2 days’ returns to be negative on days\n", + "when the market increases, and a tendency for the previous days’\n", + "returns to be positive on days when the market declines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8917085e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lda.means_\n" + ] + }, + { + "cell_type": "markdown", + "id": "14bc4692", + "metadata": {}, + "source": [ + "The estimated prior probabilities are stored in the `priors_` attribute.\n", + "The package `sklearn` typically uses this trailing `_` to denote\n", + "a quantity estimated when using the `fit()` method. We can be sure of which\n", + "entry corresponds to which label by looking at the `classes_` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efb3e9bd", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lda.classes_\n" + ] + }, + { + "cell_type": "markdown", + "id": "50697f11", + "metadata": {}, + "source": [ + "The LDA output indicates that $\\hat\\pi_{Down}=0.492$ and\n", + "$\\hat\\pi_{Up}=0.508$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efce91fb", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lda.priors_\n" + ] + }, + { + "cell_type": "markdown", + "id": "a27bc841", + "metadata": {}, + "source": [ + "The linear discriminant vectors can be found in the `scalings_` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e33cd6c5", + "metadata": {}, + "outputs": [], + "source": [ + "lda.scalings_\n" + ] + }, + { + "cell_type": "markdown", + "id": "b011d741", + "metadata": {}, + "source": [ + "These values provide the linear combination of `Lag1` and `Lag2` that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (4.24).\n", + " If $-0.64\\times `Lag1` - 0.51 \\times `Lag2` $ is large, then the LDA classifier will predict a market increase, and if it is small, then the LDA classifier will predict a market decline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc8a5178", + "metadata": {}, + "outputs": [], + "source": [ + "lda_pred = lda.predict(X_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c8a8a75", + "metadata": {}, + "source": [ + "As we observed in our comparison of classification methods\n", + " (Section 4.5), the LDA and logistic\n", + "regression predictions are almost identical." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31a6782b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "confusion_table(lda_pred, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c3534a8f", + "metadata": {}, + "source": [ + "We can also estimate the\n", + "probability of each class for\n", + "each point in a training set. Applying a 50% threshold to the posterior probabilities of\n", + "being in class 1 allows us to\n", + "recreate the predictions contained in `lda_pred`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da1bffa3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "lda_prob = lda.predict_proba(X_test)\n", + "np.all(\n", + " np.where(lda_prob[:,1] >= 0.5, 'Up','Down') == lda_pred\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ab71d7c", + "metadata": {}, + "source": [ + "Above, we used the `np.where()` function that\n", + "creates an array with value `'Up'` for indices where\n", + "the second column of `lda_prob` (the estimated\n", + "posterior probability of `'Up'`) is greater than 0.5.\n", + "For problems with more than two classes the labels are chosen as the class whose posterior probability is highest:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d7eae3c", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.all(\n", + " [lda.classes_[i] for i in np.argmax(lda_prob, 1)] == lda_pred\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "6a67baf1", + "metadata": {}, + "source": [ + "If we wanted to use a posterior probability threshold other than\n", + "50% in order to make predictions, then we could easily do so. For\n", + "instance, suppose that we wish to predict a market decrease only if we\n", + "are very certain that the market will indeed decrease on that\n", + "day --- say, if the posterior probability is at least 90%.\n", + "We know that the first column of `lda_prob` corresponds to the\n", + "label `Down` after having checked the `classes_` attribute, hence we use\n", + "the column index 0 rather than 1 as we did above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18fa175c", + "metadata": {}, + "outputs": [], + "source": [ + "np.sum(lda_prob[:,0] > 0.9)\n" + ] + }, + { + "cell_type": "markdown", + "id": "527be6fe", + "metadata": {}, + "source": [ + "No days in 2005 meet that threshold! In fact, the greatest posterior\n", + "probability of decrease in all of 2005 was 52.02%.\n", + "\n", + "The LDA classifier above is the first classifier from the\n", + "`sklearn` library. We will use several other objects\n", + "from this library. The objects\n", + "follow a common structure that simplifies tasks such as cross-validation,\n", + "which we will see in Chapter 5. Specifically,\n", + "the methods first create a generic classifier without\n", + "referring to any data. This classifier is then fit\n", + "to data with the `fit()` method and predictions are\n", + "always produced with the `predict()` method. This pattern\n", + "of first instantiating the classifier, followed by fitting it, and\n", + "then producing predictions is an explicit design choice of `sklearn`. This uniformity\n", + "makes it possible to cleanly copy the classifier so that it can be fit\n", + "on different data; e.g. different training sets arising in cross-validation.\n", + "This standard pattern also allows for a predictable formation of workflows.\n" + ] + }, + { + "cell_type": "markdown", + "id": "66ecd10f", + "metadata": {}, + "source": [ + "## Quadratic Discriminant Analysis\n", + "We will now fit a QDA model to the `Smarket` data. QDA is\n", + "implemented via\n", + "`QuadraticDiscriminantAnalysis()`\n", + "in the `sklearn` package, which we abbreviate to `QDA()`.\n", + "The syntax is very similar to `LDA()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62499aad", + "metadata": {}, + "outputs": [], + "source": [ + "qda = QDA(store_covariance=True)\n", + "qda.fit(X_train, L_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "17b38989", + "metadata": {}, + "source": [ + "The `QDA()` function will again compute `means_` and `priors_`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "296fdd89", + "metadata": {}, + "outputs": [], + "source": [ + "qda.means_, qda.priors_\n" + ] + }, + { + "cell_type": "markdown", + "id": "f439efa9", + "metadata": {}, + "source": [ + "The `QDA()` classifier will estimate one covariance per class. Here is the\n", + "estimated covariance in the first class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fbbefb0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "qda.covariance_[0]\n" + ] + }, + { + "cell_type": "markdown", + "id": "4b33a370", + "metadata": {}, + "source": [ + "The output contains the group means. But it does not contain the\n", + "coefficients of the linear discriminants, because the QDA classifier\n", + "involves a quadratic, rather than a linear, function of the\n", + "predictors. The `predict()` function works in exactly the\n", + "same fashion as for LDA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03094dcd", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "qda_pred = qda.predict(X_test)\n", + "confusion_table(qda_pred, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2037df4b", + "metadata": {}, + "source": [ + "Interestingly, the QDA predictions are accurate almost 60% of the\n", + "time, even though the 2005 data was not used to fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25022d1a", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.mean(qda_pred == L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf85b45e", + "metadata": {}, + "source": [ + "This level of accuracy is quite impressive for stock market data, which is\n", + "known to be quite hard to model accurately. This suggests that the\n", + "quadratic form assumed by QDA may capture the true relationship more\n", + "accurately than the linear forms assumed by LDA and logistic\n", + "regression. However, we recommend evaluating this method’s\n", + "performance on a larger test set before betting that this approach\n", + "will consistently beat the market!" + ] + }, + { + "cell_type": "markdown", + "id": "258f98b8", + "metadata": {}, + "source": [ + "## Naive Bayes\n", + "Next, we fit a naive Bayes model to the `Smarket` data. The syntax is\n", + "similar to that of `LDA()` and `QDA()`. By\n", + "default, this implementation `GaussianNB()` of the naive Bayes classifier models each\n", + "quantitative feature using a Gaussian distribution. However, a kernel\n", + "density method can also be used to estimate the distributions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f73eb202", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "NB = GaussianNB()\n", + "NB.fit(X_train, L_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9911dcdc", + "metadata": {}, + "source": [ + "The classes are stored as `classes_`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e860cc3", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "NB.classes_\n" + ] + }, + { + "cell_type": "markdown", + "id": "8d6b9b33", + "metadata": {}, + "source": [ + "The class prior probabilities are stored in the `class_prior_` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c28bf3b5", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "NB.class_prior_\n" + ] + }, + { + "cell_type": "markdown", + "id": "fed2c4b4", + "metadata": {}, + "source": [ + "The parameters of the features can be found in the `theta_` and `var_` attributes. The number of rows\n", + "is equal to the number of classes, while the number of columns is equal to the number of features.\n", + "We see below that the mean for feature `Lag1` in the `Down` class is 0.043." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84a9f086", + "metadata": {}, + "outputs": [], + "source": [ + "NB.theta_\n" + ] + }, + { + "cell_type": "markdown", + "id": "66f0a08d", + "metadata": {}, + "source": [ + "Its variance is 1.503." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "885f5165", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "NB.var_\n" + ] + }, + { + "cell_type": "markdown", + "id": "db0a1ce6", + "metadata": {}, + "source": [ + "How do we know the names of these attributes? We use `NB?` (or \\textR?NB}).\n", + "\n", + "We can easily verify the mean computation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61473094", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "X_train[L_train == 'Down'].mean()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d4ffc86b", + "metadata": {}, + "source": [ + "Similarly for the variance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db203a23", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "X_train[L_train == 'Down'].var(ddof=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7c9078c1", + "metadata": {}, + "source": [ + "Since `NB()` is a classifier in the `sklearn` library, making predictions\n", + "uses the same syntax as for `LDA()` and `QDA()` above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b387227e", + "metadata": {}, + "outputs": [], + "source": [ + "nb_labels = NB.predict(X_test)\n", + "confusion_table(nb_labels, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ab979471", + "metadata": {}, + "source": [ + "Naive Bayes performs well on these data, with accurate predictions over 59% of the time. This is slightly worse than QDA, but much better than LDA.\n", + "\n", + "As for `LDA`, the `predict_proba()` method estimates the probability that each observation belongs to a particular class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f14ebe61", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "NB.predict_proba(X_test)[:5]\n" + ] + }, + { + "cell_type": "markdown", + "id": "f14c520d", + "metadata": {}, + "source": [ + "## K-Nearest Neighbors\n", + "We will now perform KNN using the `KNeighborsClassifier()` function. This function works similarly\n", + "to the other model-fitting functions that we have\n", + "encountered thus far.\n", + "\n", + "As is the\n", + "case for LDA and QDA, we fit the classifier\n", + "using the `fit` method. New\n", + "predictions are formed using the `predict` method\n", + "of the object returned by `fit()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a12f4a", + "metadata": {}, + "outputs": [], + "source": [ + "knn1 = KNeighborsClassifier(n_neighbors=1)\n", + "knn1.fit(X_train, L_train)\n", + "knn1_pred = knn1.predict(X_test)\n", + "confusion_table(knn1_pred, L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "22ee6dff", + "metadata": {}, + "source": [ + "The results using $K=1$ are not very good, since only $50%$ of the\n", + "observations are correctly predicted. Of course, it may be that $K=1$\n", + "results in an overly-flexible fit to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dd2f3e4", + "metadata": {}, + "outputs": [], + "source": [ + "(83+43)/252, np.mean(knn1_pred == L_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cda2465d", + "metadata": {}, + "source": [ + "Below, we repeat the\n", + "analysis using $K=3$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ec72efc", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "knn3 = KNeighborsClassifier(n_neighbors=3)\n", + "knn3_pred = knn3.fit(X_train, L_train).predict(X_test)\n", + "np.mean(knn3_pred == L_test)" + ] + }, + { + "cell_type": "markdown", + "id": "ff4533bd", + "metadata": {}, + "source": [ + "The results have improved slightly. But increasing *K* further\n", + "provides no further improvements. It appears that for these data, and this train/test split,\n", + "QDA gives the best results of the methods that we have examined so\n", + "far." + ] + }, + { + "cell_type": "markdown", + "id": "5810022d", + "metadata": {}, + "source": [ + "KNN does not perform well on the `Smarket` data, but it often does provide impressive results. As an example we will apply the KNN approach to the `Caravan` data set, which is part of the `ISLP` library. This data set includes 85\n", + "predictors that measure demographic characteristics for 5,822\n", + "individuals. The response variable is `Purchase`, which\n", + "indicates whether or not a given individual purchases a caravan\n", + "insurance policy. In this data set, only 6% of people purchased\n", + "caravan insurance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98fef4ed", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "Caravan = load_data('Caravan')\n", + "Purchase = Caravan.Purchase\n", + "Purchase.value_counts()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4fc1a1df", + "metadata": {}, + "source": [ + "The method `value_counts()` takes a `pd.Series` or `pd.DataFrame` and returns\n", + "a `pd.Series` with the corresponding counts\n", + "for each unique element. In this case `Purchase` has only `Yes` and `No` values\n", + "and returns how many values of each there are." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1af72c0b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "348 / 5822\n" + ] + }, + { + "cell_type": "markdown", + "id": "78c25fda", + "metadata": {}, + "source": [ + "Our features will include all columns except `Purchase`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ea841e4", + "metadata": {}, + "outputs": [], + "source": [ + "feature_df = Caravan.drop(columns=['Purchase'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "55efb306", + "metadata": {}, + "source": [ + "Because the KNN classifier predicts the class of a given test\n", + "observation by identifying the observations that are nearest to it,\n", + "the scale of the variables matters. Any variables that are on a large\n", + "scale will have a much larger effect on the *distance* between\n", + "the observations, and hence on the KNN classifier, than variables that\n", + "are on a small scale. For instance, imagine a data set that contains\n", + "two variables, `salary` and `age` (measured in dollars\n", + "and years, respectively). As far as KNN is concerned, a difference of\n", + "1,000 USD in salary is enormous compared to a difference of 50 years in\n", + "age. Consequently, `salary` will drive the KNN classification\n", + "results, and `age` will have almost no effect. This is contrary\n", + "to our intuition that a salary difference of 1,000 USD is quite small\n", + "compared to an age difference of 50 years. Furthermore, the\n", + "importance of scale to the KNN classifier leads to another issue: if\n", + "we measured `salary` in Japanese yen, or if we measured\n", + " `age` in minutes, then we’d get quite different classification\n", + "results from what we get if these two variables are measured in\n", + "dollars and years.\n", + "\n", + "A good way to handle this problem is to *standardize* the data so that all variables are\n", + "given a mean of zero and a standard deviation of one. Then all\n", + "variables will be on a comparable scale. This is accomplished\n", + "using\n", + "the `StandardScaler()`\n", + "transformation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e26b14f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "scaler = StandardScaler(with_mean=True,\n", + " with_std=True,\n", + " copy=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c0d1faf", + "metadata": {}, + "source": [ + "The argument `with_mean` indicates whether or not\n", + "we should subtract the mean, while `with_std` indicates\n", + "whether or not we should scale the columns to have standard\n", + "deviation of 1 or not. Finally, the argument `copy=True`\n", + "indicates that we will always copy data, rather than\n", + "trying to do calculations in place where possible.\n", + "\n", + "This transformation can be fit\n", + "and then applied to arbitrary data. In the first line\n", + "below, the parameters for the scaling are computed and\n", + "stored in `scaler`, while the second line actually\n", + "constructs the standardized set of features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd178f38", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "scaler.fit(feature_df)\n", + "X_std = scaler.transform(feature_df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "30bfaa05", + "metadata": {}, + "source": [ + "Now every column of `feature_std` below has a standard deviation of\n", + "one and a mean of zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18929b37", + "metadata": {}, + "outputs": [], + "source": [ + "feature_std = pd.DataFrame(\n", + " X_std,\n", + " columns=feature_df.columns);\n", + "feature_std.std()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f536808", + "metadata": {}, + "source": [ + "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 198.\n", + "In this case it does not matter, as long as the variables are all on the same scale.\n", + "\n", + "Using the function `train_test_split()` we now split the observations into a test set,\n", + "containing 1000 observations, and a training set containing the remaining\n", + "observations. The argument `random_state=0` ensures that we get\n", + "the same split each time we rerun the code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d439d0b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "(X_train,\n", + " X_test,\n", + " y_train,\n", + " y_test) = train_test_split(feature_std,\n", + " Purchase,\n", + " test_size=1000,\n", + " random_state=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "87fcf773", + "metadata": {}, + "source": [ + "`?train_test_split` reveals that the non-keyword arguments can be `lists`, `arrays`, `pandas dataframes` etc that all have the same length (`shape[0]`) and hence are *indexable*. In this case they are the dataframe `feature_std` and the response variable `Purchase`. \n", + "We fit a KNN model on the training data using $K=1$,\n", + "and evaluate its performance on the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b8c0171", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "knn1 = KNeighborsClassifier(n_neighbors=1)\n", + "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n", + "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3aa030b6", + "metadata": {}, + "source": [ + "The KNN error rate on the 1,000 test observations is about $11%$.\n", + "At first glance, this may appear to be fairly good. However, since\n", + "just over 6% of customers purchased insurance, we could get the error\n", + "rate down to almost 6% by always predicting `No` regardless of the\n", + "values of the predictors! This is known as the *null rate*.}\n", + "\n", + "Suppose that there is some non-trivial cost to trying to sell\n", + "insurance to a given individual. For instance, perhaps a salesperson\n", + "must visit each potential customer. If the company tries to sell\n", + "insurance to a random selection of customers, then the success rate\n", + "will be only 6%, which may be far too low given the costs\n", + "involved. Instead, the company would like to try to sell insurance\n", + "only to customers who are likely to buy it. So the overall error rate\n", + "is not of interest. Instead, the fraction of individuals that are\n", + "correctly predicted to buy insurance is of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f31b6e26", + "metadata": {}, + "outputs": [], + "source": [ + "confusion_table(knn1_pred, y_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "de5fb529", + "metadata": {}, + "source": [ + "It turns out that KNN with $K=1$ does far better than random guessing\n", + "among the customers that are predicted to buy insurance. Among 62\n", + "such customers, 9, or 14.5%, actually do purchase insurance.\n", + "This is double the rate that one would obtain from random guessing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a8c37aa", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "9/(53+9)" + ] + }, + { + "cell_type": "markdown", + "id": "a898b7d0", + "metadata": {}, + "source": [ + "### Tuning Parameters\n", + "\n", + "The number of neighbors in KNN is referred to as a *tuning parameter*, also referred to as a *hyperparameter*.\n", + "We do not know *a priori* what value to use. It is therefore of interest\n", + "to see how the classifier performs on test data as we vary these\n", + "parameters. This can be achieved with a `for` loop, described in Section 2.3.8.\n", + "Here we use a for loop to look at the accuracy of our classifier in the group predicted to purchase\n", + "insurance as we vary the number of neighbors from 1 to 5:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "153662f8", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "for K in range(1,6):\n", + " knn = KNeighborsClassifier(n_neighbors=K)\n", + " knn_pred = knn.fit(X_train, y_train).predict(X_test)\n", + " C = confusion_table(knn_pred, y_test)\n", + " templ = ('K={0:d}: # predicted to rent: {1:>2},' +\n", + " ' # who did rent {2:d}, accuracy {3:.1%}')\n", + " pred = C.loc['Yes'].sum()\n", + " did_rent = C.loc['Yes','Yes']\n", + " print(templ.format(\n", + " K,\n", + " pred,\n", + " did_rent,\n", + " did_rent / pred))\n" + ] + }, + { + "cell_type": "markdown", + "id": "39ba18b3", + "metadata": {}, + "source": [ + "We see some variability --- the numbers for `K=4` are very different from the rest.\n", + "\n", + "### Comparison to Logistic Regression\n", + "As a comparison, we can also fit a logistic regression model to the\n", + "data. This can also be done\n", + "with `sklearn`, though by default it fits\n", + "something like the *ridge regression* version\n", + "of logistic regression, which we introduce in Chapter 6. This can\n", + "be modified by appropriately setting the argument `C` below. Its default\n", + "value is 1 but by setting it to a very large number, the algorithm converges to the same solution as the usual (unregularized)\n", + "logistic regression estimator discussed above.\n", + "\n", + "Unlike the\n", + "`statsmodels` package, `sklearn` focuses less on\n", + "inference and more on classification. Hence,\n", + "the `summary` methods seen in `statsmodels`\n", + "and our simplified version seen with `summarize` are not\n", + "generally available for the classifiers in `sklearn`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55b99a3f", + "metadata": {}, + "outputs": [], + "source": [ + "logit = LogisticRegression(C=1e10, solver='liblinear')\n", + "logit.fit(X_train, y_train)\n", + "logit_pred = logit.predict_proba(X_test)\n", + "logit_labels = np.where(logit_pred[:,1] > 5, 'Yes', 'No')\n", + "confusion_table(logit_labels, y_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4e0ebc83", + "metadata": {}, + "source": [ + "We used the argument `solver='liblinear'` above to\n", + "avoid a warning with the default solver which would indicate that\n", + "the algorithm does not converge.\n", + "\n", + "If we use $0.5$ as the predicted probability cut-off for the\n", + "classifier, then we have a problem: none of the test\n", + "observations are predicted to purchase insurance. However, we are not required to use a\n", + "cut-off of $0.5$. If we instead predict a purchase any time the\n", + "predicted probability of purchase exceeds $0.25$, we get much better\n", + "results: we predict that 29 people will purchase insurance, and we are\n", + "correct for about 31% of these people. This is almost five times\n", + "better than random guessing!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c2ea9f5", + "metadata": {}, + "outputs": [], + "source": [ + "logit_labels = np.where(logit_pred[:,1]>0.25, 'Yes', 'No')\n", + "confusion_table(logit_labels, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a6bf0a4", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "9/(20+9)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d04bbc7", + "metadata": {}, + "source": [ + "## Linear and Poisson Regression on the Bikeshare Data\n", + "Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6.\n", + "The response `bikers` measures the number of bike rentals per hour\n", + "in Washington, DC in the period 2010--2012." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "769300df", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "Bike = load_data('Bikeshare')\n" + ] + }, + { + "cell_type": "markdown", + "id": "8cc82fa9", + "metadata": {}, + "source": [ + "Let's have a peek at the dimensions and names of the variables in this dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02b351f5", + "metadata": {}, + "outputs": [], + "source": [ + "Bike.shape, Bike.columns\n" + ] + }, + { + "cell_type": "markdown", + "id": "7784e3a1", + "metadata": {}, + "source": [ + "### Linear Regression\n", + "\n", + "We begin by fitting a linear regression model to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "926c908d", + "metadata": {}, + "outputs": [], + "source": [ + "X = MS(['mnth',\n", + " 'hr',\n", + " 'workingday',\n", + " 'temp',\n", + " 'weathersit']).fit_transform(Bike)\n", + "Y = Bike['bikers']\n", + "M_lm = sm.OLS(Y, X).fit()\n", + "summarize(M_lm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b29a0708", + "metadata": {}, + "source": [ + "There are 24 levels in `hr` and 40 rows in all.\n", + "In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated\n", + "as the baseline values, and so no coefficient estimates are provided\n", + "for them: implicitly, their coefficient estimates are zero, and all\n", + "other levels are measured relative to these baselines. For example,\n", + "the Feb coefficient of $6.845$ signifies that, holding all other\n", + "variables constant, there are on average about 7 more riders in\n", + "February than in January. Similarly there are about 16.5 more riders\n", + "in March than in January.\n", + "\n", + "The results seen in Section 4.6.1\n", + "used a slightly different coding of the variables `hr` and `mnth`, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "064b24d1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "hr_encode = contrast('hr', 'sum')\n", + "mnth_encode = contrast('mnth', 'sum')\n" + ] + }, + { + "cell_type": "markdown", + "id": "fba11e8e", + "metadata": {}, + "source": [ + "Refitting again:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27c1aa54", + "metadata": {}, + "outputs": [], + "source": [ + "X2 = MS([mnth_encode,\n", + " hr_encode,\n", + " 'workingday',\n", + " 'temp',\n", + " 'weathersit']).fit_transform(Bike)\n", + "M2_lm = sm.OLS(Y, X2).fit()\n", + "S2 = summarize(M2_lm)\n", + "S2" + ] + }, + { + "cell_type": "markdown", + "id": "d057b162", + "metadata": {}, + "source": [ + "What is the difference between the two codings? In `M2_lm`, a\n", + "coefficient estimate is reported for all but level `23` of `hr`\n", + "and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate\n", + "for the last level of `mnth` is not zero: instead, it equals the\n", + "negative of the sum of the coefficient estimates for all of the\n", + "other levels. Similarly, in `M2_lm`, the coefficient estimate\n", + "for the last level of `hr` is the negative of the sum of the\n", + "coefficient estimates for all of the other levels. This means that the\n", + "coefficients of `hr` and `mnth` in `M2_lm` will always sum\n", + "to zero, and can be interpreted as the difference from the mean\n", + "level. For example, the coefficient for January of $-46.087$ indicates\n", + "that, holding all other variables constant, there are typically 46\n", + "fewer riders in January relative to the yearly average.\n", + "\n", + "It is important to realize that the choice of coding really does not\n", + "matter, provided that we interpret the model output correctly in light\n", + "of the coding used. For example, we see that the predictions from the\n", + "linear model are the same regardless of coding:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd0114c8", + "metadata": {}, + "outputs": [], + "source": [ + "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bcb6ae87", + "metadata": {}, + "source": [ + "The sum of squared differences is zero. We can also see this using the\n", + "`np.allclose()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "292585a1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5bffbdd1", + "metadata": {}, + "source": [ + "To reproduce the left-hand side of Figure 4.13\n", + "we must first obtain the coefficient estimates associated with\n", + "`mnth`. The coefficients for January through November can be obtained\n", + "directly from the `M2_lm` object. The coefficient for December\n", + "must be explicitly computed as the negative sum of all the other\n", + "months. We first extract all the coefficients for month from\n", + "the coefficients of `M2_lm`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e05e0575", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", + "coef_month\n" + ] + }, + { + "cell_type": "markdown", + "id": "d542caac", + "metadata": {}, + "source": [ + "Next, we append `Dec` as the negative of the sum of all other months." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f04a25e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "months = Bike['mnth'].dtype.categories\n", + "coef_month = pd.concat([\n", + " coef_month,\n", + " pd.Series([-coef_month.sum()],\n", + " index=['mnth[Dec]'\n", + " ])\n", + " ])\n", + "coef_month\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e3ef375", + "metadata": {}, + "source": [ + "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n", + "the labels in the index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20c7c054", + "metadata": {}, + "outputs": [], + "source": [ + "fig_month, ax_month = subplots(figsize=(8,8))\n", + "x_month = np.arange(coef_month.shape[0])\n", + "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", + "ax_month.set_xticks(x_month)\n", + "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", + "ax_month.set_xlabel('Month', fontsize=20)\n", + "ax_month.set_ylabel('Coefficient', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "6032f6a9", + "metadata": {}, + "source": [ + "Reproducing the right-hand plot in Figure 4.13 follows a similar process." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f83f4bf", + "metadata": {}, + "outputs": [], + "source": [ + "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", + "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n", + "coef_hr = pd.concat([coef_hr,\n", + " pd.Series([-coef_hr.sum()], index=['hr[23]'])\n", + " ])\n" + ] + }, + { + "cell_type": "markdown", + "id": "58200be9", + "metadata": {}, + "source": [ + "We now make the hour plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86e9a741", + "metadata": {}, + "outputs": [], + "source": [ + "fig_hr, ax_hr = subplots(figsize=(8,8))\n", + "x_hr = np.arange(coef_hr.shape[0])\n", + "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", + "ax_hr.set_xticks(x_hr[::2])\n", + "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", + "ax_hr.set_xlabel('Hour', fontsize=20)\n", + "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "5db06a3e", + "metadata": {}, + "source": [ + "### Poisson Regression\n", + "\n", + "Now we fit instead a Poisson regression model to the\n", + "`Bikeshare` data. Very little changes, except that we now use the\n", + "function `sm.GLM()` with the Poisson family specified:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9448278", + "metadata": {}, + "outputs": [], + "source": [ + "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7b3bc38", + "metadata": {}, + "source": [ + "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec8505be", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "S_pois = summarize(M_pois)\n", + "coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef']\n", + "coef_month = pd.concat([coef_month,\n", + " pd.Series([-coef_month.sum()],\n", + " index=['mnth[Dec]'])])\n", + "coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef']\n", + "coef_hr = pd.concat([coef_hr,\n", + " pd.Series([-coef_hr.sum()],\n", + " index=['hr[23]'])])" + ] + }, + { + "cell_type": "markdown", + "id": "f6fb37ef", + "metadata": {}, + "source": [ + "The plotting is as before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b8a5edf", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", + "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", + "ax_month.set_xticks(x_month)\n", + "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", + "ax_month.set_xlabel('Month', fontsize=20)\n", + "ax_month.set_ylabel('Coefficient', fontsize=20)\n", + "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", + "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", + "ax_hr.set_xlabel('Hour', fontsize=20)\n", + "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c256bf2", + "metadata": {}, + "source": [ + "We compare the fitted values of the two models.\n", + "The fitted values are stored in the `fittedvalues` attribute\n", + "returned by the `fit()` method for both the linear regression and the Poisson\n", + "fits. The linear predictors are stored as the attribute `lin_pred`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d487c7ed", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax.scatter(M2_lm.fittedvalues,\n", + " M_pois.fittedvalues,\n", + " s=20)\n", + "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n", + "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n", + "ax.axline([0,0], c='black', linewidth=3,\n", + " linestyle='--', slope=1);\n" + ] + }, + { + "cell_type": "markdown", + "id": "17ccc1bf", + "metadata": {}, + "source": [ + "The predictions from the Poisson regression model are correlated with\n", + "those from the linear model; however, the former are non-negative. As\n", + "a result the Poisson regression predictions tend to be larger than\n", + "those from the linear model for either very low or very high levels of\n", + "ridership.\n", + "\n", + "In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument\n", + "`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function\n", + "with `family=sm.families.Binomial()` to perform logistic regression. Other\n", + "choices for the `family` argument can be used to fit other types\n", + "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n", + "model." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch5-resample-lab.ipynb b/docs/source/labs/Ch5-resample-lab.ipynb new file mode 100644 index 0000000..b499676 --- /dev/null +++ b/docs/source/labs/Ch5-resample-lab.ipynb @@ -0,0 +1,901 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0c11a71f", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ad36451e", + "metadata": {}, + "source": [ + "# Cross-Validation and the Bootstrap\n", + "In this lab, we explore the resampling techniques covered in this\n", + "chapter. Some of the commands in this lab may take a while to run on\n", + "your computer.\n", + "\n", + "We again begin by placing most of our imports at this top level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60fad148", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import statsmodels.api as sm\n", + "from ISLP import load_data\n", + "from ISLP.models import (ModelSpec as MS,\n", + " summarize,\n", + " poly)\n", + "from sklearn.model_selection import train_test_split\n" + ] + }, + { + "cell_type": "markdown", + "id": "78fcfe7a", + "metadata": {}, + "source": [ + "There are several new imports needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2478aeb4", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from functools import partial\n", + "from sklearn.model_selection import \\\n", + " (cross_validate,\n", + " KFold,\n", + " ShuffleSplit)\n", + "from sklearn.base import clone\n", + "from ISLP.models import sklearn_sm\n" + ] + }, + { + "cell_type": "markdown", + "id": "713d30db", + "metadata": {}, + "source": [ + "## The Validation Set Approach\n", + "We explore the use of the validation set approach in order to estimate\n", + "the test error rates that result from fitting various linear models on\n", + "the `Auto` data set.\n", + "\n", + "We use the function `train_test_split()` to split\n", + "the data into training and validation sets. As there are 392 observations,\n", + "we split into two equal sets of size 196 using the\n", + "argument `test_size=196`. It is generally a good idea to set a random seed\n", + "when performing operations like this that contain an\n", + "element of randomness, so that the results obtained can be reproduced\n", + "precisely at a later time. We set the random seed of the splitter\n", + "with the argument `random_state=0`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99c95faf", + "metadata": {}, + "outputs": [], + "source": [ + "Auto = load_data('Auto')\n", + "Auto_train, Auto_valid = train_test_split(Auto,\n", + " test_size=196,\n", + " random_state=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "57be35df", + "metadata": {}, + "source": [ + "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41b0717d", + "metadata": {}, + "outputs": [], + "source": [ + "hp_mm = MS(['horsepower'])\n", + "X_train = hp_mm.fit_transform(Auto_train)\n", + "y_train = Auto_train['mpg']\n", + "model = sm.OLS(y_train, X_train)\n", + "results = model.fit()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f1bef95", + "metadata": {}, + "source": [ + "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n", + "created using the validation data set. We also calculate the validation MSE of our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7ea3c0d", + "metadata": {}, + "outputs": [], + "source": [ + "X_valid = hp_mm.transform(Auto_valid)\n", + "y_valid = Auto_valid['mpg']\n", + "valid_pred = results.predict(X_valid)\n", + "np.mean((y_valid - valid_pred)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6dba5d55", + "metadata": {}, + "source": [ + "Hence our estimate for the validation MSE of the linear regression\n", + "fit is $23.62$.\n", + "\n", + "We can also estimate the validation error for\n", + "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n", + "as a training and test set and returns the MSE on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a02a2d05", + "metadata": {}, + "outputs": [], + "source": [ + "def evalMSE(terms,\n", + " response,\n", + " train,\n", + " test):\n", + "\n", + " mm = MS(terms)\n", + " X_train = mm.fit_transform(train)\n", + " y_train = train[response]\n", + "\n", + " X_test = mm.transform(test)\n", + " y_test = test[response]\n", + "\n", + " results = sm.OLS(y_train, X_train).fit()\n", + " test_pred = results.predict(X_test)\n", + "\n", + " return np.mean((y_test - test_pred)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "39ab59b1", + "metadata": {}, + "source": [ + "Let’s use this function to estimate the validation MSE\n", + "using linear, quadratic and cubic fits. We use the `enumerate()` function\n", + "here, which gives both the values and indices of objects as one iterates\n", + "over a for loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51d93dea", + "metadata": {}, + "outputs": [], + "source": [ + "MSE = np.zeros(3)\n", + "for idx, degree in enumerate(range(1, 4)):\n", + " MSE[idx] = evalMSE([poly('horsepower', degree)],\n", + " 'mpg',\n", + " Auto_train,\n", + " Auto_valid)\n", + "MSE\n" + ] + }, + { + "cell_type": "markdown", + "id": "936e168a", + "metadata": {}, + "source": [ + "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n", + "choose a different training/validation split instead, then we\n", + "can expect somewhat different errors on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83432f06", + "metadata": {}, + "outputs": [], + "source": [ + "Auto_train, Auto_valid = train_test_split(Auto,\n", + " test_size=196,\n", + " random_state=3)\n", + "MSE = np.zeros(3)\n", + "for idx, degree in enumerate(range(1, 4)):\n", + " MSE[idx] = evalMSE([poly('horsepower', degree)],\n", + " 'mpg',\n", + " Auto_train,\n", + " Auto_valid)\n", + "MSE" + ] + }, + { + "cell_type": "markdown", + "id": "f5ceb357", + "metadata": {}, + "source": [ + "Using this split of the observations into a training set and a validation set,\n", + "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n", + "\n", + "These results are consistent with our previous findings: a model that\n", + "predicts `mpg` using a quadratic function of `horsepower`\n", + "performs better than a model that involves only a linear function of\n", + "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`." + ] + }, + { + "cell_type": "markdown", + "id": "6d624a5c", + "metadata": {}, + "source": [ + "## Cross-Validation\n", + "In theory, the cross-validation estimate can be computed for any generalized\n", + "linear model. {}\n", + "In practice, however, the simplest way to cross-validate in\n", + "Python is to use `sklearn`, which has a different interface or API\n", + "than `statsmodels`, the code we have been using to fit GLMs.\n", + "\n", + "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n", + "requires the use of a *wrapper*.\n", + "In the `ISLP` package,\n", + "we provide \n", + "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n", + "models fit by `statsmodels`.\n", + "\n", + "The class `sklearn_sm()` \n", + "has as its first argument\n", + "a model from `statsmodels`. It can take two additional\n", + "optional arguments: `model_str` which can be\n", + "used to specify a formula, and `model_args` which should\n", + "be a dictionary of additional arguments used when fitting\n", + "the model. For example, to fit a logistic regression model\n", + "we have to specify a `family` argument. This\n", + "is passed as `model_args={'family':sm.families.Binomial()}`.\n", + "\n", + "Here is our wrapper in action:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcfc433f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "hp_model = sklearn_sm(sm.OLS,\n", + " MS(['horsepower']))\n", + "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n", + "cv_results = cross_validate(hp_model,\n", + " X,\n", + " Y,\n", + " cv=Auto.shape[0])\n", + "cv_err = np.mean(cv_results['test_score'])\n", + "cv_err\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b0f6f30", + "metadata": {}, + "source": [ + "The arguments to `cross_validate()` are as follows: an\n", + "object with the appropriate `fit()`, `predict()`,\n", + "and `score()` methods, an\n", + "array of features `X` and a response `Y`. \n", + "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n", + "$K$ results in $K$-fold cross-validation. We have provided a value \n", + "corresponding to the total number of observations, which results in\n", + "leave-one-out cross-validation (LOOCV). The `cross_validate()` function produces a dictionary with several components;\n", + "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23." + ] + }, + { + "cell_type": "markdown", + "id": "b527f67f", + "metadata": {}, + "source": [ + "We can repeat this procedure for increasingly complex polynomial fits.\n", + "To automate the process, we again\n", + "use a for loop which iteratively fits polynomial\n", + "regressions of degree 1 to 5, computes the\n", + "associated cross-validation error, and stores it in the $i$th element\n", + "of the vector `cv_error`. The variable `d` in the for loop\n", + "corresponds to the degree of the polynomial. We begin by initializing the\n", + "vector. This command may take a couple of seconds to run." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f951ffc8", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "cv_error = np.zeros(5)\n", + "H = np.array(Auto['horsepower'])\n", + "M = sklearn_sm(sm.OLS)\n", + "for i, d in enumerate(range(1,6)):\n", + " X = np.power.outer(H, np.arange(d+1))\n", + " M_CV = cross_validate(M,\n", + " X,\n", + " Y,\n", + " cv=Auto.shape[0])\n", + " cv_error[i] = np.mean(M_CV['test_score'])\n", + "cv_error\n" + ] + }, + { + "cell_type": "markdown", + "id": "792f1304", + "metadata": {}, + "source": [ + "As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n", + "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n", + "\n", + "Above we introduced the `outer()` method of the `np.power()`\n", + "function. The `outer()` method is applied to an operation\n", + "that has two arguments, such as `add()`, `min()`, or\n", + "`power()`.\n", + "It has two arrays as\n", + "arguments, and then forms a larger\n", + "array where the operation is applied to each pair of elements of the\n", + "two arrays. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3610b5a", + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([3, 5, 9])\n", + "B = np.array([2, 4])\n", + "np.add.outer(A, B)\n" + ] + }, + { + "cell_type": "markdown", + "id": "983625b2", + "metadata": {}, + "source": [ + "In the CV example above, we used $K=n$, but of course we can also use $K 250 # shorthand\n", + "glm = sm.GLM(y > 250,\n", + " X,\n", + " family=sm.families.Binomial())\n", + "B = glm.fit()\n", + "summarize(B)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd7f2a1d", + "metadata": {}, + "source": [ + "Once again, we make predictions using the `get_prediction()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84f172f2", + "metadata": {}, + "outputs": [], + "source": [ + "newX = poly_age.transform(age_df)\n", + "preds = B.get_prediction(newX)\n", + "bands = preds.conf_int(alpha=0.05)\n" + ] + }, + { + "cell_type": "markdown", + "id": "96a82fa2", + "metadata": {}, + "source": [ + "We now plot the estimated relationship." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcf376bd", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "rng = np.random.default_rng(0)\n", + "ax.scatter(age +\n", + " 0.2 * rng.uniform(size=y.shape[0]),\n", + " np.where(high_earn, 0.198, 0.002),\n", + " fc='gray',\n", + " marker='|')\n", + "for val, ls in zip([preds.predicted_mean,\n", + " bands[:,0],\n", + " bands[:,1]],\n", + " ['b','r--','r--']):\n", + " ax.plot(age_df.values, val, ls, linewidth=3)\n", + "ax.set_title('Degree-4 Polynomial', fontsize=20)\n", + "ax.set_xlabel('Age', fontsize=20)\n", + "ax.set_ylim([0,0.2])\n", + "ax.set_ylabel('P(Wage > 250)', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "ece6ba93", + "metadata": {}, + "source": [ + "We have drawn the `age` values corresponding to the observations with\n", + "`wage` values above 250 as gray marks on the top of the plot, and\n", + "those with `wage` values below 250 are shown as gray marks on the\n", + "bottom of the plot. We added a small amount of noise to jitter\n", + "the `age` values a bit so that observations with the same `age`\n", + "value do not cover each other up. This type of plot is often called a\n", + "*rug plot*.\n", + "\n", + "In order to fit a step function, as discussed in\n", + "Section 7.2, we first use the `pd.qcut()`\n", + "function to discretize `age` based on quantiles. Then we use `pd.get_dummies()` to create the\n", + "columns of the model matrix for this categorical variable. Note that this function will\n", + "include *all* columns for a given categorical, rather than the usual approach which drops one\n", + "of the levels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4d00900", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "cut_age = pd.qcut(age, 4)\n", + "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n" + ] + }, + { + "cell_type": "markdown", + "id": "68f44046", + "metadata": {}, + "source": [ + "Here `pd.qcut()` automatically picked the cutpoints based on the quantiles 25%, 50% and 75%, which results in four regions. We could also have specified our own\n", + "quantiles directly instead of the argument `4`. For cuts not based\n", + "on quantiles we would use the `pd.cut()` function.\n", + "The function `pd.qcut()` (and `pd.cut()`) returns an ordered categorical variable.\n", + " The regression model then creates a set of\n", + "dummy variables for use in the regression. Since `age` is the only variable in the model, the value $94,158.40 is the average salary for those under 33.75 years of\n", + "age, and the other coefficients are the average\n", + "salary for those in the other age groups. We can produce\n", + "predictions and plots just as we did in the case of the polynomial\n", + "fit." + ] + }, + { + "cell_type": "markdown", + "id": "4e157a87", + "metadata": {}, + "source": [ + "## Splines\n", + "In order to fit regression splines, we use transforms\n", + "from the `ISLP` package. The actual spline\n", + "evaluation functions are in the `scipy.interpolate` package;\n", + "we have simply wrapped them as transforms\n", + "similar to `Poly()` and `PCA()`.\n", + "\n", + "In Section 7.4, we saw\n", + "that regression splines can be fit by constructing an appropriate\n", + "matrix of basis functions. The `BSpline()` function generates the\n", + "entire matrix of basis functions for splines with the specified set of\n", + "knots. By default, the B-splines produced are cubic. To change the degree, use\n", + "the argument `degree`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3817102", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n", + "bs_age = bs_.transform(age)\n", + "bs_age.shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "711c9413", + "metadata": {}, + "source": [ + "This results in a seven-column matrix, which is what is expected for a cubic-spline basis with 3 interior knots. \n", + "We can form this same matrix using the `bs()` object,\n", + "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.\n", + "\n", + "We now fit a cubic spline model to the `Wage` data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c06c0f27", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n", + "Xbs = bs_age.fit_transform(Wage)\n", + "M = sm.OLS(y, Xbs).fit()\n", + "summarize(M)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a5698ae9", + "metadata": {}, + "source": [ + "The column names are a little cumbersome, and have caused us to truncate the printed summary. They can be set on construction using the `name` argument as follows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cb32db0", + "metadata": {}, + "outputs": [], + "source": [ + "bs_age = MS([bs('age',\n", + " internal_knots=[25,40,60],\n", + " name='bs(age)')])\n", + "Xbs = bs_age.fit_transform(Wage)\n", + "M = sm.OLS(y, Xbs).fit()\n", + "summarize(M)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d709fec", + "metadata": {}, + "source": [ + "Notice that there are 6 spline coefficients rather than 7. This is because, by default,\n", + "`bs()` assumes `intercept=False`, since we typically have an overall intercept in the model.\n", + "So it generates the spline basis with the given knots, and then discards one of the basis functions to account for the intercept. \n", + "\n", + "We could also use the `df` (degrees of freedom) option to\n", + "specify the complexity of the spline. We see above that with 3 knots,\n", + "the spline basis has 6 columns or degrees of freedom. When we specify\n", + "`df=6` rather than the actual knots, `bs()` will produce a\n", + "spline with 3 knots chosen at uniform quantiles of the training data.\n", + "We can see these chosen knots most easily using `Bspline()` directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f26b715f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "BSpline(df=6).fit(age).internal_knots_\n" + ] + }, + { + "cell_type": "markdown", + "id": "e606523e", + "metadata": {}, + "source": [ + " When asking for six degrees of freedom,\n", + "the transform chooses knots at ages 33.75, 42.0, and 51.0,\n", + "which correspond to the 25th, 50th, and 75th percentiles of\n", + "`age`.\n", + "\n", + "When using B-splines we need not limit ourselves to cubic polynomials\n", + "(i.e. `degree=3`). For instance, using `degree=0` results\n", + "in piecewise constant functions, as in our example with\n", + "`pd.qcut()` above.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6109816", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "bs_age0 = MS([bs('age',\n", + " df=3, \n", + " degree=0)]).fit(Wage)\n", + "Xbs0 = bs_age0.transform(Wage)\n", + "summarize(sm.OLS(y, Xbs0).fit())\n" + ] + }, + { + "cell_type": "markdown", + "id": "53ce3ef2", + "metadata": {}, + "source": [ + "This fit should be compared with cell [15] where we use `qcut()`\n", + "to create four bins by cutting at the 25%, 50% and 75% quantiles of\n", + "`age`. Since we specified `df=3` for degree-zero splines here, there will also be\n", + "knots at the same three quantiles. Although the coefficients appear different, we\n", + "see that this is a result of the different coding. For example, the\n", + "first coefficient is identical in both cases, and is the mean response\n", + "in the first bin. For the second coefficient, we have\n", + "$94.158 + 22.349 = 116.507 \\approx 116.611$, the latter being the mean\n", + "in the second bin in cell [15]. Here the intercept is coded by a column\n", + "of ones, so the second, third and fourth coefficients are increments\n", + "for those bins. Why is the sum not exactly the same? It turns out that\n", + "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n", + "deciding bin membership.\n", + "\n", + "\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "59807efd", + "metadata": {}, + "source": [ + "In order to fit a natural spline, we use the `NaturalSpline()` \n", + "transform with the corresponding helper `ns()`. Here we fit a natural spline with five\n", + "degrees of freedom (excluding the intercept) and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c73a3da", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ns_age = MS([ns('age', df=5)]).fit(Wage)\n", + "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n", + "summarize(M_ns)" + ] + }, + { + "cell_type": "markdown", + "id": "d819f968", + "metadata": {}, + "source": [ + "We now plot the natural spline using our plotting function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d73789b4", + "metadata": {}, + "outputs": [], + "source": [ + "plot_wage_fit(age_df,\n", + " ns_age,\n", + " 'Natural spline, df=5');\n" + ] + }, + { + "cell_type": "markdown", + "id": "09616af2", + "metadata": {}, + "source": [ + "## Smoothing Splines and GAMs\n", + "A smoothing spline is a special case of a GAM with squared-error loss\n", + "and a single feature. To fit GAMs in `Python` we will use the\n", + "`pygam` package which can be installed via `pip install pygam`. The\n", + "estimator `LinearGAM()` uses squared-error loss.\n", + "The GAM is specified by associating each column\n", + "of a model matrix with a particular smoothing operation:\n", + "`s` for smoothing spline; `l` for linear, and `f` for factor or categorical variables.\n", + "The argument `0` passed to `s` below indicates that this smoother will\n", + "apply to the first column of a feature matrix. Below, we pass it a\n", + "matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\\lambda$ as discussed in Section 7.5.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e70b87d", + "metadata": {}, + "outputs": [], + "source": [ + "X_age = np.asarray(age).reshape((-1,1))\n", + "gam = LinearGAM(s_gam(0, lam=0.6))\n", + "gam.fit(X_age, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f95cf22c", + "metadata": {}, + "source": [ + "The `pygam` library generally expects a matrix of features so we reshape `age` to be a matrix (a two-dimensional array) instead\n", + "of a vector (i.e. a one-dimensional array). The `-1` in the call to the `reshape()` method tells `numpy` to impute the\n", + "size of that dimension based on the remaining entries of the shape tuple.\n", + " \n", + "Let’s investigate how the fit changes with the smoothing parameter `lam`.\n", + "The function `np.logspace()` is similar to `np.linspace()` but spaces points\n", + "evenly on the log-scale. Below we vary `lam` from $10^{-2}$ to $10^6$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efe03b12", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", + "for lam in np.logspace(-2, 6, 5):\n", + " gam = LinearGAM(s_gam(0, lam=lam)).fit(X_age, y)\n", + " ax.plot(age_grid,\n", + " gam.predict(age_grid),\n", + " label='{:.1e}'.format(lam),\n", + " linewidth=3)\n", + "ax.set_xlabel('Age', fontsize=20)\n", + "ax.set_ylabel('Wage', fontsize=20);\n", + "ax.legend(title='$\\lambda$');\n" + ] + }, + { + "cell_type": "markdown", + "id": "6590a835", + "metadata": {}, + "source": [ + "The `pygam` package can perform a search for an optimal smoothing parameter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acff2af2", + "metadata": {}, + "outputs": [], + "source": [ + "gam_opt = gam.gridsearch(X_age, y)\n", + "ax.plot(age_grid,\n", + " gam_opt.predict(age_grid),\n", + " label='Grid search',\n", + " linewidth=4)\n", + "ax.legend()\n", + "fig\n" + ] + }, + { + "cell_type": "markdown", + "id": "c979dbf1", + "metadata": {}, + "source": [ + "Alternatively, we can fix the degrees of freedom of the smoothing\n", + "spline using a function included in the `ISLP.pygam` package. Below we\n", + "find a value of $\\lambda$ that gives us roughly four degrees of\n", + "freedom. We note here that these degrees of freedom include the\n", + "unpenalized intercept and linear term of the smoothing spline, hence there are at least two\n", + "degrees of freedom." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2d25550", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "age_term = gam.terms[0]\n", + "lam_4 = approx_lam(X_age, age_term, 4)\n", + "age_term.lam = lam_4\n", + "degrees_of_freedom(X_age, age_term)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ce629653", + "metadata": {}, + "source": [ + "Let’s vary the degrees of freedom in a similar plot to above. We choose the degrees of freedom\n", + "as the desired degrees of freedom plus one to account for the fact that these smoothing\n", + "splines always have an intercept term. Hence, a value of one for `df` is just a linear fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc6322de", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(X_age,\n", + " y,\n", + " facecolor='gray',\n", + " alpha=0.3)\n", + "for df in [1,3,4,8,15]:\n", + " lam = approx_lam(X_age, age_term, df+1)\n", + " age_term.lam = lam\n", + " gam.fit(X_age, y)\n", + " ax.plot(age_grid,\n", + " gam.predict(age_grid),\n", + " label='{:d}'.format(df),\n", + " linewidth=4)\n", + "ax.set_xlabel('Age', fontsize=20)\n", + "ax.set_ylabel('Wage', fontsize=20);\n", + "ax.legend(title='Degrees of freedom');\n" + ] + }, + { + "cell_type": "markdown", + "id": "2cb73ff7", + "metadata": {}, + "source": [ + "### Additive Models with Several Terms\n", + "The strength of generalized additive models lies in their ability to fit multivariate regression models with more flexibility than linear models. We demonstrate two approaches: the first in a more manual fashion using natural splines and piecewise constant functions, and the second using the `pygam` package and smoothing splines.\n", + "\n", + "We now fit a GAM by hand to predict\n", + "`wage` using natural spline functions of `year` and `age`,\n", + "treating `education` as a qualitative predictor, as in (7.16).\n", + "Since this is just a big linear regression model\n", + "using an appropriate choice of basis functions, we can simply do this\n", + "using the `sm.OLS()` function.\n", + "\n", + "We will build the model matrix in a more manual fashion here, since we wish\n", + "to access the pieces separately when constructing partial dependence plots." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703d2570", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ns_age = NaturalSpline(df=4).fit(age)\n", + "ns_year = NaturalSpline(df=5).fit(Wage['year'])\n", + "Xs = [ns_age.transform(age),\n", + " ns_year.transform(Wage['year']),\n", + " pd.get_dummies(Wage['education']).values]\n", + "X_bh = np.hstack(Xs)\n", + "gam_bh = sm.OLS(y, X_bh).fit()\n" + ] + }, + { + "cell_type": "markdown", + "id": "34cae11a", + "metadata": {}, + "source": [ + "Here the function `NaturalSpline()` is the workhorse supporting\n", + "the `ns()` helper function. We chose to use all columns of the\n", + "indicator matrix for the categorical variable `education`, making an intercept redundant.\n", + "Finally, we stacked the three component matrices horizontally to form the model matrix `X_bh`. \n", + "\n", + "We now show how to construct partial dependence plots for each of the terms in our rudimentary GAM. We can do this by hand,\n", + "given grids for `age` and `year`.\n", + " We simply predict with new $X$ matrices, fixing all but one of the features at a time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "766a7b1f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "age_grid = np.linspace(age.min(),\n", + " age.max(),\n", + " 100)\n", + "X_age_bh = X_bh.copy()[:100]\n", + "X_age_bh[:] = X_bh[:].mean(0)[None,:]\n", + "X_age_bh[:,:4] = ns_age.transform(age_grid)\n", + "preds = gam_bh.get_prediction(X_age_bh)\n", + "bounds_age = preds.conf_int(alpha=0.05)\n", + "partial_age = preds.predicted_mean\n", + "center = partial_age.mean()\n", + "partial_age -= center\n", + "bounds_age -= center\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.plot(age_grid, partial_age, 'b', linewidth=3)\n", + "ax.plot(age_grid, bounds_age[:,0], 'r--', linewidth=3)\n", + "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n", + "ax.set_xlabel('Age')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of age on wage', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "83e636f3", + "metadata": {}, + "source": [ + "Let's explain in some detail what we did above. The idea is to create a new prediction matrix, where all but the columns belonging to `age` are constant (and set to their training-data means). The four columns for `age` are filled in with the natural spline basis evaluated at the 100 values in `age_grid`.\n", + "\n", + "* We made a grid of length 100 in `age`, and created a matrix `X_age_bh` with 100 rows and the same number of columns as `X_bh`.\n", + "* We replaced every row of this matrix with the column means of the original.\n", + "* We then replace just the first four columns representing `age` with the natural spline basis computed at the values in `age_grid`. \n", + "\n", + "The remaining steps should by now be familiar.\n", + "\n", + "We also look at the effect of `year` on `wage`; the process is the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a9ed841", + "metadata": {}, + "outputs": [], + "source": [ + "year_grid = np.linspace(2003, 2009, 100)\n", + "year_grid = np.linspace(Wage['year'].min(),\n", + " Wage['year'].max(),\n", + " 100)\n", + "X_year_bh = X_bh.copy()[:100]\n", + "X_year_bh[:] = X_bh[:].mean(0)[None,:]\n", + "X_year_bh[:,4:9] = ns_year.transform(year_grid)\n", + "preds = gam_bh.get_prediction(X_year_bh)\n", + "bounds_year = preds.conf_int(alpha=0.05)\n", + "partial_year = preds.predicted_mean\n", + "center = partial_year.mean()\n", + "partial_year -= center\n", + "bounds_year -= center\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.plot(year_grid, partial_year, 'b', linewidth=3)\n", + "ax.plot(year_grid, bounds_year[:,0], 'r--', linewidth=3)\n", + "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n", + "ax.set_xlabel('Year')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of year on wage', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "4aca2254", + "metadata": {}, + "source": [ + "We now fit the model (7.16) using smoothing splines rather\n", + "than natural splines. All of the\n", + "terms in (7.16) are fit simultaneously, taking each other\n", + "into account to explain the response. The `pygam` package only works with matrices, so we must convert\n", + "the categorical series `education` to its array representation, which can be found\n", + "with the `cat.codes` attribute of `education`. As `year` only has 7 unique values, we\n", + "use only seven basis functions for it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccf068b1", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "gam_full = LinearGAM(s_gam(0) +\n", + " s_gam(1, n_splines=7) +\n", + " f_gam(2, lam=0))\n", + "Xgam = np.column_stack([age,\n", + " Wage['year'],\n", + " Wage['education'].cat.codes])\n", + "gam_full = gam_full.fit(Xgam, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7e035d3", + "metadata": {}, + "source": [ + "The two `s_gam()` terms result in smoothing spline fits, and use a default value for $\\lambda$ (`lam=0.6`), which is somewhat arbitrary. For the categorical term `education`, specified using a `f_gam()` term, we specify `lam=0` to avoid any shrinkage.\n", + "We produce the partial dependence plot in `age` to see the effect of these choices.\n", + "\n", + "The values for the plot\n", + "are generated by the `pygam` package. We provide a `plot_gam()`\n", + "function for partial-dependence plots in `ISLP.pygam`, which makes this job easier than in our last example with natural splines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38b719f1", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_gam(gam_full, 0, ax=ax)\n", + "ax.set_xlabel('Age')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7be9454", + "metadata": {}, + "source": [ + "We see that the function is somewhat wiggly. It is more natural to specify the `df` than a value for `lam`. \n", + "We refit a GAM using four degrees of freedom each for\n", + "`age` and `year`. Recall that the addition of one below takes into account the intercept\n", + "of the smoothing spline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02142f6e", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "age_term = gam_full.terms[0]\n", + "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n", + "year_term = gam_full.terms[1]\n", + "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n", + "gam_full = gam_full.fit(Xgam, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "08085085", + "metadata": {}, + "source": [ + "Note that updating `age_term.lam` above updates it in `gam_full.terms[0]` as well! Likewise for `year_term.lam`.\n", + "\n", + "Repeating the plot for `age`, we see that it is much smoother.\n", + "We also produce the plot for `year`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94587b05", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_gam(gam_full,\n", + " 1,\n", + " ax=ax)\n", + "ax.set_xlabel('Year')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of year on wage', fontsize=20)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b373d5c", + "metadata": {}, + "source": [ + "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bba4c757", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax = plot_gam(gam_full, 2)\n", + "ax.set_xlabel('Education')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of wage on education',\n", + " fontsize=20);\n", + "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n" + ] + }, + { + "cell_type": "markdown", + "id": "38ff8ddb", + "metadata": {}, + "source": [ + "### ANOVA Tests for Additive Models\n", + "In all of our models, the function of `year` looks rather linear. We can\n", + "perform a series of ANOVA tests in order to determine which of these\n", + "three models is best: a GAM that excludes `year` ($\\mathcal{M}_1$),\n", + "a GAM that uses a linear function of `year` ($\\mathcal{M}_2$), or a\n", + "GAM that uses a spline function of `year` ($\\mathcal{M}_3$)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32368085", + "metadata": {}, + "outputs": [], + "source": [ + "gam_0 = LinearGAM(age_term + f_gam(2, lam=0))\n", + "gam_0.fit(Xgam, y)\n", + "gam_linear = LinearGAM(age_term +\n", + " l_gam(1, lam=0) +\n", + " f_gam(2, lam=0))\n", + "gam_linear.fit(Xgam, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c95b177b", + "metadata": {}, + "source": [ + "Notice our use of `age_term` in the expressions above. We do this because\n", + "earlier we set the value for `lam` in this term to achieve four degrees of freedom.\n", + "\n", + "To directly assess the effect of `year` we run an ANOVA on the\n", + "three models fit above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7ba9957", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "anova_gam(gam_0, gam_linear, gam_full)\n" + ] + }, + { + "cell_type": "markdown", + "id": "825ea833", + "metadata": {}, + "source": [ + " We find that there is compelling evidence that a GAM with a linear\n", + "function in `year` is better than a GAM that does not include\n", + "`year` at all ($p$-value= 0.002). However, there is no\n", + "evidence that a non-linear function of `year` is needed\n", + "($p$-value=0.435). In other words, based on the results of this\n", + "ANOVA, $\\mathcal{M}_2$ is preferred.\n", + "\n", + "We can repeat the same process for `age` as well. We see there is very clear evidence that\n", + "a non-linear term is required for `age`.\n", + "\\newpage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffc0099a", + "metadata": {}, + "outputs": [], + "source": [ + "gam_0 = LinearGAM(year_term +\n", + " f_gam(2, lam=0))\n", + "gam_linear = LinearGAM(l_gam(0, lam=0) +\n", + " year_term +\n", + " f_gam(2, lam=0))\n", + "gam_0.fit(Xgam, y)\n", + "gam_linear.fit(Xgam, y)\n", + "anova_gam(gam_0, gam_linear, gam_full)" + ] + }, + { + "cell_type": "markdown", + "id": "3a243d63", + "metadata": {}, + "source": [ + "There is a (verbose) `summary()` method for the GAM fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08026a6b", + "metadata": {}, + "outputs": [], + "source": [ + "gam_full.summary()\n" + ] + }, + { + "cell_type": "markdown", + "id": "839acb14", + "metadata": {}, + "source": [ + "We can make predictions from `gam` objects, just like from\n", + "`lm` objects, using the `predict()` method for the class\n", + "`gam`. Here we make predictions on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9191d615", + "metadata": {}, + "outputs": [], + "source": [ + "Yhat = gam_full.predict(Xgam)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a98a10a7", + "metadata": {}, + "source": [ + "In order to fit a logistic regression GAM, we use `LogisticGAM()` \n", + "from `pygam`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92007a5f", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "gam_logit = LogisticGAM(age_term + \n", + " l_gam(1, lam=0) +\n", + " f_gam(2, lam=0))\n", + "gam_logit.fit(Xgam, high_earn)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dd6aa2f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax = plot_gam(gam_logit, 2)\n", + "ax.set_xlabel('Education')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of wage on education',\n", + " fontsize=20);\n", + "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4e9e093", + "metadata": {}, + "source": [ + "The model seems to be very flat, with especially high error bars for the first category.\n", + "Let's look at the data a bit more closely." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a6e8754", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "pd.crosstab(Wage['high_earn'], Wage['education'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "06d2d2fd", + "metadata": {}, + "source": [ + "We see that there are no high earners in the first category of\n", + "education, meaning that the model will have a hard time fitting. We\n", + "will fit a logistic regression GAM excluding all observations falling into this\n", + "category. This provides more sensible results.\n", + "\n", + "To do so,\n", + "we could subset the model matrix, though this will not remove the\n", + "column from `Xgam`. While we can deduce which column corresponds\n", + "to this feature, for reproducibility’s sake we reform the model matrix\n", + "on this smaller subset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c92b60be", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "only_hs = Wage['education'] == '1. < HS Grad'\n", + "Wage_ = Wage.loc[~only_hs]\n", + "Xgam_ = np.column_stack([Wage_['age'],\n", + " Wage_['year'],\n", + " Wage_['education'].cat.codes-1])\n", + "high_earn_ = Wage_['high_earn']\n" + ] + }, + { + "cell_type": "markdown", + "id": "02dc4908", + "metadata": {}, + "source": [ + "In the second-to-last line above, we subtract one from the codes of the category, due to a bug in `pygam`. It just relabels\n", + "the education values and hence has no effect on the fit.\n", + " \n", + "We now fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e525e8d0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "gam_logit_ = LogisticGAM(age_term +\n", + " year_term +\n", + " f_gam(2, lam=0))\n", + "gam_logit_.fit(Xgam_, high_earn_)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9601ab00", + "metadata": {}, + "source": [ + "Let’s look at the effect of `education`, `year` and `age` on high earner status now that we’ve\n", + "removed those observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3e66cf9", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax = plot_gam(gam_logit_, 2)\n", + "ax.set_xlabel('Education')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n", + "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n", + " fontsize=8);\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd924348", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax = plot_gam(gam_logit_, 1)\n", + "ax.set_xlabel('Year')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of high earner status on year',\n", + " fontsize=20);\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d3ec90a", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8, 8))\n", + "ax = plot_gam(gam_logit_, 0)\n", + "ax.set_xlabel('Age')\n", + "ax.set_ylabel('Effect on wage')\n", + "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n" + ] + }, + { + "cell_type": "markdown", + "id": "ea4a7d79", + "metadata": {}, + "source": [ + "## Local Regression\n", + "We illustrate the use of local regression using the `lowess()` \n", + "function from `sm.nonparametric`. Some implementations of\n", + "GAMs allow terms to be local regression operators; this is not the case in `pygam`.\n", + "\n", + "Here we fit local linear regression models using spans of 0.2\n", + "and 0.5; that is, each neighborhood consists of 20% or 50% of\n", + "the observations. As expected, using a span of 0.5 is smoother than 0.2." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f2bc0eb", + "metadata": {}, + "outputs": [], + "source": [ + "lowess = sm.nonparametric.lowess\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", + "for span in [0.2, 0.5]:\n", + " fitted = lowess(y,\n", + " age,\n", + " frac=span,\n", + " xvals=age_grid)\n", + " ax.plot(age_grid,\n", + " fitted,\n", + " label='{:.1f}'.format(span),\n", + " linewidth=4)\n", + "ax.set_xlabel('Age', fontsize=20)\n", + "ax.set_ylabel('Wage', fontsize=20);\n", + "ax.legend(title='span', fontsize=15);\n" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch8-baggboost-lab.ipynb b/docs/source/labs/Ch8-baggboost-lab.ipynb new file mode 100644 index 0000000..6054a2c --- /dev/null +++ b/docs/source/labs/Ch8-baggboost-lab.ipynb @@ -0,0 +1,1052 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "257f1382", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ade0554", + "metadata": {}, + "source": [ + "# Tree-Based Methods\n", + "We import some of our usual libraries at this top\n", + "level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cc7120f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib.pyplot import subplots\n", + "from statsmodels.datasets import get_rdataset\n", + "import sklearn.model_selection as skm\n", + "from ISLP import load_data, confusion_table\n", + "from ISLP.models import ModelSpec as MS\n" + ] + }, + { + "cell_type": "markdown", + "id": "75645461", + "metadata": {}, + "source": [ + "We also collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "864fa15e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from sklearn.tree import (DecisionTreeClassifier as DTC,\n", + " DecisionTreeRegressor as DTR,\n", + " plot_tree,\n", + " export_text)\n", + "from sklearn.metrics import (accuracy_score,\n", + " log_loss)\n", + "from sklearn.ensemble import \\\n", + " (RandomForestRegressor as RF,\n", + " GradientBoostingRegressor as GBR)\n", + "from ISLP.bart import BART\n" + ] + }, + { + "cell_type": "markdown", + "id": "7b68e1e9", + "metadata": {}, + "source": [ + "## Fitting Classification Trees" + ] + }, + { + "cell_type": "markdown", + "id": "85b1d9af", + "metadata": {}, + "source": [ + "We first use classification trees to analyze the `Carseats` data set.\n", + "In these data, `Sales` is a continuous variable, and so we begin\n", + "by recoding it as a binary variable. We use the `where()` \n", + "function to create a variable, called `High`, which takes on a\n", + "value of `Yes` if the `Sales` variable exceeds 8, and takes\n", + "on a value of `No` otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfb4c83b", + "metadata": {}, + "outputs": [], + "source": [ + "Carseats = load_data('Carseats')\n", + "High = np.where(Carseats.Sales > 8,\n", + " \"Yes\",\n", + " \"No\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "8e50828c", + "metadata": {}, + "source": [ + "We now use `DecisionTreeClassifier()` to fit a classification tree in\n", + "order to predict `High` using all variables but `Sales`.\n", + "To do so, we must form a model matrix as we did when fitting regression\n", + "models. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d9e7a14", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n", + "D = model.fit_transform(Carseats)\n", + "feature_names = list(D.columns)\n", + "X = np.asarray(D)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b42d6270", + "metadata": {}, + "source": [ + "We have converted `D` from a data frame to an array `X`, which is needed in some of the analysis below. We also need the `feature_names` for annotating our plots later.\n", + "\n", + "There are several options needed to specify the classifier,\n", + "such as `max_depth` (how deep to grow the tree), `min_samples_split`\n", + "(minimum number of observations in a node to be eligible for splitting)\n", + "and `criterion` (whether to use Gini or cross-entropy as the split criterion).\n", + "We also set `random_state` for reproducibility; ties in the split criterion are broken at random." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "970c4515", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "clf = DTC(criterion='entropy',\n", + " max_depth=3,\n", + " random_state=0) \n", + "clf.fit(X, High)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ccdc9e7", + "metadata": {}, + "source": [ + "In our discussion of qualitative features in Section 3.3,\n", + "we noted that for a linear regression model such a feature could be\n", + "represented by including a matrix of dummy variables (one-hot-encoding) in the model\n", + "matrix, using the formula notation of `statsmodels`.\n", + "As mentioned in Section 8.1, there is a more\n", + "natural way to handle qualitative features when building a decision\n", + "tree, that does not require such dummy variables; each split amounts to partitioning the levels into two groups.\n", + "However, \n", + "the `sklearn` implementation of decision trees does not take\n", + "advantage of this approach; instead it simply treats the one-hot-encoded levels as separate variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d9a51dc", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "accuracy_score(High, clf.predict(X))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7fd4219f", + "metadata": {}, + "source": [ + "With only the default arguments, the training error rate is\n", + "21%.\n", + "For classification trees, we can\n", + "access the value of the deviance using `log_loss()`,\n", + "\\begin{equation*}\n", + "\\begin{split}\n", + "-2 \\sum_m \\sum_k n_{mk} \\log \\hat{p}_{mk},\n", + "\\end{split}\n", + "\\end{equation*}\n", + "where $n_{mk}$ is the number of observations in the $m$th terminal\n", + "node that belong to the $k$th class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbc04b67", + "metadata": {}, + "outputs": [], + "source": [ + "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n", + "resid_dev\n" + ] + }, + { + "cell_type": "markdown", + "id": "bca81c07", + "metadata": {}, + "source": [ + "This is closely related to the *entropy*, defined in (8.7).\n", + "A small deviance indicates a\n", + "tree that provides a good fit to the (training) data.\n", + " \n", + "One of the most attractive properties of trees is that they can\n", + "be graphically displayed. Here we use the `plot()` function\n", + "to display the tree structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "809830c7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = subplots(figsize=(12,12))[1]\n", + "plot_tree(clf,\n", + " feature_names=feature_names,\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7cf2e12", + "metadata": {}, + "source": [ + "The most important indicator of `Sales` appears to be `ShelveLoc`.\n", + "\n", + "We can see a text representation of the tree using\n", + "`export_text()`, which displays the split\n", + "criterion (e.g. `Price <= 92.5`) for each branch.\n", + "For leaf nodes it shows the overall prediction \n", + "(`Yes` or `No`). \n", + " We can also see the number of observations in that\n", + "leaf that take on values of `Yes` and `No` by specifying `show_weights=True`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abe8c7fc", + "metadata": {}, + "outputs": [], + "source": [ + "print(export_text(clf,\n", + " feature_names=feature_names,\n", + " show_weights=True))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f06c2f4c", + "metadata": {}, + "source": [ + "In order to properly evaluate the performance of a classification tree\n", + "on these data, we must estimate the test error rather than simply\n", + "computing the training error. We split the observations into a\n", + "training set and a test set, build the tree using the training set,\n", + "and evaluate its performance on the test data. This pattern is\n", + "similar to that in Chapter 6, with the linear models\n", + "replaced here by decision trees --- the code for validation\n", + "is almost identical. This approach leads to correct predictions\n", + "for 68.5% of the locations in the test data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7a1f736", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "validation = skm.ShuffleSplit(n_splits=1,\n", + " test_size=200,\n", + " random_state=0)\n", + "results = skm.cross_validate(clf,\n", + " D,\n", + " High,\n", + " cv=validation)\n", + "results['test_score']\n" + ] + }, + { + "cell_type": "markdown", + "id": "45f99d7e", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "id": "533af069", + "metadata": {}, + "source": [ + "Next, we consider whether pruning the tree might lead to improved\n", + "classification performance. We first split the data into a training and\n", + "test set. We will use cross-validation to prune the tree on the training\n", + "set, and then evaluate the performance of the pruned tree on the test\n", + "set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c663a623", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "(X_train,\n", + " X_test,\n", + " High_train,\n", + " High_test) = skm.train_test_split(X,\n", + " High,\n", + " test_size=0.5,\n", + " random_state=0)\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "9e618049", + "metadata": {}, + "source": [ + "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2f2a403", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "clf = DTC(criterion='entropy', random_state=0)\n", + "clf.fit(X_train, High_train)\n", + "accuracy_score(High_test, clf.predict(X_test))\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb59eecd", + "metadata": {}, + "source": [ + "Next we use the `cost_complexity_pruning_path()` method of\n", + "`clf` to extract cost-complexity values. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2505658", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n", + "kfold = skm.KFold(10,\n", + " random_state=1,\n", + " shuffle=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0fdf1106", + "metadata": {}, + "source": [ + "This yields a set of impurities and $\\alpha$ values\n", + "from which we can extract an optimal one by cross-validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09a7bd33", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "grid = skm.GridSearchCV(clf,\n", + " {'ccp_alpha': ccp_path.ccp_alphas},\n", + " refit=True,\n", + " cv=kfold,\n", + " scoring='accuracy')\n", + "grid.fit(X_train, High_train)\n", + "grid.best_score_\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff585d2c", + "metadata": {}, + "source": [ + "Let’s take a look at the pruned true." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a75dea32", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = subplots(figsize=(12, 12))[1]\n", + "best_ = grid.best_estimator_\n", + "plot_tree(best_,\n", + " feature_names=feature_names,\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "c81a06b3", + "metadata": {}, + "source": [ + "This is quite a bushy tree. We could count the leaves, or query\n", + "`best_` instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f1e3d19", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "best_.tree_.n_leaves\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a32f63a", + "metadata": {}, + "source": [ + "The tree with 30 terminal\n", + "nodes results in the lowest cross-validation error rate, with an accuracy of\n", + "68.5%. How well does this pruned tree perform on the test data set? Once\n", + "again, we apply the `predict()` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30f5c2f9", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "print(accuracy_score(High_test,\n", + " best_.predict(X_test)))\n", + "confusion = confusion_table(best_.predict(X_test),\n", + " High_test)\n", + "confusion\n" + ] + }, + { + "cell_type": "markdown", + "id": "adbe5709", + "metadata": {}, + "source": [ + "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "ad50114a", + "metadata": {}, + "source": [ + "## Fitting Regression Trees\n", + "Here we fit a regression tree to the `Boston` data set. The\n", + "steps are similar to those for classification trees." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ca6bc7a", + "metadata": {}, + "outputs": [], + "source": [ + "Boston = load_data(\"Boston\")\n", + "model = MS(Boston.columns.drop('medv'), intercept=False)\n", + "D = model.fit_transform(Boston)\n", + "feature_names = list(D.columns)\n", + "X = np.asarray(D)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ebebb4a7", + "metadata": {}, + "source": [ + "First, we split the data into training and test sets, and fit the tree\n", + "to the training data. Here we use 30% of the data for the test set.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15cfb553", + "metadata": {}, + "outputs": [], + "source": [ + "(X_train,\n", + " X_test,\n", + " y_train,\n", + " y_test) = skm.train_test_split(X,\n", + " Boston['medv'],\n", + " test_size=0.3,\n", + " random_state=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae21c490", + "metadata": {}, + "source": [ + "Having formed our training and test data sets, we fit the regression tree." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d18820a3", + "metadata": {}, + "outputs": [], + "source": [ + "reg = DTR(max_depth=3)\n", + "reg.fit(X_train, y_train)\n", + "ax = subplots(figsize=(12,12))[1]\n", + "plot_tree(reg,\n", + " feature_names=feature_names,\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a4deec7", + "metadata": {}, + "source": [ + "The variable `lstat` measures the percentage of individuals with\n", + "lower socioeconomic status. The tree indicates that lower\n", + "values of `lstat` correspond to more expensive houses.\n", + "The tree predicts a median house price of $12,042 for small-sized homes (`rm < 6.8`), in\n", + "suburbs in which residents have low socioeconomic status (`lstat > 14.4`) and the crime-rate is moderate (`crim > 5.8`)." + ] + }, + { + "cell_type": "markdown", + "id": "f5cad860", + "metadata": {}, + "source": [ + "Now we use the cross-validation function to see whether pruning\n", + "the tree will improve performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "619f8d94", + "metadata": {}, + "outputs": [], + "source": [ + "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n", + "kfold = skm.KFold(5,\n", + " shuffle=True,\n", + " random_state=10)\n", + "grid = skm.GridSearchCV(reg,\n", + " {'ccp_alpha': ccp_path.ccp_alphas},\n", + " refit=True,\n", + " cv=kfold,\n", + " scoring='neg_mean_squared_error')\n", + "G = grid.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b971e775", + "metadata": {}, + "source": [ + "In keeping with the cross-validation results, we use the pruned tree\n", + "to make predictions on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "779a14b6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "best_ = grid.best_estimator_\n", + "np.mean((y_test - best_.predict(X_test))**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "507d174f", + "metadata": {}, + "source": [ + "In other words, the test set MSE associated with the regression tree\n", + "is 28.07. The square root of\n", + "the MSE is therefore around\n", + "5.30,\n", + "indicating that this model leads to test predictions that are within around\n", + "$5300\n", + "of the true median home value for the suburb.\n", + "\n", + "Let’s plot the best tree to see how interpretable it is." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b04030b", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ax = subplots(figsize=(12,12))[1]\n", + "plot_tree(G.best_estimator_,\n", + " feature_names=feature_names,\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf40b6ac", + "metadata": {}, + "source": [ + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "8e9aba94", + "metadata": {}, + "source": [ + "## Bagging and Random Forests" + ] + }, + { + "cell_type": "markdown", + "id": "81afb557", + "metadata": {}, + "source": [ + "Here we apply bagging and random forests to the `Boston` data, using\n", + "the `RandomForestRegressor()` from the `sklearn.ensemble` package. Recall\n", + "that bagging is simply a special case of a random forest with\n", + "$m=p$. Therefore, the `RandomForestRegressor()` function can be used to\n", + "perform both bagging and random forests. We start with bagging." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7dedce31", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n", + "bag_boston.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b020aef3", + "metadata": {}, + "source": [ + "The argument `max_features` indicates that all 12 predictors should\n", + "be considered for each split of the tree --- in other words, that\n", + "bagging should be done. How well does this bagged model perform on\n", + "the test set?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f2c7336", + "metadata": {}, + "outputs": [], + "source": [ + "ax = subplots(figsize=(8,8))[1]\n", + "y_hat_bag = bag_boston.predict(X_test)\n", + "ax.scatter(y_hat_bag, y_test)\n", + "np.mean((y_test - y_hat_bag)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e6db784", + "metadata": {}, + "source": [ + "The test set MSE associated with the bagged regression tree is\n", + "14.63, about half that obtained using an optimally-pruned single\n", + "tree. We could change the number of trees grown from the default of\n", + "100 by\n", + "using the `n_estimators` argument:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bba2439", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "bag_boston = RF(max_features=X_train.shape[1],\n", + " n_estimators=500,\n", + " random_state=0).fit(X_train, y_train)\n", + "y_hat_bag = bag_boston.predict(X_test)\n", + "np.mean((y_test - y_hat_bag)**2)" + ] + }, + { + "cell_type": "markdown", + "id": "7fa447de", + "metadata": {}, + "source": [ + "There is not much change. Bagging and random forests cannot overfit by\n", + "increasing the number of trees, but can underfit if the number is too small.\n", + "\n", + "Growing a random forest proceeds in exactly the same way, except that\n", + "we use a smaller value of the `max_features` argument. By default,\n", + "`RandomForestRegressor()` uses $p$ variables when building a random\n", + "forest of regression trees (i.e. it defaults to bagging), and `RandomForestClassifier()` uses\n", + "$\\sqrt{p}$ variables when building a\n", + "random forest of classification trees. Here we use `max_features=6`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90457fbe", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "RF_boston = RF(max_features=6,\n", + " random_state=0).fit(X_train, y_train)\n", + "y_hat_RF = RF_boston.predict(X_test)\n", + "np.mean((y_test - y_hat_RF)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9ddb8fa", + "metadata": {}, + "source": [ + "The test set MSE is 20.04;\n", + "this indicates that random forests did somewhat worse than bagging\n", + "in this case. Extracting the `feature_importances_` values from the fitted model, we can view the\n", + "importance of each variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8aae1857", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "feature_imp = pd.DataFrame(\n", + " {'importance':RF_boston.feature_importances_},\n", + " index=feature_names)\n", + "feature_imp.sort_values(by='importance', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "74b9ffb6", + "metadata": {}, + "source": [ + " This\n", + "is a relative measure of the total decrease in node impurity that results from\n", + "splits over that variable, averaged over all trees (this was plotted in Figure 8.9 for a model fit to the `Heart` data). \n", + "\n", + "The results indicate that across all of the trees considered in the\n", + "random forest, the wealth level of the community (`lstat`) and the\n", + "house size (`rm`) are by far the two most important variables.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "82ed6937", + "metadata": {}, + "source": [ + "## Boosting" + ] + }, + { + "cell_type": "markdown", + "id": "d420075c", + "metadata": {}, + "source": [ + "Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`\n", + "to fit boosted regression trees to the `Boston` data\n", + "set. For classification we would use `GradientBoostingClassifier()`.\n", + "The argument `n_estimators=5000`\n", + "indicates that we want 5000 trees, and the option\n", + "`max_depth=3` limits the depth of each tree. The\n", + "argument `learning_rate` is the $\\lambda$\n", + "mentioned earlier in the description of boosting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "887b4fa9", + "metadata": {}, + "outputs": [], + "source": [ + "boost_boston = GBR(n_estimators=5000,\n", + " learning_rate=0.001,\n", + " max_depth=3,\n", + " random_state=0)\n", + "boost_boston.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "97019e71", + "metadata": {}, + "source": [ + "We can see how the training error decreases with the `train_score_` attribute.\n", + "To get an idea of how the test error decreases we can use the\n", + "`staged_predict()` method to get the predicted values along the path." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c56696c", + "metadata": {}, + "outputs": [], + "source": [ + "test_error = np.zeros_like(boost_boston.train_score_)\n", + "for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):\n", + " test_error[idx] = np.mean((y_test - y_)**2)\n", + "\n", + "plot_idx = np.arange(boost_boston.train_score_.shape[0])\n", + "ax = subplots(figsize=(8,8))[1]\n", + "ax.plot(plot_idx,\n", + " boost_boston.train_score_,\n", + " 'b',\n", + " label='Training')\n", + "ax.plot(plot_idx,\n", + " test_error,\n", + " 'r',\n", + " label='Test')\n", + "ax.legend();\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1a851f2", + "metadata": {}, + "source": [ + "We now use the boosted model to predict `medv` on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a636f63", + "metadata": {}, + "outputs": [], + "source": [ + "y_hat_boost = boost_boston.predict(X_test);\n", + "np.mean((y_test - y_hat_boost)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb7f53af", + "metadata": {}, + "source": [ + " The test MSE obtained is 14.48,\n", + "similar to the test MSE for bagging. If we want to, we can\n", + "perform boosting with a different value of the shrinkage parameter\n", + "$\\lambda$ in (8.10). The default value is 0.001, but\n", + "this is easily modified. Here we take $\\lambda=0.2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2ab3c7", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "boost_boston = GBR(n_estimators=5000,\n", + " learning_rate=0.2,\n", + " max_depth=3,\n", + " random_state=0)\n", + "boost_boston.fit(X_train,\n", + " y_train)\n", + "y_hat_boost = boost_boston.predict(X_test);\n", + "np.mean((y_test - y_hat_boost)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "9360b5b0", + "metadata": {}, + "source": [ + "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n", + "as $\\lambda=0.001$.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "1693f5da", + "metadata": {}, + "source": [ + "## Bayesian Additive Regression Trees" + ] + }, + { + "cell_type": "markdown", + "id": "47c83ab9", + "metadata": {}, + "source": [ + "In this section we demonstrate a `Python` implementation of BART found in the\n", + "`ISLP.bart` package. We fit a model\n", + "to the `Boston` housing data set. This `BART()` estimator is\n", + "designed for quantitative outcome variables, though other implementations are available for\n", + "fitting logistic and probit models to categorical outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0b41c04", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n", + "bart_boston.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "af48867c", + "metadata": {}, + "source": [ + "On this data set, with this split into test and training, we see that the test error of BART is similar to that of random forest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fda8fffc", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n", + "np.mean((y_test - yhat_test)**2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bad852b7", + "metadata": {}, + "source": [ + "We can check how many times each variable appeared in the collection of trees.\n", + "This gives a summary similar to the variable importance plot for boosting and random forests." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be77f0e4", + "metadata": {}, + "outputs": [], + "source": [ + "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n", + " index=D.columns)\n", + "var_inclusion\n" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch9-svm-lab.ipynb b/docs/source/labs/Ch9-svm-lab.ipynb new file mode 100644 index 0000000..9c07d86 --- /dev/null +++ b/docs/source/labs/Ch9-svm-lab.ipynb @@ -0,0 +1,977 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "67b2823a", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d45c6d2b", + "metadata": {}, + "source": [ + "# Support Vector Machines\n", + "In this lab, we use the `sklearn.svm` library to demonstrate the support\n", + "vector classifier and the support vector machine.\n", + "\n", + "We import some of our usual libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeaa5be0", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib.pyplot import subplots, cm\n", + "import sklearn.model_selection as skm\n", + "from ISLP import load_data, confusion_table\n" + ] + }, + { + "cell_type": "markdown", + "id": "26ebd377", + "metadata": {}, + "source": [ + "We also collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a59634", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "from ISLP.svm import plot as plot_svm\n", + "from sklearn.metrics import RocCurveDisplay\n" + ] + }, + { + "cell_type": "markdown", + "id": "f197b846", + "metadata": {}, + "source": [ + "We will use the function `RocCurveDisplay.from_estimator()` to\n", + "produce several ROC plots, using a shorthand `roc_curve`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9a175d7", + "metadata": {}, + "outputs": [], + "source": [ + "roc_curve = RocCurveDisplay.from_estimator # shorthand\n" + ] + }, + { + "cell_type": "markdown", + "id": "f666c212", + "metadata": {}, + "source": [ + "## Support Vector Classifier\n", + "\n", + "We now use the `SupportVectorClassifier()` function (abbreviated `SVC()`) from `sklearn` to fit the support vector\n", + "classifier for a given value of the parameter `C`. The\n", + "`C` argument allows us to specify the cost of a violation to\n", + "the margin. When the `cost` argument is small, then the margins\n", + "will be wide and many support vectors will be on the margin or will\n", + "violate the margin. When the `C` argument is large, then the\n", + "margins will be narrow and there will be few support vectors on the\n", + "margin or violating the margin.\n", + "\n", + "Here we demonstrate\n", + "the use of `SVC()` on a two-dimensional example, so that we can\n", + "plot the resulting decision boundary. We begin by generating the\n", + "observations, which belong to two classes, and checking whether the\n", + "classes are linearly separable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7216b47", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "rng = np.random.default_rng(1)\n", + "X = rng.standard_normal((50, 2))\n", + "y = np.array([-1]*25+[1]*25)\n", + "X[y==1] += 1\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(X[:,0],\n", + " X[:,1],\n", + " c=y,\n", + " cmap=cm.coolwarm);\n" + ] + }, + { + "cell_type": "markdown", + "id": "7b4aff06", + "metadata": {}, + "source": [ + "They are not. We now fit the classifier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed329198", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "svm_linear = SVC(C=10, kernel='linear')\n", + "svm_linear.fit(X, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5e6b4c79", + "metadata": {}, + "source": [ + "The support vector classifier with two features can\n", + "be visualized by plotting values of its *decision function*.\n", + "We have included a function for this in the `ISLP` package (inspired by a similar\n", + "example in the `sklearn` docs)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95494b8b", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X,\n", + " y,\n", + " svm_linear,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f6ce1246", + "metadata": {}, + "source": [ + "The decision\n", + "boundary between the two classes is linear (because we used the\n", + "argument `kernel='linear'`). The support vectors are marked with `+`\n", + "and the remaining observations are plotted as circles.\n", + "\n", + "What if we instead used a smaller value of the cost parameter?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98c2236f", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "svm_linear_small = SVC(C=0.1, kernel='linear')\n", + "svm_linear_small.fit(X, y)\n", + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X,\n", + " y,\n", + " svm_linear_small,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "906f4bb8", + "metadata": {}, + "source": [ + "With a smaller value of the cost parameter, we\n", + "obtain a larger number of support vectors, because the margin is now\n", + "wider. For linear kernels, we can extract the\n", + "coefficients of the linear decision boundary as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b498f594", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "svm_linear.coef_\n" + ] + }, + { + "cell_type": "markdown", + "id": "90a0ee53", + "metadata": {}, + "source": [ + "Since the support vector machine is an estimator in `sklearn`, we\n", + "can use the usual machinery to tune it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b65e80d6", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "kfold = skm.KFold(5, \n", + " random_state=0,\n", + " shuffle=True)\n", + "grid = skm.GridSearchCV(svm_linear,\n", + " {'C':[0.001,0.01,0.1,1,5,10,100]},\n", + " refit=True,\n", + " cv=kfold,\n", + " scoring='accuracy')\n", + "grid.fit(X, y)\n", + "grid.best_params_\n" + ] + }, + { + "cell_type": "markdown", + "id": "d390528c", + "metadata": {}, + "source": [ + "We can easily access the cross-validation errors for each of these models\n", + "in `grid.cv_results_`. This prints out a lot of detail, so we\n", + "extract the accuracy results only." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bba8fad7", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "grid.cv_results_[('mean_test_score')]\n" + ] + }, + { + "cell_type": "markdown", + "id": "703e2d43", + "metadata": {}, + "source": [ + "We see that `C=1` results in the highest cross-validation\n", + "accuracy of 0.74, though\n", + "the accuracy is the same for several values of `C`.\n", + "The classifier `grid.best_estimator_` can be used to predict the class\n", + "label on a set of test observations. Let’s generate a test data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad64269d", + "metadata": {}, + "outputs": [], + "source": [ + "X_test = rng.standard_normal((20, 2))\n", + "y_test = np.array([-1]*10+[1]*10)\n", + "X_test[y_test==1] += 1\n" + ] + }, + { + "cell_type": "markdown", + "id": "db41f5e2", + "metadata": {}, + "source": [ + "Now we predict the class labels of these test observations. Here we\n", + "use the best model selected by cross-validation in order to make the\n", + "predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5107fca1", + "metadata": {}, + "outputs": [], + "source": [ + "best_ = grid.best_estimator_\n", + "y_test_hat = best_.predict(X_test)\n", + "confusion_table(y_test_hat, y_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "bbfc8005", + "metadata": {}, + "source": [ + "Thus, with this value of `C`,\n", + "70% of the test\n", + "observations are correctly classified. What if we had instead used\n", + "`C=0.001`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0320d9e0", + "metadata": {}, + "outputs": [], + "source": [ + "svm_ = SVC(C=0.001,\n", + " kernel='linear').fit(X, y)\n", + "y_test_hat = svm_.predict(X_test)\n", + "confusion_table(y_test_hat, y_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "427d775f", + "metadata": {}, + "source": [ + "In this case 60% of test observations are correctly classified.\n", + "\n", + "We now consider a situation in which the two classes are linearly\n", + "separable. Then we can find an optimal separating hyperplane using the\n", + "`SVC()` estimator. We first\n", + "further separate the two classes in our simulated data so that they\n", + "are linearly separable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84d7e778", + "metadata": {}, + "outputs": [], + "source": [ + "X[y==1] += 1.9;\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff7bdad1", + "metadata": {}, + "source": [ + "Now the observations are just barely linearly separable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abb1f8be", + "metadata": {}, + "outputs": [], + "source": [ + "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n", + "y_hat = svm_.predict(X)\n", + "confusion_table(y_hat, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "c44297cc", + "metadata": {}, + "source": [ + "We fit the\n", + "support vector classifier and plot the resulting hyperplane, using a\n", + "very large value of `C` so that no observations are\n", + "misclassified. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e4ed2f5", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X,\n", + " y,\n", + " svm_,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2836d70d", + "metadata": {}, + "source": [ + "Indeed no training errors were made and only three support vectors were used.\n", + "In fact, the large value of `C` also means that these three support points are *on the margin*, and define it.\n", + "One may wonder how good the classifier could be on test data that depends on only three data points!\n", + " We now try a smaller\n", + "value of `C`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "164a611c", + "metadata": {}, + "outputs": [], + "source": [ + "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n", + "y_hat = svm_.predict(X)\n", + "confusion_table(y_hat, y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "39a432d1", + "metadata": {}, + "source": [ + "Using `C=0.1`, we again do not misclassify any training observations, but we\n", + "also obtain a much wider margin and make use of twelve support\n", + "vectors. These jointly define the orientation of the decision boundary, and since there are more of them, it is more stable. It seems possible that this model will perform better on test\n", + "data than the model with `C=1e5` (and indeed, a simple experiment with a large test set would bear this out)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c67591a1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X,\n", + " y,\n", + " svm_,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "25e61f65", + "metadata": {}, + "source": [ + "## Support Vector Machine\n", + "In order to fit an SVM using a non-linear kernel, we once again use\n", + "the `SVC()` estimator. However, now we use a different value\n", + "of the parameter `kernel`. To fit an SVM with a polynomial\n", + "kernel we use `kernel=\"poly\"`, and to fit an SVM with a\n", + "radial kernel we use\n", + "`kernel=\"rbf\"`. In the former case we also use the\n", + "`degree` argument to specify a degree for the polynomial kernel\n", + "(this is $d$ in (9.22)), and in the latter case we use\n", + "`gamma` to specify a value of $\\gamma$ for the radial basis\n", + "kernel (9.24).\n", + "\n", + "We first generate some data with a non-linear class boundary, as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "322be574", + "metadata": {}, + "outputs": [], + "source": [ + "X = rng.standard_normal((200, 2))\n", + "X[:100] += 2\n", + "X[100:150] -= 2\n", + "y = np.array([1]*150+[2]*50)\n" + ] + }, + { + "cell_type": "markdown", + "id": "22fe2182", + "metadata": {}, + "source": [ + "Plotting the data makes it clear that the class boundary is indeed non-linear." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04fda182", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(X[:,0],\n", + " X[:,1],\n", + " c=y,\n", + " cmap=cm.coolwarm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "64913fe3", + "metadata": {}, + "source": [ + "The data is randomly split into training and testing groups. We then\n", + "fit the training data using the `SVC()` estimator with a\n", + "radial kernel and $\\gamma=1$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c2690d1", + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, \n", + " X_test,\n", + " y_train,\n", + " y_test) = skm.train_test_split(X,\n", + " y,\n", + " test_size=0.5,\n", + " random_state=0)\n", + "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1)\n", + "svm_rbf.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5da9efdb", + "metadata": {}, + "source": [ + "The plot shows that the resulting SVM has a decidedly non-linear\n", + "boundary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eb171e8", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X_train,\n", + " y_train,\n", + " svm_rbf,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ab5b1446", + "metadata": {}, + "source": [ + "We can see from the figure that there are a fair number of training\n", + "errors in this SVM fit. If we increase the value of `C`, we\n", + "can reduce the number of training errors. However, this comes at the\n", + "price of a more irregular decision boundary that seems to be at risk\n", + "of overfitting the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6b905b", + "metadata": {}, + "outputs": [], + "source": [ + "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1e5)\n", + "svm_rbf.fit(X_train, y_train)\n", + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X_train,\n", + " y_train,\n", + " svm_rbf,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "300c1b8b", + "metadata": {}, + "source": [ + "We can perform cross-validation using `skm.GridSearchCV()` to select the\n", + "best choice of $\\gamma$ and `C` for an SVM with a radial\n", + "kernel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ab01d6c", + "metadata": {}, + "outputs": [], + "source": [ + "kfold = skm.KFold(5, \n", + " random_state=0,\n", + " shuffle=True)\n", + "grid = skm.GridSearchCV(svm_rbf,\n", + " {'C':[0.1,1,10,100,1000],\n", + " 'gamma':[0.5,1,2,3,4]},\n", + " refit=True,\n", + " cv=kfold,\n", + " scoring='accuracy');\n", + "grid.fit(X_train, y_train)\n", + "grid.best_params_\n" + ] + }, + { + "cell_type": "markdown", + "id": "1bb987ae", + "metadata": {}, + "source": [ + "The best choice of parameters under five-fold CV is achieved at `C=1`\n", + "and `gamma=0.5`, though several other values also achieve the same\n", + "value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "166a6acb", + "metadata": {}, + "outputs": [], + "source": [ + "best_svm = grid.best_estimator_\n", + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X_train,\n", + " y_train,\n", + " best_svm,\n", + " ax=ax)\n", + "\n", + "y_hat_test = best_svm.predict(X_test)\n", + "confusion_table(y_hat_test, y_test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "39ee6f32", + "metadata": {}, + "source": [ + "With these parameters, 12% of test\n", + "observations are misclassified by this SVM." + ] + }, + { + "cell_type": "markdown", + "id": "f0ea699d", + "metadata": {}, + "source": [ + "## ROC Curves\n", + "\n", + "SVMs and support vector classifiers output class labels for each\n", + "observation. However, it is also possible to obtain *fitted values*\n", + "for each observation, which are the numerical scores used to\n", + "obtain the class labels. For instance, in the case of a support vector\n", + "classifier, the fitted value for an observation $X= (X_1, X_2, \\ldots,\n", + "X_p)^T$ takes the form $\\hat{\\beta}_0 + \\hat{\\beta}_1 X_1 +\n", + "\\hat{\\beta}_2 X_2 + \\ldots + \\hat{\\beta}_p X_p$. For an SVM with a\n", + "non-linear kernel, the equation that yields the fitted value is given\n", + "in (9.23). The sign of the fitted value\n", + "determines on which side of the decision boundary the observation\n", + "lies. Therefore, the relationship between the fitted value and the\n", + "class prediction for a given observation is simple: if the fitted\n", + "value exceeds zero then the observation is assigned to one class, and\n", + "if it is less than zero then it is assigned to the other.\n", + "By changing this threshold from zero to some positive value,\n", + "we skew the classifications in favor of one class versus the other.\n", + "By considering a range of these thresholds, positive and negative, we produce the ingredients for a ROC plot.\n", + "We can access these values by calling the `decision_function()`\n", + "method of a fitted SVM estimator.\n", + "\n", + "The function `ROCCurveDisplay.from_estimator()` (which we have abbreviated to `roc_curve()`) will produce a plot of a ROC curve. It takes a fitted estimator as its first argument, followed\n", + "by a model matrix $X$ and labels $y$. The argument `name` is used in the legend,\n", + "while `color` is used for the color of the line. Results are plotted\n", + "on our axis object `ax`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0607fc41", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "roc_curve(best_svm,\n", + " X_train,\n", + " y_train,\n", + " name='Training',\n", + " color='r',\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "54446e71", + "metadata": {}, + "source": [ + " In this example, the SVM appears to provide accurate predictions. By increasing\n", + "$\\gamma$ we can produce a more flexible fit and generate further\n", + "improvements in accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5211a882", + "metadata": {}, + "outputs": [], + "source": [ + "svm_flex = SVC(kernel=\"rbf\", \n", + " gamma=50,\n", + " C=1)\n", + "svm_flex.fit(X_train, y_train)\n", + "fig, ax = subplots(figsize=(8,8))\n", + "roc_curve(svm_flex,\n", + " X_train,\n", + " y_train,\n", + " name='Training $\\gamma=50$',\n", + " color='r',\n", + " ax=ax);\n" + ] + }, + { + "cell_type": "markdown", + "id": "de7e4be8", + "metadata": {}, + "source": [ + "However, these ROC curves are all on the training data. We are really\n", + "more interested in the level of prediction accuracy on the test\n", + "data. When we compute the ROC curves on the test data, the model with\n", + "$\\gamma=0.5$ appears to provide the most accurate results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12acc4ff", + "metadata": {}, + "outputs": [], + "source": [ + "roc_curve(svm_flex,\n", + " X_test,\n", + " y_test,\n", + " name='Test $\\gamma=50$',\n", + " color='b',\n", + " ax=ax)\n", + "fig;\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb5c8aeb", + "metadata": {}, + "source": [ + "Let’s look at our tuned SVM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21c81913", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplots(figsize=(8,8))\n", + "for (X_, y_, c, name) in zip(\n", + " (X_train, X_test),\n", + " (y_train, y_test),\n", + " ('r', 'b'),\n", + " ('CV tuned on training',\n", + " 'CV tuned on test')):\n", + " roc_curve(best_svm,\n", + " X_,\n", + " y_,\n", + " name=name,\n", + " ax=ax,\n", + " color=c)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b9fefe9f", + "metadata": {}, + "source": [ + "## SVM with Multiple Classes\n", + "\n", + "If the response is a factor containing more than two levels, then the\n", + "`SVC()` function will perform multi-class classification using\n", + "either the one-versus-one approach (when `decision_function_shape=='ovo'`)\n", + "or one-versus-rest {One-versus-rest is also known as one-versus-all.} (when `decision_function_shape=='ovr'`).\n", + "We explore that setting briefly here by\n", + "generating a third class of observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fff4fa8", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(123)\n", + "X = np.vstack([X, rng.standard_normal((50, 2))])\n", + "y = np.hstack([y, [0]*50])\n", + "X[y==0,1] += 2\n", + "fig, ax = subplots(figsize=(8,8))\n", + "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n" + ] + }, + { + "cell_type": "markdown", + "id": "b7adc87d", + "metadata": {}, + "source": [ + "We now fit an SVM to the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5396f2df", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "svm_rbf_3 = SVC(kernel=\"rbf\",\n", + " C=10,\n", + " gamma=1,\n", + " decision_function_shape='ovo');\n", + "svm_rbf_3.fit(X, y)\n", + "fig, ax = subplots(figsize=(8,8))\n", + "plot_svm(X,\n", + " y,\n", + " svm_rbf_3,\n", + " scatter_cmap=cm.tab10,\n", + " ax=ax)\n" + ] + }, + { + "cell_type": "markdown", + "id": "837644f5", + "metadata": {}, + "source": [ + "The `sklearn.svm` library can also be used to perform support vector\n", + "regression with a numerical response using the estimator `SupportVectorRegression()`." + ] + }, + { + "cell_type": "markdown", + "id": "a6bc0cbc", + "metadata": {}, + "source": [ + "## Application to Gene Expression Data\n", + "\n", + "We now examine the `Khan` data set, which consists of a number of\n", + "tissue samples corresponding to four distinct types of small round\n", + "blue cell tumors. For each tissue sample, gene expression measurements\n", + "are available. The data set consists of training data, `xtrain`\n", + "and `ytrain`, and testing data, `xtest` and `ytest`.\n", + "\n", + "We examine the dimension of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f63c575e", + "metadata": {}, + "outputs": [], + "source": [ + "Khan = load_data('Khan')\n", + "Khan['xtrain'].shape, Khan['xtest'].shape\n" + ] + }, + { + "cell_type": "markdown", + "id": "bfd6492c", + "metadata": {}, + "source": [ + "This data set consists of expression measurements for 2,308\n", + "genes. The training and test sets consist of 63 and 20\n", + "observations, respectively.\n", + "\n", + "We will use a support vector approach to predict cancer subtype using\n", + "gene expression measurements. In this data set, there is a very\n", + "large number of features relative to the number of observations. This\n", + "suggests that we should use a linear kernel, because the additional\n", + "flexibility that will result from using a polynomial or radial kernel \n", + "is unnecessary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32091338", + "metadata": {}, + "outputs": [], + "source": [ + "khan_linear = SVC(kernel='linear', C=10)\n", + "khan_linear.fit(Khan['xtrain'], Khan['ytrain'])\n", + "confusion_table(khan_linear.predict(Khan['xtrain']),\n", + " Khan['ytrain'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "23043ab0", + "metadata": {}, + "source": [ + "We see that there are *no* training\n", + "errors. In fact, this is not surprising, because the large number of\n", + "variables relative to the number of observations implies that it is\n", + "easy to find hyperplanes that fully separate the classes. We are more\n", + "interested in the support vector classifier’s performance on the\n", + "test observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9058023", + "metadata": {}, + "outputs": [], + "source": [ + "confusion_table(khan_linear.predict(Khan['xtest']),\n", + " Khan['ytest'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0d5aba4", + "metadata": {}, + "source": [ + "We see that using `C=10` yields two test set errors on these data." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "ipynb,Rmd", + "main_language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/torch_requirements.txt b/torch_requirements.txt index f3b355a..0380132 100644 --- a/torch_requirements.txt +++ b/torch_requirements.txt @@ -1,6 +1,5 @@ -torch -torchvision -torchmetrics -torchdata -pytorch_lightning -torchinfo +torch==2.0.0 +torchvision==0.15.1 +pytorch_lightning==2.0.2 +torchinfo==1.7.2 +torchmetrics==0.11.4 From 25b5ab71dbacaa341045bf146a9ac24aa08e6116 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 16:13:23 -0700 Subject: [PATCH 043/195] including Auto data --- docs/source/labs/Auto.csv | 393 ++++++++++++++++++++++++++++++++++++ docs/source/labs/Auto.data | 398 +++++++++++++++++++++++++++++++++++++ 2 files changed, 791 insertions(+) create mode 100644 docs/source/labs/Auto.csv create mode 100644 docs/source/labs/Auto.data diff --git a/docs/source/labs/Auto.csv b/docs/source/labs/Auto.csv new file mode 100644 index 0000000..78a4921 --- /dev/null +++ b/docs/source/labs/Auto.csv @@ -0,0 +1,393 @@ +mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name +18.0,8,307.0,130,3504,12.0,70,1,chevrolet chevelle malibu +15.0,8,350.0,165,3693,11.5,70,1,buick skylark 320 +18.0,8,318.0,150,3436,11.0,70,1,plymouth satellite +16.0,8,304.0,150,3433,12.0,70,1,amc rebel sst +17.0,8,302.0,140,3449,10.5,70,1,ford torino 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"volkswagen jetta" +34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" +33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" +32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" +32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" +31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" +28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" +30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" +25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" +24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" +22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" +26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" +20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" +17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" +28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" +27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" +34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" +31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" +29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" +27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" +24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" +36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" +37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" +31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" +38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" +36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" +36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" +36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" +34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" +38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" +32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" +38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" +25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" +38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" +26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" +22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" +32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" +36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" +27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" +27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" +44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" +32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" +28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" +31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" From 4287e08fde9bca51db62de3e052657cc91165a90 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 16:15:22 -0700 Subject: [PATCH 044/195] remove numbering --- docs/source/labs.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/labs.rst b/docs/source/labs.rst index 074a07e..38dba3d 100644 --- a/docs/source/labs.rst +++ b/docs/source/labs.rst @@ -19,7 +19,6 @@ To ensure you have the same package versions as those built here, run: .. toctree:: :maxdepth: 1 - :numbered: labs/Ch2-statlearn-lab labs/Ch3-linreg-lab From e497e71b55dd9f3f123941c191a6724710e0ef3b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 16:58:12 -0700 Subject: [PATCH 045/195] update requirements --- docs/requirements.txt | 1 + frozen_requirements.txt | 10 ++++++++++ torch_requirements.txt | 4 ++-- 3 files changed, 13 insertions(+), 2 deletions(-) create mode 100644 frozen_requirements.txt diff --git a/docs/requirements.txt b/docs/requirements.txt index b06b49b..1e093a5 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -3,3 +3,4 @@ numpydoc myst_nb sphinx-book-theme rpy2 +sphinx_rtd_theme diff --git a/frozen_requirements.txt b/frozen_requirements.txt new file mode 100644 index 0000000..73b5e2e --- /dev/null +++ b/frozen_requirements.txt @@ -0,0 +1,10 @@ +numpy==1.21.6 +scipy==1.10.1 +pandas==1.4.1 +lxml==4.8.0 +scikit-learn==1.2.2 +joblib==1.2.0 +statsmodels==0.13.2 +lifelines==0.27.6 +pygam==0.8.0 +l0bnb==1.0.0 diff --git a/torch_requirements.txt b/torch_requirements.txt index 0380132..374ee22 100644 --- a/torch_requirements.txt +++ b/torch_requirements.txt @@ -1,5 +1,5 @@ torch==2.0.0 torchvision==0.15.1 -pytorch_lightning==2.0.2 +pytorch_lightning==1.7.7 torchinfo==1.7.2 -torchmetrics==0.11.4 +torchmetrics==0.9.3 From 3860e68656cd9e65a46ca32d3d383588b08a929d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 20:58:07 -0700 Subject: [PATCH 046/195] Adding colab link --- docs/README.rst | 9 + docs/fix_labs.py | 73 + docs/source/conf.py | 4 +- docs/source/labs/Ch10-deeplearning-lab.ipynb | 7183 +++++++++++++- docs/source/labs/Ch11-surv-lab.ipynb | 1929 +++- docs/source/labs/Ch12-unsup-lab.ipynb | 2026 +++- docs/source/labs/Ch13-multiple-lab.ipynb | 770 +- docs/source/labs/Ch2-statlearn-lab.ipynb | 5809 ++++++++++- docs/source/labs/Ch3-linreg-lab.ipynb | 1979 +++- docs/source/labs/Ch4-classification-lab.ipynb | 3209 ++++++- docs/source/labs/Ch5-resample-lab.ipynb | 498 +- docs/source/labs/Ch6-varselect-lab.ipynb | 8463 ++++++++++++++++- docs/source/labs/Ch7-nonlin-lab.ipynb | 1945 +++- docs/source/labs/Ch8-baggboost-lab.ipynb | 862 +- docs/source/labs/Ch9-svm-lab.ipynb | 1110 ++- 15 files changed, 34459 insertions(+), 1410 deletions(-) create mode 100644 docs/README.rst create mode 100644 docs/fix_labs.py diff --git a/docs/README.rst b/docs/README.rst new file mode 100644 index 0000000..cee991e --- /dev/null +++ b/docs/README.rst @@ -0,0 +1,9 @@ +Deep learning +============= + +This lab should be run as a notebook and saved + +Ridge regression +================ + +There is a snippet that should be inserted to remove the many warnings raised. diff --git a/docs/fix_labs.py b/docs/fix_labs.py new file mode 100644 index 0000000..cb616c1 --- /dev/null +++ b/docs/fix_labs.py @@ -0,0 +1,73 @@ +import os, nbformat +from glob import glob + +for f in glob('source/labs/Ch14*'): + os.remove(f) + + + +for nbfile in glob('source/labs/*nb'): + base = os.path.splitext(nbfile)[0] + fname = os.path.split(base)[1] + + colab_code = f''' + + Open In Colab + + +''' + + # allow errors for lab2 + if fname[:3] == 'Ch6': + colab_code = (''' +```{code-cell} +:tags: [hide-cell] + +import warnings +warnings.simplefilter('ignore') +``` + +''' + colab_code) + if fname[:3] == 'Ch2': + nb = nbformat.read(open(nbfile), 4) + nb.metadata.setdefault('execution', {})['allow_errors'] = True + nbformat.write(nb, open(nbfile, 'w')) + + if fname[:4] != 'Ch10': + + os.system(f'jupytext --set-formats ipynb,md:myst {base}.ipynb; jupytext --sync {base}.ipynb') + + myst = open(f'{base}.md').read().strip() + + new_myst = [] + for l in myst.split('\n'): + if l.strip()[:9] != '# Chapter': + new_myst.append(l) + new_myst.append(colab_code) + + myst = '\n'.join(new_myst) # remove the "Chapter %d + myst = myst.replace('# Lab:', colab_code + '\n# ') + + open(f'{base}.md', 'w').write(myst) + + os.system(f'jupytext --sync {base}.ipynb') + +# add a warning for ridge +# at ## Ridge Regression and the Lasso + +code_to_insert = ''' + + +```{attention} +Using `skl.ElasticNet` to fit ridge regression +throws up many warnings. We have inserted the code below to filter them. +``` + + +```{code-cell} +import warnings +warnings.simplefilter('ignore') +``` + +''' + diff --git a/docs/source/conf.py b/docs/source/conf.py index 654522f..d36b86a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -27,8 +27,10 @@ graphviz_dot = '/opt/homebrew/bin/dot' numpydoc_class_members_toctree = False -nb_execution_mode = "cache" +nb_execution_mode = "auto" nb_execution_timeout = 60*3 #*100 +nb_execution_excludepatterns = ['Ch10*'] +nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} diff --git a/docs/source/labs/Ch10-deeplearning-lab.ipynb b/docs/source/labs/Ch10-deeplearning-lab.ipynb index 6693e01..3f0f930 100644 --- a/docs/source/labs/Ch10-deeplearning-lab.ipynb +++ b/docs/source/labs/Ch10-deeplearning-lab.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "aeb913e2", "metadata": { "lines_to_next_cell": 2 @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3a3e91e7", "metadata": {}, "outputs": [], @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3f4fdb8c", "metadata": {}, "outputs": [], @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "71cc6f03", "metadata": {}, "outputs": [], @@ -134,10 +134,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "fd1290f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n" + ] + } + ], "source": [ "from pytorch_lightning.utilities.seed import seed_everything\n", "seed_everything(0, workers=True)\n", @@ -156,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c85308d1", "metadata": { "lines_to_next_cell": 0 @@ -191,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4de9f03c", "metadata": {}, "outputs": [], @@ -220,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4ef95fe3", "metadata": {}, "outputs": [], @@ -249,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "493821a9", "metadata": { "lines_to_next_cell": 2 @@ -271,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a427e224", "metadata": { "lines_to_next_cell": 0 @@ -299,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "feac4b15", "metadata": { "lines_to_next_cell": 0 @@ -335,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6a75a640", "metadata": {}, "outputs": [], @@ -360,10 +368,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "32d8de64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "259.71528833146243" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hit_lm = LinearRegression().fit(X_train, Y_train)\n", "Yhat_test = hit_lm.predict(X_test)\n", @@ -385,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "46786db6", "metadata": {}, "outputs": [], @@ -408,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "2b55b38b", "metadata": { "lines_to_next_cell": 0 @@ -433,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "cc44c559", "metadata": {}, "outputs": [], @@ -460,12 +479,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "1bd1fd13", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "257.2382010799497" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "trained_lasso = grid.best_estimator_\n", "Yhat_test = trained_lasso.predict(X_test)\n", @@ -491,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "4b2ccc5a", "metadata": {}, "outputs": [], @@ -549,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "108ad1f1", "metadata": {}, "outputs": [], @@ -584,12 +614,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "2f20059e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "HittersModel [175, 19] [175] --\n", + "├─Flatten: 1-1 [175, 19] [175, 19] --\n", + "├─Sequential: 1-2 [175, 19] [175, 1] --\n", + "│ └─Linear: 2-1 [175, 19] [175, 50] 1,000\n", + "│ └─ReLU: 2-2 [175, 50] [175, 50] --\n", + "│ └─Dropout: 2-3 [175, 50] [175, 50] --\n", + "│ └─Linear: 2-4 [175, 50] [175, 1] 51\n", + "===================================================================================================================\n", + "Total params: 1,051\n", + "Trainable params: 1,051\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.18\n", + "===================================================================================================================\n", + "Input size (MB): 0.01\n", + "Forward/backward pass size (MB): 0.07\n", + "Params size (MB): 0.00\n", + "Estimated Total Size (MB): 0.09\n", + "===================================================================================================================" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(hit_model, \n", " input_size=X_train.shape,\n", @@ -621,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "6ca3030d", "metadata": { "lines_to_next_cell": 0 @@ -643,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "86723b3e", "metadata": {}, "outputs": [], @@ -678,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "999279fd", "metadata": {}, "outputs": [], @@ -710,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "7bd9cc5c", "metadata": {}, "outputs": [], @@ -738,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "5be2f822", "metadata": {}, "outputs": [], @@ -765,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "87334d33", "metadata": {}, "outputs": [], @@ -796,12 +867,709 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "1a474999", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------\n", + "0 | model | HittersModel | 1.1 K \n", + "1 | loss | MSELoss | 0 \n", + "---------------------------------------\n", + "1.1 K Trainable params\n", + "0 Non-trainable params\n", + "1.1 K Total params\n", + "0.004 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e5fe2eab339740c99a234f68dff24548", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": 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"application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + } + ], "source": [ "hit_trainer = Trainer(deterministic=True,\n", " max_epochs=50,\n", @@ -828,12 +1596,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "e3b24643", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ee861e12d14b4d349e535c091f331ca4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 103304.8515625\n", + " test_mae 224.26962280273438\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 103304.8515625, 'test_mae': 224.26962280273438}]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hit_trainer.test(hit_module, datamodule=hit_dm)\n" ] @@ -855,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "f9b266e7", "metadata": {}, "outputs": [], @@ -874,7 +1679,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "c5752a0b", "metadata": { "lines_to_next_cell": 0 @@ -917,12 +1722,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "ff0c9fa0", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(1, 1, figsize=(6, 6))\n", "ax = summary_plot(hit_results,\n", @@ -953,12 +1769,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "1ee2b61b", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(224.2696, grad_fn=)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hit_model.eval() \n", "preds = hit_module(X_test_t)\n", @@ -987,7 +1814,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "0ca069a3", "metadata": { "lines_to_next_cell": 2 @@ -1020,10 +1847,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "af6a0a33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset MNIST\n", + " Number of datapoints: 60000\n", + " Root location: data\n", + " Split: Train\n", + " StandardTransform\n", + "Transform: ToTensor()" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(mnist_train, \n", " mnist_test) = [MNIST(root='data',\n", @@ -1059,7 +1902,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "0df8a0c8", "metadata": {}, "outputs": [], @@ -1082,12 +1925,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "0c7dd6ee", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X: torch.Size([256, 1, 28, 28])\n", + "Y: torch.Size([256])\n", + "X: torch.Size([256, 1, 28, 28])\n", + "Y: torch.Size([256])\n" + ] + } + ], "source": [ "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -1110,7 +1964,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "63e48c70", "metadata": {}, "outputs": [], @@ -1150,7 +2004,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "92405826", "metadata": {}, "outputs": [], @@ -1169,10 +2023,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "2c8f7a48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([256, 10])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mnist_model(X_).size()" ] @@ -1189,10 +2054,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "9f502df8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "MNISTModel [256, 1, 28, 28] [256, 10] --\n", + "├─Sequential: 1-1 [256, 1, 28, 28] [256, 10] --\n", + "│ └─Sequential: 2-1 [256, 1, 28, 28] [256, 256] --\n", + "│ │ └─Flatten: 3-1 [256, 1, 28, 28] [256, 784] --\n", + "│ │ └─Linear: 3-2 [256, 784] [256, 256] 200,960\n", + "│ │ └─ReLU: 3-3 [256, 256] [256, 256] --\n", + "│ │ └─Dropout: 3-4 [256, 256] [256, 256] --\n", + "│ └─Sequential: 2-2 [256, 256] [256, 128] --\n", + "│ │ └─Linear: 3-5 [256, 256] [256, 128] 32,896\n", + "│ │ └─ReLU: 3-6 [256, 128] [256, 128] --\n", + "│ │ └─Dropout: 3-7 [256, 128] [256, 128] --\n", + "│ └─Linear: 2-3 [256, 128] [256, 10] 1,290\n", + "===================================================================================================================\n", + "Total params: 235,146\n", + "Trainable params: 235,146\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 60.20\n", + "===================================================================================================================\n", + "Input size (MB): 0.80\n", + "Forward/backward pass size (MB): 0.81\n", + "Params size (MB): 0.94\n", + "Estimated Total Size (MB): 2.55\n", + "===================================================================================================================" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(mnist_model,\n", " input_data=X_,\n", @@ -1214,7 +2125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "d1a7590f", "metadata": {}, "outputs": [], @@ -1233,12 +2144,490 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "e2fe6de3", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | MNISTModel | 235 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "235 K Trainable params\n", + "0 Non-trainable params\n", + "235 K Total params\n", + "0.941 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "19132f797f854efbaa5bc297129f7288", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -1309,10 +2709,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "f5269c40", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1a7f965ce4ad4bfa92fbaa1305bbbbe5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.9681000113487244\n", + " test_loss 0.14706705510616302\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.14706705510616302, 'test_accuracy': 0.9681000113487244}]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mnist_trainer.test(mnist_module,\n", " datamodule=mnist_dm)" @@ -1333,7 +2770,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "97a0b304", "metadata": {}, "outputs": [], @@ -1353,12 +2790,430 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "ea685183", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | MNIST_MLR | 7.9 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "7.9 K Trainable params\n", + "0 Non-trainable params\n", + "7.9 K Total params\n", + "0.031 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "417a4d9bf4ce47c38cf544d9f63db3a0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "42056226aefe470d919ec951608b4d79", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a3a42f9e5c7a4dfcb73dd9f4b7d24317", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "mlr_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -1376,12 +3231,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "c0bd63e3", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92b7e09cc3df40619350090e37d39bc0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.9240999817848206\n", + " test_loss 0.3186827003955841\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.3186827003955841, 'test_accuracy': 0.9240999817848206}]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mlr_trainer.test(mlr_module,\n", " datamodule=mnist_dm)" @@ -1400,7 +3292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "6b0d3daa", "metadata": { "lines_to_next_cell": 2 @@ -1431,10 +3323,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "67517b11", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], "source": [ "(cifar_train, \n", " cifar_test) = [CIFAR100(root=\"data\",\n", @@ -1445,7 +3346,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "ee7b040e", "metadata": {}, "outputs": [], @@ -1476,7 +3377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "bd9e74ad", "metadata": { "lines_to_next_cell": 0 @@ -1500,12 +3401,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "a553b926", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X: torch.Size([128, 3, 32, 32])\n", + "Y: torch.Size([128])\n", + "X: torch.Size([128, 3, 32, 32])\n", + "Y: torch.Size([128])\n" + ] + } + ], "source": [ "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -1528,12 +3440,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "94885e68", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = subplots(5, 5, figsize=(10,10))\n", "rng = np.random.default_rng(4)\n", @@ -1567,7 +3490,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "cdc2528a", "metadata": {}, "outputs": [], @@ -1612,7 +3535,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "845be1ae", "metadata": {}, "outputs": [], @@ -1648,12 +3571,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "be768cbb", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "CIFARModel [128, 3, 32, 32] [128, 100] --\n", + "├─Sequential: 1-1 [128, 3, 32, 32] [128, 256, 2, 2] --\n", + "│ └─BuildingBlock: 2-1 [128, 3, 32, 32] [128, 32, 16, 16] --\n", + "│ │ └─Conv2d: 3-1 [128, 3, 32, 32] [128, 32, 32, 32] 896\n", + "│ │ └─ReLU: 3-2 [128, 32, 32, 32] [128, 32, 32, 32] --\n", + "│ │ └─MaxPool2d: 3-3 [128, 32, 32, 32] [128, 32, 16, 16] --\n", + "│ └─BuildingBlock: 2-2 [128, 32, 16, 16] [128, 64, 8, 8] --\n", + "│ │ └─Conv2d: 3-4 [128, 32, 16, 16] [128, 64, 16, 16] 18,496\n", + "│ │ └─ReLU: 3-5 [128, 64, 16, 16] [128, 64, 16, 16] --\n", + "│ │ └─MaxPool2d: 3-6 [128, 64, 16, 16] [128, 64, 8, 8] --\n", + "│ └─BuildingBlock: 2-3 [128, 64, 8, 8] [128, 128, 4, 4] --\n", + "│ │ └─Conv2d: 3-7 [128, 64, 8, 8] [128, 128, 8, 8] 73,856\n", + "│ │ └─ReLU: 3-8 [128, 128, 8, 8] [128, 128, 8, 8] --\n", + "│ │ └─MaxPool2d: 3-9 [128, 128, 8, 8] [128, 128, 4, 4] --\n", + "│ └─BuildingBlock: 2-4 [128, 128, 4, 4] [128, 256, 2, 2] --\n", + "│ │ └─Conv2d: 3-10 [128, 128, 4, 4] [128, 256, 4, 4] 295,168\n", + "│ │ └─ReLU: 3-11 [128, 256, 4, 4] [128, 256, 4, 4] --\n", + "│ │ └─MaxPool2d: 3-12 [128, 256, 4, 4] [128, 256, 2, 2] --\n", + "├─Sequential: 1-2 [128, 1024] [128, 100] --\n", + "│ └─Dropout: 2-5 [128, 1024] [128, 1024] --\n", + "│ └─Linear: 2-6 [128, 1024] [128, 512] 524,800\n", + "│ └─ReLU: 2-7 [128, 512] [128, 512] --\n", + "│ └─Linear: 2-8 [128, 512] [128, 100] 51,300\n", + "===================================================================================================================\n", + "Total params: 964,516\n", + "Trainable params: 964,516\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 2.01\n", + "===================================================================================================================\n", + "Input size (MB): 1.57\n", + "Forward/backward pass size (MB): 63.54\n", + "Params size (MB): 3.86\n", + "Estimated Total Size (MB): 68.97\n", + "===================================================================================================================" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cifar_model = CIFARModel()\n", "summary(cifar_model,\n", @@ -1699,7 +3679,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "67e3e2d9", "metadata": {}, "outputs": [], @@ -1712,10 +3692,486 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "1ea339ff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | CIFARModel | 964 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "964 K Trainable params\n", + "0 Non-trainable params\n", + "964 K Total params\n", + "3.858 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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"execution_count": null, + "execution_count": 59, "id": "0a9068d9", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "log_path = cifar_logger.experiment.metrics_file_path\n", "cifar_results = pd.read_csv(log_path)\n", @@ -1771,12 +4238,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "e71eff9b", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44687f1d6df745d2ae89b0d3b8371ddd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.40880000591278076\n", + " test_loss 2.4764862060546875\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 2.4764862060546875, 'test_accuracy': 0.40880000591278076}]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cifar_trainer.test(cifar_module,\n", " datamodule=cifar_dm)\n" @@ -1804,12 +4308,510 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "58eae430", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | CIFARModel | 964 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "964 K Trainable params\n", + "0 Non-trainable params\n", + "964 K Total params\n", + "3.858 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bf9f95a0186f49cda30773788fcf96cb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchmetrics/utilities/checks.py:49: UserWarning: MPS: no support for int64 min/max ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:1271.)\n", + " if ignore_index is None and target.min() < 0:\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 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"version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b0b092424de344d5805fc7b57eae4a88", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "try:\n", " for name, metric in cifar_module.metrics.items():\n", @@ -1858,12 +4860,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "638a1c8c", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([6, 3, 224, 224])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resize = Resize((232,232))\n", "crop = CenterCrop(224)\n", @@ -1886,12 +4907,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "7776ed1e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "ResNet [6, 3, 224, 224] [6, 1000] --\n", + "├─Conv2d: 1-1 [6, 3, 224, 224] [6, 64, 112, 112] 9,408\n", + "├─BatchNorm2d: 1-2 [6, 64, 112, 112] [6, 64, 112, 112] 128\n", + "├─ReLU: 1-3 [6, 64, 112, 112] [6, 64, 112, 112] --\n", + "├─MaxPool2d: 1-4 [6, 64, 112, 112] [6, 64, 56, 56] --\n", + "├─Sequential: 1-5 [6, 64, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-1 [6, 64, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-1 [6, 64, 56, 56] [6, 64, 56, 56] 4,096\n", + "│ │ └─BatchNorm2d: 3-2 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-3 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-4 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-5 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-6 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-7 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-8 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─Sequential: 3-9 [6, 64, 56, 56] [6, 256, 56, 56] 16,896\n", + "│ │ └─ReLU: 3-10 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-2 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-11 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-12 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-13 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-14 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-15 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-16 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-17 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-18 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─ReLU: 3-19 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-3 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-20 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-21 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-22 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-23 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-24 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-25 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-26 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-27 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─ReLU: 3-28 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "├─Sequential: 1-6 [6, 256, 56, 56] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-4 [6, 256, 56, 56] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-29 [6, 256, 56, 56] [6, 128, 56, 56] 32,768\n", + "│ │ └─BatchNorm2d: 3-30 [6, 128, 56, 56] [6, 128, 56, 56] 256\n", + "│ │ └─ReLU: 3-31 [6, 128, 56, 56] [6, 128, 56, 56] --\n", + "│ │ └─Conv2d: 3-32 [6, 128, 56, 56] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-33 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-34 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-35 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-36 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─Sequential: 3-37 [6, 256, 56, 56] [6, 512, 28, 28] 132,096\n", + "│ │ └─ReLU: 3-38 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-5 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-39 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-40 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-41 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-42 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-43 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-44 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-45 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-46 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-47 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-6 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-48 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-49 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-50 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-51 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-52 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-53 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-54 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-55 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-56 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-7 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-57 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-58 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-59 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-60 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-61 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-62 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-63 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-64 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-65 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "├─Sequential: 1-7 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-8 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-66 [6, 512, 28, 28] [6, 256, 28, 28] 131,072\n", + "│ │ └─BatchNorm2d: 3-67 [6, 256, 28, 28] [6, 256, 28, 28] 512\n", + "│ │ └─ReLU: 3-68 [6, 256, 28, 28] [6, 256, 28, 28] --\n", + "│ │ └─Conv2d: 3-69 [6, 256, 28, 28] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-70 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-71 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-72 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-73 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─Sequential: 3-74 [6, 512, 28, 28] [6, 1024, 14, 14] 526,336\n", + "│ │ └─ReLU: 3-75 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-9 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-76 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-77 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-78 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-79 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-80 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-81 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-82 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-83 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-84 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-10 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-85 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-86 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-87 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-88 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-89 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-90 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-91 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-92 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-93 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-11 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-94 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-95 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-96 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-97 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-98 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-99 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-100 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-101 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-102 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-12 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-103 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-104 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-105 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-106 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-107 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-108 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-109 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-110 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-111 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-13 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-112 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-113 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-114 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-115 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-116 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-117 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-118 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-119 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-120 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "├─Sequential: 1-8 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-14 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-121 [6, 1024, 14, 14] [6, 512, 14, 14] 524,288\n", + "│ │ └─BatchNorm2d: 3-122 [6, 512, 14, 14] [6, 512, 14, 14] 1,024\n", + "│ │ └─ReLU: 3-123 [6, 512, 14, 14] [6, 512, 14, 14] --\n", + "│ │ └─Conv2d: 3-124 [6, 512, 14, 14] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-125 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-126 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-127 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-128 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─Sequential: 3-129 [6, 1024, 14, 14] [6, 2048, 7, 7] 2,101,248\n", + "│ │ └─ReLU: 3-130 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-15 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-131 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-132 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-133 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-134 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-135 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-136 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-137 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-138 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─ReLU: 3-139 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-16 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-140 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-141 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-142 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-143 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-144 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-145 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-146 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-147 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─ReLU: 3-148 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "├─AdaptiveAvgPool2d: 1-9 [6, 2048, 7, 7] [6, 2048, 1, 1] --\n", + "├─Linear: 1-10 [6, 2048] [6, 1000] 2,049,000\n", + "===================================================================================================================\n", + "Total params: 25,557,032\n", + "Trainable params: 25,557,032\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 24.54\n", + "===================================================================================================================\n", + "Input size (MB): 3.61\n", + "Forward/backward pass size (MB): 1066.99\n", + "Params size (MB): 102.23\n", + "Estimated Total Size (MB): 1172.83\n", + "===================================================================================================================" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", "summary(resnet_model,\n", @@ -1911,12 +5141,198 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "5c8e359d", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "ResNet(\n", + " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, 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(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", + ")" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resnet_model.eval()" ] @@ -1935,7 +5351,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "80f19869", "metadata": {}, "outputs": [], @@ -1956,7 +5372,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "6892597d", "metadata": {}, "outputs": [], @@ -1975,7 +5391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "3eb1e1b0", "metadata": {}, "outputs": [], @@ -2000,12 +5416,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "0e89d251", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image: book_images/flamingo.jpg\n", + " label prob\n", + "0 flamingo 0.591760\n", + "1 spoonbill 0.012386\n", + "2 American_egret 0.002105\n", + "Image: book_images/hawk.jpg\n", + " label prob\n", + "0 great_grey_owl 0.287958\n", + "1 kite 0.039478\n", + "2 fountain 0.029384\n", + "Image: book_images/hawk_cropped.jpeg\n", + " label prob\n", + "0 kite 0.301841\n", + "1 jay 0.121659\n", + "2 magpie 0.015511\n", + "Image: book_images/huey.jpg\n", + " label prob\n", + "0 Lhasa 0.151153\n", + "1 Shih-Tzu 0.129804\n", + "2 Tibetan_terrier 0.102400\n", + "Image: book_images/kitty.jpg\n", + " label prob\n", + "0 tabby 0.173628\n", + "1 tiger_cat 0.110415\n", + "2 doormat 0.093447\n", + "Image: book_images/weaver.jpg\n", + " label prob\n", + "0 jacamar 0.287284\n", + "1 bee_eater 0.046768\n", + "2 bulbul 0.037507\n" + ] + } + ], "source": [ "for i, imgfile in enumerate(imgfiles):\n", " img_df = class_labels.copy()\n", @@ -2029,7 +5482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "69456669", "metadata": { "lines_to_next_cell": 2 @@ -2073,12 +5526,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "70b3ece2", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n", + " 4468], dtype=int32)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(imdb_seq_train,\n", " imdb_seq_test) = load_sequential(root='data/IMDB')\n", @@ -2106,10 +5571,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "349b2af7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\" this film was just brilliant casting location scenery story direction everyone's\"" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lookup = load_lookup(root='data/IMDB')\n", "' '.join(lookup[i] for i in sample_review)" @@ -2129,7 +5605,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "ffa8da37", "metadata": { "lines_to_next_cell": 0 @@ -2156,7 +5632,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "11eeeabb", "metadata": { "lines_to_next_cell": 0 @@ -2193,10 +5669,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "5fe55a29", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "IMDBModel [25000, 10003] [25000] --\n", + "├─Linear: 1-1 [25000, 10003] [25000, 16] 160,064\n", + "├─ReLU: 1-2 [25000, 16] [25000, 16] --\n", + "├─Linear: 1-3 [25000, 16] [25000, 16] 272\n", + "├─ReLU: 1-4 [25000, 16] [25000, 16] --\n", + "├─Linear: 1-5 [25000, 16] [25000, 1] 17\n", + "===================================================================================================================\n", + "Total params: 160,353\n", + "Trainable params: 160,353\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 4.01\n", + "===================================================================================================================\n", + "Input size (MB): 1000.30\n", + "Forward/backward pass size (MB): 6.60\n", + "Params size (MB): 0.64\n", + "Estimated Total Size (MB): 1007.54\n", + "===================================================================================================================" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", "summary(imdb_model,\n", @@ -2225,7 +5741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "e1ebbc70", "metadata": {}, "outputs": [], @@ -2248,10 +5764,496 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "9feddeb2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | model | IMDBModel | 160 K \n", + "1 | loss | BCEWithLogitsLoss | 0 \n", + "--------------------------------------------\n", + "160 K Trainable params\n", + "0 Non-trainable params\n", + "160 K Total params\n", + "0.641 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1892: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", + " rank_zero_warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3c8345a0fa7d41768ff584eca2ae7e32", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + 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"version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "imdb_logger = CSVLogger('logs', name='IMDB')\n", "imdb_trainer = Trainer(deterministic=True,\n", @@ -2272,12 +6274,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "887d7dad", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "15800f2fa0774b2d8832acab92ea61df", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8543599843978882\n", + " test_loss 1.1992340087890625\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 1.1992340087890625, 'test_accuracy': 0.8543599843978882}]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", "test_results" @@ -2297,7 +6336,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "id": "055a9aeb", "metadata": {}, "outputs": [], @@ -2321,7 +6360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "id": "e3e1e181", "metadata": { "lines_to_next_cell": 0 @@ -2346,7 +6385,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "dd64350c", "metadata": { "lines_to_next_cell": 0 @@ -2370,7 +6409,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "id": "0bf8b868", "metadata": {}, "outputs": [], @@ -2396,7 +6435,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "id": "28256828", "metadata": { "lines_to_next_cell": 0 @@ -2418,7 +6457,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "id": "a5945618", "metadata": { "lines_to_next_cell": 0 @@ -2461,12 +6500,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "id": "74704884", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", "summary_plot(imdb_results,\n", @@ -2496,7 +6547,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "id": "b1ecfca8", "metadata": { "lines_to_next_cell": 2 @@ -2542,7 +6593,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "5503cb3f", "metadata": {}, "outputs": [], @@ -2585,7 +6636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "id": "8b683641", "metadata": { "lines_to_next_cell": 0 @@ -2616,10 +6667,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "702aa8de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "LSTMModel [10, 500] [10] --\n", + "├─Embedding: 1-1 [10, 500] [10, 500, 32] 320,096\n", + "├─LSTM: 1-2 [10, 500, 32] [10, 500, 32] 8,448\n", + "├─Linear: 1-3 [10, 32] [10, 1] 33\n", + "===================================================================================================================\n", + "Total params: 328,577\n", + "Trainable params: 328,577\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 45.44\n", + "===================================================================================================================\n", + "Input size (MB): 50.00\n", + "Forward/backward pass size (MB): 2.56\n", + "Params size (MB): 1.31\n", + "Estimated Total Size (MB): 53.87\n", + "===================================================================================================================" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lstm_model = LSTMModel(X_test.shape[-1])\n", "summary(lstm_model,\n", @@ -2640,7 +6729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "id": "e48aa272", "metadata": {}, "outputs": [], @@ -2651,12 +6740,350 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "143be7e8", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | model | LSTMModel | 328 K \n", + "1 | loss | BCEWithLogitsLoss | 0 \n", + "--------------------------------------------\n", + "328 K Trainable params\n", + "0 Non-trainable params\n", + "328 K Total params\n", + "1.314 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d750346c2da241e5aa9290715ef3ab2f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + 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"output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=20` reached.\n" + ] + } + ], "source": [ "lstm_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -2677,10 +7104,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "id": "9272e2ba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1db51a56913246c59f746a80a10ad431", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8452399969100952\n", + " test_loss 0.7559056878089905\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.7559056878089905, 'test_accuracy': 0.8452399969100952}]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" ] @@ -2695,12 +7159,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "5d9d387e", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5, 1.0)" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -2715,7 +7200,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "7c6d1072", "metadata": { "lines_to_next_cell": 2 @@ -2743,7 +7228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "id": "8e2d1d5c", "metadata": {}, "outputs": [], @@ -2768,7 +7253,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "6f6d0e12", "metadata": {}, "outputs": [], @@ -2793,12 +7278,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "id": "d3c961ec", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n", + " 'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n", + " 'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n", + " 'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n", + " dtype='object')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Y, train = X['log_volume'], X['train']\n", "X = X.drop(columns=['train'] + cols)\n", @@ -2816,10 +7316,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "id": "cb89da88", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.4128912938562521" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M = LinearRegression()\n", "M.fit(X[train], Y[train])\n", @@ -2838,7 +7349,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "id": "55d921d7", "metadata": { "lines_to_next_cell": 0 @@ -2862,12 +7373,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "id": "4e87eeb9", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.45633025715446285" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M.fit(X_day[train], Y[train])\n", "M.score(X_day[~train], Y[~train])" @@ -2903,12 +7425,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "id": "3689b318", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n", + " 'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n", + " 'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n", + " 'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n", + " dtype='object')" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ordered_cols = []\n", "for lag in range(5,0,-1):\n", @@ -2928,12 +7465,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "5633e521", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(6046, 5, 3)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X_rnn = X.to_numpy().reshape((-1,5,3))\n", "X_rnn.shape" @@ -2955,7 +7503,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "979a6066", "metadata": {}, "outputs": [], @@ -2992,7 +7540,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "id": "3528f4e7", "metadata": {}, "outputs": [], @@ -3015,12 +7563,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "id": "795c283e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "NYSEModel [1770, 5, 3] [1770] --\n", + "├─RNN: 1-1 [1770, 5, 3] [1770, 5, 12] 204\n", + "├─Dropout: 1-2 [1770, 12] [1770, 12] --\n", + "├─Linear: 1-3 [1770, 12] [1770, 1] 13\n", + "===================================================================================================================\n", + "Total params: 217\n", + "Trainable params: 217\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 1.83\n", + "===================================================================================================================\n", + "Input size (MB): 0.11\n", + "Forward/backward pass size (MB): 0.86\n", + "Params size (MB): 0.00\n", + "Estimated Total Size (MB): 0.97\n", + "===================================================================================================================" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(nyse_model,\n", " input_data=X_rnn_t,\n", @@ -3041,7 +7627,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "id": "31640121", "metadata": { "lines_to_next_cell": 0 @@ -3065,10 +7651,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "af6795f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64]) torch.Size([64])\n", + "torch.Size([64]) torch.Size([64])\n", + "torch.Size([64]) torch.Size([64])\n" + ] + } + ], "source": [ "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", " out = nyse_model(x)\n", @@ -3089,7 +7685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "0d04344c", "metadata": {}, "outputs": [], @@ -3112,12 +7708,1981 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "id": "485d7bb1", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------\n", + "0 | model | NYSEModel | 217 \n", + "1 | loss | MSELoss | 0 \n", + "------------------------------------\n", + "217 Trainable params\n", + "0 Non-trainable params\n", + "217 Total params\n", + "0.001 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b52ec6ddbbe14c79b0c9b9c1a26b0ab0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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"Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ea270026d12c47eb8e3739ffbf057b9a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "33a2996806e24b838c12f07ae95d4ecd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "960fdb9f03644de9a242056f01da68b3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9c76b3b6a56441f7aee113f504fc7920", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b5f3096e1ca54276981128e117f8f8d5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4cd269135bfa46fc90362442680d92dd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "be50daf7a50d482a8846113d6a222fdf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a23c3571935747c999cf569c40a06b00", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b9221b69df044149a5d8ce0417c11096", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dc45ac5a02954e44a414719716c4aa37", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3fc7c185647a4e7f8333ddfce3d8172a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "707a4ccebba94a6bbd103b9a5f40321d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fafc0ed205274ffdb9539050a041a290", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "61ad762e9e654c558b09afbc828484e4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "793897c2fc8d425d998615bb829d8c43", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b12ad2c52494483a4d1c6e7ed5ede31", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de68e3e4b3124d3ead25d0e0a7d3ba42", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6c9a0d13daba4d72a1bb2dc20efcf412", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8a45b474bcb7441fafde536166c3b5ef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "befa8447901e4948aff679cd4b829437", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7080d064216a4d6da052593011bfeccb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a1865848fe7e4983945449f3865ddcc4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.6207807064056396\n", + " test_r2 0.41084879636764526\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.6207807064056396, 'test_r2': 0.41084879636764526}]" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nyse_trainer = Trainer(deterministic=True,\n", " max_epochs=200,\n", @@ -3146,7 +9711,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "91d6633a", "metadata": {}, "outputs": [], @@ -3170,7 +9735,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "87137503", "metadata": {}, "outputs": [], @@ -3192,7 +9757,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "3cf09a55", "metadata": {}, "outputs": [], @@ -3211,7 +9776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "930576f3", "metadata": {}, "outputs": [], @@ -3235,10 +9800,281 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "b9ae8d0e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | NonLinearARModel | 705 \n", + "1 | loss | MSELoss | 0 \n", + "-------------------------------------------\n", + "705 Trainable params\n", + "0 Non-trainable params\n", + "705 Total params\n", + "0.003 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bda98978787f41bc81b9ed227089d304", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.5648325681686401\n", + " test_r2 0.4639463424682617\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.5648325681686401, 'test_r2': 0.4639463424682617}]" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nl_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -3246,6 +10082,14 @@ "nl_trainer.fit(nl_module, datamodule=day_dm)\n", "nl_trainer.test(nl_module, datamodule=day_dm) " ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a42ae9c-f34e-41a1-8513-fb0752375547", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -3253,6 +10097,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python (islp_freeze_39)", + "language": "python", + "name": "islp_freeze_39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch11-surv-lab.ipynb b/docs/source/labs/Ch11-surv-lab.ipynb index 9e0eda6..b0131e2 100644 --- a/docs/source/labs/Ch11-surv-lab.ipynb +++ b/docs/source/labs/Ch11-surv-lab.ipynb @@ -29,9 +29,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "91ac40fd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:11.792778Z", + "iopub.status.busy": "2023-07-26T00:00:11.792414Z", + "iopub.status.idle": "2023-07-26T00:00:12.793105Z", + "shell.execute_reply": "2023-07-26T00:00:12.792749Z" + } + }, "outputs": [], "source": [ "from matplotlib.pyplot import subplots\n", @@ -52,9 +59,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "99782418", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.795280Z", + "iopub.status.busy": "2023-07-26T00:00:12.795071Z", + "iopub.status.idle": "2023-07-26T00:00:12.854734Z", + "shell.execute_reply": "2023-07-26T00:00:12.854428Z" + } + }, "outputs": [], "source": [ "from lifelines import \\\n", @@ -78,10 +92,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3137149a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.856788Z", + "iopub.status.busy": "2023-07-26T00:00:12.856526Z", + "iopub.status.idle": "2023-07-26T00:00:12.862649Z", + "shell.execute_reply": "2023-07-26T00:00:12.862372Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sex', 'diagnosis', 'loc', 'ki', 'gtv', 'stereo', 'status', 'time'], dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "BrainCancer = load_data('BrainCancer')\n", "BrainCancer.columns\n" @@ -98,36 +130,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "45963c92", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.864323Z", + "iopub.status.busy": "2023-07-26T00:00:12.864209Z", + "iopub.status.idle": "2023-07-26T00:00:12.867297Z", + "shell.execute_reply": "2023-07-26T00:00:12.867008Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Female 45\n", + "Male 43\n", + "Name: sex, dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "BrainCancer['sex'].value_counts()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "73be61f6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.868922Z", + "iopub.status.busy": "2023-07-26T00:00:12.868804Z", + "iopub.status.idle": "2023-07-26T00:00:12.871822Z", + "shell.execute_reply": "2023-07-26T00:00:12.871554Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Meningioma 42\n", + "HG glioma 22\n", + "Other 14\n", + "LG glioma 9\n", + "Name: diagnosis, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "BrainCancer['diagnosis'].value_counts()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "572f0b9e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.873332Z", + "iopub.status.busy": "2023-07-26T00:00:12.873229Z", + "iopub.status.idle": "2023-07-26T00:00:12.875929Z", + "shell.execute_reply": "2023-07-26T00:00:12.875665Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 53\n", + "1 35\n", + "Name: status, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "BrainCancer['status'].value_counts()\n" ] @@ -159,10 +250,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "92c39707", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.877558Z", + "iopub.status.busy": "2023-07-26T00:00:12.877447Z", + "iopub.status.idle": "2023-07-26T00:00:12.996625Z", + "shell.execute_reply": "2023-07-26T00:00:12.996279Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "km = KaplanMeierFitter()\n", @@ -197,10 +316,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "3fc7848c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:12.998859Z", + "iopub.status.busy": "2023-07-26T00:00:12.998718Z", + "iopub.status.idle": "2023-07-26T00:00:13.123101Z", + "shell.execute_reply": "2023-07-26T00:00:13.122758Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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coefse(coef)p
covariate
sex[Male]0.4076680.3420040.233262
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" + ], + "text/plain": [ + " coef se(coef) p\n", + "covariate \n", + "sex[Male] 0.407668 0.342004 0.233262" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "coxph = CoxPHFitter # shorthand\n", "sex_df = BrainCancer[['time', 'status', 'sex']]\n", @@ -284,10 +567,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "4716b7b0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.205975Z", + "iopub.status.busy": "2023-07-26T00:00:13.205843Z", + "iopub.status.idle": "2023-07-26T00:00:13.211669Z", + "shell.execute_reply": "2023-07-26T00:00:13.211344Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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null_distributionchi squared
degrees_freedom1
test_namelog-likelihood ratio test
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test_statisticp-log2(p)
01.440.232.12
" + ], + "text/latex": [ + "\\begin{tabular}{lrrr}\n", + " & test_statistic & p & -log2(p) \\\\\n", + "0 & 1.44 & 0.23 & 2.12 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + "null_distribution = chi squared\n", + " degrees_freedom = 1\n", + " test_name = log-likelihood ratio test\n", + "\n", + "---\n", + " test_statistic p -log2(p)\n", + " 1.44 0.23 2.12" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cox_fit.log_likelihood_ratio_test()\n" ] @@ -309,10 +673,120 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c2767d88", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.213603Z", + "iopub.status.busy": "2023-07-26T00:00:13.213471Z", + "iopub.status.idle": "2023-07-26T00:00:13.246417Z", + "shell.execute_reply": "2023-07-26T00:00:13.246077Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefse(coef)p
covariate
sex[Male]0.1837480.3603580.610119
diagnosis[LG glioma]-1.2395300.5795550.032455
diagnosis[Meningioma]-2.1545660.4505240.000002
diagnosis[Other]-1.2688700.6176720.039949
loc[Supratentorial]0.4411950.7036690.530665
ki-0.0549550.0183140.002693
gtv0.0342930.0223330.124661
stereo[SRT]0.1777780.6015780.767597
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" + ], + "text/plain": [ + " coef se(coef) p\n", + "covariate \n", + "sex[Male] 0.183748 0.360358 0.610119\n", + "diagnosis[LG glioma] -1.239530 0.579555 0.032455\n", + "diagnosis[Meningioma] -2.154566 0.450524 0.000002\n", + "diagnosis[Other] -1.268870 0.617672 0.039949\n", + "loc[Supratentorial] 0.441195 0.703669 0.530665\n", + "ki -0.054955 0.018314 0.002693\n", + "gtv 0.034293 0.022333 0.124661\n", + "stereo[SRT] 0.177778 0.601578 0.767597" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cleaned = BrainCancer.dropna()\n", "all_MS = MS(cleaned.columns, intercept=False)\n", @@ -347,9 +821,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "ede1d219", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.248407Z", + "iopub.status.busy": "2023-07-26T00:00:13.248257Z", + "iopub.status.idle": "2023-07-26T00:00:13.252004Z", + "shell.execute_reply": "2023-07-26T00:00:13.251659Z" + } + }, "outputs": [], "source": [ "levels = cleaned['diagnosis'].unique()\n", @@ -373,10 +854,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "dc032a71", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.253777Z", + "iopub.status.busy": "2023-07-26T00:00:13.253667Z", + "iopub.status.idle": "2023-07-26T00:00:13.260023Z", + "shell.execute_reply": "2023-07-26T00:00:13.259719Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexdiagnosislockigtvstereostatustime
0FemaleMeningiomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleHG gliomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleLG gliomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleOtherSupratentorial80.919548.687011SRT0.40229927.188621
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" + ], + "text/plain": [ + " sex diagnosis loc ki gtv stereo status \\\n", + "0 Female Meningioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", + "0 Female HG glioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", + "0 Female LG glioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", + "0 Female Other Supratentorial 80.91954 8.687011 SRT 0.402299 \n", + "\n", + " time \n", + "0 27.188621 \n", + "0 27.188621 \n", + "0 27.188621 \n", + "0 27.188621 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "modal_df = pd.DataFrame(\n", " [modal_data.iloc[0] for _ in range(len(levels))])\n", @@ -395,10 +982,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "e7c1fe43", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.261832Z", + "iopub.status.busy": "2023-07-26T00:00:13.261701Z", + "iopub.status.idle": "2023-07-26T00:00:13.270198Z", + "shell.execute_reply": "2023-07-26T00:00:13.269836Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sex[Male]diagnosis[LG glioma]diagnosis[Meningioma]diagnosis[Other]loc[Supratentorial]kigtvstereo[SRT]statustime
Meningioma0.00.01.00.01.080.919548.6870111.00.40229927.188621
HG glioma0.00.00.00.01.080.919548.6870111.00.40229927.188621
LG glioma0.01.00.00.01.080.919548.6870111.00.40229927.188621
Other0.00.00.01.01.080.919548.6870111.00.40229927.188621
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" + ], + "text/plain": [ + " sex[Male] diagnosis[LG glioma] diagnosis[Meningioma] \\\n", + "Meningioma 0.0 0.0 1.0 \n", + "HG glioma 0.0 0.0 0.0 \n", + "LG glioma 0.0 1.0 0.0 \n", + "Other 0.0 0.0 0.0 \n", + "\n", + " diagnosis[Other] loc[Supratentorial] ki gtv \\\n", + "Meningioma 0.0 1.0 80.91954 8.687011 \n", + "HG glioma 0.0 1.0 80.91954 8.687011 \n", + "LG glioma 0.0 1.0 80.91954 8.687011 \n", + "Other 1.0 1.0 80.91954 8.687011 \n", + "\n", + " stereo[SRT] status time \n", + "Meningioma 1.0 0.402299 27.188621 \n", + "HG glioma 1.0 0.402299 27.188621 \n", + "LG glioma 1.0 0.402299 27.188621 \n", + "Other 1.0 0.402299 27.188621 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "modal_X = all_MS.transform(modal_df)\n", "modal_X.index = levels\n", @@ -415,12 +1124,150 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "f89fbed7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.272136Z", + "iopub.status.busy": "2023-07-26T00:00:13.271995Z", + "iopub.status.idle": "2023-07-26T00:00:13.279105Z", + "shell.execute_reply": "2023-07-26T00:00:13.278699Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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MeningiomaHG gliomaLG gliomaOther
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85 rows × 4 columns

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" + ], + "text/plain": [ + " Meningioma HG glioma LG glioma Other\n", + "0.07 0.997947 0.982430 0.994881 0.995029\n", + "1.18 0.997947 0.982430 0.994881 0.995029\n", + "1.41 0.995679 0.963342 0.989245 0.989555\n", + "1.54 0.995679 0.963342 0.989245 0.989555\n", + "2.03 0.995679 0.963342 0.989245 0.989555\n", + "... ... ... ... ...\n", + "65.02 0.688772 0.040136 0.394181 0.404936\n", + "67.38 0.688772 0.040136 0.394181 0.404936\n", + "73.74 0.688772 0.040136 0.394181 0.404936\n", + "78.75 0.688772 0.040136 0.394181 0.404936\n", + "82.56 0.688772 0.040136 0.394181 0.404936\n", + "\n", + "[85 rows x 4 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "predicted_survival = fit_all.predict_survival_function(modal_X)\n", "predicted_survival\n" @@ -438,12 +1285,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "8f0329b4", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.281050Z", + "iopub.status.busy": "2023-07-26T00:00:13.280921Z", + "iopub.status.idle": "2023-07-26T00:00:13.391548Z", + "shell.execute_reply": "2023-07-26T00:00:13.391175Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "predicted_survival.plot(ax=ax);\n" @@ -464,10 +1328,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "3045bfc0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.393498Z", + "iopub.status.busy": "2023-07-26T00:00:13.393362Z", + "iopub.status.idle": "2023-07-26T00:00:13.556483Z", + "shell.execute_reply": "2023-07-26T00:00:13.556104Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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coefse(coef)p
covariate
posres0.1480760.1616250.359579
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coefse(coef)p
covariate
posres0.5707730.1759601.179610e-03
multi-0.0408600.2511948.707842e-01
clinend0.5461830.2620003.709944e-02
sampsize0.0000050.0000157.507005e-01
budget0.0043860.0024657.515984e-02
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" + ], + "text/plain": [ + " coef se(coef) p\n", + "covariate \n", + "posres 0.570773 0.175960 1.179610e-03\n", + "multi -0.040860 0.251194 8.707842e-01\n", + "clinend 0.546183 0.262000 3.709944e-02\n", + "sampsize 0.000005 0.000015 7.507005e-01\n", + "budget 0.004386 0.002465 7.515984e-02\n", + "impact 0.058318 0.006676 2.426306e-18" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model = MS(Publication.columns.drop('mech'),\n", " intercept=False)\n", @@ -568,9 +1606,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "b8ece43a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.630972Z", + "iopub.status.busy": "2023-07-26T00:00:13.630836Z", + "iopub.status.idle": "2023-07-26T00:00:13.634830Z", + "shell.execute_reply": "2023-07-26T00:00:13.634534Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(10)\n", @@ -599,9 +1644,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "3e4f766f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.636547Z", + "iopub.status.busy": "2023-07-26T00:00:13.636436Z", + "iopub.status.idle": "2023-07-26T00:00:13.644633Z", + "shell.execute_reply": "2023-07-26T00:00:13.644339Z" + } + }, "outputs": [], "source": [ "model = MS(['Operators',\n", @@ -624,10 +1676,104 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "72f42d14", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.646387Z", + "iopub.status.busy": "2023-07-26T00:00:13.646291Z", + "iopub.status.idle": "2023-07-26T00:00:13.650688Z", + "shell.execute_reply": "2023-07-26T00:00:13.650359Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OperatorsCenter[B]Center[C]Time[Even.]Time[Morn.]
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OperatorsCenterTimeWait timeFailed
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-762,10 +2039,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "2b27af56", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:13.849508Z", + "iopub.status.busy": "2023-07-26T00:00:13.849066Z", + "iopub.status.idle": "2023-07-26T00:00:14.045745Z", + "shell.execute_reply": "2023-07-26T00:00:14.045295Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Probability of Still Being on Hold')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "by_center = {}\n", @@ -786,10 +2091,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "9625598d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:14.047564Z", + "iopub.status.busy": "2023-07-26T00:00:14.047461Z", + "iopub.status.idle": "2023-07-26T00:00:14.210828Z", + "shell.execute_reply": "2023-07-26T00:00:14.210459Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Probability of Still Being on Hold')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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t_0-1
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degrees_of_freedom2
test_namemultivariate_logrank_test
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test_statisticp-log2(p)
020.30<0.00514.65
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test_namemultivariate_logrank_test
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test_statisticp-log2(p)
049.90<0.00535.99
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null_distributionchi squared
degrees_freedom2
test_namelog-likelihood ratio test
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test_statisticp-log2(p)
020.58<0.00514.85
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test_statisticp-log2(p)
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MurderAssaultUrbanPopRape
Alabama13.22365821.2
Alaska10.02634844.5
Arizona8.12948031.0
Arkansas8.81905019.5
California9.02769140.6
Colorado7.92047838.7
Connecticut3.31107711.1
Delaware5.92387215.8
Florida15.43358031.9
Georgia17.42116025.8
Hawaii5.3468320.2
Idaho2.61205414.2
Illinois10.42498324.0
Indiana7.21136521.0
Iowa2.2565711.3
Kansas6.01156618.0
Kentucky9.71095216.3
Louisiana15.42496622.2
Maine2.183517.8
Maryland11.33006727.8
Massachusetts4.41498516.3
Michigan12.12557435.1
Minnesota2.7726614.9
Mississippi16.12594417.1
Missouri9.01787028.2
Montana6.01095316.4
Nebraska4.31026216.5
Nevada12.22528146.0
New Hampshire2.157569.5
New Jersey7.41598918.8
New Mexico11.42857032.1
New York11.12548626.1
North Carolina13.03374516.1
North Dakota0.845447.3
Ohio7.31207521.4
Oklahoma6.61516820.0
Oregon4.91596729.3
Pennsylvania6.31067214.9
Rhode Island3.4174878.3
South Carolina14.42794822.5
South Dakota3.8864512.8
Tennessee13.21885926.9
Texas12.72018025.5
Utah3.21208022.9
Vermont2.2483211.2
Virginia8.51566320.7
Washington4.01457326.2
West Virginia5.781399.3
Wisconsin2.6536610.8
Wyoming6.81616015.6
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"execution_count": null, + "execution_count": 4, "id": "b127d014", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.133058Z", + "iopub.status.busy": "2023-07-26T00:00:18.132868Z", + "iopub.status.idle": "2023-07-26T00:00:18.136222Z", + "shell.execute_reply": "2023-07-26T00:00:18.135816Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Murder', 'Assault', 'UrbanPop', 'Rape'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "USArrests.columns\n" ] @@ -115,12 +594,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c7343f72", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.138449Z", + "iopub.status.busy": "2023-07-26T00:00:18.138330Z", + "iopub.status.idle": "2023-07-26T00:00:18.142002Z", + "shell.execute_reply": "2023-07-26T00:00:18.141676Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Murder 7.788\n", + "Assault 170.760\n", + "UrbanPop 65.540\n", + "Rape 21.232\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "USArrests.mean()\n" ] @@ -137,10 +637,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "34501140", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.144029Z", + "iopub.status.busy": "2023-07-26T00:00:18.143885Z", + "iopub.status.idle": "2023-07-26T00:00:18.147113Z", + "shell.execute_reply": "2023-07-26T00:00:18.146759Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Murder 18.970465\n", + "Assault 6945.165714\n", + "UrbanPop 209.518776\n", + "Rape 87.729159\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "USArrests.var()\n" ] @@ -169,9 +691,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "daf119e8", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.148889Z", + "iopub.status.busy": "2023-07-26T00:00:18.148784Z", + "iopub.status.idle": "2023-07-26T00:00:18.152181Z", + "shell.execute_reply": "2023-07-26T00:00:18.151873Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -193,9 +721,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "a0eda7c9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.153991Z", + "iopub.status.busy": "2023-07-26T00:00:18.153858Z", + "iopub.status.idle": "2023-07-26T00:00:18.155563Z", + "shell.execute_reply": "2023-07-26T00:00:18.155300Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -216,10 +750,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "1430fb3c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.157118Z", + "iopub.status.busy": "2023-07-26T00:00:18.157011Z", + "iopub.status.idle": "2023-07-26T00:00:18.160394Z", + "shell.execute_reply": "2023-07-26T00:00:18.160108Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
PCA()
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" + ], + "text/plain": [ + "PCA()" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.fit(USArrests_scaled)\n" ] @@ -236,10 +791,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "6131d8d1", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.162053Z", + "iopub.status.busy": "2023-07-26T00:00:18.161958Z", + "iopub.status.idle": "2023-07-26T00:00:18.164351Z", + "shell.execute_reply": "2023-07-26T00:00:18.164070Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-7.10542736e-17, 1.38777878e-16, -4.39648318e-16, 8.59312621e-16])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.mean_\n" ] @@ -255,9 +828,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "08246aad", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.165967Z", + "iopub.status.busy": "2023-07-26T00:00:18.165875Z", + "iopub.status.idle": "2023-07-26T00:00:18.167798Z", + "shell.execute_reply": "2023-07-26T00:00:18.167542Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -278,10 +857,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b682b632", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.169396Z", + "iopub.status.busy": "2023-07-26T00:00:18.169312Z", + "iopub.status.idle": "2023-07-26T00:00:18.171738Z", + "shell.execute_reply": "2023-07-26T00:00:18.171462Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.53589947, 0.58318363, 0.27819087, 0.54343209],\n", + " [ 0.41818087, 0.1879856 , -0.87280619, -0.16731864],\n", + " [-0.34123273, -0.26814843, -0.37801579, 0.81777791],\n", + " [ 0.6492278 , -0.74340748, 0.13387773, 0.08902432]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.components_ \n" ] @@ -300,12 +900,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c165e990", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.173597Z", + "iopub.status.busy": "2023-07-26T00:00:18.173475Z", + "iopub.status.idle": "2023-07-26T00:00:18.290430Z", + "shell.execute_reply": "2023-07-26T00:00:18.290051Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "i, j = 0, 1 # which components\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", @@ -333,10 +950,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "848c9f35", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.292401Z", + "iopub.status.busy": "2023-07-26T00:00:18.292266Z", + "iopub.status.idle": "2023-07-26T00:00:18.393479Z", + "shell.execute_reply": "2023-07-26T00:00:18.393134Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"iopub.status.busy": "2023-07-26T00:00:18.399859Z", + "iopub.status.idle": "2023-07-26T00:00:18.402377Z", + "shell.execute_reply": "2023-07-26T00:00:18.402074Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.53085875, 1.00996444, 0.36383998, 0.17696948])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.explained_variance_\n" ] @@ -404,12 +1073,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "68e47d3a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.404041Z", + "iopub.status.busy": "2023-07-26T00:00:18.403900Z", + "iopub.status.idle": "2023-07-26T00:00:18.406482Z", + "shell.execute_reply": "2023-07-26T00:00:18.406162Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.62006039, 0.24744129, 0.0891408 , 0.04335752])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.explained_variance_ratio_\n" ] @@ -428,9 +1114,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "e87fe198", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.408230Z", + "iopub.status.busy": "2023-07-26T00:00:18.408090Z", + "iopub.status.idle": "2023-07-26T00:00:18.552316Z", + "shell.execute_reply": "2023-07-26T00:00:18.551935Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -458,12 +1150,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "409fb0c6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.554504Z", + "iopub.status.busy": "2023-07-26T00:00:18.554354Z", + "iopub.status.idle": "2023-07-26T00:00:18.664965Z", + "shell.execute_reply": "2023-07-26T00:00:18.664571Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ax = axes[1]\n", "ax.plot(ticks,\n", @@ -488,12 +1198,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "e563e41b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.666854Z", + "iopub.status.busy": "2023-07-26T00:00:18.666716Z", + "iopub.status.idle": "2023-07-26T00:00:18.669407Z", + "shell.execute_reply": "2023-07-26T00:00:18.669069Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 3, 11, 8])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a = np.array([1,2,8,-3])\n", "np.cumsum(a)\n" @@ -520,12 +1247,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "f83ad0bc", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.671214Z", 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+ ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pcaUS.components_\n" ] @@ -574,22 +1359,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "4cc49622", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.684217Z", + "iopub.status.busy": "2023-07-26T00:00:18.684084Z", + "iopub.status.idle": "2023-07-26T00:00:18.686744Z", + "shell.execute_reply": "2023-07-26T00:00:18.686436Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.98556588, 1.13339238, -0.44426879, 0.15626714],\n", + " [-1.95013775, 1.07321326, 2.04000333, -0.43858344],\n", + " [-1.76316354, -0.74595678, 0.05478082, -0.83465292]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(U * D[None,:])[:3]\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "c96c9fe1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.688393Z", + "iopub.status.busy": "2023-07-26T00:00:18.688266Z", + "iopub.status.idle": "2023-07-26T00:00:18.690823Z", + "shell.execute_reply": "2023-07-26T00:00:18.690511Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.98556588, -1.13339238, -0.44426879, 0.15626714],\n", + " [ 1.95013775, -1.07321326, 2.04000333, -0.43858344],\n", + " [ 1.76316354, 0.74595678, 0.05478082, -0.83465292]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "scores[:3]\n" ] @@ -610,9 +1434,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "574409d6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.692558Z", + "iopub.status.busy": "2023-07-26T00:00:18.692426Z", + "iopub.status.idle": "2023-07-26T00:00:18.695066Z", + "shell.execute_reply": "2023-07-26T00:00:18.694787Z" + } + }, "outputs": [], "source": [ "n_omit = 20\n", @@ -643,9 +1474,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "89f190ae", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.696744Z", + "iopub.status.busy": "2023-07-26T00:00:18.696608Z", + "iopub.status.idle": "2023-07-26T00:00:18.698816Z", + "shell.execute_reply": "2023-07-26T00:00:18.698495Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -670,9 +1507,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "322f339c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.700416Z", + "iopub.status.busy": "2023-07-26T00:00:18.700304Z", + "iopub.status.idle": "2023-07-26T00:00:18.702308Z", + "shell.execute_reply": "2023-07-26T00:00:18.702041Z" + } + }, "outputs": [], "source": [ "Xhat = Xna.copy()\n", @@ -691,9 +1535,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "7e106d1a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.703954Z", + "iopub.status.busy": "2023-07-26T00:00:18.703834Z", + "iopub.status.idle": "2023-07-26T00:00:18.705823Z", + "shell.execute_reply": "2023-07-26T00:00:18.705529Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -725,10 +1575,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "7cb05ce4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.707449Z", + "iopub.status.busy": "2023-07-26T00:00:18.707325Z", + "iopub.status.idle": "2023-07-26T00:00:18.710562Z", + "shell.execute_reply": "2023-07-26T00:00:18.710239Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration: 1, MSS:0.395, Rel.Err 5.99e-01\n", + "Iteration: 2, MSS:0.382, Rel.Err 1.33e-02\n", + "Iteration: 3, MSS:0.381, Rel.Err 1.44e-03\n", + "Iteration: 4, MSS:0.381, Rel.Err 1.79e-04\n", + "Iteration: 5, MSS:0.381, Rel.Err 2.58e-05\n", + "Iteration: 6, MSS:0.381, Rel.Err 4.22e-06\n", + "Iteration: 7, MSS:0.381, Rel.Err 7.65e-07\n", + "Iteration: 8, MSS:0.381, Rel.Err 1.48e-07\n", + "Iteration: 9, MSS:0.381, Rel.Err 2.95e-08\n" + ] + } + ], "source": [ "while rel_err > thresh:\n", " count += 1\n", @@ -757,12 +1630,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "6f245188", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.712332Z", + "iopub.status.busy": "2023-07-26T00:00:18.712229Z", + "iopub.status.idle": "2023-07-26T00:00:18.714918Z", + "shell.execute_reply": "2023-07-26T00:00:18.714660Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.711356743429736" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]\n" ] @@ -798,9 +1688,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "345fb41e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.716565Z", + "iopub.status.busy": "2023-07-26T00:00:18.716476Z", + "iopub.status.idle": "2023-07-26T00:00:18.718601Z", + "shell.execute_reply": "2023-07-26T00:00:18.718325Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -821,9 +1717,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "3a8c21a2", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.720161Z", + "iopub.status.busy": "2023-07-26T00:00:18.720052Z", + "iopub.status.idle": "2023-07-26T00:00:18.750146Z", + "shell.execute_reply": "2023-07-26T00:00:18.749757Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -843,12 +1745,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "e3e35b5d", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.752342Z", + "iopub.status.busy": "2023-07-26T00:00:18.752172Z", + "iopub.status.idle": "2023-07-26T00:00:18.754770Z", + "shell.execute_reply": "2023-07-26T00:00:18.754396Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n", + " 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0], dtype=int32)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "kmeans.labels_\n" ] @@ -866,10 +1787,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "d928650a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.756939Z", + "iopub.status.busy": "2023-07-26T00:00:18.756801Z", + "iopub.status.idle": "2023-07-26T00:00:18.874421Z", + "shell.execute_reply": "2023-07-26T00:00:18.874069Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n", "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", @@ -895,12 +1834,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "92e5175c", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:18.876530Z", + "iopub.status.busy": "2023-07-26T00:00:18.876396Z", + "iopub.status.idle": "2023-07-26T00:00:18.998430Z", + "shell.execute_reply": "2023-07-26T00:00:18.998045Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "kmeans = KMeans(n_clusters=3,\n", " random_state=3,\n", @@ -927,12 +1883,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "4911ecc7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.000480Z", + "iopub.status.busy": "2023-07-26T00:00:19.000345Z", + "iopub.status.idle": "2023-07-26T00:00:19.014678Z", + "shell.execute_reply": "2023-07-26T00:00:19.014345Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(78.06117325882116, 75.0350825910044)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "kmeans1 = KMeans(n_clusters=3,\n", " random_state=3,\n", @@ -983,10 +1956,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "1b42a700", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.016810Z", + "iopub.status.busy": "2023-07-26T00:00:19.016679Z", + "iopub.status.idle": "2023-07-26T00:00:19.021325Z", + "shell.execute_reply": "2023-07-26T00:00:19.021002Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
AgglomerativeClustering(distance_threshold=0, linkage='complete',\n",
+       "                        n_clusters=None)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "AgglomerativeClustering(distance_threshold=0, linkage='complete',\n", + " n_clusters=None)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "HClust = AgglomerativeClustering\n", "hc_comp = HClust(distance_threshold=0,\n", @@ -1006,9 +2003,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "50ef7eea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.023446Z", + "iopub.status.busy": "2023-07-26T00:00:19.023277Z", + "iopub.status.idle": "2023-07-26T00:00:19.026803Z", + "shell.execute_reply": "2023-07-26T00:00:19.026438Z" + } + }, "outputs": [], "source": [ "hc_avg = HClust(distance_threshold=0,\n", @@ -1032,10 +2036,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "bf7a2408", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.028981Z", + "iopub.status.busy": "2023-07-26T00:00:19.028840Z", + "iopub.status.idle": "2023-07-26T00:00:19.034246Z", + "shell.execute_reply": "2023-07-26T00:00:19.033877Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
AgglomerativeClustering(distance_threshold=0, linkage='single',\n",
+       "                        metric='precomputed', n_clusters=None)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "AgglomerativeClustering(distance_threshold=0, linkage='single',\n", + " metric='precomputed', n_clusters=None)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "D = np.zeros((X.shape[0], X.shape[0]));\n", "for i in range(X.shape[0]):\n", @@ -1068,10 +2096,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "a118c0ab", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.036313Z", + "iopub.status.busy": "2023-07-26T00:00:19.036187Z", + "iopub.status.idle": "2023-07-26T00:00:19.293174Z", + "shell.execute_reply": "2023-07-26T00:00:19.292867Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cargs = {'color_threshold':-np.inf,\n", " 'above_threshold_color':'black'}\n", @@ -1095,10 +2141,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "b1ff41c0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.295154Z", + "iopub.status.busy": "2023-07-26T00:00:19.295014Z", + "iopub.status.idle": "2023-07-26T00:00:19.548973Z", + "shell.execute_reply": "2023-07-26T00:00:19.548621Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"code", - "execution_count": null, + "execution_count": 44, "id": "1407f7a4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.556459Z", + "iopub.status.busy": "2023-07-26T00:00:19.556356Z", + "iopub.status.idle": "2023-07-26T00:00:19.560257Z", + "shell.execute_reply": "2023-07-26T00:00:19.559863Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [0],\n", + " [1],\n", + " [1],\n", + " [2],\n", + " [1],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [1],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [1],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [1],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2],\n", + " [2]])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cut_tree(linkage_comp, height=5)\n" ] @@ -1160,10 +2311,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "2d74f224", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.561978Z", + "iopub.status.busy": "2023-07-26T00:00:19.561852Z", + "iopub.status.idle": "2023-07-26T00:00:19.829118Z", + "shell.execute_reply": "2023-07-26T00:00:19.828758Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "scaler = StandardScaler()\n", "X_scale = scaler.fit_transform(X)\n", @@ -1197,12 +2366,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "b7f7da12", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:19.831097Z", + "iopub.status.busy": "2023-07-26T00:00:19.830959Z", + "iopub.status.idle": "2023-07-26T00:00:20.036103Z", + "shell.execute_reply": "2023-07-26T00:00:20.035746Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "X = np.random.standard_normal((30, 3))\n", "corD = 1 - np.corrcoef(X)\n", @@ -1232,9 +2418,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "b94424fc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.038049Z", + "iopub.status.busy": "2023-07-26T00:00:20.037914Z", + "iopub.status.idle": "2023-07-26T00:00:20.043042Z", + "shell.execute_reply": "2023-07-26T00:00:20.042732Z" + } + }, "outputs": [], "source": [ "NCI60 = load_data('NCI60')\n", @@ -1258,12 +2451,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "cea54566", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.044884Z", + "iopub.status.busy": "2023-07-26T00:00:20.044785Z", + "iopub.status.idle": "2023-07-26T00:00:20.047376Z", + "shell.execute_reply": "2023-07-26T00:00:20.046971Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(64, 6830)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nci_data.shape\n" ] @@ -1278,12 +2488,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "4dac41bb", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.049125Z", + "iopub.status.busy": "2023-07-26T00:00:20.048998Z", + "iopub.status.idle": "2023-07-26T00:00:20.052730Z", + "shell.execute_reply": "2023-07-26T00:00:20.052393Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "label \n", + "NSCLC 9\n", + "RENAL 9\n", + "MELANOMA 8\n", + "BREAST 7\n", + "COLON 7\n", + "LEUKEMIA 6\n", + "OVARIAN 6\n", + "CNS 5\n", + "PROSTATE 2\n", + "K562A-repro 1\n", + "K562B-repro 1\n", + "MCF7A-repro 1\n", + "MCF7D-repro 1\n", + "UNKNOWN 1\n", + "dtype: int64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nci_labs.value_counts()\n" ] @@ -1302,9 +2544,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "d8ebadd6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.054502Z", + "iopub.status.busy": "2023-07-26T00:00:20.054401Z", + "iopub.status.idle": "2023-07-26T00:00:20.185686Z", + "shell.execute_reply": "2023-07-26T00:00:20.184416Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler()\n", @@ -1327,12 +2576,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "63b5efe3", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.190805Z", + "iopub.status.busy": "2023-07-26T00:00:20.190351Z", + "iopub.status.idle": "2023-07-26T00:00:20.452175Z", + "shell.execute_reply": "2023-07-26T00:00:20.451824Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cancer_types = list(np.unique(nci_labs))\n", "nci_groups = np.array([cancer_types.index(lab)\n", @@ -1380,12 +2646,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "e20c3cc1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.454082Z", + "iopub.status.busy": "2023-07-26T00:00:20.453955Z", + "iopub.status.idle": "2023-07-26T00:00:20.657428Z", + "shell.execute_reply": "2023-07-26T00:00:20.657082Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", "ax = axes[0]\n", @@ -1436,9 +2719,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "622de805", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.659393Z", + "iopub.status.busy": "2023-07-26T00:00:20.659262Z", + "iopub.status.idle": "2023-07-26T00:00:20.662015Z", + "shell.execute_reply": "2023-07-26T00:00:20.661717Z" + } + }, "outputs": [], "source": [ "def plot_nci(linkage, ax, cut=-np.inf):\n", @@ -1467,12 +2757,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "54d40449", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:20.663751Z", + "iopub.status.busy": "2023-07-26T00:00:20.663617Z", + "iopub.status.idle": "2023-07-26T00:00:22.071969Z", + "shell.execute_reply": "2023-07-26T00:00:22.071658Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Complete0123
label
BREAST2302
CNS3200
COLON2005
K562A-repro0010
K562B-repro0010
LEUKEMIA0060
MCF7A-repro0001
MCF7D-repro0001
MELANOMA8000
NSCLC8100
OVARIAN6000
PROSTATE2000
RENAL8100
UNKNOWN1000
\n", + "
" + ], + "text/plain": [ + "Complete 0 1 2 3\n", + "label \n", + "BREAST 2 3 0 2\n", + "CNS 3 2 0 0\n", + "COLON 2 0 0 5\n", + "K562A-repro 0 0 1 0\n", + "K562B-repro 0 0 1 0\n", + "LEUKEMIA 0 0 6 0\n", + "MCF7A-repro 0 0 0 1\n", + "MCF7D-repro 0 0 0 1\n", + "MELANOMA 8 0 0 0\n", + "NSCLC 8 1 0 0\n", + "OVARIAN 6 0 0 0\n", + "PROSTATE 2 0 0 0\n", + "RENAL 8 1 0 0\n", + "UNKNOWN 1 0 0 0" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "linkage_comp = compute_linkage(hc_comp)\n", "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n", @@ -1530,10 +3004,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "40ff59f9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:22.086072Z", + "iopub.status.busy": "2023-07-26T00:00:22.085951Z", + "iopub.status.idle": "2023-07-26T00:00:22.546297Z", + "shell.execute_reply": "2023-07-26T00:00:22.545986Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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AgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4huUAtHLlSvXt21d169aVy+XSwoULC9Rs375d/fr1U0JCguLi4nTBBRfowIEDvudPnDihESNGqGbNmqpataquvfZapaSklGpCAAAAAKAklgPQ8ePH1bp1a02bNq3Q5/fs2aMuXbqoadOmWr58ubZs2aKHH35YVapU8dWMHj1aixYt0vvvv68VK1bo0KFDuuaaa4KfCgAAAAAIgMsYY4J+sculjz/+WP379/cNGzhwoKKiovTWW28V+pq0tDSdfvrpWrBgga677jpJ0n/+8x81a9ZMq1evVseOHUscb3p6uhISEpSWlqb4+Phguw9UCMePH1fVqlUlSceOHVNcXFw59wgAgMqFz9qKzWo2COk5QLm5ufrss890zjnnqFevXqpVq5Y6dOjgd5jchg0blJWVpZ49e/qGNW3aVPXr19fq1asLbTczM1Pp6el+fwAAAABgVUgD0OHDh3Xs2DE988wz6t27t7788ktdffXVuuaaa7RixQpJUnJysqKjo5WYmOj32tq1ays5ObnQdidNmqSEhATfX7169ULZbQAAAAAOEfI9QJJ01VVXafTo0WrTpo0eeOABXXnllZo+fXrQ7U6YMEFpaWm+v4MHD4aqywAAAAAcJDKUjZ122mmKjIxU8+bN/YY3a9ZM3377rSQpKSlJJ0+eVGpqqt9eoJSUFCUlJRXabkxMjGJiYkLZVQAAAAAOFNI9QNHR0brgggu0Y8cOv+E7d+5UgwYNJEnt27dXVFSUlixZ4nt+x44dOnDggDp16hTK7gAAAACAH8t7gI4dO6bdu3f7Hu/bt0+bN29WjRo1VL9+fY0dO1Y33HCDLrnkEnXr1k2LFy/WokWLtHz5cklSQkKChg0bpjFjxqhGjRqKj4/X3XffrU6dOgV0BTgAAAAACJblALR+/Xp169bN93jMmDGSpCFDhmjOnDm6+uqrNX36dE2aNEn33HOPzj33XH344Yfq0qWL7zUvv/yy3G63rr32WmVmZqpXr1567bXXQjA5AAAAAFC0Ut0HqLxwHyA4CfcmAADAXnzWVmzleh8gAAAAAAhnBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjhFZ3h0AAABA5WKMUUZGRnl3I2DHjx8v9P+KIDY2Vi6Xq7y7UaEQgAAAABAyxhh16dJF3333XXl3JSi1a9cu7y5Y0rlzZ33zzTeEIAs4BA4AAAAhk5GRUWHDT0W0atWqCrW3LRywBwgAAAC2SElJUVxcXHl3o1I6fvx4hdtbFS4IQAAAALBFXFwcAQhhh0PgAAAAADgGAQgAAACAYxCAAAAAADgG5wCVgYp2LXyEl4p8bwKEH+4XAQBwOgKQzSr6tfARXrjaC0qL+0UAAJyOQ+BsxrXwAYQT7hcBAHA69gCVIa6FD6C8cL8IAAD+RAAqQ1wLHwAAAChfHAIHAAAAwDEIQAAAAAAcgwAEAAAAwDEIQAAAAAAcw3IAWrlypfr27au6devK5XJp4cKFRdbeeeedcrlcmjx5st/wo0ePatCgQYqPj1diYqKGDRumY8eOWe0KAAAAAFhi+Spwx48fV+vWrXXbbbfpmmuuKbLu448/1po1a1S3bt0Czw0aNEi//fabvvrqK2VlZWno0KEaPny4FixYYK0zPXpIkeF9IbsqubnKuwtQle7dJTc73QCUPbZFAMoK25uywXzOJzvbUrnLGGOCHZfL5dLHH3+s/v37+w3/9ddf1aFDB33xxRe64oorNGrUKI0aNUqStH37djVv3lzff/+9zj//fEnS4sWLdfnll+uXX34pNDCdKj09XQkJCUqTFB9s5wEAAABUeOmSEiSlpaUpPr7kdBDyqJibm6tbbrlFY8eOVYsWLQo8v3r1aiUmJvrCjyT17NlTbrdba9euLbTNzMxMpaen+/0BAAAAgFUhD0DPPvusIiMjdc899xT6fHJysmrVquU3LDIyUjVq1FBycnKhr5k0aZISEhJ8f/Xq1Qt1twEAAAA4QEgD0IYNGzRlyhTNmTNHLpcrZO1OmDBBaWlpvr+DBw+GrG0AAAAAzhHSKwh88803Onz4sOrXr+8blpOTo/vuu0+TJ0/Wzz//rKSkJB0+fNjvddnZ2Tp69KiSkpIKbTcmJkYxMTEFnzj//LC/CEJObq7WrVsnSbrwwgsV4eQT1ACUG7ZFAMoK25uywXzOJztbWr8+4PKQpodbbrlFPXv29BvWq1cv3XLLLRo6dKgkqVOnTkpNTdWGDRvUvn17SdLSpUuVm5urDh06WBvhkiVSACc6lacTx4/roqpVJUnHli5VXFxcOfcIgBOxLQJQVtjelA3mcz7p6VJCQsDllgPQsWPHtHv3bt/jffv2afPmzapRo4bq16+vmjVr+tVHRUUpKSlJ5557riSpWbNm6t27t26//XZNnz5dWVlZGjlypAYOHBjQFeCAiswYI2+2t7y7AQfKyMrw+9+VFbrDlIFAeSI9IT1EHgCCYTkArV+/Xt26dfM9HjNmjCRpyJAhmjNnTkBtzJ8/XyNHjlSPHj3kdrt17bXXaurUqVa7AlQoxhgN/nywNh/ZXN5dgQPlZub6/u/6Xle5Yxx8qATKTdtabTW391xCEIByZTkAde3aVVZuHfTzzz8XGFajRg3rNz0FKjhvtpfwg3LjjnGr5ZyW5d0NONymw5vkzfYqNiq2vLsCwMHC+woCQCW1fMByeSI95d0NACgT3myvur7Xtby7AQCSCEBAufBEevgFFAAAoBxwEDgAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAx4gs7w4AAICyZYyRN9tbZuPLP66yHK8n0iOXy1Vm4wNQMRCAAABwEGOMBn8+WJuPbC6X8Xd9r2uZjattrbaa23suIQiAHwIQAAAO4s32llv4KWubDm+SN9ur2KjY8u4K4GOMUfbJ3FK3k5WZ4/d/VmROMdWBiYx2O+IHAwIQAAAOtXzAcnkiPeXdjZDzZnvLdE8TEChjjD56fqOS96aVuq3MrP87nHTW2G8UE1X6dbnOWQm6+v52lT4EEYAAAHAoT6SHvSNAGco+mRuS8CNJMVEevXrHkpC0lee3PWnKPpmrqJiIkLYbbghAAAAAQBkb+lyXsAkaWZk5mj3u2/LuRpkhAAEAAABlLComImwCkNMQgApjjJSVEZq2Th73/z8qNM0qKlaq5MdnAgAAAKFGADqVMdKsXtLBtaFp76T5v/+fP1uKDlFoqddRum0xIQgAAACwgAB0qqyM0IUfSXHRLplH40PWns/BNX/2NTou9G0DAAAAlRQBqDj375aiw+zqOCczpBfOLu9eAAAAABUSAag40bFlv4fFyvlHJ0uo4zwhAAAAwA8BKJxYPf+opD1BnCcEAAAA+HGXdweQT4jPP/KdJwQAAABAEnuAwldpzj8qg/OEjDHyZnttHUdlk39+Me+s80R65GJvJgAAKCUCULgqj/OPAmSM0eDPB2vzkc3l3ZUKq+t7Xcu7CxVO21ptNbf3XEIQAAAoFQ6Bg2XebC/hB2Vu0+FN7DkDAAClxh4glMryAcvlifSUdzdQiXmzvewxAwAAIUMAQql4Ij2KjQqzeyUBAAAAReAQOAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BjcCBUAAKCSM8bIeL1lMq7cjAy//3NdrjIZr8vjkauMxoWKjQAEAABQiRljtP+mQfJu2lQm48vIzfX9v7NzF8W6y+aAI0+7dmow/5+EIJSIAAQAAFCJGa+3zMKPJMW63dp2btMyG18e78aNMl6vXLGxZT5uVCwEIAAAAIdosupbuT2e8u5GSOV6vdrVuUt5dwMVCAEIAADAIdwej9zsIYHDEYAAi4wx8maXzYmkkN+8Zr6XHU8kJxMDAConAhBggTFGgz8frM1HNpd3Vxyp63tdy7sLjtG2VlvN7T2XEAQAqHS4DxBggTfbS/iBI2w6vIk9bgCASok9QECQlg9YLk9k5TqRFPBme9nTBgAVhDFG2SdzSy4sQVZmTqH/Bysy2h3WRxAQgIAgeSI9io3iRFIAAFD2jDH66PmNSt6bFtJ2Z4/7ttRt1DkrQVff3y5sQxCHwAEAAAAVTPbJ3JCHn1D5bU9aSPZM2YU9QAAAAEAFNvS5LoqKiSjvbigrMycke5DsRgACAAAAKrComIiwCEAVBYfAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAx7AcgFauXKm+ffuqbt26crlcWrhwoe+5rKwsjR8/Xuedd57i4uJUt25dDR48WIcOHfJr4+jRoxo0aJDi4+OVmJioYcOG6dixY6WeGAAAAAAojuUAdPz4cbVu3VrTpk0r8FxGRoY2btyohx9+WBs3btRHH32kHTt2qF+/fn51gwYN0k8//aSvvvpK//rXv7Ry5UoNHz48+KkAAAAAgABEWn1Bnz591KdPn0KfS0hI0FdffeU37NVXX9WFF16oAwcOqH79+tq+fbsWL16s77//Xueff74k6ZVXXtHll1+uF154QXXr1i3QbmZmpjIzM32P09PTrXYbAAAAAOw/BygtLU0ul0uJiYmSpNWrVysxMdEXfiSpZ8+ecrvdWrt2baFtTJo0SQkJCb6/evXq2d1tAAAAAJWQ5T1AVpw4cULjx4/XjTfeqPj4eElScnKyatWq5d+JyEjVqFFDycnJhbYzYcIEjRkzxvc4PT2dEASgUjLGyJvtLbfx5x93efYjjyfSI5fLVd7dAABUIrYFoKysLA0YMEDGGL3++uulaismJkYxMTEh6hkAhCdjjAZ/Plibj2wu765Ikrq+17W8u6C2tdpqbu+5hCAAQMjYcghcXvjZv3+/vvrqK9/eH0lKSkrS4cOH/eqzs7N19OhRJSUl2dEdAKgQvNnesAk/4WLT4U1hsScKAFB5hHwPUF742bVrl5YtW6aaNWv6Pd+pUyelpqZqw4YNat++vSRp6dKlys3NVYcOHULdHQCokJYPWC5PpKe8u1FuvNnesNgDBQCofCwHoGPHjmn37t2+x/v27dPmzZtVo0YN1alTR9ddd502btyof/3rX8rJyfGd11OjRg1FR0erWbNm6t27t26//XZNnz5dWVlZGjlypAYOHFjoFeAAwIk8kR7FRsWWdzcAAKh0LAeg9evXq1u3br7HeRcnGDJkiB577DF9+umnkqQ2bdr4vW7ZsmXq2rWrJGn+/PkaOXKkevToIbfbrWuvvVZTp04NchIAAAAAIDCWA1DXrl1ljCny+eKey1OjRg0tWLDA6qgBAAAAoFRsvw8QAAAAAIQLAhAAAAAAxyAAAQAAAHAM226EGlaMkbIyAqs9mVH4/4GIipW4WV9IGGPC8t4f+fsUjv2T/rx6GDeNBAAAKFzlD0DGSLN6SQfXWn/tC2dbq6/XUbptcdmGoMLCXUkhLsyDmjFGgz8fHPY3hAzXe5S0rdVWc3vPJQQBAAAUovIHoKyM4MJPMA6u+XN80XFlM75Awl1hIa48gpoF3mxv2IefcLbp8CZ5s73cQwYAAKAQlT8A5Xf/binahi+FJzOs7y0KhWDDXVkHtVJYPmC5PJGe8u5GheDN9obtXikAAIBw4awAFB1bIb70ByWQcFdeQa0UPJEe9mQAAAAgZJwVgCqzyhzuAAAAgBDhMtgAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxCEAAAAAAHIP7AAEAAACQJBljlH0yN6jXZmXmFPp/MCKj3XK5XKVqo8i2bWkVAAAAQIVijNFHz29U8t60Urc1e9y3pXp9nbMSdPX97WwJQRwCBwAAAEDZJ3NDEn5C4bc9aUHviSoJe4CsMEbKyig4/GRG4f/niYqVbNqFBwAAAITa0Oe6KComoszHm5WZU+q9RyUhAAXKGGlWL+ng2uLrXji74LB6HaXbFhOCAAAAUCFExUSUSwAqCxwCF6isjJLDT1EOril8zxEAAACAMsUeoGDcv1uKji257mRG4XuEAAAAAJQLAlAwomOl6Ljy7gUAAAAAizgEDgAAAIBjEIAAAAAAOAYBCAAAAIBjEIAAAAAAOAYBCAAAAIBjEIAAAAAAOAaXwQYAlIoxRt5sb0jbzN9eqNv2RHrkcrlC2iYAoOIgAAEAgmaM0eDPB2vzkc22jaPre11D2l7bWm01t/dcQhAAlDNjjLJP5voNy8rMKfT/PJHR7lJvvwlAAICgebO9toYfO2w6vEnebK9io2LLuyuOZcdew/zs3IN4KvYoAsExxuij5zcqeW9akTWzx31bYFidsxJ09f3tSrXeEYAAACGxfMByeSI95d2NInmzvSHfmwTrymKvYX52v+fsUQSCk30yt9jwU5Tf9qQp+2SuomIigh43AQgAEBKeSA97VVCiirjXsDjsUQRKb+hzXUoMNFmZOYXuEQoGAQgAAJSLcN9rWBz2KAKhExUTUao9OlYRgAAAQLlgryGA8sB9gAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGNUrosgGCNlZfgPO5lR+P95omIlrt2PANh9477SKssb/5UGNw0EAADlqfIEIGOkWb2kg2uLrnnh7ILD6nWUbltMCEKxyvrGfaUVzpdm5aaBAIBAGGNkvCX/oJebryY3gHqXhx/inK7yBKCsjOLDT1EOrvnztdFxoe8TKo3KduO+8lRZbxoYij2Eod6Lx942ABWVMUb7bxok76ZNll63q3OXEms87dqpwfx/sn10sMoTgPK7f7cUXcKXq5MZhe8RAkpQkW/cV54q800D7dhDGIp5xd42ABWV8Xoth59AeTdulPF65YqtXD/EIXCVMwBFx7JHB7bhxn04VbjuIayse9sAJwr0cLDCWD1ErDDledhYk1Xfyu0p/Q+PuV5vQHuIUPlVzgCECqOww4ZKOgyIw3oQzsJhD2Fl3tsGOFGwh4MVJtgAUJ6Hjbk9HrnZW4MQIgCh3ARy2FBhX+I4rAfhjD2EAELNzsPBAsVhY6hMCEAoN8EeNsRhPQAApwrV4WCB4rAxVEYEIISFQA4b4rAeAIDTcTgYUHoEIIQFDhsCAABAWSAAAUAFU9I9h6zcT4iLigAAnIYABAAViNV7DpV02CgXFQEAOI27vDsAAAhcqO85lHdREQAAnII9QABQQZXmnkNcVAQA4FQEIACooLh4CAAA1hGAAJSopJPuA2HlxPxAcQI/AACwigAEoFhWT7oPRKgOveIEfgAAYBUXQQBQrFCfdB9KnMAPAACsYg8QgICV5qT7UOIEfgAInjFGxhvYj0e5+epyA3yNJLk8HKKM8EUAAhAwTroH4BSV9YbDxhjtv2mQvJs2WX7trs5dAq71tGunBvP/GTbTDeRHAAIAAMinMt9w2Hi9QYUfq7wbN8p4vXLF8qMZwg8BCAAQlkJx9cH87LgSYZ5w+oUfpWfXDYfDbQ96k1Xfyu0J/rBmY4zMiRN+w3K9Xu3peZnv/1NxaBzCAQEIABB27Lj6YH6hPocsnH7hR2hV5hsOuz0euYPcQ2OM0YESDqUr7JA5Do1DOCAAAQDCTjhffbAw4foLP0qPcx8LF+yhdBwah3BAAAIAhLVwufpgYcL9F36gLARyKF2u12vpIgqAnQhAAICwxi/wQHgrzaF0QHngRqgAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxuAgCgLASyM0vrdzQkhtUAgCA/AhAAMJGMDe/LOkSxNygEgAA5MchcADChh03v8y7QSUAAIDEHiAAYaq0N7/kBpUAAKAwBCAAYYmbXwIAADtwCBwAAAAAxyAAAQAAAHAMywFo5cqV6tu3r+rWrSuXy6WFCxf6PW+M0SOPPKI6derI4/GoZ8+e2rVrl1/N0aNHNWjQIMXHxysxMVHDhg3TsWPHSjUhAAAAAFASywHo+PHjat26taZNm1bo888995ymTp2q6dOna+3atYqLi1OvXr104sQJX82gQYP0008/6auvvtK//vUvrVy5UsOHDw9+KgAAAAAgAJYvgtCnTx/16dOn0OeMMZo8ebIeeughXXXVVZKkefPmqXbt2lq4cKEGDhyo7du3a/Hixfr+++91/vnnS5JeeeUVXX755XrhhRdUt27dUkwO4C+Qm2oGwsqNNwPFDToBAADKXkivArdv3z4lJyerZ8+evmEJCQnq0KGDVq9erYEDB2r16tVKTEz0hR9J6tmzp9xut9auXaurr766QLuZmZnKzMz0PU5PTw9lt1FJBXNTzUCE6tLK3KATAACg7IX0IgjJycmSpNq1a/sNr127tu+55ORk1apVy+/5yMhI1ahRw1dzqkmTJikhIcH3V69evVB2G5WUHTfVDCVu0AkAAFD2KsR9gCZMmKAxY8b4HqenpxOCYElpb6oZSuF2g86SDhO0cvgfh/UBAIBwF9IAlJSUJElKSUlRnTp1fMNTUlLUpk0bX83hw4f9Xpedna2jR4/6Xn+qmJgYxcTEhLKrcBhuqlk4q4cJlhTcOKwPAACEu5AGoEaNGikpKUlLlizxBZ709HStXbtWd911lySpU6dOSk1N1YYNG9S+fXtJ0tKlS5Wbm6sOHTqEsjsAShDqwwTzDusjbALOYfViM6W9qAx7mgGUluUAdOzYMe3evdv3eN++fdq8ebNq1Kih+vXra9SoUXryySfVpEkTNWrUSA8//LDq1q2r/v37S5KaNWum3r176/bbb9f06dOVlZWlkSNHauDAgVwBDihHpTlMMNwO6wNQNkp7sZlgthvsaQZQWpYD0Pr169WtWzff47xzc4YMGaI5c+Zo3LhxOn78uIYPH67U1FR16dJFixcvVpUqVXyvmT9/vkaOHKkePXrI7Xbr2muv1dSpU0MwOQCCxWGCcLJgL5kfikvkV+Q9GuVxsRn2NAMoLcsBqGvXrjLGFPm8y+XSxIkTNXHixCJratSooQULFlgdNQAAIReqS+YHuxe0suzRsPtiM+xpBhAqFeIqcAAA2KW8L5lfWfZosBcZQEVBAAIA4P8ry0vms0cDAMoHAQgAgP+PvRgAUPm5y7sDAAAAAFBWCEAAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxCEAAAAAAHIPLYANBMMbIm+0N6rX5XxdsG3k8kZ4Kf/d4AACAskQAAiwyxmjw54NDcuf40t4EsW2ttprbey4hyGYlBV4roZbQCgBA+SIAARZ5s70hCT+hsOnwJnmzvdy40UZWA29JoZbQCgBA+SIAAaWwfMByeSI9ZT5eb7a31HuPEJhQB15CKwAA5YsABJSCJ9LDF1kHKU3gJbQCABAeCEAAECACLwAAFR+XwQYAAADgGAQgAAAAAI5BAAIAAADgGJwDhGIVdv+Tku55wn1OAAAAEK4IQChSIPc/KeyqVtznBAAAAOGKQ+BQpGDvf5J3nxMAAAAg3LAHCAEJ5P4n3OcE5YVDNQEAQKAIQAgI9z9BuOJQTaByKOyHjPxK+lEjP37gAFAcAhCACq20h2oS7IHA2LmnNZAfMvIr6WgDfuAAUBwCEIBKg0M1AXvYvac12B8yisIPHACKQwACUGlwqCZgj7Lc0xrIDxlF4QcOAIEgAAEAgIDZvaeVHzIA2I0ABAAAAkZAAVDRcR8gAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGFwEAQCAECnsZqFFKekmokUJ9OaiAIDCEYCAMGfn3dcBhE4gNwstipVLRgd6c1EAQOEIQEAYs/vu6wBCJ9ibhVoVzM1FAQD/hwAEhLGyvPs6gNAJ5GahVpXm5qIAgP9DAAIqCLvvvg4gdLhZKACELwIQUEHwhQoAAKD0CEAAgDJX0tXSrFwhjYt+AACsIAABAMqU1aullXRYJxf9AABYwY1QAQBlKtRXS8u76AcAAIFgDxAAoNyU5mppXPQDABAMAhAAoNxwcQ8AQFnjEDgAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjsF9gIBTGGOKvat8/udKuvu8J9Ijl8sVsr4BcIbCtkMlbXvY3gBAYAhAQD7GGA3+fLA2H9kcUH1Jd6FvW6ut5vaey5cSAAELZDtU2LaH7Q1OZYyR8fqH5dx8j3O9BYO0y0OQRuVHAALy8WZ7Aw4/gdh0eJO82V7udA8gYMFuh9jeID9jjPbfNEjeTZuKrNnVuUuBYZ527dRg/j8JQajUCEBAEZYPWC5PpCeo13qzvSXuHQKAkgSyHWJ7g8IYr7fY8FMU78aNMl6vXLEEaVReBCCgCJ5ID7+kAihXbIcQCk1WfSu3p/ggnev1FrpHCKiMCEAAAACVmNvjkZs9OoAPl8EGAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOwWWwAVhijJE32+s3LP/jU5+T/ryXCXcVBwAA4YAABCBgxhgN/nywNh/ZXGRNYXekb1urreb2nksIAgAA5Y4ABCBg3mxvseGnKJsOb5I32+uYO9qzlwwAgPBFAAIQlOUDlssT6Sm2xpvtLXSPUGXGXjIAAMIbAQhAUDyRHsfs0bGCvWQAAIQ3AhAA2IS9ZAAAhB8CEADYhL1kAACEHwIQAAAAEGLGGGWfzPUblpWZU+j/+UVGuzkf1GYEIAAAACCEjDH66PmNSt6bVmTN7HHfFjq8zlkJuvr+doQgG7nLuwMAAABAZZJ9MrfY8FOc3/akFdhzhNBiDxAAAABgk6HPdVFUTESJdVmZOUXuFUJoEYAAAAAAm0TFRAQUgFB2OAQOAAAAgGMQgAAAAAA4BofAAQCACscYI2+2129Y/senPif9eW8urqwFgAAEAAAqFGOMBn8+WJuPbC6yput7XQsMa1urreb2nksIAhyOQ+AAAECF4s32Fht+irLp8KZC9wwBcJaQ7wHKycnRY489pn/+859KTk5W3bp1deutt+qhhx7y/eJijNGjjz6qN954Q6mpqercubNef/11NWnSJNTdAQAAldjyAcvlifQUW+PN9ha6RwiAM4U8AD377LN6/fXXNXfuXLVo0ULr16/X0KFDlZCQoHvuuUeS9Nxzz2nq1KmaO3euGjVqpIcffli9evXStm3bVKVKlVB3CQAAVFKeSI9io2LLuxsAKpCQB6DvvvtOV111la644gpJUsOGDfX2229r3bp1kv7c+zN58mQ99NBDuuqqqyRJ8+bNU+3atbVw4UINHDgw1F0CACCkOAEfACqukAegiy66SDNmzNDOnTt1zjnn6IcfftC3336rl156SZK0b98+JScnq2fPnr7XJCQkqEOHDlq9enWhASgzM1OZmZm+x+np6aHuNgAAAeEEfACo2EIegB544AGlp6eradOmioiIUE5Ojp566ikNGjRIkpScnCxJql27tt/rateu7XvuVJMmTdLjjz8e6q4CIcEvwYCzlPYEfA7XAoDyFfIA9N5772n+/PlasGCBWrRooc2bN2vUqFGqW7euhgwZElSbEyZM0JgxY3yP09PTVa9evVB1GQgavwQDzsYJ+ABQ8YQ8AI0dO1YPPPCA71C28847T/v379ekSZM0ZMgQJSUlSZJSUlJUp04d3+tSUlLUpk2bQtuMiYlRTExMqLsKlBq/BAPOxgn4AFDxhDwAZWRkyO32v71QRESEcnNzJUmNGjVSUlKSlixZ4gs86enpWrt2re66665QdwcoM/wSDAAAEP5CHoD69u2rp556SvXr11eLFi20adMmvfTSS7rtttskSS6XS6NGjdKTTz6pJk2a+C6DXbduXfXv3z/U3QHKDL8EAwAAhL+QB6BXXnlFDz/8sP72t7/p8OHDqlu3ru644w498sgjvppx48bp+PHjGj58uFJTU9WlSxctXryYewABAAAAsFXIA1C1atU0efJkTZ48ucgal8uliRMnauLEiaEePQAAAAAUyV1yCQAAAABUDiHfAwQAQKhxvy0AQKgQgAAAYY37bQEAQolD4AAAYa2099sCACA/9gABACoM7rcFACgtAhAAoMLgflsAgNLiEDgAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYBCAAAAAAjkEAAgAAAOAYkeXdAQCAvYwx8mZ7/Yblf3zqc5LkifTI5XLZ3jcAAMoaAQgAKjFjjAZ/Plibj2wusqbre10LDGtbq63m9p5LCAIAVDocAgcAlZg321ts+CnKpsObCt0zBABARcceIABwiOUDlssT6Sm2xpvtLXSPEAAAlQUBCAAcwhPpUWxUbHl3AwCAcsUhcAAAAAAcgwAEAAAAwDE4BA4AAABwAGOMsk/mFvl8VmZOof8XJjLaXWGvFEoAAgAAACo5Y4w+en6jkvemBVQ/e9y3xT5f56wEXX1/uwoZgjgEDgAAAKjksk/mBhx+AvHbnrRi9yaFM/YAAQAAAA4y9LkuioqJCOq1WZk5Je4dCncEIAAAAMBBomIigg5AlQGHwAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDAIQAAAAAMcgAAEAAABwDFsC0K+//qqbb75ZNWvWlMfj0Xnnnaf169f7njfG6JFHHlGdOnXk8XjUs2dP7dq1y46uAAAAAIBPyAPQ77//rs6dOysqKkqff/65tm3bphdffFHVq1f31Tz33HOaOnWqpk+frrVr1youLk69evXSiRMnQt0dAAAAAPCJDHWDzz77rOrVq6fZs2f7hjVq1Mj3vzFGkydP1kMPPaSrrrpKkjRv3jzVrl1bCxcu1MCBAwu0mZmZqczMTN/j9PT0UHcbAAAAgAOEfA/Qp59+qvPPP1/XX3+9atWqpbZt2+qNN97wPb9v3z4lJyerZ8+evmEJCQnq0KGDVq9eXWibkyZNUkJCgu+vXr16oe42AAAAAAcIeQDau3evXn/9dTVp0kRffPGF7rrrLt1zzz2aO3euJCk5OVmSVLt2bb/X1a5d2/fcqSZMmKC0tDTf38GDB0PdbQAAAAAOEPJD4HJzc3X++efr6aefliS1bdtWP/74o6ZPn64hQ4YE1WZMTIxiYmJC2U0AAAAADhTyPUB16tRR8+bN/YY1a9ZMBw4ckCQlJSVJklJSUvxqUlJSfM8BAAAAgB1CHoA6d+6sHTt2+A3buXOnGjRoIOnPCyIkJSVpyZIlvufT09O1du1aderUKdTdAQAAgAMYY5SbkVH0n9frq831eoutNcaU45TAbiE/BG706NG66KKL9PTTT2vAgAFat26dZsyYoRkzZkiSXC6XRo0apSeffFJNmjRRo0aN9PDDD6tu3brq379/qLsDAACASs4Yo/03DZJ306aA6nd17lLs85527dRg/j/lcrlC0T2EmZAHoAsuuEAff/yxJkyYoIkTJ6pRo0aaPHmyBg0a5KsZN26cjh8/ruHDhys1NVVdunTR4sWLVaVKlVB3BwAAAJWc8XoDDj+B8G7cKOP1yhUbG7I2ET5CHoAk6corr9SVV15Z5PMul0sTJ07UxIkT7Rg9AAAAHKrJqm/l9niCem2u11vi3iFUfLYEIAAAAKA8uD0eudlzg2KE/CIIAAAAABCuCEAAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxCEAAAAAAHIMABAAAAMAxuA8QAAAAgAKMMco+mes3LCszp9D/80RGu+VyuWzvW2kQgAAAAAD4Mcboo+c3KnlvWpE1s8d9W2BYnbMSdPX97cI6BHEIHAAAAAA/2Sdziw0/RfltT1qBvUbhhj1AAAAAAIo09LkuioqJKLYmKzOn0D1C4YgABAAAAKBIUTERJQagioRD4AAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BvcBAgAAgOMYY2S8Xr9hufke557ynCS5PB65XC7b+wZ7EYAAAADgKMYY7b9pkLybNhVZs6tzlwLDPO3aqcH8fxKCKjgOgQMAAICjGK+32PBTFO/GjQX2GqHiYQ8QAAAAHKvJqm/l9niKrcn1egvdI4SKiQAEAAAAx3J7PHLHxpZ3N1CGOAQOAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4BgEIAAAAgGMQgAAAAAA4RmR5dwAAAKCiM8bIm+31G5b/8anPSZIn0iOXy2V73wD4IwABAACUgjFGgz8frM1HNhdZ0/W9rgWGta3VVnN7zyUEAWWMQ+AAAABKwZvtLTb8FGXT4U2F7hkCYC/2AAEAAITI8gHL5Yn0FFvjzfYWukcIQNkgAAEAAISIJ9Kj2KjY8u4GgGJwCBwAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHAM2wPQM888I5fLpVGjRvmGnThxQiNGjFDNmjVVtWpVXXvttUpJSbG7KwAAAAAcztYA9P333+sf//iHWrVq5Td89OjRWrRokd5//32tWLFChw4d0jXXXGNnVwAAAADAvgB07NgxDRo0SG+88YaqV6/uG56WlqaZM2fqpZdeUvfu3dW+fXvNnj1b3333ndasWWNXdwAAAADAvgA0YsQIXXHFFerZs6ff8A0bNigrK8tveNOmTVW/fn2tXr260LYyMzOVnp7u9wcAAAAAVkXa0eg777yjjRs36vvvvy/wXHJysqKjo5WYmOg3vHbt2kpOTi60vUmTJunxxx+3o6sAAAAAHCTke4AOHjyoe++9V/Pnz1eVKlVC0uaECROUlpbm+zt48GBI2gUAAADgLCEPQBs2bNDhw4fVrl07RUZGKjIyUitWrNDUqVMVGRmp2rVr6+TJk0pNTfV7XUpKipKSkgptMyYmRvHx8X5/AAAAAGBVyA+B69Gjh7Zu3eo3bOjQoWratKnGjx+vevXqKSoqSkuWLNG1114rSdqxY4cOHDigTp06hbo7AAAAAOAT8gBUrVo1tWzZ0m9YXFycatas6Rs+bNgwjRkzRjVq1FB8fLzuvvtuderUSR07dgx1dwAAAADAx5aLIJTk5Zdfltvt1rXXXqvMzEz16tVLr732Wnl0BQAAAICDlEkAWr58ud/jKlWqaNq0aZo2bVpZjB4AAAAAJNl4HyAAAAAACDcEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BgEIAAAAACOQQACAAAA4BghD0CTJk3SBRdcoGrVqqlWrVrq37+/duzY4Vdz4sQJjRgxQjVr1lTVqlV17bXXKiUlJdRdAQAAAAA/IQ9AK1as0IgRI7RmzRp99dVXysrK0l/+8hcdP37cVzN69GgtWrRI77//vlasWKFDhw7pmmuuCXVXAAAAAMBPZKgbXLx4sd/jOXPmqFatWtqwYYMuueQSpaWlaebMmVqwYIG6d+8uSZo9e7aaNWumNWvWqGPHjqHuEgAAAABIKoNzgNLS0iRJNWrUkCRt2LBBWVlZ6tmzp6+madOmql+/vlavXl1oG5mZmUpPT/f7AwAAAACrbA1Aubm5GjVqlDp37qyWLVtKkpKTkxUdHa3ExES/2tq1ays5ObnQdiZNmqSEhATfX7169ezsNgAAAIBKytYANGLECP3444965513StXOhAkTlJaW5vs7ePBgiHoIAAAAwElCfg5QnpEjR+pf//qXVq5cqTPPPNM3PCkpSSdPnlRqaqrfXqCUlBQlJSUV2lZMTIxiYmLs6ioAAAAAhwj5HiBjjEaOHKmPP/5YS5cuVaNGjfyeb9++vaKiorRkyRLfsB07dujAgQPq1KlTqLsDAAAAAD4h3wM0YsQILViwQJ988omqVavmO68nISFBHo9HCQkJGjZsmMaMGaMaNWooPj5ed999tzp16sQV4AAAAADYKuQB6PXXX5ckde3a1W/47Nmzdeutt0qSXn75Zbndbl177bXKzMxUr1699Nprr4W6KwAAAADgJ+QByBhTYk2VKlU0bdo0TZs2LdSjBwAAAIAi2X4fIAAAAAAIFwQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGAQgAAAAAI5BAAIAAADgGOUagKZNm6aGDRuqSpUq6tChg9atW1ee3QEAAABQyZVbAHr33Xc1ZswYPfroo9q4caNat26tXr166fDhw+XVJQAAAACVXLkFoJdeekm33367hg4dqubNm2v69OmKjY3VrFmzyqtLAAAAACq5yPIY6cmTJ7VhwwZNmDDBN8ztdqtnz55avXp1gfrMzExlZmb6HqelpUmS0tPT8zV6XMo0+v9PSNE5JXTC5vpw7JPF+oysDOV4c/5/ebqyo7IrdX049ol63rPKVh+OfaKe9yzc63MzMnQs5//q3dklv2dWX0N9aOuzMnPkPXncVx8VE1FsfTCvof7/6vMygTGm2DbyuEyglSF06NAhnXHGGfruu+/UqVMn3/Bx48ZpxYoVWrt2rV/9Y489pscff7ysuwkAAACggjh48KDOPPPMEuvKZQ+QVRMmTNCYMWN8j3Nzc3X06FHVrFlTLperHHsGAAAAoDwZY/THH3+obt26AdWXSwA67bTTFBERoZSUFL/hKSkpSkpKKlAfExOjmJgYv2GJiYl2dhEAAABABZGQkBBwbblcBCE6Olrt27fXkiVLfMNyc3O1ZMkSv0PiAAAAACCUyu0QuDFjxmjIkCE6//zzdeGFF2ry5Mk6fvy4hg4dWl5dAgAAAFDJlVsAuuGGG3TkyBE98sgjSk5OVps2bbR48WLVrl27vLoEAAAAoJIrl6vAAQAAAEB5KLcboQIAAABAWSMAAQAAAHAMAhAAAAAAxyAAAQAAAHAMAhAAAAAAxyAAAQAAAHCMcrsPEIDS2bJli+XXNG/eXJGR4bPaV4ZpAACUrfT0dMuviY+Pt6EnqKgq7LeITz/91FL92rVr1aZNG8XExARUv2jRIjVt2lRRUVEB1a9YsUIdOnRQlSpVAqr/+OOP1aJFC0VHRwdUv2fPHg0YMEDVqlULqH7QoEEB912SMjIydPPNN1vaQOzcuTPg/kvS0qVLVb9+/YC/vP7nP//RWWedFfB0bNmyRVWrVpXbHdiOzfT0dDVt2jTgadi7d686deokj8cTUP2aNWvUtm3bgJc5q/Nn06ZNcrvdCvRWXm6329L8kaTU1FTFx8cH/Bqr9b///rulaTDGKCEhIeD2jx07pu7duys2Njag+i1btqhnz54B11td763WS9KSJUsUERERcP2RI0d0++23KzExMaD6559/XjVr1gx4nlpdzzIzMzV27FjVqFEjoHq75+mECRMUHR0tl8sVUL3VbWMw7/GJEycs1Vsdx2uvvSaPxxPwNFtdb6xu6yp6vST1798/4HVMsv4dZNKkSapVq1bA6364zSOr9b/99pvuuuuugOdp//79A16eJcnlcum1115TUlJSwK+ZOXOmGjRoYOkz+S9/+Yvi4uICqrd7W2e1fuvWrerZs2fA33Gsfs+0+lkgWd82StLQoUMD+q5cYQNQ//79LdUbYyytLMYY1alTJ+CV9+eff9bq1ast1aenpwf8obp8+XItWrQooFrpz/4//PDDOuusswKqHTp0qDZt2hRwf7799lvl5ORowIABAa8saWlp6tChg84888yA+rRp0yZ17NjRUv0999xjaZo7deoUcPuPP/64IiIilJCQUGK99OcGvV27dgHXBzN/PvzwQ7Vt2zag+pYtWyo9PV2TJ08OqE9588jqPLVab2UaGjVqZLn9yMjIgN+DPXv2KCEhIeD1YOPGjZIC/2XRan3eenbfffepatWqJdbnLafTp08PeFv0yy+/qHfv3rauZzNmzLB1W2qlPiMjQ/fdd59t28Zg3+NOnToF/GOM1XFkZmZq3Lhxtq03Vrd1Fb1+wYIFWrhwYUC1eYL5DnLFFVcE/GUx3OaR1fqPP/5Ya9euDahW+nP+TJs2Tc2bNw+o9vLLL9edd94ZcPt5r2vXrp2l72k7duwIm21dMPUzZ84MqFYK7numlc+CgwcPKjc3V2eeeWbAPwQcPHhQV155ZWA7C0wF5XK5TEpKSsD1kszatWupD1F91apVeQ/KuT4iIsJs2rQp4Po+ffqE3XtmdRokmS1bttjWn3CrZz2r/PW8xxWvnves/OtdLpdZv359wPUtWrTgPatg9cGsZ1WrVjV79uwJrD8Btxpmbr31VpOenh5w/bnnnmv27dsXcP35559v9u/fH3B9jx49zK+//hpw/XXXXWeSk5MDrr/kkkvMb7/9FnD9nXfeaY4cORJw/Zw5c8yJEycCrp8/f775/PPPTVZWVsCvef/9943X6w24/rvvvrPU/oEDB0x2dnbA9T///LPJzc0NuP69996zNI+mTp1qjh07FnC91fljdXqdaPny5ZaWoQcffNCkpaUFXH/HHXeY//3vf7bVz58/32zbts3Scjp69GhL28axY8eao0ePBlxvdbl77LHHzPHjxwOut7ottVr/9NNPm99//z3geqvbxmDe43/84x+2j8PKtsjqemN1W1fR67/55htzyy232PodpF+/fpY+88NtHlmt79Onj6XvLFa/4xhj/Xtjt27dzC+//BJw/TXXXGMOHz4ccL3d2zqr9e3atTMHDhwIuN7qe2D1s+Dpp582DzzwgOXXBLp9dxkT4MH3AAAAAFDBVdhzgFCylStX+j2+5JJLyqkn/+fAgQN+j+vXrx/SeqvTbLV9u1ntz8SJE/0eP/LIIyWOw+o8srve6jSE43JtN7uXU7vXs3BT0fsfDCdOMyq3efPm+T0ePHhwOfXEuSrydqVC7wEaOnSo30mFs2bNCml9t27d/OqXLl0a0vpGjRr51e/du7fYeqv9b9Soke9/l8tVYvtW+yP9eWUxl8vlO8EzJyenXOutTnO49d9q/dChQ33/u1yuEpcJKbjlws56q9Ng93tsdT2wu16yPg1Wt0V2r2d2b0uD2fbmsWPbGMx7XBbLXR4nbBvtrpfs/w4SbtNcFp9nVr+j5XG5XCWu98GMw+p6Fm7bOqv1t912m9/jUH8eW+1PsK8JRIXeA9SwYUNb62+99VZb6+fMmWOp3mr/9+3bZ6nean+CGUdubq6t9Xb3x2r7dvdn9uzZluol+6fBar3VabC7P1bXA7vrJevLhdVtkd3rgd3bUqv14bZMlMU4wm29r+j1kv3fQcJtmu2utzp/li1bZqk+mHFYXc/CbVtntb5BgwaW6u3+LAj2NYGo0HuADhw4oDPPPDPg+1FYrS9vGRkZ2rx5sy666CJJ5d//U/sTqB9//FEtW7a0qVcojjFGR44cUa1atcq7K0Eri2mwsoxaXQ/srkf5K4v3OByXI6vb9spWX9m/g5S1UMyfvK+0RV1yPBzeg5ycHEv3dgu3ersF059gXlOhA1BERIR+++23gL8YWa0vzIkTJ/Tuu+/q+PHjuuyyy9SkSZOQ1uf3ww8/qF27dr7dxlb7v3TpUo0cOVJr1qwpcB37tLQ0XXTRRZo+fbouvvjioPpTnD/++ENvv/223nzzTW3YsMH3mlOPFy1K3nGkVuutTrPV9k89V6IoeedQWK232p/Y2Fjt379fp59+uiTpiiuu0Jtvvqk6depIklJSUlS3bl2/98zqPLK73uo0hGq5LmoZLYmV9SBU9VaXC6vsXs8KY/e2tLj68tw2BlMfinGU13pTmevt/g5i9+dNuNWX5jvavHnz9Pzzz2vXrl2SpHPOOUdjx47VLbfc4ldXmnHk5ORo4cKF2r59uySpRYsW6tevX8BfvHfu3Kk333xTb731ln777bcKUX/kyBHt2LFDknTuuef6PqcLs2LFCr3wwgu++dO8eXONHTs24O2o1f4H+xqfgK8tF4asXh/cav3o0aPNyJEjfY8zMzNNmzZtTFRUlElISDBxcXHmu+++C7q+JJs3bzZutzvo/vft29e89NJLRT4/ZcoU079//6D7U5gVK1aYwYMHm7i4ONOkSRMzfvx4s27dOt/zLperyD+3223cbreJiIgIut7qNAfTn7zh+f9CWW+1P/mXiVOvgZ+cnGxcLpffPLA6j+yutzoNpV2uS1pGSxLIehDqeqvLRcOGDU2jRo2K/WvcuHHQ7Vt9D+zellqtL49tY2nqQzGOsl5vnFBv93eQsvi8Cbd6K/Mnz4svvmhiY2PNuHHjzCeffGI++eQTM3bsWBMbG1tgmQ92HLt27TJNmjQxsbGxpm3btqZt27YmNjbWnHvuuWb37t1Fvu748eNm1qxZpkuXLiYiIsJ06NDBPPfcc2Fff+zYMTN06FATGRnp+yyIjIw0t912W6GXpX7rrbdMZGSkGTBggJkyZYqZMmWKGTBggImKijLz588PWf+DfU1hKnwAsnLNdav1LVq0MJ988onv8axZs0z16tV994+59dZbzeWXXx50fUkKC0BW+l+/fn2zbdu2Ip/fvn27qVevXtD9yfPbb7+ZSZMmmbPPPtvUqlXLjBw50kRGRpqffvqpQG1qamqhf4cOHTLjx483Ho/HtGjRIuh6q9Nstf3NmzcX+rdp0yZf/emnnx50vdX+BBIeTn3PrM4ju+utTkMwy7WVZbQk5RGArC4XkydPLvJv1KhRxuPx+I3D7vXM7m2p1fqy2jaGqj4U4yiL9cZp9XZ/B7H78ybc6q3OnzwNGzY0c+fOLTB8zpw5pmHDhn7Dgh1Hnz59TO/evf3uv/Xf//7X9O7du9DvdatXrzbDhg0z8fHxpmXLliYiIsKsXLmyyPbDrX748OGmcePG5t///rdJS0szaWlp5rPPPjNnnXWWufPOOwvUN23atNAfWF588UXTtGnTUvcn2NcUp0JfBEGSHn74YcXGxhZb89JLLwVVf+DAATVv3tw3/Msvv9R1113nO0ns3nvv1eWXX+573mp9MKz0PyUlRVFRUUXWRUZG6siRI6XqT9++fbVy5UpdccUVmjx5snr37q2IiAhNnz690PqEhAS/x7m5uZo1a5Yef/xxud1uTZs2TUOGDAm63uo0W22/devWBdr8+uuv9cADD2jnzp0aN26c7rvvvqDrrfYnGFbnkd31Vllt3+oyGo6sLhf33ntvgTaOHj2qJ554Qq+//ro6dOigZ599Nuj2rb4Hdm9LrdaXxbYx3Ni93jitPo+d30Hs/rwJt3rJ+vyUpN9++63Qc90uuuiiQg+LCmYcK1as0Jo1a1SjRg3fsJo1a+qZZ55R586dfcNefPFFzZo1S2lpabrxxhu1cuVKtW7dWlFRUapZs2aB8YRbfZ4PP/xQH3zwgbp27eobdvnll8vj8WjAgAF6/fXX/er37t2rvn37FminX79+evDBB0vVn2CnoSQVPgBt3bpV0dHRRT5/6olwVurdbrfvhDpJWrNmjR5++GHf48TERP3+++9B13/66adF9kMq/OoaVvp/xhln6Mcff9TZZ59daO2WLVt851kE25/PP/9c99xzj+666y5L5zdJ0kcffaQHH3xQR44c0YQJE3T33XcrJiamVPVWp7k0/dm4caPGjx+vb775Rn/961/173//u9jjiq3WB9Ifl8vl956f+rgwVueR3fVWp8Fq+1aXUavrgd31p7K6nHq9Xr300kt64YUX1KBBA3300UfF/hBjx3pm97bUar3d28Zg3mO7x2H3euO0+jx2fgfJz+7Pm3CpD2b+nH322Xrvvff8vmhL0rvvvlvoexnMOGJiYvTHH38UGH7s2DG/tsaPH6/x48dr4sSJAZ0bFG71eTIyMlS7du0Cw2vVqqWMjIwCw+vVq6clS5YU2L58/fXXqlevXqn6E+w0lCjofUdhwO7jbzt27GhefPFFY4wxP/74o3G73Wbv3r2+55cvX24aNGgQdH1xx93n/wu2/yNHjjQtW7Y0Xq+3wHMZGRmmZcuW5u677w66P8b8uUvyr3/9q6lWrZq58MILzSuvvGKOHDlS7GEDy5cvNx06dDCxsbFmwoQJJjU1tdjpsFJvdZqD6c/u3bvNgAEDTEREhLnxxhv9DtcKRb2V/rhcLpOYmGiqV69uqlevblwul0lISPA9TkxMLHDYjNV5ZHe91Wmw2r7VZTSY9dLO+jxWl9Ps7Gzz+uuvm6SkJNOwYUMzb948k5ubW2S9neuZ3dtSq/V2bxuDeY/tHofd643T6vPeAzu/gxhj/+dNONUHe37OBx98YCIiIkyvXr3MxIkTzcSJE02vXr1MZGSk+eijj0IyjltuucW0aNHCrFmzxuTm5prc3FyzevVq07JlSzNkyBBf3dNPP22aNGli6tWrZ8aNG2e2bt1qjDFFLkfhVp+ne/fu5vrrr/fbXmRkZJjrr7/e9OjRo0D9a6+9ZqKjo82dd95p5s2bZ+bNm2fuuOMOExMTY6ZPn16q/gQ7DSWp0AHI7XZbWpCt1n/00UcmOjradO/e3dSuXdtceeWVfs+PGzfOXH/99UHXW2W1/8nJyaZu3bqmXr165tlnnzULFy40CxcuNM8884ypV6+eqVu3rklOTg66P/kdO3bMzJw503Tu3NlERUUZt9ttJk+ebNLT0/3q+vTpY6Kioswdd9xhfvvttxLbtVpvdZqttn/XXXeZ6Oho06tXL7Np06aQ11vtz5w5cwL6y8/qPLK73uo0BLtcB7qMhiOry8W7775rmjRpYk4//XQzefJkk5mZGdL2rb4Hdm9LrdaX5bYxXJTVeuOkeru/g9j9eRNu9VbnT34bNmwwgwYNMu3atTPt2rUzgwYNMhs3bgzZOH7//XfTr18/43K5THR0tImOjjZut9v079+/0B+Lli9fbgYPHmxiY2NNq1atTEREhPn222+LbD/c6rds2WLq1q1ratasabp37266d+9uatasac444wzz448/Fvqajz76yHTu3NnUqFHD1KhRw3Tu3NksXLgwJP0J9jXFqdABqCx+ffn666/NqFGjzDPPPFPgyhePPfaYWbZsWanqi5OTk2MWLVpUqv7//PPPpk+fPr4rr7j+/9VX+vTp4/cLaTD9Kcp//vMfM3bsWJOUlGSqVKli+vbt6zcNUVFRfr/4F/YXbL3VaQ6mPx6Px3cVmKL+SlNvpT8rVqwwWVlZAbx7/qwuF3bWBzMNpV2ui1tGSxLoehDK+mCW09jYWDNs2DAzevToIv+Cbd8Y6++B3dtSq/XlsW0Mtj5U4yjr9aay19v9HaQsPm/Crd7qd5yTJ0+aoUOHBrzOBjOO3Nxcs3//fpORkWF27dplPv30U/Ppp5+aXbt2lfja9PR0M336dHPhhReaiIgI06lTJ9/e6nCvP378uJkxY4YZM2aMGTNmjHnjjTdMRkZGgbqsrCzz+OOPm4MHDxYzJ0LT/2BfU5gKfR+guXPnauDAgcUe/16a+vKye/duzZo1S3PmzNGRI0eUlZUlqXT9//3337V7924ZY9SkSRNVr1691P0pSU5OjhYtWqRZs2b5jlefO3duQK/NO+Haan1+gUyz1fYff/zxgOofffTRoOqt9qe097ayulzYUV+aaSjNci0VvowWxep6EMp6q8tF165dSzwXzOVyaenSpUG1n19p34PyVpbbxmC2pXaMoyzXm8pcb/d3ELs/b8KtPtjvOAkJCdq8ebMaNWpUYm0w48jNzVWVKlX0008/WT7XOb+tW7dq5syZWrBggQ4fPhy29VlZWWratKn+9a9/qVmzZiW2I0lVq1bVjz/+qIYNGwZUb6U/oX6Nj+XIFEZ27Nhh1q5d6zfs66+/Nl27djUXXHCBeeqpp0pVv3///oD+gq3PLyMjw8ydO9dcfPHFxu12m0svvdS8/vrrfockWO1/SbZv326aNGkSdH/skp2dbVt9cdMcqv7YLX9/gj2euThW51Fp60M9DcG8x0Wxuh7YXV8cu5fT0qxndm9LS7PtDaT/+ZXFe1wey1Eo1xunsPs7iNMEO38GDx5c7D2uQjGO5s2bm9WrVwc0jpKcPHky7Ovr1q1b7GXzT9WvX78Ch9sHy2r/g31Nhb4K3Pjx43XeeefpwgsvlPTnVW/69u2riy++WK1atdKkSZMUGxurUaNGBVXfsGHDQn9FNcb4hrtcLmVnZwdVL0nff/+93nzzTb3zzjs666yzNGjQIH333Xd67bXX/C7rGkz/S5KZmak9e/b4DbPSH0m67bbbShyPy+XSzJkzS6zbuXOnZs6cqXnz5gV8F2Mr9VLh0xxs+1u2bNHOnTsl/XnX6VatWhXbntX6QPtT0i/9VlmZR6GqD+U0nNp+MMuo1fXA7vriFLZcLFu2TJ07dy72Skelab8kp74Hdm9Lg9n2Wum/VDbvcXkuR6Vdb5xWL9n/HSQ/uz9vwqE+2PnTpEkTTZw4UatWrVL79u0VFxfn9/w999xT6nE888wzGjt2rF5//XW1bNmyyOmcOnVqsfNB+nM5uvvuu8OyPs+IESP07LPP6s0331RkZMlRoU+fPnrggQe0devWQt+Dfv36Bd2fYKehJBU6AK1fv17jxo3zPZ4/f77OOeccffHFF5KkVq1a6ZVXXvEtyFbrN23aVOh4jTF65513NHXqVFWtWtU33Gp9q1atlJ6erptuuknfffedWrRoIUl64IEHQjK9VlntjyTNmTNHDRo0UNu2bf0uQxuojIwMvfvuu5o1a5ZWr16t888/X2PGjAlZvR39WbdunYYNG6Zt27b5ptnlcqlFixaaOXOmLrjgglLVW+3PrbfeWuLu/I8++ijgeVAe7JwGq8uo1fXA7vrClLRc9OjRQ1WqVFHHjh3VrVs3devWTR07dgzogyyQ9q2ye1tqtd6qsniPy2M5Ko7V9cZp9ZL930Ek+z9vwqk+2O84M2fOVGJiojZs2KANGzb4PedyufwCULDjGDx4sDIyMtS6dWtFR0fL4/H4PX/06FFJ0ssvv6yS5P+yHm71eb7//nstWbJEX375pc4777wCgebUz+O//e1vkgrePymv/ZycnKD7E+w0lMjyPqMwUqVKFXPgwAHf4+7du5uHHnrI93j37t0mISEh6PrCfPXVV6Z9+/amWrVq5tFHHy3xClLF1UdHR5tbbrnFfPnll36Xpy3q0n6h6H9+p94p3Gp/jDHmb3/7m6levbpp06aNmTJlit9dkotj912Mi1LU3dQDbf+nn34yVatWNRdccIFZsGCB2bRpk9m0aZOZP3++Of/88021atX85pXVeqv9cblc5oYbbjC33nprsX+hmEd21Yd6Gk5t3+oyanU9sLs+v0CXi59//tnMmjXLDBkyxDRo0MC4XC4TFxdn/vKXv5hJkyaZNWvWmJycnKDbL0kgy0Qot6WhqC+u/2XxHpflclSY0q43Tqs3xv7vIHZ/3oRbfai/4xQm2HFYudJqZRDK7xThqkIHoLp16/qO5czJyTHx8fHmX//6l+/5bdu2mfj4+KDr89uwYYPp2bOniYmJMSNGjCjxnIVA6n/55Rfz5JNPmrPOOsvUrVvX3HfffWbjxo0mKiqq0A+w0vS/MKd+4FntT54TJ06YBQsWmJ49e5rY2Fhz/fXXm8WLFxd6z5EXXnjBNG/e3Jxxxhnm/vvvN5s3bzbGFP2hbbXe6jRbbf/66683V199daHTlpuba/r37+93uV2r9Vb7Y8c5QOURgEI5DYX1x8oyanU9sLvemNKvB3v27DEzZ840t9xyi6lfv75xu91+H/J2r2f52bEtLU19IP0vi/e4LMZhZZqNsbbeOLHe7u8gdn/ehFt9KL7j5N2jpyih/h6FiqtCB6CbbrrJXHnllebAgQPmxRdfNFWrVjXHjh3zPf/BBx+YVq1aBV1vjP03CcuzZMkSM2jQIOPxeIzL5TJjx441O3bsKNX0lnRJ22rVqhX5JSWQ/hTm559/No899php3LixqV+/vvnjjz/8no+IiDAPPvhggROqi/qiZbXe6jRbbf+0004z33//fZHTv27dOnPaaacFXW+1P8Hc08DqPLK73uo0lGa5NqbkZTQ/q+uBXfVWl4vC5O0VGjx4sImPjzcejyfo9oN5D8LphotluW0MZltqxzjKcr1xSr3d30Hs/rwJt/pgvqPlefPNN02LFi189+hp0aKFeeONNwrUlWYc2dnZ5v333/fdbPWDDz4ocAuHuXPnBvQXrvWnSklJMStXrjQrV64s8XP666+/NldccYVp3Lixady4sbniiivMV199Var5E4ppKEqFPgfoqaee0mWXXaYGDRooIiJCU6dO9TtO8a233lL37t2Drv/b3/6mmTNnqlu3blq/fr3atGlTbH+s1ufXvXt3de/eXWlpaZo/f75mzZqlF154QS1bttSWLVuC6v/kyZMDHn8w/SmM2+2Wy+WSMcZ3zGd+TzzxhGbPnq233npLN954o2655ZZiTyi0Wm91mq22/8cff6h27dpFPp+UlKQ//vgj6Hqr/TElHKu+fft2zZw5Uy+88IJvmNV5ZHe91WkozXItlbyM5md1PbCr3upyIUkHDhzQ8uXLtWzZMi1fvlz//e9/ddFFF+niiy/Wv/71L3Xo0CHo9q2+B3ZvS63Wl+W2MZhtqR3jKMv1xin1dn8HsfvzJtzqrc6fPI888oheeukl3X333erUqZMkafXq1Ro9erQOHDigiRMnlnocP/30k/r166fk5GSde+65kqRnn31Wp59+uhYtWuTbXt56662qWrWqIiMji/xsc7lcGjx4cFjW50lPT9eIESP0zjvv+Jb/iIgI3XDDDZo2bZoSEhL86l977TXde++9uu6663TvvfdKktasWaPLL79cL7/8skaMGBF0f4KdhhJZjkxhJisry2zevNn8+uuvBZ7bvHlzgeN4rdTbfdOvkmzatMncfffdpZreUCqsP8b4HzZQpUoVc91115nPPvus0HMM8th9F2OrAm3/nHPOMR988EGR7bz//vvmnHPOCbrean+WL19e4BeoY8eOmTfffNN06tTJuFwu06JFiyLHHw7KYhqCWUaLUtR6UBb1gS4XjRo1MomJieaKK64wzz77rFm9enVAN5u1az0Ltxsuhprdy0RZjeNUVtcbp9UbY+93ELs/b8Kt3pjgvuOcdtppZsGCBQWGL1iwwNSsWbPA8GDG0bFjR9O3b19z9OhR37CjR4+afv36mU6dOvmGNW/e3NSsWdPce++95ocffijQzqnCrT7PgAEDTJMmTczixYtNWlqaSUtLM4sXLzbnnnuuueGGGwrUn3HGGeaVV14pMPzVV181devWLVV/gp2GklT4AFScbdu2mfvuuy/o+sceeyygv2DrQ+3U/h89etRMnTrVpKWlFahNTU0t8jkr7rrrLlO9enXTqlUrM3nyZHPkyBFLrw/1XYxLO80ltf/II4+Y+vXrm61btxZ47ZYtW0yDBg3Mww8/HHS91f7k9+2335qhQ4eauLg443a7zX333We2b99eoM7qPLK73uo0WG2/tMtoOCppuUhKSjKJiYmmb9++5sUXXzTr168v9rh4q+1bfQ/s3pZarS+LbWO4sXu9cVp9IEr7HcTuz5twqy9JUfMzISHB7Ny5s8DwHTt2WL5oQlHjqFKlivnxxx8LDN+6daupUqWK37A1a9aY4cOHm4SEBNO+fXvz2muvFbs9Cbd6Y4yJjY0133zzTYHhK1euNLGxsQWGx8XFmV27dhUYvnPnThMXF1fq/gTzmpK4jAni2sVh7Pjx43rnnXc0c+ZMrVmzRs2bN9ePP/4YsvpQ6tatW0B3a1+yZEmRzxfX/yeeeEJbtmzR+++/X+hrBwwYoNatW+vvf/970P1xu92qX7++2rZtW+xrA7mEcSjuYmx1mq22f+LECfXo0UNr167VZZddpmbNmskYo+3bt+vrr7/WhRdeqKVLl6pKlSpB1Vvtz+HDhzVnzhzNmjVLaWlpuvHGG3XTTTepU6dO+uGHHwq9F4jVeWR3vdVpsNq+1WXU6npgd31Jilpv/vOf//gOgVuxYoVOnDihLl26qGvXrrr00kvVvn17ud3uoNoP5XpWHuzeNgbzHts9DrvXG6fVFyWU30Hs/rwJt/pg5+fdd9+tqKioApdgvv/+++X1ejVt2rRSv2etW7fWyy+/XODwuKVLl+ree+/V1q1bC7Tr9Xr1/vvva/bs2Vq3bp369++vWbNmFXnLh3Cqr1+/vj777DOdd955fsO3bNmiyy+/XL/88ovf8Jtuuklt27bV2LFj/Ya/8MILWr9+vd55551S9z/Y1xSl0gSgVatWaebMmXrvvffk9Xo1evRo/fWvf1XTpk2Drj9x4oS+/PJLdevWTdWqVfN7fXp6upYvX65evXr5ZrzV+tGjRxc5PX/88YcWLFigzMzMQo8/DqT/bdq00YsvvqgePXoUOo4lS5bo/vvv991DI5j+3HrrrQHdxHL27Nkl1uTJyspSVFRUUPVWpzmY/pw8eVIvv/yy3n77bb8buw0cOFCjR48usCJarbfSH4/Ho+uuu04333yzLrvsMt8X2qioqCIDkNV5ZHe91Wmw2r7VZdTqemB3faBKWm+2b9/uOx/oyy+/lCSlpqYG1b7V98DubanVeru3jcG8x3aPw+71xmn1p7LjO4hk/+dNuNUHMz/vvvtuzZs3T/Xq1VPHjh0lSWvXrtWBAwc0ePBgv+1i/pBkZRz//ve/NW7cOD322GO+caxZs0YTJ07UM888oy5duvhq4+Pj/V67cuVKPfroo1q5cqX++9//qnr16oVOczjVz5gxQ++//77eeustJSUlSZKSk5M1ZMgQXXPNNbrjjjv86p988km98MIL6ty5s+88rDVr1mjVqlW67777/OZJ/vsyBdP/YF9zqgodgKz+cmy1fsqUKfr000+L/CW2Z8+e6t+/v0aOHBlUfWGys7M1bdo0PfXUU0pISNATTzyhgQMHBtX/atWq6aefflL9+vULHdeBAwfUsmVLpaenB9WfYNh9F2Or0xzqOwz/8ssvmjhxombMmBFUvdX+NG3aVJmZmbrpppt0yy23+DbcxQUgq/PI7nqr0xCK5doqq+tBqOtLu5ympKT49gYtW7ZMu3btUkxMjLxeb1DtW30P7N6WWq0vj21jMNvSUI6jPNabys7u7yAlKe3nTbjVBzt/unXrFtD4XC6X3nnnnaDGkX9veV5QNvlu7Jr3OO+mn7/++qvmzp2r2bNn6/jx47r55pt12223FRmIw62+bdu22r17tzIzM33bjAMHDigmJkZNmjTxq924caMaNWpUaDuncrlc2rt3r+X+BDMNJSrVAXTlrEqVKubmm282ixcv9jtJsbgbiVqpv+CCC8ynn35a5PgXLVpkLrjggqDrT/XPf/7TNG7c2NSpU8dMmzatwEnLVvufkJBgVq9eXeT4Vq9eXezxsSX151S///67+f777833339vfv/990JrGjZsWOJfo0aNgq63Os1W2y9Jae+JE0x/8s6bqVq1qmnXrp156aWXTGRkpNm2bVuh47Q6j+yutzoNpVmuA1lGT2V1PbCj3upykZKSYt59911z1113maZNmxq3221iYmLMxRdfbB555BGzbNkyc+LEiaDbt/oe2L0ttVpf1ttGq/V2jKMs1xun1Nv9HaQkZX3PNrvrQz1/ChPsOJYvXx7Q36OPPmp69+5tPB6P6d+/v/nkk08K3F4gv3fffTes6vMEel6l1fPag+lPsNNQkgodgM4991zTsGFD8+CDD/qdKF3Ugmy1PjEx0ezfv7/I8e/fv98kJiYGXZ/n888/N61btzbx8fFm4sSJftekL03/u3btasaPH19kf8aNG2e6du0adH/y7Nu3z1x++eUmIiLCuN1u43a7TUREhLniiivMvn37in1tqAU7zaFi9wdGcf744w8zY8YM35XTunbtambMmGEOHz7sV2d1Htldb3Uagmk/mGXU6npgd70VLpfLREdHm4suusj8/e9/N19//bXJyMgIWftW3wO7t6VW68tq2xjMe2zXOMpivXFavd3fQUoS7oHGan1p58+uXbvM4sWLfdu6wi78Eur34FQul8s0aNDAPPjgg2bKlClF/oVrfWllZmaa//znP0X+YBNMf+yahgodgIyx/uu3lfqqVaua9evXFznu9evXm6pVqwZdv3btWtO1a1dTpUoVM2rUqICuOmOl/x988IGJjIw0r7zyil9azs7ONlOnTjVRUVHm/fffL1V/Dhw4YGrXrm3OPPNM8/TTT5uPP/7YfPzxx+app54yZ555pklKSjIHDx4ssZ38fvnll6DrrU5zqPtTFgEof38ef/xxc/z48QI1eVeyqVWrlomMjPR7zuo8srve6jRYbd/qMmp1PbC7PlD5l4vFixeHNFCd2r7V98DubanVeru3jcG8x3aPw+71xmn1eez8DlKScA80wdQHM3/++9//mu7duxuXy2XcbrfvJshDhw41Y8aMKVAf7HuwcuVKM2jQINOpUyff9nDevHl+V0tr0KCBpb3p4Vaf3++//27eeOMN88ADD/guDb5hw4ZCvxMdP37c3HbbbSYiIsJERET43oORI0eaSZMmlao/pZmG4lToc4DyO3bsmN5++23Nnj1ba9as0aWXXqqbbrpJ/fv31+mnnx5UfceOHXX11Vdr/PjxhY5z0qRJ+uSTT7RmzZqg6t1utzwej4YPH17s8ZOnnjBmZXr//ve/a9KkSapWrZoaN24sSdq7d6+OHTumsWPH6plnnvHVBtOfYcOGaffu3friiy8KXMnF6/Wqd+/eatKkid58880i28uTnJysp556SjNnzlRGRkbQ9VamOZT9kaQffvhB7dq1C/gEdiv1hfUnIiJCv/32m2rVqlXoa7Kzs/Xpp5/qmmuu8RtudR7ZWR/MNFhp3+oyanU9sLu+JCUtp1u2bPE7+bhVq1YBtVtS+1beA7u3pVbrrfa/LN7jshiHneuN0+pPZcd3kJLY+XlT3vVW5s/gwYN1+PBhvfnmm2rWrJl++OEHNW7cWF988YXGjBmjn376qdDxWxnHhx9+qFtuuUWDBg3SW2+9pW3btqlx48Z69dVX9e9//1v//ve/A5rmimLLli3q2bOnEhIS9PPPP2vHjh1q3LixHnroIR04cEDz5s3zq7/33nu1atUqTZ48Wb1799aWLVvUuHFjffLJJ3rssccsXXiqrFSaAJRf3p3j33rrLR09elRZWVlB1c+YMUNjxozRO++8oyuvvNLvNYsWLdKNN96ol156ScOHDw+qvmHDhgFdxnTv3r2lmt5169Zp/vz52r17t4wxOuecc3TTTTfpwgsv9KsLpj9nnHGG3n33Xb8roOS3cuVKDRw4UIcOHZIk/f777/rb3/6mr776StHR0XrggQc0cuRIPfbYY3rhhRfUqlUrjR49WjfccENQ9Van2Wr7pwaJU6WmpmrFihW+DbrVeqv9cbvdSk5OLjI8FCfQeWR3fbDTEGj7VpdRq+uB3fVScOvBunXrNGzYMG3bts3vZN0WLVpo5syZuuCCC0rVft44AnkP7N6WWq232v+yeI/LYhySfeuN0+qLE6rvIHZ/3oRbfVFKmp9JSUn64osv1Lp1a1WrVs0XgPbu3atWrVrp2LFjxbYfyDjatm2r0aNHa/DgwX7j2LRpk/r06aPk5OQSx1GR9OzZU+3atdNzzz3nN73fffedbrrpJv38889+9Q0aNNC7776rjh07+tXv3r1b7dq1C8uLq1TKAJSnqF+/rdTffPPNWrBggZo2bapzzz1X0p/319i5c6cGDBigt99+268Nq/WhZHV6QyEmJkZ79uzRmWeeWejzv/zyi84++2ydOHFCknTHHXdo8eLFuv766/XFF19o27Zt6tWrl9xutx566CHf5SXzWK23ymr7Q4cODajdvEulWq232h+3262UlJSAfzEMR3ZPg9VlNBxZXS62bdumDh06qFmzZho9erSaNWvmG/7yyy9rx44dvvtdBNN+MOzelpbntrcysrreOK0+EKX9DmL350241ZekqPlZrVo1bdy4UU2aNPH78r1+/Xr16tVL//vf/wJqv7hxxMbGatu2bWrYsGGBkNW8eXPfcnH55Zfr7bffVkJCgiTpmWee0Z133qnExERJ0v/+9z9dfPHF2rZtW1jW50lISNDGjRt11lln+U3v/v37de655xZYD2JjY/Xjjz+qcePGfvU//PCDLrnkEqWlpQXdn2CnoUSWD5oLI++++67JzMz0PT548KDfVT2OHz9unn322aDr87/uqquuMs2bNzfNmjUzV111lXn33XeL7ZeV+kBZ7f+RI0fMzz//7NfGjz/+aG699VZz/fXXm/nz55e6Tw0aNDBffPFFkc9//vnnpkGDBr7H9erVM0uWLDHG/HnCqcvlMhMmTCjy9VbrrU6z1fbtZrU/LpfLJCYmmurVqxf7l5/VeWR3vdVpsNq+1WU0HFldLq6//npz9dVXF3oScG5urunfv7+5/vrrg24/2G2L3dvSQOvLYtsYbuxeb5xWb0zZfQdximDnT58+fcxDDz1kjPnzfMC9e/eanJwcc/3115trr702JONo1KiR+eqrr3zjyDvHZe7cuaZZs2a+OrfbbVJSUnyPq1Wr5qs1xpjk5GS/857CrT7P6aefbjZu3Fhger/88ktz5plnFqi/+OKLzdSpU331e/fuNcb8eQ5Qr169StWfYKehJBU6AJXVgmCXPn36mNTUVN/jSZMm+V1y87///W+pVqyBAwf6nQCYkpJiqlevblq0aGH69etnoqKizLx584LujzHG3Hvvvea8884rcKWxvPG1atXK3Hvvvb5hERER5tChQ77HHo+n2CuvWK23Os1W28/PjkurWu2Py+UyU6ZMMXPmzCn2Lz+r88jueqvTYLV9q8uo1fXA7npjrC8Xp512mvn++++LfH7dunXmtNNOC7p9q+9BuLF72xjMe2z3OOxeb5xWb0zZfgcJl0t/21kf7PzZunWrqVWrlundu7eJjo421113nWnWrJmpXbu22b17d0jG8fTTT5vmzZubNWvWmGrVqplvvvnG/POf/zSnn36674u/MX9+nuVvP394KKz9cKvPM2zYMNO/f39z8uRJX6DZv3+/adu2bYH1wBhjvvnmG1O1alVz5513mipVqph7773XXHbZZSYuLs7vAjXB9CfYaShJhQ5AZbUg2MXqimi1/w0bNjTLly/3PX7++efNWWed5bs84fPPP286dOgQdH+MMebo0aOmSZMmplq1auauu+4yU6ZMMZMnTzZ33HGHqVatmmnSpInv6iF548j/AZP/l4Ki5pGV+mCm2Ur7xth7aVWr/Tl1mQiE1Xlkd73VabDafjDLaLj9Imd1uYiJiTEHDhwo8vkDBw6YmJiYoNu3+h6EG7u3jWXxK6fVervXG6fVG1M230HC7dLfdtaX5jtaamqqefLJJ831119v+vTpY/7+97/7/ahT2nHk5uaaJ5980sTFxRmXy2VcLpepUqWKb89TsO2HW32e1NRU07NnT5OYmGgiIiJMvXr1TFRUlLnkkkuKvMLonj17zF//+ldzwQUXmGbNmplBgwaZLVu2lGr+lGYaShJp7YA5Z3G73XK5XH539w1lvTnl9KtTH5dWcnKyGjZs6Hu8dOlSXXPNNYqM/PNt79evnyZNmlSq/lSvXl1r167Vgw8+qHfeeUepqamSpMTERN100016+umnVaNGDb82e/To4euD1+tV3759FR0d7dfuxo0bg6oPZpqttH/w4EF17NhRUVFReuKJJ/zOrXj99dfVqVMnff/9977jyK3WW+1PSSdBF8bqPLK73uo0WG0/mGU0v5LWA7vr82qsLBcNGjTQunXrVK9evULbW7t2rRo0aBB0+1bfA7u3pVbr7d42Bvse2zkOu9cbp9WXBbs/b8KtPhhZWVnq3bu3pk+frr///e9Bt1OcnJwcrVq1SiNGjNDYsWO1e/duHTt2TM2bN1fVqlX9al0uV4HPtOI+48KtPk9CQoK++uorrVq1Sj/88IOOHTumdu3aqWfPngVqs7KydMcdd+jhhx/WG2+8UWy7wfQn2GkoCQGoGPv27bO13m7x8fFKTU31fdHJuypUHpfLpczMzFKPp3r16nr99df12muv6ciRI5Kk008/vdAF9NFHH/V7fNVVVxXbttV6q9Nstf3HHntM5557boFLpfbv31+jR49W79699dhjj/kulWq13mp/ggnNVueR3fVWpyGY5drKMhqOrC4XAwcO1JgxY3TuueeqZcuWfs9t3bpV999/vwYPHhx0+1bfA7u3pVbry2rbGE7KYr1xWr3d7P68Cbf6YERFRWnLli1Bvz4QERER+stf/qLt27crMTHRd/GYwhhjdOuttyomJkaSdOLECd15552Ki4uTpALrWLjVS38GGo/Ho82bN6tz587q3LlzsfMnKipKH374oR5++OFi64LtTzCvCUSFD0BffPGF78oQubm5WrJkiX788UdJ8v2CE0z9li1b1LJlS7nd7oD68dFHH+nKK68s8ItpUX766aegUq2V6e3YsaOmTp2qN954Qx999JH++OMPde/e3ff8zp07/X4hLm3KdrlcJV7K+NQvWiWxWm91mq22v3jxYr377rsF7hMhSR6PR0888YQGDhwYdL3V/uTm5hY6fP/+/Tp+/LiaNm1aYBm2Oo/srrc6DVbbzy+QZTQcf5GzulxMmDBBX3/9tdq0aaPLLrtMzZo1kzFG27dv19dff60LL7xQDz74YNDtW3kP7N6WBrPt7dChg63bxrL4ldNqvd3rjVPr7foOItn/eRNu9ZL1+Sn9eQXImTNnBnyPv2DG0bJlS+3du7fYe25Jf96TKP96ePPNNxdaE6710p+Bpn79+gHfz0n6M9QuXLhQo0ePLrYumP4E85pAVOjLYAfygZr/cAgr9REREUpOTg740rwul0vr1q3zu7dGceLj43Xs2DH16dPHl2oXLVqk7t27+6XaxYsXB9V/6c8vHj169FB6erqys7P14IMP6oknnvDV3nLLLYqLi9P06dN97VvpjyR169YtoA/2JUuWlNj39PR0zZ8/XzNnztT69euDqrc6zVbbL89LqxbWn1mzZik1NVVjxozx1Q0fPlwzZ86UJN+vb/m/2FidR3bXW50Gq+1bXUatrgd215ekqPXm5MmTevnll/X222/73Qh14MCBGj16tG/8wbRv5T2we1sazLb3nXfe0ZAhQ2zbNgbzHts9DrvXG6fVS/Z+B5HC79LfdtdbnT957r77bs2bN09NmjRR+/btfetAnpdeesn3f7DjWLx4sSZMmKAnnnii0HHEx8eX2G6eY8eOFTh0LtzqZ86cqY8++khvvfVWQId+Pvnkk3rxxRfVo0ePQudPoDf2Lqo/drymQu8BKuqX41DUG2P08MMPKzY2NuDXvPzyy0pKSgqo9uTJk7rmmmtUrVo137CSUq3V6W3VqpW2b9+uVatWKSkpSR06dPB7fuDAgX67cocMGeL3fCApu02bNkWO/48//tCCBQtK3D25bNkyzZo1Sx999JESEhJ09dVXB11vdZqttl+nTh1t27atyA36jz/+6LcMWK232p8ZM2bojjvu8D1evHixZs+erXnz5qlZs2YaOXKkHn/8cb9DDKzOI7vrrU6D1fatLqNW1wO764tS0noTHR2t8ePHa/z48SW2ZbV9K++B3dtSq/UnT55U06ZNbd02BvMe2z0Ou9cbp9VL9n4Hkez/vAm3eqvzJ3877dq1kyTfjz15Tg21wY7j8ssvl/TnuXL52zz1vMOXX3652L0gf/zxh3r37q1Vq1aFZX2eV199Vbt371bdunXVoEGDAoEm73zQPDNnzlRiYqI2bNigDRs2+D3ncrl8ASiY/gQ7DSWp0HuAAuH1euXxeCzXd+3a1dLhX5s2bVKLFi0CPgxDkhYsWKA6deoEXB8Iq9Nrh+zsbE2bNk1PPfWUEhISCt3N/euvv2rOnDmaPXu2UlNT9fvvv2vBggUaMGBAofPdar1VgbY/atQoLV26VEuWLCnwi/bhw4d12WWXqVu3bpo8eXJQ9Vb7U7NmTS1fvlznnXeeJOmuu+7SkSNH9MEHH0iSli9frqFDh4bd+Wn5lcc0BLKMhqNwWQ+ssHtbGi7bXiewut44rb4wwX4Hkez/vAm3eqvzxy6FjWPFihXFvubSSy+V9Oehff/4xz8K/RHr2LFjvhuz/uc//wnL+jyPP/54sdNr9bDpPMH0J9hpKEmlDUCZmZl69dVX9fzzzys5OTnk9WXlgw8+0HXXXVdiXWH9nzdvXkDjsHLsZEn9mT9/vh555BF5vV499NBDGj58uO8qQ5L04YcfaubMmVq5cqX69Omjm2++WX369FFcXJx++OGHAntnrNZbnWar7f/+++/q0KGDkpOTdfPNN6tp06a+cysWLFigpKQkrVmzxrfL2Gq91f7ExsZq+/btvhObW7durWHDhvl+bTlw4IDOPfdceb3eoOeR3fVWp6G0y3VJy2ggAl0vQ1Vvdblo1KhRQIfz7NmzJ6j27di2lKXy2DaWtr604yjr9cZp9acKxXcQuz9vwq0+FPPz7bffVr9+/QrssQhEKL4HfvDBB7rlllv07rvvql+/fr7hx48fV69evXT48GGtWLHC9wNMuNWHwqpVq3T++ecXeph1MP2xbRosXzg7jJw4ccI88MADpn379qZTp07m448/NsYYM2vWLFOnTh1z5plnmmeeeSbo+rKQlZVltm7danbs2OE3fOHChaZVq1YmOjraN8xq/xMTE4v8q169uomOji5w7XQr/cnv888/N61btzbx8fFm4sSJRV4nPiIiwjz44IMmPT3db3hkZGShN160Wm91mq22b8yf94u48847TfXq1X33A6hevbq54447Ctwnwmq91f40bdrUfPjhh8aYP+/0HhER4XfTsbVr15ratWuXah7ZXW91GoJZro0JfBk1xvp6YHe91eVi8uTJRf6NGjXKeDyeUq0Hwb4H4aIsto3BbEvtHEdZrDdOqy+L7yB2ft6EW30ovqOdej+sU4ViHC1btiz2PmtvvPGGiY2NNcuWLTPGGHPs2DHTpUsXc/bZZ5tff/017OtPddddd5kjR46UWJenpPcgmP6UdhoKU6ED0Lhx40xCQoK59tprTZ06dUxkZKS5/fbbzXnnnWfefvttk52dXap6u23dutU0aNDAd3Owq6++2iQnJ5tLLrnE1KhRw4wfP94cPHgw5P0/dOiQueOOO0xUVJTp1atX0P0x5s8vp127djVVqlQxo0aNKnElGT58uElISDAXXXSRef31183Ro0eNMUV/0bJab3WaS9N+bm6uSUlJMSkpKSY3N7fEPgRSb7U/kyZNMklJSWbixImma9eupkWLFn7Pv/zyy6ZHjx4l9s2YoueR3fWhmoai2re6jFpdD+yuNyY068H//vc/M2rUKBMTE2MuueQSs3r16pC2b4z1ZSLchGrbGMx7XBbjsDLNVtcbp9UbU7bfQez4vAm3+lB8xzn1JpnlMQ5jjHn22WdNfHy8WbZsmbn44otN48aNi10fw60+v5ICzansmD+lnYbCVOgA1KhRI/PJJ58YY/78MHC5XGbo0KFFrlxW6+12+eWXmx49ephFixaZm266ybhcLtO0aVPz/PPPm4yMjAL1pe1/enq6+fvf/26qVq1qOnToYJYuXVqq/hjz5x16Y2NjzahRo8yUKVOK/MsvIyPDzJkzx1xyySUmJibG9OvXz0RERJitW7cWOg6r9Vam2Wr7GRkZ5pNPPinwS7kxxqSlpZlPPvnEnDhxIuh6q/3JyckxDz/8sGnTpo3p3bu32bZtm9/z1113nXnzzTeLnkEmsHlkZ31pp6Gk9q0uo1bXA7vr8wS7HmRkZJgnn3zSJCYmmtatW5vPPvsspO0bY32ZCDeh3jYG8x6X1XIU6DRbXW+cVm+M/d9B7P68Cbf6UHxHK+nLd1mMI8/48eON2+02jRs3LnaPUbjW5wl0eq3WB9OfYKehMBU6AEVFRZlffvnF97hKlSpmy5YtIau32+mnn242bdpkjDEmNTXVuFwuM2/evCLrg+3/yZMnzYsvvmhq1qxpzjnnHPP++++HpD/GGNOgQQPTsGHDYv8aNWpU5Ot37txpJkyYYOrWrWvi4+PNjTfe6DscqjT1gU6z1fYnT55sunfvXuTre/ToYV599dWg6632pzSsziO7660KtH2ry6jV9cDu+sIEslxkZ2eb119/3SQlJZmGDRuaefPmBfwhb/d6Fi7s2jYG8x6X1XJk13rjtHpj7P8OYvfnTbjVh+I72jfffGO8Xq/v8amH2YViHH369DGHDh0q9Lmrr77a7y8mJsZceOGFBYaHa31hrAag+fPn+x06mn+vWjD9CcU0FKZCByC3220OHz7se/z/2jv3sKqq9I9/9zkSIqACoqKi6CigiOWUFypESytNBDQF856U5mjSRW1ydEbHS0+maDYkFMhMDgamTEBeMEXMzBhv8IApKmpZ3nK8iwLy/v7o4fw4cA7utWGfsw/n/TwPj56zv3vtd+291t7rPWvt93VxcaGSkpIG06uNJEl06dIlI3uKi4vN6kXtr6yspOTkZOrYsSO1a9eO4uPj65zeFbVHCeZ+TX7w4AFlZGRQWFiY0bp1Ub1onUXL79OnD2VkZJgtLzMzk/r06aNYL2pPFVW/tK1YsYJWrFhBX331ldlfgUXPkdp60TooLV8uov1AbT2ReLtITU2lbt26kaenJ61evZru37/foOWrfQ3URu17o5JrrPYxbP2aaRG1xyBqP2+0pm/IMdqOHTto9OjR1LRpU6Pv1R4HTp48WdafVvUNyYkTJ2jOnDnUtm3betmjVh1sOgrcwxLBVbFlyxZFerXR6/UoLi6Gp6cniAje3t7Yt28ffHx8jHRVCbZE7Q8MDERJSQlmzZqFmJgYs3k4qsoXtUcO169fx4YNGzBz5kxDHfr06YPo6GhERUUZ5UGq4vLly4Ys3KJ60TqLlu/m5ob8/Hx07NjRZLk//fQTHn30UVy7dk2RXtQeAMjIyEB0dDR+++03I12rVq2QmJiI0NBQo+9Fz5HaetE6KCm/Lmq2UdF+oLYeUNZvnJycMHbs2DrPQ1WCQLX7mdZQ+96o5BqrfQy1+4096tUeg6j9vNGavr5jtHPnziEpKQn//Oc/ce3aNQwdOhSjRo3C6NGjDRqlx9i9eze2bNmCs2fPQpIkdO7cGS+99BIGDBhg0pbGBBEhJycHpaWlePLJJ+Hm5mZWe/fuXaSmpiIpKQnff/89nnjiCYwaNQpz5syxoMXysGkHaMqUKbJ069evV6RXG51OZzKhVs3PVQm2RO2vnvHYVEjcmuWL2lMXu3btQmJiItLT09GsWTNcvXoVAPDtt99i/fr1+PLLL1FZWYlRo0YhOjoawcHBJssR1YvWWbR8V1dX7NmzB48//rjJ7YcOHcLAgQNx69YtRXpRe/bv34+BAwdixIgRePvtt9G9e3cAwLFjx7By5UpkZWUhNzcX/fv3V3yO1NaL1kG0fHOYa6Oi/UBtPSDeLuTm3snJyVFUfkNdA2uh9r1RyTW2RLsTqbM5zPUbe9SrPQZR+3mjNb2SMVpZWRm2bNmCzz77DN999x0GDx6Mbdu24ciRI4bcctVRcozp06cjISEBbm5u8PX1BRHh5MmTuH79OmbMmIG1a9fKKtMWuH79OmbPno3Dhw+jf//+WLlyJYYNG4b9+/cDAFq3bo3s7Gz06tXLaL8DBw7gs88+w6ZNm9CxY0f8+OOPyMnJMfsM0QJiyS80hqijYinHRi5Vgw+5iNovWr6oviY///wz1q9fj/Xr1+Onn35CVFQU0tPT8eyzzxo0wcHBCA4Oxtq1a5GWlobk5GSEhISga9eumDp1KiZNmmSUGVpUL1oH0fIDAgLwzTffmL2hZ2dnIyAgQLFe1J4lS5ZgypQpiI+PNyr3ySefxJNPPolp06Zh8eLF2Lp1q+JzpLZetA71aady2qjWzg8g3i727Nnz0DLLy8sVl1/fe4W10eI11qJNVcjpN/aoV3sMovbzRmt60fMza9YsbNy4Ed26dcP48eORmpoKDw8PODg4QK/Xm9xH9Bjp6elYv349kpKSMGnSJMOPB5WVlUhOTsbrr7+OIUOGGPLT9O7d2+QPDC1atICvry9mz55tlFdNa/p33nkH33//PSZNmoTMzEy88MILICJ8//330Ol0mDt3LubPn4/MzEwAwMqVK5GUlIQbN25g7Nix2Lt3Lx599FE4ODjAw8Oj1nFF7VG6jyyEF80xTDXKysooLS2NnnvuOXJycqKIiAjatGmTUPjckydP0nvvvUfe3t7k4OBAoaGhDaoXpa7y4+PjydnZmTIzM2vtl5GRQc7OzhQfH69YL2qPm5tbnS9w5ufnU8uWLess39qoXYeGaKNapK52kZqaWue+5eXlD31pVO1+xmgb0X5jb3pLoPbzRmt6UZTk8RMlNDSU3n33XbPb586dSyNGjDB8/tvf/mbyLyYmhgYMGECPPPII7du3T7P6du3a0Z49e4iI6Pz58yRJkiH3DlHtvHxV16Dm+4TmroGoPUr3kYPNO0C7d++mDz/80FD5devWkbe3N7Vq1Yqio6NrvUQtqtcaWrPf09OTgoODKT4+3pA7hEj8BnT79m2Kj48nd3d3WQkURfWi1FX+uHHjSJIk6t69O4WHh1N4eDj5+/uTTqejqKioWmWJ6kXsadq0KZ09e9bsfmfPnq31EqjWULsODdVGtYi5duHo6EjZ2dkm96lyfqq/mCpaPtP4Ee039qavQu0xiNrPG63pRc5PSkoKDR48mJydnWnMmDGUmZlJFRUVDXrN2rdvTz/88IPZsg4cOEDt27c3u70m7733Xp2R8ayt1+v1RhHunJyc6NSpU4bPFy5cMHoWLFu2jLp160be3t40d+5cQ0Adpc9XUfuV7kNk4w5QQkIC6fV66tq1Kzk6OtKyZcvI2dmZpk+fTjNmzKDmzZvTvHnzFOvVRpIk0ul0hn8fhqj9ouWL6ol+//V+wIABlJCQQDdu3DB8L7fx5+bm0qRJk8jFxYWaN29O0dHRRgkaRfVK6qDEntTUVAoLC6MePXpQ9+7dKSwsrM5f3UX1cu0JDAykpKQks/snJiZSYGCg0XdqtwtRvWgdRMsXbaNaOz+meFi7WL16Nbm4uNCBAweM9nvw4AGNHDmSWrduTYWFhYrLb4g6WBMtXmOt2STab+xNT2S5MYjazxut6JWen5KSElq4cCF17NiRWrVqRTqdzmx4d9FjODo60i+//GK2bufPnxf6ga6wsJA8PT01qzcVXbJ6COyLFy+avH/s2bOHJk6cSM2aNaNevXqRXq9XNCsjar/SfYhs3AEKCAigjz76iIiItm3bRk2aNKHk5GTD9rS0NPrDH/6gWK82Z8+eNfp7GKL2i5YvqiciKi0tpQ0bNtCgQYPIycmJRo4cSVu2bCEHBwezD41ffvmFli5dSt26dSNJkuipp56ipKQko7jxSvVK6iBqj9qI2LNq1Spyd3c3mdwyKyuLPDw8aOXKlUbfq90uRPWidRAtX7SNau38VCHaThcuXEju7u4GR6eiooJGjRpFnp6eJsNeq93PtIQWr7HWbBLtN/amJ7L9MYjWqO/5qayspO3bt9Po0aPJ0dGR2rdvT7NmzarXMSRJMgqbXRNzDoE5fvzxR/Lw8NCsXpIkWrp0qSHxb9OmTWnBggWGz0uWLKmzvjdv3qR169ZR3759Sa/XU1BQUK0xSEPar3QfIht3gJycnIxu5A4ODkZZ5M+dO2eUu0JUryb5+fn04MED2frCwkIh+0XL37x580NzhdS0p7y83Oi7U6dO0fz586lDhw4kSRK9/PLLlJ2dbbQ29IUXXqAmTZpQ27Ztae7cuXT8+PE6jyOiV3JOn3/+eSF71Eb0/Dx48IBeeuklQxb4iIgICg8PJz8/P9LpdDRy5Eijc6J2u1DSju7fvy+7DkqucfV2+rA2qsXzU15eLtwuqpg5cya1a9eOTpw4QaNHj6ZWrVpRfn5+LZ3a/azmvcKaaPEaHzp0SHM2ifSbmtiL3pbHIFqkIc/P1atXKTY2lnr16lWvY0iSRNOmTaM333zT5N+0adOEHKClS5dScHCwZvVyEgL7+PjIKrugoIBmz54tNDsjar/SfYgaQR6gixcvGnJTuLq6Ij8/H126dAEAXLp0Ce3atTMKAyqiVxO9Xo+LFy/C09NTlr558+a4ffu2bPtFy5ckCXl5eejTp49se44ePWo4dnUqKyuxY8cOJCYmIjMzEy4uLobQoSNGjMDUqVMxfPhws1FaqiOiV3JO+/bti1mzZsm2pyr0LMkMGSuqFz0/VaSmpmLjxo0oLi4GAPj6+iIqKgpRUVFGOrXbRX3akZw6KLnGptqpuTaq1fMTExOjqF0AwPjx47F582a4uLhg165dtcKXAur3M3P3CmugxWt8584dzdkk0m/M0dj1ao9B1H7eaFGv9hhN9BiiKQU++ugjk9tv3LiBQ4cO4euvv8a2bdswePBgTerVoLy8HA4ODortUasONh0GW5Ik3Lp1C02bNjV0sNu3b+PmzZsAYPhXqV5NiAgLFiwwm4yuJmVlZUL2i5YPALGxsUahbh9mjzmqEo0NHToUV65cweeff27YlpGRAQAoLS3Fzp07jQa7Q4YMgZOTk1FZInol5zQhIQFdunSRbc+ZM2dkla1UL3p+qoiMjERkZORDy1e7XYjqq7cjOXVQco1NYa6NavX8iLaLt956y/B/Nzc3EBEee+wxJCcnG+mqEqGq3c+0hBavsRZtMkVd93Z71Ks9BlH7eaM1vej5CQ0NxZgxY/DSSy+ZfTbW9xhyUgpUJzY21uT3zZs3h5+fH/bu3YugoCDN6pVQWlqKQ4cOwd3dvVY46nv37iEtLQ0TJ05UbI9adbD5GSC1k9OphdxfFaqTm5sr237R8o8cOYKAgAA88sgjsvdJSUmBl5dXnZqSkhJMnz4d2dnZhu8yMjIQHR2N3377zUjbqlUrJCYmIjQ01Oh7uXol5zQlJQX//e9/ZZVfUFCAnj17GiUUrIstW7Zg+PDhss9pUVER/Pz8sHXrVqHzUxeHDx/GwoULkZWVBUD8HIm2CzXaUfU6KL3GdZVfvY1q+fyI9JtBgwY9tFxJkrB7927DZ7X72cPuFZZCi9f4/v37hsz0WrFJpN/IoTHq1RyDqP280Zq+qKgIgYGBwudTr9fD2dkZY8eORXR0tNmcQ1VoaRyoVUQcmuLiYjz33HP46aefIEkSnn76aXzxxReGe4clV1aJYtMOUG5urixdSEiIIr3WsEX78/Pz8cc//tHQ+Pfv34+BAwdixIgRePvtt9G9e3cAwLFjx7By5UpkZWUhNzcX/fv3V6QXRaR8SywrTExMxLhx44Tqu2PHDuzcuROPPPIIoqOj0aVLFxw/fhzvvvsuMjMz8fzzzxslQtUi1qxDzTaqRbTUDxgGEO83jVGv5hhEa0s1LbHsMj4+Hu3atXuotup86nQ6FBYWIjs7G0lJSQYnKjo6GuPGjYObm1utfUWvmZJZJltG1KGJiIhAeXk5kpOTcf36dcTExODYsWPYs2cPOnbsyA4QY7/UfGgMGzYM3t7eiI+PN6mfNm0afv75Z8NgV1Qvikj5Op0Or732muxlKrGxsRg7dqzsZSdxcXHo378//Pz8ZNc3MTERr776Ktzd3XHt2jV4eHhg1apVmDVrFiIjIzF79mzDYFarWLsOtuAAaakfMAxgGw6KNfX1Re3njdb0cXFxOHbsmNC7gjXf58nLy0NiYiJSU1NRVlaG8PBwREdH45lnnpFdpqljiMwy9ejRA/v27YO7uzsAYMaMGVi8eDFatWoFALh8+TJ8fHxw9+5dTepFHZo2bdrgm2++QWBgIIDfZ9BmzJiBrVu3IicnB87OzkZ6UXuU7iMHdoAYVan50HB3d0dubq6hs9SkoKAAISEhuHbtmiK9KCLlW2JZ4dGjR7F3717Z9e3VqxcmTJiAOXPmYPPmzRg9ejT69++PtLQ0dOjQQfZxrYm162ALDpBoP1i8eLGschcuXKiofIbRmsOhNX190dpSTS0su6xJTQeoirt37yItLQ2JiYnYv39/vYMmiMwy1bSpZkCRS5cuwcvLC5WVlZrUizo0zZs3xw8//FDrR8qZM2fiq6++QkpKCgYOHGg2EMjD7FG6jxxsOgiC2hFJtIat2w/8vra0efPmZre3aNEC9+7dU6xX0x7RlyGV4OTkJFTf06dPY/To0QCAkSNHokmTJlixYoXNOD9A46iD2oj2g/T0dLNaSZJw4sQJ3Lt3z+AAqd3PGKYxouYYxBLPG63RUGOcZs2aYfLkyZg8ebIhoEt9jtGqVSvExMQgJibGMMv0l7/8BXPnzn3oLJOpOYa6HFtr60tLS9GkSROjbZ988glmzpyJkJAQpKSkGO3r7++PgwcP1nKAPv74YwC/RxitC1H7le5jCpt2gNSOSKI1tGh/796962x4Nacku3Xrht27d2PKlCkm9bt27UK3bt0U60VRu3y17SktLTUskZAkCY6Ojpp52VwuatdBtI1qEdF2ceTIEZO6o0eP4t1330VhYSFeffVVxeUzjR/RfmNvesD+xiBqI3p+QkJCHjrD5OvrW69j1KRv377o27cvYmNjDbNMQ4YMsckfpE0h6tBERERg48aNmDBhQq2yPv74Y1RWVmLdunXqGVwPbNYBslREruqesDURra+l7A8PDxfST5kyBe+88w7atGmDYcOGGW37+uuvMXfuXLz33nuK9aKoXb4l7Pnss8/g4uICAKioqEBycrJhbWwVb7zxhrqG1xM16yDaRrVIfdvpmTNnsGDBAqSmpmLkyJEoKioycmi01g8Y6yPab+xNb29jELVRMsbZuXOn0PlRcgxzmJtlkiSpliNdl2OtNb2oQ/PnP/8Zf/7zn82WFxcXh7i4OMX2KN1HDjb7DpA1E31aA1tPPlhFZWUlIiMjsXnzZvj5+aF79+4gIvz44484efIkwsPDsWnTJsMNSlSvtj1qI2qPj4+PrJtHSUmJJcxXRGOog9oobae//fYbFi1ahISEBDz99NN4//33Td4DtdYPGEbr2NsYRG2UjHHi4uIQFRUl2wlScowePXpg+/btaNmypax9dDodevbsabCpoKAA/v7+Bse3oqICRUVFRu/EaEnfUFRUVJi8LkrsUasONusAWSIil2hEEjURra+l7L98+XKtFxCrU1FRgcOHD6Nv375G36empmLjxo1GCRejoqIQFRVlshxRvShql2/r9tgyStuoFpHbLu7cuYMPP/wQq1atQteuXbF8+XI899xzDVY+0/gR7Tf2pre3MYjaKBnjlJeX48KFC4br1r9/f2zevBnt27dvsGOIXoNFixbJ0v31r3/VpN4c5hya7du3o3379ggMDERlZSWWLl2KdevW4eLFi/Dy8sLMmTMxb948w4+cSuxpqDrUxGYdIK0k+rQUWk0+qNfrjW5AgYGB2Lp1K7y9vQFoOwlWY4GIcOrUKZSVldnskgk162CPbbRt27a4desWZs2ahbFjx5q9d/Tq1cvCljG2gmi/sTe9vY1B1EbJGCc3N9coOpirqyvy8/PNOixKjjFjxgxERETY5HNVCaIOjb+/Pz799FMEBwdj+fLlWLlyJebPn4/u3bvjxIkTWL58Od58803MmzfPyjUzATFMPZAkiS5dumT47OLiQqdPnzZ8vnjxIkmSZA3T7IKSkhLq2bMn6XQ60ul05O3tTXl5edY2Swi162CPbVSSJMOfTqcz+Vmn01nbTEbDiPYbe9Mz1udh16wh0Ol0Rsfo168fnT9/XnF5+fn55ODgoFm9n58f7d27l4iIli1bRh4eHrRq1Sratm0brV69mtq0aUPvv/++Qe/o6Ejnzp0jIqKePXtSWlqaUflZWVnUtWtX1exXug8RkX24tIxVqf6Li9ZCl2sttLioPXPmzEFFRQU2bNiApk2b4sMPP8T06dNx6NAhC1lcf7RQh4Z4oVJNRNuFaKQjrfUDxjYQ7Tf2pmfUpebL8aZelq8vVGORVFFREe7fv1+v8ioqKjSrP3v2LDp16gTg9xnITz75xJCm4oUXXkDXrl0RExNjmNFxd3fHr7/+io4dO+LKlSvo2rWrUfm+vr745ZdfVLNf6T6ADUeBY2wTrYUN1VpYUlF79u3bhy+//BJPP/00gN/XQHfo0AF37tyBs7OzGiY2OI2hDmoj2i6qHmBqlc8wDGNtiAjPPvusYXna3bt3ERoaWmuZ4eHDh61hnlm05qhX14s6NBEREVi6dCn+85//ICwsDHFxcUhISDCUuXbtWjz22GOq2q90H3aAmHohSRJu3bqFpk2bGn49vn37Nm7evAkAhn8B7YUN1VpocSX2XL582SicsZeXF5ycnHD58mV07txZFTsbGrXrINJGtYiSdlFcXAy9Xl9rW4sWLeDr62v0XoHW+gGjDUT7jb3pGetT86X3sLCwBj+GJWaZtISoQ7Ns2TIMHjwY/v7+CAoKwqZNm7Bz5074+vri1KlT+N///ocdO3ZYqTZ1Y7NBEBhtULV0poqqB0fNzw8ePNBc2FCthRZXYs+dO3dQXFxstE+HDh2wb98++Pj4GGm1il6vV7UOIm1UiyhpF7dv3za7XZIkREVF4dNPP0WzZs001w8YbSDab+xNz9gHDwvBXEXVLNPDHOWCggKEhIQY2pHW9Ddu3MDgwYNx/fp1g0PTpk2bWg5Nv379DGWUl5cjMTERmZmZKCkpQWVlJby8vPDUU0/h9ddfR4cOHQxaUXuU7iMH/gmPqRc5OTmytUSEBQsWyA5BCfweOlRu2NCysjLZ5SqxR7R8UZTYQ0S1Ml0TEXr37m34v9Yf2mrXQaSNahEl7eLUqVMmHZQbN27g0KFD+NOf/oQlS5Zg2bJlmusHjDYQ7Tf2pmesz71795CdnY1BgwbB1dXVaNvNmzexZ88ePP/883B0dFR8DNFZppYtW9Y5Q1TTsdaavkWLFti/f7/BofHx8UFlZSXKysowduzYWg4NADg4OGD69OmYPn262eMotUfpPnLgGSDGYmgtbKjWQosrsWfWrFnw8PB4qC4kJESpWaqTm5srS6flOqiJGu1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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hc_pca = HClust(n_clusters=None,\n", " distance_threshold=0,\n", @@ -1627,6 +3385,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch13-multiple-lab.ipynb b/docs/source/labs/Ch13-multiple-lab.ipynb index a7c1cdc..d759391 100644 --- a/docs/source/labs/Ch13-multiple-lab.ipynb +++ b/docs/source/labs/Ch13-multiple-lab.ipynb @@ -25,9 +25,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1f928b2d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:24.664836Z", + "iopub.status.busy": "2023-07-26T00:00:24.664535Z", + "iopub.status.idle": "2023-07-26T00:00:25.648869Z", + "shell.execute_reply": "2023-07-26T00:00:25.648538Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -48,9 +55,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "eb4b32aa", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.651065Z", + "iopub.status.busy": "2023-07-26T00:00:25.650806Z", + "iopub.status.idle": "2023-07-26T00:00:25.653087Z", + "shell.execute_reply": "2023-07-26T00:00:25.652803Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -81,9 +94,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "e12ac0cd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.654827Z", + "iopub.status.busy": "2023-07-26T00:00:25.654707Z", + "iopub.status.idle": "2023-07-26T00:00:25.657085Z", + "shell.execute_reply": "2023-07-26T00:00:25.656796Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(12)\n", @@ -104,10 +124,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "04d0f49e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.658750Z", + "iopub.status.busy": "2023-07-26T00:00:25.658609Z", + "iopub.status.idle": "2023-07-26T00:00:25.662657Z", + "shell.execute_reply": "2023-07-26T00:00:25.662324Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9307442156164141" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "result = ttest_1samp(X[:,0], 0)\n", "result.pvalue\n" @@ -133,9 +171,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d1f0c695", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.664332Z", + "iopub.status.busy": "2023-07-26T00:00:25.664234Z", + "iopub.status.idle": "2023-07-26T00:00:25.684874Z", + "shell.execute_reply": "2023-07-26T00:00:25.684597Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -163,12 +207,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7a9594a0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.686565Z", + "iopub.status.busy": "2023-07-26T00:00:25.686471Z", + "iopub.status.idle": "2023-07-26T00:00:25.694731Z", + "shell.execute_reply": "2023-07-26T00:00:25.694405Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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H0TrueFalse
Decision
Reject H0515
Do not reject H04535
\n", + "
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H0TrueFalse
Decision
Reject H0240
Do not reject H04810
\n", + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "m = np.linspace(1, 501)\n", "fig, ax = plt.subplots()\n", @@ -287,10 +475,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "9ce7a19f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.952085Z", + "iopub.status.busy": "2023-07-26T00:00:25.951946Z", + "iopub.status.idle": "2023-07-26T00:00:25.992310Z", + "shell.execute_reply": "2023-07-26T00:00:25.991986Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00620236, 0.91827115, 0.01160098, 0.6005396 , 0.75578151])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Fund = load_data('Fund')\n", "fund_mini = Fund.iloc[:,:5]\n", @@ -337,12 +543,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "de6cffed", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.994255Z", + "iopub.status.busy": "2023-07-26T00:00:25.994126Z", + "iopub.status.idle": "2023-07-26T00:00:25.996766Z", + "shell.execute_reply": "2023-07-26T00:00:25.996489Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, False, False, False])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n", "reject\n" @@ -359,10 +582,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "0de71500", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:25.998481Z", + "iopub.status.busy": "2023-07-26T00:00:25.998341Z", + "iopub.status.idle": "2023-07-26T00:00:26.000891Z", + "shell.execute_reply": "2023-07-26T00:00:26.000558Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0.03101178, 1. , 0.05800491, 1. , 1. ]),\n", + " array([0.03101178, 1. , 0.05800491, 1. , 1. ]))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bonf, np.minimum(fund_mini_pvals * 5, 1)\n" ] @@ -382,12 +624,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "f7e87bdb", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.002751Z", + "iopub.status.busy": "2023-07-26T00:00:26.002618Z", + "iopub.status.idle": "2023-07-26T00:00:26.041360Z", + "shell.execute_reply": "2023-07-26T00:00:26.041041Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ True, False, True, False, False]),\n", + " array([0.03101178, 1. , 0.04640393, 1. , 1. ]))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]\n" ] @@ -403,12 +663,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "e88be376", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.043177Z", + "iopub.status.busy": "2023-07-26T00:00:26.043041Z", + "iopub.status.idle": "2023-07-26T00:00:26.046129Z", + "shell.execute_reply": "2023-07-26T00:00:26.045861Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Manager1 3.0\n", + "Manager2 -0.1\n", + "Manager3 2.8\n", + "Manager4 0.5\n", + "Manager5 0.3\n", + "dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fund_mini.mean()\n" ] @@ -425,10 +707,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "41149af6", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.047795Z", + "iopub.status.busy": "2023-07-26T00:00:26.047695Z", + "iopub.status.idle": "2023-07-26T00:00:26.050538Z", + "shell.execute_reply": "2023-07-26T00:00:26.050256Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.038391072368079586" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ttest_rel(fund_mini['Manager1'],\n", " fund_mini['Manager2']).pvalue\n" @@ -459,12 +759,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "61aabda7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.052147Z", + "iopub.status.busy": "2023-07-26T00:00:26.052048Z", + "iopub.status.idle": "2023-07-26T00:00:26.543846Z", + "shell.execute_reply": "2023-07-26T00:00:26.543509Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n", + "===================================================\n", + "group1 group2 meandiff p-adj lower upper reject\n", + "---------------------------------------------------\n", + " 1 2 -3.1 0.1862 -6.9865 0.7865 False\n", + " 1 3 -0.2 0.9999 -4.0865 3.6865 False\n", + " 1 4 -2.5 0.3948 -6.3865 1.3865 False\n", + " 1 5 -2.7 0.3152 -6.5865 1.1865 False\n", + " 2 3 2.9 0.2453 -0.9865 6.7865 False\n", + " 2 4 0.6 0.9932 -3.2865 4.4865 False\n", + " 2 5 0.4 0.9986 -3.4865 4.2865 False\n", + " 3 4 -2.3 0.482 -6.1865 1.5865 False\n", + " 3 5 -2.5 0.3948 -6.3865 1.3865 False\n", + " 4 5 -0.2 0.9999 -4.0865 3.6865 False\n", + "---------------------------------------------------\n" + ] + } + ], "source": [ "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n", "managers = np.hstack([[i+1]*50 for i in range(5)])\n", @@ -491,10 +819,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "cbcad4de", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.545878Z", + "iopub.status.busy": "2023-07-26T00:00:26.545735Z", + "iopub.status.idle": "2023-07-26T00:00:26.635976Z", + "shell.execute_reply": "2023-07-26T00:00:26.635663Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", "tukey.plot_simultaneous(ax=ax);\n" @@ -514,9 +860,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "b5842190", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:26.637968Z", + "iopub.status.busy": "2023-07-26T00:00:26.637827Z", + "iopub.status.idle": "2023-07-26T00:00:27.041757Z", + "shell.execute_reply": "2023-07-26T00:00:27.041385Z" + } + }, "outputs": [], "source": [ "fund_pvalues = np.empty(2000)\n", @@ -536,10 +889,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "7c9d8bed", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.043624Z", + "iopub.status.busy": "2023-07-26T00:00:27.043492Z", + "iopub.status.idle": "2023-07-26T00:00:27.046326Z", + "shell.execute_reply": "2023-07-26T00:00:27.046047Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.08988921, 0.991491 , 0.12211561, 0.92342997, 0.95603587,\n", + " 0.07513802, 0.0767015 , 0.07513802, 0.07513802, 0.07513802])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n", "fund_qvalues[:10]\n" @@ -562,12 +934,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "bfa39f7c", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.048023Z", + "iopub.status.busy": "2023-07-26T00:00:27.047902Z", + "iopub.status.idle": "2023-07-26T00:00:27.050271Z", + "shell.execute_reply": "2023-07-26T00:00:27.049978Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "146" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(fund_qvalues <= 0.1).sum()\n" ] @@ -589,12 +978,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "70b69b47", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.051940Z", + "iopub.status.busy": "2023-07-26T00:00:27.051831Z", + "iopub.status.idle": "2023-07-26T00:00:27.054171Z", + "shell.execute_reply": "2023-07-26T00:00:27.053894Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(fund_pvalues <= 0.1 / 2000).sum()\n" ] @@ -622,9 +1028,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "4c0ddea1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.055856Z", + "iopub.status.busy": "2023-07-26T00:00:27.055741Z", + "iopub.status.idle": "2023-07-26T00:00:27.058467Z", + "shell.execute_reply": "2023-07-26T00:00:27.058182Z" + } + }, "outputs": [], "source": [ "sorted_ = np.sort(fund_pvalues)\n", @@ -649,12 +1062,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "0314eac9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.060132Z", + "iopub.status.busy": "2023-07-26T00:00:27.060006Z", + "iopub.status.idle": "2023-07-26T00:00:27.320810Z", + "shell.execute_reply": "2023-07-26T00:00:27.320465Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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VqxdiYmLQvXt3A1elmUSpLrZZuIfnCKWlpSE/P58ryIiITEj7hXtxr0xRbR+eKq879+/fh6+vLzIzM1XXHnvsMaxatUrtpon6VFBQACcnJ42/v416jpCYOEeIiMh0DY/7RWMI4rwg3bK3t0dkZCQAoEGDBli+fDkuXbqEF198UfR5QNVhECIiIrOSnJWH5Oz8avtwqXztXL9+HV9//bXa9hEjRmDFihVIS0vD3LlzUa9ePQNWVzt8NKaBtkNrRERkHFrP3V3tERoAH4nV1N27dxEVFYUVK1agpKQE586dg6+vr9hlVYuPxoiIyOK0DKs+BEnAEFQTSqUSW7duRbt27bBo0SLcvXsXcrkcoaGhYpemMwxCasTFxcHX19coZ7gTEVFlyVl5aBm2u9o+EgCZDEFaO3nyJHr16oWxY8dWOXczPj4ee/bsEaky3WIQUoOTpYmITMOKvZcw/JNfNfb7gZOjtZKdnY1XX30VPXr0EDybUyqVYvLkyejatasI1eme6PsIERER1VZyVh7WHhE+wuFhPq4NODlag+LiYkRGRmLVqlW4d++eYJ/+/fsjOjoafn5+Bq5OfxiEiIjIZI3+VPNIkI0U2D+zjwGqMU0KhQJbtmxBWFgYrl27JtinTZs2iIqKwpAhQ4x6KXxtMAgREZFJitx3GeUaVoe5NrTFyfkDDFOQCfr1118xY8YMtdNAnJycsGjRIrzzzjuwtbU1cHWGwTlCanCyNBGRcYs7dLXadp9m9RmCNFi/fr1gCJJKpXj77beRnp4OmUxmtiEI4D5CGnEfISIi4+M9bzeq2zh65ciOGNPdy3AFmagbN27Ax8cHRUVFqmsDBgxAdHQ0OnToIGJldcd9hIiIyCwNj/ul2hD07jNtGIK01Lx5c8ybNw8A4OPjg127dmHfvn0mH4JqgnOEiIjIZGhzfEbfts0MVI1pOHbsGG7evInRo0cLts+cORPOzs6YOHEibGxsDFyd+DgiREREJmPZ7kvVto/o7M5l8v/KzMzE6NGj0bt3b7z55pvIzc0V7Gdvb48333zTIkMQwCBEREQm5PLNArVtXTydED2mswGrMU4FBQUICwtDu3bt8P333wMA8vLysGTJEpErM04MQmpw1RgRkXFJzspDUYlcsK2hnRV+mPqUgSsyLnK5HOvWrYO3tzdWrlyJ0tLSSu2ffPIJ0tLSRKrOeDEIqcEjNoiIjEvcoQy1beeXDDJgJcbn0KFD6Nq1K9544w38/fffVdqdnZ0RGxuLxx9/XITqjBsnSxMRkUk4mlb1FzwAPO7iYOBKjEdGRgbee+897Ny5U7Dd2toa06ZNw8KFC9G4MedOCWEQIiIio7ctKQulwk/F8LhLfcMWYwTy8/OxdOlSrF69GmVlZYJ9hgwZgg8//BA+Pj4Grs60MAgREZHR++HMX2rbpvb3NmAl4lIoFPjf//6HhQsX4vbt24J9OnTogOjoaAwYwF21tcE5QkREZPRO/ZEneL1JfRuLWi4vkUiwZcsWwRDk4uKCtWvXIjk5mSGoBhiEiIjIqEXuuwy5msOggtq7GrYYkUkkEsTExFQ6Ad7GxgazZs1Ceno63nzzTVhb82FPTTAIERGRUTtyRXiSNAC8FGB5R2l07doVISEhAIBhw4YhNTUVkZGRaNSokbiFmSgGITW4jxARkXG4cL1Q8Lqbo51ZPhYrLy9HXFwcEhIS1PZZtmwZEhISsGPHDrRp08aA1Zkfnj6vAU+fJyIST+fwfci7Wy7Y9u4zbTBzQFsDV6Rf+/btg0wmQ2pqKtq3b49z587xUVct8fR5IiIyaduSstSGIMC8Dle9dOkSnn32WQwaNAipqamqa5999pnIlZk/BiEiIjJKy3anqm3zcW1gFo/F/vnnH0yfPh0dO3bE3r17q7QvXrwYxcXFIlRmORiEiIjI6GxLykLBfTU7KALYP7OPAavRvbKyMnz00Ufw9vbGxx9/DLm86s/auXNnbN++HfXrW96GkYbEB49ERGR0Yg+qPxx0oK/pPhJTKpXYs2cPQkNDceXKFcE+bm5uWL58OcaNGwcrKysDV2h5GISIiMioJGfl4UZ+idr2Z0x076CLFy9CJpNh//79gu12dnYIDQ1FWFgYGjZsaODqLBeDEBERGZVvErPUtnk2tseY7qa1d1BBQQHmzp2LtWvXQqFQCPYZM2YMVqxYgZYtWxq2OGIQIiIi45J3t1Twup21BMfmPGPgaurOxsYGu3btEgxB3bp1Q2xsLHr16iVCZQRwsrRa3FCRiEgcJeXCoybhL3QwcCW6Ua9ePaxatarSNXd3d3zxxRc4efIkQ5DIGITUmDp1KlJTU5GUlCR2KUREFiM5Kw9H0oRPVTdlL774Ip588knY29tj0aJFSEtLw7hx4yCV8tew2Pj/ASIiMhozvklW23Y2+47hCqmhnJwcvPnmm2pXgkkkEqxfvx5XrlzBkiVLuCTeiHCOEBERGYXkrDz8mXtPbbufZyPDFaOlkpISrF69GkuXLkVhYSGuX7+O//u//xPs265dOwNXR9rgiBARERmFw9WcMt+soZ1RrRZTKpXYvn07fH19MWfOHBQWVhwMu2vXLhw4cEDk6qgmGISIiMgo3My/L3jdWgokzg8ycDXqJScno2/fvhg1ahR+//33Ku0LFy4UoSqqLQYhIiIyCm5O9oLXp/ZrY+BKhN24cQOTJk1C165dcfToUcE+ISEh2L59u4Ero7rgHCEiIjIKP5z5S/C6e6N6Bq6ksvv37yMmJgbLly9HUVGRYJ9evXohJiaGW66YIAYhIiIS3bakLGTnCT8as7ES5+GFUqnEd999h9mzZ+PPP/8U7PPYY49h1apVGD16NCQSiYErJF1gECIiItElXMpR29bKxfBLzdPS0jBx4kQcP35csL1+/fqYN28eZs6ciXr1xB2xorphECIiItFl5d4VvN7OrQE6ezU2cDVAgwYNkJxcdU8jiUSCCRMmYOnSpWjevLnB6yLd42RpIiISVXJWHi7fFJ57M6FXKwNXU8Hd3R1hYWGVrvXu3RunTp3C+vXrGYLMCIMQERGJavgnv6ptE2t+EACEhobC09MTrVq1wvfff4/Dhw+jS5cuotVD+sEgREREomkZtrvadn3ODzp58iSef/553LlzR7DdwcEB8fHxSE1NxciRIzkZ2kwxCKnB0+eJiPRnW1KWxhDU2auRXuYH/fXXX3jttdfQo0cP7N69Gx988IHavr6+vrC3F97fiMyDRKlUKsUuwpgVFBTAyckJ+fn5cHR0FLscIiKT9/TKBLVL5R+wsQLSlz2n0/sWFxcjMjISq1atwr17/51pZmNjg4sXL8Lb21un9yNxafv7myNCRERkMNqEIEC3IUihUGDz5s1o27YtlixZUikEAUBZWRmio6N1dj8yLVw+T0REBlHdpokP+2OF7kLQr7/+ihkzZiApKUmw3dHREYsWLcI777yjs3uSaWEQIiIig1i2O1VjH12FoD///BNhYWH45ptvBNulUikmT56M8PBwNG3aVCf3JNPEIERERHoXsuEkCu7Lq+2jixBUVFSEFStWICoqCvfvC48+BQUFITo6Gh07dqzz/cj0MQgREZFeJWfl4UjabbXtDjZSpH4wuM732bNnD15//XXcuHFDsN3HxwdRUVF47rnnuBSeVDhZmoiI9CrzdnG17V+/0UMn92nSpIlgCGrUqBFiY2Nx/vx5PP/88wxBVAmDEBER6VV1myL29XHR2V5BgYGBePXVV1Wvrays8M477yAjIwPvvvsubG1tdXIfMi8MQkREpFdfnvhD8LpnY3tsmhio03tFRESgXr16GDRoEM6dO4ePP/4YTZo00ek9yLwwCBERkd4kZ+VhR/J1wbYnW7vU6LPkcjnWr1+PgQMHQi4XnnjdokULXLhwAXv37oWvr2+N6yXLwyBERER6M/pT9Qeq5t0t1fpzDh8+jG7duuH111/HgQMHsH79erV9H3/88RrVSJaNQYiIiPSic/g+lFdziNMz7V01fsbVq1cxYsQI9OvXDykpKarrCxYsQH5+vg6qJEvHIERERDoXsuEk8u6Wq233bGyPMd291Lbn5+dj9uzZ8PX1xY4dO6q037p1C99//71OaiXLVusgdPXqVSxYsAAvv/wy/v77bwDA3r17cfHiRZ0VR0REpmdbUla1+wbZWUtwbM4zgm1yuRyfffYZvL29ERkZidLSqo/PnnjiCezbtw+TJk3SWc1kuWoVhI4cOYKOHTvi5MmT+OGHH1BUVAQAOHv2LBYvXqzTAomIyHSs2HsJc7afr7bPN5N7Cl5PSEhA586d8dZbb+HWrVtV2ps0aYJPPvkEKSkpGDhwoE7qJapVEAoLC8PSpUtx4MCBSvsy9O/fH7/99pvOiiMiItORnJWHtUd+r7bPiM7uVfYNSk9PxwsvvICgoCCcP181RNnY2CA0NBQZGRmYMmUKrK15KALpTq2+TefPn8eWLVuqXG/WrBlu31Y/HEpEROYpOSsPIz9Rv0IMALp4OiF6TOdK11asWIFFixahrKxM8D0vvPACIiMj4e3trbNaiR5WqxGhRo0aCW5jnpycDA8PjzoXpUvZ2dno27cvfH190alTJ3z33Xdil0REZFZmbkvG8E9+haKaPg3trPDD1KeqXPf09BQMQR07dsTBgwexc+dOhiDSq1oFoZdeeglz5szBzZs3IZFIoFAocPz4ccyaNQvjxo3TdY11Ym1tjdjYWKSmpmL//v2YMWMGiourP/eGiIi0E7LhpNoNEx+QAji/ZJBg28svv4zAwP92l27atCk+++wzJCcn45lnhCdUE+lSrYLQ8uXL0a5dO3h6eqKoqAi+vr7o3bs3nnzySSxYsEDXNdZJ8+bN4e/vDwBwc3ODi4sLcnNzxS2KiMgMDI/7pdrVYQ9sf/tJKBTC40VSqRSxsbGws7PD7NmzkZ6ejsmTJ8PKykrX5RIJqlUQsrW1xeeff46rV69i165d2Lx5My5fvoyvvvqqxl/eo0ePYsiQIXB3d4dEIsHOnTur9ImLi0PLli1hb2+PwMBAJCYm1qZsnD59GnK5HJ6enrV6PxERVSyP95m/G8nZmjc0DOnijE1R72PMmDFq+/To0QPZ2dlYuXIlnJycdFkqkUZ1mnrv5eUFLy/1G2Jpo7i4GH5+fpg4cSJGjBhRpX3btm2QyWRYu3YtAgMDERsbi+DgYFy5cgXNmjUDAPj7+6O8vOrGXfv374e7uzsAIDc3F+PGjcPnn39ep3qJiCzZ0ysTkJ13X2M/qaIco+pfwsdvrUJeXh4A4NChQ+jXr59g/6ZNm+q0TiJtSZRKZTUboAubOHFite0bNmyoXTESCXbs2IFhw4aprgUGBqJ79+5Ys2YNAEChUMDT0xPTpk1DWFiYVp9bUlKCAQMG4I033sBrr72msW9JSYnqdUFBATw9PZGfnw9HR8ea/1BERGai4+J4FJYIH3b6gFKphCTrDOyTt+DKlSuV2vz8/HD69Gk+9iKDKCgogJOTk8bf37UaEXqQ7h8oKyvDhQsXcOfOHfTv3782HymotLQUp0+fxty5c1XXpFIpgoKCcOLECa0+Q6lUYvz48ejfv7/GEAQAERERWLJkSa1rJiIyRx0Wx6NIQwgqvfUnio6sR+HVM4Ltly9fRnJyMrp166aPEolqpVZBSOjcF4VCgSlTpqB169Z1LuqB27dvQy6Xw9W18sF8rq6uuHz5slafcfz4cWzbtg2dOnVSzT/66quv0LFjR8H+c+fOhUwmU71+MCJERGSpIvddrjYEye/m484vW1B0di+gZlL0mDFjsGLFCrRs2VJPVRLVjs6255RKpZDJZOjbty9mz56tq4+ts6eeekrtagUhdnZ2sLOz02NFRESm5YvjfwheV8rLUHhmN+4c3wplifC2JN26dUNsbCx69eqlxwqJak+n+5RfvXpVcNJybbm4uMDKygo5OTmVrufk5MDNzU1n9xESFxeHuLg4yOXVDwUTEZmzbUlZKCqt/PegUqnEvYxE5B1aj/I84T2E3N3dERERgVdffRVSaa3P9ybSu1oFoYcfHQEV/1LcuHEDu3fvRkhIiE4KAyqW6Xft2hUJCQmqCdQKhQIJCQl45513dHYfIVOnTsXUqVNVk62IiCzRp4evVnqtVCpx64cPcC9DeBsTe3t7zJ49G7Nnz0b9+vUNUSJRndQqCCUnJ1d6LZVK0bRpU0RFRWlcUfaooqIiZGRkqF5nZmYiJSUFzs7O8PLygkwmQ0hICLp164aAgADExsaiuLgYEyZMqE3pRESkpdBvU/DHP3crXZNIJLBt1lowCI0dOxYRERF13laFyJBqtXxelw4fPiy4r0RISAg2bdoEAFizZg0iIyNx8+ZN+Pv746OPPqq0Jbs+abv8jojIXCRn5eGl/51ASbnwrwdF6X3c3zINt3Iqzpx8sMdbjx49DFkmUbW0/f0tehAyVg/PEUpLS2MQIiKLsGLvJezbcQxeudeR2cgVWS5VR3fauTXAK02yMG/ePKxcuRIvvfQS5wGR0dF5EOrcuTMkEolWNz9zRngPCVPEESEishTnzv0ODAyGIicDMwEEAhjUqgumD52NAvsGqn7vPtMG7z7jjfv378PBwUG0eomqo/MNFR/e7ZmIiMzM1atw8ffBIqUCGwAoAfwKYFJmMj76aRXGvxiu6tq3bTNIpVKGIDILWgehxYsX67MOIiISyf3UVMQ+8QSWASh66HopgLlQ4vvMM2iZew1/OHtgSp/H0dmrsUiVEumeTvcRIiIi06FUKrH9iy/w3oQJ+ENNnyQAeQDGNS1D5zefZAgis1Or2W1yuRwffvghAgIC4ObmBmdn50p/zEFcXBx8fX3RvXt3sUshItK506dPo0+fPhitJgTVB7AUwGUAjQFMHD+QIYjMUq2C0JIlSxAdHY0xY8YgPz8fMpkMI0aMgFQqxfvvv6/jEsUxdepUpKamIikpSexSiIh05vr165gwYQK6d++OY8eOVWmXAJgAIA3AfAD1AKBbN8Db26B1EhlKrZbPt27dGh999BGee+45NGzYECkpKaprv/32G7Zs2aKPWkXBVWNEZA7u3buHqKgorFixAsXFwueCPQ0gBkDXhy9aWQG3bgGNORpEpkXnq8YedvPmTdXp7Q0aNEB+fj4A4Pnnn8fChQtr85FERKQn+fn58PPzw59//inY3hLAhwBGoGJEqJL0dIYgMmu1ejTWokUL3LhRsaNo69atsX//fgBAUlIST24nIjIyTk5O6NmzZ5XrDQGsAHAJwEgIhKDly4FWrfReH5GYahWEhg8fjoSEBADAtGnTsHDhQnh7e2PcuHE1PmvMWHGyNBGZkxUrVsDe3h5AReB5HRXzgOYAsFf3plGjDFIbkZhqNEdozZo1ePXVV9GoUaNK10+cOIETJ07A29sbQ4YM0XWNouIcISIyFXfv3oWdnR2srKwE2xcsWIDju3Yh5uxZ+Gv6sE6dgLNndV0ikcHo5awxJycnlJWVYfjw4Zg0aRL69++vk2KNGYMQERk7hUKBLVu2YO7cuViyZInakfmysjJYN2oEyd27gu2VJCVVrBYjMlHa/v6u0aOxmzdvYu3atbh+/ToGDBiAVq1a4YMPPkB2dnadCyYiopo7ceIEevbsiddeew1//fUX5s2bh8LCQsG+Nl9+qV0IevpphiCyGDUKQvXq1cO4ceNw6NAhpKen47XXXsP69evRqlUrDBo0CN999x3Kysr0VSsREf0rKysLY8eOxZNPPonExETV9ZycHERERFTunJtbEWxef13zB9evD/z4o46rJTJetZosDQCPP/44wsPDkZmZib1796JJkyYYP348PDw8dFkfERE9pKioCIsWLULbtm2xdetWwT4XLlyAUqkE9u0DXnwRcHUFTp/W7gaHD3O5PFmUOp81JpFIYG1tDYlEAqVSaTYjQnFxcYiLi4NcLhe7FCIiKBQKfPXVV5g7d65q+5JH+fj4ICoqCs+1aweJoyNQVCTYT4gSgKR/fz4SI4tTq52lASA7OxsbN27Epk2bkJWVhd69e2PSpEkYOXKkaommOeBkaSIS2y+//IKZM2fi1KlTgu2NGjXC+++/jymtW8N23braPdp67DEgOZmjQWQ29LKzdGlpKX744Qds2LABP//8M5o3b46QkBBMnDgRjz/+eJ2LJiKi//zxxx+YM2cOvv32W8F2KysrTJkyBe/7+KDJrFlAaWntb/b55wxBZJFqFITc3Nxw9+5dPP/88/i///s/BAcHQyqt9TQjIiJSIzk5GT179kRJSYlg+6BBgxDVqxd8P/igTgFICUDSpAkwYECtP4PIlNUoxSxYsADZ2dn4/vvvMXjwYEilUmzdulXtAX5ERFQ7fn5+6NChQ5Xr7dq1w56lS7H3yBH4LlxYt1EgAJLGjSv2DCKyULWeI/SAo6MjUlJSzPbRGOcIEZFYfvnlFzz99NMAAGdnZyyZPRtv7t0LmyNH6v7hNjbAN98AI0bU/bOIjJBeT59/WB1zFBGRRcvJyYGrq6tg21NPPYVXXnkFLi4uWLRoEZxfeAHKX36p+027d69YWs85QUS130eIiIhqLz8/H7Nnz4aXlxeOHTumtt9XX32F2EWL4Pzss8Avv1Q9IV5bAwYAS5cCaWlAYiJDENG/ajQipFAoEBkZiZ9++gmlpaV45plnsGfPHrPcRJH7CBGRPsjlcqxbtw4LFy7ErVu3AAAzZsxAUlKS4OITiUQCjB0L5cmTtQtB9esD588DrVrVrXAiM1WjEaFly5Zh3rx5aNCgATw8PLB69Wps3LgRdnZ2+qpPNFOnTkVqaiqSOImQiHQkISEBnTt3xltvvaUKQQBw5swZfPnll8Jv+ugjYN++moUgJydgzBhg//6KTRUZgojUqtGI0JdffolPPvkEb775JgDg4MGDeO6557Bu3TouoyciUiM9PR2zZs3CTz/9JNhuY2ODG+fP//foqkkTIC8P2Lq1ZqvC7OyAPXuA/v11VDmR+avRqjE7OztkZGTA09NTdc3e3h4ZGRlo0aKFXgoUG1eNEVFt3blzBx988AE+/vhjtccPvdChAyJzcuD90AhRrXz8MfDOO3X7DCIzopdVY+Xl5VWOz7CxsTGb88WIiHShvLwc//vf/7Bo0SL8888/gn06Wlsjprwcz1y4UKd7KQFIgoMZgohqqUZBSKlUYvz48ZXmBN2/fx9vvfUW6tevr7r2ww8/6K5CIiITsm/fPshkMqSmpgq2NwWwFMCk8nJY6eB+kh49Kh6hEVGt1CgIhYSEVLn26quv6qwYIiJTtnHjRkycOFGwzRbADADzADjp6obdugEnTujq04gsUp13ljZ3nCNERNoqLCyE9+OPI+f27UrXRwBYBaC1Lm/m7AxkZHA/ICI1DLazNBGRxUpLA44cAXJygLt30fCbb7D89m1M+rfZH0AsgD66vm+PHhWrwxiCiOqMQUgNbqhIRFX8G3wyTp9Gm/37gczMKl1CAGwHMPLff9bFPCAAgIcH8N57wLPPAt7euvpUIovHR2Ma8NEYkYVLS6sYffnoI6RmZkIG4DCAKwAe08f9bGyAV1+t2BQxNxfw8QFefJHhh6iG+GiMiKimHn7U1bAhsHEjcPYsbgN4H8BaAA/GiMMA6Gytlrs78PzzwKhRFWeCEZHBMAgREeXmVjxyOnmy0uVSAJ8AWALgziNv+QbANABP1uZ+LVoAvXoBHTtytIdIZAxCRGSZHoz+ZGZWnOdVXKxqUgLYBSAUQLqatzcHkFfTe7ZtC2zeXLHsnYiMAoMQEVmOB/N91q4FrlwR7HIegAzAQTUfYQ9gFoA5ABpoe18GICKjxSBEROYvN7di/s2hQ2q73AKwCMD/ACjU9HkZwAoAXtrcs1Ur4N13ucqLyMgxCBGReUtLA15+GcozZyARaC4B8DGADwAUqPmIAAAxqGY+UKtWwNixQL16gKsr0KcPww+RiWAQIiLzlJsLvPAC8MsvACAYgoCKx1xr1LR5AFiJipEg6cMND4JPy5YMPUQmjkGIiMxPYmLFMvQCdWM8/5Gh4nFY6UPX6qFiDtAsAKrjpFu3BqZN46MuIjPDIERE5uORUSBttAIwExUjPwDwKoCIxx5Di5kzAV9foLwcaNOG4YfITDEIEZF5qGYU6D4qlsF3VPPWeQAuAZjv7Y2ALVu4uovIgjAIEZHperAcft064OLFKs1KVJz79R4qHn1dwSNL3v+d6+PYsiV+5FwfIovEIEREpkeL5fCnUfHI69hD11YBCAeggATSp3oBx44JvpeILIdUcxfLFBcXB19fX3Tv3l3sUojogbQ0YO9eYOBAKNWEoOsAJgDojsohCAAiAWQBkAYPBH76Sa+lEpFp4OnzGvD0eSIjkJgITJkCnDmjtss9AFGo2PCwWE2fp9u1w+exsWgbHKyHIonImPD0eSIyXWlpwNWrFed/RURUG4CUALahYrl7lpo+Ld3dEbl6NUaOHAmJRN2OQkRkiRiEiMh45OZWbFS4b59W3RNRMQ/oVzXtDaysMH/+fMyYOxf29va6qpKIzAiDEBEZj7FjoThwUOPkxb8AzAWwWU27BMCkF17AB2vXws3NTaclEpF5YRAiIuOQlgbs26fVCo6XABxX09a3a1fErFsHf39/3dVGRGaLq8aISHyJiRVHV2hpqcC11l5e2LFjB35OSmIIIiKtMQgRkXhyc4FBg4DAwIrJ0VrqC2D4v//saG2NyCVLcDEtDcOGDeNkaCKqET4aIyLDeLASzMoKkMsrzu+aOBGKX44L/hdZFoBCAE882tClCzB/PiLv3IFbQgLej4lBs2bN9F4+EZknBiEi0i8NK8EeDUFFqNgBOhJAJwAnHvTp0gX47DPVOWCtAXwycaKeiiYiS8FHY0SkP2lpwIABUBw4oLGrAsAXANoC+AAVB6UmAtgyaVLF55w+zcNQiUjnGISISDceHH+Rnl4x+blrV6BtW+DMGUgVimrf+guAQADjUXFExsPC4uNR7O6un5qJyOLx0RgR1U0NN0F82B+o2BH6WzXtVlZWeOGFF1BeXl6HAomI1GMQIqK60XITxIcVouJMsCgAJWr6DBw4ENHR0XjiiSrTpYmIdIZBiIhqb98+rTdBBAA5KuYBzQdwU02fdu3aISoqCoMHD+ZSeCLSOwYhItLswdL3Nm0ApRJISQHWrAGOHdP6I46g4lywZDXtja2tsWTpUrwlk8HGxkYHRRMRacYgREQVHg07V68CLi7AwoW1mv/zsHsAXgTwt0CbtZUV3n7lFSyOiYGzs3Od7kNEVFMMQkSW4uGg4+3932sNYUeJikNM66IegA98ffFmamql68899xw+/PBDtGvXro53ICKqHbMPQnfu3EFQUBDKy8tRXl6Od999F2+88YbYZREZjtCqriZNgH/+Ub2sLuzUeZbOvxshTurcGXFduuDcuXPw9fVFdHQ0goOD6/rpRER1IlEqlUqxi9AnuVyOkpISODg4oLi4GB06dMCpU6fQpEkTrd5fUFAAJycn5Ofnw9HRUc/VEunBoEEVq7oUctUlXYzyCPkZgPfmzfAMCAAyMv4bffrXkSNHcPHiRUyePBnW1mb/32FEJCJtf3+b/d9EVlZWcHBwAACUlJRAqVTCzLMf0X/S0gRXdek6BKUDmAXgJwCv7N2Lza+8UikAPdCnTx/06dNHx3cnIqo90XeWPnr0KIYMGQJ3d3dIJBLs3LmzSp+4uDi0bNkS9vb2CAwMRGJiYo3ucefOHfj5+aFFixZ477334OLioqPqiYzEw7s6P/z66FG93vYOKgLQE6gIQQDw9ddf4+TJk3q9LxGRrog+IlRcXAw/Pz9MnDgRI0aMqNK+bds2yGQyrF27FoGBgYiNjUVwcDCuXLmiOnHa399fcOfZ/fv3w93dHY0aNcLZs2eRk5ODESNGYNSoUXB1ddX7z0akd1rM/6krocdo5QDWAVhobY3bAv/uzZw5E8ePH+c+QERk9EQPQoMHD8bgwYPVtkdHR+ONN97AhAkTAABr167F7t27sWHDBoSFhQEAUlJStLqXq6sr/Pz8cOzYMYwaNUqwT0lJCUpK/tvrtqCgQMufhMhAHl79NW1alV2dlf/8Uym4PHgQ/Oi16l4/TBIcDCxdCty6BbRpgwPHj2Pm++/j4p9/AgIhqGnTpggJCYFSqWQQIiKjJ3oQqk5paSlOnz6NuXPnqq5JpVIEBQXhxIkTWn1GTk4OHBwc0LBhQ+Tn5+Po0aOYMmWK2v4RERFYsmRJnWsn0jk1Z3ppmv8jFEUkj4waPRp2AFSZ7HzlyhXMksmwa9cuwfJsbGwwY8YMzJ8/H05OTjX4wYiIxGPUQej27duQy+VVHmO5urri8uXLWn3Gn3/+icmTJ6smSU+bNg0dO3ZU23/u3LmQyWSq1wUFBfD09KzdD0CkS7U406uSzz8HPDz+Czfp6YIru1T+vZabm4vw8HDExcWpPfx0+PDhWLVqFdo8CFFERCbCqIOQLgQEBGj96AwA7OzsYGdnp7+CiLT16E7PNTjTS1CfPpUDj7e3cAD6V1lZGT777DMsXrwYubm5gn38/PwQGxuLvn371qUyIiLRGHUQcnFxgZWVFXJycipdz8nJgZubm17vHRcXh7i4OMjlcs2diXRJ6BFYly5av/3R+T4KqRWkA4KqDT1CsrKyIJPJUFZWVqXN1dUVy5Ytw/jx42FlZVWjzyUiMiaiL5+vjq2tLbp27YqEhATVNYVCgYSEBPTs2VOv9546dSpSU1ORlJSk1/sQVVn6/u8jsIcpklO0/jjJI5uFSgcEAVu31ris1q1bY/r06ZWu2draIiwsDGlpaZg0aRJDEBGZPNFHhIqKipCRkaF6nZmZiZSUFDg7O8PLywsymQwhISHo1q0bAgICEBsbi+LiYtUqMiKTJTTy89RTwC+/VPkvFKlSAQBQSKWQKhSq66rRno8/rjzfR9P8Hy0tWLAAX3zxBW7fvo3Ro0dj5cqVaNWqVa0/j4jI2Ih+xMbhw4fRr1+/KtdDQkKwadMmAMCaNWsQGRmJmzdvwt/fHx999BECAwMNUh+P2CC9ETj6QiGRqkKPoC5dgDNn/nsdHFwx2tO4ca1KKC0txfr16xESEqLagf1RO3bsgIuLC55++ula3YOISAza/v4WPQgZq4fnCKWlpTEIkW6lpQFt29bufUCdR3uUSiV27dqF0NBQpKenY8mSJVi0aFGtPouIyBgxCOkIR4RIJx5eAebtXTEn6Nln1XZ/dGRI9QgsPr7OpZw/fx4ymQwHD/43D8nBwQFpaWnw8PCo8+cTERkDbX9/G/VkaSKTl5sLDBpUMfrz7LOAj0/Faw3n3Umf6lX5dS0nPD/s1q1bmDJlCvz9/SuFIAC4e/dupY1LiYgsheiTpYnMmsAmiKrXwcFV5wg9PPKjownPJSUl+Pjjj/HBBx+oPTImICAAb731Vq3vQURkqhiEiDR59LGWtn3S0gQ3QZQq5BUrxZKSKtoeWjVWaeRHw4aHmiiVSvz444+YNWsWrl69KtjHw8MDK1aswNixYyGVcoCYiCwPg5Aa3FCRBJe3P7pKq7o+asKHyq1bOh35eVhKSgpmzpyJw4cPC7bXq1cPc+bMwaxZs1C/fn2d3JOIyBRxsrQGnCxtwYSWtz86abm6Ph99VP3KsLQ0nQWfB3JycrBgwQKsX78e6v7VfvXVVxEREYEWLVro9N5ERMaEk6WJ6uLBYy1F5RFB1WOt9HTNfSSSinlA0sq7LyukVhWjRjoOQQBw7tw5rFu3TjAE9ezZEydPnsRXX33FEERE9C8GIbJMjx5r8ShNj7UyMrTrs3VrxejQQ3SxAkydAQMGYMiQIZWueXp6YuvWrTh+/DgCAgL0cl8iIlPFOUJk+rSZzPyANvN+AKB16+o/58GJ8Jr6NG6st3lA6nz44YfYu3cv7OzsEBYWhtDQUNSrV0+v9yQiMlUcEVIjLi4Ovr6+6N69u9ilkDrq9ujJy1P/HqEDTQ8cBF5+uXI/Hx/Nj7W06fOAtzcweLBOQtCNGzewbNkytXOAfHx88NVXXyEtLQ0LFixgCCIiqgYnS2vAydJGTJvJzA/TdKzFo5OX8/IqAlJ1o0fa9NGRe/fuITo6GhERESguLsY333yDMWPG6PQeRETmgkds6AiDkJGqaagBNB5rgT17KkZtHqXNYy09PvpSKpX49ttvMXv2bGRlZamue3l54fLlyxzxISISwFVjZN60maj8KG3m/QjR5rGWDh99PSwpKQlPP/00XnrppUohCACysrIQGxur0/sREVkaBiEyTbUJNTWZ0yOya9euISQkBAEBATh+/HiVdolEgkmTJmHChAkiVEdEZD4YhMg01TbUGHg5e03dvXsX4eHh8PHxwZdffinYp0+fPjh9+jTWrVsHNzc3A1dIRGReuHxeDR6xYQK2boX0kYnKGkONCMvZtaFUKrF161bMmTMHf/31l2Cfxx9/HJGRkRg+fDgkEomBKyQiMk+cLK0BJ0ubACMLNTWVmJiId999F7/99ptge8OGDbFw4UJMnz4ddnZ2Bq6OiMg0afv7myNCZPrqeEq72E6dOiUYgqRSKV5//XWEh4fD1dVVhMqIiMwf5wgRiWzy5Mnw9fWtdK1///5ITk7GZ599xhBERKRHDEJEIrO2tkZMTAwAoE2bNvjxxx9x8OBBdOrUSeTKiIjMH4MQkQEcP34cy5cvV9s+cOBAfPfdd7h48SKGDh3KydBERAbCOUIknpoclmqi/vjjD8yZMwfffvstgIrA061bN8G+o0aNMmRpREQEjgiRGGpzWKqJKSwsxPz589GuXTtVCAKAGTNmqD0slYiIDI9BSA2ePq9H2p4Ab4IUCgU2btwIHx8fLF++HCUlJZXajx8/jp9++kmk6oiI6FHcR0gD7iOkY7U5LNVEHD16FDNmzEBycrJge+PGjfH+++9jypQpsLGxMXB1RESWhYeuknGqzWGpRu7333/HqFGj0KdPH8EQZGVlhenTpyMjIwPTp09nCCIiMiKcLE2GVdsT4I1QQUEBli9fjpiYGJSWlgr2efbZZ/Hhhx+iffv2Bq6OiIi0wREhMiwTOgG+Ohs3boS3tzdWrlwpGIJ8fX0RHx+P3bt3MwQRERkxBiEyPCM/AV4bZ8+exd9//13lepMmTRAXF4ezZ88iODhYhMqIiKgmOFlaA06W1iMTPiw1NzcX3t7eyM3NBVCxO/T06dOxYMECNG7cWOTqiIiIk6XJ+Hl7A4MHm1wIAgBnZ2csWbIEADB06FBcvHgRUVFRDEFERCaGk6WJBJSXl2PdunWQy+WYOnWqYJ8333wTHTt2RJ8+fQxcHRER6QqDENEjDhw4gJkzZ+LixYto0KABRowYgebNm1fpZ2NjwxBERGTi+GhMDe4sbXmuXLmCIUOGYODAgbh48SIAoKioCAsWLBC5MiIi0hdOltaAk6XNX15eHsLDw7FmzRqUl5dXaZdIJEhPT0drTXsgERGR0eBkaSINysrKsGbNGrRp0waxsbGCIcjPzw8JCQkMQUREZopzhMgixcfHQyaT4dKlS4LtzZo1w/LlyzF+/HhYWVkJ9iEiItPHIEQW5dKlSwgNDcXevXsF221tbSGTyTB37lw+CiUisgAMQmQRysrKIJPJ8Omnn0Iulwv2GTVqFFatWoVWrVoZuDoiIhILgxBZBBsbG1y9elUwBHXp0gUxMTHo3bu3CJUREZGYOFmaLEZUVFSl+T5ubm7YuHEjkpKSGIKIiCwUgxBZjPbt2+Ptt9+Gvb095s+fj/T0dIwfPx5SKf81ICKyVNxHSAPuI2Q6bt26hcWLFyMoKAgjRowQ7JObm4vCwkI89thjBq6OiIgMifsIkcUoLS1FdHQ0vL298emnn2LWrFm4f/++YF9nZ2eGICIiUmEQIvXS0oC9e4H0dLErEaRUKvHjjz/iiSeeQGhoKPLz8wEAmZmZWL16tcjVERGRKWAQoqpyc4FBg4C2bYFnnwV8fCpe5+WJXZnKuXPnEBQUhGHDhiEjI6NKe0xMDEpKSkSojIiITAmDkBoWfejq2LFQHDhY6ZLiwEHg5ZdFKug/OTk5mDx5Mjp37oyff/5ZsM8rr7yCpKQk2NnZGbg6IiIyNZwsrYHFTZZOS6sYCaqu3dvbcPX8q6SkBKtXr8bSpUtRWFgo2KdHjx6IjY1FYGCggasjIiJjw8nSVDtXr1bfLvAYSp+USiW2b98OX19fzJkzRzAEeXp6YsuWLfj1118ZgoiIqEa4szRVpumU9TZtDFMHgJs3b2LMmDE4evSoYLuDgwPCwsIQGhoKBwcHg9VFRETmgyNCVJmPDxAcDIW08onrCqkVEBxs0MdiTZo0QU5OjmBbSEgI0tLSsHDhQoYgIiKqNQYhqmrrVkgHBFW6JB0QBGzdatAybGxsEBUVVelar169kJiYiE2bNsHDw8Og9RARkfnhZGkNLG6y9MPS0yvmBLVpI8oEaaBijtCgQYNw5coVrFq1CqNHj4ZEIhGlFiIiMh2cLE115+0NDB6s1xD04MDT5ORkwXaJRIJNmzbh0qVLePHFFxmCiIhIpxiESBTXrl1DSEgIAgICcOzYMcycORPqBiebN2+OevXqGbhCIiKyBAxCZFB3795FeHg4fHx88OWXX6quHzlyBDt37hSvMCIiskgMQmIx8nO8dE2pVGLLli1o27YtFi9ejLt371bp88knn4hQGRERWTIGIUMzgXO8dO23337Dk08+iVdeeQV//fVXlfaGDRti1apV2LVrlwjVERGRJWMQMjQjPsdL17Kzs/HKK6+gZ8+e+O2336q0S6VSTJ48Genp6Xjvvfd4NhgRERkcd5Y2pLQ0YN++KulTqpAD+/ZVPCYTaZm6LhUXF2PVqlWIjIzEvXv3BPv0798fMTEx6NSpk4GrIyIi+g+DkCFpc46XiQeh3377DSNHjsT169cF29u0aYOoqCgMGTKES+GJiEh0fDRmSEZ0jpe+tGnTBsXFxVWuOzk5ISoqChcvXsTQoUMZgoiIyCgwCBmSEZ3jpS8uLi5YvHix6rVUKsXbb7+N9PR0yGQy2NrailgdERFRZTxiQwOdH7GRl1cxMXrfvv+uBQdXnOPVuHHdP99AFAoFpFLhHF1aWooOHTqgVatWiIqKQocOHQxcHRERWToesfGIu3fv4rHHHsOsWbPELaRxYyA+vmLi9J49Ff83Pt5kQpBCocDGjRvh6+uLv//+W7CPra0tfv31V8THxzMEERGRUbOYILRs2TL06NFD7DL+Y4BzvHTt6NGj6N69OyZOnIgrV65g0aJFavu6uLhwHhARERk9iwhC6enpuHz5MgYPHix2KSbp999/x6hRo9CnTx+cOXNGdf3zzz/HuXPnRKyMiIiobkQPQkePHsWQIUPg7u4OiUQieN5UXFwcWrZsCXt7ewQGBiIxMbFG95g1axYiIiJ0VLHlKCgoQFhYGNq3b4/t27dXaVcoFPj+++9FqIyIiEg3RN9HqLi4GH5+fpg4cSJGjBhRpX3btm2QyWRYu3YtAgMDERsbi+DgYFy5cgXNmjUDAPj7+6O8vLzKe/fv34+kpCT4+PjAx8cHv/76q95/HnMgl8uxceNGzJ8/X+08oPbt2yM6OhqDBg0ycHVERES6Y1SrxiQSCXbs2IFhw4aprgUGBqJ79+5Ys2YNgIpRCE9PT0ybNg1hYWEaP3Pu3LnYvHkzrKysUFRUhLKyMoSGhqqd31JSUoKSkhLV64KCAnh6eupu1ZiRO3ToEGbOnImzZ88Ktjs7OyM8PByTJ0+GjY2NgasjIiLSjlmsGistLcXp06cRFBSkuiaVShEUFIQTJ05o9RkRERHIzs7GH3/8gQ8//BBvvPFGtZN8IyIi4OTkpPrj6elZ55/DFGRkZGD48OHo37+/YAiytrbGjBkzkJGRgalTpzIEERGRWTDqIHT79m3I5XK4urpWuu7q6oqbN2/q5Z5z585Ffn6+6k92drZe7mNMNmzYAF9fX8H5WQAwZMgQXLhwATExMWhsIsv8iYiItCH6HCFDGj9+vMY+dnZ2FncKekBAAORyeZXrHTp0QHR0NAYMGCBCVURERPpn1CNCLi4usLKyQk5OTqXrOTk5cHNz0+u94+Li4Ovri+7du+v1PsagQ4cOmDx5suq1i4sLPv30UyQnJzMEERGRWTPqIGRra4uuXbsiISFBdU2hUCAhIQE9e/bU672nTp2K1NRUJCUl6fU+hlRaWqq2LTw8HE2bNsWsWbOQnp6Ot956C9bWFjVgSEREFkj033RFRUXIyMhQvc7MzERKSgqcnZ3h5eUFmUyGkJAQdOvWDQEBAYiNjUVxcTEmTJggYtWmJS8vDx988AHi4+ORnJws+OivadOmyMzMRP369UWokIiISByiB6FTp06hX79+qtcymQwAEBISgk2bNmHMmDG4desWFi1ahJs3b8Lf3x/x8fFVJlBTVeXl5fjss8+wePFi/PPPPwCANWvWIDQ0VLA/QxAREVkao9pHyJjExcUhLi4OcrkcaWlpJreP0L59+yCTyZCamlrpuqOjIzIyMtC0aVORKiMiItI/s9hHSEymOkfo0qVLePbZZzFo0KAqIQgA7t+/j+PHj4tQGRERkfFhEDIT//zzD6ZPn46OHTti7969gn1GjRqFS5cuVdq5m4iIyJKJPkeI6qasrAyffvop3n//feTl5Qn26dKlC2JiYtC7d28DV0dERGTcGIRMlFKpxJ49exAaGoorV64I9nFzc0NERATGjRsHqZSDf0RERI9iEFLj4cnSxmjKlCn47LPPBNvs7Owwa9YszJkzBw0bNjRwZURERKaDq8Y00HbWuaH9+OOPgnN9xowZg5UrV+Kxxx4zfFFERERGgqvGzNzQoUPRv39/1etu3brhl19+wTfffMMQREREpCUGISOmVCpRWFgo2CaRSBAdHQ1PT0988cUXOHnyJHr16mXgComIiEwb5wgZqXPnzmHmzJmQSCQ4cOAAJBJJlT5+fn64evUqbGxsRKiQiIjI9HFESA2xTp/PycnB5MmT0blzZ/z8889ISEjArl271PZnCCIiIqo9TpbWwFCTpUtKSrB69WosXbq0yuMwHx8fnD9/Hra2tnq7PxERkTnhZGkToVQq8cMPP8DX1xdz5swRnBN07949/P777yJUR0REZN4YhESUnJyMfv36YeTIkYJBx8HBAeHh4bh8+TLatWsnQoVERETmjZOlRXDz5k3Mnz8fGzduhLonk+PGjcPy5cvh4eFh4OqIiIgsB4OQAd2/fx8xMTFYvnw5ioqKBPv06tULMTExBp+kTUREZIkYhNTQ9REbCoUCPXr0wNmzZwXbH3vsMaxatQqjR48WXCpPREREusc5QmpMnToVqampSEpK0snnSaVSvPrqq1Wu169fH8uWLcOlS5fw4osvMgQREREZEIOQAU2bNg2tW7cGULEz9MSJE5Geno558+ahXr16IldHRERkeRiEdOzevXvIz88XbLOzs0NkZCR69+6NU6dOYf369WjevLmBKyQiIqIHGIR0RKlUYuvWrWjbti3mzZuntt+wYcNw+PBhdOnSxYDVERERkRAGIR14cODp2LFjkZ2djbVr1+LChQuCfSUSCecBERERGQkGoTr466+/8Nprr6FHjx44ceKE6rpCoUBoaKjaPYKIiIjIOHD5fC0UFxcjMjISq1atwr179wT7lJWVoaioCA0bNjRwdURERKQtjgipIXT6vEKhwObNm9G2bVssWbJEMAS1bt0aO3fuREJCAkMQERGRkePp8xo8OL32wIEDmD9/PhITEwX7OTo6YtGiRXjnnXdgZ2dn4CqJiIjoYdqePs9HY1oaMGCA4HWpVIrJkycjPDwcTZs2NXBVREREVBcMQnUQFBSE6OhodOzYUexSiIiIqBYYhGrBx8cHUVFReO6557gUnoiIyIRxsnQNNGrUCLGxsTh//jyef/55hiAiIiITxxEhLU2ePBnLly9HkyZNxC6FiIiIdISrxjTQdtY5ERERGQ+uGtORBzmxoKBA5EqIiIhIWw9+b2sa72EQ0qCwsBAA4OnpKXIlREREVFOFhYVwcnJS285HYxooFApcv34d/fv3x6lTp9T26969O5KSkrRuKygogKenJ7Kzs436kVt1P5cxfX5tPqcm79Gmr6Y+/I6I9/nm+P0A+B3R5eeb43fE0r8fSqUShYWFcHd3h1Sqfm0YR4Q0kEqlaNGiBaytrav9IllZWaltr67N0dHRqL+g1dVuTJ9fm8+pyXu06aupD78j4n2+OX8/AH5H+B3h3yHqVDcS9ACXz2tp6tSptW7X9F5jpu/adfX5tfmcmrxHm778jhjv5/P7IS5+R/gdqY7YtfPRmEi4Go004XeENOF3hKrD74d2OCIkEjs7OyxevJgHtJJa/I6QJvyOUHX4/dAOR4SIiIjIYnFEiIiIiCwWgxARERFZLAYhIiIislgMQkRERGSxGISIiIjIYjEIGaldu3ahbdu28Pb2xrp168Quh4zM8OHD0bhxY4waNUrsUsgIZWdno2/fvvD19UWnTp3w3XffiV0SGZk7d+6gW7du8Pf3R4cOHfD555+LXZJouHzeCJWXl8PX1xeHDh2Ck5MTunbtil9//RVNmjQRuzQyEocPH0ZhYSG++OILfP/992KXQ0bmxo0byMnJgb+/P27evImuXbsiLS0N9evXF7s0MhJyuRwlJSVwcHBAcXExOnTogFOnTlnk7xmOCBmhxMREPPHEE/Dw8ECDBg0wePBg7N+/X+yyyIj07dsXDRs2FLsMMlLNmzeHv78/AMDNzQ0uLi7Izc0VtygyKlZWVnBwcAAAlJSUQKlUwlLHRRiE9ODo0aMYMmQI3N3dIZFIsHPnzip94uLi0LJlS9jb2yMwMBCJiYmqtuvXr8PDw0P12sPDA9euXTNE6WQAdf1+kPnT5Xfk9OnTkMvl8PT01HPVZEi6+I7cuXMHfn5+aNGiBd577z24uLgYqHrjwiCkB8XFxfDz80NcXJxg+7Zt2yCTybB48WKcOXMGfn5+CA4Oxt9//23gSkkM/H6QJrr6juTm5mLcuHH43//+Z4iyyYB08R1p1KgRzp49i8zMTGzZsgU5OTmGKt+4KEmvACh37NhR6VpAQIBy6tSpqtdyuVzp7u6ujIiIUCqVSuXx48eVw4YNU7W/++67yq+//tog9ZJh1eb78cChQ4eUI0eONESZJKLafkfu37+vfPrpp5VffvmloUolkdTl75EHpkyZovzuu+/0WabR4oiQgZWWluL06dMICgpSXZNKpQgKCsKJEycAAAEBAbhw4QKuXbuGoqIi7N27F8HBwWKVTAakzfeDLJs23xGlUonx48ejf//+eO2118QqlUSizXckJycHhYWFAID8/HwcPXoUbdu2FaVesVmLXYCluX37NuRyOVxdXStdd3V1xeXLlwEA1tbWiIqKQr9+/aBQKDB79myLnMlvibT5fgBAUFAQzp49i+LiYrRo0QLfffcdevbsaehySQTafEeOHz+Obdu2oVOnTqq5I1999RU6duxo6HJJBNp8R/78809MnjxZNUl62rRpFvv9YBAyUkOHDsXQoUPFLoOM1MGDB8UugYzYU089BYVCIXYZZMQCAgKQkpIidhlGgY/GDMzFxQVWVlZVJqXl5OTAzc1NpKrIWPD7QZrwO0Ka8DtSMwxCBmZra4uuXbsiISFBdU2hUCAhIYGPNojfD9KI3xHShN+RmuGjMT0oKipCRkaG6nVmZiZSUlLg7OwMLy8vyGQyhISEoFu3bggICEBsbCyKi4sxYcIEEasmQ+H3gzThd4Q04XdEh0RetWaWDh06pARQ5U9ISIiqz8cff6z08vJS2traKgMCApS//fabeAWTQfH7QZrwO0Ka8DuiOzxrjIiIiCwW5wgRERGRxWIQIiIiIovFIEREREQWi0GIiIiILBaDEBEREVksBiEiIiKyWAxCREREZLEYhIiIiMhiMQgRkcWSSCTYuXOn2GUQkYgYhIjIJI0fPx7Dhg0TuwwiMnEMQkRERGSxGISIyOT17dsX06dPx+zZs+Hs7Aw3Nze8//77lfqkp6ejd+/esLe3h6+vLw4cOFDlc7Kzs/Hiiy+iUaNGcHZ2xgsvvIA//vgDAHD58mU4ODhgy5Ytqv7ffvst6tWrh9TUVH3+eESkRwxCRGQWvvjiC9SvXx8nT57EqlWrEB4ergo7CoUCI0aMgK2tLU6ePIm1a9dizpw5ld5fVlaG4OBgNGzYEMeOHcPx48fRoEEDDBo0CKWlpWjXrh0+/PBDvP3228jKysJff/2Ft956CytXroSvr68YPzIR6QBPnycikzR+/HjcuXMHO3fuRN++fSGXy3Hs2DFVe0BAAPr3748VK1Zg//79eO655/Dnn3/C3d0dABAfH4/Bgwdjx44dGDZsGDZv3oylS5fi0qVLkEgkAIDS0lI0atQIO3fuxMCBAwEAzz//PAoKCmBrawsrKyvEx8er+hOR6bEWuwAiIl3o1KlTpdfNmzfH33//DQC4dOkSPD09VSEIAHr27Fmp/9mzZ5GRkYGGDRtWun7//n1cvXpV9XrDhg3w8fGBVCrFxYsXGYKITByDEBGZBRsbm0qvJRIJFAqF1u8vKipC165d8fXXX1dpa9q0qeqfz549i+LiYkilUty4cQPNmzevfdFEJDoGISIye+3bt0d2dnal4PLbb79V6tOlSxds27YNzZo1g6Ojo+Dn5ObmYvz48Zg/fz5u3LiBV155BWfOnEG9evX0/jMQkX5wsjQRmb2goCD4+PggJCQEZ8+exbFjxzB//vxKfV555RW4uLjghRdewLFjx5CZmYnDhw9j+vTp+OuvvwAAb731Fjw9PbFgwQJER0dDLpdj1qxZYvxIRKQjDEJEZPakUil27NiBe/fuISAgAK+//jqWLVtWqY+DgwOOHj0KLy8vjBgxAu3bt8ekSZNw//59ODo64ssvv8SePXvw1VdfwdraGvXr18fmzZvx+eefY+/evSL9ZERUV1w1RkRERBaLI0JERERksRiEiIiIyGIxCBEREZHFYhAiIiIii8UgRERERBaLQYiIiIgsFoMQERERWSwGISIiIrJYDEJERERksRiEiIiIyGIxCBEREZHFYhAiIiIii/X/XSL/1Wv4qiwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(np.arange(0, sorted_.shape[0]) + 1,\n", @@ -682,12 +1112,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "b59b8137", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.322735Z", + "iopub.status.busy": "2023-07-26T00:00:27.322608Z", + "iopub.status.idle": "2023-07-26T00:00:27.397859Z", + "shell.execute_reply": "2023-07-26T00:00:27.397542Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2 29\n", + "4 25\n", + "3 18\n", + "1 11\n", + "Name: Y, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Khan = load_data('Khan') \n", "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n", @@ -712,12 +1163,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "96fb2f61", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.399700Z", + "iopub.status.busy": "2023-07-26T00:00:27.399569Z", + "iopub.status.idle": "2023-07-26T00:00:27.403423Z", + "shell.execute_reply": "2023-07-26T00:00:27.403115Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-2.0936330736768185, 0.04118643782678394)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "D2 = D[lambda df:df['Y'] == 2]\n", "D4 = D[lambda df:df['Y'] == 4]\n", @@ -748,12 +1216,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "fdc229fa", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:27.405191Z", + "iopub.status.busy": "2023-07-26T00:00:27.405090Z", + "iopub.status.idle": "2023-07-26T00:00:28.262471Z", + "shell.execute_reply": "2023-07-26T00:00:28.262168Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0398" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "B = 10000\n", "Tnull = np.empty(B)\n", @@ -782,12 +1267,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "e3894695", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:28.264140Z", + "iopub.status.busy": "2023-07-26T00:00:28.264026Z", + "iopub.status.idle": "2023-07-26T00:00:28.488348Z", + "shell.execute_reply": "2023-07-26T00:00:28.487989Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", "ax.hist(Tnull,\n", @@ -825,9 +1327,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "3b7392cb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:28.490300Z", + "iopub.status.busy": "2023-07-26T00:00:28.490163Z", + "iopub.status.idle": "2023-07-26T00:01:51.994441Z", + "shell.execute_reply": "2023-07-26T00:01:51.994033Z" + } + }, "outputs": [], "source": [ "m, B = 100, 10000\n", @@ -864,9 +1373,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "cac15616", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:01:51.996501Z", + "iopub.status.busy": "2023-07-26T00:01:51.996360Z", + "iopub.status.idle": "2023-07-26T00:01:52.220394Z", + "shell.execute_reply": "2023-07-26T00:01:52.220065Z" + } + }, "outputs": [], "source": [ "cutoffs = np.sort(np.abs(T_vals))\n", @@ -898,10 +1414,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "9661eb10", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:01:52.222331Z", + "iopub.status.busy": "2023-07-26T00:01:52.222198Z", + "iopub.status.idle": "2023-07-26T00:01:52.225010Z", + "shell.execute_reply": "2023-07-26T00:01:52.224751Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['G0097',\n", + " 'G0129',\n", + " 'G0182',\n", + " 'G0714',\n", + " 'G0812',\n", + " 'G0941',\n", + " 'G0982',\n", + " 'G1020',\n", + " 'G1022',\n", + " 'G1090',\n", + " 'G1320',\n", + " 'G1634',\n", + " 'G1697',\n", + " 'G1853',\n", + " 'G1854',\n", + " 'G1994',\n", + " 'G2017',\n", + " 'G2115',\n", + " 'G2193']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])\n" ] @@ -917,10 +1469,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "18ad4900", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:01:52.226732Z", + "iopub.status.busy": "2023-07-26T00:01:52.226617Z", + "iopub.status.idle": "2023-07-26T00:01:52.229181Z", + "shell.execute_reply": "2023-07-26T00:01:52.228899Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['G0097',\n", + " 'G0129',\n", + " 'G0158',\n", + " 'G0182',\n", + " 'G0242',\n", + " 'G0552',\n", + " 'G0679',\n", + " 'G0714',\n", + " 'G0751',\n", + " 'G0812',\n", + " 'G0908',\n", + " 'G0941',\n", + " 'G0982',\n", + " 'G1020',\n", + " 'G1022',\n", + " 'G1090',\n", + " 'G1240',\n", + " 'G1244',\n", + " 'G1320',\n", + " 'G1381',\n", + " 'G1514',\n", + " 'G1634',\n", + " 'G1697',\n", + " 'G1768',\n", + " 'G1853',\n", + " 'G1854',\n", + " 'G1907',\n", + " 'G1994',\n", + " 'G2017',\n", + " 'G2115',\n", + " 'G2193']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])\n" ] @@ -937,10 +1537,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "28c276b6", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:01:52.230871Z", + "iopub.status.busy": "2023-07-26T00:01:52.230743Z", + "iopub.status.idle": "2023-07-26T00:01:52.309521Z", + "shell.execute_reply": "2023-07-26T00:01:52.309196Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(Rs, FDRs, 'b', linewidth=3)\n", @@ -954,6 +1572,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch2-statlearn-lab.ipynb b/docs/source/labs/Ch2-statlearn-lab.ipynb index 9b826df..674581e 100644 --- a/docs/source/labs/Ch2-statlearn-lab.ipynb +++ b/docs/source/labs/Ch2-statlearn-lab.ipynb @@ -101,10 +101,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "88d0bfff", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.336320Z", + "iopub.status.busy": "2023-07-25T23:59:03.335906Z", + "iopub.status.idle": "2023-07-25T23:59:03.345178Z", + "shell.execute_reply": "2023-07-25T23:59:03.344494Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fit a model with 11 variables\n" + ] + } + ], "source": [ "print('fit a model with', 11, 'variables')\n" ] @@ -119,9 +134,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ebe7aca8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.348214Z", + "iopub.status.busy": "2023-07-25T23:59:03.348018Z", + "iopub.status.idle": "2023-07-25T23:59:03.350992Z", + "shell.execute_reply": "2023-07-25T23:59:03.350615Z" + } + }, "outputs": [], "source": [ "print?\n" @@ -137,10 +159,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "df2f8168", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.353557Z", + "iopub.status.busy": "2023-07-25T23:59:03.353389Z", + "iopub.status.idle": "2023-07-25T23:59:03.357986Z", + "shell.execute_reply": "2023-07-25T23:59:03.357641Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "3 + 5\n" ] @@ -159,10 +199,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4cc5c2c1", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.360072Z", + "iopub.status.busy": "2023-07-25T23:59:03.359922Z", + "iopub.status.idle": "2023-07-25T23:59:03.362322Z", + "shell.execute_reply": "2023-07-25T23:59:03.362042Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'hello world'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"hello\" + \" \" + \"world\"\n" ] @@ -190,10 +248,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "e5d81180", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.364153Z", + "iopub.status.busy": "2023-07-25T23:59:03.364018Z", + "iopub.status.idle": "2023-07-25T23:59:03.366529Z", + "shell.execute_reply": "2023-07-25T23:59:03.366235Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 4, 5]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x = [3, 4, 5]\n", "x\n" @@ -213,10 +289,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "425a714c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.368332Z", + "iopub.status.busy": "2023-07-25T23:59:03.368198Z", + "iopub.status.idle": "2023-07-25T23:59:03.370618Z", + "shell.execute_reply": "2023-07-25T23:59:03.370345Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 4, 5, 4, 9, 7]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y = [4, 9, 7]\n", "x + y\n" @@ -269,9 +363,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "2f88669a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.372629Z", + "iopub.status.busy": "2023-07-25T23:59:03.372498Z", + "iopub.status.idle": "2023-07-25T23:59:03.424071Z", + "shell.execute_reply": "2023-07-25T23:59:03.423098Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -299,9 +399,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e8ec382a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.429959Z", + "iopub.status.busy": "2023-07-25T23:59:03.429583Z", + "iopub.status.idle": "2023-07-25T23:59:03.436080Z", + "shell.execute_reply": "2023-07-25T23:59:03.435328Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -332,12 +438,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "04de39ad", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.441429Z", + "iopub.status.busy": "2023-07-25T23:59:03.440989Z", + "iopub.status.idle": "2023-07-25T23:59:03.449302Z", + "shell.execute_reply": "2023-07-25T23:59:03.448567Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7, 13, 12])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x + y" ] @@ -362,12 +485,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "fee5d798", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.454224Z", + "iopub.status.busy": "2023-07-25T23:59:03.453864Z", + "iopub.status.idle": "2023-07-25T23:59:03.463532Z", + "shell.execute_reply": "2023-07-25T23:59:03.462539Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x = np.array([[1, 2], [3, 4]])\n", "x" @@ -395,10 +536,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "5871ca00", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.468600Z", + "iopub.status.busy": "2023-07-25T23:59:03.468292Z", + "iopub.status.idle": "2023-07-25T23:59:03.475945Z", + "shell.execute_reply": "2023-07-25T23:59:03.475149Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x.ndim" ] @@ -415,12 +574,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "5a32759c", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.481118Z", + "iopub.status.busy": "2023-07-25T23:59:03.480680Z", + "iopub.status.idle": "2023-07-25T23:59:03.488333Z", + "shell.execute_reply": "2023-07-25T23:59:03.486555Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x.dtype" ] @@ -438,12 +614,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "55f01b16", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.495516Z", + "iopub.status.busy": "2023-07-25T23:59:03.494194Z", + "iopub.status.idle": "2023-07-25T23:59:03.507143Z", + "shell.execute_reply": "2023-07-25T23:59:03.506347Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([[1, 2], [3.0, 4]]).dtype\n" ] @@ -460,9 +653,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "2a9de618", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.516160Z", + "iopub.status.busy": "2023-07-25T23:59:03.515522Z", + "iopub.status.idle": "2023-07-25T23:59:03.522135Z", + "shell.execute_reply": "2023-07-25T23:59:03.521022Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -480,12 +679,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "33e3e6ea", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.528006Z", + "iopub.status.busy": "2023-07-25T23:59:03.527073Z", + "iopub.status.idle": "2023-07-25T23:59:03.535281Z", + "shell.execute_reply": "2023-07-25T23:59:03.533210Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([[1, 2], [3, 4]], float).dtype\n" ] @@ -501,12 +717,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "623f8434", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.540712Z", + "iopub.status.busy": "2023-07-25T23:59:03.540315Z", + "iopub.status.idle": "2023-07-25T23:59:03.545866Z", + "shell.execute_reply": "2023-07-25T23:59:03.545195Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x.shape\n" ] @@ -527,12 +760,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "235738a7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.550091Z", + "iopub.status.busy": "2023-07-25T23:59:03.549770Z", + "iopub.status.idle": "2023-07-25T23:59:03.555222Z", + "shell.execute_reply": "2023-07-25T23:59:03.554501Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x = np.array([1, 2, 3, 4])\n", "x.sum()" @@ -548,12 +798,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "7f3494e1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.559801Z", + "iopub.status.busy": "2023-07-25T23:59:03.559468Z", + "iopub.status.idle": "2023-07-25T23:59:03.565409Z", + "shell.execute_reply": "2023-07-25T23:59:03.564548Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x = np.array([1, 2, 3, 4])\n", "np.sum(x)" @@ -577,10 +844,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "0ca8f936", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.570100Z", + "iopub.status.busy": "2023-07-25T23:59:03.569563Z", + "iopub.status.idle": "2023-07-25T23:59:03.575143Z", + "shell.execute_reply": "2023-07-25T23:59:03.574126Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "beginning x:\n", + " [1 2 3 4 5 6]\n", + "reshaped x:\n", + " [[1 2 3]\n", + " [4 5 6]]\n" + ] + } + ], "source": [ "x = np.array([1, 2, 3, 4, 5, 6])\n", "print('beginning x:\\n', x)\n", @@ -609,12 +895,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "23fba624", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.579248Z", + "iopub.status.busy": "2023-07-25T23:59:03.578903Z", + "iopub.status.idle": "2023-07-25T23:59:03.584347Z", + "shell.execute_reply": "2023-07-25T23:59:03.583515Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x_reshape[0, 0] " ] @@ -630,12 +933,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "1a1df926", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.588305Z", + "iopub.status.busy": "2023-07-25T23:59:03.587875Z", + "iopub.status.idle": "2023-07-25T23:59:03.596795Z", + "shell.execute_reply": "2023-07-25T23:59:03.596057Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x_reshape[1, 2] " ] @@ -654,10 +974,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "4bfcc1e1", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.601576Z", + "iopub.status.busy": "2023-07-25T23:59:03.600688Z", + "iopub.status.idle": "2023-07-25T23:59:03.607941Z", + "shell.execute_reply": "2023-07-25T23:59:03.606891Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x before we modify x_reshape:\n", + " [1 2 3 4 5 6]\n", + "x_reshape before we modify x_reshape:\n", + " [[1 2 3]\n", + " [4 5 6]]\n", + "x_reshape after we modify its top left element:\n", + " [[5 2 3]\n", + " [4 5 6]]\n", + "x after we modify top left element of x_reshape:\n", + " [5 2 3 4 5 6]\n" + ] + } + ], "source": [ "print('x before we modify x_reshape:\\n', x)\n", "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n", @@ -688,12 +1032,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "973c562a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.616078Z", + "iopub.status.busy": "2023-07-25T23:59:03.615705Z", + "iopub.status.idle": "2023-07-25T23:59:03.778706Z", + "shell.execute_reply": "2023-07-25T23:59:03.778393Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m my_tuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + ] + } + ], "source": [ "my_tuple = (3, 4, 5)\n", "my_tuple[0] = 2\n" @@ -710,10 +1072,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "56b5c382", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.780591Z", + "iopub.status.busy": "2023-07-25T23:59:03.780456Z", + "iopub.status.idle": "2023-07-25T23:59:03.782964Z", + "shell.execute_reply": "2023-07-25T23:59:03.782700Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((2, 3),\n", + " 2,\n", + " array([[5, 4],\n", + " [2, 5],\n", + " [3, 6]]))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x_reshape.shape, x_reshape.ndim, x_reshape.T\n" ] @@ -732,10 +1116,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "2e624622", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.784591Z", + "iopub.status.busy": "2023-07-25T23:59:03.784467Z", + "iopub.status.idle": "2023-07-25T23:59:03.786863Z", + "shell.execute_reply": "2023-07-25T23:59:03.786571Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.23606798, 1.41421356, 1.73205081, 2. , 2.23606798,\n", + " 2.44948974])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.sqrt(x)\n" ] @@ -750,10 +1153,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "6f45bd25", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.788435Z", + "iopub.status.busy": "2023-07-25T23:59:03.788312Z", + "iopub.status.idle": "2023-07-25T23:59:03.790500Z", + "shell.execute_reply": "2023-07-25T23:59:03.790237Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([25, 4, 9, 16, 25, 36])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x**2\n" ] @@ -768,12 +1189,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "6980f628", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.792166Z", + "iopub.status.busy": "2023-07-25T23:59:03.792045Z", + "iopub.status.idle": "2023-07-25T23:59:03.794224Z", + "shell.execute_reply": "2023-07-25T23:59:03.793976Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.23606798, 1.41421356, 1.73205081, 2. , 2.23606798,\n", + " 2.44948974])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x**0.5\n" ] @@ -797,10 +1236,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "89e32100", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.795835Z", + "iopub.status.busy": "2023-07-25T23:59:03.795720Z", + "iopub.status.idle": "2023-07-25T23:59:03.798190Z", + "shell.execute_reply": "2023-07-25T23:59:03.797947Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.16941099, -1.65360995, 0.385241 , 1.37725872, -0.33090576,\n", + " 0.95225998, 1.38477584, 0.03399787, -1.01406121, -0.30788104,\n", + " 0.93226375, -0.70561647, -0.59502338, 0.0957298 , -0.54067538,\n", + " 1.23057972, -1.09400881, 0.08400338, 0.42994564, 0.30192903,\n", + " -1.35097931, -0.5317783 , -0.00317528, 0.95776295, -0.38856995,\n", + " 0.89187793, -0.83973487, 0.36758703, -1.94125031, -0.4910692 ,\n", + " 1.43410404, -1.68234372, 1.23075143, -0.26745071, 0.71955302,\n", + " -0.76446116, -0.69642788, 1.18213409, -1.93529368, -0.32528807,\n", + " -0.86482767, 1.11610801, -0.23379597, -0.67712932, -0.55311178,\n", + " -1.22714226, 0.17073539, -0.73072959, 0.50918715, 1.73268696])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x = np.random.normal(size=50)\n", "x\n" @@ -816,9 +1282,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "e3428155", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.799778Z", + "iopub.status.busy": "2023-07-25T23:59:03.799649Z", + "iopub.status.idle": "2023-07-25T23:59:03.801472Z", + "shell.execute_reply": "2023-07-25T23:59:03.801218Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -837,10 +1309,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "df70172e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.803109Z", + "iopub.status.busy": "2023-07-25T23:59:03.802986Z", + "iopub.status.idle": "2023-07-25T23:59:03.805319Z", + "shell.execute_reply": "2023-07-25T23:59:03.805046Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.64138518],\n", + " [0.64138518, 1. ]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.corrcoef(x, y)" ] @@ -858,12 +1349,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "1d996956", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.806958Z", + "iopub.status.busy": "2023-07-25T23:59:03.806838Z", + "iopub.status.idle": "2023-07-25T23:59:03.809025Z", + "shell.execute_reply": "2023-07-25T23:59:03.808727Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.0841301 3.82641665]\n", + "[ 1.97096548 -10.3124576 ]\n" + ] + } + ], "source": [ "print(np.random.normal(scale=5, size=2))\n", "print(np.random.normal(scale=5, size=2)) \n" @@ -894,10 +1400,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "37b3a30b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.810769Z", + "iopub.status.busy": "2023-07-25T23:59:03.810650Z", + "iopub.status.idle": "2023-07-25T23:59:03.813103Z", + "shell.execute_reply": "2023-07-25T23:59:03.812814Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 4.09482632 -1.07485605]\n", + "[ 4.09482632 -1.07485605]\n" + ] + } + ], "source": [ "rng = np.random.default_rng(1303)\n", "print(rng.normal(scale=5, size=2))\n", @@ -924,12 +1446,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "b4f99e0e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.814729Z", + "iopub.status.busy": "2023-07-25T23:59:03.814608Z", + "iopub.status.idle": "2023-07-25T23:59:03.817050Z", + "shell.execute_reply": "2023-07-25T23:59:03.816803Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.1126795190952861, -0.1126795190952861)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rng = np.random.default_rng(3)\n", "y = rng.standard_normal(10)\n", @@ -946,12 +1485,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "dbd70319", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.818703Z", + "iopub.status.busy": "2023-07-25T23:59:03.818597Z", + "iopub.status.idle": "2023-07-25T23:59:03.820913Z", + "shell.execute_reply": "2023-07-25T23:59:03.820642Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2.7243406406465125, 2.7243406406465125, 2.7243406406465125)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.var(y), y.var(), np.mean((y - y.mean())**2)" ] @@ -967,10 +1523,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "bf281844", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.822530Z", + "iopub.status.busy": "2023-07-25T23:59:03.822409Z", + "iopub.status.idle": "2023-07-25T23:59:03.824738Z", + "shell.execute_reply": "2023-07-25T23:59:03.824473Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.6505576756498128, 1.6505576756498128)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.sqrt(np.var(y)), np.std(y)" ] @@ -986,10 +1560,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "f751b1dd", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.826313Z", + "iopub.status.busy": "2023-07-25T23:59:03.826201Z", + "iopub.status.idle": "2023-07-25T23:59:03.828777Z", + "shell.execute_reply": "2023-07-25T23:59:03.828496Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.22578661, -0.35263079, -0.28128742],\n", + " [-0.66804635, -1.05515055, -0.39080098],\n", + " [ 0.48194539, -0.23855361, 0.9577587 ],\n", + " [-0.19980213, 0.02425957, 1.54582085],\n", + " [ 0.54510552, -0.50522874, -0.18283897],\n", + " [ 0.54052513, 1.93508803, -0.26962033],\n", + " [-0.24355868, 1.0023136 , -0.88645994],\n", + " [-0.29172023, 0.88253897, 0.58035002],\n", + " [ 0.0915167 , 0.67010435, -2.82816231],\n", + " [ 1.02130682, -0.95964476, -1.66861984]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X = rng.standard_normal((10, 3))\n", "X" @@ -1005,10 +1606,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "cbf92de5", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.830471Z", + "iopub.status.busy": "2023-07-25T23:59:03.830350Z", + "iopub.status.idle": "2023-07-25T23:59:03.832659Z", + "shell.execute_reply": "2023-07-25T23:59:03.832413Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.15030588, 0.14030961, -0.34238602])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.mean(axis=0)" ] @@ -1023,12 +1642,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "efacc213", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.834289Z", + "iopub.status.busy": "2023-07-25T23:59:03.834173Z", + "iopub.status.idle": "2023-07-25T23:59:03.836374Z", + "shell.execute_reply": "2023-07-25T23:59:03.836131Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.15030588, 0.14030961, -0.34238602])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.mean(0)" ] @@ -1076,10 +1712,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "637fb67d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:03.838008Z", + "iopub.status.busy": "2023-07-25T23:59:03.837889Z", + "iopub.status.idle": "2023-07-25T23:59:04.142408Z", + "shell.execute_reply": "2023-07-25T23:59:04.141987Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib.pyplot import subplots\n", "fig, ax = subplots(figsize=(8, 8))\n", @@ -1100,10 +1754,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "109d681d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:04.144429Z", + "iopub.status.busy": "2023-07-25T23:59:04.144254Z", + "iopub.status.idle": "2023-07-25T23:59:04.222416Z", + "shell.execute_reply": "2023-07-25T23:59:04.222078Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(x, y, marker='o')\n" @@ -1209,10 +1943,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "16a9cb99", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:04.508470Z", + "iopub.status.busy": "2023-07-25T23:59:04.508345Z", + "iopub.status.idle": "2023-07-25T23:59:04.612557Z", + "shell.execute_reply": "2023-07-25T23:59:04.612259Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(x, y, marker='o')\n", @@ -1232,12 +1984,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "6707420e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:04.614466Z", + "iopub.status.busy": "2023-07-25T23:59:04.614335Z", + "iopub.status.idle": "2023-07-25T23:59:04.681782Z", + "shell.execute_reply": "2023-07-25T23:59:04.681462Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fig.set_size_inches(12,3)\n", "fig" @@ -1268,12 +2038,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "6c78ef8b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:04.683675Z", + "iopub.status.busy": "2023-07-25T23:59:04.683534Z", + "iopub.status.idle": "2023-07-25T23:59:04.980489Z", + "shell.execute_reply": "2023-07-25T23:59:04.980118Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "axes[0,1].plot(x, y, 'o')\n", "axes[1,2].scatter(x, y, marker='+')\n", @@ -1326,9 +2131,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "80e18950", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:05.188103Z", + "iopub.status.busy": "2023-07-25T23:59:05.187975Z", + "iopub.status.idle": "2023-07-25T23:59:06.284103Z", + "shell.execute_reply": "2023-07-25T23:59:06.283751Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -1347,10 +2158,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "fe8c404d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.286372Z", + "iopub.status.busy": "2023-07-25T23:59:06.286067Z", + "iopub.status.idle": "2023-07-25T23:59:06.544075Z", + "shell.execute_reply": "2023-07-25T23:59:06.543737Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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kxyRCYz4REn3xoqXVqHrvcb1Wc0jVe49HdZ43O8/thAkj8rTse1cpNyswOZublRE3K8YCAJBsGG4JAEg6paWlKikp8b/3er1JnSjzNebL39gRMLdXblaGyiYOd31j3pfom/nCVqVIAcPb4iXRFw8qtx9u94zkRekZsfPcTpowIk83Ds/VptoTOnLytLJ7n0vWRvostrSaqJ0LAAC0R5IMAJB0PB6PPB6P02G4SrQb87EW74m+YNyUEKncflgzX9jabn6tuobTmvnC1i71jLLz3G6QlpqiwqEXdvk8iZpIBADATUiSAQAASdFrzDsl3hN9bcU6IdJRQq6l1aj8jR1BJ6A3Otdbr/yNHbpxeK7lsrbz3Ikk0ROJAAC4BUkyAEDc++STT7Rnzx7/+9raWtXU1Khv3766+OKLHYzMvZzupWTX9eM90SfFPiHSWUJuU+2JgJ+dz0g63HBam2pPWC57O8+dKEgkAgAQOyTJAABxb/Pmzbr++uv9733zjU2bNk2rV692KCr3cnrYltPXd7NYJ0TCScg1nW0N61xHToZOdnX1mEjOnShIJAIAEDusbgkAiHvjx4+XMabdiwRZe76kyPmNbl9SpHL74YS+vttZSYh0VWcJOelcQq7f58Kbvy+7d0bnO0V4TCTnThQkEgEAiJ2IkmRLly7VoEGDlJGRoYKCAm3atKnD/T/++GPNmjVLeXl58ng8+sIXvqD169dHFDAAAIhMuEmRltZge8T/9eNBLBMi4SbklHKup1+ofmspOvfzsYP7Wo5h7OC+tp07Glpajar3HtdrNYdUvfe4I88miUR70J4BAARjOUm2du1alZSUqKysTFu3btXIkSNVXFysI0eOBN2/ublZN954o/bv36/f/e532r17t1asWKGBAwd2OXgAABC+WPZScsP13ZDgsCqWCZFwE23HPmlS2cThkhQ0mWUk3Tzi3IIJVss4LTUl5Ll978smDndkrq3K7Yd1zZPvaMqKD3TfmhpNWfGBrnnynZj3dnR7IjEe0Z4BAIRiOUm2ePFizZgxQ9OnT9fw4cO1fPly9ezZU6tWrQq6/6pVq3TixAm9+uqruvrqqzVo0CBdd911GjlyZJeDBwAA4XN62FYsr++WBIdVsUyIWEnITRiRp2Xfu0q5WYHH+HJXK/+0P+IyDnXu3KwMx1ZtdNOwYDcnEuMV7RkAQCiWkmTNzc3asmWLioqKPjtBaqqKiopUXV0d9JjXX39dhYWFmjVrlnJycjRixAg98cQTamlpCXmdpqYmeb3egBcAAOgap4dtxer6bkpwWBXLhIjVhNyEEXl6f84N+vWMr+j7Vw+SJJ3fcSzSMm577p99Z5R+PeMren/ODY4kyNw4LNiNicR4RXsGANARS0myY8eOqaWlRTk5OQHbc3JyVFdXF/SYffv26Xe/+51aWlq0fv16PfLII/rpT3+qxx57LOR1KioqlJWV5X/l5+dbCRMAAATh9LCtWFzfjQkOq2KVEIkkIZeWmqKxg/vq99uD/93XlTJOS01R4dALdeuogSoceqFjPaOcHpYcTEurUe+M7vrmlQM1adQA3TN+qF78QYFjicR4RnsGANCRbnZfoLW1VdnZ2Xr22WeVlpam0aNH69ChQ3r66adVVlYW9JjS0lKVlJT433u9XioWAAC6yJcUmfnCVqVIAYmkWAzbisX1rSQ4CodeGPF17DZhRJ5uHH5unq8jJ08ru/e55GG0PxtfQq78jR0B5ZablaGyicODJmASpYxDcXpY8vkqtx/W3Jc/1MenzgRsf2nTAS385uUkyWKA9gwAJA9LSbJ+/fopLS1N9fX1Advr6+uVm5sb9Ji8vDx1795daWlp/m1f/OIXVVdXp+bmZqWnp7c7xuPxyOMJb7lxAAAQvkiSIvF0fbclOLrC17PKblYTcolUxsE4NSy5pdW0+ww27KjT3S9sDbr/x6fO6O4Xtmo5wy0toT0DAOiIpSRZenq6Ro8eraqqKk2aNEnSuf9Zqaqq0uzZs4Mec/XVV+ull15Sa2urUlPPje7829/+pry8vKAVCgAAsFeseik5cX2n512LV1YScolexr5hwXUNp4MO203RuaRuNIclV24/3D5xnOnR6bOtnR77k9f/qhuH5zJxf5hozwAAOmJ5dcuSkhKtWLFCzz//vHbu3KmZM2eqsbFR06dPlyRNnTpVpaWl/v1nzpypEydO6L777tPf/vY3rVu3Tk888YRmzZoVvbsAAACWOD3/k13Xd3retWSQ6GUc69UkQy404W1qN8QymDpvU0znR0sEtGcAAKFYnpNs8uTJOnr0qObPn6+6ujqNGjVKlZWV/skvDxw44P8fFknKz8/XW2+9pQceeEBXXHGFBg4cqPvuu09z5syJ3l0AABDngg21omeIdU7Pu5YMkqGMYzUsuaOFJqyI16GtTqE9AwAIJcUY497lnf7F6/UqKytLDQ0NyszMdDocAIh7fK8Gcro8gg21yovRHGGJijK1XzKUsd3J6+q9xzVlxQddPs+vZ3zFdYskOP296jaUBwBEl13fq7avbgkAAELzDbU6/3+s6hpOa+YLW7WMSbkjEq15z+jhF5rTc9vFgt2LJ0SjB1hupiduh7YCAOA2JMkAAHBIR0OtjM4NXSt/YweTckeoqwmOZOgp1VWxWoEzUUVjcYOffONLfD8AABAllifuBwAA0bGp9kS7ybrbMpION5xmUm4HhJxM/V89/Cq3H3YosvjX0mpUvfe4Xqs5pOq9x9XS6vqZP2wTziIIfXp2V5+e3dv9rE/P7lpOT1MAAKKKnmQAADgk3KFWTModW/Twsw+98wKFswjCwm9erhuH5+qDfcdVvfe4JKPCIf30FQdWpQUAINHRkwwAAIeEO9QqGkOyED56+NmD3nnB+VbSzM0K/D3Pzcrwz0mYlpqiqy/tpx8WX6YfFg/T1Z/vR4IMAAAb0JMMAACH+IZa1TWcDtprKUXnGspMym2v8yfnr2v4NKzj6OEXPnrndSwZFkEAACAekCQDAMAh4Qy1Kps4nIayjYIN/+v7ufSwjo1lD794X2XTSu8830IA8X7PVoWzCIKvTOoaPtWJxmb17eVRbmbilw0AALFCkgwAAIui2Xj3DbU6P1GTG+fzNMVDgsM3/O/83k3/bGzu8LhY9/BLhHm8rM6/Fw/3HOtnPFiZ+LitbAAAiFckyQAAsMCOxvv5Q6369fJIRjrW2KTqvcddmWDqSLwkODoa/hdKrHv4hUrk+ebxWhYnqxtamX8vHu451s94qDLxOeyisgEAIJ4xcT8AAGGyc+Jx31ArT7dU/fC3/63vrvyL7ltToykrPtA1T74TN5Oax8vk7J0N//Pp+7nuAe/bTqZut3ASeeVv7FBLa0dpPXfwzb8XKq2YonNJptGXXOD6e471M97Rc3A+p8sGAIB4R5IMAIAwxCJhES8JplDiKakT7vC/R/6/L+nXM76in31nlH494yt6f84NMeupk0irbPrm35PULlHWtnfelr//0xX33NJqVL33uF6rOaTqvcf9z6wTz3i4Cd14eh4AAHArhlsCABCGSCYetyIRVv+zu4yiKdzhf7mZGY7FanUeL7cLZ/6912oOhXUuO++5o6GUWT3SY/6MW73XeHkeAABwI5JkAICEsHTpUj399NOqq6vTyJEj9cwzz2js2LFRO7/dCYt4SjCFEk9JHd/wv7qG00ETk7GenD8YK/N4OcnKBPbnz793/v5O33Nn86FNv3pQWOeJ5jNu9V6dfh4AAIhnJMkAAHFv7dq1Kikp0fLly1VQUKAlS5aouLhYu3fvVnZ2dlSuYXfjPZ4STKE4neCwwjf8b+YLW5WiwMn6Yz05fyjxkMiLZAJ73/x7wTh5z+H05nyt5qOwzhXNZ9xXJp0NuXTD8wAAQLxjTjIAQNxbvHixZsyYoenTp2v48OFavny5evbsqVWrVkXtGuFOPB5pAzWeEkyh2F1G0eYb/pebFVimsZycvyPhzuPlVCLPjjn0nLzncHpzHm9sVt/PdY/pM56WmqJvjAzvWXQ6sQsAQLwjSQYAiGvNzc3asmWLioqK/NtSU1NVVFSk6urqoMc0NTXJ6/UGvDpjd+M93hJMwbg9qRPMhBF5en/ODY5Nzt8Ztyby7JzA3ql7DreX5v9v1EBJsXvGW1qNXv/vjhOOqSnS0tucT+wCABDvGG4JAIhrx44dU0tLi3JycgK25+TkaNeuXUGPqaioUHl5ueVrhTPxeKTiYfhfOOwsI7t0NPzPDTqbx8sOnc0zZvccek7cc7i9NIuG5+rLg/vG7BkPZ3XLViNd8Ln0qF4XAIBkRJIMAJB0SktLVVJS4n/v9XqVn58fsE+oJEG0G+++69R5T+vEJ026Y9wleu2/P9KJxjP+fdycYArGiQRHootlIi+cecZiMYderJOXVuZDS0tNidkzngjzFQIAEC9IkgEA4lq/fv2Ulpam+vr6gO319fXKzc0NeozH45HH4wl5zs6SBNFqvAe7jk/fz6Vr0qgBunF4bswSTFZWKeyM23tnIbjOVnf0DXdMhDn0zme1N2esnvFELGsAANyKOckAAHEtPT1do0ePVlVVlX9ba2urqqqqVFhYaPl8dkxGbuU6Picam/Xcn/ar4dPmmCTIKrcf1jVPvqMpKz7QfWtqNGXFB7rmyXeidr9wPyvzjCXCHHrBuHEOuEQtawAA3IgkGQAg7pWUlGjFihV6/vnntXPnTs2cOVONjY2aPn26pfPYORl5uNc5/5rRuF5nYpUYhLtZmWcsHhdpCJfbFnNI5LIGAMBtSJIBAOLe5MmTtWjRIs2fP1+jRo1STU2NKisr203m35kt+/8ZdpKgK8KZiNsnGtfrSKwSg/GupdWoeu9xvVZzSNV7jydkeVid+8qNva6ixTeU8tZRA1U49ELHE1CRlHUyPLMAAEQbc5IBABLC7NmzNXv27C6d4+gnsZkg2+rxdk7IbfcqhYkgnInsE0Ekc1+xSEPsWCnrZHlmAQCINpJkAAD8S/9esZkg2+rxdk7Izcp5HQt3IvtEYGV1x7bidZGGaC5UESvhlHUyPbMAAERbRMMtly5dqkGDBikjI0MFBQXatGlTWMetWbNGKSkpmjRpUiSXBQDAVqMHXRCTCbI7m4i7Lbsn5GblvNCSbShqMs19lagLVSTbM9tVtGkAAOeznCRbu3atSkpKVFZWpq1bt2rkyJEqLi7WkSNHOjxu//79+uEPf6hrr7024mABALBTrJIEba/TkZQoXa8jrJwXmpWhqG7SlbmoEnmeMZ9EXqgiXp9ZJ9CmAQAEYzlJtnjxYs2YMUPTp0/X8OHDtXz5cvXs2VOrVq0KeUxLS4u++93vqry8XEOGDOlSwAAA2ClWSQLfdfKygvfQyotRUiKZeg9ZFY9DUaPRQ8ptqztGU6L3tIrHZ9YptGkAAMFYmpOsublZW7ZsUWlpqX9bamqqioqKVF1dHfK4BQsWKDs7W3feeaf++Mc/dnqdpqYmNTU1+d97vV4rYQIA0CWxmoy87XXqvKd14pMm9f1cunKzesR0fiRfwu78ib5zk3ii75ZWo2MnmzrfUe4ZihrNuajidZ6xziT6QhUMnw5PLNo0tGcAID5ZSpIdO3ZMLS0tysnJCdiek5OjXbt2BT3m/fff18qVK1VTUxP2dSoqKlReXm4lNAAAoipWSQK3JCNYpfAzwVYGDCbURPZO6KyHVIrO9ZC6cXhuUn6mPone0yrSxReSTSzaNLRnACA+RTRxf7hOnjyp22+/XStWrFC/fv3CPq60tFQNDQ3+18GDB22MEgAASJ8l7G4dNVCFQy9MymRKqPmqzue2oajMRRWeRO9pxfBpe0TSpqE9AwDxyVJPsn79+iktLU319fUB2+vr65Wbm9tu/71792r//v2aOHGif1tra+u5C3frpt27d2vo0KHtjvN4PPJ4PFZCAwAACa6l1dja062j3ljnc9tQ1ETvIRUtydDTiuHTnYtFm4b2DADEJ0tJsvT0dI0ePVpVVVX+JY9bW1tVVVWl2bNnt9t/2LBh+vDDDwO2zZs3TydPntTPfvYz5efnRx45AABIGsGGQOb9q9EfrWGinfXG8nnkli/qjqsHO94bp23SMN7mT3OKr6fVzBe2KkUKSJQlUk8rhk93jDYNACAUS0kySSopKdG0adM0ZswYjR07VkuWLFFjY6OmT58uSZo6daoGDhyoiooKZWRkaMSIEQHH9+nTR5LabQcAIJnZ3UsqnnU0If3dL2xVn57d9fGpM/7teRH2mAm3l1W/3h7HP5tgScPUFCnUooyJ0EMqWpKlp5Vb5jt0K9o0AIBgLCfJJk+erKNHj2r+/Pmqq6vTqFGjVFlZ6Z/48sCBA0pNtXWqMwAAEkpHvaQSpcEeqc4mpJcUkCCTIlvNUYqf+apCJQ07SpBJidFDKlroaQXaNACAYFKMMeFMveEor9errKwsNTQ0KDMz0+lwACDu8b0ayMnyCJXw8DXVrSR6otUbzU292qr3HteUFR9YPs7Xc+r9OTeEHXtLq9E1T77T6XxVVs4Zbb4YOxoWen6PMrsTrm56XuAe1DOBKA8AiC67vlct9yQDAADR0VkvqRRJ5W/s0I3DcztNOkSrN5rberVFOtF829Ucwx1yFg/zVYUzb1qrOTdvWr/eHtuTVm57XgAAALqCPsQAADiks4RH20RPR3y90c4/l2/YYeX2w2HFE63zRFNXhzZaTbL55qvKzQq8bm5WhuXhm3awMm/araMGqnDohbYmyNz2vAAAAHQFPckAAHBIuAmPjvaLVm+0aPZqi6axg/sqLysj5BDIzkSSZHPzfFVumTfNrc8LAABAV9CTDAAAh0Qj4RGt3mjROk+0+YZASp8NeQxHis4N+4t0NUffyoB298ayypc0DBVNV+87XG59XgAAALqCJBkAAA6JRsIjGr3RonkeO4QaAtmnZ3dJ7ZNnbpk/zA4dJQ1jed9ufl4AAAAixXBLAAAcEo2J4qM1/M4tw/hCCTUEcsOOunYTx+cm+MTxvqShk/cd7nOw/1ijzZHEP1YHBQDAPUiSAQDgoK4mPDqbsyvlX+fqbPhdtM5jJ98QyLbcPH+YnZy+77GD+yo3M0N13o57iv160wHNvuHzCf95RIrVQQEAcBeSZAAAOKwrCY9o9EaL5nmcECx5lgycvO+01BRNGXux/vPtv3W4X523SZtqTyTl59MZ3+qg5yelfauDumE1VQAAkg1zkgEA4AJdmSg+1JxduVkZlhra0ToPksOgfj3D2o95ydrrbHVQ6dzqoC2tkazpCgAAIkVPMgAAEkC0ht85PYwP8cPKPHbMuxXIyuqg9MIDACB2SJIBAJAgojX8LlmHL8KacOex+2djk6558p2EmXcrGgk/VgcFAMCdSJIBAADAsnDmsfvGyDzNemlbwsy7Fa2J9t2+miwAAMmKOckAAHHt8ccf17hx49SzZ0/16dPH6XCApNLRPHZLb7tKr//34YSZd8s30f75wyR9Cb/K7YfDPpevF16o/mcpOpd8c3I1WQAAkhE9yQAAca25uVnf+ta3VFhYqJUrVzodDpB0Qs1jl0jzbnU20X6KziX8bhyeG9NVaQEAQHSRJAMAxLXy8nJJ0urVq50NBEhiweaxS6R5t+xI+Pl64Z0/fDM3judrAwAg3pEkAwAknaamJjU1Nfnfe71eB6MBElMizbtlV8KP1WQBAHAXkmQAgKRTUVHh74EGwB7hrn7pxLxbVleotDPhx2qyAAC4BxP3AwBcZ+7cuUpJSenwtWvXrojPX1paqoaGBv/r4MGDUYwesK6l1ah673G9VnNI1XuPx81k9h3xzbslqd0E9U7Ou1W5/bCuefIdTVnxge5bU6MpKz7QNU++0+HE+0y0DwBAcqAnGQDAdR588EHdcccdHe4zZMiQiM/v8Xjk8XgiPh6Ipsrth9vNS5VncV4qqz2jYsVt8275Vqg8PwXpW6Fy2feuChoTE+0DAJAcSJIBAFynf//+6t+/v9NhALaLNGlz/jm6mmSzk1vm3erqCpVdTfi5NZEJAAA+Q5IMABDXDhw4oBMnTujAgQNqaWlRTU2NJOnSSy9Vr169nA0O6EBXkzZSdJJsseCGebeisUJlpAk/tycyAQDAOSTJAABxbf78+Xr++ef976+88kpJ0rvvvqvx48c7FBXQua4mbaKRZEsm0Vqh0mrCL14SmQAAgIn7AQBxbvXq1TLGtHuRIIPbdTVpYyXJBntXqAyls0SmdC6RmQgLNQAAkAhIkgEAADigq0mbaPWMShZOrFBJIhMAgPgSUZJs6dKlGjRokDIyMlRQUKBNmzaF3HfFihW69tprdcEFF+iCCy5QUVFRh/sDAAAkg64mbZzoGRXPfCtUSmpX5natUEki091o0wAAzmc5SbZ27VqVlJSorKxMW7du1ciRI1VcXKwjR44E3X/jxo2aMmWK3n33XVVXVys/P1833XSTDh061OXgAQAA4lVXkzZO9IyKd74VKnOzAhOHuVkZtswNRiLTvWjTAACCSTHGWJoEoaCgQF/+8pf185//XJLU2tqq/Px83XvvvZo7d26nx7e0tOiCCy7Qz3/+c02dOjWsa3q9XmVlZamhoUGZmZlWwgUABMH3aiDKA07qysqHvknhJQXMe+VLnDEpfHAtrcbyCpWRXueaJ99RXcPpoPOSpehcgu79OTck3OIKbv9ejXWbxu3lAQDxxq7vVUurWzY3N2vLli0qLS31b0tNTVVRUZGqq6vDOsepU6d05swZ9e0b+n81m5qa1NTU5H/v9XqthAkAABA3JozI043DcyNK2vh6Rp2fZMsNM8mWrKyuUNmV65RNHK6ZL2xVioInMqM9xBOdi0WbhvYMAMQnS0myY8eOqaWlRTk5OQHbc3JytGvXrrDOMWfOHA0YMEBFRUUh96moqFB5ebmV0AAAAOJWV5I2XUmywX4kMt0nFm0a2jMAEJ8sJcm6auHChVqzZo02btyojIzQcy+UlpaqpKTE/97r9So/Pz8WIQIAAMSdWPWMwmesDNkkkZlYwmnT0J4BgPhkKUnWr18/paWlqb6+PmB7fX29cnNzOzx20aJFWrhwod5++21dccUVHe7r8Xjk8XishAYAAADERCTzyJHIdI9YtGlozwBAfLK0umV6erpGjx6tqqoq/7bW1lZVVVWpsLAw5HFPPfWUHn30UVVWVmrMmDGRRwsAAAA4yLdYQtsEmSTVNZzWzBe2qnL7YYciQ7ho0wAAQrE83LKkpETTpk3TmDFjNHbsWC1ZskSNjY2aPn26JGnq1KkaOHCgKioqJElPPvmk5s+fr5deekmDBg1SXV2dJKlXr17q1atXFG8FAID4FqsV9wBEpqXVqPyNHUFXqjQ6Nxl/+Rs7dOPwXH53XY42DQAgGMtJssmTJ+vo0aOaP3++6urqNGrUKFVWVvonvjxw4IBSUz/roLZs2TI1Nzfr3/7t3wLOU1ZWpp/85Cddix4AgAQRyfAtALG1qfZEux5kbRlJhxtOa1PtCYZWuhxtGgBAMCnGmGD/GeYqXq9XWVlZamhoUGZmptPhAEDc43s1kNPl4Ru+dX6F7OuHsux7V5EoA1zgtZpDum9NTaf7/ew7o3TrqIH2B+RiTn+vug3lAQDRZdf3qqU5yQAAQHR1NnxLOjd8q6XV9f+nBSS87N6hV2ePZD8AAOAuJMkAAHCQleFbAJw1dnBf5WVlKNRsYyk6N0x67OC+sQwLAABECUkyAAAcdORk6ARZJPvBXVpajar3HtdrNYdUvfc4PQLjXFpqisomDpekdoky3/uyicOZtB8AgDhleeJ+AAAQPQzfSlwsxpCYJozI07LvXdXus83lswUAIO6RJAMAwEG+4Vt1DaeDzkuWonONb4ZvxZdQizHUNZzWzBe2shhDnJswIk83Ds/VptoTOnLytLJ7n/sdpQcZAADxjeGWAAA4iOFbiYfFGJJDWmqKCodeqFtHDVTh0Av5HQUAIAGQJAMAwGG+4Vu5WYFDKnOzMuhxFIdYjAEAACA+MdwSAAAXYPhW4mAxBgAAgPhEkgwAAJfwDd9CfGMxBgAAgPhEkgwAAKADLa3GUg8/FmMAAACITyTJAAAAQqjcfljlb+wImGMsLytDZROHh5wrzrcYw8wXtipFCkiUsRgDAACAezFxPwAgbu3fv1933nmnBg8erB49emjo0KEqKytTc3Oz06EhAVRuP6yZL2xtNwl/XcNpzXxhqyq3Hw55LIsxAAAAxB96kgEA4tauXbvU2tqqX/7yl7r00ku1fft2zZgxQ42NjVq0aJHT4SGOtbQalb+xI+hwSaNzPcLK39ihG4fnhuwRxmIMAAAA8YUkGQAgbk2YMEETJkzwvx8yZIh2796tZcuWkSRDl2yqPdGuB1lbRtLhhtPaVHuiw8UWWIwBAAAgfpAkAwAklIaGBvXty4To6JojJ0MnyCLZDwiH1UUiAABAdJEkAwAkjD179uiZZ57ptBdZU1OTmpqa/O+9Xq//3zRSIUnZvTM638nCfkBnIlkkAgAARBcT9wMAXGfu3LlKSUnp8LVr166AYw4dOqQJEyboW9/6lmbMmNHh+SsqKpSVleV/5efnS5I27KjTNU++oykrPtB9a2o0ZcUHuubJdzqcoB2JaezgvsrLylCo9GiKziUwxg6m1yK6riuLRAAAgOhJMcYEm5PWVbxer7KystTQ0KDMzEynwwGAuOf279WjR4/q+PHjHe4zZMgQpaenS5I++ugjjR8/Xl/5yle0evVqpaZ2/H9AwXqS5efn6+L7f6MUT8+AfX1JElYkTD6+xIWkgAn8eSYQTS2tRtc8+U7IOfBSdG5V1Pfn3BBXvVrdXs/EGuUBANFl1/cqwy0BAK7Tv39/9e/fP6x9Dx06pOuvv16jR4/Wc88912mCTJI8Ho88Hk+77b5VC4Nt62wlQySeCSPytOx7V7UbApfLEDhEUbQWiQAAAF1HkgwAELcOHTqk8ePH65JLLtGiRYt09OhR/89yc3Ojdh0aqclrwog83Tg8l3nqYBsWiQAAwD1IkgEA4taGDRu0Z88e7dmzRxdddFHAz+yYTYBGanJKS00hOQrbsEgEAADuwcT9AIC4dccdd8gYE/RlBxqpAKKNRSIAAHAPkmQAAPwLjVQAsZaWmqKyicMltf8O8r0vmzicIb4AAMQASTIAANqgkQog1nyLRORmBfZWzc3KYBVVAABiKKIk2dKlSzVo0CBlZGSooKBAmzZt6nD/3/72txo2bJgyMjJ0+eWXa/369REFCwCAnRZPHkkjFYAjJozI0/tzbtCvZ3xFP/vOKP16xlf0/pwb+O6xEW0aAMD5LCfJ1q5dq5KSEpWVlWnr1q0aOXKkiouLdeTIkaD7//nPf9aUKVN05513atu2bZo0aZImTZqk7du3dzl4AACi6cbhuTRSATjGt0jEraMGqnDohfRetRFtGgBAMCnG4uzGBQUF+vKXv6yf//znkqTW1lbl5+fr3nvv1dy5c9vtP3nyZDU2NurNN9/0b/vKV76iUaNGafny5WFd0+v1KisrSw0NDcrMzLQSLgAgCL5XA1EeABBdbv9ejXWbxu3lAQDxxq7v1W5Wdm5ubtaWLVtUWlrq35aamqqioiJVV1cHPaa6ulolJSUB24qLi/Xqq6+GvE5TU5Oampr87xsaGiSdKwQAQNf5vk/tWgUy3vjKgXoGAKLDzfVMLNo0tGcAwF521TOWkmTHjh1TS0uLcnJyArbn5ORo165dQY+pq6sLun9dXV3I61RUVKi8vLzd9vz8fCvhAgA6cfz4cWVlZTkdhuOOHz8uiXoGAKLNjfVMLNo0tGcAIDaiXc9YSpLFSmlpacD/1Hz88ce65JJLdODAAddVsk7wer3Kz8/XwYMH6a4tyiMYyiQQ5dFeQ0ODLr74YvXt29fpUFzBVw7xUM/Ey/McL3FKxGqXeIk1XuKU4ivWZK9naM90Lp6e51igPAJRHu1RJoHsqmcsJcn69euntLQ01dfXB2yvr69Xbm5u0GNyc3Mt7S9JHo9HHo+n3fasrCwehjYyMzMpjzYoj/Yok0CUR3upqREtcpxwfOUQT/VMvDzP8RKnRKx2iZdY4yVOKb5idWM9E4s2De2Z8MXT8xwLlEcgyqM9yiRQtOsZS2dLT0/X6NGjVVVV5d/W2tqqqqoqFRYWBj2msLAwYH9J2rBhQ8j9AQAAAMAutGkAAKFYHm5ZUlKiadOmacyYMRo7dqyWLFmixsZGTZ8+XZI0depUDRw4UBUVFZKk++67T9ddd51++tOf6pZbbtGaNWu0efNmPfvss9G9EwAAAAAIA20aAEAwlpNkkydP1tGjRzV//nzV1dVp1KhRqqys9E9keeDAgYDubuPGjdNLL72kefPm6aGHHtLnP/95vfrqqxoxYkTY1/R4PCorKwvaZTkZUR6BKI/2KJNAlEd7lEmgeCqPeIk1XuKUiNUu8RJrvMQpEWs0xbpN4/bycAJlEojyCER5tEeZBLKrPFKMG9dlBgAAAAAAAGLIfTNpAgAAAAAAADFGkgwAAAAAAABJjyQZAAAAAAAAkh5JMgAAAAAAACQ91yTJli5dqkGDBikjI0MFBQXatGlTh/v/9re/1bBhw5SRkaHLL79c69evj1GksWGlPFasWKFrr71WF1xwgS644AIVFRV1Wn7xxurz4bNmzRqlpKRo0qRJ9gboAKtl8vHHH2vWrFnKy8uTx+PRF77whYT6vbFaHkuWLNFll12mHj16KD8/Xw888IBOnz4do2jt9d5772nixIkaMGCAUlJS9Oqrr3Z6zMaNG3XVVVfJ4/Ho0ksv1erVq22PM5Yef/xxjRs3Tj179lSfPn3COsYYo/nz5ysvL089evRQUVGR/vd//zdgnxMnTui73/2uMjMz1adPH91555365JNPuhSr1XPu379fKSkpQV+//e1v/fsF+/maNWtiGqskjR8/vl0cd999d8A+Bw4c0C233KKePXsqOztbP/rRj3T27NmYxnrixAnde++9/u+Jiy++WP/xH/+hhoaGgP2iUa7R/hsonGc3EtH+2+SOO+5oV3YTJkzocpxWY129enW7ODIyMgL2satMrcYa7PcnJSVFt9xyi38fO8rVrnol0r/v3Ir2THu0aQLRpglEe6Y92jSfcaxNY1xgzZo1Jj093axatcr89a9/NTNmzDB9+vQx9fX1Qff/05/+ZNLS0sxTTz1lduzYYebNm2e6d+9uPvzwwxhHbg+r5XHbbbeZpUuXmm3btpmdO3eaO+64w2RlZZl//OMfMY7cHlbLw6e2ttYMHDjQXHvttebWW2+NTbAxYrVMmpqazJgxY8zNN99s3n//fVNbW2s2btxoampqYhy5PayWx4svvmg8Ho958cUXTW1trXnrrbdMXl6eeeCBB2IcuT3Wr19vHn74YfPyyy8bSeaVV17pcP99+/aZnj17mpKSErNjxw7zzDPPmLS0NFNZWRmbgGNg/vz5ZvHixaakpMRkZWWFdczChQtNVlaWefXVV81///d/m2984xtm8ODB5tNPP/XvM2HCBDNy5EjzwQcfmD/+8Y/m0ksvNVOmTOlSrFbPefbsWXP48OGAV3l5uenVq5c5efKkfz9J5rnnngvYr+29xCJWY4y57rrrzIwZMwLiaGhoCLifESNGmKKiIrNt2zazfv16069fP1NaWhrTWD/88EPzzW9+07z++utmz549pqqqynz+8583/+f//J+A/bparnb8DRTOs2uVHX+bTJs2zUyYMCGg7E6cOBFxjJHG+txzz5nMzMyAOOrq6gL2saNMI4n1+PHjAXFu377dpKWlmeeee86/jx3lake9Eunfd25Fe6Y92jSBaNMEoj3THm2aQE61aVyRJBs7dqyZNWuW/31LS4sZMGCAqaioCLr/t7/9bXPLLbcEbCsoKDD//u//bmucsWK1PM539uxZ07t3b/P888/bFWJMRVIeZ8+eNePGjTO/+tWvzLRp0xKqQjHGepksW7bMDBkyxDQ3N8cqxJiyWh6zZs0yN9xwQ8C2kpISc/XVV9sapxPCqVB+/OMfmy996UsB2yZPnmyKi4ttjMwZzz33XFhJstbWVpObm2uefvpp/7aPP/7YeDwe8+tf/9oYY8yOHTuMJPP//t//8+/z+9//3qSkpJhDhw5FFF+0zjlq1Cjz/e9/P2BbOM+CFZHGet1115n77rsv5M/Xr19vUlNTA5IUy5YtM5mZmaapqSmmsZ7vN7/5jUlPTzdnzpzxb+tquUb7b6Bwnt1YxHm+YH+b2FU/W421s+8Fu8o0kljP95//+Z+md+/e5pNPPvFvs/vvnmjVK129d7ehPdMebZpAtGkC0Z5pjzZNaLFs0zg+3LK5uVlbtmxRUVGRf1tqaqqKiopUXV0d9Jjq6uqA/SWpuLg45P7xJJLyON+pU6d05swZ9e3b164wYybS8liwYIGys7N15513xiLMmIqkTF5//XUVFhZq1qxZysnJ0YgRI/TEE0+opaUlVmHbJpLyGDdunLZs2eLvvrxv3z6tX79eN998c0xidptE/k6NVG1trerq6gLKJSsrSwUFBf5yqa6uVp8+fTRmzBj/PkVFRUpNTdVf/vKXiK4bjXNu2bJFNTU1Qb//Zs2apX79+mns2LFatWqVzv3NEZmuxPriiy+qX79+GjFihEpLS3Xq1KmA815++eXKycnxbysuLpbX69Vf//rXmMfaVkNDgzIzM9WtW7eA7ZGWqx1/A4Xz7Fpl598mGzduVHZ2ti677DLNnDlTx48fjyjGrsb6ySef6JJLLlF+fr5uvfXWgGfNjjLtSqxtrVy5Ut/5znf0uc99LmB7tMvVqs6e02jcu5vQnmmPNk0g2jSBaM+0R5um66L1vdqt813sdezYMbW0tAT8MSxJOTk52rVrV9Bj6urqgu5fV1dnW5yxEkl5nG/OnDkaMGBAuwckHkVSHu+//75WrlypmpqaGEQYe5GUyb59+/TOO+/ou9/9rtavX689e/bonnvu0ZkzZ1RWVhaLsG0TSXncdtttOnbsmK655hoZY3T27Fndfffdeuihh2IRsuuE+k71er369NNP1aNHD4cic46vPumorqmrq1N2dnbAz7t166a+fftGXB9F45wrV67UF7/4RY0bNy5g+4IFC3TDDTeoZ8+e+sMf/qB77rlHn3zyif7jP/4jprHedtttuuSSSzRgwAD9z//8j+bMmaPdu3fr5Zdf9p83WLn7fhbLWNs6duyYHn30Ud11110B27tSrnb8DRTOs2uVXX+bTJgwQd/85jc1ePBg7d27Vw899JC+/vWvq7q6WmlpaTGL9bLLLtOqVat0xRVXqKGhQYsWLdK4ceP017/+VRdddJEtZRpprG1t2rRJ27dv18qVKwO221GuVnVWr/zzn//s8jPlJrRn2qNNE4g2TSDaM+3Rpum6aLVpHE+SIboWLlyoNWvWaOPGje0mnU0GJ0+e1O23364VK1aoX79+TofjGq2trcrOztazzz6rtLQ0jR49WocOHdLTTz+dEJWKVRs3btQTTzyhX/ziFyooKNCePXt033336dFHH9UjjzzidHgI09y5c/Xkk092uM/OnTs1bNiwGEUUWrixdtWnn36ql156Kehz3HbblVdeqcbGRj399NPtkjl2x9o2yXT55ZcrLy9PX/va17R3714NHTrU0rliVa5er1e33HKLhg8frp/85CcBPwu3XJNZqL9NvvOd7/j/ffnll+uKK67Q0KFDtXHjRn3ta1+LWXyFhYUqLCz0vx83bpy++MUv6pe//KUeffTRmMVh1cqVK3X55Zdr7NixAdvdUq5AV9CmoU1zPtoz7dGmsYfjSbJ+/fopLS1N9fX1Advr6+uVm5sb9Jjc3FxL+8eTSMrDZ9GiRVq4cKHefvttXXHFFXaGGTNWy2Pv3r3av3+/Jk6c6N/W2toq6Vyvgd27d1tuhLlNJM9IXl6eunfvHvA/yF/84hdVV1en5uZmpaen2xqznSIpj0ceeUS33367fvCDH0g614hobGzUXXfdpYcfflipqY6PRI+pUN+pmZmZru5F9uCDD+qOO+7ocJ8hQ4ZEdG7fs1NfX6+8vDz/9vr6eo0aNcq/z5EjRwKOO3v2rE6cONHu2Qs3VivnDOZ3v/udTp06palTp3a6b0FBgR599FE1NTXJ4/HEPNa2cUjSnj17NHToUOXm5rZbycn3fDpRridPntSECRPUu3dvvfLKK+revXun9xOsXIOx42+gcJ5dq2L1t8mQIUPUr18/7dmzJ+JkTldi9enevbuuvPJK7dmzR5I9ZdrVWBsbG7VmzRotWLCg0+tEo1yt6qxeSUtL6/Ln5Ca0Z9qjTROINk0g2jPt0abpumi1aRwvtfT0dI0ePVpVVVX+ba2traqqqgr4X722CgsLA/aXpA0bNoTcP55EUh6S9NRTT+nRRx9VZWVlwJwr8c5qeQwbNkwffvihampq/K9vfOMbuv7661VTU6P8/PxYhm+LSJ6Rq6++Wnv27PFXrpL0t7/9TXl5eXFfoURSHqdOnWpXafgq3K7M0RSv4vU7tX///ho2bFiHr0if78GDBys3NzegXLxer/7yl7/4y6WwsFAff/yxtmzZ4t/nnXfeUWtrqz/xYzVWK+cMZuXKlfrGN76h/v37d7pvTU2NLrjggnaJnFjF2jYOSf7kQ2FhoT788MOApNaGDRuUmZmp4cOHxzRWr9erm266Senp6Xr99dfD6s0QqlyDseNvoHCeXati9bfJP/7xDx0/fjwgERWrWNtqaWnRhx9+6I/DjjLtaqy//e1v1dTUpO9973udXica5WpVZ89pND4nN6E90x5tmkC0aQLRnmmPNk3XRe171dI0/zZZs2aN8Xg8ZvXq1WbHjh3mrrvuMn369PGvbHX77bebuXPn+vf/05/+ZLp162YWLVpkdu7cacrKyhJqyWSr5bFw4UKTnp5ufve73wUs933y5EmnbiGqrJbH+RJtJRhjrJfJgQMHTO/evc3s2bPN7t27zZtvvmmys7PNY4895tQtRJXV8igrKzO9e/c2v/71r82+ffvMH/7wBzN06FDz7W9/26lbiKqTJ0+abdu2mW3bthlJZvHixWbbtm3m73//uzHGmLlz55rbb7/dv79vueQf/ehHZufOnWbp0qURLZfsZn//+9/Ntm3bTHl5uenVq5e/fNp+T1522WXm5Zdf9r9fuHCh6dOnj3nttdfM//zP/5hbb73VDB482Hz66af+fSZMmGCuvPJK85e//MW8//775vOf/7yZMmVKl2Lt7Jz/+Mc/zGWXXWb+8pe/BBz3v//7vyYlJcX8/ve/b3fO119/3axYscJ8+OGH5n//93/NL37xC9OzZ08zf/78mMa6Z88es2DBArN582ZTW1trXnvtNTNkyBDz1a9+1X/M2bNnzYgRI8xNN91kampqTGVlpenfv78pLS2NaawNDQ2moKDAXH755WbPnj0B9evZs2eNMdEpVzv+Bgrn2bUq2n+bnDx50vzwhz801dXVpra21rz99tvmqquuMp///OfN6dOnI44zkljLy8vNW2+9Zfbu3Wu2bNlivvOd75iMjAzz17/+NeB+ol2mkcTqc80115jJkye3225XudpRr3R27/GG9kx7tGkC0aYJRHumPdo0gZxq07giSWaMMc8884y5+OKLTXp6uhk7dqz54IMP/D+77rrrzLRp0wL2/81vfmO+8IUvmPT0dPOlL33JrFu3LsYR28tKeVxyySVGUrtXWVlZ7AO3idXno61Eq1B8rJbJn//8Z1NQUGA8Ho8ZMmSIefzxx/0NvURgpTzOnDljfvKTn5ihQ4eajIwMk5+fb+655x7zz3/+M/aB2+Ddd98N+p3gK4Np06aZ6667rt0xo0aNMunp6WbIkCHmueeei3ncdpo2bVrQMnn33Xf9+0gKuO/W1lbzyCOPmJycHOPxeMzXvvY1s3v37oDzHj9+3EyZMsX06tXLZGZmmunTp3f5j/nOzllbW9sudmOMKS0tNfn5+aalpaXdOX//+9+bUaNGmV69epnPfe5zZuTIkWb58uVB97Uz1gMHDpivfvWrpm/fvsbj8ZhLL73U/OhHPzINDQ0B592/f7/5+te/bnr06GH69etnHnzwQXPmzJmYxhrq90iSqa2tNcZEr1yj/TdQOM9uJKL5t8mpU6fMTTfdZPr372+6d+9uLrnkEjNjxoyoJUisxHr//ff7983JyTE333yz2bp1a8D57CpTq7EaY8yuXbuMJPOHP/yh3bnsKle76pWO7j0e0Z5pjzZNINo0gWjPtEeb5jNOtWlSjEnCfngAAAAAAABAG47PSQYAAAAAAAA4jSQZAAAAAAAAkh5JMgAAAAAAACQ9kmQAAAAAAABIeiTJAAAAAAAAkPRIkgEAAAAAACDpkSQDAAAAAABA0iNJBgAAAAAAgKRHkgwAAAAAAABJz3KS7L333tPEiRM1YMAApaSk6NVXX+30mI0bN+qqq66Sx+PRpZdeqtWrV0cQKgAgGVDPAAAAAHCC5SRZY2OjRo4cqaVLl4a1f21trW655RZdf/31qqmp0f33368f/OAHeuuttywHCwBIfNQzAAAAAJyQYowxER+ckqJXXnlFkyZNCrnPnDlztG7dOm3fvt2/7Tvf+Y4+/vhjVVZWRnppAEASoJ4BAAAAECvd7L5AdXW1ioqKArYVFxfr/vvvD3lMU1OTmpqa/O9bW1t14sQJXXjhhUpJSbErVABIGsYYnTx5UgMGDFBqanxPT0k9AwDuk0j1TDS0trbqo48+Uu/evalnACAK7KpnbE+S1dXVKScnJ2BbTk6OvF6vPv30U/Xo0aPdMRUVFSovL7c7NABIegcPHtRFF13kdBhdQj0DAO6VCPVMNHz00UfKz893OgwASDjRrmdsT5JForS0VCUlJf73DQ0Nuvjii3Xw4EFlZmY6GBkAJAav16v8/Hz17t3b6VAcQT0DAPZK9nrmfL5yoJ4BgOiwq56xPUmWm5ur+vr6gG319fXKzMwM+r/7kuTxeOTxeNptz8zMpFIBgChKhCEf1DMA4F6JUM9Eg68cqGcAILqiXc/YPkFAYWGhqqqqArZt2LBBhYWFdl8aAJAEqGcAAAAARIPlJNknn3yimpoa1dTUSJJqa2tVU1OjAwcOSDo3hGXq1Kn+/e+++27t27dPP/7xj7Vr1y794he/0G9+8xs98MAD0bkDAEBCoZ4BAADJ7lTzWQ2au06D5q7TqeazTocDJA3LSbLNmzfryiuv1JVXXilJKikp0ZVXXqn58+dLkg4fPuxvyEjS4MGDtW7dOm3YsEEjR47UT3/6U/3qV79ScXFxlG4BAJBIqGcAAAAAOCHFGGOcDqIzXq9XWVlZamhoYAw/AEQB36uBKA8AiC6+VwNRHgiXr9fYqeYWjXnsbUnS5nlF6pmeJknqme7KtfeAmLPre5XfMAAAAAAAXGD4/LfabfMlyyRp/8JbYhkOkHRsn7gfAAAAAAAAcDt6kgEAAAAA4AI7FpybUzXUcEsA9iJJBgAAAACACwSbc6xnehpzkQExwnBLAAAAAAAAJD3S0QAAAAAAuEjP9G5M0g84gJ5kAAAAAAAASHokyQAAAAAAQNw51XxWg+au06C563Sq+azT4SABkCQDAAAAANiCJAaAeMKcZAAAAAAAIG74Eq6nmlvabPvs36wGikjx5AAAAAAAoookBuw0fP5b7baNeext/79Z9ACR4psJAAAAABBVJDEAxCOSZAAAAAAAIG7sWFAs6VzvRF/ydfO8IvVMT3MyLCQAkmQAAAAA0IGKigq9/PLL2rVrl3r06KFx48bpySef1GWXXeZ0aK5FEgN2CjZct2d6GsN40WWsbgkAAAAAHfiv//ovzZo1Sx988IE2bNigM2fO6KabblJjY6PToblWz/Ru/3qltdmW5t8OAG7EtxMAAAAAdKCysjLg/erVq5Wdna0tW7boq1/9qkNRAeiZ3o357RBVJMkAAAAAwIKGhgZJUt++fYP+vKmpSU1NTf73Xq83JnG5EUkMAPGE4ZYAAAAAEKbW1lbdf//9uvrqqzVixIig+1RUVCgrK8v/ys/Pj3GUAIBIkCQDAAAAgDDNmjVL27dv15o1a0LuU1paqoaGBv/r4MGDMYwQABAphlsCAAAAQBhmz56tN998U++9954uuuiikPt5PB55PJ4YRgYAiAaSZAAAAADQAWOM7r33Xr3yyivauHGjBg8e7HRIAAAbkCQDAAAAgA7MmjVLL730kl577TX17t1bdXV1kqSsrCz16NHD4egAANHCnGQAAAAA0IFly5apoaFB48ePV15env+1du1ap0MDAEQRPckAAAAAoAPGGKdDAADEQEQ9yZYuXapBgwYpIyNDBQUF2rRpU4f7L1myRJdddpl69Oih/Px8PfDAAzp9+nREAQMAEh/1DAAAcItTzWc1aO46DZq7TqeazzodDgAbWU6SrV27ViUlJSorK9PWrVs1cuRIFRcX68iRI0H3f+mllzR37lyVlZVp586dWrlypdauXauHHnqoy8EDABIP9QwAAAAAJ1hOki1evFgzZszQ9OnTNXz4cC1fvlw9e/bUqlWrgu7/5z//WVdffbVuu+02DRo0SDfddJOmTJnSaa8AAEByop4BAABucKr57L9eLW22tfi3O4nebYA9LCXJmpubtWXLFhUVFX12gtRUFRUVqbq6Ougx48aN05YtW/yNlX379mn9+vW6+eabQ16nqalJXq834AUASHzUMwAAoC0nk0HD57+l4fPf0pjH3vZvG/PY2/7t6BiJPMQjSxP3Hzt2TC0tLcrJyQnYnpOTo127dgU95rbbbtOxY8d0zTXXyBijs2fP6u677+5wGExFRYXKy8uthAYASADUMwAAAKH5kk3n927z6ZnO2nxAV9j+G7Rx40Y98cQT+sUvfqGCggLt2bNH9913nx599FE98sgjQY8pLS1VSUmJ/73X61V+fr7doQIA4hD1DAAAiccNyaAdC4r91/X1Jts8r0g909Nsv3YowXqwte3ptn/hLbEMJyg3fHZApCw9nf369VNaWprq6+sDttfX1ys3NzfoMY888ohuv/12/eAHP5AkXX755WpsbNRdd92lhx9+WKmp7Ud8ejweeTweK6EBABIA9QwAAJDckQwKlszpmZ5GkqcTbvjsgEhZmpMsPT1do0ePVlVVlX9ba2urqqqqVFhYGPSYU6dOtWugpKWdy7wbY6zGCwBIYNQzAAAAoe1YUKwdC4q1ed5n87dunlfk3w6gayynwEtKSjRt2jSNGTNGY8eO1ZIlS9TY2Kjp06dLkqZOnaqBAweqoqJCkjRx4kQtXrxYV155pX8YzCOPPKKJEyf6GzEAAPhQzwAAADcNdeyZ3s01vZ/ioXebmz47wCrLv0mTJ0/W0aNHNX/+fNXV1WnUqFGqrKz0T7J84MCBgP/RnzdvnlJSUjRv3jwdOnRI/fv318SJE/X4449H7y4AAAmDegYAAMRDMgjB8dkhnqWYOBiL4vV6lZWVpYaGBmVmZjodDgDEPb5XA1EeABBdfK8Gojwid6r5rH+Oqx0Likm0xBE+O9jJru9VnlIAAAAAgCu5aagjrOGzQzyyNHE/AAAAAAAAkIhIkgEAAAAAACDpkSQDAAAAAABIMqeaz2rQ3HUaNHedTjWfdTocVyBJBgAAAAAAgKTHxP0AAAAAAABJwtdr7FRzS5ttn/07mVciTd47BwAAAAAASDLD57/VbtuYx972/zuZVyVluCUAAAAAAACSHj3JAAAAAAAAksSOBcWSzg2x9PUg2zyvSD3T02IWw6nms/4ebTsWFLtmiKc7ogAAAAAAADiPW5Mp8SxYGfZMT6NsRZIMAAAAAICYIOGDZOf2RQP4jQQAAAAAII4kQ7LN7ckUO8T6c+2Z3i3mk/S7fdGAxHuqAAAAAABwkWRM+HSVG5MpTiQnkyEh6iaULgAAAAB04L333tPTTz+tLVu26PDhw3rllVc0adIkp8NCHIlWwodkW2Lq6HP1/SxRuGHRgI7wGwQAAAAAHWhsbNTIkSP1/e9/X9/85jedDgdJzI29q+zipmSK3cnJzj5XO67pFLcvGuCOKAAAAADApb7+9a/r61//utNhII65KeETL9yUTHFLcjJRE6JuQpIMAAAAAKKoqalJTU1N/vder9fBaOAG0Ur4kGxLTME+10TnxKIB4SBJBgAAAABRVFFRofLycqfDQAJyU++qWHFDMsXu5GSwz2/zvK+pZ3o3EqIxlup0AAAAAACQSEpLS9XQ0OB/HTx40OmQXOVU81kNmrtOg+auS7hJyTvjS/jsX3hL3Ca2wvn8Eukz9q0ueW7IpfFv9yUn7focfedumxSz+5qgJxkAAAAARJXH45HH43E6DCQwN/SucgNfAks619sr3pNHfK7Oi+8nCAAAAAAQF+xeIRDWWUkyhfP5JdJnHOxepBT/0MtY3QuJs9iKnycUAAAAABzwySefaM+ePf73tbW1qqmpUd++fXXxxRc7GFl8ccsKgbCubTKtrfM/v1h9xrFIxvG8JieSZAAAAADQgc2bN+v666/3vy8pKZEkTZs2TatXr3YoKsQTq8MC7R5GGO89vkhgwS7ufvIBAAAAwGHjx4+XMabzHdEhu1cIRPjCTTIFS6b94YGv6qb/fE9S+88vkT7jRLoXhC+i1S2XLl2qQYMGKSMjQwUFBdq0aVOH+3/88ceaNWuW8vLy5PF49IUvfEHr16+PKGAAQOKjngEAIPEk42p9p5rP/usV2GPLt72r+9vNt6pj2wSaL0Emtf/8YvUZ71hQrB0LirV5XpF/2+Z5Rf7t0ZCMzysi6Em2du1alZSUaPny5SooKNCSJUtUXFys3bt3Kzs7u93+zc3NuvHGG5Wdna3f/e53GjhwoP7+97+rT58+0YgfAJBgqGcAAHBOoq0W6DSrwwJjNYzQjb2krDx7wX7mS2ABXWH5CVq8eLFmzJih6dOnS5KWL1+udevWadWqVZo7d267/VetWqUTJ07oz3/+s7p37y5JGjRoUNeiBgAkLOoZAAASG6v1RUdXEprhJpk6SqZ1dP1E+owT6V7QOUtJsubmZm3ZskWlpaX+bampqSoqKlJ1dXXQY15//XUVFhZq1qxZeu2119S/f3/ddtttmjNnjtLSgmepm5qa1NTU5H/v9XqthAkAiFPUMwAAOCPeJ3J3K6s9ttzWwysWPba68uyRwEK0WXqyjx07ppaWFuXk5ARsz8nJ0a5du4Ies2/fPr3zzjv67ne/q/Xr12vPnj265557dObMGZWVlQU9pqKiQuXl5VZCAwAkAOoZAACcwWqB9rCaZApn/2gmNCNJMkU7ocqzBzex/b8DWltblZ2drWeffVZpaWkaPXq0Dh06pKeffjpk46W0tNS/rLJ07n/48/Pz7Q4VABCHqGcAAEAycSKp1DaZNmjuuphfP5i2wz0l5tBDdFh6gvr166e0tDTV19cHbK+vr1dubm7QY/Ly8tS9e/eAIS9f/OIXVVdXp+bmZqWnp7c7xuPxyOPxWAkNAJAAqGcAAHCG24b5JRqrPbaSaRghzx7cxFKSLD09XaNHj1ZVVZUmTZok6dz/4FdVVWn27NlBj7n66qv10ksvqbW1VampqZKkv/3tb8rLywvacAEAJC/qGQAAnMFqgfHD6aRStK9v9dk79slpjXmsqt32458061T6WfVM78Zzi4ilWj2gpKREK1as0PPPP6+dO3dq5syZamxs9K9CNnXq1IAJl2fOnKkTJ07ovvvu09/+9jetW7dOTzzxhGbNmhW9uwAAJAzqGQAAgNB8SaC2SSlfUikWySGnrx8sQSZJ1z71rsY8VhV0OCoQLstP8OTJk3X06FHNnz9fdXV1GjVqlCorK/2TLB84cMD/P/mSlJ+fr7feeksPPPCArrjiCg0cOFD33Xef5syZE727AAAkDOoZAACck0zD/OAunT17vgUDADulGGOM00F0xuv1KisrSw0NDcrMzHQ6HACIe3yvBqI8ACC6+F4NRHkkrraTxzNxvL2CLRjQ1h9/fL16pKcy3DJJ2PW9ypMDAAAAAJBE0gfx68Je6Tyv6DKeIAAAAAAALPAN/TvV3NJm22f/djJZk6iJzmALBgDRlhi/LQAAAACAiLk56eNGwSaHb5u4YV636Av2DCZSEhDuwNMEAAAAAEmOpE/8c0OiM1F7sSF58MQCAAAAAGBBsKF/m+cVqWd6mmMxJUuikxVYYSeSZAAAAACQ5NyY9HGzYD2keqanJW3PKTf0YgOigScVAAAAAJIcSZ/4t3ne1zTmsarztsUm0ZksvdiQ+PjGAwAAAAAkJLvnyHLT0L9IE52xmEfsVPNZ287bNkHHPGjoKp4eAAAAAIAkdyV93CCWE9FHeq1gQx3b/iwWSaNgw3X/+OPxuvapjRGdjwUA4BSeNAAAAABAQonlHFlOJ3SCDXX0GfNYVcikZzTLKPi+KV0+byinms/qVPNZfdrcGrD9+CdNOpV+LjHo5sSa088MQuOTAAAAAAAklK7OkRXrJFusrtWW3fOIXfvUu5bPG25ZhEoMtu25Ro9IRIIkGQAAAAAAbYSTQIpWcqurySo3rUwaaULv/LnF2kqkBQBYBdT9+AQAAAAAIAxLly7V008/rbq6Oo0cOVLPPPOMxo4d63RYCCIWiSO3rOgY6YT9dpVRLMp+x4Ji/3DLtj3W/vjj8eoRhVVZ7RoO6ZZnBqGRJAMAAACATqxdu1YlJSVavny5CgoKtGTJEhUXF2v37t3Kzs52OjycJ9LEkU8se2c51ROsq2UUKpFk5bzBelb98cfjJaX4k1/BysI359ixT04HbL+wl8cVvbGYcyx+8UkBAAAAQCcWL16sGTNmaPr06ZKk5cuXa926dVq1apXmzp3rcHSItnASPdFKbnU1WdX2PPHWEylYz6rzV8SMpCwiZfdwSDcNjUVwJMkAAAAAoAPNzc3asmWLSktL/dtSU1NVVFSk6urqdvs3NTWpqanJ/97r9cYkTrRnZ+IoWsktp1kto3ATSXaWvS+Gtitobp5X5P9ZpJ9BrBZ8+Cx+STJx98wkMj4JAAAAAOjAsWPH1NLSopycnIDtOTk52rVrV7v9KyoqVF5eHqvwYKNY9s6Kl55g0ZxXq7OeVaGSR26d28utcSF8JMkAAAAAIIpKS0tVUlLif+/1epWfnx+z6zMfUmzFS3KrqzpagTJSbuuNZ/dwyGA94KQU/3Z+V53HJwAAAAAAHejXr5/S0tJUX18fsL2+vl65ubnt9vd4PPJ4PLEKDzYIJ9F4ftIomRKSm+d9TVKKY/Nq2ZXMsmPBB5/N875GT7M4kOp0AAAAAADgZunp6Ro9erSqqqr821pbW1VVVaXCwkIHIwt0qvnsv16B8yH5tieqU81nNWjuOg2au8719xlPsfoEe67O9YQy/ne+RFKkSUJfb7z9C28J6xy+a7VNinU1ho6E+7l9dn0T5KcpQbbBbZIjzQ0AAAAAXVBSUqJp06ZpzJgxGjt2rJYsWaLGxkb/apduQC+Vrgtn4nVf0ujTgKSRdPyTZp1KP2tbosYpnT1Xiej8IbRWE5pjHqsKsu2zMts8r4jVLV0qcX5zAQAAAMAmkydP1tGjRzV//nzV1dVp1KhRqqysbDeZP2In3JUEOzr+/CGV4SQaQ83Lde1T7wbsF81Y3czJ5Kvd88HZ9bkF6wEHd+CTAAAAAIAwzJ49W7Nnz3Y6jJDsnnTcbaLZcy7aE9KHc347e/lFc/GGeHquor1oRaSfWzyVGQKRJAMAAACABOC2lQLdKljvIJ8//ni8Luzl6TC5sWNBsX+45bVPbWxz7PXqkZ6acOXNc2VdOGXG8Gd3iuipXrp0qZ5++mnV1dVp5MiReuaZZzR27NhOj1uzZo2mTJmiW2+9Va+++moklwYAJAHqGQAA0JlIe+t01Gvs2qc2/uu87Sel/+x9N//cZG1d2Cs9ZOIoVj2L4m1YZ7R6ftl13/QISz6Wn5S1a9eqpKREy5cvV0FBgZYsWaLi4mLt3r1b2dnZIY/bv3+/fvjDH+raa6/tUsAAgMRGPQMAQNfYPU+TW9jVw8mOoZex6o1l57BONz9Xdt13Vz83N5cZgku1esDixYs1Y8YMTZ8+XcOHD9fy5cvVs2dPrVq1KuQxLS0t+u53v6vy8nINGTKkSwEDABIb9QwAALDTjgXF/h5CHdm/8BbtX3hLyISILwHS2X7x5lTzWQ2au06D5q6zvKqjlWucewX2/PJtdwtfWdg9Zx3cw9JvcXNzs7Zs2aLS0lL/ttTUVBUVFam6ujrkcQsWLFB2drbuvPNO/fGPf+z0Ok1NTWpqavK/93q9VsIEAMQp6hkAAGCV1d46vmTW5nlf05jHqgJ+ZvdQOrt7FsXL8MBo9/yKxX1HYyEAuJ+lT/jYsWNqaWlpt8xxTk6Odu3aFfSY999/XytXrlRNTU3Y16moqFB5ebmV0AAACYB6BgAAxEqiTkh/fgIq3HuKt/nM2or2Z9nVsoj2KpuIHVs/qZMnT+r222/XihUr1K9fv7CPKy0tVUlJif+91+tVfn6+HSECAOIY9QwAINHZ0dimAX+Or1dX2/JIZnbOZ3Y+t/d4i2VZwF0sfRv269dPaWlpqq+vD9heX1+v3Nzcdvvv3btX+/fv18SJE/3bWltbz124Wzft3r1bQ4cObXecx+ORx+OxEhoAIAFQzwAAgFhLhMnVg/V8clPS6Xx29eJz+rOM5954OMfSJ5Senq7Ro0erqqpKkyZNknSuMVJVVaXZs2e323/YsGH68MMPA7bNmzdPJ0+e1M9+9jP+1x4AEIB6BgCAc+xobNOAj28d9QCMRs8nt/fuiqVIy4IeaPHP8rdgSUmJpk2bpjFjxmjs2LFasmSJGhsbNX36dEnS1KlTNXDgQFVUVCgjI0MjRowIOL5Pnz6S1G47AAAS9QwAAJI9jW0a8InDruGykc5nFimne36Fkqjz1aFzlj/hyZMn6+jRo5o/f77q6uo0atQoVVZW+idZPnDggFJTU6MeKAAgOVDPAACAeBGL+d2C9QBs+7Oe6d3oBeYSfA7xL8UYY5wOojNer1dZWVlqaGhQZmam0+EAQNzjezUQ5QEA0cX3aqBIyqNtYiRYY7urwy2jdc5kF4sk2aC56zr8uS8x0zO9W0Tx8FxEH4tj2M+ueoZPCgAAAABcxo7hXgwhi57O5neTQg+JjHYCxXeuSIctRjoMl0QQEhFPMQAAAAAAFnSWWPL17ooG37mCXfN8bp3jK9nwOcQvkmQAAAAA4FJ2NLYTvQHvhh5OwXqY+eYPs7q6qG/75nlf05jHqgJ+Fo35rqzOo8UqqUhkPL0AAAAAAFgQKrHk+3fbXmVt/30+K6uL2jVc1up5WSUViYwkGQAAAAAg7sWyh1OoxJKdfD0A2/aUAxBdJMkAAAAAAHHPDT2cOhq62Ha4ZTjDGkOxa7hsuOcNdo/SueGgDLVEvOMJBgAAAAAgAuEklnxDFxNlddFzib6zksx5P0mR9Nnca3awc765WM5l54Z58xAcnwQAAAAAIO5ZnYAekXNDrz3ADiTJAAAAAABxz009tTrqYZboq4vawc755mI5lx0rg7ofnwAAAAAAAAhbrHvt2dlzLZa94uiB534kyQAAAAAACYOeWvYL3uPJMM8W4h5PLQAAAAAAcIVgk9rb2XMtlr3imDfP/UiSAQAAAEAHHn/8ca1bt041NTVKT0/Xxx9/7HRIgCucn8DyifY8W3bONxfLuezcNG8eguOTAAAAAIAONDc361vf+pYKCwu1cuVKp8MBXCVa82wxqT3cgKcMAAAAADpQXl4uSVq9erWzgQAJLJxkm53zzcVyLruuXivYkFREByUJAAAAAFHU1NSkpqYm/3uv1+tgNIC9mGcLiYQkGQAAAABEUUVFhb/3GZDoojXPFsm2zjEk1X6UIAAAAICkM3fuXD355JMd7rNz504NGzbM8rlLS0tVUlLif+/1epWfn2/5PEAiC2fIIJPaB4rW/G8IjacNAAAAQNJ58MEHdccdd3S4z5AhQyI6t8fjkcfjiehYIBbsmNMqlnN6AXYhSQYAAAAg6fTv31/9+/d3OoyExuTi8ScWn1k4QwZJtgXHkFT78S0FAAAAAB04cOCATpw4oQMHDqilpUU1NTWSpEsvvVS9evVyNjjAgs4SVLEQyZBBEq7nMCTVfpQkAAAAAHRg/vz5ev755/3vr7zySknSu+++q/HjxzsUlXsxubh7dZag2jyvyP9vPjMkoxRjjHE6iM54vV5lZWWpoaFBmZmZTocDAHGP79VAlAcARBffq4GSrTwGzV3X4c8ZSueczj6bUKL5mbVNogYbMtg2IWdlXyQXu75XeaIAAAAAAEgCoea0atubzG5WhgyymiNiLTWSg5YuXapBgwYpIyNDBQUF2rRpU8h9V6xYoWuvvVYXXHCBLrjgAhUVFXW4PwAA1DMAAMSvHQuKtWNBccDQvc3zivzb4Zye6d3+9Uprsy2Nz8wmp5rPatDcdRo0d52/VxzczXJPsrVr16qkpETLly9XQUGBlixZouLiYu3evVvZ2dnt9t+4caOmTJmicePGKSMjQ08++aRuuukm/fWvf9XAgQOjchMAgMRBPQMAQHxjcvH448RnFs4qlqzmiFiz3JNs8eLFmjFjhqZPn67hw4dr+fLl6tmzp1atWhV0/xdffFH33HOPRo0apWHDhulXv/qVWltbVVVV1eXgAQCJh3oGAADAXr4E1f6Ft7g6eRmq55tvu1udaj77r1fg4hW+7XAvS09Vc3OztmzZotLSUv+21NRUFRUVqbq6OqxznDp1SmfOnFHfvn1D7tPU1KSmpib/e6/XayVMAECcop4BACBxhNNTCO7CZxYdzKUWvyz1JDt27JhaWlqUk5MTsD0nJ0d1dXVhnWPOnDkaMGCAioqKQu5TUVGhrKws/ys/P99KmACAOEU9AwAAgPPFS883xL+YPl0LFy7UmjVrtHHjRmVkZITcr7S0VCUlJf73Xq+XBgwAoFPUMwAAAHAac6nFL0tJsn79+iktLU319fUB2+vr65Wbm9vhsYsWLdLChQv19ttv64orruhwX4/HI4/HYyU0AEACoJ4BAABIbqeaz/qHK+5YUByXPcdYvCJ+WRpumZ6ertGjRwdMhuybHLmwsDDkcU899ZQeffRRVVZWasyYMZFHCwBIaNQzAAAAAJxiOY1ZUlKiadOmacyYMRo7dqyWLFmixsZGTZ8+XZI0depUDRw4UBUVFZKkJ598UvPnz9dLL72kQYMG+eeU6dWrl3r16hXFWwEAJALqGQAAgOTjW/Xx/BUhfeKxFxYLIcQfy0/Z5MmTdfToUc2fP191dXUaNWqUKisr/ZMsHzhwQKmpn3VQW7ZsmZqbm/Vv//ZvAecpKyvTT37yk65FDwBIONQzAAAgniTC8EA3YEVIuEFEv72zZ8/W7Nmzg/5s48aNAe/3798fySUAAEmMegYAAABArJHiBgAAAADAokQcHugkVoSEG/BbCwAAAACARQwPjC5WhIQbWFrdEgAAAAAAAEhEpGQBAAAAALAo3oYHxssCA6wICSe587cCAAAAAAAbdTVpxPBAIPHw2wsAAAAAQIJigQEgfPw2AAAAAACSRrSTRm4fHsgCA0D4SJIBAAAAAJIGSSMAoZAkAwAAAAAgQcXbAgOAk0iSAQAAAACSRrIljVhgAAgfvxUAAAAAgKSR6Emjrq7aCSSzVKcDAAAAAAC32r9/v+68804NHjxYPXr00NChQ1VWVqbm5manQwMs8S0wsH/hLSTOgBD4zQAAAACAEHbt2qXW1lb98pe/1KWXXqrt27drxowZamxs1KJFi5wOD13g9lUprYr2qp1AMuK3BAAAAABCmDBhgiZMmOB/P2TIEO3evVvLli0jSQZXYdVOoOsYbgkAAAAAFjQ0NKhv375OhwEAiDJ6kgEAAABAmPbs2aNnnnmmw15kTU1Nampq8r/3er2xCA1JLllW7WRhAtiJnmQAAAAAks7cuXOVkpLS4WvXrl0Bxxw6dEgTJkzQt771Lc2YMSPkuSsqKpSVleV/5efn2307iKJTzWc1aO4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+ "text/plain": [ + "
" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "axes[0,1].set_xlim([-1,1])\n", "fig.savefig(\"Figure_updated.jpg\")\n", @@ -1378,12 +2208,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "a99034cb", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.545997Z", + "iopub.status.busy": "2023-07-25T23:59:06.545858Z", + "iopub.status.idle": "2023-07-25T23:59:06.652996Z", + "shell.execute_reply": "2023-07-25T23:59:06.652585Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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j6MqMs0YSZjOzZUc5S9fsUh3nsMIjbPz67tMB+OC1+RTvqVKcqGt46fFvcLa46T+sGyefM0x1nMPy+fz896MlAMw4awThdjkmqI3fr/GPL+YBcOHYwfRKTVCcSHQ1UjxFl9ErNYELxw4G4B+fz8Mnm1PaxcWEc970oUBwjHpOPHkAI8b3xuPx8dwjX+o+b7BbsWAbC3/Iw2gyctsfz8Zo1PePjrlLt7G7qGaf0XwR8MXKTWwuqiAqzMbN08eqjiO6IH0/ewhxnN08fSxRYTa2lFTyxUo51/OnLjl7FFarmbxtpaxcX6A6zmEZDAZuue9MLFYzq5fsYP63G1VHClkup4fnH/0SgPMuH0ePPqmKEx2e36/x5kdLAbj4zBFEhNsUJ9KPZpeHf32zCIAbTxpNfKRcHyo6nxRP0aXER4Zz40mBI3n+9fVCml1uxYn0IyEugnNODowIv/7hYt2PIqZ3S2DGdZMBePHxmdTXNilOFJreemEOpUW1JKZEc8XNJ6qOc0QLVuSzs6CKiHArF54+XHUcXXn9xxVUOprITIjhsklDVccRXZQUT9HlXDZpKJkJMVQ1NPPajytVx9GVy88djdViYv3mYtbk6X8d7MXXTqJbzyRqqxr5558/1X1ZDjZrlu7gozcCu59vue8s7DofPdQ0jTc+DKztvPD04UTrfANUZyqra+CNuasAuPPMSVjNZsWJRFclxVN0OVazmTvPDOzkfOPHlVTUNypOpB+J8ZGcMW0QQPt0pZ5ZbRb+77GLsVhMLJ27pf0qR/HL1dc28cT9H6FpGqdfNIpxU/V/JNGilTvZvqsCe5iFi88coTqOrvx71mKcHi/De2Zw0qAc1XFEFybFU3RJJw3KYWh2Gi6vj//8IGXlpy4/dzRms5FVGwpYt7lIdZwj6tU3jWt/Ox2A/zzxDbvzyxUnCn6apvH0g59RXdFAVo8kbrzrNNWRjigw2rkYgPNPHUZMlF1xIv3YWV7Dl61r2u86a7LuD/4XoU2Kp+iSDAYDt582AYCPl22guKZecSL9SE2K5vSpAwF480P9j3oCnHP5WEZO6I3b5eXv93yI2yUHy/8SX3+4giU/bsZiMfF/j11EWBAcR7Rs7W627CgnzGbmkrNltPOnXvhuCX5NY+qAXgzqpu/NYSL0SfEUXdaonCzG9M7C6/Pz0vfLVMfRlSvOG43JaGD5ut3kbStVHeeIjEYjv//bBcTERbBrWxmvPf2d6khBa8+OCl76x9cAXPPbU8jpl6440ZFpmsbrHwRGO885ZQhxMRGKE+nH1pJKZq3dBsAtp45TnEYIKZ6ii7utddTzi5Wb2C1XabZLT4ll+pQBALzZehC33sUlRPL7v50PwGdvL2H5ArnL/Vi5XR4e+78PcLu8DB+fw7mXB0dRWbm+gLxtpVitZi49Z5TqOLry3KzA9++pQ/uQm56kOI0QUjxFFzekexon9O+Bz6/x149my6HyP3Hl+WMwGg0sXrWTJat3qo5zVEZPyuWcywJl6bG7P2DzOv3vzNcLt9vL337/Hru2lRETF8Fdf7tA9wfFA7g9Xv79xo8AnH3SYBLjIhUn0o85G3fwY94OjAYDN50SHC8iROjT/7OKEB3srrNPwG41szy/kNfleKV2mWlx7ecg/v2F73A0OhUnOjrX/e4UhozqQXOTiz/e9AZbN+p/g5RqHo+XR//wPsvnb8VqM3PfEzOIT4xSHeuovPHhUnYUVBEbbeeqC8eojqMbZXUN/On9wJKTX00eTs+UeMWJhAiQ4im6vOykOO49dyoQOHJk/R79r2nsLDdeNpHMtDiqahp55rU5quMcFavNwkPP/opBI7JpbnTxx1+/wfZNJapj6ZbX4+Oxuz8IbCaymnnwX1cwZFRP1bGOyub8Ut7+NLA++/c3niRrO1v5/H7ue2cW9c1O+mUmc8fpE1RHEqKdFE8hgHNHD+DUoX3w+TXufutrGp0u1ZF0Icxm4f7bTsNoNPDtvE0sWJ6vOtJRCQu38pfnfkX/Yd1obHBy742vs2OLvKDYn8/r4/H7PmTR7E1YLCb+/PRlDB8XHGc8utxeHn72G3x+jWkT+jJ1XK7qSLrx2pyVrNhRhN1q4fErTsdiNqmOJEQ7KZ5CEDhe6YELp5EeF01xjYO/fRwco3udYWBuOpecPRKAx1/8jvqGFsWJjo493Mbfnr+KfkOyaHS0cO+Nr7NrW5nqWLrh8/n5xx8/Zv63GzGbTdz/z8sYObGP6lhH7dX3FrG7qIaE2AjuvGGa6ji6sW5PKc99G9jhf995U8lOilOcSIh9SfEUolW0PYy/X34aJqOBmau38OXKTaoj6cZ1MyaQnZlAbX0zT78aPKU8PCJQPnMHZuKoa+aeG16XA+YJlM6nHviEud+sx2Q28scnL2HM5OAZMdy4rYT3vgysx/7Db06Ww+JbNbS4+L+3vsbn1zhtaC7njOqvOpIQB5DiKcRPDO2Rzm9OGQvA3z6ZQ0FVndpAOmGzmvnjbadiNBr4fsFm5i/brjrSUYuICuPhF6+id/906mubuOf61yjYWaE6ljJ+v5+nH/yU2V+txWgycu/jM4LiOsw2LpeHR56dhd+vMf2E/kwcFRxLAzqapmn89ePZFNc4yIiP5oELp8kNRUKXpHgKsZ8bpo1mRM8Mml0e7n7razxen+pIutAvJ43LWs9IfOI/3wfNlDtAZLSdR166ml5906iraeL/rn+NPTu6Xvn0enw889DnfP/5GowmI/f8/WImnjRAdaxj8ur7iykoCUyx33HNVNVxdOOLlZv4Zs1WTEYDj11+GlF2m+pIQhyUFE8h9mMyGnnsstOIttvIKyzn2VmLVUfSjWtnjCc7M4GauuCacgeIignn0f9cQ48+qdRWNXL7pS8w65OVaJqmOlqnKCuq4a5rXuHbT1dhNBr4w8MXMPmUgapjHZN9p9hPIVqm2AHYU1nLw58EzjK9efo4hmbr/7Yp0XVJ8RTiIFLjovjLjFMAeP3HlSzZtkdxIn2wWszcd2twTrkDRMcGyufQMT1xOT08/eBnPPKH92l0BM/o7c/x49fruPni59iyvpCIqDDue+ISpp4+RHWsY+Jye3n0361T7JP7M3FUL9WRdMHj9XH3W1/T4vYwqlcm150oNzcJfZPiKcQhTBuUw0XjBgFw3zuzqGlsVpxIH/r3Dt4pd4DY+Ageeelqrv3tKZjMRhZ8t5GbL/o3G1fvVh3tuGtucvHE/R/z93s+pLnRRf+h3Xjug1uCbnod4NX3F7GnuHWK/VqZYm/z7DeL2FRUQUx4GI9cdiqmILhtSnRt8i9UiMP4w9kn0CslnqqGZh5477suMy17JNdcvHfKPVgOlv8po9HIxddO5qk3byQtK56K0nruvvZV3nphDr4QWdO7La+YW2c8zw9frMFoNHD5r6fyj9euIzUj+I7X2bithPe+2LuLXabYAxZv3cPrc1cB8NDFJ5MaGxy3TYmuTYqnEIdht1r4+xWnYzWbmL95l1yp2cpm3Tvl/t38zcxetEV1pJ8ld1Amz31wC9POHIrfr/HWC3P4/dWvBPU1my3NLt549nvu/NV/KCmoJjElmr+/ci2/umUapiA8SLyxybV3F/tk2cXeprTWwR/fnQXAxeMGM22QPC4iOEjxFOIIctOTuOusyQD8c+ZCPl22UXEifejfO43Lzx0NwMPPfsO6TcFZ1sIjbPzhkQu5+9ELCY+wsWV9IXdc9iJP3P8x1RUO1fGOmt/v5/vPV3PdWU/z3svz8Hp9TJjWnxc+upVBI3uojvezuD1e7nv8s8Au9jiZYm9T3dDMjS99QlVDMzmpCdx19mTVkYQ4agZNx3OHDoeDmJgY6uvriY6OVh1HdGGapvHklwt4c94qjAYDT199FlMHyuYGn8/P/U98wYLl+URG2Hj+b5fSs1ui6lg/W1W5g9ef+Y7ZX60FIMxuZcZ1kzn/ygnYwixqwx1G3po9vPj412zPKwYgLSue6+88lfEn9gvasxz9fo2Hnv6K2Yu2Em638u+/zKBPzxTVsZRrcrq59oUP2VRUQXpcNG/ecjGpcTLFLtQ6lr4mxVOIo6RpGn96/3s+W5GHzWzi5d9cwLAeGapjKedyefjtXz5iw5ZikhOieOGRS0lJDO7v160binjx8ZlsXlcIQHJ6LNfcdjLjp/XXTQHVNI09Oyp49z9zmTdrAxAYvb30ximcc/k4rFaz4oQ/n6ZpPPvGXD74ahVms5F/3HcBo4Z0Vx1LOY/Xx82vfMbS7QXER9r5760z6C5XYgodkOIpRAfx+vzc8foXzN+8i2i7jTdvvZic1OAd4TteHA0t3Hz/u+wuqiE7M4HnH76U6Mgw1bF+EU3TmPvNel7957dUlQem3K02M0PH9GL0pD6MnpxLclpsp2ZyuzysX7GL5Qu2sWzeFspL6gAwGAycev4Irrz1JOISIjs1U0d45/MVPP/feQD86bdncMqk4LlZqaP4/Rr3vPMN36zZit1q4fWbL2RAVqrqWEIAUjyF6FAtbg/Xv/gx6/eUkhITyVu3XSJTXUBZpYOb7nuHyppGhvTL5Kk/XYgtiEfd2jhb3Hz830V8+8lKKkrr93lfdu8URk/OZczkXPoOzsJkOv7L5qvKHaxYsJVl87eyZukOXE5P+/ssVjMjxufwq5un0atv2nH/2ip8N38Tf3nmawBuueoELj1bzqXUNI3HP5/HWwvWYDYaee76cxmfKyPAQj+keArRweqaWrjquQ/YWV5Dz5R4/nvrDGLCg3uE73jYsaeSW+5/j8ZmFyeM6c1ffn9Wh5QxFTRNY09+Bcvmb2H5/G1sXleA37/36TMqxs7IiX0YPakPIyf0Jiom/Gd9HZ/Pz7aNxSxfsJXl87eyY0vpPu9PSI5qLbt9GTq6J2Hh1l/059KTFet2c9fDn+Dz+Zlx5ghukysxAXh1zgqenrkQgEcvO5UzR8gIsNAXKZ5CdIKy2gYuf/Y9KuobGdI9jZd/cwF2qz7W/6m0emMBv//rx3i8Ps47dSh3Xj8taDe4HI6jrplVi7azbP5WVi7avs/tR0aTkf5DssjqmXRMf/bmRhdrlu6gvrap/W0Gg4HcQZmMntyHMZP70jM3NSQfz607y7n1gfdocXqYNqEvf/7tGRiNoffnPFafLc/jgfe/AwLnCl95wnDFiYQ4kBRPITpJflkVV/77AxpaXEzu14OnrzkLiyn4zko83uYs3sqfn/oSTYPrL5nA1ReNUx2pQ/m8PjavK2T5gq0sm7eVPTsqftF/LzzSxojxvRkzOZeRE/sQGx9xnJLqU1FpLTff/y41dc0MH5jFE/dfgNUS/Ms0fqn5m3Zy++tf4PNrXDNlBHeeJccmCX2S4ilEJ1qzq5gbXvwYl9fHuaMG8JcZJ4fkiNSx+ujr1Tz9auBWozuuPZGLzug6IzVlxbWsWrSduprGY/o8k8lIvyHdGDCsO2ZL13gBU17l4Jb736Os0kFOdhL//sslREbYVMdSbu3uEm548WOcHi9nj+zHX2dMlxFgoVtSPIXoZD9u3MFv3/gSv6Zx/bRR3HH6RNWRdOG1Dxbz2vuLAbjvllM5/cSBihMJPamtb+KW+9+noKSGzLQ4nv/bJcTHhvbo7tHYWV7Nlf/+gPpmJxP7ZvOva8+WmRSha8fS10Jj1b8Qik0d2Is/X3QSAK/MXsFb81crTqQP11w0jhlnjgDgsRe+5cclWxUnEnrR0OTkzr9+TEFJDcmJUTz954ukdAJldQ38+j+fUt/sZHC3VJ688kwpnSKkSPEU4jg5f8xAbj9tAgB//3weX68OzvvLjyeDwcCtV0/hrJMGtd5EM5Ola3apjiUUa3G6ufvhT9i+q4L42HCe+fPFpCbJrFZ9s5ObXv6UsroGspPi+Pd15xJukw2LIrRI8RTiOLp+2igunTAUgPvencWHS9arDaQDBoOBu248mWkTcvF6/dz72Gd8PUfuu++qKqobuPVP77NhawlRkWE89cBFZKXL7TslNQ6ufu4D8suqSY6O4KUbzycu0q46lhDHnRRPIY4jg8HAPedO4ZxR/fH5Nf7y0Wye/HL+Puc9dkUmk5EHbj+dqeP64PH6eOS5Wfz7zbn4fH7V0UQnyttWyg13v8XWHeXERtt58v4LyMlOUh1LuY0FZVz2r3fJL6smKTqCF288n/R4GQEWoUk2FwnRATRN46UflvHcrCUAnDw4h0cuO42wLn5EjN+v8fqHi3n9g8DjMnZYDx783Zmyi7kL+HbeJv7+wre4PT56dUvksXvPIy05RnUs5WZvyOeet7/B6fHSJy2R5647V25CE0FHdrULoRMzV2/hgfe+w+PzMbhbKv+69hwSon7ejTahZM7irTz87De43F66Z8Tz2D3nyXRriPL5/PznnYW8/dlyACaNyuGBO04n3B46Ny79HJqm8daCNfzji3loGkzom82TvzqDiLCu/biI4CTFUwgdWbmjiN++8SX1zU4y4qN5/vrz6JkSrzqWclt3lnPvY59RUd1AVGQYf/39WYwcLPdPh5KmZhcPPT2Txat2AnDlBWO5/pIJXf48Sp/fz98/m8e7i9YCcPG4wdx73lTMIXK9rOh6pHgKoTO7K2u5+eVPKayuJ8pu45lrzmZUr0zVsZSrrm3ivsc/I29bKSajgduvPZHzTx0qB/CHgOKyOu557FN2FVZjtZq55+bpnDJJ7hhvdrm5+62vmbcpcLrDnWdO4uopI+TfvAhqUjyF0KGaxmZuf+0L1u0pxWwy8pcZp3DWCPlB7HJ7efzF7/h23iYAzjllCHdcO1WuTAxiqzcU8MCTX1Lf0EJCXASP3XMu/XLSVMdSrtLRyC2vfs7mogpsZhOPXHYqpwzpozqWEL+YFE8hdMrp8fLHd2fx3brtANx8ylh+c8rYLj/aoWka736+ghfemo+mQe8eyTz42zPonpmgOpo4Bl6vj1ffX8xbny5D06BfTiqP/t+5JMZHqo6m3LbSKm555TPK6hqIj7Tzr2vPYUh3KeMiNEjxFELH/H6NZ75eyGs/rgTg7JH9ePCik7GY5XaSJat28rdnv6G+oQWb1cxt10zlnJMHd/liHgyKy+p48Omv2Ly9DIDTTxzI76+fhk0OQGfx1j38/r9f0eh0k50Ux/M3nEtWQqzqWEIcN1I8hQgCHy5Zz8OfzMHn1xidk8VTV51JTHiY6ljKVdU28vCz37Bi3R4gsAv6/24+hdhoOQ1AjzRNY9bcPJ56ZTYtTg+RETbu/s0pnDg+V3U0Xfhk2Ub++tFsvH4/I3pm8Mw1Z8v3uQg5UjyFCBILt+zm9//9imaXhx7J8Tx//blkJsjZhn6/xgdfreLFt+fj9fpJiIvggdtPl13vOtPQ5OSJl75n9qKtAAztn8n9t58u118S+Df871mLeXl24BipM4b35S8zTsZqlrXLIvRI8RQiiGwtqeTmVz6jor6R+Mhw/n3dOQzqlqo6li5s21nOQ0/PZE9xDQYDXHr2KG64dCIWiyxLUG3dpiL+8sxMyqsaMBkNXHfJBC4/dzQmORIIl8fLA+99xzdrA4X81yeP4Zbp42TJiAhZUjyFCDLl9Y3c+spnbCmpJMxi5k8XncSZw/vKDyrA6fLw7Btz+fy7dQD06ZHMXb8+mf69ZWOGCs0tbt74cAnvfbkSv18jIzWWP//2DPn7aFVW28Ddb33Nmt0lmI1G/nzRSZw7eoDqWEJ0KCmeQgShJqebP7z1NQs2B873mz6kDw9cOE3Wg7Wav2w7f3/hO+obWgA4feoAfn35ZBLiIhQn6xr8fo1v52/ixbfmU13bBAT+Dn573bQufwtRm5mrt/Dwx3NocLqICrPx1FVnMrZPN9WxhOhwUjyFCFJen59X5iznpe+W4fX7SY6J5G+XnMK4PrK2EQIbj158az6z5gbO/Ay3W7n6wrFcdMYImX7vQJu2l/L0q3PYtL0UgIzUWG67eioTR/VSnEwf6pudPPzxnPap9cHdUnnkslPpniTXwIquQYqnEEFuY0EZ974zi92VtQBcMWkYd5wxkTA5VB2AjdtKeOa1Oe1H96QlR3PJWSM5Y9ogwuT4nuNm47YS3v50OQuW5wNgD7Nw1YVjufjMEXLAf6tl2wv447vfUl7fiMlo4NcnjeGGk8bI9ZeiS5HiKUQIaHZ5eOqr+by/eD0AOakJPHb5aeSmJylOpg9+v8a38/J48a0FVNcFpn5jo+1ceMZwzp8+lOgou+KEwUnTNJat3c1bny5jbV5R+9tPndKf31w+WQ6Db+XyeHnm60X8b/5qALolxvLoZacyWA6FF12QFE8hQsj8zbv40/vfUd3QjNlk5LZTx3PVlBGYjDKiAuByeZj540be/XwlpRX1QGBk7uyTBjPj7JEkJ0QpThgcvD4/Py7eytufLSd/dyUAJpOR6ZP7cdm5o8mWW6TabS2p5J63vyG/rBqAi8YN4q6zTiBcRttFFyXFU4gQU9PYzIMf/MCPeTsAGNEzg0cuPZX0ePm+aHOw4mQ2GzllkhSnw3G6PMycs5H3vjiwuF981ghSEuXfWBu/X+O/81bxr28W4/H5iI8M56GLT2LKAFnrKro2KZ5ChCBN0/h0eR6PfTaXFreHyDAr9503lTNH9JNjl36ibar47U+XsyavsP3tk0blcPl5oxmYm64wnX44Gp18MmsNH81cTZ0jcFKALFU4tNJaB39891tW7AgsP5jSvycPXnwyCVFyo5YQUjyFCGGFVXXc+84s1u0J7DA+ZUhv/nThSXLs0kHkbSvl7c+Ws2D5dtqe6Yb0y+SK80YzZlgPjMauV9grqht4/8uVfPH9elqcHqB1c9bZozjjxIGyOWs/mqYxc/UWHvnkRxqcLuxWC3efcwIXjBkoL/iEaCXFU4gQ5/X5eXXOCl78bmng2KXoCP56yXTG58qxSwezp6iadz5fwbfzN+H1+gFIiItg4qgcJo7qxYhB3UJ6l3ZBSQ0LluezcHk+G7eVtJfwXt2TuPy80Zw4Pld2YR9EXVMLD38yh1lrtwGBY5Ievfw0uiXGqg0mhM5I8RSii8grLOOet/ceu3TB2IHcddZkIsNsipPpU2V1A+9/tYovf1hPU7O7/e32MAtjh/Vg0pjejBveg6iI4B499vs1NueXsWD5dhauyGd3Uc0+7x86IJPLzx3N2GE9ZNTuEOZs3MFfPvqB6obmwDFJJ4/lhmmjpaALcRBSPIXoQlrcHp6euZB3Fq4FIC0uiocuPlkOnT8Mt8fLmo2FLFiRz8IVO6iqaWx/n8lkZNiALCaNDoyGBsvmGrfHy+qNhYGRzRX57bcLQeDPNHxg258pR3b6H0Z9s5PHPvuRr1ZtAaBHcjwPXzqdQd1SFScTQr+keArRBa3IL+SB97+juMYBBI54+f2Zk4kIk+sMD8fv19i6s4z5y/JZsDyf3UXV+7w/t1cKk0blMHZ4D7IzE3SzBtLv16iobmDDlmIWLM9n6ZpdNLfsHcUNt1sZN7wHE1uzB/sobmeYm7eDhz78gaqGZowGA1dNGcEt08dhC+FlGEIcD1I8heiiml1u/jlzIe8tWgdAYlQ4N00fx/mjB8oU4VEqLKllwYpACd24tZj9nyGT4iPJTIsjMy229fc4stJiyUiJxXacS6nfr1FV20hRSS2FpbUUldZSVFZHUWktxeX1uN3efT4+IS6CSaNymDg6h+EDs0J63erxtL20in/OXMiCzbsAyE6K42+XTmeIHAYvxFGR4ilEF7c8v5AHP/iewurAuYw9kuO54/QJnDiwl6zpOwY1dU0sXrmT+cu3s3FrCY5G52E/Pjkhap9CGh15bKOMXq+Pkop6ikrrKG4tma79yuVPmUxGsjPiGT+yF5NG59C3V2qX3Kn/c5XVNfDcrCV8sXITfk3DbDRyxeRh3HLqeLmeVohjIMVTCIHH6+ODJet58bul1DUHCtOw7HTuPGsSQ7PlLMufw9HQQmFpYMTxp6OPhaW1NDa5OuRrmkxG0pJjAqOqqYHR1bZim5IULSPZP0NDi4tX56zgrfmrcXl9AJw8OIfbT59IdlKc4nRCBB8pnkKIdg0tLl7/cSX/m78apycwenbSoBzuOEN+yB4vmqZR39ASKKIlta1ltI4Wp/vIn/wTRqOB1KRoMtPiyEiNJSstjtSkaMxmUwcl71rcXi8fLF7PS98va38xNrxHOr87U16MCfFLSPEUQhygvL6R52ct4bMVefg1DZPRwFkj+nPjyaPJSohVHU+IDuPx+fhixSb+88NySmoDm+96JMfzuzMmMmVAT1l+IsQvJMVTCHFI20ureHrmQua3bqQwG42cPTJQQDPiYxSnE+L48fh8fLlyM//5YVn7aQ+JUeHcPH0c58mGOyGOGymeQogjWru7hBe+XcribXuAQAE9Z3R/bpw2hvR4+X4Twcvr8/PlqkDhLGrdYJcQFc61U0dx0bhB2K36OBJLiFAhxVMIcdTW7irh+e+WsGRbAQBmk5HzRg/ghmmjSYuT7zsRPLw+PzNXb+al75e1n+gQHxnOtVNHcvH4wVI4hegguimejz76KJ988glbtmzBbrczfvx4/v73v5Obm3tUny/FU4jOs3pnMc9/t4Rl2wuBQAE9f/RAbpg2mtQ4uelG6JfX5+frNVt46ftlFFTVARAfaeeaKSO5ePwQwnVy6L8QoUo3xfPUU0/lkksuYdSoUXi9Xu677z42btzIpk2biIiIOOLnS/EUovOt3FHEC98tZXl+oIBaTCbOHzOQ66eNIjVWCqjQD5/fz9drtvKf75exu7IWgLgIO1dPGcElE4YQbpNbu4ToDLopnvurrKwkOTmZefPmMXny5CN+vBRPIdRZsaOI579dwsodRcDeAnr1lBFkJsgmJKGOx+vj6zVbeGX2ivbCGRsexlVTRnDZxKFSOIXoZLotnvn5+fTu3ZsNGzYwcODAA97vcrlwufYewuxwOMjKypLiKYRCy/MLee7bJazeWQyAyWhg+pBcrj1xJLnpSYrTia6k2eXm42UbeXPuKsrrGwGICQ/j6ikjuHTCUCLCpHAKoYIui6ff7+fss8+mrq6OhQsXHvRjHnzwQR566KED3i7FUwi1NE1jxY4iXp29on0XPMCEvtlcNmEI43Oz5Wga0WGKquv5ZNlG3l+8DkdLYHAiMSqcKyYPZ8b4wUSG2RQnFKJr02XxvOmmm/jmm29YuHAhmZmZB/0YGfEUQv82FZXz+o8r+W7ddvytTx/J0RGcM3oA540eIIfRi+PC5fEyZ+MOPl62oX3DG0C3xFiumTqSs0b0wyb3qQuhC7ornrfeeiuff/458+fPp0ePHkf9ebLGUwj9Kqyq491F6/hy5ab26wcBxvTO4vzRA5k2KEeKgThm20qr+HTZRr5ctZn61n9XBgOM7d2Ni8YN5sSBvTAZZXRdCD3RTfHUNI3bbruNTz/9lLlz59K7d+9j+nwpnkLon9vr5ce8nXyydCNLtu+h7Rkl2m7jrJH9OGtEP/pnpsi1hOKQ6ppa+G79dj5bnseGgrL2t6fERHLe6AGcO3qA3KolhI7ppnjefPPNvPPOO3z++ef7nN0ZExOD3W4/4udL8RQiuJTUOPhsRR6fLs+jrK6h/e2ZCTGcOrQPpw7NpU9aopRQgaPFyZwNO5i1bhvLthXg9fuBwA1aUwf25PwxgxjXp5uMbgoRBHRTPA/1w+X111/n6quvPuLnS/EUIjj5/H6WbCvgs+V5zN+8kxa3t/192UlxTB/ah1OH9iEnNVFhStHZGp0ufszbybdrt7Jo6x68Pn/7+/qmJ3HG8L6cNbI/CVHhClMKIY6VbornLyXFU4jg1+zyMH/TTr5dt435m3fh9vra35eTmtBaQnPJTopTmFJ0lLa//1nrtrFA/v6FCElSPIUQutTkdPNj3g6+XbuNhVt3HzDiNX1oH6YP6UNWYqy6kOIXa3F7WLRlN7PWbpMRbyG6ACmeQgjdO9QaP4DuibGM7dOd8bndGZ2TKec06pzfr7GttJLFW/ewZFsBq3cV7zOyKWt8hQhtUjyFEEGlrqmF2RvymbV2Gyt2FOLz731aMhkNDO6Wxrjc7ozr042BWalyWL0OlNc3sqS1aC7dvoeaxpZ93p8eF80pQ3pz6tA+cqqBECFOiqcQImg1Ol0szy9iybY9LNm6hz1Vdfu8PyrMxujeWYzv041xud3lwPpO0uzysGpnEYu37WHptgLyy6r3eb/damF0Tibj+nRnXJ/u9EiOk7IpRBchxVMIETKKa+pZsq2AxVv3sGx7QfuViW0yE2IY1SuTgd1SGZSVSk5aAhaTSVHa0FHpaGRDQTkbC8tYu6uEtbtL8fj2Tp8bDDAwK5Wxfboxvk93hnRPw2KWx12IrkiKpxAiJPn8fjYVVbSuJdzDut2l+6wNBbCZTfTLTGZgt1QGZqUyqFsqWQkxMvp2GI1OF3mF5WwoKGv/vby+8YCPS4+LZlxuN8b16c7Y3t2ICQ9TkFYIoTdSPIUQXUKT083KnUWs213aXpoanK4DPi4qzEbv9ET6pifRNz2JPulJdE+K7XKblrw+P2V1DeSXVbGlpJKtxZVsLamksLr+gI81GKBXSgKDuqUysFsqY3Ky6JYYKwVeCHEAKZ5CiC7J79fYU1XLxoIyNhSUk1dYxubiyn2miH8qNjyMjIQYMuNjyIiPJjMhhoz4GLISYkiNiwq6KXtN06hrclJUU09xdT1FNa2/qusprnFQWuvYZ+PWT6XHRTOwWwqDslIZ0C2V/hnJRIRZO/lPIIQIRlI8hRCilcfrY2dFDVtLAqN7W4sr2V5WdcAu7P0ZDQZSY6PaC2labBTR4WFE223tv0fZbUTbw4gOD8NmNnXIaKDP76ehxUVDiwtHi5P6ZhcNTheOZieOFhfVDc2txTJQMptdnsP+9ywmE9nJcfRNTyI3PYk+6YnkpicRHym3BQkhfh4pnkIIcQRNTvc+o4E/LW/F1fW4vAcfJT0Ui8lEdHigiAYKqQ2bxXxM/w2f30+T042jtWQ2tLhodLqP6b8BkBwd0T56G/h972hucnQkRqNMlwshjp9j6WvH9qwohBAhIiLMSm7rqN/+NE2jqqGJ4moHhTWBQlpe19heBh0te0ccG1pc+DUNj89HdUMz1Q3NHZLXbrXsHWVtG3m1hxEXYd+nWGbERx9z4RVCiM4iz05CCLEfg8FAUnQkSdGRDO2RftiP1TSNJpcbR7PrgGJ6qLWlh/nCRNqs+0zpR4UFRk/lqCIhRCiQ4imEEL+AwWAgMsxGZJiNdGRJkBBCHI7cOyeEEEIIITqFFE8hhBBCCNEppHgKIYQQQohOIcVTCCGEEEJ0CimeQgghhBCiU0jxFEIIIYQQnUKKpxBCCCGE6BRSPIUQQgghRKeQ4imEEEIIITqFFE8hhBBCCNEppHgKIYQQQohOIcVTCCGEEEJ0CimeQgghhBCiU0jxFEIIIYQQnUKKpxBCCCGE6BRSPIUQQgghRKeQ4imEEEIIITqFFE8hhBBCCNEppHgKIYQQQohOIcVTCCGEEEJ0CrPqAEej2ePB7PGojiGEEEIIIfbTfAwdLSiK59hXX8QYFqY6hhBCCCGE2I/f6Tzqj5WpdiGEEEII0SkMmqZpqkMcisPhICYmhtKqKqKjo1XHEUIIIYQQ+3E4HKQlJlJfX3/EvhYUU+3hFgvhFovqGEIIIYQQYj/eY+hoMtUuhBBCCCE6hRRPIYQQQgjRKaR4CiGEEEKITiHFUwghhBBCdAopnkIIIYQQolNI8RRCCCGEEJ1CiqcQQgghhOgUUjyFEEIIIUSnkOIphBBCCCE6hRRPIYQQQgjRKaR4CiGEEEKITiHFUwghhBBCdAopnkIIIYQQolNI8RRCCCGEEJ1CiqcQQgghhOgUUjyFEEIIIUSnkOIphBBCCCE6hRRPIYQQQgjRKaR4CiGEEEKITiHFUwghhBBCdAopnkIIIYQQolOYVQcQQohg4PZ5qfc4cbhbcHic1HtacLidODwt1LtbWt8X+P8OjxOHx4nH7zumr2EAIi02oi12oi1hRFvDiGn/33ZiWn+PtrS+3RpGpNmGwWDomD+0EEIcZ1I8hRAC8Gl+ylocFDXVUthUG/i9ua79/9e6m1VHPCiL0URmeCyZEXFkRcSRFR7X/r8zI+KIMFtVRxRCiHZSPIUQXYamaRQ317G5vozCnxbMplpKmuvwaP7Dfr4B2kcfoy1hRFvsxFjD2v93+wilNfD/rUbTMeXzo9HoceFoG0FtHVX96UhrYIQ18Lvb78Pj97GrsZpdjdUH/W/GW8PJjIijW0Q8mRGxZEXE0TMqib4xKYSZLMeUTwghfikpnkKIkKRpGkXNdeTVlZBXW0peXSmb6kqp97Qc8nMsBiMZbaOF4bHto4ZZEXGk2WOIsoRh1NG0ttPnocrZSFFz3T4luqg58Hudu4UadzM17mbW1xbv87kmg4FeUUkMiE1nQGwa/WPT6BuTit0sZVQI0XEMmqZpqkMcisPhICYmhvr6eqKjo1XHEULolKZpFDbVkldXGiiadaVsriul3uM84GMtBiO9Y1LoGZnYPgKY2TpFnWyPwmQInT2XDR4nRU11+5TRgqYattaXU+1qOuDjTQYDPaOSGChlVAhxDI6lr0nxFEIEHb+msaW+jCUVO1lauYv1tcU4DlYyjSb6RCczIDYtMLIXl07v6ORjngIPNZqmUe5sIK+2pLWsl7KproSqg5RRIwZyopMYldidcUk9GZ2UTZQlTEFqIYReSfEUQoSc4qY6FlXsYEnlLpZW7qTOve+UucVoIjc6pX2kTkrmsdE0jQpnw95R49blCVWuxn0+zmQwMDA2g3HJPRif3Ish8ZnyGAvRxUnxFEIEvVpXM8urdrOkYidLKndS0FS7z/vDzVZGJ2YzLqkHIxK7S8nsIBUtDaytKWJp5U6WVO5i936bmOwmCyMTuzM+qSfjknvSOzpZV+tghRAdT4qnECLoeP1+VtcUsKAsnyWVO9lUV8pPn5xMBgND4jIZl9yT8ck9GRSXgUWKZqcraa5naeVOFlfsZEnFTmr2O2YqwRbB2KQejE/uyQmpfUiwRShKKoToLFI8hRBBodnrZmF5PrNLtzKvbPsBO85zopIYl9yTcUk9GZXYnUiLTVFScTB+TWO7o4IlFTtZXLmTlVV7aPF52t9vAIbGZ3FiWi7T0vrSIypBXVghRIeR4imE0C2f5mdpxS4+L1zP7JLNNP+kqMRY7ExO7c3E5F6MTe5BcliUwqTiWLn9PtbVFLG4Ygfzy/PZVFe6z/sHxqZzVtYgzsgaJCOhQoQQKZ5CCF3RNI3N9WV8Wbierwo37rNhJSsijpPS+jI1LZdh8VmYjaFznFFXV9pcz49l25hTupVllbvwth7QbzIYGJ/ci3OyBnNiWl85rkmIICfFUwihC6XN9XxVtIEvCtaT31DZ/vZYq53TMgZwdrchDInLkLvGu4AaVxPfFOXxReH6fQ6zDzdbOSW9H2dlDWZMUnZInaMqRFchxVMIoYxf01hUsYO3dyxnfvn29g1CVqOJE9NyOStrMBNTcmQHehe2q6GaLwvX82Xheoqa69rfnh4ewyXZI7kgexjxMhUvRNCQ4imE6HT17hY+3bOWd3etpKCppv3tIxO6c3a3wUxP70+0VQ4eF3tpmsaamkK+KFzPrKK89pumrEYTp2YM4PKeoxkcn6E4pRDiSKR4CiE6zaa6Ut7ZuYKZRRtw+rwARFlsnNdtKJf0GCU7mcVRcfo8fFOUxzs7V7CxrqT97QNj07m050hOzxxImEnWggqhR1I8hRAdyu3z8m3JJt7ZuYK1NUXtb+8bk8KlPUZxZtYgws1WhQlFMFtfU8w7O1fwdfFGPH4fEDjx4ILuQ7mk5yiyIuIUJxRC/JQUTyFEhyhprueDXSv5cPfq9oPDLQYjp2T057KeoxgWnyUbhcRxU+Nq4uPda3hv90pKmuuBwNmgk1J6c1nPUUxKyZFbkoTQASmeQojjan1NMa9sX8Tski34W7cLpYRFcXGPEVyUPYKksEjFCUUo82l+5pVt552dK1hUsaP97d0i4rgyZyzndxsmRzIJoZAUTyHEL6ZpGosrdvLytoUsq9rd/vYxidlc1nMUJ6b1lTM3Rafb1VDNe7tW8GnBWho8LgDireH8KmcMl/YYRYzVrjihEF2PFE8hxM/m0/x8V7yZl7ctZHN9GQBmg5GzsgZzTe9x9I5OVpxQiMB1q58WrOW17Yvbp+EjzFZm9BjJVb3GkmyXW6+E6CxSPIUQx8zt8/JZwTpe3b64/Tgku8nCRdnDuSpnHOnhMYoTCnEgj9/HrOI8Xt62iO2OCgAsRhPndhvCtb3Hkx0ppyoI0dGkeAohjprb7+PdnSt4dfsiKp2BqyxjLHau6DWay3uOJs4WrjihEEemaRrzyrfzyrZFrKouAAIbkaZn9Of2flPpEZWoNqAQIUyKpxDiiDRNY07pVh7f+H37CGeqPZqrc8ZxYfZwIuQ4JBGkVlUV8Mr2Rcwt2wYElopc1nMUN/c9QdaACtEBpHgKIQ5rW305j274lqWVuwBItEVwW7+pnNt9qFxlKULGtvpy/rlpTnsBjbHYua3/FGZkj5SNcUIcR1I8hRAH5XA7+feWH3ln5wp8mobVaOLqnHHc2GciERab6nhCdIhF5Tt4bMO35DdUApAbncIDQ05nRGI3xcmECA1SPIUQ+/BrGl8UrOOJvB+odjUBcEp6P+4eeAoZEbFqw4Uol8+Fy+86ps8xYCDSHCmH8HcAr9/PB7tX8a9Nc9rvhD8razB3DTyJ5DDZAS/ELyHFUwjRblNdKX9d93X71ZY9IhP445DTmJDcS3Gy4Of1e6lyVVHmLKfMWdb6e+BXraf2Z/03bUYbKWHJpIalkhqW0vor8L8jzBHH+U/Q9dS6mvnnptl8tHs1GoEjmG7tN4XLe47GIstMhPhZdFM858+fzz/+8Q9WrVpFaWkpn376Keeee+5Rf74UTyF+vjp3C//aNIf3d63Cj0a4ycLNfU/gVzljZR3nMWrxtbC7ac8+5bK0pYxKVyV+/J2WI8ocdUAZzQzPJMWWLKOkx2hDbTF/Xfc1G2pLAMiJSuKBIaczOilbbTAhgtCx9DVzRwZpampiyJAhXHvttZx//vkd+aWEEK38msbHe9bwz7zZ1Lbep3565kDuHngyKXZ5AXe0KpyVrK1bx9q6dWxp2IpP8x3046xG6z5FsO1/p4QlE2k+tqtEDzWCWuoso85TR4O3gYbGBrY35u/zecm2ZIbGDmZo7BByo/pgNnboU3tIGBSXwXsnXM/He9bwVN4P5DdUctXCN+V7RYgO1mlT7QaDQUY8hehgMorz8/k0H/mNOwJls3YdJc7Sfd6faE0kw54eKJb2vSUzzhLXKaONTp+T8p9M5Zc5yyh1llPYXIhX87Z/XJgxjEExAxgaO4TBsYOJtsj6xSM52OzATX1P4EqZHRDiqOhmqn2fL3QUxdPlcuFy7V2M73A4yMrKkuIpxBHIurWfp8nbxIb6jaytW8f6ug00+Zrb32cymOgT2bt9JDHVnqow6aE5fU7yHJtYW7eedXXrqPc42t9nwECvyJ4MjR3CkNjBZNkzZUr+MGQ9tBA/T9AWzwcffJCHHnrogLdL8RTi4Pyaxke7V/NU3g/tO3XPzhrMXQNPJins2KZ5uwqHx8GiqiWsrVvHtobt+6zRjDBFMCR2EENjhzAwZiAR5uC6tcmv+dndtKd9icCe5oJ93p9gjWdo7BBGx48iN6qPlNCDONgJENPT+3Pv4Oky/S7EIQRt8ZQRTyGO3tb6ch5c+1X76IycTXh4Oxt38X35bJbXrNhnajrDns7Q2CEMjR1Cr8iemAyhM0Jc465hXd0G1tatI69+Ex7N0/6+LHsmJ6WcyLiEsdhMcobr/ho8Tp7dPJd3di7Hp2lEmK3c0f9ELus5CpNBDp8X4qeCtnjuT9Z4CnGgZq+b5zbP5c0dS/FpGuFmK7f3m8rlPUfLbSz78fq9LK9ZyQ/ls9nRtLP97T0jejA+YRxDYgeTHJakMGHncflcbG7YwqraNSytXobb7wYgwhTO5KRJTEuZSpKtazwWx2JLfRkPrpnJutrAC7z+sWk8OPQMBsVlKE4mhH5I8RQiROXVlvDb5R9S1FwHwMnp/bhv8KmkyhTgPjx+D/MrF/Blydft52maDCbGxI/m5JRp9IzsoTihWk3eJhZULuSHih+pdAVu8zFgYGzCGM5NP0u361lV8WsaH+5exVN5s3F4nBiA3+RO5pZ+J8jopxDoqHg2NjaSnx849mPYsGE89dRTTJ06lfj4eLp1O/J0oBRPIQI0TeOD3at4eP0sPH4fafYYHhhyOlPT+qiOpitev5cFVYv4ouQratw1AMRaYjgxeSpTkicTY4lRnFBf/JqfdXXr+b58NnmOTUCggE5IHM856Wd1mdHgo1XlbOTxjd/xZeEGAMYl9eSJUecTb5OD/UXXppviOXfuXKZOnXrA26+66ireeOONI36+FE8hAlPrD62dyReF6wE4MTWXR0acQ4zVrjiZfnj9XhZXL+Hz4q+oclcBEGuJ5ez0M5icNAmL0aI4of7tatrNZ8VfsLZuHQBGjExMmsDZ6WeSZEtUnE5fvihYz4Nrv6LF5yElLIp/jr6IYQlZqmMJoYxuiucvJcVTdHW7Gqq4Y/mHbHdUYMTA7wZM47re42U3citN01hSvYxPiz+nwlUBQIwlhjPTTmdK8glYpXAesx2NO/m0+HM21G8EAksUJidO5NyMc4i1yohxm+2OCu5Y9gG7GqsxG4zcNfBkruw1Rr43RZckxVOIEPBNUR73r/mCZq+bRFsET46+kNGJ2apj6Ualq4rXd73ZPkUcZY7izLTTmJo8RXZpHwfbG/L5tPjz9sc3whTOZd0vZULCOClXrZo8Lu5f8yWzivOAwLFLfxt+NpEW+fcnuhYpnkIEMbffxxMbv+N/O5YDMDKhO0+OvoDkMLmBBgLrEmeX/8iHRR/j8ruwGCycnXEm01NOlsLZAbY4tvJOwXvtZ4IOihnINdlXkmBLUJxMHzRN4+2dy3l8w3d4ND/ZkQk8M/oi+sSkqI4mRKeR4ilEkCprcfC75R+2n815fe8J3NH/RDkmqVVpSxmv7nq9/a7yPlG9ua7HNaSGyQ/5juT1e/mm7Fs+K/4Cr+YlzGjj4qyLmJp8AkbZ1Q3A2poi7lz+IaUtDsJMZh4aehZndxusOpYQnUKKpxBBaFnlLn6/4mOqXU1EWWw8NuI8TkzLVR1LF3yaj29Kv+Wz4s/xSPFRpqSllFd3vUF+a/HPjerDtT2uluLfqtbVzB9WfsKiih0AXNpjJPcMPlXuexchT4qnEEFE0zRe3b6Yf+bNxo9G35gUnhl9Md0i41VH04WC5kJe2fk6e5r3ADLVq5pf8/ND+Rw+LPoYt9+NxWDh/MxzOTX1FHkRAPg0P89vmcfzW+YDMCQuk6fHXCRn7YqQJsVTiCDR6HFx3+rP+b5kMwDnZA3hz0PPwG6W3dgev4cvSr5iZuk3+DSfbG7RmUpXJa/tepNNjsC/3R4RPbi+x9VkhmcqTqYPc8u28X8rP8XhcRJvDefJ0RcyNqlrX1wgQpcUTyGCwHZHBbcv+4DdjdVYDEbuG3IaM7JHSKkC8ht38OquNyhpKQFgZNwIftX9cjnOR2c0TWN+1ULeK3ifZl8LJoOJs9LP4Ky0MzAbzarjKVfYVMvty95nS325HIcmQpoUTyF07uuijTyw+guafR5S7dE8PfoihsTLSJHL5+Ljok/5rvwHNDRiLNH8qvvljIofqTqaOIwady3/3f0Wa+rWApBpz+S6Hld3+atJAZw+Dw+tnclnBYGD+U9K68sjI84hyhKmOJkQx48UTyF0yuv384+N3/HfHcsAGJvUgydHXSBX7hFYy/l8/ouUOssAmJAwnsu6zyDSHKk4mTgamqaxrGY5b+15hwZvI0aMnJ95Lmemnd7lR/j2v/K2e0Q8/x57CTnRciWpCA1SPIXQIa/fzz2rPmVmUeBGmBv7TOT2/lMxyYYMtji28s9t/8LpdxJnieOaHlcxJHaQ6ljiZ3B4Gnhrz9ssq1kBwKTECVzT4ypMBtnZvb6mmN8u/4DSFgexVjuvT7ySvjGpqmMJ8YtJ8RRCZ7x+P3ev/IRvivMwG4z8Y9T5nJoxQHUsXVhdu5bn81/Ao3npG5XLbTk3E2mRUc5gpmkacyvn8+bu/6GhMTxuGDf1+rVcYQrUuJr4zZJ32FBbQowlUD77xUr5FMHtWPqaDLUI0cE8fh9/WPkx3xTnYTEYeXr0RVI6Wy2sWsyz25/Do3kZFjuU3+f+TkpnCDAYDExNPoHbet+MxWBmde0antr6NC2+FtXRlIu3RfDK+F8xOC6Dek8L1yx8k7y6UtWxhOg0UjyF6EAev48/rPiEWcWbsBiMPDPmYqal91UdSxdmlX3HyztfxY+fiYnjua33zTIiFmJGxA3n97m/I8wYxuaGLTy2+R84PA2qYykXbQ3jlQlXMCQuk3qPk+sW/pe82hLVsYToFFI8heggbr+PO5d/xLclm7AYTTwzZgZT5SYiNE3jo6JPeLfgfQCmp5zMdT2ukTWAIapfdF/u6fcHosyR7G7ewyObH6PaVa06lnJRlkD5HBofKJ/XLvof61qvyhUilEnxFKIDuHxeblv6Pj+UbsFiNPHsmBlMTeujOpZyfs3Pm3ve4suSmQBckHkel3abITfehLgeEdnc1+8e4q3xlDrL+NvmRylpkenlSIuNV8ZfwfD4LBweJ9ct+h+rqgpUxxKiQ8mzvRDHWbPXzU1L3mV++XbCTGZeGHcpJ6T2Vh1LOa/fy4s7/sOPFXMxYOCq7F9xdvqZXf6ona4i3Z7G/f3uIS0slRp3LY9sfoxdjbtVx1IuwmLjPxOuYExiNk1eNzcsfoullbtUxxKiw0jxFOI4avK4uHHx2yyp3Em42cpL4y9nQnIv1bGUc/lcPL39WZbVrMBkMHFTrxs5MXmK6liikyXYEriv3z30iMimwdvIY1seZ7Nji+pYykWYrbw4/jImJveixefhN4vfYUF5vupYQnQIKZ5CHCcOt5PrFr3FquoCIs2BKbTRidmqYynX6G3k8a1PsqF+I1ajld/1vp0xCaNVxxKKRFui+L++d9Evqi9Ov4sntv6TVbWrVcdSLsxk4bmxlzA1tQ8uv5dblr7H7BIp5SL0SPEU4jiodTVzzaL/sq62iBhLGK9PvJJhCVmqYylX667l0c2Pk9+4gwhTOP+X+3sGxQ5UHUsoZjfZuTP3twyPG4ZX8/Ls9ueZX7lQdSzlrCYzT4+5mOnp/fH4ffx2+YfMKs5THUuI40oOkBfiF6pyNnLdov+xzVFBvDWc1yZeSW5MiupYylU4K3l86xNUuqqItcTwh9w7yQzvOvfR+/xOPP7mY/ocg8GI1RjTZda9+jQfb+z6L/OrAqXz0qyLOTVtuuJU6nn9fu5b/TlfFq7HiIFHRpzDOd2GqI4lxCEdS18zd1ImIUJSeYuDaxf+j52NVSSFRfL6hCvpJfcvU+6s4LEtj1PjriXZlszdfe8kyRbaj4um+ah1baKiZSkVzUupdq5Hw3vM/51wczrJ9rEkh48l2T4aqymmA9Lqg8lg4toeVxNhjuCbsm95t/ADfJqPM9JPVx1NKbPRyKMjzsFmNPHRnjXcu+oz3H4fF2UPVx1NiF9MRjyF+JlqXE1cOu81CppqSLNH8/rEq+geGa86lnI17lr+kvcwtZ5a0sLSuKfvXcRaY1XH6hA+v5PyliWUNM6mtHkBHr/jOH8FA/G2gaRHTCMjchoRltAdMf6s+As+Lf4cgMu6XcL01JMVJ1LPr2k8vP4b3tkZuPf+4eFnc373YYpTCXEgGfEUooN5/X5+v+JjCppqyAiP5c2JV5EREas6lnJev5d/b3+eWk8t6WFp3NPvD8RYQmvEzuNvoqxpASVNsylrXohPc7a/z2KMIsk+imT7GJLDxxJhzjqmaXOvv5mqllVUtCyjonkpDs8OalwbqHFtYGPN08RYc8mInEZ6xIlEW0PrtIRzM87GgIFPij/j3YL36RaeRb/orn3Ll9Fg4P7Bp2E1mngjfykPrp1Jr6gkhsSH7gsQEfpkxFOIn+GJjd/z6vbFhJssvDflenpHJ6uOpAv/2/02P1TMIdwUzkMD/kRyWGhMr3v8TZQ0zqa4aTYVLUvxa+7299nNqWRETCM9YhrxYYMwGo7f6/kWbwWlTXMpbppNVcsqNHzt74uy9CA9YhqZkacQYwuNc2I1TeOVXa+xsGox0eZoHhr4J+KtcapjKadpGncs/5DvSzaTEhbFR1NvJDEsUnUsIdodS1+T4inEMfqueBN3LP8QgKdGXchpmQMUJ9KHxVVLeWnnywD8rvftDI0Ljc0QxY2zWVf1GE5fVfvbIi3dA9PfEScSa+vfKZuBXL5aSpvmBcpv89J91o9mR53PwITfYjVFdXiOjubyufjb5kcpaC4kJ7IX9/a9G7NRJucaPS5mzH2FnY1VjErszmsTrsRslINphD5I8RSig+Q7Kpkx7xWavW6uyRnH3YNOUR1JF4qai3ho08O4/W7OTj+TCzLPUx3pF2vxVrCu6u+UNM0BINycQfeos0iPmEa0tZfSneceXwNlzQspavyO0ua5AISZEhmSeA8ZkdOU5Tpeyp0VPJj3F5p9LZycMo0rul+mOpIu7Gyo4qK5L9PsdXN1zlj+b5CcACD04Vj6mrxcEuIoNXpc3L7sfZq9bkYnZnPngJNUR9KFZm8z/9r+PG6/mwHR/Tkv4xzVkX4RTfOzy/Ex3xdeQEnTHAyYyY29jpOzPqZf/K+JseUoP+7IYooiK+o0xqX9k8nprxBp6Y7TV8Wy8rtYWvZ7WrwVSvP9UilhydzY83oAvi+fzZLqZYoT6UPPqEQeHX4uAG/kL2Vm0Ua1gYT4GaR4CnEUfFpgM9GuxmpS7dE8NfpCmeaibU3e65S7yom3xnNTrxsxGoL3cWlw72ZByQ2sqfwbXn8jcbYBnJj5NgMSbsVktKmOd1CJ9hFMy3yf3LjrMWCmpGkO3xdewM76j9A0v+p4P9uwuKGclXYGAK/teoOi5iLFifThlIx+XN97AgD3r/6cvNoSxYmEODbB+xNCiE70xMbvmV++HZvRzLNjZpBgi1AdSRe+LpvFqtrVmA1mbs25iShLcK4x9GsettS+wuyiGVQ5V2MyhDE44S6mZLxJjK2P6nhHZDLaGBB/CydmvkOcbSBefyNrqx5mfsn1NLh3q473s52feS4Dovvj9rt5Nv95WnwtqiPpwm8HnMiklBycPi83L32PipYG1ZGEOGpSPIU4go92r+aN/KUAPDriXAbGpStOpA+bHVv4sPBjAC7vfim9InsqTvTz1Dg3MKfoMjbVPIdfc5NsH89JWR+TE3s5BoNJdbxjEmPrzZSMNxic8AdMBjvVzjXMLryYLbUv49c8quMdM6PByG963Ui8NZ4yZzmv7HwNHW9L6DQmg5EnR11Ar6hEKpwN3LL0PVq8wff3K7omKZ5CHMaKqj38Ze1MAG7pe4LsYG9V467l+fyX0NCYkDCeqUknqI50zLz+ZtZV/YO5xVfhcOdjNcYyMvlhJqT9mwhL8L64MBhM5MRexklZH5FiH48fD5tqnmdO0WXUODeojnfMoi1R3JpzEyaDiZW1q/mm7FvVkXQhyhLG82MvJcZiZ2NdCX9c/bmUchEUpHgKcQiFTbXcvux9PJqfUzP6c3Pf4CtXHcHr9/Jc/gs4vA6y7JlclX2F8s02x6qsaSHfF17Ajvp3AI2syDM4udvHdIs6Pej+LIcSYUlnfNq/GZX8CFZjLA53PnOLr2Jd1eN4j/EOedV6Rfbk8m6XAvBB4UdsdmxRnEgfukXG868xF2M2GPmmOI/nt85XHUmII5LiKcRBNHpc3LTkXercLQyITeOR4ediDJFC8ku9V/gB+Y07CDfZua33LdhM+tx0czCaprGx+l8sLruNFm8Z4eY0JqQ9x6iUv2Ezhd51pwaDgayo0zi52yd0izwD0NhR/y5zii6jyVOsOt4xOTF5CuMTxqGh8Xz+S9S4a1VH0oXRSdk8MCRwt/2/N89lVnGe4kRCHJ4UTyH207aDfUdDJUlhkTw39hLsZovqWLqwpHoZ35fPBuDGnteTEhY8Nzb5NQ+rKh5gW93rAPSKaZ2ODh+vOFnHs5niGJnyNyakPY/dlEKjZw9zi6+izhU8I4cGg4Grs39Flj0Th9fBc/kv4vV7j/yJXcDFPUZwZa8xANy76jPZ6S50TYqnEPt5M39p+w7258ZeQopdLi8AqHPX8cauNwE4K+0MhsUNVRvoGGiaxurKv1LQOBMDJoYnPciQxD9gNoarjtapUsLHMSXzv8RYe+PyVbOg5Nc0uveojnXUbCYbt/W+mXCTnfzGfGaWfqM6km78YeAp7Tvdf7v8Q5w+2Wwk9EmKpxA/Udpcz3Ob5wLwxyGnMSguQ20gHfmo6BOcfhc9I3pwfua5quMck211r1PQ8CUGTIxNfZLs6OA+5P6XsJuTmZz+KnG2gXj8DhaX3YHb51Ad66ilhKVwZfcrAPiq9GuZcm9lNgZ2uqeERVHUXMfL2xaqjiTEQUnxFOInHtvwLc0+D8Pjs7ig+zDVcXRjZ+MuFlYtBgJHJwXTIfHFjbPJq3kWgCGJd5MWIZvELKYoxqX+E7s5lUbPHpaV/yGojlsamzCGnMgc3H43HxR+pDqObkRZwrh38KkAvLxtEbsbqxUnEuJAwfPTQ4gOtqA8n+9KNmMyGPjT0DNkM1Erv+bnrT3voKExPmEcOZG9VEc6arWuTaysuB+AXjGX0jPmYsWJ9CPMnMj41GcwG8KpbFnO2qq/B81xPAaDgSu6X4oBA0uql7KtYbvqSLpxSno/JiT3wuP38fC6b4Lm71R0HVI8hQBcPi9/W/c1AFf0HENuTIriRPqxqGoxO5p2Ema0MSPrQtVxjlqzt5wlpXfg05ykhE9gUMKdqiPpToytD6NSHgOM7HZ8TH79W6ojHbUeEdlMTpoEwP/2vI0/iK8HPZ4MBgP3DzkNi9HEwoodfFeyWXUkIfYhxVMI4JXtiyhoqiUpLJJb+01RHUc3mr3NfFgUuJ3onIyzibXGqg10lLz+ZpaU3oHTV0W0pRejUx7DaDCrjqVLaRGTGNxayjdU/5PSpnmKEx29CzPPJ9xkp6C5kHmVcoZlm+zIhPb73B9dP4smj0txIiH2kuIpuryCxhr+s3UBAPcMmk6kJXjOpexon5d8Sb3HQWpYCqeknKQ6zlHRND8ryv9IvXsrNmMc49L+hcUYqTqWrvWKuYwe0RcAGsvL76XOtVV1pKMSbYnivIxzAfio6FMavY1qA+nIjbkTyQyPpdzZwPNbgufFhAh9UjxFl6ZpGg+vn4Xb72NcUk9Oy5ArMduUtJS0n9l5ebdLMRuDY8Qwr+ZZSpvnYjRYGZv2VFBff9lZDAYDQxL/jyT7aHxaC0vKfkuLt1J1rKMyLWUqGfZ0Gr2NfFL0ueo4uhFmsvDHIacB8N8dy9jmqFCcSIgAKZ6iS/uhdAvzy7djMRh5YMhpIXNd4i+laRpv73kPn+ZjaOwQBscOUh3pqOx2fMa2ujcAGJH0IAlhQ5XmCSZGg4UxKf8g0pJNi7eMpWW/w+d3qo51RCaDiV91vxyAORU/UtBcqDiRfkxJ7cO0tL54NT9/Xfu1bDQSuiDFU3RZzV43j66fBcC1vcfTIypRcSL9WF23ho2OPMwGM5d1u0R1nKNS2bKSNZUPA9A37tdkRZ2mOFHwsZqiGZ/2L6zGWGpdeays+BNaEGza6Rfdl1HxI9HQAicwSMFqd+/g6YSZzKys3sMXhetVxxFCiqfoul7cOp/SFgfp4TH8Oney6ji64fa7eWfP+wCcljo9KK7FbHTvYVnZXWh4yYycTr+4X6uOFLQiLVmMTX0CA2aKm75nc+2LqiMdlUuzLsZqtLK1YRvLa1aojqMbGeGx3JwbOLv2Hxu/x+HW/yi2CG1SPEWXtKW+jNe3LwHgj4NPk7vYf+Kb0m+pclcRZ4njrPQzVMc5IrevPnD7jr+eONsgRiQ9KEsmfqFE+wiGJz0AwJbalylomKk40ZEl2BI4M+10AN4r/ACXT3Zyt7mq9zh6RiZS7WriibzvVccRXZwUT9Hl+DQ/f1z9BV7Nz8np/TgxLVd1JN2odtXwVWngPNNLul2EzaTvHf6a5mdZ+d00evZgN6cyLvUpTMYw1bFCQvfos+kTew0Aqyseosa5QXGiIzstbTqJ1kRq3LV8War/stxZrEYTDw47E4APd69mScVOxYlEVybFU3Q5s4o3samulGhLGA8MOV11HF2ZXTEHt99Nn8jejIkfrTrOERU3fU9ly3JMBjvjU58hzCzrdI+nAfG3kh5xIn48rKv6h+7XTlqNVi7tFridanb5HBn1/IlRid25tMdIAJ7eNEf3f5cidEnxFF2Kpmm8sm0hAFfmjCUpTM53bOP1e1lQuQiAU9NO0f10tV/zsqkmsP6wT+xVxNj6KE4UegwGI0MT78VkCKPWtYGy5gWqIx3R8LhhJNmSaPa1yFrP/dzc9wRsRjPra4tZXrVbdRzRRUnxFF3KgvJ8ttSXE262cnlP/Y/odaZVtWtweB3EWmIZGjtEdZwjKmz4mkbPbqzGWHJiL1cdJ2SFmRPpFRM42WBTzfO63+VuNBiZkhTYLPhjhRyc/lOJYZGc330oAP9pfQEuRGeT4im6lLYn2xnZI4i12hWn0Ze5lXMBOCFpEiaDSW2YI/BrHjbXvgRAn9ir5WaiDtY79irMhgjq3VspbpqtOs4RTUqagMlgYkfTTjnXcz/X9Z6AyWBgccVO8mpLVMcRXZAUT9FlrK4uYFV1ARajiatzxqmOoytlznI2ObZgwMDkpImq4xzRbsdnNHtLsJkS6Rlzseo4Ic9miiUn9goANte8gKb5FCc6vBhLDMNjhwIwt0LucP+pjIhYTs8MXAgho55CBSmeosv4z9bAk+w5WYNJtkcpTqMv81p/OA+OGUiiTd8bdHx+J1tqXwagb9x1mI0yct0ZesdcjtUYQ4NnF4WN36iOc0RTk6cAsLh6iWwy2s8NfSYA8H3JZnY2VClOI7oaKZ6iS9haX8688u0YMXBd65OuCPD4PSyoCpTyKcknKE5zZDsdH+H0VWI3p5Idfb7qOF2GxRRF79irANhc8xJ+zaM40eH1i+5Lki2JFl8Ly2qWq46jK72jkzkxNRcNeHX7ItVxRBcjxVN0Ca9sCzy5npLRn+zIBMVp9GV17RoavI3EWeIYEjtYdZzD8vqb2Vb7GgB9427AZLAqTtS19Iq5BJspgSZvEXsavlAd57CMBiNTWzcZyXT7gW7IDSyp+bJgPWUtDsVpRFcixVOEvMKmWr4u2gjsnWISe7Xt/J2cNFH3m4p21L+Ly19LhCWL7lFnqY7T5ZiNdnJjrwVgS83L+Pz6nsKe1PpvWjYZHWhofCajErvj0fy8vn2x6jiiC5HiKULeq9sX4UdjUkoO/WPTVMfRlbKWMjY3BDYVnZA0SXWcw3L7GthW9yYA/eJ+g9Eg15yq0CP6AuymFFp85exyfKw6zmFFW6IZHjcMgB8r5qoNo0M39gmMen64ezW1rmbFaURXIcVThLRKZyOf7lkLwA199L9bu7PNrWzbVDSIBJu+lyDk1/8Pj7+BKEtPsiKnq47TZZmMNvrG3wDA1rpX8fpbFCc6vKlJgXXLi6uWyiaj/UxI7kW/mFRafB7e2rlMdRzRRUjxFCHtv/lLcft9DI3PZGRCN9VxdMXj97CwKrD2darONxW5fLXk170NQP/4mzHofElAqOsedTYR5kxcvhp21L+nOs5h9YvuS4otGaffyVLZZLQPg8HAja1rPd/esZwmr1txItEVSPEUIavF6+G9XSuBwJSS3q+A7Gxr69a1byoaHDtIdZzDyq9/B6/WTIy1L+kRJ6qO0+UZDRb6xf8agO11b+p6rafRYOSE5MAmo/mV+r/ys7OdnN6P7MgE6j1OPmudHRKiI0nxFCFraeUuGr0u0uwxnJAq93jvb0N9YMPV6PiRut5UpGkaRY3fAoE72eUFhD5kRZ5GmCkZt7+eihZ9jySOSxgDwI7GnTR7ZS3jT5kMRmb0GAHAD6VbFKcRXYEUTxGy5pVvA+CE1N4YpawcYLNjKwD9o/spTnJ4DvcOmjyFGA1WUiP0vQGqKzEYTKRHTAWgROfXaMZb40mxJaOhsbVhm+o4ujOl9YX5yqo9NHr0O3otQoMUTxGSNE1jftl2IFA8xb6qXTVUuCowYKBPlL4fn7ZSk2wfi8UYoTiN+KmMyGkAlDbNw695Fac5vL7RfQHY7JBRvf1lRybQPSIer+ZnccUO1XFEiJPiKULSdkcFpS0ObEYzYxJ7qI6jO5sbAj98syOyCTeHK05zeCVNcwDIiJimOInYX0LYMKzGWNz+Oqqda1THOax+bcWzQYrnwbS9QJ/X+oJdiI4ixVOEpLYnzzFJPbCb5bzH/bWN+vSLzlWc5PAaPYXUu7dhwERqxGTVccR+jAYzaRGBExGKG/U93d4vKvBvvbC5iEZPo+I0+tO2Dn5++Xb8mqY4jQhlUjxFSJpXLtPsh6JpGnn1mwDoH6Xv9Z0ljYHRziT7SGymWLVhxEGlt45ElzTNQdP8itMcWqw1lnR7Ohoamxo2q46jOyMTuxNutlLlamJTXanqOCKESfEUIafO3cKa6sD1eFI8D1TcUkKtpxaLwUJutL53+7et75QjlPQr2T4asyECp6+SWtdG1XEOa1D0AAA21OcpTqI/VqOJ8Uk9AZhXJhuwRMeR4ilCzqLyfPxo5EQlkREeqzqO7mxsPUapb3QuVqNVcZpDa/FWUOPaABhIa909LfTHZLSRGhE4hLy4dT2uXg2KGQgEvgc0mU4+QPs6z3JZ5yk6jhRPEXJkmv3w2kZ7BsYMUJzk8EqafgQgPmwwdnOS4jTicNqn2xvn6LrQ5Ub3wWKwUOOupcQp08n7m9z6nLmhtoQqp6yDFR1DiqcIKT7Nz4LyfAA5NP4g3H53+zmGbaM/erV3N7tMs+tdavgEjAYrTd5CHG79jpZZjVZyowLPC20XKIi9ksOi6B+bBtD+PCrE8SbFU4SU9TXF1LlbiLaEMSw+S3Uc3dnq2IZH8xBvjSM9LE11nENy+WqpalkFyPrOYGA2hpNiHwcEw3R7YKR/o6zzPKgTUuRYJdGxpHiKkNK2KH5Cci/MRvnnvb/1raM8A2MG6vrqydKm+Wj4iLHmEmHJVB1HHIW2FwglOi+eA1tH+rc4tuL2uxWn0Z+2JUqLKnbg8fsUpxGhSH4yi5CysroAgEkpOYqT6FN+Y2D6bIDOr8msbFkGQFrEFLVBxFELnOdpwOHejtNbozrOIWXY04m1xODRPOxu2qM6ju4Missg1mqn0etia3256jgiBEnxFCFlT2PgB16f6GTFSfSp3FkBQLo9XXGSw3O4dwEQa9P3AfdiL6sphnBzYPlGo2eX4jSHZjAYSLcHcla4KhWn0R+jwUCvqMBmvj1N+n0BIYKXFE8RMpq8bqpcgZ2YWRHxitPoT5O3mSZfEwDJNv3uEtc0jUZPYCQqyiLXnQaTKEs2AA0efY8kJtkCL0wrnVI8D6Z76/NnQaMUT3H8SfEUIaOw9dV5nDWcaGuY4jT6U9k6uhNtjibMpN/Hp8VXgU9rwYCZCEuG6jjiGERaswFodO9WmuNI2l54VbgqFCfRp26RrcVTRjxFB5DiKUJG26vzbjLaeVBtxTM5TL+jnbC3tERYMjAaLGrDiGOyd8Rzt9IcR9L2PVDhqlKcRJ+6yYin6EBSPEXIaFuP1PZqXeyronV9Z5KOp9lhb2mJtHRXG0Qcs6jWEU+9F8+274FKWeN5UDLiKTqSFE8RMtpenXePiFOcRJ/aRnf0vL4ToLG1tERZZX1nsGl7sdDsKcGn6feoorbvgXpPPS6fS3Ea/enW+hxa5WqiySOPjzi+pHiKkFHQVAvIiOehtK1n03vxbHC3bSySEc9gE2ZKwmyIQMNHk6dQdZxDijBHEGEKB2TU82CiLGHEWwOPT9vzqhDHixRPETLapoVkjefBVTjb1njq+6iphtajeCJb1wuK4GEwGIi0Bl4wtL2A0Kuk9g1GUjwPRqbbRUeR4ilCgtPnoazFAUjxPBiv30u1uxqAJFui4jSH5vW30OItA/auFxTBpe0ILD2f5Ql7X4BJ8Ty4LNlgJDqIFE8REgpbp4OiLDZirXbFafSn2l2NhobFYCHWEqs6ziE1egI3T1mNMdhMslY3GEW1jXjq/izP1hFPOcvzoNrWyssh8uJ4k+IpQsJPj1LS8x3kqrSN6iSFJen68WmUHe1Br22JRLCc5SlrPA9OptpFR5HiKUKCrO88vMog2dHevrFIdrQHreA7y1MOkT+YtufSPTLVLo4zKZ4iJDS0Hvkh0+wH1+JrASDSHKE4yeF5/A0AMs0exGymQGFp+7vUq0hzJLD3e0PsK651V3ujx6k4iQg1UjxFSNHzNLI+yOMjOpr8GwsF8lwqOooUTyGEEEII0Sk6pXg+99xzZGdnExYWxpgxY1i+fHlnfFkhhBBCCKEjHV4833//fe68807+/Oc/s3r1aoYMGcL06dOpqJAF3UIIIYQQXUmHF8+nnnqKG264gWuuuYb+/fvz4osvEh4ezmuvvXbAx7pcLhwOxz6/hBBCCCFEaOjQ4ul2u1m1ahUnnXTS3i9oNHLSSSexZMmSAz7+0UcfJSYmpv1XVlZWR8YTQgghhBCdqEOLZ1VVFT6fj5SUlH3enpKSQllZ2QEff++991JfX9/+q7CwsCPjCSGEEEKITmRWHeCnbDYbNptNdQwhhBBCCNEBOnTEMzExEZPJRHl5+T5vLy8vJzU1tSO/tBBCCCGE0JkOLZ5Wq5URI0Ywe/bs9rf5/X5mz57NuHHjOvJLCyGEEEIInenwqfY777yTq666ipEjRzJ69GiefvppmpqauOaaazr6SwshhBBCCB3p8OI5Y8YMKisr+dOf/kRZWRlDhw5l1qxZB2w4EuKXMLZe7+b1+xQn0SdD6zWGPk3fj4+hdRLGr3kUJxE/l5+2vzt9X4zX9r1gkCs+D6rtuVSuzhTHW6dsLrr11lu59dZbO+NLiS4q1R4NQElzveIk+hRnjQOg2l2tOMnhhVvSAGjyFCtOIn6uJk/gNJIIc7riJIdX7aoBIN4arziJPhU31wGQZo9RG0SEHH2/JBXiKHWLDPzwKGiqUZxEn5JtSQBUOqsUJzm8SEs2AI2ePWqDiJ+t7e8u0tpdcZLDq3AFbs9Lav3eEPva0/pc2j1Sirk4vqR4ipDQPSLw5FjcXIdHptsP0FY8az21uP1uxWkOLcoSKCuNnkKZbg9SDe5dAES1vojQq0pXJQDJYVI8D6agMVA8u0VI8RTHlxRPERKSwqIIM5nxaRqlMt1+gEhzJGHGMACqXPod9bSbUzAZwtDw0uQpUR1H/AwNbSOeOi+eFc7W4ikjngdV2FQL7J1NEuJ4keIpQoLRYCArQqbbD8VgMLSP7LT9wNUjg8FIZPuo5261YcTP0ugOFM8oa7baIEfQPuJpS1acRJ/ankdlxFMcb1I8Rchoe4LcI8XzoNpGdipc+i2eAFGtawMbZJ1n0PFpbpq8gY1hep5q92k+qlo32skazwP5ND8FrSOe3aV4iuNMiqcIGd0jAju329YmiX21jey0barQq7Yp2ra1giJ4BHa0+zEbI7GZElTHOaQady0+zYfZYCbOGqs6ju6UtzTg8fuwGIykhkerjiNCjBRPETL27myvVZxEn5Jap9ordbzGE/aOlMnO9uCzd2NRd12f/9g2zZ5kS8RokB+D+2ubZs+MiMMkj484zuRflAgZbVPtMuJ5cO1T7U6dj3i2rg1sdO9WmkMcu7blEXqeZoe93wMyzX5wsqNddCQpniJktD1JFjbX4tP8itPoT9sP2UpXFX4dPz5tRyq5/LW4fXJCQTBpe7EQqfONRRWto/6yo/3g2tbJd4uMU5xEhCIpniJkpIZHYzEY8fh9lLU4VMfRnQRrPEaMeDQPdR79FjqzMRy7KXClboPsbA8qbX9fuh/xbDs8Xs7wPCgZ8RQdSYqnCBkmg5HM1g1Ge2S6/QBmo5kEW2DDh/6n21t3tst0e9DQNK39CCy9n+FZ6ZSjlA5HjlISHUmKpwgpvaMDP0jW1BQqTqJPGfbA/dk7m/S9YzzWmgtAVctKxUnE0ap3b8Xjb8RkCCPS2k11nENy+VwUtQSOfMqwpylOoz/17hZ2NASKeU60FHNx/EnxFCFlYkoOAPPLtitOok8DovsBsKF+o+Ikh5cWMQWA0uZ5cnVmkChumg1ASvh4TAar4jSHtrlhC17NS6I1UUY8D2JhxQ58mkavqCTSw2NUxxEhSIqnCCknpPQGYENtMdWuJsVp9GdgzEAAtjVsx+VzKU5zaAlhQ7CZ4vH4G6hsWaU6jjgKJY1zAEiPOFFxksPbWJ8HwKCYAbo+8kmVea0v2k9I7a04iQhVUjxFSEm2R9EvJhUNWCCjngdIC0slwRqPV/OypWGr6jiHZDCYSI+YCkBJ0w+K04gjaXDvosGzEwNmUsMnq45zWG2j/W0vwsRePs3PgnIpnqJjSfEUIaftCXNeuRTP/RkMhvYfuHqfbk+PmAZASdNcNM2nOI04nJKmwGhnsn00VlOU4jSHVumqosxZjhEj/aP7qo6jO+triqlztxBlsTEsPkt1HBGipHiKkHNCah8AFpbvwOOXwrK/QTEDgL1TjnqVZB+JxRiFy1dNtXO96jjiMIpbi2d65DTFSQ5vY+uLrV6RPQk3hytOoz9tL9YnJOdgMZoUpxGhSoqnCDmD4tKJs4bT6HWxplp2t++vf3R/jBgpdZZRpePrM40GC2mt07YlrRtXhP40e0qpc20CDKSFT1Ed57A2tK/vlGn2g5kv6ztFJ5DiKUKOyWBkUuvu9nll2xSn0Z8Iczg9I3sC+h/1bBtBK2mag6ZpitOIgylp+hGAxLBhhJn1e+6jT/Ox2bEZgIGto/5ir/IWB5vryzAAk1ufP4XoCFI8RUiSdZ6H13as0maHfjcYASTbx2IyhNHsLaXOvUV1HHEQbaPRbWty9WpPUwHNvhbCTXZ6RGSrjqM7bbvZB8VlEG+LUJxGhDIpniIkTUzOwWQwsKOhiqKmWtVxdKdf68aKzQ1bdD2SaDbaSQmfAOw9rkfoh9NbTZVzDUD7KQR6tbkh8MKlT1QfjAb50be/+bKbXXQS+e4TISnaGsaw+MDtKfPkWKUD9IrshcVgpt5TT6mzTHWcw8po390u6zz1prR5HqARa+tPuEXftwBtdgSKZz/ZzX4At8/LkoqdwN7NmUJ0FCmeImTJdPuhWY0WekX2Avb+QNar1PCJGDDT4NmFw71TdRzxE8WNgRcDGTo/NN7r97KtIfA80C9Kiuf+VlTtodnnISkskv4xqarjiBAnxVOErCmtr9yXVe6iRm4xOkDbyI/ez/O0mKJIDh8DQEHDV4rTiDbN3nIqW5YD+l/fub0xH5ffRYQpgqzwTNVxdGdWcWCT4eSU3nKbk+hwUjxFyOoVlcjA2HTcfh//27FMdRzdGRk3AoB1deupc9epDXMEPaIvAGBH/Xs4vTWK0wiArbUvo+ElMWw4UdZs1XEOa37lAgBGxA+X9Z37qWhp4PPCwDm553YbojiN6ArkO1CELIPBwA19AhtT3tm5giaPfu8mVyEzPIPekTn48TO/aqHqOIeVFj6FWFt/fFoL2+peVx2ny2vyFLHb8TkA/eNvUZzm8Bo9jayoWQnA1KQTFKfRnzfzl+Dx+xgen8XIxO6q44guQIqnCGknpfejR2QCDo+T93evUh1Hd6YkB34Qz6uYj1/zK05zaAaDgQGtBWen40NavBWKE3Vtm2v/g4aXZPt4Eu3DVcc5rIXVi/FoXrqFZ8kxSvupd7fwXuvz4g25ExWnEV2FFE8R0owGA9e3jnq+kb8Et8+rOJG+jI4fSYQpnCp3te4Pk0+2jyMhbBh+zcWW2ldUx+myHO6dFDTMBKB//M2K0xyepmnMrZgPwNTkKbJ+cT/v7FxBs9dNn+hkTkiRY5RE55DiKULemVmDSbVHU+ls5LOCdarj6IrVaGVC4ngAfqycpzjN4RkMhvais9vxKU2eYsWJuqbNNS8BftLCpxAfpu8bgLY2bKPUWYrNaGNcwhjVcXSlxetpX/t+Q5+JUspFp5HiKUKe1Wji6pxxALy6fRFev36nlFVom25fW7uOWre+D9tPso8k2T4WDS9bal9WHafLqXNtpbjpO8Cg+9FOgLmtL6bGJozGbrIrTqMvH+1ZTa27mayIOE7N0PcLCBFapHiKLuGi7OHEWu0UNNXybckm1XF0JcOeTp/I3oFNRpX63mQEe6d39zR8SYN7t9owXcymmucByIw8hRibvqdmA5uKAusXpyZNURtGZ9x+H69vXwzAtb3HYzZKFRCdR/61iS4h3Gzlil6BqbZXti3U9TWRKrRvMqrU9yYjgPiwQaSGTwb8bK59UXWcLqPGuYGy5vmAkX5xv1Ed54gWVi3Cq3npHt6dHpHZquPoyszCDZS2OEi0RXJet6Gq44guRoqn6DIu7zmacLOVLfXlLCjPVx1HV0bFjyDCFE61u4YNOt9kBHtHPYsav6XetU1xmq6hbbSze9RZuj+3U9M05la2bSqarDiNvvg1jVe2LQLgqpyx2ExmxYlEVyPFU3QZsVY7M7IDh6b/Z5v+p5Q7U2CTUWD3/9yKuWrDHIVYWy4ZEacAsElGPTtcZctKKlqWYsBM37gbVcc5osCmojLCjDbGyqaiffxQsoWdjVVEWWxc0mOk6jiiC5LiKbqUq3LGYjEYWVVdwOrqAtVxdKVtZGht3XpqdL7JCKBf/K8BI6VNP1Lj1P8obbDSNK19tDM7+jwiLOmKEx3ZjxVtm4rGyqain9A0jZdbX3Rf1mMUkRab4kSiK5LiKbqUFHs057ReC/fSVhn1/Kl0ezq5UX3w42de6zSlnkVbe9It8nQA8mr+Jet2O0hZ80KqnWswGmz0jbtedZwjcngaWFnbuqkoWW4q+qnFlTvZWFeCzWjmVzljVccRXZQUT9HlXNdnAiaDgfnl2/mhZIvqOLoyNXkKAN+VfY/D06A2zFHoF/9rDJipbFnO5tqXVMcJOY2eAlZVPABAz+iLsJuTFSc6ss+Lv8CreekRkU12hFwB2cbl8/Lwum+AwCkfCbYIxYlEVyXFU3Q52ZEJXNs7cGj6Q2tn4nA7FSfSjzHxo+ge3o1mXwsfFX2sOs4RRVgyGZb0RwC21L5EYcM3ihOFDrfPweLSO3D764mzDQiKczsLmguZXfEjABdnXag4jb68sGUeuxqrSbRFcmu/KarjiC5Miqfokm7pO4UekQlUuRr51+Y5quPohtFg5IrulwEwv3Ihuxp3qw10FLKjz6VP7NUArKp8kGrnWqV5QoFf87Cs/A80enZjN6cyLvVpzEZ9r5XUNI2397yLhsaouBH0j+6nOpJu7Gyo4rXWczv/NPR0Yqz6/rsUoU2Kp+iSbCYzDwwJrA98d+dK8upKFSfSjz5RvRmfMBYNjf/teVv353oCDIi/jbSIqfg1N0tL76TJU6I6UtDSNI21VX+nsmU5JoOdcalPE2ZOVB3riJbXrGBLw1asRiuXdLtYdRzd0DSNv677Go/mZ0pqH05K66s6kujipHiKLmtcck9OzxyIH42/rJ2JXzantLs460LCjDZ2NO1kcfVS1XGOyGAwMir5YWKsfXH5a1lSejsef6PqWEFpR/077HZ8DBgYnfIosbZc1ZGOyOVz8V7hBwCckXYaiTb9F+XO8nXRRpZW7sJmNPPHwafKnexCOSmeokv7v4GnEGG2sr62mI92r1YdRzfirHGcnXEWAB8UfkSLr0VxoiMzG+2MS3uaMFMiDs8Olpffg1/zqo4VVEqb5rO++kkABiX8jrSI4NgV/lXp19S4a0m0JnJ62qmq4+hGg8fJYxu+A+DXuZPIjIhTnEgIKZ6ii0u2R3Fbv6kAPJU3m1pXs+JE+nFKykmk2FKo99TzefGXquMclXBzCuNSn8ZkCKO8eREbqp9SHSlo1Lu2saL8XkAjO+p8cmKuUB3pqFQ4K/mmdBYAl3abgdVoVZxIP57dPJcqVyPdI+K5rnVDpRCqSfEUXd7lPUeTG51CvaeFJ/N+UB1HNyxGC5d3vxSA78p/oKQlONbBxoUNYGTyXwHYUf8uO+o/UJxI/5zeKhaX3YFXaybJPpqhSfcEzZTsuwXv4dG8DIjuz4i4Yarj6MbmujLe3rEcgAeGnI5VrsYUOiHFU3R5ZqORPw89A4CP96xhTXWh4kT6MSR2EENjh+DTfLxT8F7QHNKeEXkSA+JvBWB91eOUNy9RnEi/fH4nS8rupMVbRqSlO2NS/oHRYFEd66hsqNvI6rq1mAwmruh+adCU5Y7m1zT+sm4mfjROzejPhJReqiMJ0U6KpxDAsIQszu82FIC/rJuJ16//ndyd5bJuMzAbzGyo38iaunWq4xy1PrHX0i3qLDR8LCu/G4d7h+pIuqNpGqsq/0ytawNWYwzjU5/BaopWHeuoeP1e3i54F4CTkk8k3a7/qzw7y6d71rK2pohwk4X/GzRddRwh9iHFU4hWvx94EjGWMLbUl/POzuWq4+hGSlgKp6aeAsA7Be/h9nsUJzo6BoOBYUn3kxA2DK+/kcWld+Dy1aiOpSuba1+iqPE7DJgZk/oEkdbguenn+/LZlDrLiDZHc27G2arj6Eatq5kn8r4H4NZ+U0i1B8cLCdF1SPEUolW8LYLfDZgGwL82/0iFU/9XRnaWs9LPINYSS6Wrku/Lvlcd56iZDFbGpj5JhDmTZm8xS8vuwqe5VcfShcKGb9jSes3osKT7SbKPVJzo6Dk8Dj4vCWx4uyjrfMLN4YoT6cfTm2ZT524hJyqJK3qNUR1HiANI8RTiJy7MHs7guAyavG4eWP0lviA4PL0zhJnCuDjrAgC+KPmKspYyxYmOns0Ux7i0Z7AYI6l2rmFZ2V14/fo/HqojFTd+z6qKPwPQJ/ZqsqPPUZzo6Gmaxlt73qXF10J2eHcmJk5QHUk3Fpbn82HrsXB/HnoGFqNJcSIhDiTFU4ifMBmMPDj0TGxGM/PLt/NU3mzVkXRjXMJYcqP64PS7eDb/eVw+l+pIRy3a2pMxKU9gNNgoa17AgpJf4/R2vWl3TdPIr3uHZeX/hx8PGRGnMCD+NtWxjskPFXNYVrMcI0Z+lX05RoP8GAPY4ajkd8s/QgNmZI9gZGLwLJsQXYt8xwqxn36xqTw8PLBm7LXti/l0z1q1gXTCaDByc69fE2OJpqilmNd3/zdodrkDJIePYVL6i1iNMdS6NvBD4QUUNMwMqj/DL9HkKWFR6a2sr/4HoNEz+mJGpzyCIYiKW35DPu8WvA/AjKwLyYmU3doQWNd589J3afS6GJHQjfsGyyH6Qr+C5xlHiE50RtYgfpM7CYA/r/mSVVUFihPpQ6w1lpt7/QYjRpZUL2V2xY+qIx2ThLChnJDxOtHW3rj9daysuJ9FpbfS5ClWHa3DaJqP7XVv8UPhBVS0LMaIhUEJv2NI4j0YDMEzFevwOPh3/ov4NB+j4kYwvXXDW1fn9vv47fIPKWiqJSM8ln+NuVjO7BS6JsVTiEO4rd9UTknvh0fzc9uy9yluqlMdSRf6RudycdaFQGCXe35jcB1TFGXtwYmZb9M//laMBisVLYv5ofBCtte9hab5VMc7rupd25hbfBUbqp/EpzlJDBvOtKwP6B17ZVCdeenTfDyf/xK1nlrSwtK4ruc1QZW/o2iaxsPrvmZ51W7CzVaeH3cp8bYI1bGEOCwpnkIcgtFg4NER59IvJpVadzM3LX2XJk/wrGvsSKemnsKouBH4NB//3v4CDo9DdaRjYjRY6Bt3HdMy3ycxbDg+zcmG6ieZW3wVda6tquP9Yj6/i7zqfzOn6HJqXXlYjJEMS7qfSekvE2XNVh3vmH1c9CmbG7YQZrRxe++bsZvsqiPpwls7l/PB7tUYgCdGXkCf6GTVkYQ4IimeQhxGuNnKc2MvIdEWwXZHBX9Y+YnsdCdwRuZ1Pa8hLSyNWk8tL+x4CV8QjhZGWbOZlP4yw5IewGKMpNaVx49FV7Cx+ll8fqfqeD9LZctKZhddzNa6V9Hwkh5xIidlfUyP6AuCaj1nm1W1q5lZ+g0A1/W8Rg6Kb7WwPJ/H1n8LwO8HnMTUtD6KEwlxdILvWUiITpYWHsO/x16C1Wjix7JtPJ03R3UkXbCb7Nze+2ZsRhubHFv4uOgz1ZF+FoPBSI/o8zkp6xPSI6ah4WVb3WvMLppBZctK1fGOmtvXwOqKv7Kg5AYaPQWEmRIZk/IkY1OfxG4OzpGwMmc5L+98DYDpKSczOn6U4kT6sKuhijtXfIQfjfO6DeXa3uNVRxLiqEnxFOIoDInP5OHhgbMOX9m+iO+KNylOpA/p9nSu63ENADNLv2Z17RrFiX4+uzmJsalPMCblScJMiTR6ClhQcgOrK/6C26fvpQTFjbP5vvB8djd8AkCP6As4KesTMiJPVJzs53P5XDy7/TlafC30iezdvq64q2vyuLht2Qc0eFwMj8/iwaFnyHpXEVSkeApxlM7MGsQ1OeMAuHf15+xwVCpOpA9jEkZxSsrJAPxn56uUOcsVJ/plMiJP5OSsT+gRHSg6uxs+5fvCCyhsmIVf09d1oY2eQpaU3cmy8rtw+aqItGQzOf0VhiXdj9UUpTrez6ZpGq/v/i9FLcXEWGK4Jec3mI2yU1vTNP64+gt2NFSSFBbJ07KDXQQhg6bjQ+wcDgcxMTHU19cTHS33zQr1vH4/1y36H8urdtMjMoEPptxApMWmOpZyXr+Xv295gm2N28m0Z/Kn/vdhMwX/41LVsprVlX+l0bMbAKsxhtSIE8iImEZy+FhMBmunZ2pw76K4aQ4ljbOpc28GwICZPnFX0zf2ekzG4H/cfyifw//2vI0RI/f0+wO5UbJ+EQLnCv9j4/dYDEbenHQ1wxKyVEcSAji2vibFU4hjVO1q4sIf/0NZi4OT0vryrzEXy1QXUOeu4095D1HvcTA+YSw39rw+JB4Xn9/F1rrX2FX/IS5/bfvbzYYIUiMmkhExjZTwiZiNHbPTWtM06t3bKGmaTXHjbBo8O3/yXiPJ9tEMSriTGFvvDvn6nS2/cQePbP47Ps3HpVkXc2radNWRdGFp5S6uW/g//Gj8acjpXNpT1rsK/ZDiKUQHW1dTxK8WvIHH7+OO/lP5Te5k1ZF0YYtjK3/f8gR+/FzR/TJOTpmmOtJxo2k+qpxrWgvgHJy+ivb3mQxhpISPJyV8Asn2sURYftnOa6+/hWrnGiqal1LSNJcmb2H7+wyYSbaPJj1yGukRU7CZ4n/R19KTOnc9D+b9lVpPLaPiRnBLzk0h8eLllypuruOiH1+m1t3Mud2G8Mjwc+RxEboixVOITvDBrlX8ee1XAPxh4Mmys7TVN6Xf8l7hBxgwcG2Pq5icNEl1pONO0/zUuvIobppNSeNsmrxF+7w/wpxFcvgYIi3dgKMvCF5/E1XO1VS3rMHP3vWkRoONlPDxZERMIzV8clCv3zyUOncdj215glJnKWlhafx5wB/lvE6goLGGaxb9l5LmevrFpPLOCdcSZrKojiXEPqR4CtEJNE3jX5t/5MWtCwC4vd9UbuorI5+apvG/Pe8wuyJw7NRV2b/ixOQpakN1oLap8NKmuVS0LKPGuR6NX36mqd2cSrJ9DKnhEzp0Kl8Pql01/GPrk5Q6y4i3xnFP37tJCQvOI6COp10NVVy98L9UOBvIjkzgjYlXkmKXn4VCf6R4CtGJXtw6n2c2Be4s/3XuJO7oN7XLT4NpmsbbBe/yfflsAC7MPJ8z007vEo+Lx99IVcsqKlqW4/bVHdPnGgwm4mz9SbaPJdLSvUs8XqUtZTy+9Ulq3DUkWOO5p+/dJIclqY6l3DZHBdct/C9VriZyopJ4beKVJIVFqo4lxEFJ8RSik72+fTGPb/wegKtzxnL3wFO6RGk4HE3T+LjoU74snQkErtm8JEs2Yom9djft4YmtT9HgbSQ1LIW7c39Pgi1BdSzlNtWVct2i/1HnbqFfTCqvTvgVcbZw1bGEOKRj6WtyjqcQx8E1vcdz/5DTAHgjfyl/Xfc1fv2+pusUBoOBC7PO59KsiwGYVfYdr+x6PSiv1hTH32bHFh7d/DgN3ka6h3fnj/3uldJJYOPiNQv/S527hcFxGbw+8UopnSKkyMmzQhwnl/ccjc1o5k9rvuTdXStx+b38ZdhZmILwfuzj6dS06USYI3ht15ssrFpEs7eJm3J+g9UoGyS6qtW1a3g+/0U8mpe+Ubn8ts9tspEIWFVVwK+XvE2T183w+CxeGn+5nBMsQk7X/okoxHF2YfZwHhtxHkYMfLJnLfes/Ayv3686lnKTkiZyW++bsRjMrK5by5Nb/0mLr0V1LKHAwsr/b+++w6OsszaOf2cmmfTeQwoJJdTQE6qCdJWmYlesK7a1vSrqquuuXbfady2sK66CCmKhKFUFElqAQCoB0ntvkynP+8ckERSQaJLnmeR8risXZGbIHDLJzD3n137glazXMSsWRvuO5IG4+yR0AjtLc7h1xwc0WFpIDOzLvyZdK6FT9EgSPIXoZPOj4vlLwqU46fR8mX+IB3Z/QotNhpdH+43igbj7cNW7kl6XwfNpL1FrrlO7LNGN1hdt4N/H3sWGjcmBk7hrwB3S+Qa2F2exdOeHNFnNTAnpz5sTr8bDqftPxRKiO0jwFKILzOkzlH8kXo6z3sDGwjTuT14l4RMY7D2IZYMfxMvJk+ONJ3gm7TkqTBVqlyW6mKIofJL3Gf/LWwnYF5rdHHMDBp1B5crUt6Uok7uSPqbFZuWC0DheTbxC9ukUPZoETyG6yAVhcbw2/kqMegObijK4N2klLVaL2mWpLsajr30hidGf4uYSnk57jsKmQrXLEl3Eptj4z/H/tu9ucFnEJVwZeTn6Xj73GWBLUQb3JH2M2WZldvgQ/p64GKNBll6Ink1+84XoQlNC+vP6+Ktw0TuxpTiTe5JXSfgEwtxCeWzwI4S5hlHZUsUzaS+QU39M7bJEJ7PYLLxx9F9sKduGDh039L2eeeEXyZZawKbCdO5JWolZsTGnz1BeGncJznrpAIueT4KnEF1sUkg/Xp9gD59bizO5O2klJgmfBLj489jgh4nxiKHeUs8L6S9xuOaI2mWJTtJsbeZvmf8kuXI3Bp2BO/rfxrTg89UuSxO+KUzj3uRVmBUbF0YM46WxEjpF7yHBU4huMDE4ljcmXIWrwYntJVn8bscKKkwNapelOi9nLx4e9ABDvAfTbDPx18x/sKlkCxo+10Kcg+LmEp5Ne5HU2sMY9UbuG/h7EvzHqV2W6hRF4X85u7k/+RMsio2LIobxwphFOOnlpVj0HnJykRDdKKnsGHfs/B+NVjMhrl78PfFyRvpHqF2W6sw2M28d/Te7q/YCMNR7CDfGLCHIJVDlykRH2BQbG0u+5dP81bTYWvB08uS+gb+nv2c/tUtTXZPFzFMpX/F53gEA5kXG8+zoBRI6RY8gR2YKoWHZtWX8PuljjtVX4KzT80j8HK6MGdvr570pisI3Jd+yMu9TzIoZF70LiyMvZXrwNFmI4gAKmgp5J+c9jjbkADDEexC3xt6Mv9Ff5crUl1tfye+TVpJRW4IeHfcPnc5NAyb2+t950XNI8BRC4+rNJh7b9zkbC9MAmB8Zzx9HXoybk2yjUtxcwjvH3iOzLguAAZ79uTnmRsLcQlWuTJyOxWbhq6J1rC38Eotiwc3gxpWRizk/6DwJVti3S3p472fUmU0EuHjwl3GXkhgUo3ZZQnQqCZ5COABFUVievZO/HP4Wq6IQ5x3CPxMvJ8pTOkQ2xcaW0m2szFtFs82Es86JhX0WMDdstuz9qCHHGo7zTs575DXlAzDSdwRL+l4rXU7Aqth4LW0bb2RsB2CkfwR/T1hMiJu8lomeR4KnEA4kuew49+/+hApTA17OLrwwZhHTwuLULksTKkwVvHf8fQ7VpAIQ7R7NLbE3EuUeqXJlvVuLrYU1BWtZV7QBGzY8nTy5NuoqxgckSpcTqDI18uCez/ih9CgA18Ym8ODwWRhl5brooSR4CuFgSppquS/5E/ZX5gFwW9wU7h48FYPMbURRFH6o2MmHJ/5Hg7URg87ARWFzmR9+Mc5y3GK3y6zL4p1j71HcXAJAov84ro2+Gm9neY4GSK0q5J7klRQ21uBmcOapURczLzJe7bKE6FISPIVwQC02Ky8d2sgHOckAjA+K4eVxlxLg4qFyZdpQ3VLDf0+sYE/ryvdwt3BujrlBVkx3k2ZrM6vyPmVT6RYUFHydfVjS9zpG+41SuzRNUBSFj4/v5dmD6zHbrER5+PNK4uUM9AlRuzQhupwETyEc2Fd5h3h8/xc0tW659NeEyxgdEKV2WZqxu3IP7x9fQa2lFh06ZoXM4NKIRbgYXNQurcc6VJPK8mPvU95SAcB5gZO5MupyPJzkTRFAo6WFP6Z8xRd5BwGYHjaI58YswMvZVeXKhOgeEjyFcHDZtWXck7SSnPpynHR6Hhg2gyX9xsv8uVb15no+zP2YHyp2AOBv9GN++MVMCZyMk17Ouu4sJxpyWVPwOfuqUwAINAZwY8wShvkMVbcwDcmpK+eepJVk15Vh0Ol4YOgMbug/QX5XRa8iwVOIHqDB0sIT+7/g63z7wppZ4YN5ZvQCPJ2ls9fmQPUhlh9/n8qWSsAejOb3mcekgAkSQH+D/MZ8VhesbZ/WoEPH9OBpLI68FFeDdPHarMs/zB/2r6XR0kKQqyd/HXcZYwOj1S5LiG4nwVOIHkJRFD48tpsXDm7ArNiI9vDnH4mXEyfzxtq12MxsLd3Gl0VfU2OuASDIJYiF4fOYEDhetl/qgMKmQtYUrCW5cg8KCjp0JPiPY2GfeYS7hatdnma02Ky8nLqR/x61z8dOCOzLy+MuJcjVU+XKhFCHBE8hepiDlQXcm7yKoqYaXPRO/GHEXC6NHiXDeScxWU1sKd3Kl0XrqLPUARDiEsL88ItIDEiQFfBnkdeYz1dF69hVkYSC/SVhnN8YFvaZT4S7HOl6svyGKv5v92ccqLLvXfq7gZO5e/A0OfpS9GoSPIXogapMjTy8dzXflWQD9lXvT4y4kBgvOc/8ZCariW9LN/N10XrqLfUAeDt5My34fKYFn4+f0U/lCrXBqljZX5XCNyWbSK/LaL98tN8oFvVZIHul/oTZZmV59k5eT99Gs9WCt7Mrz49ZxLSwgWqXJoTqNBE8n3nmGb766itSUlIwGo1UV1d3+GtI8BTiVDZF4d2sHbyathWTzYKz3sCtAyfxu4FTcDHInMaTNVmb2FSyhW9LNlNlrgLAoDMw1m80M0KmM8Czf6/sGNeZ69hW9h2bSre0z43Vo2eM3yguCr+QGI++6haoQXvKT/BUyldk15UB9qH1Z0bPJ8JD3sQIARoJnk8++SS+vr7k5+fzzjvvSPAUohPlNVTx5wNft3c/ozz8eXzEXCaH9Fe5Mu2x2Czsq97PNyWb2s9/B/tCpBG+IxjpG88g70EYe/BQfHFTMSnVB0ipPkhmfRZWxQqAl5MnU4POZ1rwVAJc5JjLn6o0NfCXw9/y2YkUAPyN7jw0fBbzI+N75ZsWIc5EE8GzzfLly7n33nvPKXiaTCZMJlP757W1tURGRkrwFOI0FEVhY2Eazx5cT2mzfU7jjLBBLIufTR93X3WL06gTDbl8W7KJXZXJtNha2i930bsw1GcII31HMMInHl+jj4pV/nYWm4Ws+mxSqg+wv+oAJaaSU66Pdo9mVsh0EgISenTg/rWsio2Pju3hn0e2UGtuBmBx39HcP3QGvkY3lasTQnscNnj+8Y9/5KmnnvrZ5RI8hTizerOJV9K2sCInGaui4Gpw4ncDp3DTgIky/H4GJquJI7VppFQfJKX6ANXm6lOuj/GIYaRvPKN8RxDlHuUQ3a16cz0Hag5xoPoAh2pSabQ2tV9n0BkY5BXHSN94RviOIMQ1WMVKtW1/RR5/PvA1aTXFAAz2CeXxERcyKkDmvApxJg4bPKXjKcSvl1lbytMHvmZ3+QkAojz8eCR+DlNDZfHD2SiKwonG3Nah6AMcazh+yvV+zn6M8I1nuM9Q+rj1IcglUPU9QhVFoc5SR3FzCVl1WaRUHySrPrt9RTrYh9FH+MYz0ncEw3yG4maQTt3ZlDfX83Lqt3yedwAAb2dX7hlyAVfEjMGgkxXrQpxNlwXPZcuW8cILL5z1NmlpaQwaNKj9844Ez5+SOZ5CdIyiKHydn8qLqd+0D79PDR3Io/FziJSFEOekuqWaAzWHSKlKIbX2yClD8mBfiBPkEkioawghrqGEuYUS6hpCqGsIvs6+6DsxpDRbmylpLqGouZji5pL2j5Lm4lM6mm0i3SIY2TpvNdYztlNr6aksNhsf5iTzStpW6i32xsel0aO4f+h0/F3kSFAhzkWXBc+ysjIqKirOepvY2FiMRmP75xI8heh+DWYTr2ds5/3sXVgUG0a9gVsGTuLWgZNxNcicvnPVYjOTXptOSvUBsuqzKWkuxWQznfH2Rr2xNZDag6inU8c2FLfaLJSayiluDZo/nQJwMh06AowBRLj3Id5nGCN84wl0ka21OiK5/DhPH1hHVm0pAMN8w3l8xIXE+/dRuTIhHIvDDrX/lARPIX6bnLpynj6wjp1lOQD0cfdl2fDZTA+Lc4h5i1qjKApV5mpKmkvaw2FxcwlFTcWUmcqwYev0+/Ry8mrvqIa6/thdDXYNkYVBv1JpUx0vpm7kq9bjaH2c3bh/6HQu7TtKhtWF+BU6kte6bKJSbm4ulZWV5ObmYrVaSUlJAaB///54esqxYkJ0h1ivQN6ZdC0bC9N44dAGChqruTvpY6aE9OeR4XOI8QpQu0SHotPp8Df64W/0Y7D3oFOus9gslJvKW8OoPZQ225o79vXREdg6jB/qGkqIa3CHu6bizFpsVj44msRr6dtotLSgAy6PGcM9gy/Az8Vd7fKE6BW6rON5ww038J///Odnl2/ZsoWpU6ee09eQjqcQnafR0sK/Mr7j3eydmG1WnHV6rus/nqVxU/BydlW7PCG6jKIobC/J5oVDGzhWb58uFu/XhydGXMhQPzmDXojfSlND7b+FBE8hOt/x+gqePbi+ffN5f6M79wy5QIYZRY+UXVvGC4c28H3pUcD+8/7AsBksjBqJXqabCNEpJHgKIc7qdB2g/l5B3BY3hTl9huKklwAqHFtOXTnvZP3A57kHsCqKdPiF6EISPIUQ58Rss/LRsT28lraVmtYTWvq4+3LTgIlcEj1SVsALh3OwsoB/Z37PpqL09l1NZ4QN4v+GzSTaU44FFaIrSPAUQnRIbUsz/zu2m/ezd1HZ0gjYhySv7z+eq2LG4W2UDpHQLkVR2FGWw9uZP7Cr7Fj75dPDBnHLwEmM9I9QsTohej4JnkKIX6XZauazEym8m7WDgsZqADycjFwRM5Yl/cYT7OalboFCnMSq2PimMI1/Z/7AkeoiAJx0euZFxnPzgIn08w5SuUIhegcJnkKI38Ris7GuIJV/Z/7Qvrm2s97AvMjhXBObwBDfMJUrFL1ZvdnEmtwU/ns0mdyGSgDcDM4s7juaJf0nEO7uo3KFQvQuEjyFEJ1CURS2lWTxduYP7K3Ibb98pH8EV8eOY3b4EIwGdc8tF71HZm0pH+Yk80XuQRqtZsC++fu1/RK4JjZB9uIUQiUSPIUQnW5fRS4rcnazseAIFsV+Qk+AiweXRY/iipixhEmXSXQBs83KpqJ0VhzdzZ6KE+2X9/MK5KqYcSyMHomHk/EsX0EI0dUkeAohukxZcz2rju/l42N7KW2uA0CPjmlhcVwTO47xQTFyHKf4zUqb61h1bC8rj+9r/zkz6HRcEDaIq2PHkRjYV37OhNAICZ5CiC5ntlnZUpTBhzm7SSo/3n55rGcgV8WOZUHUCNkvUXSIoijsqcjlw5zdfFuY1t5ZD3Tx4LK+o7kiZiyhbvJaIITWSPAUQnSr7Noy/ndsN2tyD9BoaQHA3eDMvKh4FkePZohvmHSnxBnVtDTxdX4qHx3bQ2brYjaA0f6RXB07jpl9hmDUG1SsUAhxNhI8hRCqqDebWJt3kA9zdnO0rqz98n5egcyLjOfiyOH0cfdVr0ChGS1WC9tKslibe5BtJVmYbVbAvjr94sjhXB07jkE+oSpXKYQ4FxI8hRCqUhSF3eUn+OjYHjYXZWCyWdqvGxsQzfyoeGaHD5GN6XsZRVHYV5HHF3kHWV9wuP20LIBBPiEsjBrJoqiR8nMhhIOR4CmE0Ix6s4mNhUdYm3uQ5PLj7ccYGvUGpoXGcXHkcCYF98PNSY7n7IkURSG7royv81P5Mu8Q+a0HEwAEu3pxceRwFkTGM9AnRL0ihRC/iQRPIYQmFTXW8GX+IdbmHiT7pKF4V4MTE4P6MS1sIFNDBxLo6qlileK3sths7KvIZXNRBpuLM8hrqGq/zt3JyOzwIcyLHE5CUF8MOr2KlQohOoMETyGEpimKQnpNCWvzDrChII2ipppTrh/sE8qEoBgmBMcyJiBauqEapygKOXXl7CzLYWfpMZLLj1NvMbVfb9QbmBjcj4sjh3NBaJw8nkL0MBI8hRAOQ1EUMmpL7N2xogwOt5653cZZb2CUfyQTgmOYEBTLML9w6ZJpQGlTXXvQ3FmW077XZhtfoxtTQwcyLTSOSSH9ZJN3IXowCZ5CCIdV3lxPUtkxdpTlsLM0h6Km2lOu93J2ITHQ3g2dEBRLX09/2aqpGzSYTSSXn2gNmzmnTJUAe1dzTEA0E4JjmBgUyyDfUHmDIEQvIcFTCNEjKIrCiYZKdpbmsKM0h6TyY9SZTafcJszNm7GB0QzxDWOobzhDfELxcHZRqeKewaYonKiv4HB1EalVhRysKuBQVUH7hu4AOmCob3h7J3pUQCSuBhlCF6I3kuAphOiRrIqNw1VF7V23fZV57fs/ttEBfT0DGOobzlDfMIb4hTHEJwxPCaOnZVMUjtdXcLi6kMNVRRyuLiKtpoiG1oMAThbl4ceEoFgmBMeSGBSDr9FNhYqFEFojwVMI0Ss0Wczsq8jlQFU+R6rtoan4J0PzcPowGuMZSKCLR68apm+ymMlrrCK9utgeNKuLSKspbj9t6mSuBicG+YQytLWTPC4wmggPPxWqFkJonQRPIUSvVWFq4HCVPVQdri7iSHXhz+aJtnE1OBHh7kekhx8RHq1/tn7ex93X4VZf2xSFsuY68hqqyG+oIq+xiryGavvfG6ooN9Wf9t+dHDKH+YYzxC+cWM9AnPQyR1MI8cskeAohxEkqTA2tHVH7cHJ6TTGFjTXYOPvTX5CrJ5Hu9lAa5u6Dr7Mb3kZXvFv/9HF2w9vZFW+jG+4G5y7pnpptVurMzdS0NFNrbqLW3ExNSxM15iZqW5qpMNW3h8v8xipafjL14Kd8nF2J9Qpq7WSGScgUQvxmEjyFEOIXmG1WihprWruCVT92CVs/Tt6H8lw46/R4ObviY3RrD6YueqcOfQ2bolBnaaa2NWTWmJtPOwx+NgadjnB339bOrW97JzfSw58Id198ZF6mEKKTdSSvdexZUQgheghnvYEoT3+iPP1/dp2iKNSYm9uDaH5DFcXNtdS2NLd3Gk/uPloUG2bFRmVLI5UtjV1Sr6eTy8+6rN7Orvi7uLdPD4j08CPUzUe6l0IIzZLgKYQQP6HT6fA1uuFrdGOYX/hZb6soCo1WM7UtrUH0pGD60xX3v3i/6PB0drGHy7YhfWdXvJxdJUwKIXoECZ5CCPEb6HQ6PJyMeDgZCcNH7XKEEELT5C20EEIIIYToFhI8hRBCCCFEt5DgKYQQQgghuoUETyGEEEII0S0keAohhBBCiG4hwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFhI8hRBCCCFEt5DgKYQQQgghuoUETyGEEEII0S0keAohhBBCiG4hwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFhI8hRBCCCFEt5DgKYQQQgghuoUETyGEEEII0S2c1C7gXDSazTiZzWqXIYQQQgghfqKxAxnNIYLn+HfeRO/qqnYZQgghhBDiJ2zNzed8WxlqF0IIIYQQ3UKnKIqidhFnUltbi4+PD0Xl5Xh7e6tdjhBCCCGE+Ina2lrCAgOpqan5xbzmEEPt7s7OuDs7q12GEEIIIYT4CUsHMpoMtQshhBBCiG4hwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFhI8hRBCCCFEt5DgKYQQQgghuoUETyGEEEII0S0keAohhBBCiG4hwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFhI8hRBCCCFEt5DgKYQQQgghuoUETyGEEEII0S0keAohhBBCiG4hwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFk5qFyCEEI6g0WSmtqmZuiYTtU3N1DaaTvr8x8var28yYbHaOnw/Hq5GvN1c8HZzxdvdBS9XF7zdXe2Xubvi5eqCj3vb9a54uhox6KWHIIRwDBI8hRACMJktFFbVUlBZS35FDQWVNa1/1pJfWUNdk0ntEk/LSa8n1M+LCH8f+gR4E+Hv0/p3+5++Hq7odDq1yxRCCECCpxCiFzFbrBwtqSCrqJy8k8JlfmUtZbX1KMrZ/71Br7N3Gt1+7EJ6ndSd9HZzbf3c/nejk6FD9dkUhQZTC7WN9o5pbWMztc2m9u5q22VtXdamFjMWm83+f6iogayff013F2d7ED0pmMYE+zOoTxD+nu4dqk8IIX4rCZ5CiB6pqcVMekEpqXklpBeUklFYztGSirMOf7sZnYkI8KGPvzcRrR3Dts/DfL1xd3HWVPfQbLFSUddIfqU9RP+0W1ta20CjyUxmUTmZReU/+/fB3h4MDA9iUJ8ghkaEMCwylBBfT039H4UQPYtOUX7pPb56amtr8fHxoaamBm9vb7XLEUJolMVq42hJBam5xaTmlZCaW0xWcTlW28+f3rzcXBgYFkjfIL/WUPlj0PTzcOtRoctktlBQWfuzaQPZReWcKK8+7b8J9HJnWFQow6NCGRYZytDIEHzcXbu3cCGEQ+lIXpPgKYRwOFabjfSCUnZk5LIz8wSpecU0tVh+dru2EDWkTzBxfYIZFB5EmJ9XjwqXv1ZDcwtZReVkFJWRVlDK4dySM4b16EBfEgZEMmFgNAn9IyWICiFOIcFTCNHjFFXVsjMzlx0ZJ9iVlUtNY/Mp13u4GBkaGcKwyBB7ty4qlBAfGTbuiKYWMxkFZRzKK+ZQbjGpucXkVdScchu9TsewyBAmxEUzcWA0w6NDcTZ0bC6rEKJnkeAphHB4Dc0t7D6a1x42j5dVnXK9p6uRhP72Lty4fhHEBPuj10vI7GzVDU0cOFHEzswT7MzMJaek8pTrPVyMjOsfwYSB0UyMiyY60FfCvhC9jARPIYTDURSFzKJyth7OYUfmCQ4eL8Ji+3EhkEGvY1hkKBNbO23DokJxMsj+ld2tuKquPYTuzDxB9U86z+F+3kwYGMWkQX2ZPKgvbkZnlSoVQnQXCZ5CCIdxtLiC9SkZbDiQxbHSU7tpkQE+TIyLtnc1+0fg7SZzC7XEZlNILyy1T3/IzGXfsULMVmv79W5GZ6YOiWX2yIFMHtQXF2fZSEWInkiCpxBC006UVbE+JZP1KRlkF1e0X250MjApri+TB/dlwsAoIgN81StSdFijyczenHx2ZJ5gS+pRCipr26/zdDUybWg/5owcyISB0Th3cI9TIYR2SfAUQmhOfkUNGw5ksiElk7SC0vbLnQx6JsVFM3tkHNOGxuLp6qJilaKzKIpCal6JvZudkklJTX37dV5uLkwf1p85IweSMCBSFicJ4eAkeAohNKG4qo4NB+ydzdS8kvbLDXod4wdEMXtkHBcM6yfb8/RwNpvCgROFrE/JZOOBTMrrGtuv83V3ZUb8AOaMHMjYfhFy7rwQDkiCpxBCNS0WC5sPHeXTpFSSsnPbj6HU63SM6xfB7JEDmTF8AH6ebuoWKlRhtdnYl1PA+pRMvjmYRVVDU/t1wT6eLBw3lEUJQ4kI8FGxSiFER0jwFEJ0u6yicj5LSuWLvWmn7LE5OrYPc0YMZGb8AAK9PVSsUGiNxWojOTuPDSmZfHsoi9omU/t1iQMiuTRxOBcM6yeLkoTQOE0Ez+PHj/PnP/+ZzZs3U1xcTHh4ONdeey2PPfYYRqPxnL6GBE8htK2huYX1KRl8lpTKwdzi9svbOlcLE4bIAiFxTto65Z8lp7IzM7f9ch93V+aNGcyixGEMDAtUsUIhxJl0JK912dvI9PR0bDYbb731Fv379yc1NZVbb72VhoYGXn755a66WyFENyisrOU/2/ayOvkwTS1mAJz0es4fGsslicOYFBctc/VEhxidnJgzKo45o+LIr6hhze7DrEk+TElNPR98t58PvtvPiOgwbpw2lmlD+8lhAUI4qG4dan/ppZd44403yMnJOe31JpMJk+nHoZba2loiIyOl4ymERmQWlfPelt2s25/RfqZ33yA/Lkkcxryxgwn0kqF00XmsNhs7Mk6wOjmVLak57QcKxAT7c9O0sVw0epBsyySEBmii43k6NTU1+Pv7n/H65557jqeeeqobKxJCnIvDecW8sXEX244ca79s/IAobrpgLOMHRMkRiaJLGPR6pgyOYcrgGMprG1jx/X4++uEAx0orefzjjby6fgc3XTCOSxOHyTxQIRxEt3U8s7OzGTNmDC+//DK33nrraW8jHU8htOVwXglvbtzF1iP2UQqdDmbGD+CmaeMYGhmicnWiN6pvNrFq5yH+u30fZbUNgH1O8a3TE7gkcShGJwmgQnS3Ll1ctGzZMl544YWz3iYtLY1Bgwa1f15QUMD555/P1KlTefvtt8/5vmRxkRDqSMsv5fWNO9l62B449TodF40exO9mJtI3yE/l6oSwL0ZanXyYf3+b3L45fYiPJ7fOSGBRggRQIbpTlwbPsrIyKioqznqb2NjY9pXrhYWFTJ06lfHjx7N8+XL0HVhwIMFTiO6VUVjG6xt2sjn1KGAPnBeOjuN3MxKJCT7zNBkh1NJisfDprlTe3ryb0tYAGurrZQ+g44bKHFAhuoEmtlMCe6dz2rRpjBkzhg8++ABDB49Fk+ApRPfIKCzjzY27+PZQNmAfUp87chC3zUwkNkQCp9A+k9nCp0mpvLMpmdLWIfhwP29unZHAgrFDJIAK0YU0ETwLCgqYOnUq0dHR/Oc//zkldIaGhp7T15DgKUTXyiwq582NO/nm4I+Bc86IOJbOSiQ2JEDl6oToOJPZwie7DvH2puT2ozn7+Htz6/QE5o8bIufCC9EFNBE8ly9fzo033nja6871LiV4CtE1ckoqeG3DTjYeyALsgXP2iIHcNjOR/qGySbdwfM1mC6t2HuSdzbupOCmA3jZzPPPGDMbJIPvMCtFZNBE8O4METyE6V2lNPa9v3MnqpMPYWn/1Z40YwNKZ4xkgp8KIHqipxcyqnYd4d8uPAbRfiD/3XTyF8wbHyFZgQnQCCZ5CiFPUN5t4b8te/rt9L00tFgAuGNaPO+dMlGMIu4CiKFRUN9DUZO7Qv9PrdQT6e+JilBXZna2pxczKHQf596ZkahqbARgT24cH5p3H8Khzm/4lhDg9CZ5CCADMFiurdh3irW92UVnfBMCI6DAemDeFUTF9VK7OsSmKQmV1I/lFVeQVVZHf9lFcTUFxNU3NHQudbXQ6CAn0pk+oL5FhfkS0f/gSHuKDUTZK/01qm5p5Z9NuVny3H5PFCtinmdxz4SQiA33VLU4IByXBU4heTlEUvjmYxT++/oHc8mrAfrTlvRdN5oJh/WR4sYPyCqs4lF7wY8Asria/qOqs4VKv1+Hm6tyh+7FYbJhaO9Jn+pohgV4/htFQX2KjAhkxJEICaQcVV9Xx6oYdrN1zBEUBJ72eyyfGs3TmePw83dQuTwiHIsFTiF6srLaeP63a1H7aUICXO3fMGs+ixGGyovcc2WwK6UeL+S45m++SszieX3na29mDoDcRYb5EhPm1dih9iQj1IyzYB2fnjn2/FUWhqqbRHmwLf+ykFhRXk3eWoOvuZmT8qBgmJ/RnwugYvDxcO/x/7q0yCsv4+1ff8336cQD8Pd34w6XTmRk/QN3ChHAgEjyF6IUUReGrfek8t3oLtU0mnAx6br5gHDdNG4u7i1Ht8jSvxWxhX2oe3ydn893ubCqqGtqvMxj0DB8UTt+IACLD/OgT6kdkmC9h3Tj0ffLQfn5xFflF9jB6KL3gZ7WOGhrJlIT+TB7Xj5BAee48F0lZuTy/ZivZxfYDUuaOjOPRS6bh6yHdTyF+iQRPIXqZ8toG/vTJJrYctp84NCQimKevnC0r1X9BfYOJnfty+C45m137j9HY1NJ+XVsXcUpCf8ZruIt4anc2m+P5p54sF9cvhCkJ/Zkyrj+xUYEyzeIsWiwW3tyYxDubd2NTFAK83Hn80ulMH95f7dKE0DQJnkL0IhsPZPKnTzZR09iMk0HP0pnjuemCsTKsfgYNjSY2bk9je3IW+w/nYbHY2q8L8PNgyrj+TE7oz+hhkQ45bzK3sLK1a3uU1IwCTn6GDw/xYcq4/sw6fwhxsSHqFalxqbnF/OGjDRwtsU+xuHjMIB5ddAFebi4qVyaENknwFKIXqGsy8fyaLazdkwbAoPAgnr5qNnHhQSpXpk0VVQ2s+movazYcoL7R1H553wh/piQMYPK4/gzuH4pe33M6gpXVDezYk8P25Cz2HDxBi9naft2Y4VFcuyiRsfFR0gU9DZPZwusbdrJ8615sikKYnxfPXDWHcf0i1C5NCM2R4ClED7c3J59HP9xAYVUtep2Om6eP4/aZ4+U86tPIL6rif2v3sG5Lanvw6hvhz4XThjE5oT9R4b3jLPrGphZ2HzjO5h0ZbN2ZidVmf+ofGBvCNQvHMXX8QAxyms/PpBwr5JH/rSe/ogadDm44fwx3zZ2I0cnxuuFCdBUJnkL0UGaLlVfX7+C9rXtQFPsRgM9dPUf25DyN9KPFrFidzLakLGytIWtYXDjXLExg0th+Paqz2VFFpTV8/MUevvj2UPv2TRGhvly5YBxzpw6VDex/oqG5hRc/38ZnyakAxIUH8dzVc2QOtRCtJHgK0QNlF5fzyIr1pBeWAbAoYSgPL5iKh6usWG+jKAp7D+Xywepk9hw80X75hNExXLsokfjBfWRY+STVtY18+vV+Pl23n9p6+2k+/r7uLL5oDItmj8TTQ+Y0nmzToWyeWvUtVQ1NGJ0M3HPhZK6dMqpXv4kRAiR4CtGj2GwKH36/n7999T0tFiu+7q788fKZstL2JFarje3JWXywOpmMoyUAGPQ6ZkwezNULx9EvWua9nk1jUwtfbjrER1/sobS8DgAPdyMLZo3g8ovHEOjnqXKF2lFe28ATK7/hu7RjACQOiOTpK2cT6uulcmVCqEeCpxA9RElNPY9/tIGdmbkATB7Ulz9dMZMgbwkCYA+c67Ye5oPVyeQXVQHgYnRi3ozhXDFvLGHBPipX6FgsFivffJ/Oh2uSOZZn35bJ2cnAnKlDWXLZeEKD5HkY7J31VTsP8tLa7TSbLXi5ufD4pdOZOypO7dKEUIUETyF6gPUpGfz5k03UNplwdXbigXnnccXEeBkqbpVyOI9/vLeFrGOlAHh7unLp3FFcMncUfj7uKlfn2Gw2hR17c1ixJplD6QWAPdBfsyiBaxaMw8WlY0eB9lTHy6p4ZMU6UvPsXfYLRw3isUun4e2mzT1fhegqEjyFcGB1TSaeXb2ZL/emAzA0MoTnrp5DTHDvWH39S4rLann9/W1s3pEBgKeHC0suG8+CmSNwd5P5rp3tQFo+//7we1KO5AMQEujFnUumMm3CQHkTBJitVv71TRL/3pSM1aYQ6uvFM1fNJqF/pNqlCdFtJHgK4aBSc4u5//0vKaqqQ6/Tccv0BJbOSpTN4LEPq3/0xR7e/XgHphYLer2O+TPiueWqSfh6S4ezKymKwuYdGbz2/rb2OaCjh0Wy7I7ZhIf4qlucRhw4UcQjK9aR17rt0k3TxvH7uZNk4ZHoFSR4CuGANh3KZtmKdTSbLUQE+PDcVXMYGROudlmaUFJey5//+TUph+1dt5FDIrjnpgsYEBOscmW9S7PJzIo1yaxYs5uWFgse7kYeuHUGs84bonZpmtBoauHFtdv4dJd926UZw/vz7NVzcDPK1ATRs0nwFMKBKIrCB9/t56W121AU+wKil667EE9X2coGYMvODF588xvq6ptxc3Xmnpsu4KILhskwr4oKS6r58z/Xtc//nHXeYO6/ZYZsv9Tq633p/OGjjZitVoZHhfLPm+YT6OWhdllCdBkJnkI4CKvNxgtrtvG/H1IAuHxCPI8smoaTnCBDY1ML/3xvC19uOgTA4P6hPHHPRUSG+6lcmQCwWG28/+kulq/aic2mEBbszRP3XMTwQXKYAdhPF7vnvS+oaWymj783r9+ykNiQALXLEqJLSPAUwgE0mlp46IOv2XbEvh/gAxdPYcnUMdLJA9Kzi/nj378iv6gKnQ6uXZTIzVdMxEmOBNWcQ+kF/OkfX1FUWotBr2PJ4glcf+l4efOEfdX7Hf9eTV5FDV5uLvzjhnmMk0VHogeS4CmExpXW1HPXO5+TVlCKi5OBZ6+ew6wRA9UuS3U2m8KHnyfz7//9gNVqIzjAiz/8fi6jh0WpXZo4i/oGE397exMbth8BYPigPjxxz4WyjypQWd/IPe+tJeV4EU4GPX+6fCbzxsqcWNGzSPAUQsMyi8q58+01FFfX4e/pxj9vWsCI6DC1y1JdXUMzT/71S5JTjgMwdcJAHrptJt5ebuoWJs7Zxu/S+Mu/vqGhsQVPdxeevO8iJoyOVbss1ZnMFh773wY2HMgE4I5Z41k6a7yMbogeQ4KnEBq1I+ME9//nSxpMLfQN8uP1WxcSGeCrdlmqyy2sZNlza8gtrMTVxYl7b54uC4gcVGFJNX/6x9ekZhSi1+u447rzuWKeTCGx2RT+8fX3vLtlDwDzxw7mj4tn4izTR0QPIMFTCA36dNch/vzpJqw2hbH9Ivj7DfPwcZcTTnYfOM7jf/mC+gYTwYFevLBskWyT5ODMZit/fftbvvjWvjDswmlD+b/bZmJ0dlK5MvV9susQT7c+DyT0j+SvSy6W5wHh8CR4CqEhNpvCK+t/4O1NuwG4eMwgnrp8Jkan3v0irCgKn3y9n1eXb8FqUxgWF86zDy3A31e2nekJ2h7fV5ZvwWZTGB4XzjPy+ALwQ/pxHnj/KxpMLcQE+/P6LQuJCJD5sMJxSfAUQiNMZgt/+GgD61Psc7uWzkzkjtkTev2wo70jtokvvj0ISEesJ0tOOc4Tf/mC+kYTIYFePP/IIgb0lY52RmEZd769hpKaevw93Xn15gUMjwpVuywhfhUJnkJoQGV9I/e+9wX7jxfipNfzx8tnsGDcULXLUl11bSN/eGktKUfy0et13H7deVw5b2yvD+M9WW5BJQ8/v5q8wipcXZx4/J6LOD9xgNplqa6kpp673l5DemEZLk4GnrtmLjPj5fsiHI8ETyFUdqy0kjvfXmPfv8/Vhb/dcDGJA2RLoKMnylj2/GqKSmvxcDfyx/sullXPvURtfTN//OuXJB84DsAtV05iyWWysruhuYUHP/ia79Ls+/nef/EUbpD9fIWDkeAphIqOlVay5NWVVDU0yYklJ0k/Wsw9f1xJQ2MLfUJ9eeGRRfSNkO9Lb2Kx2nj9/W2s/HIvAIvmjOT+W6b3+pBlsdp48fMfTzD73YxE7p47Ud2ihOiAjuQ1OVpCiE5UWlPP0n+tpqqhicERwaz4/VUSOoFjeeU88OdPaWhsYcTgCP79/DUSOnshJ4Oe3984jYeWzkKng9XrU3hrxXdql6U6J4OeRy+ZxoPzzwfgX98m8eH3KeoWJUQXkeApRCepbWpm6b9XU1hVS3SgL2/euogAL3e1y1JdQXE19z61ipq6JgYPCOWlxy6RTeF7ufkz4/m/380E4IPVyfz3sySVK9KG688fzR2zJwDw/Jot7RvOC9GTSPAUohM0my3c/e5asorKCfRy583fXYK/p4TOsoo67n1qJRVVDcRGBfKXxy7F3c2odllCAxbMGsEd19s7fG+t+I5P1+1XuSJtWDozkSsmxqMo8MiK9SRn56ldkhCdSoKnEL+R1WZj2Yp17MspwNPVyJu/u0T25MO+ev2+P62iqLSWPqG+/PWJy6TTKU5x9YJxLLlsPAB/e3sT67ceVrki9el0Oh5ZNI2Z8f0xW638/t21pOWXql2WEJ1GNs0T4jdQFIWnP93MpkPZOBsM/PPG+cSFB6ldluqamlt48NnPOJ5fSXCAF39/cjGBfp5ql9XlWkxmDu45TvL2DPb8kEVNVUOH/r3BoGdQfCSJ58WRcN5AgkJ9u6ZQDbnlykk0NLbwydf7eO619fh4u/X6nQ4Mej3PXT2XqobV7Dmaz+1vr+a/d18hx+uKHkFWtQvxG7y+YSdvbNyFTgd/uf5i2YMP+wrdR19Yw469OXh7uvL6M1f16IVEFaW1JH+XSfL2DPbvOkpzU0unfe2YgaEkTBlI4vmDiBsegcHQMwepbDaFZ15Zx4btR3B1ceKVP13B4P5hapeluromEze+voqMwjKiAn15/64rZN640CTZTkmIbrByxwH+/OlmAB6/9AIunzhC5YrUpygKz7++ga82p+JidOIff7ycYXHhapfVqWw2G1mHC0nank7y9kyy0wpPuT4g2IuEKXEknBdHRN/ADm0V1NhgYt/ObJK3Z5B+MA+b7cenZ29fd8ZOHkDClDjGTBqAl3fPmrZgNlt5+LnVJB84jq+3G288czWR4X5ql6W6stp6rnvlYwoqaxkSEcy7ty/Gw1XmSQttkeApRBf75mAWD7z/JYoCt88a374Stbf79/++5z+f7EKv1/HsQwuYPK6/2iV1iuamFvZ8n0XS9nR2f5dJdeWPQ+g6nY64YX0Yd14ciefF0W9QWKfsS1lT1cCeH7JI3p7B3h+yqK9rbr9Ob9AzdFQUCVPimDBtMBF9A3/z/WlBY1MLdz3xMZk5JYQF+/DWc1fL2e7AibIqrn/1Yyrrmxg/IIrXb1mIs5NB7bKEaCfBU4gutDs7j9v+tRqz1cpl44fzxGWyATbAmg0pvPyvbwF4aOks5s+MV7mi385ms7H5ywO898+NVJTWtV/u7uHC6In9STgvjnGTB+IX0LXzV60WK0dScknankHyd5nkHv1xsYlOp2PG/JHccPdMAoId/3mysrqBpY9+SGFJDQNjQ3j1T1fITgjA4bxibnz9E5pazMwdFcfzV89Fr5fnHaENEjyF6CIZhWXc8NpK6ptbmD68P3+5/iIM+p45764jtiVl8fjLa7HZFG66YiI3Xe74p64c3n+CN1/8mqzDBQAEhfoweeZQEs+LY+joaJyd1VubWZRfSfL2DJK2pbNv51EAXN2MXHnLeSy6bhIurs6q1dYZ8gqruP2xD6mubSJhRF9eeGQRzs7S4duRcYI7316DxWbj2imjeGjB+fKmV2iCBE8hukB+RQ3XvfIR5XWNjIntw1u/uwQXFcOHVhxIy+e+p1bRYrYyf2Y8D94206FfDEsKq3j37xvZtv4QYO9uXnXrVBZcOwGjUXuPd9qBPN568SvSD+UDEBzuyy33zWbKrGEO/TikZRdx9xMf02yyMPu8ITx2t3T4AL7al86yFesAuOfCSdwyPUHlioSQ4ClEpyuvbeC6Vz8mv6KGAWGBLL9zMd5urmqXpbrj+RUsffRD6htMTB7Xj6cfXICTg668tpitfPT2Nla+u50WkwWdTsfsRaNZcvfMLh9K/61sNhtb1x3k3b9tpLy0FoCho6K558kFRMUGq1zdr7dr/zEefm41VquNaxYmcPt156ldkia8v20fL63dBsjCRqENEjyF6ERWm43b3vqMpOw8IgN8+M9dlxPkre0g0h0sFiu3LltB1rFShseF87cnF+Pq4phDvEX5lbzw8Mr2ruHwsX1Z+tBF9BvkWFv6NDe28Ml/vmfVe99hajbj4urM0ocuZM6lYx22+7luSyrPvLoegL8+fhkJI/uqW5BG/HPdD/z722Sc9Hrev/sKhkeFql2S6MU6ktccszUhRDd6b8sekrLzcDM68dotCyV0tvrvZ0lkHSvFx8uNZx5a4LChc/NXKdy5+DXSD+Xj6eXKshcu58V3bna40Ang6m7k2tsv4O219zB6Qj9MzWb+8afPeeaBj6iraVS7vF9l7rRhXDp3FADPv7GBhkaTyhVpw91zJjJrxAAsNhsPf/A1Dc2dt3+sEF1JgqcQZ3Eot5jX1u8E4JGF04gJ9le5Im3IOl7K8k92AXDfLdMdcsubxgYTLz36CS8+8gmNDSaGjY7m9U/uYurceIftDrYJCvXl6TeWcMv9c3ByMvD9t4e5/bJXObTnmNql/SpLr51CeIgPpeV1vPqfbWqXowk6nY4nF88gzM+LvIoanvlss9olCXFOJHgKcQb1zSYe+uBrLDYbs0cMZGHCULVL0gSLxcqzr67HarVxXuIApk+KU7ukDss4lM+dl7/Gpi9T0Ot1XHfHBbzw9k0Eh/mqXVqn0ev1XHbDZP72we/oEx1AeUktD9/yLu+/9i1Wi1Xt8jrEzdXII3fOAeCLbw+SnHJc3YI0wtvNlReumYtep+OLvWl8sTdN7ZKE+EUSPIU4g2c+20x+RQ3hft48sVj26mzTNsTu7enK//1uhsN9XzZ/lcL9S/5FUV4lwWE+vPTeLVyz9AIMPXRD7gFD+vDqx3cwc8EobDaFD9/aymNL/0Nzo2MNzY4aGilD7qcxKqYPS2cmAvD0p5vIK69WtyAhfoEETyFO44s9R/hybzp6nY7nr5kjK9hbOfoQ+5avD/DyY59itdiYPHMor6+6i6GjotUuq8u5ubvwwJ8vZdkLl+Pu4UJKcg5//P0HnXqufHc4ecj9tfdlyL3NrTMSGR0TTqPJzMMr1mG2OlZHW/QuEjyF+Inc8mqebp0vdfvs8YyK6aNyRdpgsVh57qQh9hmTB6ldUods35jKS499is2mMPfSsTz60hV49rDzzn/J1LnxPP3mEtzcjaQk5/Cne1fQYjKrXdY5c3M1suyO2QCs/eYguw8cV7cgjXAy6Hn+mrl4ubmcMi9dCC2S4CnEScwWKw9/8DWNJjNjYvtwq2zO3O6D1clktg6xP3CrYw2xf//tYZ5/eCU2q41ZC0dz9+Pz0ffSE6eGjIjiz69fj6ubkX07j/Knez+kpcWidlnnbPSwqB+H3F+XIfc2YX7ePHX5TADe3bKbpKxclSsS4vR65zOvEGfw6vodpOaV4O3mwvNXz5XjMFvZh9jtXZR7b5lOgJ/jDLHv3JLGcw99jM1qY/rFI7nnyYW9NnS2GTa6L3967TpcXJ3Z80MWT9//P8xmxwmft10zhbBgH0pklfspZsYP4NLxw1AUeOTD9VTVN6ldkhA/07uffYU4ya7MXN7bugeApy6fSaifl8oVaYPZbOXpf67DYrExJaE/Mx1oiD1pewbPPPARVouNaRfGc/+fL8HgoCcrdbb4sTE89cq1GF2cSN6ewbP/9zEWs2PMDXR3M/LoXT+uct+13zG3ieoKD82fSkywP2W1DTzx8UY0fEaM6KXkGVgIoLK+kUf/tx5FgcvGD2dG/AC1S9KM5Z/s5OiJMny83BzqHPY9P2Tx9H0fYrFYOW/2MP7v6UsldP7EyMR+/PGf1+JsdGLnljSef3ilw4TPUUMjuezC0QC88PoG6hqaVa5IG9xdnHnpugtxNhjYeiSHj344oHZJQpxCnoVFr6coCk98vJGy2gZiQ/x5aMH5apekGWnZRXzwWRIAD/xuhsOsYt+3K5un7lmB2Wxl8oyhPPTs4h67XdJvNXpCf578+9U4O9s3mn/x0VUOs8/n0munEBHmR1llPf94VzZQbxMXHsQD86YA8PIX28ksKle5IiF+JMFT9HqfJaWy7cgxjE4GXrz2QtyMjnn0Y2ezWm0899oGrDaF6ZPiuGCiY2wUn3WkkKd+vwJzi4UJ0waz7IXLcXKW0Hk2YycP5A9/uxonJwPbN6Ty6rNfqF3SOXF1ceaxu+ag08H6rUdIkiH3dldPHsl5g2NoaV0wabHa1C5JCECCp+jlms0WXttgXzTz+7mTiAsPUrki7diyM5Oc3HK8PF25/9YZapdzTpobW3j+4ZWYms2MnTSAR1++QkLnOUo8L47H/nIlOp2OdZ/s4buNqWqXdE6GD+rTPuT+9sc/yJzGVjqdjj9fOQsfd1eyiyv4ap+caiS0QYKn6NU+3nGAstoGwvy8uGryCLXL0QybTWH5Knsgv2LeGHy8HGO/yzdf/IqCE+UEBnvz0HOLcXZ2UrskhzJh2mAuv8k+RPuPp9ZQVlytbkHn6LpLEnExOpGWVSzHaZ7E39Odm6aNBeCNjbswO8gUCtGzSfAUvVZDcwvvbNoNwO0zx2N0kpDSZltSJsfzK/B0d+GyuaPVLuecfP/NYdZ/thedTseDz16Gt6+72iU5pOvumM7AYX2or2vmxUc+weoAQ7T+vh4snGV/47h81U7pep7kqskjCfByp6CyltW7D6tdjhASPEXvteL7/VQ1NBEV6Mu8sUPULkcz7N1O+7GYiy8ajaeHi8oV/bKy4mr+/tQaABbfNIURCbHqFuTAnJwNLHv+ctzcjRzae5yV72xXu6RzctXCcRidDRzKKGRfap7a5WiGm9G5/SCMt75JwuRA+7WKnkmCp+iVquqbWL5lLwB3zJ6Ak2yz0+6HPUc5eqIMdzcjiy/SfrfTarXx4qOfUF/bxMBhfbj+julql+TwwqMCuPPReQD8943NpB/UfpAL9PNk3ox4AN5btUPlarRl8YThhPp6UVpTz/++T1G7HNHLyaut6JXe2LiLumYTceFBzBk5UO1yNENRFN5rndt56dxReDvA3M5V727n0J7juLoZWfa8rGDvLNPnjWTq3HhsVhsvLFtJQ73298m8ZlECzk4GUg7nk3JY+2G5uxidnLhj9gQA/vVtspxoJFQlwVP0OjklFazcad9U+cH558mxmCfZte8YmTkluLk6c8W8MWqX84uOZRbz3zfs+zfe+ejFhEcFqFxRz6HT6bj7D/MJCfelKL+Kd/62Qe2SflFwgBcXXTAMgOWf7FK5Gm2ZP3Ywg8KDqGs28frGnWqXI3oxecUVvc5fvvgOq01h6tBYEgdEqV2OZpzc7Vw0eyS+3tpenGOz2XjlmbVYLTYmXjCYGfNHqV1Sj+Ph5cr/PX0pAOs+2eMQQ+7XLkrAYNCz5+AJDqUXqF2OZhj0eh5sPRxj1c6D5JRUqFyR6K0keIpeZUfGCbanHcNJr+eBeeepXY6m7D5wgiNZRbgYnbhy/li1y/lF367dz5H9ubi6Gbl92UUOc5Snoxk+NoYZ80ehKAqvPL1W86cahQb7MHfqUMB+3Kv4UUL/SKYN7YfVpvDyF9+pXY7opSR4il7DarPx0tptAFw5aQR9g/xUrkg7Tu52Lpg1QvNHY9ZWN/L2X+1Dv9fefgFBob7qFtTD3XL/HDy93TiaXsQXHyerXc4vuu6SRAx6HUn7j5OWXaR2OZrywLwpOOn1fJd2jB0ZJ9QuR/RCEjxFr/FZUirZxRV4u7mwdNZ4tcvRlP2peRxKL8DobODqBePULucXvfePjdRWNxLdL5iF10xQu5wez9ffgxvvmQnA+69+S0VprcoVnV2fUF9mnWffIq1tazBhFx3kx5Wth2W8tHabHKUpup0ET9Er1DebeHW9vaO3dNZ4fNxdVa5IW9q6nfNmxBPo76lyNWeXdiCPdZ/uAeDuP8yXVezdZO6lY4kbFkFjg4l/vbxO7XJ+0XWXJqLX6/hhz1Eyc0rULkdTls4cj7ebC9nFFaxOdoyjUUXPIcFT9Aqf7DxEZX0j0YG+XDlRjsY8WfbxMvYfzsPJSc/VC7Xf7Vz+z40AzFwwimFj+qpbTC+i1+u56w/z0et1bFt/iKPp2h7Cjgr3Z/qkQQCs+mqfytVoi4+7a/v2Sm99k4TVJl1P0X0keIoeT1EU1rQeFbdk6hicnaRDdrKtOzMAmDAqlpBAb5WrObvcnFIO7D6GXq/jOtkovtsNGBLO5Jn2hTtfrkxSuZpfNn+mfUP57/ccxaLxRVHdbfGE4Xi7uVBSU09SlvZ3KxA9hwRP0eOl5pVwtKQSFycDc0bGqV2O5mxLygLg/PEDVK7kl3250r6wJfH8QQSH+apbTC918RWJAGz+8gANddreVD5+UB/8fNypq29mv2wofwqjkxMXjrZ3hNckyxnuovtI8BQ9XtuT6vThA/By0/65490pt6CSY3kVGAx6Jo3rp3Y5Z9XUaOLbtfsBuPiKBJWr6b2Gj+lLVL9gTM1mvv1iv9rlnJXBoGdKQn8Atu7KUrka7Vk4zr4Aa1NqNjWN2n4TIXoOCZ6iR2s2W1i33z6UvDBhiMrVaM/WXZkAjBkehZeHthdcbfn6II31JsKjAhg1XtshuSfT6XTtwf/Lj5NRFEXlis7u/ER7J397UhZWWcF9iiERIfQPDaDFYmVDSoba5YheQoKn6NG2pB6lrtlEmJ8Xif3llKKfahtmnzpe2+fVK4rCFx/Z5xRedHkCejnmVFXTLx6Jq5uRvGNlHNx9TO1yzmr0sCg8PVyoqmkkNaNQ7XI0RafTsTDBPmd3ze4jKlcjegt59hY9Wtsw+/yxQ9Dr5WSbkxWV1pBxtAS9XseUBG13ENMO5HEssxijixOzFsjRmGrz8HRl+sUjAfjiY20vMnJ2NjBprP3nu63DL3508ejBOOn1HMot5mixHKMpup4ET9FjFVfVsTPLfjLHgnEyzP5T21u7nfGD++Dno+2TitrCzdS58Xj5aPsM+d7iotbh9h2b0zS/oXxbR39bUpbmpwZ0twAvdyYP7gvA57tlkZHoehI8RY+1du8RFAXG9osgMsBX7XI0p22xhdaH2asrG/h+o32T67YV1UJ9sQNDGToqGpvV1r6hv1YljIjGzdWZ0vI60o8Wq12O5iwcZx9u/2JvmpxkJLqcBE/RIymKwuetc5YWSrfzZ8qr6knNKADgvERtb6O0cc1ezGYrA4f1YeDQPmqXI07Stsho3ae7sZi1u0+mi4sz40fFALB1p6xu/6nzhsTg7+lGeV0jP2QcV7sc0cNJ8BQ90r5jBeSWV+NmdGZmvLaDlRq+S8pGUWDIgDCCA7zULueMrFYbX7Xu3SndTu2ZNGMovv4eVJTWsWtbutrlnNXUCa3D7bsyZbj9J5wNBi4aPRiQPT1F15PgKXqktm7n7BEDcXcxqlyN9mxpPa1I65vG7/khi5LCajy93Th/9nC1yxE/YTQ6MeeSsQB8qfFFRhNGx2J0NpBfXE32iTK1y9GctnnwW4/kUFXfpHI1oieT4Cl6nEaTmQ0H7KtXZe/Onyuvqm8/xWXaBG3P72zrds5eOBoXV2eVqxGnM/eysej1OlKScsg7pt1A5+5mZPzoWAC+/U7b3Vk1xIUHMTgiGIvVxtf75fsjuo4ET9HjHDxRRKPJTKivF6NjZE7gT6UczkdRYGBsCOEhvmqXc0YtJjP7d2YDMGvRGJWrEWcSEu7HqPH204F2f6/t7Yqmtnb49x3OVbkSbbpolP0IzR0ZJ1SuRPRkEjxFj3Mo175qdWTfMHQ62bvzp9KyiwAYOjBM5UrOLvNwAWazFb8AT6Jig9QuR5zFiAT7wp3D+7QdWIYMsP/MZx8rw6zhxVBqGRkTDtifQ2UerOgqEjxFj3Mw1x6shkdpO1ipJeNoCQCD+4WqXMnZHd5vDzFDR0XLGwiNGzoqGrA/ZloOLH1CffHydMVssZKTV652OZozKDwIJ4OeqoYm8itr1C5H9FASPEWPoihKe8dzeFSIytVoj9VqIyPHHjwH9dd68LQPh7aFGqFdA4b2wdnoRHVlA4W52j39RqfTMaif/XkhLVv28/wpF2cnBoXbRxcOnZDvj+gaEjxFj1JcXUdFXSNOej2DIyR4/tSJgkqams24uToT3cdf7XLOyGazcaSt4zlagqfWGY1O7XustnWqtWpQa6c/XYLnabWNFB3Kk++P6BoSPEWPcrC12zkgPBBXZyeVq9GejNZTWwbGhGAwaPfXP/doGfV1zbi6GekXp+3OrLBre4OQqvXg2drplxOMTq9tpEg6nqKraPeVR4hfoe3JMj5KwsrptA0vDuqv7W5wW9ds8IhIDE4GlasR56J9nuc+ba8YH9waPI/lltNsMqtcjfYMj7Z3PNMKSjFbZAGW6HwSPEWPcqh9YZEEz9NJa+3yDNL4wqLUkxYWCccwZGQUAAUnyqmuqFe5mjML8vfE39cdq00h61ip2uVoTnSgL95uLrRYrGQWaXdfVuG4ujR4zp8/n6ioKFxdXQkLC+O6666jsLCwK+9S9GJmq5Uj+fYXElnR/nNms5Wjx+0vJIM1v7BIgqej8fJ2o29rJ/1wina7nvYFRm3D7SUqV6M9Op2u/Y37QRluF12gS4PntGnTWLlyJRkZGXz66accPXqUyy67rCvvUvRi2UUVNJsteLm60DfIT+1yNCcnr5wWsxVPDxf6hPqqXc4ZlRVXU1pYjd6gZ1B8hNrliA44eVslLWt74yULjE6vLXi27RAiRGfq0tUX9913X/vfo6OjWbZsGQsXLsRsNuPs/PPj70wmEyaTqf3z2trarixP9DBtw+xDI0PQ62Xfx59qn9/ZL1TT+2K2baPUb1AYbu4uKlcjOmLo6Ci+WpWs+Y3k2zqesqXS6bXN85TgKbpCt83xrKysZMWKFUycOPG0oRPgueeew8fHp/0jMjKyu8oTPUDbivbh0doeRlZL24p2RxlmHybD7A6nreOZnV5Ic2OLytWcWdvK9tzCShoaTb9w695neKT9+3O8rIqaxmaVqxE9TZcHz4cffhgPDw8CAgLIzc3l888/P+NtH3nkEWpqato/8vLyuro80YO0z++M1HawUktm60KKtg20tSrtgP33vm2xinAcwWG+BIZ4Y7XYyDpSoHY5Z+Tn405okDeALDA6DT9PNyIDfABIy5fvj+hcHQ6ey5YtQ6fTnfUjPT29/fYPPvgg+/fvZ+PGjRgMBq6//vozHqnm4uKCt7f3KR9CnKuKukYAwv3l5+Z0KqsaAAgJ1Pb3p6LMPsUmPCpA5UpER+l0OsIi7QcTVJTVqVzN2QUHegFQWdOociXaFO5nf56oqJfvj+hcHZ7j+cADD3DDDTec9TaxsbHtfw8MDCQwMJCBAwcyePBgIiMj2bVrFxMmTOhwsUKciaIo1LYOCfm4u6pcjTbVNdi/P16e2v3+KIpCXXUTAN6+bipXI34Nbx93AGo1Hui8POy/B3X1MpR8Ot6tz6My1C46W4eDZ1BQEEFBQb/qzmw2G8ApC4iE6AyNJjOW1p8vCZ4/ZzZbaTZZAPDy0O6CncYGE5bWTau9WgOMcCzefq3Bs0rjwdPT/nvQ9oZMnMpHgqfoIl22qj0pKYndu3czefJk/Pz8OHr0KI8//jj9+vWTbqfodLVN9idHZ4NBjso8jbYXV50OPDS8Uryu2h5WXFydcXUzqlyN+DUcr+MpjZDT8XazP0+0PbcK0Vm6bHGRu7s7n332GdOnTycuLo6bb76Z+Ph4tm3bhouLdl/4hGOqaR9md9H0VkFqaRtO9HB30fQZ7bU19mF2Lx8ZZndU3r724Nn2JkKr2qacSMfz9No6nrXS8RSdrMtaQ8OHD2fz5s1d9eWFOEWNzO88q7oGe1enrcujVbWtC6B8/DxUrkT8Wm3Bs0brwVPmeJ6VDLWLrqLd1ocQHSDB8+zaXly1vLAIfhyelY6n43KYjqdH2xxPGWo/HQmeoqtI8BQ9QtuTo7cEz9NqX9Gu4YVF8OOCFOl4Oi5vX/tjp/mOpwy1n9WPwVOCuehcEjxFjyAdz7NzvI6nrGh3VG3bYGm+4+kpQ+1nI9spia4iwVP0CLVN9nfl3m7aDlZqcZw5nq0dT18Jno7Ku7Vb3dTYQkuLReVqzkyG2s/Op/W5tLap+YyHvgjxa0jwFD2CbB5/dj92PDU+1N7W8ZTg6bA8PF3Q6+07S2i569nW8axvaMZmk2D1U20dT4vVRqPJrHI1oifR9IaHbe+yamtrVa5EaF1JRSVWUzPOikV+Xk6jrLwSi7kZJ51V09+fspIKLFYTBqO26xRn5+qhp7a6kfz8Epw1+l5QsVqwmO1vyIpKyjQ/GtDdFEXBYDXTYrGSV1xCuL+P2iUJDWt7vj6X7rhO0XAPPT8/n8jISLXLEEIIIYQQvyAvL4+IiIiz3kbTwdNms1FYWIiXl1eHNgWvra0lMjKSvLw8vL29u7BC8UvksdAOeSy0QR4H7ZDHQjvksdCGX/s4KIpCXV0d4eHh6PVnn8Wp6aF2vV7/i8n5bLy9veUHWCPksdAOeSy0QR4H7ZDHQjvksdCGX/M4+Pic23QMWVwkhBBCCCG6hQRPIYQQQgjRLXpk8HRxceHJJ5/ExUXbW8f0BvJYaIc8Ftogj4N2yGOhHfJYaEN3PA6aXlwkhBBCCCF6jh7Z8RRCCCGEENojwVMIIYQQQnQLCZ5CCCGEEKJbSPAUQgghhBDdQoKnEEIIIYToFj0+eM6fP5+oqChcXV0JCwvjuuuuo7CwUO2yep3jx49z8803ExMTg5ubG/369ePJJ5+kpaVF7dJ6pWeeeYaJEyfi7u6Or6+v2uX0Kq+99hp9+/bF1dWVxMREkpOT1S6p19m+fTvz5s0jPDwcnU7HmjVr1C6pV3ruuecYN24cXl5eBAcHs3DhQjIyMtQuq1d64403iI+Pbz+xaMKECaxbt65L7qvHB89p06axcuVKMjIy+PTTTzl69CiXXXaZ2mX1Ounp6dhsNt566y0OHz7M3/72N958800effRRtUvrlVpaWli8eDG333672qX0Kh9//DH3338/Tz75JPv27WPEiBHMnj2b0tJStUvrVRoaGhgxYgSvvfaa2qX0atu2bePOO+9k165dfPPNN5jNZmbNmkVDQ4PapfU6ERERPP/88+zdu5c9e/ZwwQUXsGDBAg4fPtzp99Xr9vFcu3YtCxcuxGQy4ezsrHY5vdpLL73EG2+8QU5Ojtql9FrLly/n3nvvpbq6Wu1SeoXExETGjRvHq6++CoDNZiMyMpK7776bZcuWqVxd76TT6Vi9ejULFy5Uu5Rer6ysjODgYLZt28Z5552ndjm9nr+/Py+99BI333xzp37dHt/xPFllZSUrVqxg4sSJEjo1oKamBn9/f7XLEKJbtLS0sHfvXmbMmNF+mV6vZ8aMGezcuVPFyoTQhpqaGgB5XVCZ1Wrlo48+oqGhgQkTJnT61+8VwfPhhx/Gw8ODgIAAcnNz+fzzz9UuqdfLzs7mlVde4bbbblO7FCG6RXl5OVarlZCQkFMuDwkJobi4WKWqhNAGm83Gvffey6RJkxg2bJja5fRKhw4dwtPTExcXF5YuXcrq1asZMmRIp9+PQwbPZcuWodPpzvqRnp7efvsHH3yQ/fv3s3HjRgwGA9dffz29bIZBl+noYwFQUFDAnDlzWLx4MbfeeqtKlfc8v+axEEIILbjzzjtJTU3lo48+UruUXisuLo6UlBSSkpK4/fbbWbJkCUeOHOn0+3HIOZ5lZWVUVFSc9TaxsbEYjcafXZ6fn09kZCQ7duzokhZyb9PRx6KwsJCpU6cyfvx4li9fjl7vkO99NOnX/F7IHM/u09LSgru7O5988skp8wmXLFlCdXW1jMSoROZ4qu+uu+7i888/Z/v27cTExKhdjmg1Y8YM+vXrx1tvvdWpX9epU79aNwkKCiIoKOhX/VubzQaAyWTqzJJ6rY48FgUFBUybNo0xY8bw3nvvSejsZL/l90J0PaPRyJgxY9i0aVN7yLHZbGzatIm77rpL3eKEUIGiKNx9992sXr2arVu3SujUGJvN1iVZySGD57lKSkpi9+7dTJ48GT8/P44ePcrjjz9Ov379pNvZzQoKCpg6dSrR0dG8/PLLlJWVtV8XGhqqYmW9U25uLpWVleTm5mK1WklJSQGgf//+eHp6qltcD3b//fezZMkSxo4dS0JCAn//+99paGjgxhtvVLu0XqW+vp7s7Oz2z48dO0ZKSgr+/v5ERUWpWFnvcuedd/Lhhx/y+eef4+Xl1T7X2cfHBzc3N5Wr610eeeQR5s6dS1RUFHV1dXz44Yds3bqVDRs2dP6dKT3YwYMHlWnTpin+/v6Ki4uL0rdvX2Xp0qVKfn6+2qX1Ou+9954CnPZDdL8lS5ac9rHYsmWL2qX1eK+88ooSFRWlGI1GJSEhQdm1a5faJfU6W7ZsOe3P/5IlS9QurVc502vCe++9p3Zpvc5NN92kREdHK0ajUQkKClKmT5+ubNy4sUvuyyHneAohhBBCCMcjk+yEEEIIIUS3kOAphBBCCCG6hQRPIYQQQgjRLSR4CiGEEEKIbiHBUwghhBBCdAsJnkIIIYQQoltI8BRCCCGEEN1CgqcQQgghhOgWEjyFEEIIIUS3kOAphBBCCCG6hQRPIYQQQgjRLf4f+kQqQaGyWJMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "x = np.linspace(-np.pi, np.pi, 50)\n", @@ -1402,12 +2249,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "c613a5df", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.655043Z", + "iopub.status.busy": "2023-07-25T23:59:06.654897Z", + "iopub.status.idle": "2023-07-25T23:59:06.789816Z", + "shell.execute_reply": "2023-07-25T23:59:06.789401Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.contour(x, y, f, levels=45);" @@ -1431,12 +2295,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "bef21527", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.793238Z", + "iopub.status.busy": "2023-07-25T23:59:06.793090Z", + "iopub.status.idle": "2023-07-25T23:59:06.903016Z", + "shell.execute_reply": "2023-07-25T23:59:06.902666Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.imshow(f);\n" @@ -1462,12 +2343,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "4435ed50", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.904985Z", + "iopub.status.busy": "2023-07-25T23:59:06.904852Z", + "iopub.status.idle": "2023-07-25T23:59:06.907631Z", + "shell.execute_reply": "2023-07-25T23:59:06.907307Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "seq1 = np.linspace(0, 10, 11)\n", "seq1\n" @@ -1485,10 +2383,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "7a2c664a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.909501Z", + "iopub.status.busy": "2023-07-25T23:59:06.909370Z", + "iopub.status.idle": "2023-07-25T23:59:06.911861Z", + "shell.execute_reply": "2023-07-25T23:59:06.911478Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "seq2 = np.arange(0, 10)\n", "seq2\n" @@ -1508,12 +2424,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "810eafdf", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.914013Z", + "iopub.status.busy": "2023-07-25T23:59:06.913879Z", + "iopub.status.idle": "2023-07-25T23:59:06.916405Z", + "shell.execute_reply": "2023-07-25T23:59:06.916096Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'lo '" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"hello world\"[3:6]" ] @@ -1529,10 +2462,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "f7a9f652", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.918188Z", + "iopub.status.busy": "2023-07-25T23:59:06.918057Z", + "iopub.status.idle": "2023-07-25T23:59:06.920501Z", + "shell.execute_reply": "2023-07-25T23:59:06.920172Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'lo '" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"hello world\"[slice(3,6)]\n" ] @@ -1578,10 +2529,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "24427538", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.922296Z", + "iopub.status.busy": "2023-07-25T23:59:06.922171Z", + "iopub.status.idle": "2023-07-25T23:59:06.924760Z", + "shell.execute_reply": "2023-07-25T23:59:06.924501Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11],\n", + " [12, 13, 14, 15]])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A = np.array(np.arange(16)).reshape((4, 4))\n", "A\n" @@ -1598,10 +2570,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "18684ed5", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.926485Z", + "iopub.status.busy": "2023-07-25T23:59:06.926366Z", + "iopub.status.idle": "2023-07-25T23:59:06.928716Z", + "shell.execute_reply": "2023-07-25T23:59:06.928382Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[1,2]\n" ] @@ -1621,10 +2611,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "id": "34d27b73", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.930420Z", + "iopub.status.busy": "2023-07-25T23:59:06.930302Z", + "iopub.status.idle": "2023-07-25T23:59:06.932693Z", + "shell.execute_reply": "2023-07-25T23:59:06.932407Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 5, 6, 7],\n", + " [12, 13, 14, 15]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[[1,3]]\n" ] @@ -1641,10 +2650,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "1bb799c5", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.934417Z", + "iopub.status.busy": "2023-07-25T23:59:06.934293Z", + "iopub.status.idle": "2023-07-25T23:59:06.936701Z", + "shell.execute_reply": "2023-07-25T23:59:06.936383Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2],\n", + " [ 4, 6],\n", + " [ 8, 10],\n", + " [12, 14]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[:,[0,2]]\n" ] @@ -1661,10 +2691,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "de853e7c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.938410Z", + "iopub.status.busy": "2023-07-25T23:59:06.938279Z", + "iopub.status.idle": "2023-07-25T23:59:06.940843Z", + "shell.execute_reply": "2023-07-25T23:59:06.940553Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 14])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[[1,3],[0,2]]\n" ] @@ -1679,10 +2727,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "e71bf672", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.942617Z", + "iopub.status.busy": "2023-07-25T23:59:06.942490Z", + "iopub.status.idle": "2023-07-25T23:59:06.945126Z", + "shell.execute_reply": "2023-07-25T23:59:06.944774Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4, 14])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([A[1,0],A[3,2]])\n" ] @@ -1697,10 +2763,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "458fe5c9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.946927Z", + "iopub.status.busy": "2023-07-25T23:59:06.946793Z", + "iopub.status.idle": "2023-07-25T23:59:06.973966Z", + "shell.execute_reply": "2023-07-25T23:59:06.973611Z" + } + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[63], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mA\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n", + "\u001b[0;31mIndexError\u001b[0m: shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) " + ] + } + ], "source": [ "A[[1,3],[0,2,3]]\n" ] @@ -1717,12 +2802,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "703832ac", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.976099Z", + "iopub.status.busy": "2023-07-25T23:59:06.975973Z", + "iopub.status.idle": "2023-07-25T23:59:06.978782Z", + "shell.execute_reply": "2023-07-25T23:59:06.978390Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 6],\n", + " [12, 14]])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[[1,3]][:,[0,2]]\n" ] @@ -1748,12 +2851,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "dbf328b7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.980758Z", + "iopub.status.busy": "2023-07-25T23:59:06.980608Z", + "iopub.status.idle": "2023-07-25T23:59:06.983480Z", + "shell.execute_reply": "2023-07-25T23:59:06.983138Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 6, 7],\n", + " [12, 14, 15]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "idx = np.ix_([1,3],[0,2,3])\n", "A[idx]\n" @@ -1773,12 +2894,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "65784eb0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.985438Z", + "iopub.status.busy": "2023-07-25T23:59:06.985317Z", + "iopub.status.idle": "2023-07-25T23:59:06.987978Z", + "shell.execute_reply": "2023-07-25T23:59:06.987611Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 6],\n", + " [12, 14]])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[1:4:2,0:3:2]\n" ] @@ -1825,12 +2964,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "3ddb0d83", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.990333Z", + "iopub.status.busy": "2023-07-25T23:59:06.990160Z", + "iopub.status.idle": "2023-07-25T23:59:06.993012Z", + "shell.execute_reply": "2023-07-25T23:59:06.992640Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "keep_rows = np.zeros(A.shape[0], bool)\n", "keep_rows" @@ -1846,10 +3002,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "90e72009", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.994861Z", + "iopub.status.busy": "2023-07-25T23:59:06.994740Z", + "iopub.status.idle": "2023-07-25T23:59:06.997372Z", + "shell.execute_reply": "2023-07-25T23:59:06.997059Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True, False, True])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "keep_rows[[1,3]] = True\n", "keep_rows\n" @@ -1867,10 +3041,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "fb3b6534", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:06.999118Z", + "iopub.status.busy": "2023-07-25T23:59:06.998976Z", + "iopub.status.idle": "2023-07-25T23:59:07.001465Z", + "shell.execute_reply": "2023-07-25T23:59:07.001171Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.all(keep_rows == np.array([0,1,0,1]))\n" ] @@ -1895,10 +3087,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "b146db22", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.003376Z", + "iopub.status.busy": "2023-07-25T23:59:07.003257Z", + "iopub.status.idle": "2023-07-25T23:59:07.005689Z", + "shell.execute_reply": "2023-07-25T23:59:07.005311Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 3],\n", + " [4, 5, 6, 7],\n", + " [0, 1, 2, 3],\n", + " [4, 5, 6, 7]])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[np.array([0,1,0,1])]\n" ] @@ -1913,10 +3126,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "6bf7b4a3", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.007666Z", + "iopub.status.busy": "2023-07-25T23:59:07.007533Z", + "iopub.status.idle": "2023-07-25T23:59:07.009740Z", + "shell.execute_reply": "2023-07-25T23:59:07.009471Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 5, 6, 7],\n", + " [12, 13, 14, 15]])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A[keep_rows]\n" ] @@ -1941,10 +3173,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "288011d9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.011387Z", + "iopub.status.busy": "2023-07-25T23:59:07.011272Z", + "iopub.status.idle": "2023-07-25T23:59:07.014033Z", + "shell.execute_reply": "2023-07-25T23:59:07.013669Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 6, 7],\n", + " [12, 14, 15]])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "keep_cols = np.zeros(A.shape[1], bool)\n", "keep_cols[[0, 2, 3]] = True\n", @@ -1962,12 +3213,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "3e3b1954", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.015743Z", + "iopub.status.busy": "2023-07-25T23:59:07.015607Z", + "iopub.status.idle": "2023-07-25T23:59:07.018145Z", + "shell.execute_reply": "2023-07-25T23:59:07.017835Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 6, 7],\n", + " [12, 14, 15]])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "idx_mixed = np.ix_([1,3], keep_cols)\n", "A[idx_mixed]\n" @@ -2036,10 +3305,222 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "d02bfb28", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.020113Z", + "iopub.status.busy": "2023-07-25T23:59:07.019977Z", + "iopub.status.idle": "2023-07-25T23:59:07.197194Z", + "shell.execute_reply": "2023-07-25T23:59:07.196835Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130350412.0701chevrolet chevelle malibu
115.08350.0165369311.5701buick skylark 320
218.08318.0150343611.0701plymouth satellite
316.08304.0150343312.0701amc rebel sst
417.08302.0140344910.5701ford torino
..............................
38727.04140.086279015.6821ford mustang gl
38844.0497.052213024.6822vw pickup
38932.04135.084229511.6821dodge rampage
39028.04120.079262518.6821ford ranger
39131.04119.082272019.4821chevy s-10
\n", + "

392 rows × 9 columns

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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration year \\\n", + "0 18.0 8 307.0 130 3504 12.0 70 \n", + "1 15.0 8 350.0 165 3693 11.5 70 \n", + "2 18.0 8 318.0 150 3436 11.0 70 \n", + "3 16.0 8 304.0 150 3433 12.0 70 \n", + "4 17.0 8 302.0 140 3449 10.5 70 \n", + ".. ... ... ... ... ... ... ... \n", + "387 27.0 4 140.0 86 2790 15.6 82 \n", + "388 44.0 4 97.0 52 2130 24.6 82 \n", + "389 32.0 4 135.0 84 2295 11.6 82 \n", + "390 28.0 4 120.0 79 2625 18.6 82 \n", + "391 31.0 4 119.0 82 2720 19.4 82 \n", + "\n", + " origin name \n", + "0 1 chevrolet chevelle malibu \n", + "1 1 buick skylark 320 \n", + "2 1 plymouth satellite \n", + "3 1 amc rebel sst \n", + "4 1 ford torino \n", + ".. ... ... \n", + "387 1 ford mustang gl \n", + "388 2 vw pickup \n", + "389 1 dodge rampage \n", + "390 1 ford ranger \n", + "391 1 chevy s-10 \n", + "\n", + "[392 rows x 9 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "Auto = pd.read_csv('Auto.csv')\n", @@ -2056,9 +3537,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "ca6090af", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.199106Z", + "iopub.status.busy": "2023-07-25T23:59:07.198977Z", + "iopub.status.idle": "2023-07-25T23:59:07.202273Z", + "shell.execute_reply": "2023-07-25T23:59:07.201978Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -2087,12 +3574,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "7220aa87", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.204037Z", + "iopub.status.busy": "2023-07-25T23:59:07.203937Z", + "iopub.status.idle": "2023-07-25T23:59:07.206918Z", + "shell.execute_reply": "2023-07-25T23:59:07.206626Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 130.0\n", + "1 165.0\n", + "2 150.0\n", + "3 150.0\n", + "4 140.0\n", + " ... \n", + "392 86.00\n", + "393 52.00\n", + "394 84.00\n", + "395 79.00\n", + "396 82.00\n", + "Name: horsepower, Length: 397, dtype: object" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Auto['horsepower']\n" ] @@ -2110,12 +3625,42 @@ }, { "cell_type": "code", - 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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130.03504.012.0701chevrolet chevelle malibu
115.08350.0165.03693.011.5701buick skylark 320
218.08318.0150.03436.011.0701plymouth satellite
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
33827.24135.084.02490.015.7811plymouth reliant
33926.64151.084.02635.016.4811buick skylark
34025.84156.092.02620.014.4811dodge aries wagon (sw)
34123.56173.0110.02725.012.6811chevrolet citation
34230.04135.084.02385.012.9811plymouth reliant
34339.1479.058.01755.016.9813toyota starlet
34439.0486.064.01875.016.4811plymouth champ
34535.1481.060.01760.016.1813honda civic 1300
34632.3497.067.02065.017.8813subaru
34737.0485.065.01975.019.4813datsun 210 mpg
34837.7489.062.02050.017.3813toyota tercel
34934.1491.068.01985.016.0813mazda glc 4
35034.74105.063.02215.014.9811plymouth horizon 4
35134.4498.065.02045.016.2811ford escort 4w
35229.9498.065.02380.020.7811ford escort 2h
35333.04105.074.02190.014.2812volkswagen jetta
35533.74107.075.02210.014.4813honda prelude
35632.44108.075.02350.016.8813toyota corolla
35732.94119.0100.02615.014.8813datsun 200sx
35831.64120.074.02635.018.3813mazda 626
35928.14141.080.03230.020.4812peugeot 505s turbo diesel
36030.76145.076.03160.019.6812volvo diesel
36125.46168.0116.02900.012.6813toyota cressida
36224.26146.0120.02930.013.8813datsun 810 maxima
36322.46231.0110.03415.015.8811buick century
36426.68350.0105.03725.019.0811oldsmobile cutlass ls
36520.26200.088.03060.017.1811ford granada gl
36617.66225.085.03465.016.6811chrysler lebaron salon
36728.04112.088.02605.019.6821chevrolet cavalier
36827.04112.088.02640.018.6821chevrolet cavalier wagon
36934.04112.088.02395.018.0821chevrolet cavalier 2-door
37031.04112.085.02575.016.2821pontiac j2000 se hatchback
37129.04135.084.02525.016.0821dodge aries se
37227.04151.090.02735.018.0821pontiac phoenix
37324.04140.092.02865.016.4821ford fairmont futura
37436.04105.074.01980.015.3822volkswagen rabbit l
37537.0491.068.02025.018.2823mazda glc custom l
37631.0491.068.01970.017.6823mazda glc custom
37738.04105.063.02125.014.7821plymouth horizon miser
37836.0498.070.02125.017.3821mercury lynx l
37936.04120.088.02160.014.5823nissan stanza xe
38036.04107.075.02205.014.5823honda accord
38134.04108.070.02245.016.9823toyota corolla
38238.0491.067.01965.015.0823honda civic
38332.0491.067.01965.015.7823honda civic (auto)
38438.0491.067.01995.016.2823datsun 310 gx
38525.06181.0110.02945.016.4821buick century limited
38638.06262.085.03015.017.0821oldsmobile cutlass ciera (diesel)
38726.04156.092.02585.014.5821chrysler lebaron medallion
38822.06232.0112.02835.014.7821ford granada l
38932.04144.096.02665.013.9823toyota celica gt
39036.04135.084.02370.013.0821dodge charger 2.2
39127.04151.090.02950.017.3821chevrolet camaro
39227.04140.086.02790.015.6821ford mustang gl
39344.0497.052.02130.024.6822vw pickup
39432.04135.084.02295.011.6821dodge rampage
39528.04120.079.02625.018.6821ford ranger
39631.04119.082.02720.019.4821chevy s-10
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mpgcylindersdisplacementhorsepowerweightaccelerationyearorigin
name
chevrolet chevelle malibu18.08307.0130.03504.012.0701
buick skylark 32015.08350.0165.03693.011.5701
plymouth satellite18.08318.0150.03436.011.0701
amc rebel sst16.08304.0150.03433.012.0701
ford torino17.08302.0140.03449.010.5701
...........................
ford mustang gl27.04140.086.02790.015.6821
vw pickup44.0497.052.02130.024.6822
dodge rampage32.04135.084.02295.011.6821
ford ranger28.04120.079.02625.018.6821
chevy s-1031.04119.082.02720.019.4821
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mpgdisplacementhorsepower
name
chevrolet chevelle malibu18.0307.0130.0
buick skylark 32015.0350.0165.0
plymouth satellite18.0318.0150.0
amc rebel sst16.0304.0150.0
ford torino17.0302.0140.0
............
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chrysler lebaron salon3465.01
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datsun 200sx2615.03
mazda 6262635.03
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volvo diesel3160.02
toyota cressida2900.03
datsun 810 maxima2930.03
buick century3415.01
oldsmobile cutlass ls3725.01
ford granada gl3060.01
chrysler lebaron salon3465.01
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chevrolet cavalier wagon2640.01
chevrolet cavalier 2-door2395.01
pontiac j2000 se hatchback2575.01
dodge aries se2525.01
pontiac phoenix2735.01
ford fairmont futura2865.01
volkswagen rabbit l1980.02
mazda glc custom l2025.03
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plymouth horizon miser2125.01
mercury lynx l2125.01
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toyota corolla2245.03
honda civic1965.03
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toyota celica gt2665.03
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dodge rampage2295.01
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" + ], + "text/plain": [ + " food bar pickle snack popcorn\n", + "0 0.345584 0.821618 0.330437 -1.303157 NaN\n", + "1 NaN -0.536953 0.581118 0.364572 0.294132\n", + "2 NaN 0.546713 NaN -0.162910 -0.482119" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rng = np.random.default_rng(1)\n", "A = rng.standard_normal((127, 5))\n", @@ -2692,12 +7617,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "d8ae1dcc", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.352395Z", + "iopub.status.busy": "2023-07-25T23:59:07.352287Z", + "iopub.status.idle": "2023-07-25T23:59:07.354931Z", + "shell.execute_reply": "2023-07-25T23:59:07.354663Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column \"food\" has 16.54% missing values\n", + "Column \"bar\" has 25.98% missing values\n", + "Column \"pickle\" has 29.13% missing values\n", + "Column \"snack\" has 21.26% missing values\n", + "Column \"popcorn\" has 22.83% missing values\n" + ] + } + ], "source": [ "for col in D.columns:\n", " template = 'Column \"{0}\" has {1:.2%} missing values'\n", @@ -2731,12 +7674,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "30a8a663", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.356627Z", + "iopub.status.busy": "2023-07-25T23:59:07.356508Z", + "iopub.status.idle": "2023-07-25T23:59:07.458864Z", + "shell.execute_reply": "2023-07-25T23:59:07.458518Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'horsepower' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[102], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m);\n", + "\u001b[0;31mNameError\u001b[0m: name 'horsepower' is not defined" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.plot(horsepower, mpg, 'o');" @@ -2752,12 +7723,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "id": "81fdedb1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.460807Z", + "iopub.status.busy": "2023-07-25T23:59:07.460673Z", + "iopub.status.idle": "2023-07-25T23:59:07.556888Z", + "shell.execute_reply": "2023-07-25T23:59:07.556504Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');\n" @@ -2777,12 +7765,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "id": "61850515", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.558736Z", + "iopub.status.busy": "2023-07-25T23:59:07.558598Z", + "iopub.status.idle": "2023-07-25T23:59:07.668649Z", + "shell.execute_reply": "2023-07-25T23:59:07.668299Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", + " scatter = ax.scatter(\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Horsepower vs. MPG')" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = Auto.plot.scatter('horsepower', 'mpg');\n", "ax.set_title('Horsepower vs. MPG')" @@ -2800,9 +7823,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "id": "f2e0c165", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.670624Z", + "iopub.status.busy": "2023-07-25T23:59:07.670488Z", + "iopub.status.idle": "2023-07-25T23:59:07.713512Z", + "shell.execute_reply": "2023-07-25T23:59:07.713197Z" + } + }, "outputs": [], "source": [ "fig = ax.figure\n", @@ -2824,10 +7854,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "35c72e77", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.715540Z", + "iopub.status.busy": "2023-07-25T23:59:07.715402Z", + "iopub.status.idle": "2023-07-25T23:59:07.907697Z", + "shell.execute_reply": "2023-07-25T23:59:07.907310Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = subplots(ncols=3, figsize=(15, 5))\n", "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n" @@ -2854,12 +7902,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "2b6fb1ad", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.909709Z", + "iopub.status.busy": "2023-07-25T23:59:07.909567Z", + "iopub.status.idle": "2023-07-25T23:59:07.912864Z", + "shell.execute_reply": "2023-07-25T23:59:07.912497Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "CategoricalDtype(categories=[3, 4, 5, 6, 8], ordered=False)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n", "Auto.cylinders.dtype\n" @@ -2876,10 +7941,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "id": "1f19f48c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:07.914563Z", + "iopub.status.busy": "2023-07-25T23:59:07.914464Z", + "iopub.status.idle": "2023-07-25T23:59:08.031995Z", + "shell.execute_reply": "2023-07-25T23:59:08.031639Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "Auto.boxplot('mpg', by='cylinders', ax=ax);\n" @@ -2895,12 +7978,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "929b6901", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:08.033904Z", + "iopub.status.busy": "2023-07-25T23:59:08.033752Z", + "iopub.status.idle": "2023-07-25T23:59:08.146764Z", + "shell.execute_reply": "2023-07-25T23:59:08.146364Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "Auto.hist('mpg', ax=ax);\n" @@ -2916,12 +8016,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "8d48f1e9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:08.148651Z", + "iopub.status.busy": "2023-07-25T23:59:08.148520Z", + "iopub.status.idle": "2023-07-25T23:59:08.257224Z", + "shell.execute_reply": "2023-07-25T23:59:08.256869Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "Auto.hist('mpg', color='red', bins=12, ax=ax);\n" @@ -2941,12 +8058,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "400690e6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:08.259243Z", + "iopub.status.busy": "2023-07-25T23:59:08.259102Z", + "iopub.status.idle": "2023-07-25T23:59:09.325925Z", + "shell.execute_reply": "2023-07-25T23:59:09.325581Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7ljWYzIuype/D7uHsZRkALW2O8uCuYSq2h1OdJ9f1yZctdgyk+Z171wCwu7rbA58nDk5ydLJEezLEO65quyS7uouJNe1xeieLxIIqQU0hVTQomja26+MDVtUK49h0kWeOTHHfxg6KpsNDr44yXbRY1hzlrjUtZ1xoXxnKkq/YBDUZ0/Y4OFbgpuUL1zfAk4emePFYitFsBcfzKdsui5siNER0wGc8Z1A2HezqpkZXJEZzFR7aM0pdOMBwusxorkx9RKc9EeLhvROM50yWNUe5tqeeREh7Q2cvzwU3LG0gU7ZIFS1WtERZ3izKhA/tGaN3sgjAytYY965v48B4np/uGSNbsQlpMs3xICtbY6gypEsmng+2q9ISC/HY4QkmcpUav3LnwPHuyIrlMpY3WXUqz/yi48evjtbENq/qTF4SEdzDE4JLBSIguxBccQHQ2fC1r32Nj33sYyxfvhxd1/nud7+70AE2h1BkiS2L63BPOtlMx+One8epWC6aIvGylmX3cIb3Xd1FUyzApq7knOwQIgFlVvtsPHh5nr7XLKojElDxTiKbOj5MFkz+5yMHeNvaNnJlm6FMmYm8QaFiUxfWmchLPHtkmnvfYKraixsjvH19G3/y2GFKpkPFdvF8fxaNpGQ6HJsq8eTBKW5a3lSzW2lNBNk/mmdRQ/i0xOj+6ZIIIKdKJMMaK1tixC7Tc+NioneyyLGpIk2xABur11zFcnlw1zBHpwq4nihZT+QMnjsyzfuv6cJ2fVRZQlNlwgGZoCb0vYqGw6HxIi1xh5CuoEgyEhLZaiYvVTR5rnea53qnWdee4L2bO15Xa/2VhJFshf2jeWJBlWsW1aEqMpIkMm2j2QpNUR1JkjAdl97JIuWqTcOh8QJ3rWlh73COsVwF1/MpmRIvHUvxSzcuJqirtCWC2K5PIqSRq9i0xIKYjkf1Po9UXVOyFZv2qERX3fxzgAzb5ad7xtgzkgNJYjRT5q61rfMu8tpXzay9Hlzxq8STTz5Z+39LSwuPPvropRvMGxy5ssXfb5tAlmVgdsqxP1VCkiCkqeiKy0TBpKsuTCyo4Xr+BfM6TkRDNMBda1rZPZQlGlTnVeDtfPD4wUlMx60uCLODxaLh8nxviqcOTZMMaWQrFo7noyoShycKbOyqq5nMvlEwkCrRpQb5P08eYXtfCh8oVCz8kzZtri+6jfaN5fiDh/Zj2C6posV00WRpU5RM8fSdHi8eS9EYDVAyXQqGQ3d9mA2XYWbwYmI4U+Ynr47W5tTzBfn5az89wP6xPKYjsju+D4ossgdTeYOK5WJXO5IkfEDC88H1fCq2S7ZsMZAqo6sytudhVzw2ddfRnyrhej5eVezw6FSJla0X3yx1uCqK2Vl3aZTBs2WLB3cO1zIyFcvl9lXN/O3z/bxwVEhcDKTKdNaFuW5JPRN5o3ajXtYcRVVkbNdHU8Q8q7LEjIpgV12I3skCiuyTDGs0RHVSRZMT95sz72s7HlN5k4o9/2uF67ps60tjOuK9X+jLXBIhxNgc8PzmNQCqq6s7bSZAkiSCwSDLli3jk5/8JL/0S780n8O66Hij+If900tDvNhXwPGEhPiJl57jgSJBxXZxXKHpMnMjH89dGEHtdFjTHmfNJdKdOFcMpctkyg5hXaFkzV6gZBkKhoPn+wRUGccTN6aK7TGeM3DaPa5b3HCJRn5x8JNXxnDUDNv7M+L88HyQIKTJOB641V2tIov5mCyYqOMFrltcT7pkMZKtoMgSL/Wl6KgLnWKLoSkiazGjlXLj8sY3LJfqTJjIm/i+6OoyHI+RTJmrF9WxeziLLImyluEI7ynT8bBdj4f2jFE0bXzfx3Y81rbHqYvoBDQFfOidLJApWfRNl2iJB7m6ux7TcdFVGUWWiAc16qqt3POhAv+z/RPsHRGyEus6Erx1TctFf8+TMV20Zsl9jOfF2jZTmp/BSLaM7dahqxIly0EGdEXGcT2uXpTgH1+S8TwHZKkmhNgc05nMG5iOTzyg0BwL8GzvFLJ0wjbKF/Ps+WB7Hi8cnZ53GkDJ8vB8v7a+O644nwLy/JKhO+aAYjGvAdDv//7v85WvfIV77rmHa6+9FoBt27bx8MMP8+u//uv09fXxmc98Bsdx+NVf/dX5HNoCzgG262F7YveiKnAy78z1QfF9YkEdRZFwqnnbpliAgVSJ5ljwknUMzCcWNUSQAcP2UCRq6WsQgWKuYhHQFEqWQ8VycX0fXZHRFJn3bG6n9Q2ofDxVMIkHVfIVG/CRkZAlCcdzq1L/EkZ1R5kt23i+T3MsQMl00BSZlc0xshWLF45O090wu7Hh9lXNPLRnjIJhs7ErSVti/ssClxpd9SEM22X3UAbD9pDwuXNNC2vb4xyZKNYMaGVJWNnsHMggS2A4orvL9X0mCibpsk1LPEhYV0gVbQxHlG/CuoyuyrQng/zC1Z2kihZPH5kiW7ZZ0x6/6GKVluPVgh+AvSM5blvZdEZF6ouF1kQQRZYYSJWI6GpNbfuWFU0cGi/g+T6RgMINSxuRJXjxaJqRrMhaWa6HIks4nvC9Mx2PgCozkwJ6cNdI9dyHQ5NFXh7McOfqZv7Hj/fXuC51YUHp8HwwHZ+GC7SAeD2IBpTqvIsbQFBTLkn5Mxl5/Z99XgOgZ599lj/4gz/gP//n2aLV3/72t3n00Uf5wQ9+wIYNG/jzP//zhQDoMoQiSyyqD/FCpozvn1reAfCRaEsE2diVZGN3ktZEiJ0DGSzHIxpQuW+jYOzVR/R5X7wuNlzX5S+fOsZwpsySpjAV28VyPVxn9jz5QFRXkKriYZInsqAF0+bV4RyNsSD902WaYoFzEosrWw4Fw6Ehol+2PIywrrComrnJVWzCusJoVZ3Y9fxZu2ofqJgO2bJNRzLEZMHkpYE0QVXh2GSJeFjj7rWttSxPWFe4a00LybB2Xq7lVypyFRvTcWmKBmpz0BwLUh/RKRouqiJxaKLIrsEMHckwzTGd0ayB5Pmosphf2/Vqcy4huvNyZZt4SOXQeJ6AKjOcKRHSxS3C8XxuWdHI2vYE+YpNxXZ576bOedvQaIpot65Ud10hXZnlkfV6sX80z0i2QmddqKaBZNguO/ozWK7Lxq466iM6+LB/NMdguoymyFy/TARAd6xqJqTKHBgvcPvKJtqSIdJFk4mCUdsIzpDNj0wWRLeiKoMkcahqcNo/XRJlJR/ynseRiQLdjRGWt0QZSJVRZYlViQi7Thj34SrBej7hI9HdEGYoXUaWJLrrL82Go3fi9X/2eQ2AHnnkEb72ta+d8ve3vOUtNUXne++9l/vvv38+h7WAc8Sq1hivTE4hST72mVj3EtieT0M0wKbuOp46NMVUwSQWUBnPGfz5z4/QGA1QH9H5wDVdb6iM0O/9aB9PH5kCRBvs6rYYLxyzOZkv5XpQtFzaEwFiaEwUDHRVpiGi89KxDEcmS0R0BUmSeNva1rOW/IYzZf5t9yiW49EYC/CBa84swHap0BjT2dBUz79XxxnWFaYLJvg+nje7lDoDyxOfrTkeIBnWGM8alD0X8PnHFwcxHZd3b+wkU7L4151DlEyXWFDlA1sungzD5YB9ozke2z9JtmKxqD7MJ27oqQVBRdMhWiV/W47Hkwen2D2UpT6ikzMcioaD6wufKkUGJAnX9ZFlCVkSFg0j2QqO5wlTWsvF9aExqtNVF2Z9R5Lneqd58eg0QV0lFlT58HXdhKtBku16jGUNokFVBAtzCEmSeNfGdp4+LK6vW1Y0XVCZ03E8dg1liAZV1rSJ0tGh8QKP7BsHRGZJlSWWt8T46d4x9o3kcT2fIxMFfunGJewYSJMp2zX1+5/tm+CedW0cnSqyrT+D5/v87MAEvxgLoCkysiTVNiWyLKPKEnUhnYJpU7E8VEWqaVb5HM8Wyz5EQioVyyWoyjTHhByB58/mwUW0+b/Wg6pMSFMJaSqSBGFdnXcCNMDRqSssAKqvr+fHP/4xv/VbvzXr7z/+8Y+prxck2VKpRCx28cl0lwuuJH7QtoE0pu1hOmcIfhC7+ZCmsL0/LawghrNoikxTLICmSNRHRGtyumRxaKLAxgvwJLocYbse+8eOq4nPOEPrMpzsZe4jNJVKlkddWBUZoaBKNKhybLrIaE4hGdJY2RqjP1U6awC0azCLVS0dTRfOLsB2qfDBLd3E43F+sEv4GBUNm6LhYJ+lc1VClGoOjRdY3hxjZWucQ+M5BlIGsiTx3RcGWdYUI1WyanNdMITI4unsGd4o2NaX5shkgamCyf7RPPGQxns3dwKwubuOI5NFSqYoWz11eIp0ycTxfIKqTCyoYTqi+66aaEBXFZqiOrbnMVmwsF0P34dcyUKShO1B3nDIlG2+/ON97B7MkKs4dNWH2dydZDBdZlVrHMsRGl1TBRNZkrh7Xeuck6LbEiE+uOXCvfM8z+NLP97HkWrW5O0b2vj49T01Hs8MxvMGy1tibO/L1G6yozmNX9hs0RwLYLseRdNBlSXiIXFt7hvJMZqtULYc6iM6fdMl1rUn6GmIsHc0hwSsaYujawqDmTKVGR6N45/2Ri4BluXRUxdmNGfW/MLqTqAH6orETcvPrBZ9sWC5PmFdxnRcJEkirCtVi5/5DYImc1dYF9gXv/hFPvOZz/DEE0/UOEDbt2/nP/7jP/jWt74FwM9+9rMzavcs4NLi2ESBfRMmzlkaDzwf9o/liAU0JAlkSaJkCkLwhs4kQe14iSZ0CXYvFwuqLNEQ1WsdH4osIZ/AazkZHqLt23A86kI6LfEgiaBKrEGlb7pMtmJTtlxa4mfXsgmeJMB2OWbUHtw5TEUKkC3bTOQNUTg9TQVVk0WXkueDooiyh65KrO2McXisQH+6XMsg9U+X+POfH+G9mztmHSOkKfi+z96RPHnDZmVr7A0liKjKElOF44TbwxOF2s3npuWNWK7HZMFkW1+KbNlClSVMx6NgOIR1ocFk2C7RoIrpeDiuj+V6VBwP3/fQZAnT9ak4PqoEuiahyhJjuQrPH53GcoQGWKZsUTBs3lOd/6FMuTYuz/fZPZSZ8wDo6FSRJw5OAoL3tfQ8eUe9UyWePDTJdFEEdxXL5ePX97CoPszLg5man9WielF2rlTb10GUwzwfOpJhGiKCrBwOqFzVJTYbo9kKuwYz2K5HRFd518aOaoDg05EMISHmxXF9Bqods4L9I5EqWQCoioxSJTzLkoQiS+wczJA3bFzPR5KOl7zkahPBpegCA4+D44WauOmBsfwl6QKbLr3+zz6vAdCv/uqvsmbNGr75zW/y4IMPArBy5UqeeuopbrjhBoBZ5qYLuLwwkqtgu2fnmAg6q0TRcuibKpE3bCK6SslyeedV7RydKjJVMFnaHGVFyxvH5dv3YVVrnGzZpmQ61bKDjXGWbFnecIgGVMo44PssbYoiSRKaopCr2Ny+qpnNr2E3cNPyRoqmQ7pksbI1dt43hfnAWM7Akn0kfJIRDcP2sGyPkyMg2wOFKi8K0UVouz7Zkk3ecEmEdDIlE9PxCaiCCzORN1nZGmM0W6GzLsyGziTP9abY3p/Gcj1+9PIId65u4eYVjbVSzZWMt61t5dneaSqWS3syRF1YF8Ra12PnQAZNkXjb2hZyZZvD4wUcT9x0NUWYoEqS6NYsWw6SJEphuYoQpHQ90SKvSKKzLhxQcFyf+oAoxRjVm63riW7P6aLJD3eN8Nm3rqBsOYxmK0QDKvGQhixJPHloEkWWuGZR/esOzD3P5/s7htk9JIQAU0WL//dtK89LgDFdMJkqWvi+D76Q7gDhtv4LmzsZyVboSAqbD4BN3XUExkQJrC0RJBpU6Z0scmiiQLZiUzRdtvWleM+mTtIlC9NxMW0PTZGYyBusaY8TUFVa4uKzz3CW1rXH+I9Xx7CrkgQ9DSLg6q4Pi0yPD0FNZllzjExZtLr7iDVmJujwfCiYLkPpEjC/Gc+y6TKZN2pr23i2gut5yPPcBRbWZbKvs8F43leEG2+8kRtvvHG+3/aKxOVWHlNk+XQb9xokxAUNotaeLVuUbRdNlvE8n8MThVq63vf9N1Srsut5mI7HytYYYU1Fln1+tn/irK/xfJEFsl2Z8WyFXFMUVZFY2Rpj65J6ljWfuoM+ed7Culqb08sZru+jKgoRTSNfqcwiPZ8IH1BlcQOYMdvd0Z+p2QMYlkzJ8nB9H8cV0gGfuKFn1jGGMmXA58Bonort8mzvNFNFk49uXXTRP+fFRnM8yO+9fQ3P9k5jux6W4/GXTx6lYNjVVnSJg+MF3nVVG4/uF7wWSQLL8VFkIU/h+z5qtSUboFKVavCBqKYgSxKJsEZEF51KdRGdXFmoFluuh+v5tdbsg+N5Dk0UePzAFIos0Z8uc/0SIV8wnBF8oj3DOT5+Q8/r8mdzfZ+fH5yolfdSJYvP37UC+Sy5h0zJomg6tCWCqIqMqkhEdNF9KUlSraMKIKSLI4X04xu8axbX8cShSSq2yx2rmgjrKruHskzkDNGFhcdzvUL7J1O2yFdEB5fj+Viu8Fv74JYuvr9TlH4/tKULTZVpioUE78r3kYGmaobSRwSeQqtJwvE84mGd6MyYkUiGNY5Vx+f5c0MEPl9U7Nkbu7Ijguf5Zt5d01PHv7969jX2tTDvAZDruvzwhz/kwIEDAKxZs4Z3vetdqOqVvzt7oyMa0FBKNt4ZMo8hTUZVRLusjFg8ZUnCQyj+ZstC1O7fd49SNB3Wdya4feXlKWZ4vvj5wUkOjOUZSJWIBzW2LK6nIRJgJGvUWpBPB88X7bGG42M5HnWRIB+5rvuU4HAwVeane8ewXY8bljW+ZmbocoKuSQQ0jbqwxmC6fIoA4onwAMcFNSDTXR9BV2XyFUtkgio2IBEPaTRFA6iKfFrF5/ZkiOFMmUrVnDKsyUwVTBzXu2y75M4VzxyZ4uXBLJGASn1Yo78oGGb7RvN01oVojAYwbY+y5RIPaowgCNGeDwEJNFkmFhDzd3CigOl4NRK6BAQ0hfUdCaaLFt31Ie5Z30p9JMDgdInHDk3SN1mkP1VGVSRMW4gopgoGtuvREg/SEg/SWRfm0HgB03HZN5LHcgXf5QPXdNEcvzCJB8N2UU64JhRJwrA9omf4Pg+O53lk7wSe79OaCPK+qzu5uqeezvowfVNFJEnixmUic9I7UeB/PHQAwxZmzl98xxqWNkX55uO9HJ4oAPDtp/u4cVkjli287PyqaKRfPZklSSagCi5MQJOxqgS3pc0RuuvDIMGSJpHpOTBeIBJQa3ppw9VuSFmSiAdV8V2pCgXD4c41LXQ3hOmfLiFJEjd0NrFj1ie9MAuI14NC5VTfLdv1CDK/GaB17YkrKwDat28f9913H+Pj46xcuRIQ9hVNTU38+Mc/Zt26dfM5nAWcJ65bXE8wZHBgPE/5NAxWHw9ZkqkLaWTLVlVwzSdbtrFdj3/ZPsRotkIkIPYKuwezLG+OXjJV17nE4fEirufTGhc6IYoksbI1zlCmwmTBPOPrfERJYThb4YEdQ7QnQ7zv6k7aEiGePjLFeM5gUUOY/aN5ytUW4KcPT7GiJXbFOJ5/bGsPSiDMVMHkC//6CpZzduNCDzAdV9gKxAKM5U0awhqyBDnLxfMcLMfFJ8REwWDnQJqrFx1XGr95WSMhTeaVoRwl0+HgRJE7VjVf8cHPWK7Cjn5RAspXbAZSpZoQYSKk1bJqkYBCpiI4OhXTqXUW2Y6PIvtkqhsRyxU8H2vGZkECw3aoj+p88Z1rSIY0kmGd0WyFg2N5NnfVsakryU9eGWUyb+IjJAheOJbm5cEsdWGdFS1RuupCpEsWuwczWK5HIqRhuz57RnK85QIDoFhQKCOPVIOFxY0RokFhN/Onjx3h1ZEcLfEg/+/bVtAYDfLyYBbPP95+PpKpEA2qrG2LC1FCVaEhKubukf0TNSf3iuXys30TLL0tyu6hLOmihY/I1PZOldjQlSQaVClbLrIksbxacu5pCHMoIWwrogGFtmQQx/X4xs97SZcsJOAbj/fyfz68mZUtUSxHZO8UWarpVq1pj9E3XcTzBcl4facIRNsTIaYLJromc3KeIBGa/45H23FO+Ztzlk3exUI4qKFX9egu9Mqe1xX0U5/6FGvXrmXHjh3U1YkdbCaT4ZOf/CSf/vSnef755+dzOAs4T2iKzOaeerKGw0CqdAoZ2nLAdITPk+sdl213faEzMlU0ee5oio2dSTRFJhZST/EVu9jwPB+vWgKYS+iKVK3xy4Q0hURYIx7S8H2fF4+lGEiVT9vufSJcH4YyFT799zvYUjWZ9HyxgJdMG9cTpYBESMO9BAvOhSKoKcSjARIhjU3ddYzvrSBJokMOJFzP5+SP43lCabZiuySDKqO5CoZ93Dssb7gYU0W66sP8x54xRrIGsYDKmvY4LfEgYzkDWRKlhGRIozl2ZiK06/m1stDljJOvlc66kFARt1yuXiQEIIumwy0rmtg1mKFozu60cwHbcWsdYD5wIkVNAhIhHcvx+LeXR3jXpg5eHszy5KFJCobgvJiui+9DQ7WrcyJvsrgxytKmCLmKzYqWGOs7kyxviaHKErKcoTkmgp6zcbCGM2Weqra4r4gIUvBThye5arFKd0MYx/Eo2y4zlJ+y7eI4Hj8/OMlTh6eo2C5TBZO/e66fz79tFeETOEeSBOGAguv69KfKQuka6JsW2bOZIHIGiWppzJhRLcfHdCRCmkx9MsQ7NrTTPy2kKu5Z3wrA5kVJ/nXnMGXTIaSHWN+RpGK7HJsqMp43kIDWShDL9QjrKvGQSr7iENAUmqslsI5kmPqwXjWrjeJ4PgXDYueAIEJLwM7i7J7SHf3pM87pxUJ97FTdn1hw/psvIprCzF7qQunQ8xoA7d69e1bwA8Ie4ytf+QpbtmyZz6Es4ALQGNOJRiN039jD3z3XR+/U7IuxttOsukqfCMeDXNmmPqxxcDyPrip014fPemOaaxybKvLTvePYrrCbuH7p3FlOdDdEMGTR6bSiJUbFdsiVbZrjQX7ppsX8y7ZBjkwWa+7nZ4PpeGzvTxPQFPRqmactEWTvSB5VkdjUVUc8dGVkf06ED3QmA6xtT9CfKpEr2YQDKoZlkz4prW67PlFJ3ISCmoznnZrst1yRRdwzkmPfSJ6re+rYP5bnXRvbefGY8CpSZIliVU36dDg8UeCRveO4vs/NyxtnZZIuN3QkQ6xsjXFovICuyty9to3meIBU0WT3cJaXh7Loisw/vTRItmSJTNtJkzbTNCQKOMcfnikuWY7gVWXKFtv60ni+z6GJArGASsFwREu9JtMYDdCRDOH5PpYjSmGN0QDJKl8oEdb44JYuWhNBBtNlWuJBtvScvmzreT4/fmWsloUZGBSdXj/bO8GBaYePX99DPKTSP12u2S/0T5dxPI+RbJmpolnjJfWnxZp0x6pmJvODTBRM3ramheZYkFTRpKc+yIv9GcKaXLv+33d1J6PZCsemiyxrivL+KqcuqivkKzY+EFAlFEmmJR7kXRs7eK53isZogDtWiwDoqUNTNEZ03LCGKks8f3Sa21c2MV0yj8tUFEVXXrZsEw9qaLKMpsoULEFsfulYirG8gef57BnOkSnZ5MoWmbJVW1tHSpVZczddts719JkzaIo0y5RaPuH/84ldg9kLDnxmMK+r6IoVK5iYmGDt2rWz/j45OcmyZcvmcygLuAB84JounuwrcnSyeFaxvTMldVwfxnIma9sSJMI6tuvx1KFp7lwzP+WJxw9O1hajF4+lWNMen7MU8gM7htDDEX7x2m7uWNXM/32mDxDdMv+6Y4i+6RKe99rBD0CqYNCaDGFYLnpIJlO2GMtVUCUZw/MwHZds2aZujsXmLjbyFZtXRvIYtofr+lieTxgwXf9Ubzkfpko2kYALZem0mcIZrSBdkZgqGvROFlnVGmcsZ9AQ0UkVhaO2qshcu+R4YNM/XSJdtljcEOGxAxO1TOUzR6ZZ05a4LKUEQIgB3ru+jVtWNKFXuXZF0+GhPeM8sncM2/PZ3J3k8ESRRQ0hXI9aGWgG/kn/12RxvcqShOeLTJjn+0zlTfqmS/j4uJ5PwXDQFZmAJlMXEZ1nK1pigM9PXh2nYolSW7pksrM5zr3rW1neEuO2c+D4ub5fC34AzGqU9lJ/in0pm2sX13P1ojoUiRqfLqyLTVV3fQjb8QRHSJZq/lDP96Z4/OAUnu+TLlqs70gQD8j8dP9k1Y4FdHWSz965Al2V+cLbVp4yLqUqZDgz90FNdCY+fnCCnQMZIgGVTV1JVrcnAAlZlqrq7sLg1HR8QlVeEAiOpGW7LG4MYzpejVDemRQUgMF0mYol2u19bIbSpZpPHlBtpZ89Ru81yskXA54vzQp4PF9cr/NdjDPn4LPPawD01a9+ld/8zd/kS1/6Elu3bgXgxRdf5L//9//O1772NfL540Jy8fjlbXj5ZkS6ZHFsSrhAl61T68CvBVUWi+zBiQJ5w0GWYCRbIV02+fB1V36Hju/DI/vGeeeGdhqjOv3TZUZzFYqGc0q54WzIGS4Nrs/1S+pr3InJgknFEou8aXt88kbriguA4LhAZLrq9yUgocpC/flkWLZHLKjNukGeiJmuuKmCSbbs0BDVWdUSYyhdRqmeb3eubqmpQ+8ZzvHYAUGcfFFLYViiNfxKwoncr0PjBXb0p8mWbQzH5eWBLIbjkavYuNW297NF3Y4nuu5A3MhyFZv9o3l8X+gDKZIk2uUl0FWJWEAjEdTY2FVHT2OEJw9NYdhiLVBlODpVYklTjF2DGZa3xEiXLAzbpTUePGPLuqbIbOxOsnswC8DaqvBnqmRRxuDoZIEblzUSC2o1YnssqBHQZBRZJhnWKBgSAVUhpIm5eXjfeO38GslW2DGQYTxvUDSOKykfHBcE57Ll8L8fPUzfdJElTVG+8NYVBHW1lhXzfZFdMmyfbf1poWjuesjAnzx2hL/6+DXctbaZp49MUTQcuhpC3LysibCuUB8NUEyLMTfGgoSDGqoic+fqFtIlk3hIo6Xq/WdU290lqdpJ5risaI2jKTJedeMWC8wOzi9FJXxgunDK33Jle943DouaXj93dF4DoHe84x0AfOADH6h1ucyw6N/5znfWfpckCded/8j2SsKZWuQvdnu80Lpwa+2o5wefeFBHV2QqlkN9JIDpeDxzZJq2RJCAqrB5UR3BswgkjmQrTOQNuurCNJ1n+ez2Vc08fEIJ7GIQCKMBFVmWSIZ1cpUspiNKOIosnVVC4ET4QKZo0BQPcGA0j+16mLZbbVuWyRk2f/tcP792+zJaLpBUeikQCahs6EgwmqtwbKqIWTXiDGoyEV1hsnhqOt/xoFBtLz7d/Pm+j2W7IElYrs/Th6ZZ2TLCx7YuYixvENKUmi1D72SRnx+cwHI8dFXGtD1WtkZFUF8tgV2u2Z8zQVMksmWLaFDFN8B0XeojOr2TRTJlcbOfya3OBHon3zTdqsiMj8iwWK6LTJWgj09cV2mI6qytKow3x4L86i1L+Jtn+xhMlyiawtBWV0VWCgTf55WhLD95dZRcxWZ1W5xPXN9zxiDo9pXNrKl6cNn5aaDKA3N8MmUbyYfWeKCWTWmNB5Cr49Vk0eKuKRIzOdawJjORN3Bcj2RYpz6i47iiG27mXJoJkL6/c5hnDk9RshyGMxV66sN89PoeylWjYt8XmVxdkRhJlylUhQmBmp/dRN5kVWuUVNGmuyHEYLrMkqYI1/bU1+ZkU2cS2/VY3BhhPF9hJGOQCGq872pRcuusCzJZMITkg67QkgiRDOv0NEYYSpVQZIl1dXFeOWHeopfA9kVXTg0bkqH5588FFfWUzPH5Yl4DoCeeeGI+324Bc4wf7Bqm6KiMZAWRUMI+ryZMryr8Far6XIm10GckW+GZI9MENYXBdJkPXXt6ufveySI/eXUU3xfZpA9e21UjWJ4LljZF+eCWLgzLpaNubg386iMa9XVhPnPrUo5MFNg3mqOjLkx7MoSuyII/4bgY5xjXpysu246lUGSJiu2IdLnvE1KEn1DJcvjHFwf4lZuW1EiblzuCmsK7N3fwfO80HckQz/ZOY9gebQmFvunS6QMcqBF5T+YazHTQebKE5/rIkk/OsPm33SOsaYuzZfHxsteLx1K8cDTFUFpwRjZ0JNBUmasX1XP3urYaCbpsCduHxqh+2XmqnQ7LmiJUbJfhTAVNkdnQFCdbsnGqxG5pJgUknT5bIFfV2t3qxM7cTFyEIKIiS2xalOS2Vc3cd1UHEV2tckCEOnRAFQFmwXRY1RLjmsX1dNSFuH1VM3/99FH2juTxfJ+BVJl1HQm29NTjej4TeYOIrs46d2eC+eGZQoAvghRNlfElWNUWr22cV7bG8ABVlnE80W2qSjPUbhGAFQ0H2/MI6Sp1YZ2IrhLWlVoWsjUu1oBXhjIMZ8q4nk+2ZPHKcI6PIjqbFEkCyUdCYrposqghTKCqpi0h0Z4UY351OMszR1J4nk/vVJHbVzazuk1kwGZO6mzFRlNk+qZLZEo2luNRsBwOjBVY3ZagMRpEV2Q82Sekq7TEAoBPWFNojAeRJQlZns27lC9B+jJ0msSz6UrM91ZsKF28sjhAt956K4Zh8OqrrzI5OYl3kqDMfffdN5/DWcB5wnZ8GiIBFEnCsn1Klku+IrqTXisQkhF8A4BYUGVxY6Qmz+96FvtG8yiyJCTnPWHQ+FzvFAfHC2zoSLJlcT1Hp4q1G6Dj+fRPl88rANo5kKmZKS5vifL29W1zJsb49Q9uIhSJ8v2dw4xkKuwZzrGkKUJ9JMA1PfV8+LpFfP1nh+idKGA43lntRGawbzRPSFPIG05VY8QjrMn0NETIlm2GMxXhu7S+lVWtV0bJeGlTtKZW/bm7BO/iTx87TN90CU0RWZwZSNWfmak6HbfMB+Tqvt/1wXE9ChWbZ49Msak7WeOWHZkoMF000RSJhohOUzzATcuaaE3MnD9CvfcHu4YxbY94SJB4L3epgf3jBUKaQmM0gAQUDeFRJUszitrVIEcG+yRO9MyZL1UfP1nfS5ElFjWE2dxdT7po890XBnjnVe30NAo9m5UtMfCFWWpbMsSv37ZsVlm2bLm1LIuEaON3PZ8f7BLXiCxJ3LW2pea+fjKCukwwqJIMaWiKIF7v80R01BQVZqOO75Au25i2i2G7lKuZ6d6pIgFNJoAIkA6O52lLhGhLBGtyEo3VDHLRcGpqy5br147RHAtQshxAIhZUaU2EUBWZu1a30JcqEwko3LtOkKB7J4sYtovv+7i+T990ieuXCp5Ppiza4E1HtMb3Tors50y28VC1FKfIUBfRsRyXZEgjW7HpCKi0JoJo1SxSkz77Swpfgoylrp0aAV0KM9TdQ7nXfYx5vboffvhhPv7xjzM9PX3KYwtlrysHsiR0LwqGTUpTKBoOOePUktiJO3ofiAZ1OurCtCVCrGlLULFd1rTF+NvnB8iWK2iqzKKGMBXb5X89cpAfvzIGiOzK+zZ3MZY3GMlWWNYcRa8arJ4PTmwZPTJRJLPUnlPX6qF0mfGcgSJLLG2Okq/YrG5LcMeqZuIhlZUtMY5NFavt8mJXaZ9FBqBiubhVLoAsizb7la1RuuojHJoo0p4M4QM7+jNXTAB0OojW6TivDGbxTQdZFoTKkvPa6W0fZgWTjutRsT3yhs2fPXYYWRYE6l2DWUYyFeoiGvWRAO/c0E7DSR5hrwxlawTcfMXm0Hj+su4KA9BkqaZqbLsekYDG8uYo6YpdzVJAyfI5HV90hm+C7wt+njc7C+QD43mTXQNpVrUnGMoYfP1nh7l5eSN3r2vl9lXNOJ4gSW/sSp7CSbtnXRt90yUc16ejLkR3fYTRbIWRjCgbeb7PzoHMGQOgsKbSEA2wpacO2/WYLBi1G+1EVXxxaLqCabuYjocqSwylxbElhHaP54sgIRZU6awLs7gxynTRRJKoGTEPZSqz1qnBaidZZ12IvlQJz4d4UKM+qhPWVXoaoxyeKKAEVa7pEedHQFNrpqCqIhNUFSRgIFU+3t1WHduK1hiRgErJdNAU6YTPL0jWkiQ8BEOaQl0kQFsiSLpsoSkyS6rB5wzK5vzfM8Pa7DVLOADMfwB03eJ6dg/nX/uJZ8G8BkC/8Ru/wfvf/35+//d/n5aWlgs6hmEYfOhDH2L//v2EQiGam5v5y7/8S5YtW8bk5CQf//jHOXr0KIFAgL/4i7/glltumeNP8eZFczxAb8agYrs0xwIkwhorWoR410CqzHTJnvX8Ey8TVRZ1++UtMeEVJEusaI0R0oWYWdF0kC2oC2o8c2Sa53qnyVUsfF+QFP/t1VHuXNVMKaThuB7v3NDO4pMWg9dCWFdquz9Flgioc1u3PlHnpC6sc01PPfdd1Q4gunRcH11V8Hyhm1GxHGzrzAGQ5wvdFtsTv1j47B0tENAU1rXHa/X/S7ELnEvctaaFxqjO97YN8vJAFg/RdaR6Nq5/PPMjS5yRCwRVHzGgPqKSr9i8eCxNNKgylhWaQEFdlC4W1YdrJZ8TcbJOzQyh9nLG+o4k1y2p56d7xnE9n45kmILp0FUXwnQ8yoZD2RLcqtPNmeeBVL0MZuwtjvuxQVNUJ284HBwTQpyN0UAtk3rriiY+dVMPri9IySdj86I6Pnvnilob/FVVYb8TW6jPdu4GNIWOZAhVUZB8ag0YIP4vA6myXZXdEA73mWqHV0tMdKp5rkc0oBLSVKJBlS/ft5af7Bklomu8d5O4NuvCOoo00w0HyWog158uEwmo+D4UTYehVBldlfjXnUMUDJuRnMHfPt/P/+/ta7hxaT3P905j2C4NUYXrljRUxyQscgAcV2SI1rcn+OA1Xewfy9OeDPK2tSKL1FUfYjAdwnE9GmMBdE3BcFwCmkJ7MoQqS1jubAf0vDF7zZ0PBHW9di2CmLOAOv8B0LrOJMoZSrvnijm/wp9++mluuOGGU6wtHMdhZGSEz33ucxcc/Mzg05/+NPfccw+SJPHNb36TT33qUzz55JPcf//9bN26lYcffpjt27fznve8h76+PjTtyuBIXO74hc2d/N2OyVpae1lTjEhAYSxncOfqVv5lx9BpF1kJUfbSFKES/alblmC7Ho2RAC8em6ZvukjZ9pGAR/aP0xgLMJiu1Hb2FdvDdVwkSaI1HiAa1Gq19xORLVscGi8QC2qsboudUt66e10bPz8wgel4XL+0gcgclzdaE0FuWdHEq8NZ4kGN21c21R7bN5bn2HQRXZHwfYWWeJDBVOksRwPb8zlxefMROi3TRYublzdRNAXh9S2rxfVUsVz2jeZQFZl17fHLXthvBqoic+3iBlwfJvMmY7mKKJkqMlSJqyBumjPf6AxJl5P+dTw4MlWid6qEJsskwhoFw6Zsu1WPJXHDOzYl5BxaE0GaY0EiAZVrF9dXDVYNFjdFWN02t27mFwOyLPHBLd3kKjbpolBfH5guUR/VUWVw/dPn0GaCSA9qGkszXYqqDGFNQdcUJCTSZYui6dJdF0JT4OkjUzWH+EzZ4ldvWXrG8a3rSLCuSp4GaIoFuH1lMy8Pijbyt6w6873A8TwOTxTZP5pjcWOEpU3R4wamDRE8QFflavs+ePhI1c8wVhA6SI7nkytbta7V1kSQ+67qQFdkgtWA961rWnl5KIPpeARU0aEl5kES55k0w5WCbX0ZoaLteNXfRVb5hd4U6ZJQjbayhuAAJkPIEjXtohm+jixLvG1dK2+rls9msKo1zmjOoGw5LKqL0BgJYDkemiLTVVXLt/Ozvb/mehN3LshXrFkObLIkUbY8osH5HctwuoKuyjiuj6dcWAA25wHQ7bffztjYGM3Ns/UfcrkchmHw5JNPsnTpmS+Y10IwGOTee++t/b5161b++I//GIAHHniA3t5eALZs2UJ7eztPPfUUd9555wW/3wKOQ5Ylgppcy6IUTOFmXh/R6UsVkU8TjUuI3aRfff2B8QKm7dJalX83LYdSleXqI3ZdOwbStV1T7TgSGJbDU0em0RWJ53qn+eydK9i8SIirlUyHf94+RKFiY3s+fdMJOurCtMaDNZ5HUyxwRoL1XOHqRXVcvehUwbdkSCOkKYR0FV31WdsRZzhTPs0Rzg5NkUiXbfqnS+iawh0rm4UytOfz/Z1DNX7Buo4477yq45JI5V8o0kWLVW1xkYWQJfpT5VoQPEOAVmRBfI0GZDJlZ9b5NnPGVCwPCSjjUajyOU6sNB6ZLPBXTx2tetXJLG+JsnVJAzcsa+TtG9rm46POLXyfY1MlERwXjBo5uWJ71EU0dNXFckWQoEoi0Dl5ozKjhSQhfLY8QPJ9RnMV1GpXY7pscmzaJlUwiQQUPM9n50CWXz3P4V7VleSqavnptaAoEmXLRVNk3rK6he3VMvaWnnrhWK8ptfKLJEmEq5uakUyZsj2ja+QwVTDxfZ+H9oxxZKKIJMFtK5vZ2JXExyOgKiiSVN00iLlY2RqnPzWO7/s0xSIsbY7Sny7huF6V6yPVNhmPHpiozant+XznuWPcvbaVfMXBdmbKqs4s0vIM13EGQU3BdjyqZvVoqkRc01neEq2N+WQPwMn82TdRFwOJkM6sNB5cku7J7oZwVUXex79AR4E5D4DO5PKdSqWIRqM8+OCDPPPMM6xfv/6UzMxv/uZvnvf7/dmf/Rnvete7SKVS2LZNa+vxqLqnp4fBwcHTvs40TUzzuEfTiRpECzg9fvTyCEVTpmQ5dNWFyVUsZFmiuz7McKZCezLIUMaoPV+TpWrQJCFLEtNFC8f1SZetWgA006o7A9+H0eypgUG2YvMfe8YpGDaxkEaqZPFHDx/kG7+4iZZ4kIm8wXjO4NBEAcNyea53ijtWtaDKMu/e1M6ihsgJ7+EzVTDRVZlkeH60dDZ0JpkuWpiO8BD6yNZFPLrv/I38ogGN9R1xhjJCrbdvusQX7lpJfURnz0iOgVSZkunwwrEUo1mDZc1RblvZPKdcp4uFrvoQ/7FnjELFZrogdpkhTXS9mY5Xs28Av/rdaZiWS9E+NcsxsxyevC66HqSLNtv701XCtcRAqshozmAgVeYjW7uxXSHM1xQNnLFt+3KC50NHMsiLx9IMZ0TQaDgOki/sM1a3xSgaDkPZCvYZxKhmMkIii+bjO0LnZia7MpE3mC6ahDQFz/cp2x4BzUc9w867Yrk8sm+c6aLJytYYNy9vOu3zzoamaJBoPFjj6qSKJj/dK3iBM1yYxqhOSJMxHA9NlmrK8tmyXeM0Oa7PeN4gXbJ4vjfFcKaMKovM0cauJAOpMpbtYns+nic61gCG0iU0WcL1ReZ1IFVmaVOMnoYIfakSQVXm5uVCTdo46RycKphUbLeq7SPGUbFdkQ2SJH7y6ijHpko0RnXetalDGNdmK7PWqcm8SU9jhLevb2NqsSkMUlOz14y88TrqPxcIw3bRRHIWEBlD6wRS93xCV2Rcz73gZpY5C4De+973AiIK/+QnP0kgcJxg6Lour776Kt3d3Tz66KMEg0GefPLJWYOWJOm8A6A//MM/pLe3l5///OdUKpXXfsEJ+OpXv8qXv/zl83rNmx0vD2bwNeE4fceqZh7aM4ZpC8f3SEBhNOfPqqUHNZm6sC7sDmyXoKagyPDC0RRFw2VjV5LFzTGCqljAQGRKpgun6sGM54//rWA6ta6X7f1p3lEltB6bKjCSKVEyRbnjZ/smaEsGWdseqy0sJ+8Cb1/ZfM670deDa3rqCekK6ZJFQJUZTJVJhLRTAsCzQQK66oPkyjYjWYN4SMzrd57rp6chzKtDWfKmg2m5xIIae0ZyPH5wkicPTvL+LV21m5DtehQNh3hIuyTkxdPhyEShaiMSZc9wFk2V0FwZTZFY3hzBdMF1fUqWUHZuiwfpT5UonIZ8fzZtkJmbfME8TvUdyRpEAwUm8waHJvLEghrxoMbixgj3XdV+2QRB/dMlHtk3jiTBLSuaiOgqfdMl4kENRZZJl6xawDdzDeqqTDSoUzJdNFnG4gyCktV/j4v/iRJkxfEwqm7yjusLexEJAopMeyLE29YcL2FNFUwOjueJBzUmCwZ90yI7saM/Q3syVOv+O1fYnscty5tY057Aslz+5yMHyZTEOvA/HznIPWtbiQY1GqIBMiWLoK7WRAVP/EwzGUXb9TkyUaBgChHW4KTI3qSKFrbn17Jg6aoeVd90qdoFJvTHKrZLSJcxq7wiWZKoVNU7VYlZvmvJkIqqCHmBmUDB9XxkWebAWJ5jU2JuposWLx1L89Y1LbTGg7VMVUBTqK+atUqSVOt2PVmC0L8EfUOC7yMhV+uNkiSjyfMfiGVKVtXXUcK7wOrbnAVAiYSo8/q+TywWIxQ6rrOi6zpbt27lT/7kT/jyl7/M/fffjyy/vnrhH//xH/Pggw/y2GOPEQ6HCYfDqKrK+Ph4LQvU399Pd/fpSx6/8zu/w+c+97na7/l8nq6urtc1pjc6BlJltJAwQJwumrxzQztPHprEB/7TLUv5jX9+WewIfXEDWtoc5Y6VzYznTaYKBmXTJVOx6J0okq84jGYrvHVNM1d1Jtg/lkdRZD58XRd/98LAWcfh+lAwbda2x2seT57nM5o1GE4bx0shtstEwWBde4I714hzYjJvcHAsjyLL+L6o4c9HAASwtj3BYKrMgy8P4/viJnI+8IFDE0U2dddh2i6Ttst41mDfSJ5YUKVsu0wXTDxPSBRkKxaqLBNQZf7u+X6uX9JA0XT4/s5hCoZQTX7f1Z1nNamcDxwYy/Pw3nFABGfRoAomhHQV3/fZvKiB913dyTef6CVTsmiJB9kzkhW1/9OQmV9L/fhkuD4cm64Q0i16p4rEAio3L29iIm+wd0RwOYKaQtl26WkIc8vyJlzfP6tg51zCcjz2jeb4u+f7GcsZonuqP0NLPEgyrDGQKjOWq2C73ikWBbbjsnckJ+wszqFjKKTJxENCeTugK4Q9QeR1PTEOv9o6Fg2qXLekgdurfJm8YfMv2wfJlm00Rap2LR4/vyvW+d+pU0WTf9o2yIbOBOs7E0wWzJoJ8GTBpOK61ZZ/iYCmCH2i6mtPVKr3fDBtFxDlkorlIElSLVgsmw5O1aPPcX0qVWVr2/VPKMH6wtC5YFI2HfJVTZ/BtAhkIgGF7AkiX+3JCPgzWmfVY8gS+N4p5+zM72vaYjx1eIp8xebWlU019fKzYf6dwECWZOJBlUzVhywRUvEu2I/9whEPqWiqjFvdhF8I5mzl+853vgOIstMXvvAFIpFTO3T+1//6X3zwgx983cHP17/+db73ve/x2GOPkUwma39///vfz7e+9S2+9KUvsX37dkZGRrj11ltPe4xAIDArS/VGwZkUouH1q0TPXMwhXSGoKXTVh/nY9T21xzd31bF9IA2+TyKs87/et4HlLXH6p0v8644hdgykSRctdg9l2dSVFHyWks3WpY1sWlSHhERTLIgqyXCGneoMXE8oC9+4rBGAfaM5BtKzHdc930eRpVpJbf9ojj9+5BBHJousao2xpj1BcJ7TtsPZcu0mFdQV9JO0b14LnucTUGVCusJItoLj+miKMPx0XK8qgAdetWSkSD6D6TJjOYO/evoYTdFALWuSKlrsHclz7eJ6ypbD7qEsiiSxsTs5ryKAw5nj2VtNkbljdTO7BrK4ns/Klhi/9dYVBDWF/3TLUn62f4KiaeP70FEXwvd9xk/KGEqS6GQ6n+4Q0XkmFKcrpsvD+8YIqKIL6aW+FMmQzuZFdTx7ZJr/eHUMD5/1HQl++aYlczQLp0fRdPiX7UMcGM2xf0zoQglvOCEJkQxrpIoWmipkEk7+yEXTJRJQT9FcOxM8X4gMThZMkVmSoOK4DKcrpEuiLKkpEq2JYM1HC2AsW2H3UJZC1eJmbXuCcETFcjwaozrLms8v+wMiiLEVm2d7p1ndFp+lCaUCnisI9DOdYa7nVyUmjhPmRZGTmmZWSzxY2zTNlMt8qp1wvjh3Zo7nue5xsnj12GXLFoGYJ7JhA9WW+aCucqLKaUCTcX2vpj4N1ALU1W1xDo4XGMlUiIc0rqsKdj57NEV9RKhWD6TKjGYrtCfPLtj6eoUALwS6ppIM67UOtPpI4JJkknsaIrQlQuK6vSBngovAAfpv/+2/nfGxT3ziE/zLv/wLv/u7v3vBxx8eHubzn/88S5Ys4fbbbwdEMPPSSy/xta99jY997GMsX74cXdf57ne/u9ABNodY35lEDoRJBNWa6eCJ+NC1XbTEA1Qsl+UtUTrrRBDc0xhhVVuMJw9PUbZdKjlx4/5oW5yGqD5rJ91ZF2J1e5znj6bOOpagJrOxK1kTqrNdD+ekO57tCC5HV73ooPgfP9nPvtEcliPEydZ1JLh77exOjP/zxGF+uGuUkK7w396xhmsWz51jPEBnMsw2KY1fbR1e0hRhKF053u7O2ZMXmqpwdKpIvmITUBUkBK/ArZYoZl4vAa7nYXoiE1Yf0TAdl4PjBaJBFcv1ODSeZyJv4HoeLx5L8cpQDkmCG5c18okbekiXLOJB7bxq+0XT4ZG94+QqNk1y8bVfgHA53zsiRM0kCT54dTcfvKaLguEKzafqTXZNe5yOuhD5ik26ZDGcqdBVH8ZyXSqWh111BAfRwy37Pm61FCRzeq8xCdAVqdpKLf7m+aIN39F88obFZN7ksFPg0Hieiu3iAw0RnUPjBXoniqzvSrKpO8mugSyW43LDssaqUej5w/N8Hj84yUC6THsiSCKkka8I3hvAQLqMWS0nZ0tmVfBQpiUurqNS1UxzBo7rkS4eL42dnBw78XchmiixujXGypYYrwxlGcuL91jWHGHfqIuuyMSCGld1JmvfCyBa7qtZHmHm6fPJG3pqmUbtAjoSTccHx6ctESQeEmXJbEUEu/GgRjSk4nge9ZEAtuuJm3A1E9CRDHGkWmaSgHvWtZKsGjAfmSigKlIt8GhLhPA9EUzI1cAaQFZk/KqGjySJjrB8xQXPw3GrPODqORUPqUwWTPzq+dYUDYgMnOfXLEgcT1ynAUXm/Vd3Ytii62ymxOp5s6/80xkAn4xLwuzzPcq2IHRLkkTxAnwh5wKLGqLcvbaV8byB4lR48QKOMecB0MTEBF/4whf4+c9/zuTkZM3rC0R5LB6P88gjj7Bhw4ZTgpOvf/3rr3n8zs7OWcc8ES0tLTz66KOv7wMs4Ix465pmHCXEytbYacnDb1ndQkNUp2y5rGuf7aqdKgm11khAwXJ8QrrM6rYYYV3lA9d0sn8sT1hX2diVZDJf4fB44XiKNaiQrszOCNWHdepOGENDJHDK4u4h0vZWtQh/cKxAwRA3MMV1aYrpNMUCFAyblwezvHh0mr9/cQDH85Elid/90V4e/a1bKZoOrw5n0RWZq7qSF7SYA/zklVGGMxVWVfWP1rXH2Tea5+WhDD4+9eEAuwYzDGcqp5B3ZUkQ/lpjGhVbpPIjutBAtl3xf8N2Maok15n2ZqpzIhywJXoahU7MU4cmmSgYjGQq9E+XMB0XvZr1efzgJCXToT9VJhnWeOeGdsqWS2si+Jo7+acOTdWE5AamMuc0L2va46iKxHjOYFFDuBawng6JkEYipPG7b1/Nv708iuf73LZqA997aZB9I3mWNEWYLlocHMuTN4RVS0iVyFScWYxoTRa6PwFVIlO2OWVJ8cRNvW+qXMskzXiVCVVfA12RCesKTfEA/+fxaZZUOS4P7x2nsy50zqXFgVSJoXSFtmSQkunwbO80uYrNS8dSyEjomsyi+jCaIkxJXc+nZIpslTgXPYYyBopEzerheEcS4AulZ1UCRZaxHNElJ8sS8aAQUDSr2YmGqM6hiQKRgMZwtoJTJQaHdIXu+gi269GaCBLWFfaO5FjbLuwpmmIBFjeG6Z8uE9YV1rUniATU1yU14Xk+QU2isy6E5MPixgj91X3R4oYIkg+ddWGaYjp90yXqI3rNT2xjd4LBdBnH82mKBUiEdYYzZfaO5IR4o+/z2IEJPnfXSnLG8bZuCchWxA1dkWWkaiZalgHJJ6BKGDOK2j7kqgHZpu46RrMGjusT1hU2dCWRgaAq18p2QVWpZdElSTplY3HT8kb+/ZVRTNtjdVuMznOw67kUShdlS5wroaphrOv5uK6HIs9vNj0R1vjwdd0cHC+gOBX+8AKOMecB0Cc/+UkGBwf54he/SFvbbKuB3/u936OxUZQs9u7dO+t1c2VJsICLh3UdSeLxMysOK7J0RuXc1a0xWhJBhtJlIgGZ5c0xuutFhqghGpjVJRLSNd5/TSdHqi3dtuPxYt9xFWcZCAdUehqP3ygVWUI5jZy/7fo8fXia37jDrd0QocojMmxs1+OBHcNs70txaDxPyXRryqbjOWGm+MD2IXIVG9/3OThe4L6N7eQrohW9gXPb/fzjSwP8++5RANQjEl++by3LWmLctqoZ1xN8Etfz+dPHDvHdFwcpmQ6u51OVwkFTJJqiOpKskAjLyLKMYTu0RFVyqiAG64oMhlPrmJo1Dx5I+NRFdIbSZaaLJgXDwfMgU7aIBlSaYmIBq9gOLxxN4QN9U0WOTBRq3+u969tY2SqyG57nM10yUWVJcCh8n97JQs1s9HywoiV2XlmThkiAX75pce33L75jLSA2WX/55FHRLWho4PtUbJep4nGyuQTUhzWiQY2xnMFpmsiqqRH/tGU0H3FeeZ5LumQzkCpTMB2mCyZBXSYa0DBtj3NpMNw9mObFYaOq9yKhKTL7RnNCeC9dJhZQCekKpuNg2h4SUi0z4PvgSYIH5QOmB6oi1c4Zn+OdypIkVbvqFAKqXFNIXtkSY0lTlLFshVeHM3i+T7pkE9IUmmNBDNshbwhFZarB10imQiKk8bP9E+QNmxuWNhINqDWpCx/mRGPL9SFddtjRl+GWFc2saY/X+Dmr2+P4kuA4HZkokCnbFE2HsZwop77Ul8GtlrQyZZt9ozk668IUKoI4K4Hw6aIqsDjznsCxSUE1DqiChO/7YvOhyTI7BmYH9flqOfnmZU2kihZ5w6Y1HmR1W4KgrtJVFxYbAgm668Nn1ebqrAvz6ZuXYLv+OWddT5fVvNiIhVS668OMZkXH76KGcM2qY77RHA/SHA9ecBf3nAdAzz77LM888wwbN2485bF3vetdc/12C7hCsHlRHR+4posdAxlCmsKHtnSd4MN00nO76xjOlFnbnqAxFmAsW2bHQKbWpaGrwg9LOYFLJnF6zocPZMsm33m+75THy5ZNrmKTr9jkDYdgtaXXA3xP7OTyhlMLfg5NFNgxkGFbX4odAxkkIGhlz+nzHxg9foE6ns/e0TzLWmJoisxMBVCRJTZ31/Efe8ZRZYmC4aArMooi0dMQJhnSaUkGMG1R6glpMg1R0Q3VP11i0jFq6fbT8YAf2TvK/rEoh8aLFE0H2/UpmjaOJ7Rf0iWTDZ1JGqN6zWfHdDzUEyKEoXRZGFF6Pj/aPcLRqSIHxvJENIV94wUCioztety2srnmtTRf8H2fn7w6xrZ+UWJc1Rqjd7JIAL9mPYAksijxsM5E3sSoZkPgeOkwoEq1IOdscH0Yygirg7ZEkCcnCiiyxDuv6iB5jga1331xkCNZl0RQJVdxSEZ0siULw3ap2B62axG0FZZ4PmvaE7xwdPq4mznU+GOqMvv32pxU/7VdwWFZ1x5nLG/ieh7Zik3OsAloMtctaWAkbyAhMmOr2+OoSomxXAXT8WlLhChbDs3NUcZzRq37aThdgaXCDb0pGqQpKq7pVGnu6Lk7+lNoiszLgxkOT4iyqiKLYPG53hTDGZGpyhsOTxya5MPXLRKcuBo3yKNQceleHMBFwnBEWaqumo45+dbtV8+Irqotj+f7NIQDNMQCLGmOiI2Wf1zgFeC2Vc0UTYfposnylhgrWqJ4vsiKD6SF0W9PY/Q1uTKqInM+9LtLYR6lqwr337OK720bQpYlPnpd9xWbwJjzAKirq+uMJaorBWcjEi/gwqApMu/a2MG7Nna85nO7G8J84oYeitV29z3DOf7t5VEy1UCkpzHCNT11s4wqTyQXn4yiYdc8nk7Ev78yzl1rOogFVWJBlWxJmdU6q6kysaBKPKQxkikzUl1oD40XsByXeEgjVzm3NvYVrTGOTB5fvNe0nz6Ttqotzt1rW5kqmuiKREhTcXzR5ZAI6dyyvIldgxlURaKnPsQfPHSQVNlCRmjByLLERM7gdHIvhyeEOFyuYmO7Lo7rM1O+z1ZsZFniI9d105YI8fkHXmG8IMo8J5akZvgRI9kK2/rSHK62r9uuW2u9ToQ1YkGVdy5v4/yVvS4cU0WT3ski0YDKSKbCVNFkeUuU+rCGrhSYLBiiDCFLtUyI5YiS0EzwI1P1JXS8U8qQp4Pt+qSKBgFVxnRcChWHf3ihH9vx+NxdywmeYKeRKpo8dmACw/ZYFBKZCsv1sByPVMnG830CVdPP8VwFGR9FlvE8H8v2KJoG5kmmXscDnLOPU5FAl+HAeIFkWGdFc4xjqRKm49GfKtMWD/KWlc3kDcEtu7angV+5aQmHxgs0RkUb/UN7xrAcj8m8Ubvxz5wPTbEAuirmEjgtR/BCkTddposGvZPFWvbryESR6aLBseki5dq17XOkGiAZJ3Sd+UDFshnMmkLsUBbf8UywuKQpyoHxQu38XdEiSpkNYV2YM/sQDihoisIvbO7m318e5dh0iaCm8Gu3LQMgGlBPEVlVJHj3pg6ePDQJwB2rWq7YQOFkXNVVx1Vdpwq+XmmY8wDoT//0T7n//vv59re/TU9Pz1wffgFvEsSCWs1faPOiOr5031p+/OoYmgL3XdXO5kX1s3ZTY7nKKRmPGUQCp9+NFyo23376GL999ypMx8WwXKTxQu040wUTTZG5d30rf/d8Xy0gm8wbWK5Xc64/F3z0um5iAY2hTJkbz0KSbUuE+NC13RydKtIUC7C6Nc4rw1nyhsOq1hgt8SAbu5OAIHXnKpYo8zgeI1kDXZVRFQnnNBGQJIuduet6+EjIMjV+gml7jOcNfrp3jHdsaGdla4ymeICILrr9fF+UqWaMGyuWS990SfBHHGHa6vk+kgURXa16OM3vYh9QFSRJWBiIcpJCV12Y65c0cMPSJqFRE9JY1xHn+ztHhMllyGWqYFTNNGUkSZT2NE3BPsfWbcsV5pyu52N7Pr7t8sShSeqjGmXT5fBEgVtXNuH5QtwOoH9AGEInQhoNliret8ppCQcUmuMB1NE8lusR1hXqIjqHJwqosowiu7MMYM+GmW9AkQBJtGXHgirZik3JdIgEVDIli8mCyYe3LuLIRIH6iM76jgSSJLHpBOXhZFjwg65b3IAsi9+v6kzUPsf7r+7kwHiBeFDlqs7kuQ3wNSADm7qTBCUZ8wSiuuX6hGWZQ2Ozifapkpjf8kkbnr1jeW5e0YxczRwBtY6xa5c0sGMgQ8F0iAdUtlQNTrf3Z2rdZH3TZSbzFVa2Jfirj2/hwHiBWFBl42t8zp7GCJ9sXHzW5yzg0mHOA6APfvCDlMtlli5dSjgcPoXonE6nz/DKBSzgzHjr2lbeelLH1okI6xohTaZywsInTPpk7lrbOosvNANVlskbNo/sG8dyRHv5iaUjWZJwXI+H946TrzgENYWi6dBZF6I/VcbzIRk6tz4MWZZ5z+bXzn4BdNXPJgJv6j79TitTMmspc1FKkwhqKnnVQVd88ifpvuhylTeChISPIkk4VZ1aH8CDpw9PM5CqsLotTrzagbR3JM+y5ih7RnJ0N4RpT4aYyBu0JoKkiyayJKHJEpNFEwlxg9m8qA7M7Dl93rlCIqTxllUt/MOL/bTEBWE7rKvEQhp3rG7hHVVjWoB4UOfhveNs708RC6qYVQd50xakeUWWMG1X8Eiqr5nxhZqxKpjBjD2H63nIkpAlsByPH708yki2gu/7bOtLs6k7ybqOJHBc+6WnMUJzgyD0p8sWDRGdrvowd6xo5htP9HJoIk9XfYRYQGF7fxrvBGG9c0FAFSUdRZaI6CrrOpJEgyq5ik1LPEhQVYgGVXqaInQkQ2fN3LQmgmcsW8NxPsZcIajJ9LRE+B/3rcH2JeJBlWxVODQWVEGViQRmF7DOpK2VL1u014XY0J7gwEQBVZK4bYXgo3Ykhdp03rCJh7Ra67nv+7VgSZZgLGeysm3uP+frgXZl2P1dtrgoGaAFLGC+cc/aFv7xpQH6pko4nihpxIIaEV3l3nVtbFnSwP0/eFW01lZRH9G4aWljrYzVGtdRqlLWEqIlP2847BzIiKyP4xEJqCxqCLO2I0FzLEh2apwnL8knhg9e280rwzkMWyg///rtSxlMV3h5MEPfdAnTNYQPkQQhXSYZ0mu7fwkoW25VIdfFrRJoC4bNYLrMdYvryBsuZdulJX6cy7OtL8XOgazwWrJcVrXFuKYnQjx0/OYEghPSdQlkttZ3JvjCXSv5/q5hTNsjFlRZ1Xpqtm1dR4KxnIHv++wbzWNpLrbnEdGFRICuSNWyljjG+o4EAU3m8HiRwXSZiuXgeiL4CWoKsaBCDJVUySKkq8RDQgPH8/yqRIPHsakSq9riqLJMd4MIcH/lpiVnbCz47XtW1f7/zy8NkK04QtBPgrqATNY4TnYPaRLGCZ56uiw4QZqioCky1y2px3J8uhvCNER07lnfxrNHpkmXLDRFYuscyz3MBX7phsV0ttTjIqPIsLIlxlDVP6+rLowqi+zsC8fSNcLzzGZhxvNsBmta4zREdD54XTdHq6XoW6tmxVd11XFookjFckUHVzWrc9PyJp44KKwnOuvD3LTs9A0elxLXdJyqt7eAc8ecB0Cf+MQn5vqQC1jAayIa0vnuL1/Lt585xoHRAo7rUR/TaYgEKFYJEus6EhyeyGM7Hookcf2yJt5xVRs/eXVMCLgpCosawriu8Jpa35HE8wRnRpIkGqM6saDGL17bzUvVrrRLWdK/bnEDf/fL17J3JM/6jjgddWFyFZtfuLqTqXyFf94+JMp4qkJdWBPEaV1lMF1GUyTqIjr4cN3ier7y0AEkSXAjFFl4laVKgiD+k1dGOTSex7A9EmGN8ZyBJEn4vs+xqSIbOhKsakvwXO90bWztySCcpz3NXKE5HuQT1/eQKVs0RgOnVWzOVWz2juRQFZl1HQkc1+O9V3dgOYL8XrZcdFUiW7LwkaqmmfCT3SP8+yujDKTKZCs2kYBCWFfRq3yxNW1xrl3SwO0rm/n6o4dqvlKSJNGeDLGlp56VrTGc/PQpYzobJosmiaBKWZYoWw6KorKmXSeoKhQNl1BAwfNEoJsuWvj4TBctQrrCyuYY1y1poC0e5OB4gdZEkI5kiF+8tpupokk8qNbKzZcTHtk/Rte0xVVdSTZ0JnnHVe3sHxXk/DXtCXRVpi0ZZl1HnFTRJBrQ2VgNgJIRnVRRuLPLEnQ0hJEkiXduaGM8L7htDVERoddHdD5xfQ+pkklDJFDrwPrzX9zEz/ePky5bvGdjO6p6aRXTT4fuluSlHsIVjYvyjR49epTvfOc7HD16lD/7sz+jubmZn/70p3R3d7N27dqL8ZYLeJ04G/H72f/nqnkcyYVjJGcwkTdxfZ+i5ZLwZrReRJ74bWtaCWoKg6kyDREdXVH4+cFJ3n91F7uHs9iuh6rITBVMNFlmQ1eCcEBomozlKiiyxMqWGDcsayQZ1pnIGwRa4MuX8DN31oXprDteLpvRyWmM6nQkU7TERTo/rCvcs66No9NF3hHWq8J1Dus7ElXfsDx7RnIoElzdU09QUwmoLpmyheN5tRvGeM4Qho4I4nRHIEyqZDOcKXPP+lbGsgbdDWEWNUQYHj43HaCLgdfSoNEUYaEwoxZeHwmyouXMEg8zCOgqpuvTHA+iawqtiSCaLFVd5yUmC6boMguq/O7bV2N7HofGC3TVhbimp56mWIC2RIjhwvlFzl11EZriQQzbxXI8OpJBfvHaRbzjqnaePTLFzsEMixsjLG+O8c/bBjkwlidXcYgFNSqOR99UiUxJZOiG0hVeOJri9lXN50RWniqYHJksUBfWaxyw+UCuZGFMFtk9lGVDZ5L3bu6gp+rpt3lREoD2ZJhN3fVkShZhXall+96yqpnHDkxguR4tsSCbqzIOkiTRljj1M4d0hU791DL5W9acuex+OWBR/UIG6PVgzgOgp556invuuYcbb7yRp59+mq985Ss0Nzfzyiuv8Dd/8zd8//vfn+u3XMA54o3e3faz/RMMpMr4vvDt8X1Y0hThmurid9/GDhzfRyZFZ30IXZUpmQ6JsMatK0Q6vCkaYFtfmqCu8La1LYR1lXde1c7zR6fRFJm71grvozXtcda0xxkevhRuPK+NgKpw97pWnjo8hSJL3Lm6ha76cK30cjLed3UnTbFA7bnC22kIy/E4OlWqBlUBIrpKxXarJO1grZutZLmsao2zqnX+bpCvB2Fd5a61LTzXK77Xt55g6nk21Ic1WuJB0iWLnojO+zZ3cGiywL6RPGM5g4rlMpYzeGDHEB++bhHf+MXN/HTvGJN5kyVNUVZf4Py8c0MbhycKHBzP01kX5gt3rSBRFRq6aXkTN1V1tHzf5+blTfi+EMgMakK5uTEaqLWFAzWTz9dCrmzzwI6hWndX0XRqJOGLDcvzkV0fp9r5FtZVblreOOs5K1qi3LKikSMTRRqjAW5YKh7/tduXYbk+uYrFzcubamWtNwokCUKqzKp5DEjfiJjzAOj+++/nD/7gD/jc5z5HLHa89n7HHXfwzW9+c67fbgELqMF2/RpvIxFS+U+3LmFte6L2eGsiyGduXUpHMlRzYz5ZuPGqruQp5qgzwc6VhuUtMZafo7jgyc89PFGo3fTaEoL0DEKi4APXdKHIEj/YOcxItoIkweZqZ9qVhNVt8fPOaKztSLB2NE/ZEnYUV/fUkwzruFW+uSxJhHUF2/UZzVZY15HgPZs6X/dYVVWexQk6EyRJ4s41LdywrIF/3iYEPDVF4u51rTxzZLpmVnquXVpj+UrtPABhiDxfAVA0oBINaSw5i/q4JEncsaqFO1bNDmAXNUT4kw9uvMgjvHRojQeoS0boSF4eZOwrFXMeAO3Zs4d/+qd/OuXvzc3NTE+fX917PuFWV7Dh4eHzrs+/0TE0NATA4ODgLPPZyw1RJ0d3oEJZ8qiPakTsHMPDhVOet6nep0OT0VWZeqXE8HDpgt/zSpmb84VVtimmJ3BcHxm4uT3GqlaJppjE5LhQtL6uBSZDEiFdJeEVTpnrN+rcvKVbJV30qIsqGNkpOnS4o0ulHoljU0WyUyUUWcIpKKc9/2B+5ua2TpnpgkQ8rBG0ctzacfx3qZxmuPzaHbl22aaQmqjp7/QEkwwPX5Th1jAzNyujFp0tkHCLDA+fm97WGx0zc7MqarOkBfxy5ozn2JsJM0rQM/fxc4Y/x+jo6PCfe+453/d9PxqN+kePHvV93/cffPBBf8mSJXP9dnOGbdu2zXQDL/ws/Cz8LPws/Cz8LPxcYT/btm07r/v+nGeAPvShD/Hbv/3b/Ou//iuSJOF5Hs899xxf+MIX+PjHPz7XbzdnWLZMKHoODQ3Nakt9rnea3YNZSpbDkYkCLw9l8X1Qq+2v//Cp62qeVm9UDA8Ps3bt2lPmZgFv7rn5P48f4YnDU8JiwvfJlC1UWSakK9y5poWPrIvN69ykiib/vG2o9nsirPLRrT1zdvzpgskX/20v47kKrudTF9bZ0JXk1uVNXLO4nlzF5ievjJAtO3TVh2iOBdg5kK29fmN3khuXCY7KzHnT29fPU8eK7BrMkClbrGyJoyky6bLJ0cnjmcnOuiDZik3RcAnqMu/Z1MnbzqKLdSVjZm5WfPbvCQSjfHhrN792+/LzOsZ3nuvj+d5pbNenKarzm3euOKvJ7pWCmbnp+rW/RQtG+MLblvORrQtCi/l8nq6urtp9/Fwx5wHQH/7hH/Lrv/7rdHV14boua9aswXVdPvzhD/N7v/d7c/12cwZFEa2P8Xi8tlgPpspYUoBxQ6J/2kLXQjTWifZSZIloVGeiIrM8HCFwPgYuVxhm5iMej7PhD5854/P6/+jt8zWkywYnzs0bNQB6dN84u4eyLG+O8Y6r2tjRn2HPSJZtIwbxWBwT4SEVkYUVR0ssyG+8bQNOQVh3z9fcxONw3SqPV4ZyaIrEXevaiMfP7l5/Pnji2AgNdQnGyxJl06EzFmVZRxM3rOniyESRn+6dIm9INESiTJmwsiuJNmnTnyrRkQxx89rumn3LzHwM5H1yrkZnSwO5kTwZR2VlY4wP3NDMHzx0gOFMhcaozl2buvmHFwYoeDYVS+JI2mFFzjuFr/ZGwMzceGoYLRzllrXdZz1/jk0V6Z0s0hgLsKkriSRJ6KEIJT+P5XkklCCxWIx4/MrfqNbmQQ+jBMNsWtL2hl13LgQz9/FzxZwHQLqu89d//dd88YtfZO/evRSLRTZt2sTy5ecXwV9qTBVMfrR7hPGcwVi2goRwU26JB2mIBnBcj3hIZ+dAhqLpnJPH1QIWcKXhmSNT/M2zfQDsHMiwdzRLNKAxkRdeWy3xAM3xIPURnT9673oyZZvORAhdV7gU1IQ7VrWwdUkDqiyftyP9a0F4dlk1r7NrF9fzkesWsW80z2MHJhhKl0UbfKtEMqyTKpnIkhDt0xSJ/ukS6zoSpz12QFXY0JVgeUuMt61pZTxvsK49zpLGKGFdZjhTwfeFsnm6ZHJwvEBIFx5Tb8QgCKCnMUwiFjrF4PVEjGYr/PsrozUfQM/zuaannsm8SUhXCGoKhu1SPEdbkysFiiQUyker1ioLuDBcNCHt7u5u7r33Xj7wgQ+8ruDnO9/5DpIk8aMf/QiAyclJ7r77bpYvX866det4+umna88922Pni6mCiev5FE0HSZJIhFR0VaE+EqA1ESIW1OiuD6Mpcq1DZgELeKPh8PjsKKa3ajbZEg/RngziuD5hXeXTtyylLhJgSVMUXb+02dAZYcK5xrr2OHLV6qM9GSKgysiyxHj1+u9Ihmp2Kd31YaIBTWQjVBlJkk67TqzrSNTsJVoTIe5a00JIVxjPGciyTDSoIssytuMTD2m4no+mKjWxvvE38NoTC2jUR4NnFWmcyBuzTJBn5qMpHiCgKiiyREM0QGCefekuNmY8zc7RinABZ8CcZ4B83+f73/8+TzzxBJOTk3jebOOaBx988JyP1d/fz1//9V+zdevW2t/uv/9+tm7dysMPP8z27dt5z3veQ19fH5qmnfWx80VHUujEJMMaE3mDlkSQ9kSI+za2054M8Y8vDpA3hJbGjDjXAhbwRsPmRXU8dmBSGJ1KcE1PXc2I887VLfzC5k6hMi2/8U2JlrXEuH1VE+mSjSJLLG4U5bXFjWH2juQIaAqbupK8Z3MHixoijGYr7BzI1OZu0WnWiaCm8IvXdmM67qwy+qKGMNv60rXXbumpw0cIXx4cz9MUEyrGb+S1Z3N3HclknEVn0K4C4ZunylJN42hmPjZ2JjEs4eQeC6q0zaE7/eWA+rBGIhHkusWXnz3HlYQ5D4A++9nP8u1vf5vbb7+dlpYWpAv0CvA8j0996lN84xvf4POf/3zt7w888AC9vb0AbNmyhfb2dp566inuvPPOsz52vkiENVa2RJkqGNSFNQqGwz8dGuTvX+gjHNDoaYhgux6DaSG893fP9/O/P3AVradRGV3AAi4Fvr9ziL97vh9Fkvkvdy7j9qpWSrZs8YNdwzy8d5yK5ZKv2AQ0oaJ7/dJG7ljVzL/tHmH3UJb2ZIjfeMtSDo0XWd0a57ol9RwYK1CyHFa0xEiEZm8uSqbDz/ZPkKvYNMlXRnvu7qEsrwxliQVV7lzTQvykjIPn+Tx1eIonDk5ydKpI0bSpWB4/2jXMXz19lI9ct4jbVjbx8N5xUiWLB7YPsbYjQSyo4vkeRyaK9DRGODReYGd/GtP1WBScnbmZCX4cx+ObT/ZyaLwguD9rWlnUEKG7IYymKDy0ZxTH83l1KMtYvsL/9+xRQGZJU4Qti+v57FtWEA2KZf253mmOTBRoigW5c03zFcdTLNkOGxsjNFYtK366Z4xvPXUUgP9861LuWd9GYzSA5Xps60vRURemq25G+Vzmhy+PUDIdbl7RRFg/863O9XwePzjJSKZMZ12Y21c1o8hnvm/9y/ZBnjo8RV1Y5zffspyWS2CMOlGwyLklut7gDTgXG3MeAP3DP/wDDz74IPfee+/rOs7Xv/51brzxRq6++ura31KpFLZt09p6vPuhp6eHwcHBsz52OpimiWker5/O6AjMYDBVZs9InqLpkinb7B/LYdoejuuTrTjkyhaG7eEjpPT3j+X5ykMH+MaHN7+uz72ABcwFMkWLbzzei1O1rfjD/zhYC4Ae3TfBT14ZY7JgMJk38X0fXVOYyBs0RnV2D2Y4PFmgYrkMZ8okQhq/+ZbjZeyziUI+dXiKvmnRvXRsKnMRP+HcYLJg8MRBwaXpnSxweKLAh7Z009N4/MaydzTHk4cm2TWYYapoUrFcDFsYyRZMh+8838+tK5ooWQ5D6TIjmQqZskXZcpEkSJUsFFni4HiBtkSQxmiA/oGp2vH7pksMpcu0J0PsGc7xwlFBHk+XLJY0Fbl5RROO6/FiX4qxKicxV7EpmYLX4uNxcCyP5/v8faSfX7ttGUcmCmyr+tVlysKz7LaVzfM1rXOCbMli50CGVa0xupNh/ucjh7CqqtD/85FD3LWmmScPT1c/p8RIpsLfvtDPf3nLCn77wb1ky0Kl/fEDE/z4lRHeedXpeZq7hzLsHREeY5lyjvqozuaqp9jJeHU4y4O7RgBIFS3+4smjfPm+S2PvZLnw0f/7At/91PWX5P3fCJjzACiRSLBkyZLXdYy9e/fygx/84HVxeF4LX/3qV/nyl8/s4jQjFW+7HkXTwbQ9PN/HByQfKrZw0Bbw8X2fnLEg1rWAywM5w6oFPwCG7eK6LoqiULIcDNvFtD1cT5RYXM/H83yOTBbxgam8KViWJjX37HNByTw3i4VLiVzZZnt/GlmG1uruPVU0OTJZJBJQ+dHuEd67qbNmG1IyXSxXzJXv+7V1AB88HxzXI106Pt+e72PaXm3tSJfERktXpJqP2oyw4ECqxGO9IlO2cyCDdYKQW9ly2DGQZlN3HataY1iOh2G72K6H5Rx3ghfvCZ4nPhsIa5ITUb4CScAHxwuMliVuXd5EUyyAYTuYzvE5LloumdJsK5p8RZx/5ZPOw5HsmY15ZwLJGZTNM8/Vye9XqFzaNb9/6tyvzQWcijkv3H/pS1/iy1/+MpXX4QT9zDPP0N/fz/Lly+np6eHFF1/k05/+NA888ACqqjI+Pl57bn9/P93d3TQ0NJzxsdPhd37nd8jlcrWfGYXNGSxujNAQ1VEkKBgOiZCGj4QiIdKjHqjVLKnvi1r+L8yB5P0CFjAX6GmMzrIBuWlZY61F9OpFdcRDGqbrodbIoT66KmM5LuFq9wwIsuWys1gRnIxN3XW18kFDVJ+bDzOH8DyfH+waZs9IjleGcmzrz9CaCJI3bCSgLRHE92E4U669Zk1bnM66MPURjZCmEA9qRAIqiiIR0hVWtMR4+/o2QbZVZeojOp31IXHcio1RDVziIY2GiCjnzGjSjGVnl8IWN0SIBBQqtkuu4pAIajxxcJIjE0XWdSRojOpYjk9IU5A43g0UD6kkwhr3rhcZ8BUt0Vp5UlMkNnSevvvsckbZcsiWbYqmQyKk0xgNYDsetuPRGA2QCOnctqqJ5Amf85514vO/fX1bjX6RDOu87+ozr81r2+M1UnlIV1h7lgznlsX1NTNVWRIWI5cSv3rT+eneLGA25jwD9IEPfIDvfe97NDc309PTcwoBedeuXa95jM985jN85jOfqf1+22238dnPfpZ3v/vdvPTSS3zrW9/iS1/6Etu3b2dkZIRbb70VgPe///1nfOxkBAIBAoHAGccQ1BQ+tKWbkKpQF9bxfEiXDOrCATZ3J/ib5/rxPA/T9YloKn/0vg2svEKMIC8WzmS2+mbUB7oc8Jcf2cSTh6cIqErNLBNgQ2eSu9e20lUXxsenbDqULYcljREURUGSYFlTlKmCSTykccuKprO8y2wsa47yiet7KJg2Tj7If7kYH+x1wHQ8cifs2jMli1+7TfjDPdc7TVATn7+z7jjxNhHW+JWbFnP3uhZKpoMsSbiuz1TRIBxQuWZRA7oqs7wlRtG0USSJuojOvtE8nieMK30fNnUnuWFZI6bj4VbtdtrrguxP2bVOpk3dddy1tpUf7hpmJGvUHO0n8gZvXdOC63kEVBnD9pBkEdDduqKZ+ojO8pZYjY8S1lU+srWbybxJMqydtZPqckVjJEAkHiASUDAdlztWtdA3LTIeixujWI5HYzTIH3/gKg6M5emqC9fIzvffu5pbVjRybKrEOza0koycmafTEA3w8esXkSpaNEYDtWDodAjrKn/03nXsHRVE9NMR2+cDMpDUoKNhgXP6ejDnAdAnPvEJdu7cyUc/+tHXRYI+E772ta/xsY99jOXLl6PrOt/97ndrQdbZHjsfTBVMdg5kCGgy6zsTjOQq2LZHb9khEtCoiwa5Z307h8YLqLLEnauba8GPYbu8eCyFYXtsXpSkObZgVreAS4O8Icq0piNKMTMifCB4PHtGcpQsB0kSLdiKohAJqCxpinD1ojqOTZVoigXOaBg6kq2wZzhLJKBy3eKGWut5IqyRCGsMly6/zrCgJtOeDDJazbz0NIYJaGLX3zddJF2yaIwE2T+WQ1flWot6UFNY2nR6Y9my5fCTXaMcniiwpj3OXWtaCWoKy5qj7Ijq2FUdmxWtsRqhd7go1sXu+gjv2hivcYBmsm13r2/jX3cM10qUi5siHBjLky3buB60J0PIssTb17fVTGwf2TvOjoE0i+rDfPi6bgZSZY5WBQKv7q5DPgux93KEpsp014XZ0JkkoCqzlJy76sO18y0W1Lh2ccOs15Ythz0jedIlk8OTJa5dfPZ1OKyrhOvP7XYY1FWuOUdD2FzFZltfGgm4dkn9KQT7C4WqyjQkw2zuXugCez2Y8wDooYce4pFHHuGmm26as2M++eSTtf+3tLTw6KOPnvZ5Z3vsXGE6Lj/YM0zFErX2kK6wvDnKP7w4wIHRHDv70/zHKyMsaYoRCSgoisL2/jQ7B9Ks60hg2H6t3tw3XeKXbuyplRNmMJwpUzJdFjWEZz1WMh2GMmXqwvol6SxYwOWFVNFkqmjSlgid0m1l2C4DqTKRgFLLVoxmK/RNFckbDs8cmWTXQBZFlihaDhFd5Z51rSxqjBCUJR4/PE2uYjGeN8H3CTSFOTZVZEtPPfesbUHX1FlZkJORN2x+uGu4dnMvmQ53r2u7eJMxR5Akifds6mT/WB7f9zFsh79/oY+jEyXiYY19oznSxSkaojr/sm2I1kSQomkTUGXaEyGWNEc5NFEgX7LpaQpj2D5TRZNDYwWyFZvHD0wwmTf51M1LaIwG+OCWbgbTJZqiwRqn6GQsboywuHF2JqEtEeKDW7oYSpdpTQSxXZ9/e3mEgumQM2wUGTZ1JfnBrmEW1YexXJe/fPIYBcNGlSX+4cUBWmJCs2yqaNAQCXD7qiaWNcdPWXdOxlhOkKwX1UdmZUPKlsNgukwypNcCw4uJZFgjGlQxqryft61p4W+f76/9fwbbjk3z41fH2NCV4P1XC8rDNx/v5fs7hzBtl+d6p/nLj1xDV0MY2/Xomy4RUOVZ2Zt9Izn2j+VZ2x5nTbV07Ho+39s2QL7i8KFrO6mvZpHGcxWe7U3REgtw81myo77v848v9PPI/gkA7pls5T/fdvaS1Ui2QtFwXvM7shyPo1NleqcLXBttOOPzFnB2zHkA1NXVdUVLc5cMh4rlYjkee0dz5Mo2mbLFSKbCDKW0DOwaztVe89ThKXRFIhHS6KwPc/1SoURr2C55w551Iu8cSPP0YZH+bojqfGhLN7oqUzIdvrdtkIIhduR3r2tl1Zu8pPZmxlC6zA9fHsH1BDfnQ1u6aKhmDyzH44EdQ6SKgpB5y4omWhNBvr9jmL0jOV7qS1EwHE7WSDs4VqA+oteyPq4nMiKeJ24Kru8zlKnQn67wFx85ezdjtmTXgh8QWdMrBboqs6EjwV89c5Qf7hohXxGfZWlzhKF0mUzZhgmqXZ6CY+MjOH+yLOH7goTrAUFFQpYlZFkWz/N9Htg+xPVLG1jbnqApFqhp9pwOpnNmwm1LPFjbCP1s/zivjuSYLpiM5w3CusIDO4ZRJHCRUICKc5z0njOKHJooEtEVHM9HlSWeODTJ1iUNXLu4gQ9d23Xatvi9Izl+Vr1hJ0IaH76um6CmULFc/uml4+vTW9e0zOKYXQxkyza9k0X6poWVyNcePsSRSUEYn8ib/MF71vH80Wl+/R934bge/7Z7lIHpMl942yr+6aUBslVCdH60wBOHxvnI1sU8uGu4lv3b0lPPTcsb2dGf5us/O4zr+fzwZYkv3LWSzYvq+Ny/7GbHgOike3T/BP/8qeso2i6/96O9FKoacIPpMh/Zuui04zcdj79+tq/WGDCUrvArNy9BU06fGX15MMOTh0R3YH1EP+N3dCL++w9f5Se/dfv5TOsCTsCc56j/9//+3/zX//pf6e/vn+tDzwviIY3GqE6uYouuC8etLXZngg+4vo/t+RQMu9aJUB/RqQvPJoLuHz3ebp8qWjV12IFUuXZR+T4cGJvdlr+ANxcOjRdqnUKW49F7QifWeM6oBT8A+8fyDKRK2K5H3rApmacGPwAekCpbWI6H6/rVDIgQi7NO6Bh7dTiL6569a6g5HiAWPL5/WtI0d55b84FH90/w4K4RxnIGru/jeL6wm6g+brvHu6xm/nV9sF0f1/fxfHGdOv5M15eL7/vVYEiadZ2fDf/36T6+++IAZevs3XOed7zT1PeFQr3jiXH7njcr+DkRleq4XM/HcUV2Ol2yGM+dXkH6xHHnKnaNDD6YPnl9uvgaT2O5CuM5E8fxKBp2LfgBODJZoGg4/HDXSK37zvd9nqgGECd2wfnA9v4M6ZJVC34A9o2KTeyLx1K1a831fF48JmQIXh05vsmdKhjsG8+zZzhXmwcQnXtnQqpozvpey5ZDwbDO+Px9J8z92b6jE7F3ovyaz1nAmTHnGaCPfvSjlMtlli5dSjgcPoWDk06n5/ot5xSqIvP+a7p45rC4kCbyBgfH87Vd4JkgIaFIEosawtyzrpWQrrC6LX5KtJ8I68JMFdFNNnMTSYZnz1MydPl10Cxg/nDK+XBCIB0PqSiyVFu0kyGN1ngQRRZdSYos4bn+ac9XpUrIlSQJRYa6sE40oDKUqaBIorMlFlRf01QwqCl86NpuDk8UiAZUVrScnh9zOSJdstgzkiWgykgSmLZHMqzRkQwTDSrsHcmTrdi4ljtrDiUEoRmoZYZkIKSphAMKuiIsKtqTwVnf12thqmDy8mC25hR/OixuirK2I0HRdJisDqpiuUiShA9oCrMycgAKVD+jhFTtXo0HNWRJOiMXJRHWaiV8SRIbQhDn4wyZG6h1Xl1MxEMasZCK7XlEAirJkEa2SmCvC2tEAgqrWmI8vHes9pqZ0lwipNU2CbIE1y9pJBpQq52OXu0zgVDdfubI8fftrnKNGiI6YzkxF7qq0FUXphB2Zl17bckzlwJbYjrxoFbLAEWDaq2MdjrUhfVaJvVs39GJaI1eeeT2ywlzHgD96Z/+6Vwfct4R1BTeuraVlkSQ7X0pEiGVbMni+aMpDNdHAlRZLIAhXbSfxoMqS5qivH19G9GQwpL6KHnDxvH8WeTTt65uQVdEyeuqrmRtoWxPhnjrmhYOjheoC2tnXQwX8MbH5u46DNtjPG+wqCHMytbjAUYyrHPv+jZeGRIE5FtXNCFJcN2SeurCKomQyrb/P3v/HWbJWZ17oL+KO4fenfP09OQoaTTSKI4iyggjBAabZBscOMbngM2Ba/ABBww+gA3nYnONARMsDEjkKCSU00ijybF7pnPunVPlun/U7j3Tk0NPEv3q6UezU1Xtb1d9tb613vW+B1OUDQvNssGFaEBFAOojPrJlE0UWWFgfYeOSekqmTTKv88pAmrBf5gO3LTnp8SULOpIoHFcw7mKD67qMZTU0wyagSsiiyJWdNUiCQLpksLotxhuvaKWkO7TXhBhIFsiWTYZSJYqGJw0QD6osqAswktLJ6SYCLi4Ca9vjvPeGhWweSDOS0bi8Pc6NS+b2+l3SGOGmJfX4JJGhdAm/IjGULjGR1VFlkWu7a3hizyTjOQ0XCMoCtVE/Vy+oZTSrUdBsGiJ+rlmUYFFDpNpddiQ2LqlHFASyZZNVrdFqE0dj1M/rVjSxeyxHPKBwwxx/v2Mh4lNYWB+iJR5AEAQ+cvdyvvqcZ8z7B9d1IQgCf3jjQnqm8zzXm6Q9HuRfHlwDwDfefSXv+NorlHSbW5bV8bZKmeq+Nc08sW+SoCpzR6WF/d41LaRLJjuHs6xpi3PPGo/L9n/ftIa//sEOiobFX9y6hIaonwbgj29cyM93jtMY9fHHN3ZXjzdVMBjPlVlYF8KvysiyzOceXMunfrkPgI/eu+yE3/fW5Q3IkkBBs1jbHqMmdPIg+pH/ccPpDeo8ZuGcdIG9FmBYDjtHcuQ0m+6GCE1RP7rtsnMkS06zMB0vAHIMr9wQCyiYtss//mIvLp7/zNULEt7Nak0z3ZUSQUCVjqsdsao1dly36Hn8dkEUBa5ffPybzKKGcLVjKFnQeXjzMHvHc2wdzOBTJOIhlbHDnKKdssmnHljDQLJEPKjwxivajiJWnyqe2DfJ1sEMANctquOqi9yPyHVdHt48zE+2jWJYDitaoqzvSrBvPM9l7XFeGUjz4sEUz/UmWdIYpjEaYHlzlOcOJCkaNmXTwbAdfIpEUFG5fmmU6ZzG9pEsIVVCQEAQBN51XdcZHV99xHfSQHIyp/GtFwdIl0wCqsT/uGUxSxojlA2b77w8yGimjIOAJEoUDBvdcklrZUYzY/hlgeZ4kOmSJ/Y4mTfYNZrjLevbjwqE/IrE7YcRjA/HipboCVXA5xojmRKOUqC5woMayZSrlIKRTJkFdSEyJYN82aIlFkCWBfpSZdYEfWwbyRFUJXyyyGTBpGzY+BWRXaM5UkWTdMlkYLrE6rYYubIndNscD6BZNrmK7tsPtowwmC7jui7//fIQtyxvxLIdJvI6TVE/sugZ3C6sD7NtKM1nHt2PYTk0RH38/f2riAVVBFHktsp4ekvn48OvSNyx8vR0hcZTGq2vMZ+z84k5D4AADhw4wNe+9jUOHDjA5z//eRoaGvjFL35BR0cHK1deGNnwU8XB6QKZMY1tQxn2jOdZUEmHfnvTIKmiQfaw+q8LWA4UNIv+6RL7xnPggiSJpIsCsiiwti3OM/unqgHQPOZxNrAdl97JApII3fVhPvfrffxs+xhBVca0HTIlA72iWnw4DNvldUviqP6WU3ZKLxneee1XRHomvYzIovpwNfgBjz+xfkHNnMtdnC1sxyVV1PHLEqWKNMVUXidbNhnNlpFEePuGDv7vL3sYzZQwLE/heetghtpwmUwpxN6xHJrleGrPjlcO39Q/TUiV0W2XgmbhVMySn9g7SUdtkFzZJFs26UgET0l75z03LqQuET/hexzH5YdbR0hVVIin8zqf+eVerl1Ux7aKJ1imaDGW1TAqZbCZX79o2OgWSHmNTElgYLpIV30InywR8Utcv6iemqCC/wReWUXdoj9ZpCao0nIeb7bZsomS13li3ySvv6yVl/sP0Sde7k9xVVeCx3ZPsG8iT6ZoElQlfrhlhDVtcb7z8jBaRb1/OFXilzvHuHFJPbtGc4xmyiiSwIsHk6xui7F9JENOMzFtB6fsLXKvW1THT3eMUdBMXGDHcIatg2miAYWeiTwTOY2AIrGpL8XC+jA/2jpKumhQNm100+Y3e6e4Z00zLxycZqxSUnRcl6u7ak/oM3a6+MNvbmLr/7ljzrb324Y5D4Ceeuop7rrrLq677jqefvpp/uEf/oGGhga2bdvGV77yFR5++OG53uWc4idbR9mT9IIcqyI5v7o1xkRWYzR7bHVrB8gfLr3uOGimN2H+Zu8k3fUhmmKBC64aOo9LG67r8sMtIwymPOLjntEsj+2dxDkROe0whMOnHoSXDKva9XNgqkDYJ9MY9TOUKmFYTrWzcYZjcjEhWzL54hM9bB/JElAk7r+shfGsRt90EcN2EIBf7Jjg17snvQyCZnmk5srnx7IaybyG4czm/emWy2hGRxR1wj4JxxXQLQfXddk9luPTv9jLcLrE8uYoNUGVt13dcdIg6GTBqGbafG/zMJv704xkypi2S6Zk0J8s8tT+KRRZoGQ4SIKAeZwTwXK88oyDl7WeKuiEfTJbhzI0x/ppjPr5qzuXsqL56OxzUffOg0Jlfrt9ReN5y1KXDIeRjMb2oQy/c3kbinSIv6NIIpIgMJAq0j9VxAHSJdg2nAGgbFhVL7DDuXE/2zHKdF4HQeCKDo333LgQ14Xtw1k00yagSNWMZqpgkKsseMuGQ1AVMW2Hp/ZNVTophervN5Iu0zOZx3W9/emmiSQKbOpLMVoJgNozGnMtxaSXL37rmYsZc94F9uEPf5i///u/59e//jWqeqiGecstt/Diiy/O9e7mHGZl9Ww7LvURP67r0lYTwHYcTmeeFwWwba/9NB5U2TOWOyVW/zzmcTwUDbsa/AA8dyB5ysEPgGmeum/RSLpc7XbRTJvpwiFy5lVdCWIBhUTI4yJdbNg8mGLPmHczKhk2zx9Iejeqim0EQEH35C0sx0WRxWqgI1beY7kQkIWjihaiCCIClg2JgEwipNKeCNIaDzBd0D3eVlajZHg6TWeLnZXW9yWNYS+bZVjIkuh1drkuhlXpXnJPXGCZIW3PEJk108G0bAqaRcmwefiV4WN+bihdqgY/4HUnni/MHO9IplwVfYwHFeJBhXtWNyOKAuNZDVH0fidREKq+Xi3xAKosIgoCsYBMxK9wYLJAQfMCF1HgsGvJJaRKKJJA0CdVo16/4n1eEAR8ikjRsBnPetw3SRBQJIFsJQApGhaS4C0GZFFgIu91gBmWgyyKVVkU/TjdemeK4/eUzeNUMOcZoB07dvDQQw8d9XxDQwPT09Nzvbs5h1+WiPoFSoZNV12IqxcmuGFxPd98cYC8bpE/wihPET0jwsMbMES8boq22iCKKFbJbHOZ+pzHbx/8skhQlarGlhG/TOmITqUjMeMXFfDJp6WKHjus66ch4ieve8FTLOAR9G9dfuIusQsJSRRnLVZEQWBte4x943lKhkXZ9DJYkiiQ1yxqggqW7VA0DrWMSwK01gSqWjK241DQbRTJ0/uJBRXedGU7b7qyndGMxtP7pwgoEhnMatfVqZBYT/5dvC8iiiLLW6LUF3weMVv3jEFlScB1XRRZgopJq08WUSSRsmGjyiKm4xILyOTKFpbjYNkuQZ+EZbv4Kpk8RTr23FQTVGd1f9WEzl/XkSwJyJLA+orq8oK6EO+um82zWtEc5Ve7JnBdL8RbUBGbbE8Eq9eJgNfxpcoifkWqZm1ClbKfKktVNW3vsfd6cyxQHX9FEmmKBvDJJvGgUu2Oa4x6Gk91Yd+ssWmI+vHLIrVhXzVbGvHLqMfRADpT1IbPCYvltwZzPnrxeJyxsTG6umafqFu2bKG1tXWudzfnWNIUYXlnkOZYgNaaQFV+/W/uWc5HfrATIadhWg4+RSQWVHAcCKoiZcMiq9nYjktHIsgtyxroSIToTxYJKBJXLkicUBBtHvM4GWRJ5Hcub+WFg0lkUeRzb76M//XdrWRKJjUBhcaojx2jOVzXu1Fd3h5n+3AWvyLxsXuWn9a+GiJ+7l7dzK7RLGvbY1XCbXvixAq1FwOuWpBg92iW5w8kifplHriilXWdNWRKFtuGM4QUie6GMKokcmC6SKZksLIliiR6vBDHhcvb4kiSiGF7q/awKjOYKjKS0agJqjxwRSv3rm0hHvRMOodSJVxcasMqSxojrGqNzQk5dXVrjMFUif7pEusX1OC68NyBaUq6VZEz8AI8vyJ5AZoIVOQ4pvI6Bd0ioMi0xv1sHcpgOQ4BVWZ5U4ThdJmyaVMf8fOOaxccc/+NUT93rmpiz1iOeFDluu7z153aFPNx3fJW3nNYp9WReOe1XfROFnm5P0Vz3M8nf2c14HVq/b/f9JItm9y4pL4q2vjWqzr44dYRVFnkr163FPA82obTZUbSZdpqAlxeIaR/4vUr+b+/2kfZsvm9qzpojgdojgd48Mp2nu6Zoiao8p4bFgLwoTuX8uFHdjCZ11nbFufBK9qRZZH/ccsiHnppEAF4+zWdc25Hsumj8/yfs4Hguu5pJNFPjr/8y7/kpZde4nvf+x5Llizh1VdfZWJigne84x284x3v4P/8n/8zl7ubM+RyOWKxGNlsdk6UrHeNZpnM63TXhY8rgX+pYHh4mPb2drLZLGs++cxpf/61bIZ6+NicTwX0qbzOrtEsEb/MZe01F2V28UKNzangQo/fhRiboVSJ3qkC9WHfRd1tejGfN6eLkmGxpdI0cEVHzQmNVk8FM2Pz01d6Wb+kdd5rsoIzvX/PeQbok5/8JO973/tob2/Htm1WrFiBbdu87W1v46Mf/ehc7+6CQbdseiYKWJV0+WhWYyqvM53X0C0H3XIYy2r0TxdZ0x7nA7cvmeXvNZ7V2DqUxq9IbFhYe05W1RM5jV/tGkc3Ha7prr2oJ73fRoxlyzy6a4Js2etgaYkFqIuoNET8LKh4Q03ky7z365uZLujct6aZv7xjGVuHMvx8xxhhn4xQ0Wy5ZdmxW5d/WzCaKfHDLaPols2SxijXdCd46KVBsmWTNW0xfJLIk/unqAv7uGNVE0/tn2Iqp6NZNtuGslzVlWBRQ3jWdbh1KMNopkx7TZDVbZfGteO6Lj/eOsrWoQztiQBvvaqTgCoxmdN4ZPMw0wWdTNlgSWOUq7sSrF+QqGYlnto/xd6xHDUhlduWN3JgMs90wWBVa5T2RIhdo1me6ZmmKern3jXNyKdQztk1mmUgWaIx6ueKjvhZEeZtx62qQS9uiJxR0KpbNr2TBXyyRHd9CEEQMG2H770yTM9EnqVNEd60rq3KszowVUC3HBY3RKqlscFkqRI8K1y9MIEiiTiOyy92jjGUKnN1V4LLO70s0rdeHGT7cAYBz4rmD284famEHcNZXjyYxK+IrI57Zb33/derdLcM8KM/v3GWztw8Tg9zPnKqqvLlL3+Zv/mbv2HHjh0UCgUuv/xyFi9ePNe7umCwbIdvvNDPE3snGUmXkUQB03bRDIuMZnk1eUlElUVCqsyO4Qz//vRB/vSmbqbyOvGgwvdfHal2NOQ0i9evbZnz43x013hVDfWxPRN01AbnzI34fGDBh3923NcuxazSzpEsQ+kSi+vDmI7L43smMCyHR3ePUzJsmqJ+gj4ZCa8F+PLOOI9sHiFVMhGAf3+6j2zZQpVF9o7nqQurLGqIMJI+dnfibwuGUiX+3296ODBVwLRcxrMaX3+hj0zJMzH98dYRNNNBrHh5/XT7KGG/jG46aKbNCweSHJjKUxP08eC6VkqmQ0G3eLbH4yzuG8/jU8RLQu16U1+K77wyhO14nWk+WeKWZQ38YMsIL/UlSRUN0iWD7cNZhlJFdo5mCftkUkWD/ukiBd0i4ld4pT/FUMojQNdH/NxYaQvXLRsBgb3jOd557QKaY4fKfKmCwUv9SZqifi7vqOHAVIFHd3m+YvvG854sSHv8jL/bI5uHeHizR9Z+cF0bD67vOOH7Nw+kmcxrXLOwlnhQxXZcHnppkM0DaVRJ5L7LWrh5aQOP7p7gGy/0kykZvHgwSTykcOdKTzDx25sGMSyHm5bU845ru8iVTR7aNMBgqkTIJ6OZNretaOThzcM88qp3bM/0TvGxe1fQmQjybM9Ulfj8TM8U77i287heYMdCTjN5fO8ErgsFHZ6a9Gw6HBcOTJf518f386G7V5zJcM6Dc6QDBJ4pant7+7na/AXFaEbjP57pI1sy0KzjtZ46lE0H03ZpjPoZTpV46KVBbMfFsG1sBwKV1ea5MpI8vOPAdakGXPM4/3h01zhffa4Pw/K0em5Z1si+iTzTeY2DU0Vc12UypxHxK0xVgtZXDtPbcSt/L/UluWNlM7IkkCwYLGpwaUtc2iXWs0XvlNfdM5nTsR2XR3eXMCyPHK4ZziwfPwFIFk1EvI4uSRDwyRKv9KfxyRJbhtKs66hhPKeRCKlVM8qpvH5JBED9yeIsDaidI1l+vWeCZEFj/0QRRRIoGzayKPDzHWP8Zu8kpu0iClA2HaJ+GVkSCPtkdMubv4ZSJQZTBaZyhmclYTsUdln4FYnblntt8ZmSwUe+v71qVfHmK9torZl9Xp7NPGdYDv/0q71Vn8WD0wXuv7ztuDIC331liEcqwdKPt47yqTeupmjYfOflIfKad4zJksHNSxv42bZReibyuJVj/Nm2Me5c2cznHt3PQLIIwP6JAnetbGY0V+bXuyY8hXU8jabbVjSy+zDvRqsimLugNoQsCcw00Uni0V2Fp/K9DyepHNlF9oMtw/MB0FlgztvgH3jgAT796U8f9fw//dM/8eCDD8717i4IeqZylA0viJnB8U5s23EQcGmvDWI7ntiagJd2nUF3feicHOeGhbWIlZTz8uYIdeF5EvaFwjM909W2bN1yGE6XaIkHGMloCIJHZjVtt3oDOR4aon5UWWRlc4xlzRFuWdbIxsX15+lbXBxwKjd413WxbYeaoIIkeM85rotlOyiS6F1vR3z2cHd3F49AbLvejd4TsXMomTZBVSZX+S1EQaiWJC92rGmNVRdWsuh1UZUNG8cVUCSvjDRThbId1zPOdV0MyxMN1EwbEQHdtLFsF9txKuPsjZdm2jiOiyQKuC7VG/+rg5lZ5+5zvUm66kLIlTKVIMDCs5jn0kWdXNmq/m65skW6ePyA6vneSqbEcUgVDbaPZJnKa5Xv671noiJLMtMOPxM3zjyesRVx8fSQDk4XmCros4yDJytB3ZJGT2PLdb2xWd4UrahqN9FWE6CtJsAdK5tOqWx4OGpDKsuaIrgVmYN1nbMVw6fypy5tMY+jMecZoKeffpqPf/zjRz1/11138dnPfnaud3dBsKQhQsQvY9supnOo1fJYuSDdctk5mmXfRA5ZlDAdF78ict+aZjYuqSfok1h6jlaWq1pjdNYGMSyH2vng54KiMepj/0S+2m4c8XsGpu2JAD3jeYzKnKoKLseb0kTBmxCDPonlzRGu6a47bTuLmZLbeFajszbErcsa5rwz5VxhNFPmJ9tGKZs2HYkgu0ZzbBvKMJIpU9RNLNsh6JOxHBcHF/M42VkXTxpABCIBiYhfpTakEvZJjGZ1do/miAUU3nnNAhRZpK0mMIu/dzHCsh1+tmOMXaM5HNclGpDZ0FXL0qYIe8Z6PIVw06lKdtgcMsvVLBtVBNMBx7ExLIeakOLpITkukijikwXqQiq5soGNZ5jrOG7VmqKtJkBBt8hrngDgiuYojVE/b7mqneF0mcao/6y64mpDXhv7jM5PyCdRe4KWfFGAfeM5LMczbm2JB6gJqIR8MuPZMpIgsLzZI8t21wXZWXFit13orgS7tSGVgWQJ13WJBhQWNoTJaRYhVSJZ9LJhM4vXy9rifO7X+8hrFgtqQ9Vtx4MKfdNeFumuVYc0s1zXrXToSdWgKFsy+dmOUdIlgw0L61jX6Sms14RUdMshoEqEfbO/87rOS4ObdrFizgOgQqEwSwBxBoqikMvljvGJSw/tiRAfvnMZ/7VpkN7JAqblYNo2JfPYE65hU5GotxDxVrAv96d54xXtLGs6t10OpyLHP49zjz+4vgvbdRnPaNRHfHTUhrAshxXNUXaNesROATiRsKvrwv6JPIsbIty4pJ7gCewLjodX+lP0TBQArzzSGPWxpi1+Bt/o/OOJfZNVbZcfbRmhaNiMZcukCjqyLOI4UNRt/LJErmwelf05HK4LNpAuWqiSRNQvk9ctVEkgpErEAgoO7lEr7osVe8byHJwq0jdVRLccYgGFvG6xrDnKZe0xJnIaiiRiOS627R61WJsJwB3Xy0Qmiyaq6KUmFzeEGcmWvdZ7oDasElJlgj6pavoa8ct01QXpny4RUCSWNHkZkYaIf046lRxEEkGVsuHx3RJBFecEBYx8xYjacVxs1yFVMKgL+agNqaQKOpIo0lzR8EkfkXXNlL0SdEci4AVAeAbEQZ+M7bpeJ1cRQKChIm3yVw9vJ130bDMOTBX41C/28MHXLeVfn+glVfS2/4Xf9HDz0npsF77/6jBjWY2wT+aBdW0kQirffWWQJ/ZNYTsuu0ZytMVX4FMkXjiQxK9IngRC79SsY907nj3rsf1txpyXwFavXs13vvOdo57/7//+b1asOLVapaZpvOENb2DJkiWsXbuW22+/nd7eXgAmJye58847Wbx4MatWreLpp5+ufu5Er8017ruslb+5dyW/d3UnG5c20Bg7NR7GTLnDK4XYJ//APF4TCKoyf3HrEv7xgTV84HVLedO6NrKaiSKJVfXhk+lRuHiEectxz5jPpR1xzpWNS+ccPFz12j3iD7ei0lxR7ZVO0lTp4GWBZElgqmBQNh3aa4IEVJm2RJDGqB/NvJTGxhsc0/HOC5cZxWebaxfVsaIlekrCjC7e2NiOC4KXSUmXDAQEJNGzn8D1FJNXtcaqHKmyYdNdH+HW5Y1cu6iuWnqfKximhQM0xfw0xfw4leeOh1TJIBZQqAmpiILAWKZMqqhjOS5d9Z40yYxZ8HDas6iY+RtIeUFW33SZ2rBKfcRHybAYTpY4OFlElSUW1odpqwlwsJLdGcuVq9ev48KusRxl0yZdNJk5S1NFA91y2DueY6xSfivoFpv6PI+z3WP5Kn8rWTToT5aOUno/8nH23NBHf2sw5wHQxz72Mf7u7/6Od77znXz961/n61//Ou94xzv4h3/4Bz72sY+d8nbe+973sm/fPrZt28b999/PH/3RHwGe1caGDRvo6enha1/7Gm9729uqEv8neu1cQK6UMxqjPoKqeFKfFwGPCKdIIsubIid1gJ7HaxuS6KW34wGlyss4jiAv4E3OSxrCrGqNEg+emcrwmrZ4VYskGlDOq7v32WLj4voq6fWOlU2sbInSEPERCyoEVYlE2E9bwlPvrQ/7kITjc/Nmgk5J9FSbJVGgPuKr2icEVOmSyYwBLG+O0hoP0BoP4FckWuMBOmuDtMQCXLkgweXtcSJ+BZ8segTwIwZGFr2bgYCnbi+LAj5ZJOiTUSTRs4lQpKqNhF+RWH2YrEZHxQ7E25bAlRX15rlCwOedq0LlvxUtUYK+42e3r+muQxa9c6Uh6ufyzjiJsK9q5ioKVKkHG5ccCtgkQeDmpfWVz/mYoS1HK8FUeyJAxC9Xt7GowdvG2rZDLf6yKHD3qmbCPplFDYf895Y2RvAr0lHB4QxPamF9qDoPRP0yrTUBEiG1moVUJIEN3bWzPttdM98CfzaY89G77777+OEPf8gnP/lJHn74YQKBAGvWrOGxxx5j48aNp7QNv9/P3XffXX28YcMGPvOZzwDw3e9+t5oNWr9+PS0tLTz11FPcdtttJ3ztXGBRfZjlzRH2jRd4w+VtLGvM8vOd42imU5lcPSPCGeJlQ1hhSVOUmpDChq5avvzMQcJ+hZuW1FcVp+dxdrhUWuef651m80CKJ/dNYdqeF5EkQF6zEfAmRcdx8asgihJNUR/XdtfzvlsWUX8WfK66sI93XbvAU48OKdUV/MWOom7xykCSsWyZsCpTE1JY1hRBFQVuWlLHwekSe8dzTOe9Vb5PFlnc4PEzVFnCcWEwVcSyPW+/Kzpr+M3eSXTLZXVrhKsX1mI7Ln9w3QKWN8eIB5WLXvH6cKiyyJvXt3Pf2hZ0y+Px1IV9iKJA1K/wxxu7uawtzjM9U/xq9wSTubJnauuTcCwXzXYRJe/8aIr6GMtpSII3ds1RP1NFA9fxlK5vWtbA2zd0zgrCZUnkgXVtTBd0gqo0J6X3e7/wDNcub+NTD6xFEgU+ePtSfr5jDIC7K15gx8PvX91JpmSQLBi8bmUT7QnvXHj92hZ+sGWYoCrzxnWeM8EHXreUZEGnZ6LAkqYIf3HbEgDede0CPvPoPgzL4XUrmmiuBE9vvaqDl/pSJIIKb1rXBsBf3LqIrUNpirrHT3vLle3Iksi7rlvAV57tQwDeee0CRNHjHvUnixyYLFIXUatBzT2rm9kzliNTMrm2u462ShfdjUvquaorgSwKjI+Nzvqej//veSXos8E5CR/vuece7rln7m42n//857n//vtJJpOYpklT0yFX9QULFjA4OHjC144FXdfR9UP5wzPhJ4miwJ2rmrljpYsgeG2lrTVBHMdzMX5y3yRZzes6cPFqzcPpMrrl8Miro/gUkbXtcX6yfZQ/ubH7kiGjzuPscGCqwKa+FE/vn65031DxofJeVySBiE/iP/9gAyuaw0iShOM4iOLcJGz9ikRT7NK5uQM8vX+Klw6m6U8WcVyXTf0pJEGgaHjdQHnNxqgYGbuAIYvEAwqvW9nEe25YSCyo4rouruvOGsfDx9V13YvO2f50EVClY6oND6fLvNCXYrpoUDQsXEHEwcWriAo0RFREUSSkSrjAgtowsuDSlyyh2R6n6P61zbz16s7jjpEkCnNKFp8uaPxs+xjdDWHec0M3S5siLG06tYaR3WNZaiucn9FMmZzmaUL1TObpqvOyMjtHcnTXR4j4Fb7wtnVHXWMTOa1KXJZEgZJhEVRlbl3eyC3LGmaNw1ef6yeoyvhlibJp851XhnjrVR3sHstVM/27RnPcsLgOSRS4d03LUefb9uEsixu8jq+sZjKZ16r8qeMF43/+Xy/z/35v/WmM6jwOx0WfP/vkJz9Jb28vjz/+OOXy3Am+/eM//iOf+MQn5mRbMydxumQgCAKSJFE0LEyHWRoOXhu0BfgomzayJJAvmxycLPDV5/q4ckGCy85CKGweFz800+aZ/dP0ThawbGcWbwAOcYEM2+MxSJLEUKpEtmzSVRci9Fuq+lo0bMoVTo5d4UBZjtfybtouhu21cR/OkdAtB8100CyHGN516rrw4sEk6aLBkqYI3fWHShSXevBzIqRLRuX/Jq57SErAsj1+StGwCShg2F5JUJVd9Eow6bguoiCQ1+1ZY1Q2bF48mES3HNZ11pwzr8P9FdL+6WCGYwMChuUwndeJ+JVqF9ns93g4coFRPIwfZzsuuukwk/Q68lzJaVZlG97zyYKB5bhkSyajGe++1ZYIYDtulTpx5DaKhlV93nU9DSs4pF7tVySOHOHdY6c/NnOBbMnkpb4koiBw1cLEJSWwezjmnANk2zaf+cxnuOqqq2hqaiKRSMz6Ox185jOf4fvf/z6/+MUvCAaD1NbWIssy4+Pj1ff09/fT0dFxwteOhY985CNks9nq39DQ0Jl94cOwvDmK47iYttfd01bjr7Y9C3guzV31IURRIBFS0C2LXWM5+pNFfr17gm8838evdo7TO5nHcVzymsne8RyTee3EO57HJYNHd08wVdAo6CZhv1LlY/hlEQnvPBGA+ojCksYwW4cy/OfzfXx70yCf+MkufrZtBM04QavYaxRXdMSJ+mXKpoU8I2CoCKiyiFJxPwevwUAWvdJNY8TPsqbIrJLhiweTvHAgyd7xPD/ZNspE7tK6tjTTZt94npHMocWg47j0ThZ4tmeKPWO5o5orXNelIeLDp4g0x/yokkjU79moyJJQ4fUIWI5LUJFY1BCmLuxjWVOUeFBBFkWCqsSdq5pmbffnO8bYOpRhz1iOR14dPiekcUUWebBSZjodLKg9pDkUUCUao37iQYV4UDnsPYdoB05FE8k5LIJefxiPaXFjeNZnj8RMyQs8vtAD69pQJIFsyWDfeJ5943lyJfOEOkBXdiYo6BbJgk5DxEdL3I9Vsel4dNcEP946yosHk7M+85G7Ts/keC7gOC4PvzrMrtEcO0ay/GjLyHk/hrnCnC8nP/GJT/Af//EffPCDH+SjH/0of/3Xf01/fz8//OEP+Zu/+ZtT3s7nPvc5vv3tb/PYY48Rj8erzz/44IN86Utf4uMf/zgvv/wyIyMjVW7RiV47Ej6fD59vblYsmwfSDCSLCIIXrRcNm/ULEnz9D67ip9vGGMt6oncLakP4FZnNg2kGpos4rp+eiTySKDBd0JnIaV6XQl2YppifbNmkbNiIgsC9a5tnrVZ3jWbpnSxQF/axYWHtRWmGOY+jMZXXkUWR1a1xuuvDLKwLYtoOC+tCpAomX3m+HweXe1Y1E/UrfPulQXaO5BhOl8iUTJ7tmeJfnzrAn2zs5pruOqI+mZf6U0zmdJY3R1jR8trUBVFlkVhQoTMRZOtQ1rsZObC2Lca2kSyO4xL2ycQCCjcsrmVlS4yVbTEW10eqq3LbcRnLHgocXNf7PRqjfsazGi/3p1AkkesX112U/kq6ZfPNFwbYPpzBtF3ecHkLd65q5ruvDPHDrSNMFwwWJIJsXFrPg1e2I4sCLvCDLSOMpMsokleyv2dtMwcmC+im54Nmu16HkmY63Lu2mTsrSuOW7aKIAv2pEt31oaOI95OHKTuXDZuCbs0pb+r+y1t58OolXHEGhOpru2uJBxXymsWypkg1c/rmK9vZNZrDJ4tVb8SibvHIq8MkCwZ1YZUH1rURVGWWNkXomy5QMmzWddScMEN41+pmFjeE2D6S4+YlDdSEVTTTZu9EnlxFeXr3WB6zItK5cyTLgYox7YaFtYiiQLZsMJIuUzZt4kEF23XJls1ZCtoHpvLVf/tlgdrwmTVDnA10y2EiW2YgWUIQQKsNYVfEMc83Ht8zwZP7poiIZ9bsNOdX+X/913/x5S9/mXvuuYePf/zjvPWtb6W7u5s1a9bw4osv8v73v/+k2xgeHuaDH/wgCxcu5Oabbwa8gOWll17i05/+NG9/+9tZvHgxqqryrW99C0XxIvMTvXau8PPto3zt+X6m8jqGZdMaD3LVwgT7xvMsa4rwpivbUSXR676onCBbhzLUhn24uOwZc8mUTFwcSoaNaXurqJf7UySCnnCX47rsGctVA6ChVKnqsXNwygu8ru2uO6ffcx5zg+76EFsGM0iiQHsiyJvXd6LKIpbl8JPto1y/uA6fLGHYLi/2TTOY8mwdMiUT03bIlExymsVXnunje5uHaYr6mcrrlAwbVRb5qzuWXlLdS6cCt3L+43qlBtN2MG0HvyKxZTBD2XS80oYAtWEfed1iMF1mLKejrBJZWB9mz1iOX++eYDyrYTsOTbEAqizSXhNEM22+v2UY3fRKDtmywVtO4jN1ITCe1XhlIEWm5E32D28e5qoFCX62Y4yxjIblOOyfLJAIqeyfyFMTVOmsDdI7niOvWdSEVLJlg9etaCJVMNg5ksOlUt6pyCoMp8pky+Ysrk3tcUpb3fUhdlUEBOvCXjfjXOIjd604Yzd4QRDorg9TNuxZYqEhn8xVXbMDqq1Dmapn4nTBYOtQhmu76/j59rEq5+wHW0f4g+u6jhvgDSSLfOqX+0gXDV7pT/Px16/EtB1yZbMaGGTLJrbtMJop8+iucXTLC4ZEUWDDwlp+8OoIyaKO47i8MpBm/3ieRRUT1nzZRBQF4gHvt5AET6Ty2d4kl3fObcfdyeCTRV7qS3FwqgiC17J/IYKfvWM5vvz0Qa80bpTOaBtzHgCNj4+zevVqAMLhMNmsJ9R07733nnIbfFtbG+7h5JnD0NjYyKOPPnrar50LOI7LE/unyJW9m1PZdMiUDfaM5SjqNvsmPEVZ3XKpDSksboxw56om9o3nGEyVKeoWhm0TUEXSJRvbcXm2J8W+8XzVZqOjNsRl7XHigUORfrLoXayO6zKR03ilL8UVHTUXbdfKxdKZdTEcx8Yl9TTF/JQNm7qwJ362ZSjFL3dMYNlel82NixvwKSKffXQ/fVNFrEpaXhaFinu1y2CqSKioUDZspvM6DVE/huXw5L7J11QA1Ddd5Bc7x9gykGb7SJaCZlI2nVmrYkmgKnpY0E0GU2WaY54C+m/2TtJVF+I3eyexHZf6iI9s2WDDwgTLmqLEgoqnz2Ie0lWaub4uNkT9Ctphx6lIInndBNeT5LAcMCybp/ZPUh/2YVbKOsmiiSR6oqhtNX6+9lwfj+6eQDdtFElgPKejWw5hn8ze8TyP7Rln21CaeNDzQmuK+Y9JPr55aQMlw6aoW9y56vRtHs4lXh1I8YXf9FI2bNa2x/nwncuO22RyZFu6VHm8eyzH3jEvSGyM+ihWMlzposH+iTwRv8Ly5giCIPAfzxxk50gWy3aZzOv8fMcYr1/bTGs8QO9UAQFPWNGvyoznsrxwMMlEzhNCbEsE2LCwlpxmMpAs4TgukYCM7Xiii4ok8FTPFH5Z5A8v97JWtguuA1315797OF0ymM7r+BXv9x7LlNENG98xyPfnEv3TRQ5MFTx7loslAGpra2NsbIyOjg66u7t59NFHueKKK3j55ZfnrOR0MSERVKsXkF/xFGUnczqtNQEyJZOJnE5AFsmUdZIFg8f3TCAKXofDeFZHELwmeVX2vItKlkVBt5BEgaAqktcsEkGFgCLx6K5xVrZGifoVhtMlpvIauuWiSiIPbx7mbVd1zHeSXWSwHZcn900ynC4T9knUBBSSlWzOb/ZM0DuepT9z6IY7mtV5dM84zVE/0wXd0wUSBBRZJOyT0SwH17CwHC8gsm2HGfEQWRKoD1/clg2nAttxsR0XteLkni+b9E4WSBUNTPvohZF9GIE8WfAE8PaP56uraVkSyJXN6gIhHlS5orOmKgEQCyg0xbwyGMCyU+w0Ot+oCancs6aZx3ZPoEgil3fEaYkHed3KJp7aP8lQqoRm2mimw3CmhG7hZZRdMAVvXDf1Z6gP+8iXDYbS5Yr/l4MkCpQNi7xuki8bSJJIUbdoivlZ1RLjusV1bOiqxbAddNPmsT0T7BjOIooCfkXioZcG+KMbFhKoqJMPJIs80zONJArcvLSBptjJz8v0KQSepQr/7UgVdM208clitUz10KbBqnL1tqEML/enuHqh125uWN73nclaXN4RZyBZYCBZoqs+zGUd8eq+bNetOLF7mfmCbvG5X3smqX5F4i3r27l1eSP7xvOezxheiejAVB5RbEWVRc+SRaB6vvVO5Nk9msOyHSZFgRd6k/zu+g5KukWmZHgkdddBlgSGUkUeemmQbMlAEODfn57NAfrpthHuXdN60nGbS/hkCQTPqV5AIBj1ocjn/76jWTZ5zcSwXVz9zPhncx4A/c7v/A6PP/44V199NX/+53/O7//+7/OVr3yFwcFB/tf/+l9zvbsLClEUuG9tC7bjsm04Q2s8wDXdtUzmdAq6xZPTk6SKBobldU+YjovjgGHbuI63avW6fjyH+MNhOS45zcYnO+wYybJ/Io8kiuydyOE60FoTYKpgsLY1Rm3Yx1Rep2hY6JbDSMV751QmnXmcW2wbzrB9OMurAyk29aUwHbdCepYoHEeF2bQcCoZX6vGIql5p593XdfJyf5qeiULFjNelPRHk1sYIw+kyLfEAt61oPM/fcG7RO1ngR1uHmchqbBnMMJQuY9sOtssJrS3A093yKZ6Q4Y6RDNMFLzWvvTzE6vYYITWAi8DGpfWz9I8kUeCBK9romczjk8VZXLuLDW++sp3L2+PsGcvRkQgiCgJvu7qD9V01/PemQfaM5hhIl8hp9qwOVFnwOr5e7kuiWQ7pklntmBMFL5tcNmxKhldujfhlRAQKusVgqsRLB1O01QaoDSo8vneqGmg1x/xEKyUmVZZ493VdCAL8dPtYVa38p9tH+aMbFp7wez3XO11VRF7gPzYx/ZHNQzy82SPcvmldKw+sa8eyHX60dZTBVMkjH1/RSjyoopsOY1kNx3UJqnJV+HFmP4okcNdqj1dZ0E2e2j/FSNrjtdy2rBFf2BMsHEqVsB2XJU0RfIrEjuEMLxycrnIzf7xtlFuXN9Ic97NtOIvteKXZzkSQbMmgd7JYJaXvm8ijGRY7RnOYto3jgOC69FUc53uni1UZh6LhsG+sQFPMx0i6hOl49wotk581JtsG0qd03swlfLKIbjkYlouA15E5VxIdp4OJrMeZdd3Z3dangzkNgEzTZHJykj/+4z8G4C1veQsdHR288MILLF68mPvuu28ud3dRYFVrrGp8N7NqPThV4EdbR7FdF920KZpewMOM5cFhk/nJfjfLdhlKl5FEAVUSCagSsih6rdKW1+IL3io2r1k8snkYy/HaVu+/rOWScbF+raKk21iOw/aRrOdN5Hot78cLfsBbbVqOhipLWI5LxK9w45Ja7lndQrJgIOBxFfyKxCfuX0k86MO0nWqJ7FLGo7vG2TKYpWcyz0RO97zzTvGztuMFlqrsEXjdigRpsmSQ12z+9vWLEA9b+R8OVRZZeYkQyLcPZxlMlTgwVWQgVeL+y1rpnyqyeSDNVF5nuuBlEYSKpoKLJ8iqyp5HVsFwZskFOK4XBNmuF0SKAmiGDXiZR910SJUMEDz+YarC+RAET19osSoRVCXymsmBqULVgHkGJcM+ocaSaTu83J+qPt46lDnqPZph8fDmkarlx8ObR7hvbSv7J/JV9/Zc2eTFgynuXNXEwgo/ybA9Y9fGqJ9MyagGWabt8sTeSbrrw3zzhQF2jngmsumSyTde7ON/3raUvukClu3iuC6TOY10ySBXsipj4wWNM9whZ8Z3zPG2LYmCJ2yqHyLn5jULURQJ+zxhTgcQXJAr41LQvKDUrYxJpqwTUgVmqp5uZSwPR/ZE5oHnCGPZEkXdomIVR1azKGgWYf/5bRwwbYeZ0+zMjIHmOABSFIVHHnlkFtfnmmuu4ZprrpnL3Vx0OHxC3dSX4uBUnm1DaSazOgiemLoNJ492jrltTxjPdjyJgYLm4lNENNMmpMrUhlXWtsdIhHz810sD7BvPY1ievowowh9e33VGppnzmBusaIny+N6JykR6ap9xgbLp0lHjIxpQCPtlNh1M86Z9z9MS97O4MUJXXZiruhLEg15ZWbmI+BdnAsdxeXUwzcOvDDKe09AqxsKnO7F11QZojAXony5V+FIOjuOpPyvypT1G4JV6Zm744DVB9EzkeejlQbJlEwEvgJFkEdvx5gFFFDBsF8Py/o51GkqigCi4ngGqKOCTJYKqSEhVmMprlA0L3bSoD/sR8MpmqiSgyhI1QYVFDREkUcSvSARVmeXNUZ4/4GVKblpaf8LAXBI8e6CZoGmmnPLkvknWdEksqAshiiKW41TLZIf7mj26a4yhVJlwQOKv7/TawhNhlaAq4uoudSFPEVtAIF3U2TeRR5HEauPIZF4nWzLQbQefJDKd9/aRLZkIgscRKho2pmWzuDFMNKAwmimjyiKXd8SqvwOCi5cIcdk6lOXu1S10JkIMpbwMT2dtCEmAhXVhfLJI2fQWLUsavUVqxK+QK3vCubIoEA0oZI8o7Rw5iuIFuO5jfgXD8rKyXnRtE1DO/8JrqnD2Rmhzcmf88Y9/XP33unXr+Pu//3vuv//+Y7739a9//Vzs8qLEU/sn2TaU5TsvD1LQLTTTxnbOKO6pwidLhH0ypuN42YSK4NvAdJGmeADTctk25KX7i7rJlsFM5T1e14jrek7kF2Nb728DUkWdgCKxfkENz/RMcwwKy3ExkCoTD5ikSmaV+zKcLmHYDp958DI6a18b2b2yYfPRH+7kFztHKRlnupbzrrNXh7Jcpypc1ZVg61CGfNmkszaITxZJFQ0Sp2AIejFDlUQifpm8NiOa57W5DyZLpIsGYmXBpYigV5ID9imedJYNhu0iCi5Rv4oqiUzmy+Q0CwEB3XKQRBHH9TLTlu2yviuGXMlMr+usoauScW6J+ZFFgVhAob9ybMczYxVFgXtWN/PEvkkANjQ28jHgR1tH2DKu88c3dtMY9RFSZXZXus7aaoIoksAvto+wd7yAi5dV/dLTB7nv8jZ+vWuCfRMFXNdlumCQLZtE/Qov9aUYy2oIAtXWeNO2yet2JatuY1XoCCG/Ql+yhOu6NEb9hP0KhUrDi1wxhi1odmXsPFVtz4TWqwSossjSpgipkpe1Xd4SRZJEMhXJAVwwHZeRjHcjb476GcuUq0HryuYYsiRUSpSV31+ZHfDccQFK3tlCYdY8ZjlguwLnuwWnWL5IAqA3vOEN1X+7rssTTzzBV7/61aPeJwgCjnPmE9zFjrGshm17XRGueyhan1H3PV14qzmBeNBrX83YJmJlVVt2wEgWSBd1SqZX74/6ZSzbpmQ4qLKI49jsHs3x460j3Le2ZU78eeZx6ihoFt98YYC+qSKaZZ9yBmgGuuVQMh1mzp4Zp+582ap2hl3KmMxr9E4U6J0q8MTeiTMKfoKKWBkjrxvMdlxyusk1rXWVxYdbITV7fI5LPQASK3ylFw8mEQSBgCry5N4p6sM+XNelaFiosoJxGqKEIqBb3vkkALiQLmrkRInqswJIkkjJtKmP+EgVDWzHZTSjcd/aBK9b0TTLWLcvWazON4blMJwun9CNfkFdiHfXdQGeDArA8wem2Tllcs3CWm5cXM9gqlhtaR9MFdFNh2d6k7Pm1qFKdmz3WA6nwqcpmxaP757gukX1pGaI1i7sH/f4NPvHC0gi1bLhnvFC5bhtAopY5ZeUdJveqQKm7QU4ggADSW9/kYCMJHqBiiKJqJIXMMb8MgtqgwhA2Cdh2g49UwWESlAjAlMVoduA6pnNOq5L0CeTLhvky8asecMwZ1/3PRPF447pucIjWyaPem7vcIY1c2yAezK80Jc5623MSQB0eFDT1dV13Pdd6vyEk6EzEWIyp3upy7LpqdS6IAouJfPUSyAzcMHTNEkWsFwq9c5DG9Ft0IuHasxl07u4BcAxbTJlm6mCRs9Ege++MszbN3RW3bS3D2eYyOl01YVY1BBGM212jWZRJI8L8dsmrHi2LfLf3jRAfSJOIqgyktVoiwf4zKP7GZgukDvDDoV4QObWFY08tnuCXKXWr4gCLTUBWi9BgrtlO/x0+yg/3zGOgMtIVqOkW4xntWoQczoQgFhQhZK3ohYEz5Tz9hWNvG5FE68OpumbKgIeX6Uh+troQq0Jqdy12vOoerk/RVYzEQSBiF+pdmF5Ctendt4dPvIzVQ3vdLMRBS8wEAWwHQfbhkxF0dh2vNdLus0r/Sme3D9JXdjHmrYYjVE/PRMFRjNlbNfl8EpNtmyyfyJPSJVZ3ux13O0ey1Ey7FkdeJrhYBU93s51i7wOrhm7CL+i4rgu6zribB855OPYWPmNRZiVpYgEZMJ+T19LM20EAexKZNORCLJnPA94PKWZLFZJt8hXvByFCjcqEVLJFHVymo0owtqKdVFtSD2sPOXSURvCr0i80JdiJO0FSZmyiSKJxAJKtTJgu1Rd63eO5DAqB50sGkzmdPqmZ3tUmkfcRHzq+Z+n1zYfPfd0XIBsdL589nIVc14X6evrm+tNXjK4blEtNSGFlS0Rntw3xXO909QEFUzHa6k0TIeyYVEybTTr1KIhx4WieXqR08yFlSkZ9E54mimXd8RJFZtpivnZNpThN3u9KH7nSJbWuJ9f7Zrw/Mtsh4X1Yf767uXEg+osMuM8jo9vPj9AyhzGcV2ifsXTqzkO3+JUIAD/9KY13LyskR92jfDDrcOUdIdrF9XyjmsW4L8EeV3ffL6Pf/j5Xk7x1D8hJAHa4gH+7JZF+ESBR7aMkCmb3L2qiXde04Uqi3Qkgrw6mGYqb7C4MUxzLHD2O76IsHs0x7M93hxj2x43UBIEgqrErrEcLx5MnXwjJ8HM/dZ1vUyOLArIlZbu7roQbbVBRjNlfrN3grxuURdWWd4UpS7iY994DseFhfVhnu6ZZmF9GNeF77w8yEi6TKZksr4rQVtNgC2DGcBrWb+5Xaru27JdcmWDkM/L/JRnSMAhCPrko+wpZqQOQqrIdCU5IrjQXhMgoEqIgiexIAgQrLz3D6/v4sn9U5QNL+Pz7msXAB7BeyaIypYsMmWT3SNZMpWyl+PAs/unAM83zJoJair6S9miznRer3rYeYKllkcKP+yY9UrJLa8duqE7Luwdy540YzmWPfsy0Oni8A7KGbgXYL0sShJwdiTwczKLPv7443z2s59lx44dKIrCihUr+J//839y2223nYvdXTQQBMGT4G+JsbotTk1QJVk0UCSRaEXYaudoDtt2Gc2UGU6XTosTcrqwXY+hn9MsRjMa/VNFNnTXkiyajGc1WuMBNNPm0d3jTOd1cmWTgCozkdP58tMH6awLMZIuI5SSJ9/ZGeJEmZdLCWPZMrbiiZLpBeOseF9BVeTj963k9pXeKv/W5Y1c1VVLc9x/SZOdv/jkgTMOfpY0BFlYH64aWHYkQrzjmk7Wd3mZgTesaz/qM4IgsO48q+SeT/RV7vBhn0LYp9AS9zOa0SgZFumSgSzCXK1fHLwbfl7zsie67XD36mbWd9Xwz4/2UDRsdNMmWTDYNpLFdb2Mn+24FHSLyZzG5OomXBfGMhp7K+Wnx/dMsKQxUg1c8ppFMu8FBJLodee1xAPkyga5slXVXMuVLfJlk6d6kogcyouPZLzzw6+qBBUT13VRJJGpvEEipKOZHj9ScGG6Ug57eLNXcvMrXnftI68Os7ajppqNobL93cMZntw/PWtcypUBnhFMBG/MNx1I8tarOkiVDolsuq6BKolHta4PVspoR1YIJAmigRPfogv6mVlAnA1e6jv6fjCS1qgJn98Ma2YOOuDmPAD6l3/5Fz7wgQ8A3gT013/91/T29nLHHXdw77338qMf/Wiud3lRIh5QCfsVZEmkbNoMp8u01QSI+GQaIj5sx0YzbXIVZdtziZlVyWCqxEReZ0ljBNN26E8WEQXwSWK1/dKyHVRJZPdYDlEQEEWBdPH8X2QXE44XpM0qjQmHJ8DPHO01ft60rp3XX+aJm/VM5Pn5jnEc16Uu4uMtV7ZXy5iXCn64ZZixknBGLbsBWWBZc5SP3rOCdQsSOI7rkX5TJZ7tTSJJIld01JyDo7740RTzsX/CCyQEAa5fXM9opsw3nu+nqFmcOfvw+PBIvlDSHX60dYS2mgABRayUzD3+iluRA7FsKhkPmZJh0zdV5IrOGrTDzFpDPnnWEaqySDzk3ZYao35q4iE2Lm1AFER0y8Z2Djmki6LANQtr2TacrQrBtNZ4Wb4VzRFymhcA+WSRKxbUkMnr1bnWxePoAUzkdc+EuHINj1c4OYIwW1+mJqRw5YIafrNvqvrcjP7fkRkQu7J9r/3fe85xXTTDqgZeMzArOzlSy8Z1BcqGM+tXlI7Yz7rOOOcbkcDRWamL1YXgZJjzAOijH/0obW1tPPTQQ9x55528613vYuHChUQikWMSo19LmC7o/HSbJ8pVMmwSYZX6sI+gJaFK3iSxqCGMKgkokkRj1IcsCTiOy3Re9+TNgbAKAVUhWTRPOUMk4q0YRLwVyExAMwNB8Nrpy6bnGdVVF6KgW6xpi/PY7gkaIl5AFvHL1IZUFlZc6+dxalAk4RQZF4fgkwXuXd3ErtEc4zmDxqiPdZ1xeiby/K/vbOW6RbUVVVjvl5zO6wymSixquHiF+o6FH24ZZaIsHNfe5niIqhLxkMry5ljVZmGqoM9qA391IP1bGwBd0VGD48Kvd09gWA7P9kzRGgswmC6RKXs3f0mYzYU51ZBo5n0zQcCR23HxsjX7x3Ncu6iO3aM5BlMlQj4Jw3KrgpSiINCZ8HiGtutpWr1lfTtffbYfWRJojQe4bXkDRd0zkV7TFsPMeVmWu1Y1c+OqDi7vqMG0bBRZpFgpgSmyRzT+4OuW8spAil0jOWpCCv/5risB+PObu9kzniNbtrh9RRNLG6P8YGRo1nec4dPctaqJ7cNZLNtBlkTuXuVlXpujPsZzOi4QUiU66yJkNZuQKlEyPP7T4kaPs9ReEyRTzOK4XuZqWVOUoCoRVmVKlRJYyCfjU2RWNEd55sChLEq80o3ml0UKhy2GA4rE6y9r5nOP9VT1f9rrggxWXpeA313feQq/5tzi+qWN8Ov+Wc+1xc8/J3Euwvs5D4BKpRJf+9rXuP7662eRnh988EH+/d//fa53d1HhyX1TpEsGe8fz2I5bEQezuLa7lmd6vItarJAVL+uIM5HV0C2n2lXhl0VCfplru+uoCar882P76JnIky1ZWI5TrTHPQBY8oqwgev5QUb+C6XjqnKIgUNItBEHAp4o4tossSdSEJVriAeJBlau6arlrVSNLGyNsH8ngV0RERM+bpquWH28fZSRdPqrOPo+jce2iWp7rL2G7nto3wtEp7Rm0xFTedGUH77lhIYok8v976iCO6zKSKbNrLI+/UmPfMpihIeKflfG5VOUMHFwvoD5JRF8fVrl1eQOWA/vG81WxtSf2TbKoIUxAlZBEAbsyuJfqeMwFBEEgoEjVv9GMxisDaabzOlG/jCQIhPwyK5ujyJXFTzJvMFXQvfnBtHBcl6JmeyKIgsedCaoyTTE/JcMh5JM8rzDTIlOyZhGmZVGgbDp85KZFPN87zfMHksQCCiXDIlc2WdwYIVXUUSSJkE/isnYvUL2svYYP3emjf7pEY9RXDSJmMFzh/f7P25dUzVB1y8tMhyrcN7WiGzSYLrJhYR0bFnqaPq8O5bh9RYCvvzhILKASC6jsHM2yZyxX4QB516UAVWL2A+va8SkSr/SlWd9Vwz1rWgD4i9uW8O9PH8RyXG5aUs+y5ihFw6Ix6ke3bAQEFlZUwx9c10a6aFAybJqiPm5Z3kDIr/CeGxfy7U1eyPL2azpRZJH33bKIV4fSlAyPU/Xem7oB2LColmd7pnFciPhlbl/ZTF0kyM/+/Ab+6dG91AZ9vPfKOO1/6/mKXbu8jVuXN83pOXUqqI0eTXiWL8Bl2ByWGC2cWYPJDOb8sEVRZPPmzTz44IOznv/5z39+zp3ZLzSsioPpzIrdcb3S04rmKHndYjhVoiUeQBIF0iWT1hqPM7KhK0GmbHJwukh92Mc13bWIgsAVHQlWNMcYyZTpmcgxltUxbAfH9oKmhmiAZc0Rbl/eyDM904xly0zkdOojKrcsa+TqrgTtiSDjOY2fbRsjo5ncsqyB5c1RdNMmEVIRBIEN3bVs6K496vu8+cp2dMtmajzIB87fMJ4SLjbu0OL6KJrroy9ZwrIdwn4Zw3TIlnUKhnc+iMBVC+J8+jANn7JhHzpfHBfHpnpVOi4sb47g4rmgr2qJXrL2JomgimY6iBVVYFUG3fSUc0UBGqIK3Q1R1rTF8SsSpu0yli1j2R5p1XU9e5hYQOHOVU1s6ksRUCRuXd5wob/aBYVpzy6fh1SJiF/xhFJ9ChuX1PPRe1dUX3ddl1/sGOPgdJHhVJl0yWA8W2ZhQ5iJnE5zzM9dq5tZ0hDh4c1DXhOGbjGYLtI/VaKoGaRLJoos0loTYFVbjLDPEz6ccYafEUJ84xVtnqJxySQakGeRZ9tqgrTVnLqRp0/2Fm4zJrj1ER8+RcI6IqCemYNndJK87wyposFVC+poqwkwltUQEbiq61Dm8N41LdxbCXxm8Ob1HdywuJ6SaVftUdZ1Jnj7NR08sXeKREjlL+9YCsCdq5rJli1SRYNFDWFWt8YA+JObFvGmdW2IolglNAdUmT+4fiG5sknYJ7Gg1tv2396/mn95bB/JosFdK5urDgOddSG++LZ1wCGJgK++az2LWhsuSJY+GlBZ1xFj11geAVjTFkcQz38JbMOiBr6/deystnFOzFA///nPs2PHDgzD4Itf/CL79u3j17/+NZ2dnXzhC1+ovvf973//XO/+jGFXmPjDw8PVVcfpoiug09s/SY1bpqBbOHmDhYkEqalxFodgcUgGTPK6hZ2fJq9ZxIMKdZJKe43M6hrv5paaHAcgYufIZErUApcvD6OZAXaMZinoDp0JmQW1Pm5dHkOVTfRGl922weW1KjcurSfiE0HPMD6WAeCeRT7AB5TITHklhJHMqX2voSEvdTw4OIiVmz7Ju397MDw8XB0bqZxkUUilVnDpqg3x4Pp2BEHgP5/v44XeJCXL5uoFCW5aGkMqpxkePkSEXODX2DqUwW87LIk4HjnecamJxekKRCv6KRJQZHj4/Ot+nClmxmZxSCMW91G/JM6LB1LsHs9T61dZ0hnld65oIRZQ+OXOcYq6gaKlubq1jt/sm6TG1Zguachlg7a6GPnkBHkgCNzU5k24hdQkhbNvdjrvOPyaisfjZ7ydqGMjaSmSeYOQT+KuJfUMDZWZ1DSCksjG1sbqTXMGaxKwJhEAPL5MybAYy2rE/F73FphQTrEo5LmetwQV3npDA6/0p9k9muPAlEFz3EfEJ9CulhkeHkYAWpUSe8byhHwSizqaZu13Knv2Y/PWlSEeedW7bh5YmWB8bJSw5aBongVI0CfS2iEzPDzMZQmHgwNT2I5LY9RHg1iglNH4y2vreGLfFH5V5Hcuqz1qbI4FHzA8nKk+vr1T5fbOigFpMcVw0TsB7+n2YdgyfllgfGz0qO2UKpd8xLZR9QwUdRxLpFXxVY/j/Rvqqu8/1rHNjI2RnWJUuHDczDsX+lgV94LNrnqVsdGR834Mf7I+wa9f2UOmZIHp3dNs+/QyQoJ7uoX5k6C5uZmJiQnC4TCFQoFwOIxpmui6TlNTU9URXhAEDh48OJe7Piu8/PLLXHXVVRf6MOYxj3nMYx7zmMcZYNOmTaxfv/6U3z/nARDAgQMH+NSnPsW2bdsoFApcccUV/O///b9ZvXr1XO9qzpBOp0kkEgwNDc3KAL06kOKFA4eWmJv6U54Yl+uiShLruxKolWLysuYIty6/tN24j4Xh4WFWrlx51Ni81pAtmfzXSwPVboy6qMpbruw46n1/+5OdPLF30itxFqbZ8fn3vObH5kxwvs+bvGbyXy8OVvlBjVEfb7ry6Pb4M8GBqQK/3DGO67rsncihGQ6r22LIoshNS+tZWSl5HI6dIxme2ncoY3pNd4IrKm35xxqbiazG0z2T9E4VmciW2TWaI+JXiPoVVrfFqtwwgCsX1HD1wqPL1q8FzIzNTR9/mHA4wns3drNxSf1pbePvf7qb3slC9fGH7lzCipb4HB/p+cfM2Cx8/zdQA0H+/g2ruX3F+ecBXWzI5XK0t7eTSqWoqTn1pohzQl3q7u7my1/+MuClpHbs2EFbW9u52NWcQZK8ySUajRKNRikbNpIo4Koa/lDF8dd1GS+nEfC4TKoosqariemC5zF0x+qm16Tx6MwEPTM2rwXM/L6HE4yjUbhnncxLfUn8isQdK5uIRmZrW0wXNPpzLobolSvNyv/PdGzOVoH6Ysb5Pm80NJRAiBmmoaPIc7bfy6NRcrbCD7cMkzEVVEXkYNZhdWsE1CD+YBinojmjmTZBVQLFwB861LHnqsFZYzLz/0gkwoGpAj/bm8F2FfK2gia4RCKe6rMoCBQchXgogGm7yKIAh23reHBdl5Jh41ekS0rZfeZ75W0ZwxBJ6sJp/45L2xuZNkQ006ElHqCprpboMci7lxqq46AEENQQluh/zczJc4GZ+/ipYs7v1suXL2fDhg187GMfo7OzkxtvvJEXXniBYDDIT3/6U2666aa53uWc4+n9U2weSCOLAuu7EvgUkem8zsGpIpbtkNdMZFGgKR7kthWN8x5blxie3DfJlsEMsihw1+omFjUc6kJZ3RZjddvRq3mA//urfbzSn2IkU652zUivcXuXSwl1YR8diWC1Tf6Kzrltj1/WFCFZNJBEgWzZRKw4hcsifOmpAxR0i5JuURv20RL3c9PSBraPZCkbNj5FZGXL0TeqsmHzo5cG2TKQJlk0WNEcpbs+zI2L/fxi5zglw5OteMNlrTy0aZBU0SCoSty7tuUYR3gIpu3wg1dHGMmUifhl3nhF2yXng5YumQQFi8YzUPDubgjxm30T2I4XMLZcgDbtcwnTdpFsh2UNp04kn8fRmPMAaHBwkFKpxNe//nUSiQSlUom/+7u/Y3R0lL/+67/mueeem+tdzimyJZPNFaXOkUyZHc/2cfeaJnBd/IpUnVxjfoXGyKU1ofw24ZX+FIOpEk0xP9csrK1KMqSLRlV233Jcntw3NSsAOh6e753ixYPTyKJIxK+giAJLm6PEHZXd5/KLzOOUIYoCb7i8ldFMGb8iUR+ZW2XaFw4k8ckSKdvAsh0CqsTvX9PBI5tH0C2bvqkCmbLJOr/MaEZjPKvxjms6mc4bJMLqMVv2d41kmc4bhHwyI5kyY9kynbUhVrTEuG15E3vHc3TVhRjNanTWBon6ZWIBheF0iSWNxz5vtw1leHLfFH3TBdoTQfKaxaa+FHeuurRKJaok4JNFxivq38eDZTukSyYRv1wV5JvM6axsjmHYDmGfp27fnnjtBAve4stl/3SZtQsu9NFcupjzAMi2bZ577jkEQeDtb387U1NTfOtb32L//v2nLISmaRq/+7u/y+7duwkEAjQ0NPBv//ZvLFq0iMnJSd7xjndw4MABfD4f//qv/8qNN94IcMLXThWC6OmOpIsGLx1MYruelHtHbZDGiJ+GqA9FEmirCbCkMXJJWxO8VrF3PFfVXRpIlvArUlUsTzwiY3MqpYFHd43z1P4pxrMa8aBKPKCghH08cEU7CTfPZ+b+K8zjDCGJwjm70QkCNEV8DCaLyJJIY9TP3rECmmmzbSjLZF5DNx0s28Une8cSVGU6ao8/zQqV6SMRUumuD9MQ9XHLsgZWVThF1y7yuoL6k0X2jOXQTAdFEo6bpeyfLvKbvZNM5TWv3VvwxuNSKoHNQJVERFHkREeumTbfe2WI6YKBX5F44xWtNEb9CILnrh7AC4hei6Kujiswn4A+O8z53buxsZHdu3cTjUbZuXMny5YtIx6PI0kSonjqu3vve9/Lvn372LZtG/fffz9/9Ed/BMCHP/xhNmzYQE9PD1/72td429vehmmaJ33tVBH1K9ywuI7pgo7luNQEVWzHxbQcYgGFpU0RVrXGuKy9hrtWN1+yEuCvZaQKs6XmM6VDj2NBhesX11VuThK3nYS0ni2Z7BrNURf20VUXJq9ZRPwKf3nHUu5c1URLzWvLYHMex8cNi+vxqxI1QZWljWGaY35SJYOGiB/XdYkHFGrDKtmySXdDuKrjciKsbo3TkQgiCHBZe5w/2djNmrb4Ue8TBaE61/hkieOFBanKuV4X9pEIqeiWTV3Ex4aFHvn6HPS8nDP4VYmWuL+qqXMs7J/IM1253jXTrmbvNy6tJxpQEAWByzritMZfW9epKAqE/TItrzGD3/ONOc8AdXR0cPfdd+M4DrIs09zczO///u8zODjIQw89dErb8Pv93H333dXHGzZs4DOf8dbZ3/3ud+nt7QVg/fr1tLS08NRTT3Hbbbed8LXTRU1Qra6adMsm7JO5e3UzjVEfQkVlee9Ent2jOZY3R2apXs/jwsB2XH6xc4xtQxn6possbggTUOWjSgXrFyS4srPmlH4zRRYwLJueCU/f5Pevbue9Gxedq68wj4sYiZDKO69ZwIce2c5AqoRmOdy+ools2eTKBQkq5hHcvLSeyyoZx8mcxlC6RFMscMybsCqLPLCureIZNft8nPlsY9RPUJVZ1hStEpvH8xpDqdJR2a6FdSFeOphCM22WNUW5Z43HcXMcl59tH6NnMk9t2Mf9l7UQvci5i9GAQlMsUBUhPBaOdCb3VZoaGiJ+blxcR7ZssqTp5CXuSw1+RaA+7KMtMR8AnQ3mPAB67rnniEajXH/99XzoQx+qlqC+/vWv8+EPf/iMtvn5z3+e+++/n2QyiWmaNDUdqmUvWLCAwcHBE752LOi6jq7r1ce5nKdimirqPLE3SW3Yx6qWGIPpElG/QtAn852Xh3jTlW00RHx8b/MwqYqp3WimzG0rXnvt75ca9o7n2DuWR5VEuuvD1IV93H95K3XHcCk+1YBVFkV2jeXony4iIFB3iRFJ5zF3MG2Hrz7Xh245uK6nYJ0rm1y5oIbpgs54VqM9EaxyysayZb73yjC24xlivn5tS9U64UgceT6OZzW++8pQ9bP3rmlmRUuUncNZRrNlfIrII68Oc++a5lkctnhQ5fc2dDCcKlMXVmmIeuTffRP5qnHqdF7nhQNJ7lh56pygcoWMfV5LaS4k855z/LGU6gGWNIYZycTomShQF/ZxbbdXMnzxYJIXKn5brw6m+f0Nna+pDl3bgUzJrBq6zuPMMOdnxJYtW3jqqad48skneeCBB1BVlY0bN3LTTTexfPny097eJz/5SXp7e3n88ccpl8tzdpz/+I//yCc+8Ymjnv/G8wPsmjZpjQfobggT9Ek0xw4ppm7qS3J1V4JkQSdbMtEsG0Fw5wOgiwB9U0U2D6RxXJfWeIDLO+LHDH6Oh8mcRsm0mMxpxIM+ljRGGEqX0E2neg4MpufuHJzHxYWCbpEqGNRHfATUo0vb6aJBsqhXTTNTFe8nnyxx/2WtTOV1frhlhC8/c5DO2iCNUX9Vk8h1oW+6eMwAqGRYTOcNasMqoQpRum+6OOuz/dMl7ljZRCKkVvksrgs7R3JV3aqmmL+qG7SiZXZ2xz7CmO7Ix8eD47j8ZPsoB6eKBFWJ37m8tRpUnWukiwaWZDCYLrGBYwdAgiBwbXcdi+ojxENK9Xc7MHVIA6io20zkdLrq5uZ2Z9oO41mNsE+uqLSff1iOi2Za7B3LsaLl+CXCeZwYc3JGfOELX+C9730vfr+fp556CoCbbrqJm266iZGREZ588km+853v4LoujuOcZGuH8JnPfIbvf//7PPbYYwSDQYLBILIsMz4+Xs309Pf309HRQW1t7XFfOxY+8pGP8IEPHHK4mhFSUiSR5pifkUyZlniAjUsa2D+RJ1nU6Z0oUNQt0kWT/RN59o3nMWyH9poge1bmTqnmP49zh8G050ad1yym8vpp/R4vHkzyXM8UT+ybQjNtasMqb7yijXtWtxCsuD8DNF+iXlzzODEm8xoPbx5GNx2CqsRb1rcTD86+ublAumgyXdDJaxYNER/7JvJcvTBBxK/w4sEkBd1bkQ8kS0T8s6fXhsjR506mZPCLLVNeIKWIPLiuveLzNztwn3lcf1hAnyubPNc7zSOvepYJV3TU8Jb17bQco9S2tCnC7rEcI+kyYZ/M1V2JUxqXg9MFDk559islw+b5A0necHnrKX32bFEyLRzNJKAcnzta1C3+++UhcmUTRRK4/7JW2hNBGiJ+JnNehl8WBRLBuQlUDMvhe5uHmMx5hrJ3rmpi6QUosRmWS1735ql5nDnmJAD653/+Z37v934Pv9/P5z73OQzDQNM0NE1D13Ucx0FVVfz+U795fO5zn+Pb3/42jz322Cw/mAcffJAvfelLfPzjH+fll19mZGSEjRs3nvS1I+Hz+aq2HEeiszZEa02AP7h+AYmQj7ahAN/bPERXXYjasI+xbBnXBVkSUWURURTYMpiZD4AuMBTR01oxbBdVEqk9jezP5oE0UwWDdIVEatkuj++Z5C3rO/jI3cv5wasj+BSRd2xYcI6Ofh4XErtGcuimtzgrGTZ7xvJcc0TZpW+6yJLGCAXdIqh6bueG5bB/osC6zpqjOgw7a0O0xAMMJj05hmN1bu0by1eDa9102DWa5aalDXTXh3ndysZDn60QgRfUhbhzVRP900UOTBUo6BaG5R33eFZj+3D2mAGQIok8uK6NomETOA1hxCNLc+ezm6o+7CMaC2CfYM3cM1kgV/YaXUzbZftwlvZEkJuW1hNUJfKaycqWGLHg3PCdhtKlamDluC5bBtMXJABSRI8/dmCyyMal5333rxnMSQDU19dX/Xc2m6VQKLB27dpq6euGG244LcO/4eFhPvjBD7Jw4UJuvvlmwAtYXnrpJT796U/z9re/ncWLF6OqKt/61reqLvMneu1UocgCkiiwcWkjlu3yXO80ByYL5MsWMyKTsiRSF1HJlE0MyyFTMtg2lOGqrgSLGo5P2JvHucVtKxr5xc4xJNHlukW1R+muFMoGX3jiAEXd5N3XdrGoQo7OayZTeZ28ZuI4YDoOJcOm0+/94EsaI/zvu5ad9+8zj3OLgm6xfTiDKolV8uwMjjx3BpMleiYKGLZDfcRHybApGzZDqRIDySJr22Jct6iWqbxGpmyypDHCovowYzmNdNGslraORNAnAYc6VQ/f78qWGCsPK2/0TOR5ct8UggDd9WFiAZlMySBZ0BFFjxR7LK2hGQiCcMLXj4WFdSGWN0fZO54j6le4flHdyT80R6gN+2mIB6sLS09uIAPA2vY4fkUi7JtdqgxVHksCjGfLTBcMFp2ARH0iWLbDtuEMuuWwujVGxK8Q9smUDItkwcAniyxquDDq0rbjySzML7rPDnMSAB1eStq4cSOtra34fD5c1+WJJ57giSeeALwL8LOf/exJt9fW1nbcds3GxkYeffTR037tVPGeGxYSiUTJlj1fqP0TeTYPpAn7FDTLRjNs7lvbQsgnI0mTPN8zjeW4HJgq8O9PH+APr194QVYE84CuuhB/urHbI6geY6X6P/57K/vG8+C6vHQwxTf+8CrqI36++8owiiRUFHsFsAQs2+Hy9rlTEj6R5cU8zj8s2+F7rwyRKXnBx4LaIF11ISbzGksaI6xq9W4sZcOmd7LA43smcFyXiZxGsqCjSiIHpzyhwYFkkV/uGufeNS2867ouHMdFFAUmchoPvzKMU5nL9OUOC+tD1YwNeEGOIfkZSpVoiQW4vOPY59xwusQnf76HqbxOqmjQHPMTUEWyJYucZqLKIo7rYjsOumUf1R11phAqZZ7XrWg871o6t69sYO3Clup8+oMtI+yf8JpVDk4XeetVHSxqiLB+gc6OkQytNYFq1u7fnjrI43smsBxvEft/H1xL42lyl365a5yeCY9LtHcsz9uv6SSoSmimTarkBUB++cIQqx1AqxDT53HmmJNfb8uWLbMeP/HEE1iWRVdXF+BxcSRJYt26dXOxu3MKQRAQRYGxrIZpu0xU0p3pkk62bDKR1Ximd5qlDWGWtcRY2x5nuEKMzZZNhlKl+QDoAkIQji8O1jNRoKCZmLZLumTyz7/u4Q2Xt5IrmwRVmc7aAFuGMli2Q1G3eWr/FG+/ZsF5Pf55nB/kNasa/AA80ztNe42nx5MIqQiCQP90kZ9WCMBFw2JpY4SIX2Y8q2E6nvqwQJmwX5mlBzYTKAyny9XgB+CFg9P8Zu8kjutSJxar7715acNJj3fPWI6DU0UKuklBtyloJkaFjC2JAg0RP0XD4ruvDLN3PM8br2ibUyXsCyEk+HtXL6j6XOmWzS92jFXJzQcmizxwRRuO69KfLKKZDqMZjWzJpCEq8WzvNL2TBVzXJaBK7B7JnnYANJQ61PCQLZvkyibpkkki5CMR8sZ2uqgf7+PnHJYLP90xwroFp8bnmsfRmJMAaCbDAx5xeWxsjNHRUfbs2QNAOBympaWFu+66ay52d17QGPUhV9LKw+kSubKFabk4uJg2jOd0htMTCKJHSIsHFNriflrnhfEuWkiiR2I0K63Fo9kSm/pSGLbD/vE8qaJOQbNwK3ou24ezF/qQ53EOMJop81TFKqIh4keWBIqVdmLXhWd7p1nTFufFg0kMyyGnmRyYLOC4LmFV9rR4KmRnw3ZIFw2miwYfengb03mD5c1RLuuIs6wpjCgI1SBoPKtVfQN7Ky3px4Prujx/IMlAskRzzE/ZtEDwurdM28GyvWN1XAdJFJgu6NiOS9Fns3ME2mqC3LOm+RyO4rnHd18ZYu3CZtYvSGA7Ln3TRYq6x5fqmy7iOA77JwtM5b0gpGx4Qoh3rW4mldcxLAcXF1enSk4/FhzH5ZneaUbSZdpqAly/qA6x4h82QwCP+GWiAQVZEkmXDEbSZVT52P5u5xO50nwb/NlgzvN3n/jEJ1BVlU9/+tNcd911ADz77LN87GMf4xOf+AR/9Vd/Nde7PCeoDft4YF0bu0azgMuvdo0zs5azXUgWNBAk/IqIKHiEtCVNETTTpm+6SFfdpe88fKExlCoxXdBZUBuak3bTpY0Rpgs6hu3ik0VKukPPZJ6u2hARv0zJMLGdmfDH9W4683jNIKeZ9EwU+OXOcYKqREs8wFhWY01rDPMwpq1asbdRZZHxnEZes6qGyA3NfpY0ReidLBBUJUI+hcaoj72jOfKaSbJoMJAq4rgOu8ey3sLJcbhnTTP7xw+pFp9Mhmr3WI5NfSkAJnIaTTE/13bX8ps9E0iC15HmuN7NWxTAtByyZY9rNJAs8cKBaW5aWn9c7tGlgJ3DGSY1kdqQSns8gCBQWZx44ycJAqosUjZtsiWDoCpXS0KRCn/PdT2uTDRw/HHYNpzh1YqC9EROIxZQWNse565Vzbw6mMawHNa2x1EkEd30Sl9hv4xPFmeVM+d6vjoVnEZT9TyOgTm/OorFIh/+8If50z/90+pza9asIZVK8Td/8zdzvbtzipZ4gP7pIt0NYRIDPqZyejUIMm2QJQfTpmq8uGs0R8nwzsibltYft54/j5Njz1iOX+4cB0CVk7ztqo6zmlRc1+NpmbaLgKflocqe5IEsiXTWhhAEkKVMVXwuHphvMX2toKBbfPulQXKayfbhDJ21QVRJZDxbpiMRRLccAopLIqTyupWeptfNSxvYNpRBM20s22VKM9hqpemqC7OyNUaubLKgNsTq1hhff74fq6KtY9oO0wWDkmFVRQpTBYPbVzTxy51j6JbDygpXRTNtIsdQgc4fIXBXF1K5cXE9O0eyFHQby3awcLFdwIHakIIriEzmNWRRZCKn8d+bBvm9DZ2XrF1P/3SJSV1idHEdbTVBakO+asdXbcgHlQz9VF5nPFMm5JOrbeG1ET9BXwnbdokFFfzK8W91R451TvP2ocoiGxbO7gTM6xZBVWZBxd9tJrO0ezTHr3Ydmq9+7+qOo2QUzgVc4dKxNrkYMecMKkEQ+Ld/+ze+//3vMzw8zPDwMI888ghf+tKXTssL7GKB1ymSxXFcZEmotB8KqJLXLSYIAqIgEPHLs0743snCCbY6j5Ph8PEzLIfBVOmMt5UpGXz1uX5GMxq27YIAAtBe4+f25U10VOwE4kGFppifppifxuix25bncWliNFOmZNjIokhtWKVnosCm/hTZskW2bFIb8nHtolr+4Pou2mq886EmpPK+WxbRVhNAkUSESqbXchwEoDUeQBIFYgGFla0xAqqEKAg0Rv34ZXGW7o/puDTF/Lzrui7+eGN3VbzuK8/08a2XBikZs2/CSxsj1cBFlUVWtcW4a3Uz961tJRFS8SkSsigQVASvrV2SiAcVBEHAtB0m8zpP7Z/iwCU8D43lykwXvPKWi0trTYClTVGWNkVprQlUxSWLuoWDV47cN+6VFpc0RmiJB2iOB+hIhE4oiLqsOVLNHPkUkWVNxy9rtdUEqkGWIFCdI3qnZs9XA8kzn69OB8ETaCTN4+SY8wzQFVdcga7rs4xIZVlmyZIltLS0zPXuzjlcXGzH9fR+BLAckICAX0IRJa7ojLGgLkJbPMCzPdPgehNn3RwSEH8bUR/xVYMgQeCsBL9ePJjyVo6CiwPgguDCHSsb2dBdi+24DKVK+BSRKzsTPLZ7gqBP5t3XLZiT7zKPC49ESK3ycdriAQzLoU0JMJnXGUiWqAmqNBzjmq0P+7hxcQOmPU6yYJDXLfZPFNg5mqMmqNAU9dM7WeD/c88ybl3WgGk7NMX8xIMKT+ydYjhdJqBKbDiB8OB0XufVgQzXLz7UYl4TUnn7NZ1M5DTqwj5iAY87dPuKRoZTJXon8/ROFYj4ZEzHy1wBhFSJiZyG7TgVjaI8K1tj6JbN871J8rrFmtYYCy6BEn0i5CMcUDBsB1USuaIjXp0TFjWEUWWRTMlkNFOmoFv4ZJGJrAZ4ekn+AxKG4BAPKEcJSx6Ohoift1/TyVRepyHiq/K0jgWf7IlkjqTLRPxKlWheH/ZVg01BoDr/p4oGLx5MIgDXdNfOeVaos/bi/x0vZsx5APSZz3yGe+65hwULFlStL/bs2UN/fz8///nP53p35xRlwyZTNFnUEGa6oJMu6bgWaJaLVrBQBIt0wSSgaAxOF5ElgYFUiVhQpi6kViXz53H6uKrS2ZAsGCxqCFdX5SfD5x7dS06zwHH4+c5xblvWwMblFc+jw7LFDjCd9/gYkihUbwjXL5YYzZRpjPpneSzN49JGLKDQGvfz6mCGFS0Rz1Fd9NSUy4aFbtocnCoS8slsOphElSU2LEzwz7/u4ZmeCbKajSwIiCJE/Sq65ZVUp/I69RGVX+2c4K1XdTCU8rwD68J+3rSujbxuefNI2STok4+rw3N4k1VOMxnPajREfLOMQMcyZXom8qxoieBXRFrjAdIlg/aaIAXd5NneJNmyQcl0UUWBsm7x8OZhZAme2jeNi0eO7pnIc9PSejprT5wZudCwHIeWSjZWEAQ6E0Ge7pkG4NZlXuec6TiM58rkNQtVFilUSNKa6VkUFcom8RaZ6YJBxK9QKBv8aNsYsaDMvWsOKVq7jkcwP9whZDKv8dlf7aOgWbznxm4u64gDXhDUWhNAOayicXVXAkHw5qvFjWFa4wFc1+WRzUMMpcsIeFnIP7xh4ZyOkTTfBn9WmPMAaOPGjezfv58vfvGL7N27F4A3velN/Nmf/dkllQEqGRYPvTRIumjQnywxmimjmZUMQgWmCy/0paEvjSRAXVjFdmEqr7FjJMftKxr53fUd59dA8DUCURSOqr+fDPd84Rl6JvIY9qFZ7KFXRtg5luONV7RjWIeed4GHXx2msy7MtRVxt7Jh866vvsxk3ltFvjqY5uOvX3X2X2YeFxSW7fD//U0PP9gygu247BzJ8rtXtfPDrSMMpUpkyyY7R7M88uowqixWVN69a3YyW8awqV73kgiZkoUkQslwMEydsmnzXM8Urw54hNkFdSHecHkry5ujZIomP9zq7TegSrx1fcdRqsSNUX+VL5gs6HznlSF000GRBN54RRst8QB9UwU+/pPdZEoGA8kSiZBKqmRQG1LZ1J/GdhyKxqHZyXBcDM3mpYNJXjiYRBRAEkWurChW58peO/e9a5tP6LZ+ITGSLpG1UvzxTd1YtsP3t4xQrqhmf3/LCBuXNrBjOE2qaOK4YNg2m/q9AOkbz/exb8Lr4BpIl3hgXTtG1M+7vv4KYxmvvf2F3iT/8MY1jGXLfPbR/aSLBomwyl++bgmN0QB/8s3N9FS69bYMZ/jZn19PPKjyq13j7BnLE1Al3nBZK00x/zHnK8N22DKYIVkxzZ4uGLxzjhfFj20b44+u756z7f224Zy0CLS0tPAP//AP52LT5w390yXymoUsiaxujbHpYJIT+Qc6LkwVDFRJRBE9Ub3vvTKEJAi8+cr2C6Kj8duGnokcpn308ztG8vz4z7v425/unpUFMiyHnaPZagC0qS9ZDX4Anu9NnutDnsd5wHTB4MUDSXJl70apWw5TuTIiAiGfQrLSmVUybJJFg4Ai4bgumulw1CXvemVxw/IIlDMdWHsn8rTGvSzl/ok8O0eyLG+OsnssWzUeLRs2vVN51nUeKof98caFJGri1cf7JwpVSw7Tdtk7nqMlHuDpnmk008sk2Y7DdEHDsFyG9BKO62IdpxtoJuZ3XBAFl11jWda0xgn7ZBzXZddo7qINgMI+BUUS+OWOcRbeFCZVCSTAKy3ZjsvuUS9AmQkpJrLee/oP4+DYDjy0aZA3Xt5aDX4AXqx02f1k22gleLXxKRI/3T7GH16/kANTBfTKwNpFg+1DWbrqw3zjhQGGUiX8iojtuLzv5kXkNZOfbh9jOq+zuDHCHRUive26Fc6YgH0ccd+zwYtD81IdZ4NzEgCl02m+8pWvVHWAVqxYwbvf/W4SiUtHsOko75gKcfZ4p7CLxyuxbE8gLag6SJLAb/ZNEPUr3LaicV618xzDOEbwA54ZIsCR809IlavcCoD2RBBJFKo3rLnyD5rHhcFQqoRhO9SGVHTbxbRdbNfFcV1yml1panA9rRgHLBykConYst3jXusBRcZyHEzL0+CRRIGaoEq65C2AZojRANHA7HModsTjI7MBR74eCyj0TuaZyGkUdQuxogWkSB6fSRTAOs55fzgEvOxVU9TPqtYYsiQynC7RP12gbFg8uK7tomtS0S0bwXbpqA3iU0RWt8XZPerd8Fe2xFBlkfULEjx/IInluEgCdNfPcGJmz9Y1QYW2RADHhYJmIgiC10kGjGTK6Nah0tlIRdhWlkQct6IPhUhrjZ8dIxkGkofMYZ/ZP8X7bl7Ec73T/GjLCMmCzpLGCF11IRY3hKkN+RhJlxEEjyc015QIdX5dfVaY8wDo6aef5r777iMWi3HllVcCnlv83/7t3/KTn/yEG2+8ca53eU7QGg9w2/JG9ozlqAmpLGkIs2Mkh3mCNJCLt+KyLQcBF9tW2DWSo2wMkC4bvOXK9nlO0DnCk/snjvvaW69qA0CRhFnlsY7aIHetPiQWt7A+zJ/fspjvvjJEyCfz0buXn7sDnsc5xXO901UdnbaaALcsrfMCIsuzD9g/maerLkSyYFAb8mHZDobt0h4PcGC66K1mjrjURcAvi0QDMqLgBdYlw6E94SfkU5jM6RQNizVtMW5cUg94XDYv46SzsD50Ul7ZipYoOc2smqAmQio/3DKKIokEVImiYVc4MZ5hryAIZEo6uuWgWcefmwQB/IrMZ9+8ltGMzrbBFLtGckQCMt9/dYSSYfPu67rOasznGqos0VEb5LpFdQiCwB/d0OU1mgDXL/aee+O6Nn6yfYyxrNcG/+4bFgBw1YIanjuQxHUh7JO4ZVkj8aBKTUjxAiCgI+GJ1l7ZUcPLfWnKpkVQkbmy0ytHrmqO8upgGseFtniAaEAl6pcJqp4fmCQKVbLzQ5sGeXXQ0xIaSpe5sivBwvoQJcMir1sIQNGwsB13TikR3Y3zJOizwZwHQO973/t4y1vewr/9278hVdxDbdvmz/7sz3jf+97Hjh075nqX5wyr22LVNsdX+lNMF00mchqi4LnxFgyvHRYOCZOJlUwRgkBeswj7ZSRBYDKnM5HXaT2GU/OZYIYLUBfxzdk2zzW2DKbZNpwloIjcsbJpTjsiHt40dNRzQUX0OB2VBHl3fZh943lcPNPE+y5rJXpEx8eDV7bz4JXtc3Zc8zj3mMxpjGU1WuKBalfOjpFDpYHhdJklDRHqKxw903YQBIFEyEdLPMANS+pJFQx2j2UpmTb1ER+ZkklBt6oxkE8WiPgVFtSGaKsJUDZtljVFUGWJ1rifA1NF2mpMJEHghiV11RZ2WRJPyericGxYWFvlk7x48FAZti7soynqJx5U0SybmF/GJ0vsGMkymi3TN1VkJr4XBZAFQBCQJYH6iI+2eJCvPz9AcyzA/skihmWTLbtE/UpV8fhiwhsvb8MfCjOR02mOBWiI+HnjFW2z3jOe1bhjZVP1cbnCg7q8I8FAqoTtuNSGfSyoC3qdWz6lKkFQqKSMb1rWwFC6XDmH/NxY+b0W1ofRLa8M2hj1I4kC1yys4+quBPsm8vhkkbdv6AQ8vpLjurguuKLL7jFPOmXfeB6rIrK5t+JDCALposFgqkR9xDsHzxTG8dLe5xiposFLB5MIgsA1C2svWLY8r5n0TRcRrfLJ33wMzHkA1Nvby8MPP1wNfgAkSeIDH/gA3/jGN+Z6d+cNq1qi/HLnGK7rIksibYkgk3kNzfDk1rVK3d5xvYknoHi6HImQSk3IhySevhPz8ZApGfzXS4MYloMgwF2rmi96/7E9Yzke2TxMT6VVdNdojv9125I5U0xdt6CWn+6cnQUK+2VkUSQR8i7Ot1/TyY+2jlIyLBbWhy+ZwHEex8dQqsT3Xx3BcV1kUeBNV7bRHAtQ0CwGU0XiQZXGqJ+hdJnasI9U0SBVNMmWDMqGRSyoMJ4ps3ciT0m3PKFMwTNHHUyXqqTbmoBCQJWZLugoksAtyxp48/oO/IrIVF5nIFmmphLQdyTmblXekQjy0sEUjuupl+c1k8m8DrgEVZmYX2F1a4yO2iCG5TCd13FcL9sZVOVqaSfqVxhKldg/kSfil9FMh5JhE/JJWLZ7UWpebRvO0NognvA6basJMpwuMZXXCfpkbqhICTTF/NyzuhnNson4ZMqmQ0dtkJqgQrriAbe00eM+RfwKG5fUc2CqyKKGcHWeXtoUoW+6iOU4tNX4iVWsMN5weQvP9CRpiPhYUbHCqAv5GEx53V6CINBdF8Z2PHPabEW8MeyTsR2XrGbw7U2H5u97VjezuPHM5u+rFp5/WonjuHz/1eGqgOR4tsy7LkD2sKhbfHvTIEXdRiuemd7VOdEB2rNnD0uXLp31/J49e1i7du1c7+684cfbxygZNj5ZxLQdirrF2rY40wWDsUwZUbAp6HZVLM2viCxvjtKRCNBRG2JdZ4JYQMGtEA8LusXypugZRc79yVJVgt11oWcyf9EHQNMFneF0mVzZc67Olk36k8UzCoAm8xoHJovUhdXqxNEc8x3F0VIkkau6ElzT7U2KtyxrZP9EgYJmce2i2nm7ktcAeqcKVa8ty3E5OFVkOFVmMFVkMqdTMizuXNnEpv4USxojvNSXpD7i6ersmyjQVRdErwQD2bKFXxYomQ4Rv0xT1I+LiySIdNT4kWUJF+9GZthula/TVhPkgXWtDKXKtMT9c6rN0hIP8KYr2xhMlrh3TTNfebYPQfBKXvsn8tRH/IzlNNYvSPDgunZ+tHUU07ERBQHTcljaFKeoW5RNz0BVsxwKuoVflqgNq9SFfXQkgrz5sKznnrEc2bLJksZIVV/oQqAurBIPqmTLxnGNXTMlo5pJDqoy+Yoy8+rWGJN5DbXyPVvifnyyxN/ev4qf7Rgj5JN5w2VeG/z+iTyP7vYWTwemCqiywKKGCLrl0BjzYzsuoiBiVBS+n+nxsnKTeZ0n9k3x+rUtPLi+nfG8Z8baVRfimu46FFEgr5kYlqc+X9BMZEmgP1k8Yv4unHEAFA+d/8yLbjmkSwaTOR2hwkmb69LeqWAkU656w50p5jwAev/7389f/MVf0Nvby4YNGwB48cUX+eIXv8inPvUptm/fXn3vmjVr5nr35wypgo4qeyUV3XRojgdY2RJj12iOnGZi2g4+WaiknL1WWkUSGEiW6W4I01YxSX26Z7rqO7NlMM3vXN5GQ8R3Wl1itUdMSjNkvosRtuOSKRnsGc3SM1nAtD013u768BlpkAynS3zn5SHEinXAzctsLmuPc2CqiCRS7YYR8UoGd6xsYl2lpr9lME0s4GmxZMsmVoVMOo9LE1sG0+wezTGWLZMIqUiCRz7+5gsDTOQ8BWFVlpBEgUUNYV7pSxFUZaJ+Bd2ykUSXveN5mqJ+WuJ+MiUDzfLEBEM+hSs74tVAx3FdSqZdLZmatjNr0m+rCZ6yVtXpojUeqGZBrumu5eBUkR3DGQRBqBL8bcfhz29dSjSg8NPto4xlyhQNi6JucVVXglf60/gUibLlHbcoevPIsqYINy9rwLIdirrNrtEsL1W4U68Opnn7hs4TCgOeS7TGg/h9MqUTlHlKhk34MH2lmYzd0qYIB6c9naZrFtbik72KREPUfxTXaSyrzXo8mtFY1BChbNrE/AqW45HdvUB5tmJ3ufJYlUQaIwHqQj4Cquxl2Sq8LKGyLCuZDrrlUHfEfH34fK6ZNrrlEPXLR9mjHAsHp86s9HM28CsikzmdvmmvbKpUiP/nG4mQSqakM5LRkC+WEthb3/pWAD70oQ8d8zVBEHAr3je2fWHql2eCW5c18NDLQ7iuS31E5e5VTTx/wBMe000bEJBEEct2cARPOXo4U0ZA4MWDKSRR5P7LWqsdBEXdYvNAnomcztKmCK9f23LKxoXtiSB3rmqid7JAXdjH1SdQmb2QKBs239s8xOb+NAemCkT9EuM5C1XyVusnUmc9FnYMZ3lo0yAHpwq0xP10JEIMJItc1h7n3rXNfOnpg9W0rKqIfPSe5azvOqTN4bXGChUtF5Ns2byoheDmcXzsHs3x5L4pXNdlOFViKFViQV2Ygm7NCmpzZYv2RBBZEqoBrxcou7QngmRKBpmySViVPaVox0W3HQzLZiyn01YTQBQFOhMh4gGlyi1a11lzQSb9O1Y28fyBaWIBmWLlZi+JAtdUFM1VSWAkXaKg2xR1i91jOUI+mTtWNvIfz/ZVrHugORbg6oW13Lummcaon6+/MECu7PEpZqw/dNOZ5WB/IVATVFh8AuL4ksYIrw6myZS8zPLa9jgAT+ybrPKafrlznPqI77icw45EkC2DaVzXI4t31nqBrGE5/HzHGC4ua9vjxIMKYb9Mc8zPWFZDEoXq4iqnmTiug2Y5hHweR6Y2pKJKItaMzYYs4uI1X9yxsokDU978vb4i+jqQLPKTbaPolsPixgj3rWk+aRC0ccnpaaXNBTTTwXKcitik1613ITJAtuNyYKrEWLaMaF4kAVBfX99cb/KiwJ/evIgVrTGGkiVuXdGAabkMJMsosohmOuQls5oK7KwNeqlmzSLiVwipEr/aNc6zFS2P9kSQ0UwZx3Up6hbfeKGfH28dYXVbjPaaIEFV5rpFdYxmy/ROFKiLqNywuB7lsA6y5c1Rljcf37PmQmAip/H/ftNDpmTywOWthPyexkrRsBAFgemSieU4JIs2LxxM8pWnD/Jntyw+5rZM2+GZnimm8waLGsNc0VHD0z1ThFSpoqqq0RQL0BzzVsadtWHefGUbL/QmEQSX9V0Jlhzh6dMc81dXLWGffBQBeh4XHpppky4ZxAMqAfUQj9BxXJ7tnWbXSJZdYzlyJZOSaSOJMJAsEQsoJEIGW4fSdNaGEEWBiZyGX5F4uS/F/ok8tWEfiZDXrt7d4BGI6yM+ZFFgKFVm45I6do3m0EyHiF+hIeJjWVOUDd0JGiIeCXZNWwxBEI5bkjnXGEyVmC4YNMcCfPy+FRyYKqJbNr/ZO8lDLw0RDchIooBmem3+AUXCdV2u6KyhbdcERd2kZHjBkeu6pEsm4zm9ajIqCh6pvLUmiCJduO8JcMPiWi7rbp11HhyJgCrxe1d3Ml3QiQaUaiZoxkMMvGA3XTKPGwAtqA3SmQiyZzzHiuZY1Rtw61CahqgP1/XsSgZTJTprQ7xpXRuTeZ2Q75CMxlTeIKd5HDJcg6JuEfHLtCcC7BzNIeDZVgRV7/hWtESr/KEZ/GLnGC8eTGLaLv3TRS5vj9OeOH5WUZEgHvQf9/VzBUFwGUyWvMyZ4MlCXIg8+o7hDBM5Dc20cXXr5B84BuY8AOrs7JzrTV4UEASBmw7r5uifLlAyrWrK1a9IFHQLy3EZTBYxbZeAKqNKIpv6kkzkdOxK5qsjHWRRvbda3dSfpKhbTOc09k/kaYwGuKa7ttplIAoCI5kyiiRyw+L6C/X1q3Acl57JArbjsqQxPKut/wPf2cqmPo+w+cKBJH//hpUA1IZ95DULnyKSKXnp4ELZ4FubBlnVGqt2XRyO/3y2j2+9NIBhuSyoC/JnN3ezZzTL3vE8Rd2iLRFg4+J61i+oqX6mORbg9kpHiCAc0v+ZwV2rm9jcn0a3HC5rj8/rMl1kyGkm3315iLxmEVAlHlzXRm0lQ/fqYJon903yxL5JMkUD2wXDshEQ0G2HTMlgMq+hmw5/vLGbtpoAP985hk8W2Tma5eW+JGG/590VUGXWddbw0sEUec3iusV1TOR0GmMhCrrNQLJEe41ndJou6bNS/A3R83/DmUGqaPCLHeOzOE83Lq7j/f+9hd7JAmXDJhpQPHNQ10VwvazDRF7n/z3ey2ROw6ys3DXD5nuvDHNgsoAii4xly7TFg7TGA7TEAzTF/HQkggylPJsJvyKxcyTDUKrM0qYIVx9Dpb2oW/RNF4n45TnhQT3Tk+RA1uXNV7YTUCVsx2X/xCGzU0n0MnZP7Jukd7JAfdjHPWuaCfk8/tZPto1SMmyWN0VoqvxuumXTM1HAr3hleEEQ2Dac5bHdEwymSoykyiysD7GmLY4sili2pxulyN5cDh5n6KGXBumsDfKeGysqzK6LaTmUTRu/LOK4YFkuk3m9Wq6fzGlHf8nDcHCyyHRBx7Rcj9Be0GlPBHm5P8nPto8TUiVu7Th0yzZtj1d0vmE73hjkK/wb28lxCtW6OYcgCJQNC8NycI+nBHoSnBMhxG9+85t86Utfoq+vjxdeeIHOzk7+5V/+ha6uLu6///5T2sb73/9+fvzjHzMwMMCWLVu47LLLAOjp6eGd73wn09PTxGIx/vM//5OVK1ee9LW5hGU7PNubJK+Z7BvPUTYdSrqNadtYTkUUERCKJlN57f/P3n+HSXaWd/7w58TKVV2d83RPDpqo0SiigEQQYCQhG2OwAXtZwLtgbK/tF6/DGu/aeK/1YnD8OYINXsAWNjkogABlaWY0OfVM51xdOZx83j+e0zXdk3OQ53tdDZqu0KdOVT3nfu77G7Bcvy5PlQDLKZOrWDiOx2zVQkbM8kXwqmg7x0KKaKEGs+t5JcHVxnf2TtUXoQOTUR69+bgs9ZXhXN0nKVuxGJwtsaw1ge16jMxViOoiwdp0XHwkClWbf90+RksyvKib9bVXx/mzHxyhaLhIQKZi8t//bS+zJaPudTJbthjOlLlt2fGF+E3r2vnevilMx+N1K5pPGimGVIXblzXh+dyIJ7kGsX+iWB9h1iyX3WMF7ljexPbhHD84OMP24RyTuRrmAi8nWRLSY8PxMRyH54/N0dsUpTGmMVs08YGJfJWi4eB5sEvKs6I1wf6JPAOzFRRJ4vH9k9yyJM1Yrgr49KQjHMtUiOoGuirzxRdH+KmtPbSnrl7xA0Ly6y1w8yzUbA7PlBnN1ZgrW6KjbDk4rl8Pbp6rWFiuhyyJaIZ81cYM1EezZZOnDs4gS2LtOTxV5r7VLXzsgRVYrs8/vzBM1XIpGTa5qs3OkRy249GcCPHe25fw6M3HidM1y+WLL43U3797VrWwpTd90ms4X2QrFkdmSmzobuCbuyfqgaOHp0s8tKmLQ9Ml9k8UAUGKff7oHA+sbWPHcI7ZkonteBybrTBVrNHbGOOx7WNMFQwkYPOSNPetauWrO8d54sA0PnBoukR7KsyG7gZWtcfZMZLD9XxuXpKmNRnm6EyZ9/7DSxiB5fy+iSKfftdmclWbXNXCCaYAtuNSshxKhsP8RDZfsynXLOIR0Yk6MSuyaNjBZACqtig6s2WTv3r6GJWgwzE6ttiu4EeHZnnntivbdJgt1uqZayDGzFXTIXaFO+odDaIrW7Nd5GulAPqrv/orfvd3f5df/uVf5g/+4A/qPJ+GhgY+/elPn3MB9JM/+ZP8xm/8Bnfdddei33/oQx/igx/8IO9///t57LHHeP/738/LL7981tsuJbJVi9mSSUxXsRxfzH89IYGfX5784Kd2gjmZj1gsZjyTVEQjpMp4wXsnSTJFw+HgVJGedJSpgkFXQ4T2VIS118C4S3R/SvV/j2SrVC2n3tZdCB8YzRl88J4VfP/ANAcmiziuh+mKWbGPhK5IxHSFw9OlRQXQt3dPUjTc+vPYrs94rrYoh83zfb65d5KfvvX4l7+nMcoHzhA2OJip8O09k9iux+1Lm065i30toO/j3zrl74f+6K1X+EjODxFt8agjrCl8e88kQ5kqY7kqM0UDRQF/AXVwQS2EhLiovDyYFa7JgUNztiLUT54n3J13jeSRZfF9lQBJljg4XaY3HcXxhDO0hERDREdTZBxPBJ9e7QKoIxWhOa6TCaI7bupK1f1+ZFnCc338oECSJQlJEoVQyXAo1hw0RUZTJEyHeqyPMAYV/zAdh2eOZPjargnWdSSpWi6+77NnvMBwpkLJFEZ+uZrFPz4/xFs3dNb9jsbztXrxA3B4qnRJCiAQnwvL8Xjh2ByDmQoSonh78KYObHfxhW/+3y8OzjFbMvF9n4rlcmSmTDyksWM4x1iuJkjNjsd9q1rZNZrHCYJQZUnI7wF2DOfRFRlP9pnI15jM1/jm7gnKhlMvROfNNkezVUqmKLItx2MsX+OesBiRzQXjuIaoRlhXcVyPb+yeYCgjMt0e3txFKqJRNh0kSQrGSRJzZaGyqiwY7yyM6gH40ZGZS3KOzwenioc5rW36ZcRc2UJTFaGuVi6sQXDJC6A/+7M/42//9m95+OGH+aM/+qP677du3cqv/dqvnfPznMoxemZmhldeeYXHH38cgEcffZSPfOQjDAwMkEwmT3vb8uXLL/JVLUYipGG5HvsmiuSqVj1F+Fw/A5IEvu9TsxwUSWZ5WxRdlcnXHFoTIQo1m7AmszQYk/30tm7ak4u9MPJVixcHs0jArUubTrLQvxx4aTDLRL5GWFNIR3XiIbWurgDY2J3klaE8HkKFdXCqxD88O1jfRUmShIxPf1MMF+FNkozqJ6nYDkyenG9zYn0vIdrg54OnDkzX5afPHZ1jdUfyipy3C8XpChm49ouZC8H6rhSzJZOhuQqdDRHWdib411dGMR0vUG8JX5t55c3CzUYdEpRMG8MWaqdM2ayThefhItr487YJkuvjuB6HZ8q0xkPEgr+z8OJ6NeXg89BVmXfe0sNotkospNKRiuB5Hr1NMeYqFr4qIUmC/6PIMp7liP93XHzAcFwU6czrVLZi86dPHmbrEmH253oepuOjKiIixPfFaLliuuwYyXFHYDGRjmqCRB4UBpfifCUiKhuWpFneGg9yvwoMzMzHUIhYkFXtCfaMF5gpmkR0hZuDkXjNdusFnOeD53l4vs+BySIT+RqyLNU3bpJ0vCAURbEoQQTfysT3xfPVbLE+u76PFzxgfoo+MFNi/uNiuz77J4voisy7t/Xw8pBQbsMtDQABAABJREFU/d7an0ZVZPaOF3hlKEeuYhHVVdpTYd60rh0JcW49T1wfdFVmSWOMjoYwk3nRtVrbsdivyXavfOXR3RAlpsuUA9PJREQldhXW0bAqo847op8pqPMMuCwk6M2bN5/0+1AoRKVycW6jo6OjdHR0oKrzH1yJ3t5eRkZGSKVSp73tVAWQaZqY5nGiXLFYPOfjmC9gLMcVO8bzKX4QrWnf9zEdFxkXz/Npiul0p6MoksRQplInSzfFQotkk/M7sv/34ghhTVS/kwWD993Rd87Hf6H40svDmJLYBb9tQydvXNeGIkvMFA0GZ0scna0sSs2eLhj843NDmI6H5XpoikxIlWlJhYhpKivbk9zS10jFsPiFz77EkqYov/yGVeSrZ6/m37C2jV9/0+qLe0FXYddyA6eHLEvc0tfIcLbC3/7oGJ/4+j5sT3RuDNvF9X0imoJ6QqTJPMToWcL1IKLJTBaMM8dDzP9dSXRCQqqM73skIyrd6Qi39TehazI9jdFrRnAQUpVFcRqjuRpLW+KUDIehuQpLmqLMlgyyZZuwJkbOCzJEzxjoDGKjMZE3+E5pEl1VcD2PmK7QkYqQrzpYrosiS3Skwou+P03xED+xsYO9E0WSYbVeGF0M9ozm6W0VkReOY3N4ulzfwByeLiNJEiFFJluyeGkoS19jtN5FLNecesfPclwcx6dYsxjOVETAqUQ9V2x1R5wdI/n6313dIfhL5aDj5SPI+ZbjsaYzSUiRqbhCAbV6XmixYKLuI7rlkiTxk1t76W6MISEsDEDI7geCUd5cxeLIdIk3rWunMxi9Oq5POqqTCGlEdIUP37OMf3l5lERY5U39Ov9jwTm6GvFtZculNRnGztVAgtZEGMvxrjinMqqrHJ4u4wHegmv5+eCSF0D9/f28+uqrJ5Ghv/vd77JmzbWTrfTJT36ST3ziExf02GLNJqQq9DbFGMsbgowpSedUjc/fY/6uLrBrvMjh6RKKLKOpMroiU7NcHt8/ze+/fd2iOfGOkTzfPzhdbwWv6xI7ghPnyQMzJbIVm6UtsUsm9c5XHbSIOPCudJhEWONfXhnlK6+M8mKwy5mH7cFE4MXSltCRgn1VeyrMxq4GZFkmHdUIqxIf/tfdWK7HMwMwmKkGr+P0FgmqDA+u7yB0nl+4e1e18t29k9iuz639jTfCTq8xjOdr/OmTh/j6rslTJ7EDtuueVnEiIYqZXMB7OVWRJAGKBLoWXNwDbxzL8WiM6pieT086yk1dKd6+sfOaz+7LlE0imlKXbouNGYR1hZJhU6mdn9WIhFibXMfHdsUYUbY95ioWEV1GdcXZl2XYfMKIa2lLnKWXMFn+xwOzvDxpkIyorGpP1IsREBLouZLJS0NzfOnlEUzH49hMmWhI4Xfetg7XX/D58UVXcHiuiul6zE/9ijVRGW4fXLx2bR/KA9TtFOY7M44rIi4c/7h/2ECgKj1hEldfmxpjOm9ZkDcIkIqohFThIB4PDDcBetJRWhJhTMeltzFKWJMxHZfv7JlkMFNBV2SSJ7ydV4MaqimQKVuimPbFZ/Bq2Kl9/Cu7TpoMnC8ueQH0q7/6q/zX//pfMQwD3/d56aWX+OIXv8gnP/lJ/u7v/u6inrunp4fJyUkcx0FVVXzfZ2RkhN7eXpLJ5GlvOxV+8zd/k1/91V+t/7tYLNLTc24ZUI0xnaa4Tk9DRBQXvuABFWvOBb8hgivkLoo0z1QsfuOxV3n/nUvxgaXNMSzHQ5VlkhGNmaLB9qE5GqI6394zSV9zDHzhjrt/Qvh/vDyU5d3bei9J5MRsyQBT/O1kROPvf3yUv/rhUUrGmRfZ6ZJYaEwHqqZD2XJJhmUqlsu/7RijZrt1PsaroznB2zhDFyikKjy+b5p7VracV57Y8tY4v3jvcuGXckMBds0gWzb52Jd2sm+iSM1yTuLNnYjT3eojzOY0BbxTfCRlCRJhlc5kiK50jINTRZriIaYLIsYiGdWJaDI/vbWbVSeMGq5VLGmKoSlzNEQ0RiQh185WTEHEPUOi/emw8P6uD67r47kumiI6a/GQgiL5TOZNPvmdA7QkQuiKzB3Lmk+K0xjNVvnWnklqlktfc5QHb+qoc4bOBVXLw5JtvrV7nDuWb0SVjy+PqgzpuM7OkTxzZRPHF3ynnUEnZ16MAiKgOqapxMNysBGbV6SINSCzsEUGzAQcK9ERFL+TJYnmeAjPl1Ak0SlbqDS1nMUy7KlA8fWpxw/x+eeHAPiFu/r56P0riSgy24eyVG0PVYY3rW0DBOfN80S+pGl7IqalYPCFF4bJVW1kCY4OLeYAXQ1RYs32qFkOQfoTVVOYyirn/tZeEhyZLp39TmfBJS+APvCBDxCJRPjt3/5tqtUq7373u+ns7OQzn/kM73rXuy7quVtbW9myZQtf+MIXeP/7389XvvIVuru76yOuM912IkKhEKHQhXVGVEXmnVt7ODRV4u5VLUwXDYbnqvzj88NndC29EEyXbP715WHuWtnG4GyFVEQjX7NoS4aYKdSQFZmS4fB/Hz/M0uYoFcvDcFx0RWJtR4qq5fKN3RO8fWPnRYePJiIakq6Rjmroisyf/+DoSfyKM8FHLDYzRYNkWGPrkjS+f1wh5yM6WSvbEoxkq8eDHTk+p/cRC9NwtsLh6RLb+s+PyKzI0g0F2DWGj315J88dnTsvHt2ZYJ/mI5kMC4fequ3RnRYp49GQQqZkMFkwcTyflkSIluS1nRHn+z5jOWH81tMY5We29TKSrRLRZZ7cP4OMj+2ff/FzOrhArio4NzVbmN5FdZevvzpOayLMm25q56mD0/Q1R+umiQeninzu2SEGMxVURSg/W+Jh7lpx7qMxzxeFjO16SL7PQqGP4wV3wMdcUOhVTbFxKi8gDvvAQKbMxiXddKfDzJYtZAnWdIhRYk9jhLnK8Q1XX5ASb7v+ohFT0bDZ0tMQGGZ6yLJET4PovCXCGpnK8b/Z3xyjbDj87Y+P1blkf/XDY/ziPUv51JOHqQTVg+3B3z87yAfuXsZkoUambGLYHrqiMFsy2T+eqx+b58PhqcWZV2dR1l8WOI5bL34ATJfjReUVRCqiYJSvsSgMgPe85z285z3voVqtUi6XaW09vzRkEIqub33rW0xNTfGmN72JRCLBwMAAf/3Xf8373/9+/vAP/5BkMslnP/vZ+mPOdNulRlhT6q6jAIemivzziyPI0tnn7OeL4ZxBdd8UjuvTENVwfZ8ljVFcYLZoCBK25zOaq1G1XRzXQ1cVDDtHPKTSENX411fGeO8dSxaRls8X2/oakUJR4iGVcmCodr7wfPjZ25awvDUhMpnCCv/38cP1r4/t+fQ0RlBlCdf1g3BBUCSJeEimYnlULBdVktg5kj/vAugGrg0YtkuhZqNIsG+8eMmKn9NBdA98XA9iEZV4WOXnbl+CqsiUTYfvH5yhYjps7m24JgjPZ8L39k1xYFLsfjd0p7h/TRsHp0o8vn+GTMlEVhQikkfF9i7ZWuQTKMs47kHk+ZCrWpQNpy5Hni+ADk2VcII/7rg+haqNdQHO/zKAJDFdMnA8vz7+dDyf2bLBcLa26HNTCJRoC1+3hCjcutMxbulv4pkjGTRF5m0bOgF43fJWDk6WsVwPXZG5e6XwW7McTxCSEcWQ6bjkqg7NQaiupsroqjii0ILOloSIhygapniOecWd41E2xThxYW5hNZCUf//ADFNFMxjZFZjMV9kznl90Pk6cMFzsCOhCcCxzMpe3WLZpTl+WcuK06ErHmS6fLJg5H1zyI67Vavi+TzQaJRqNMjs7y6c//WnWrl3LG9/4xnN+nr/+678+5e9XrVrF888/f963XW5kyhbd6QiDmQo1+9J+LH2PevBczXZJRbS6F4rrCQXLPDlQOMDKhDVROKxqTxALqZRNh0LNpjWh1B9XtRxiunrOOWTxsIoS0uhKR1jXnrigC9Ydy0Q4qReYms3b1c/DcX1aYiFWdyTZN1GsqyFCqhTM2f2gqNOveX7GDZwambLJPz03xLFMhYopTA/PNPI8G2ROvhDIiAuRh+j6RTRZZGcpEo1RjT3jeb700gj3r22jIxXh7Rs7L/wFXUEYtlsvfgD2jBe4d1UrPzg4Q0RTCGkKluuRiOqEHVFkXqqmtOv7qIqMFngv+cGy8cpwlp7GKF9+aZS+piiNcRE225oIMVsyMWyXxrjO5p7zk8VrioSuyaxojdMc1wkFrvsgusCNMZ3mmLaomIhq4pK2pDHC4ekyPiKn6/ZlTZQM4cumKrIYJ82KbkoqqpGKaliOR0iVSQQ+Pabj1T9XtuuhKQph1a13aaSgiBcQth4g1t2q5ZKOhtBkCSNYqzVZIhZW+YW7lvLf/uVV3GDs//o1ouCaq1j1kZvn+bw6UsD3r701rr3h5MlJInZlix+ARzZ3s3O0cFEbp0t+1A899BDveMc7+PCHP0w+n2fbtm3ouk4mk+FTn/oUv/iLv3ip/+RVg+N4qAGXpDstUt9zVQupZuN7QjLqeGIGPd/JkBGz9fN50+qLuw8Vy6Vmu5RMBxmI6gqeJzpSYlQkyHqG7fH2DS0oQZGQCKs0BF/sfNXise1jlAyHtmSYR2/uOqfO0Htv70MNC0XDb31933m8AvH6l7dE+E+vE86p39wzydGZMqbtBPwCcZJiusKmJWmKlsOmnhSFms1Pb+3l//vRMY5MFwlrCpt6GljdkWRpy7WV5n4m2foNHMf24Rw7RnKUTZfpYo3iBbrZKpIYy0Y0hamCUf9OKZL4XiSD2zQFVrYlWdmeZGCmxO6xAo7r8529U8yUTT5097JzzuG72tAUmbCm1I34YrqIvmiMCc+ijlSY1kSIhzZ28LVdk5QmClyK3pqMKDpiIZXmmM5U0cD1xfdVU2TKhsMzAxkmCzFaEmHWdSa5fVkTy1ridKbC3NzXeN68u9ZEiKUdDTy6tYewrvFTW3v49x1jADyypZuwrrG0OY4iB7YGEvQ0ClLMh+9ZxqeeOEzVclnflWRbfzNj2SqvDOWpWA4S8NSBGX7v7RBSZDRZwkZwesLBWmgv6Fj5PsyWasyUbezAHXrejwhEbMdYtorj+4Q1hbfc1IHrQTSs4gTFfSyk4roeG7pTNMZ0shWLsC5z7woxIVHl4++U50NPOszazgSffX6kfhwnko1DV6M+8k/eMLvelacV/NwdffzBt/dhXFgKBnAZCqAdO3bwJ3/yJwA89thjtLe3s3PnTr7yla/wu7/7u6+ZAuizzw7y+L5pQprML96zjFuXNrG2I0FEU4jqMqbjs7QlioLEs0fnGM5WMG0h6TUdT5hbSXAhBpaeL8zNZKBkCqfReFhFwg8KCQnLcYmHFNZ2NaDKEp0NEXaN5WmK6RybrdRNy6aLBvsmiudkWqbKEiXD5i9+cJiv7pw4r2NOhBUe2tTD61a0YDle3dFVVxU2dKeYK5vIssw7Nndw69Im4mGVfNVmeWucTMmgbNhIkkQspNAcD/HAmjbWd10fRNX/6JgtGqhhkXF3cKrI80czHJkuM1exFil7zgeaLLGpp4E3rG1jdXuSo5kS39w9hWk7GI6PFWwSfB/uW9XKPatamCmZjOdrWI5LSyIsxiglk0LNvm4KIEWWeGhTJ88cyeDjE9IUHts+xqbeBmq2y4HJIjFdYc94kaMz5TPaAJwJwWSnvvkSCjsJXZaYKBh1TyDXF+714/kapiNiNpKRGvGQyn+++/SmpCciUzZ5diADQH9EkJB/7+03sb6/nfZkRHS5g+43gBWM+iVZkJNNx0NTJGIhcXvRcEjHdCK6h66pVC2HsVyViuXUR1LTgangbMkQrtGuyGacLgp+1cLGuCRBLKxRnK0Io0xJWC6YwQJ+69Jmnj40S64i1qy2VBjwURacQzkwpvzcc4MUazaSBLbj87nnh3loS3fdRR9EIVS2Pdb1xAmpEmbwPjYldI6XQ1yVTeD+idxJvyvWLKLhK/sd+sgXXrmo4gcuQwFUrVZJJAS57PHHH+cd73gHsixz2223MTw8fKn/3FXB8FyF7+6dAoSr8+eeG+LWpU1s62+qyyHjIZWfvW0Jg5kKVVv44Ixmq3USro+PIkmUDIdTrVEL27qnQzARCmbx4pOgSGKRtHz46x8epSUZYUVbnOb48TTkRFgNWrpi+zCvZCjUbF4KzBW3LW0kERKJ027wxfziS8N8be8ch2cvLHn3/kDtoCkSibBKyXCCjLU21nTEkWWZbUEy8rrO48XNy0NZZkpip2W7HiXDWcS/uoFrG//yyhjJVJG7VjTzpRdH2D9ZYLp0br4duiI6mvPfBR/xGe9tivILd/Xz4HrhBizLEm9Y45OIqHz/4AwHJ4WvV75qsW9SRGrMlkw6UxEKVRvDdtEUmdZEmKb4tc35ORGdDRHeeUsPzx3N8OKx407Em3sakBDflxOdmc8Xp+pSC86fT9Xy6oaKc2ULkALDRI+aJry+poo1Ht83hSJLYkNzhgLT932+unO8fryHirOA8M1JBoR0z4fv7puqiy6+u2+K//nwerYsaeCfX1SCZPLj68ero3lCqkJIVchVLI7NlrEDhdU8lCDA6vljc4KvJAlu0fPH5vhlhDP/fPniI9atVe2JwGvKR0L4twF8fec4o9karu+zd6LAjqEsj2zpomAcL7gKhouuqcwWzQVxLj6ZYvWkcyJCaU0MU0SbzMO0Fr+n8TMExV4unMp+RLsKOvgdoycXYueLS14ALV++nK9+9as88sgjfO973+NXfuVXAOHinExeG2ZiFwvnBGbhfIFw29ImmuM6RcNhRWuciK4cd0aN6kwXjPqiq2sSM0WTsVxVhBMuqILCqkxEkylcgKze9RGW+EDOcMkbZUZzVcKawvquFG2JMEXTxnEFl+b1a1pZ25HE932+sn2sPtMezlaIaCrTRQOnJHZmX3h+iEnj7B8ZGQhpsuh4EXivBBlgIMZ079jSzXNHxfPesaz5jMTTsCrTmghRs8Uur/E6u2BdDlxv4zbTdvk/3z3Iq6OFRdb+Z4MsScTDChVTRDlIEixrjfO2DZ3cs0pwJ54+NMvecUGGdDyPZS0xjkyX8HyRFq4rMi8ey5KO6fSko/i+GEds7Uvz5pvaL0oYcDVxomHowSnBDXI8n6LhXNTg61SP9YFK4P7r+MfHMZoikwxrjOerTBUMdFVheK5KPOjGTBUN3rMgsuZE2K6/qFirmCeTlvJVs+7uDWDYHoWahSzJrO1Ikq1YREMK8aAL0dsYZTwnCNKxkEpbIkzvCdyVec6OKsvIsoQfhFWr9d9LdadnRZbJlS0hXVckHI96yCnAnokCVrD7rVkuLw3l2NqfXnStcFyP2WKNmr348z+vCFMXPJ/nw7a+Bg5OlRbFvRSNxVeE4gWmoF8MTvX+VCyfKy1HyRWts9/pLLjkBdDv/u7v8u53v5tf+ZVf4f777+f2228HRDfoVA7R1yOWtcR53Ypmfnwkg6ZIvPvW415DC11aQZCQD0wWMQPX6CWNUZa1xnlkSxdPH5zmSy+PMTJXZTwnXJQ9T1je39yXZmSuyrHZyik7RGeCf8J/12wP2/WYKtYYz9fY1NNAdzoK+NyxrBlVkeuqnHkcmirRlggjyxLlYHGqmmcvx9IRla19jTTFNb69Z5qa5aLKcPfKlmC3KNAY0+sqjLPh7pWtPH1oliMzZWIhhXdtOze/phu4dpCrWgxmKovkyWdDKqISVmRCmsJb17cH4waJ25Y18sCa9nqUwcJ8JNv1ed2KZo7OVDgwWSAW0uhOR4kFuUyFms22pY08uqX7vDxprkWsbk9wZLqMF/BObupM8spwjvZkmOG5KmFNxnK8RRfQC8XpOtKr2hNYjoflCjNYTRWRGYbtYgUhyAenirxueTO9p0mI11WZFW1xjkyLsXj/KcY687ls85Ly+aIrqtusaEsw7wM+73f2k1t7cD0hXd+6JE1fc4wn9k2J0GlvXl0qCo6fvb2XQ9MlzIAE/d7b+gBY0RZn30QJ3xfOzFuWNPL4/um6ykuWJAxnnpStBAkBYlwW0WXSUV38jWATLEkSyYjO0AkqqkrQ1Sme0LF78sAMS044Zyemrvc0XHkjoBOvcQAN0Ss/Qq5dgs/1JT/qn/zJn+Suu+5icnKSjRs31n9///3388gjj1zqP3fV8JHXr+Bnb1uCrsqnDAOdh6bI/NTWHmqWiyZLWJ6HKktULZejmSrrOpP17oflivyhDd0NdKWjfOB1y/jtf9vNQObkFimINmlEkag6Z+dReB5UDIeQptLVMO9zctwTJ6wpdKUjjAf+Ih0NkZNWvFRUoXCGNJOYLrOhp4G1nSmSEY0v3rqEv3t2iHRUIxnR6W++sHm1rsr8r0fWky1bxMPqDRPD6wyxkEK27JKrntuOTQZWtse5tb9JxDkoEg9v6qKnMYoiS/XR7TyWNseZCVzH4yGVrUuauLm3kf/vh0cpGg6piMaSpiiPbO6iZrtENKV+8buesbQlzs/c2kO2YtHVECEeEvL+FW1xOhrC5CoWLw/lMGyXquVQNt26ykiV4VRiVRkIazKuD7ZzPDBNkhYHz8pAU0znps4URcMhW7FQZZloSKG7IUJIUxiYKVOs2aSjOl/fNcHP39lf51pVrflwVvFevuWmDo61i8VFN04ebciyxNKWGINB8dDfHEOWJVa2xRnPpTgwKVLcb18qfIa6GiIi1d7xiARjoiVN0YB+IDDf+XvrBhFGumM4x819jdy5XHQW37ahE88bx3I9tvU1EdIUljbHCGkKtuHg49PXKNa0bX1pRuYqOJ4oRu9d1UpIVVjVFmNoTqzfS5tjKJJESyLMUPZ40R7RTn39qFkur1/Vwv95/NBxDlBMZ3TBfWKRK98NX9aaWlQQKwE/6kojqUHxIp2wL2kBZNs2kUiEV1999aRuz7Zt2y7ln7omcD7GgvNfwok5g2/snqBYsxnLVVneGqc7HaUtGSamK8yWrTrRz/d9fmpbD/+2fZyJIHKDwDgwpImFoz0Zomr7FGs2qix2XzKiDW66PqpCEGAIDfN5Y7IITdzW31gvvkqGTVsyhON6rG5PsrYjwff2i8iNloRoHS9rTTA2VD2tt8jylgSP3txNdzrKqjYhv//NByOM5qq0pyILCq8Lw43R18XhQsdmFxu8uq2/kSM7ZheNDE4HTYF0VCeqqfXNxar2OMtb46ctWm5ftmD03Bavf9c+ev8K9k8UkSWJNR0JJEk642blekRrIkxr4ngXYHNvms29ae5b1cqh6RI/e1sfrwxl+faeSUqGzWTBQNcUOpJhTNtlpmSKzCtfEJ/TMR3DdomoMnpUY01ngqrpMVWoijUoqInCusJPb+vhnpWt9DZG+fGRDD86MkvVdFnbmeLtmzr5mx8dI6NKyJLEVNGgbDpEdaXuY6SrMm/f2ElnQ4RXR/NULYd1nSmq1snvs+fDTV2pegHV3xwT3lGeT6Ysok9yVUsUuMH7r8hS/b8BWpMRXreihVeGsqiKzMObRAfa83xsF9LxMJYrPNVkWSJTEWnjSDKG41IxHXRNob8pSsEQBVxnWpz7m5c0cmy2QrZq0tccoykeIqyrfOS+lXzxZUFb/tlbl6CqMu/c2sveiSKGLTbDb9kgojLSUb3uSi0Dty1tpqspxvtuX8Lj+2cIqwo/ubqF//x/xOtRJHh4U/el+zCdIwzbpTGqkqs5wXFruJ5/xQ1m71nVwjf2zl7Uc1zS1UDTNHp7e3EvwPDqPwqeGcjU/SZMRxB6E2GNrX1p+ppifGPXZL2l/eJglv2TItJiXWcCRZFpietMF00OBCTPmu2TCCm8cW0rxzIVjs1WiOkq6ZhOS1xjomCSKZukoxobe9I0xUJ84HX9RPXju+BXR/L83Y+P4vlit+54PluWpHloUxe+7zM+Ps5HEWMrVa6dMmMpFlL49Tet4q7ARGwerckwrVfDr/0Grhn84OAstSBMciGiqkgPN13Bt0iEFXwkUhGdlW1xqpbLL9zZL7qRZ4EYgyyGpsj/YcnysZBaV3Zu7GngzuXNPH9sLhhJSzQnQsiSxA8OzfDisQyW4xNSJRRZpma7Qu4eD/GmdR11iXvJdOuPX9YaZyxXo6cxSmsyzKM3d/OOLV2AGOW8NJjFdjxGszVCmkLJcJktCbftA5MlKqbDRN7ma69OsDpIdAfYN1Hkgd6TL0u6KgeFrtiMrWiLo6siWX08L7rWJcPhpcE53nxTB4bt8syRDCXTZn1XA8tb48RCKo1xneVtcWRJpiVYl14dy/PqaB4QUSINEY3NvWn2jhWYCJ67ZjkokkRTTGdjT5pM2SQU8CpBdMhKpgNIzJXtOun7LRs6ePNNbcgLLKXfuK6d7x+aZmC6THMixHtv7wPgHVu6+MauCQzboTsd47YgPDUR0dnSmxYCGkW0PFJhhRU9KTaeg3r3UiOkyvUQcBAF5NXIAlvdkb62CiCA3/qt3+K///f/zuc//3kaGxsv9dNf95gvkiVJYk17ktuWNdGWDPG1neN84YVhGiIa79rWy5KmKL/2L7vrKeo12+V1fU0osrhAHJ0t19vHpuPzji096KrMM0cyTOar9DbHefe2XuYqJgMzZQ5OlpAkQTheKPl94ViGzzx5hKOzIoG+NRFiomDwxrXttKfCi3bdI9kaUU3Bdk8mWHalwmzpu/Jfxhu4ujhdV+nErtHKtgQ7RnJUDAcrcPj2AB8JXRHxMhFdJawpdKbCDM5V0RSJf3lljJ/c2n3R3cP/6FjdkWT1CYn2vu8T1gSxXEFirmoxmTdIhEXWHwju1kObupBlib6mGKO5Kpmyia4qTOQNvr1nktXtSfaMi0wuXVXYO16gbDqUDQfL9ehpjLKkKcZkwaAprlMxHfZNFPF8X3RtLKdualqzXLJlUSh/9tljbOjv5N5VLUiSFIzJBE9oabMIXT2xKTjfZfzm7gm+s2cKy/F4/ugcH39wDa7n05oI0xhwc+YJyuUTuDfzJF9ZkmiKh4LzpJCpmCxvTfDA2jZ2j+VJhjXuWyWUraO5GrJ0XOE7ka/Vg2HlEyLbp0sGuapNWFcwHI9jMxVWtyf5L/ctpyGmkytb3LO6he7GKDNFA8f16WkUkRszRUF0f9NNHbQ1pSnW7CvuXG67vhinBv8uLvjvK4mq7RCSwfbrsW7njUteAP35n/85AwMDdHZ2smTJEmKxxbyPHTt2XOo/eV3hvtWtfGPXBBXT5eb+NHcub+abuyZ4KUhTr5guLxzLsqG7AVWR6i6usZBaL55iIZUVrfH6bLmnKcq6jiSqKrPmhEWuIxWhIyVav6fC7rGC8GLxxU/FcoiHVIbmKrSnFndummI6k1Ubw3Ux7MUf+Zrtocs3uDmvRVwKxZmmyGzoTrF7tIDuejhBKrckiR2kj7BnSEX0ulXE0pY4nu8zlKncKIAuAyRJYmtfI0emyzieT3djlAdvCvGdvUK8EA+p3LOylY5UhJ+/sx+Ax7aP8q+vCDNCWZIo1GxeODZHybDZN1FEUyTmgpFUIqRiOC4E1hzdabEWtSXD7B0voKsyfU2xRRfPiK7QGA94QqbHq6N5utIRVrYlkGXpJALu6vYkg5kKAzNlmuKhetfkxcFsnXA/PFdlYKbMpp6Guv0GCPNagLWdSfZOFDBtj7Cm1DPCNvQ08OKxOUBwInuDIuS2pU3ctnSx5skJApZ1REHpnIF5vn04h+9TH8fuGM3xlg0dNER1/su9i7MrU1Ft0TF3pMQxNMVCxMIqzYkLy7O8GEzkK4toEK7nUzUdkheZNXm+eNuGLr78yhim7eL6FyZouOQF0MMPP3ypn/KKYH5sNzY2dtnl+m/u1wPJpcnY2Bgjo+NUczP12yfGDTLTEW5the3DRSQJti1pYn2HzFiuSksixFuWtvP9g1P4Prx1QztTU+dnTDiPqF3AKmaIOTZGxSClR2iRNNxShrEx0f4dHRW0u4RbZGVCpU122D9VpGy4SJLgIyXdBIOjI6cl9L1WMX9uRkZGcIqZq3w01w7Gxsbq52Z10sRTFbZsSvH5ao6jmQoFw6RiuyRCGhXToashQpcGWzrDrOmI88PDGahkyVfAa/YYG7sKqY+XEQs/Nw0NDVf1WO7skBjMVGmIaqxqj7Ii1shotkpfc4yEW2RsrFi/78a0ywuhGrmKRTqq0yTpDMyUKdRsqrkyIVXGrTlUTZtoPESrLrM0EmZrc7T+XOvTDmMJ4R9kF00296eRJJEltrItyWywlmVnJojGkwyP2ETt05uebmqEjekIkiRRzExTBLRajmpOjNVCqoyRb2BGq3JHh8zBSQNdlbgpFWZsTBRzD/SqZCsWjTGFWn6WsTy8Y2WYNiVMzXK5fVmKmanJ0x5Df9jgiFyhZjk0xHSSXpGxsVOT/uNOgVp+pj5CSrZTP45TYf6YQ6pE0g1EKlqZLR0N5GenyJ/2kZcHdtnAL2fqnneqIjE3M0XxCotTEsDv39/Ot/ZM0qRH+J9w/vQb/ypiyZIl/sqVK/2NGzf6Gzdu9L/0pS/5vu/7hw8f9m+//XZ/xYoV/tatW/29e/fWH3Oht50NL730ko8gtt/4ufFz4+fGz42fGz83fq6zn5deeum8ahDJ9+fr0EuL7du3c+DAAQDWrVt3Sg+gvr4+vvrVr7Jp06ZFv3/961/Pe9/7Xt7//vfz2GOP8b//9//m5ZdfvqjbzoZcLkdjYyOjo6P1DlDFdBjPV0mFdZ4ZmGWqcNy99s3r21kWzHhf6xgbG2PdunWLzs1rETNFgy+9NEq+ZgmFR0OY993Rf8bHXMvnxvV8hjLlOn/jVCqqv/vxMcwFeuif2dZDY/zStNWv5XNztXGx52bHcJbnj2br/17ZnuANgdP6PEqGzUS+RmNMpyVx/QgRTnVuLMfjb390bNH9/vPdS89oiTGRr1E2bXobY9e959M85s/NR/7mCVobG3h4S9dJXkH/EVEsFunp6SGbzZJOnzsX9ZLPK2ZmZnjXu97F008/XW/t5vN57rvvPr70pS/R0nJqLsrCx7/yyis8/vjjADz66KN85CMfYWBggGQyeUG3LV++/LR/bx6KIr4gyWSSZDJJxXT45qsjFGsWPzqSIVcxaYqHuHtlC7Ik09vWRPISXSiudcwvQvPn5rWG0WyVlwazOK7H0YJL2fSRcEk3hEgmk0wWarw0mCWkyty5vJmYrvLSUJbpokE04EJda+fG932++uo4Q4GH1LqazBvXtddv9zwfx/Ppam1kIi8kylOFGq9MGrx+dWoRsXLHSI7huQrtyQi3LW08ZSHl+z4vD+WYLNRY0hRjU0/Da/5zczG4kHOzazTP0FyFveMFxnJVijWHLb0NyLJMe3OaRCJRf28KVZtvHJilZrnIUo23bYxeNxu2U50b3/eJJhLsnxBjrbWdKZrSqdNaIzw3kOHTTw5QtTw29Tbw229d+5oogubPx5MDBXraNT70hjTJGyrbOuav4+eKS14AffSjH6VUKrFv3z7WrFkDwP79+3nf+97HL/3SL/HFL35x0f3f+9734vs+27Zt44/+6I8YHR2lo6MDVRWHJkkSvb29jIyMkEqlLui2UxVApmlimsc7OsVicdHtw3NVyqbDc0fnODhVBCTmKjYdDRF+9Q2raPoPUvy81lGzXL6+awLL8SibDtNFIwhWlHFcD9Nx+fed4/UuSb5qs6ItzvNHBTkyPzt3NQ//tKhYbr34ATgwWeINa9uQJImRuSrf2C1e8+qOBMmwxrf3TtGejDCarfH1V8d5f0B6PTxd4oeHhNR0KFMlpMmnDM59dTRfD7M8NlshqitEz+E4z0Swvlj/odcSBmbKfP/gDIOZMtuHc6QiGpIkMZav0ZYM8+KxOQ5Pl3jHlm5SEY1jmTK1QEHh+T4HJ0vXTQF0Wvh+Pb+LswwuPv3kYQ5OlfF9n8mCwRvXtHL3qrYzPuZ6gmF7jMxV2TGc483rO6724Vy3uOSspe9+97v85V/+Zb34AVi7di1/8Rd/wXe+851F9/3Rj37E7t272bFjB83Nzbzvfe+71IdzWnzyk58klUrVf3p6FscrNEQ1JElIFn1fyNdlSWQaLW+9zheSG6ijYjl1fxrX9SmbDs3xEKmIxmzZomq6i0ZEcxVrUaTHtYqwKi8ygROfZ3Hx+OHhmfprPjhZYk1HkqXNsXqOUqHmMD8Zz1YWv9Zc5dSv/cT7XQ/n6HrC/Pmdj0uwgyw/GSmIYZDIV21eHhRjsfQJipx09Mo79V5K2K6P5wu/pxVtCTz/5EzGhZgpWfXPsOV4DGdP7aZ/vUKWhYfW4eni2e98A6fFBRVAv//7v0+1evIHqlarYRgGmnbyl03TNDxvsRFab29v/bZf/uVf5sc//jE9PT1MTk7iOOKL7vs+IyMj9Pb2XvBtp8Jv/uZvUigU6j/zqowXj83xw8OzxEIqb1jbxpr2JKosieLHcclUTH5wcPpCTtsNXGaYjstzRzP88PDsolyzE+9TXZCo3BjV6xJrTZXoSUcpGw6m49HfHCMV0egI7AAqpoNhu5SM4wWCpl6bkQqqIvPI5i66GiL0NkV5+8YFuWuShON5DMyU2DdRYDRbZemC/KV1nUkkSWKyUGOqYDBbNvF94fS6coHh4FTB4PF9Uzx3NENfc6zuwaIpEstab/ASLiX6m2MUDRt8cD2I6opwcPaETcB86PJ8g6SvOcZ9q1vpaYyyqSfFmo4Ejuud4S+cHhP5Gj84OMPOkRyXiTJ6Svzo8CwjgdWHrsq0p8IcmCxyYLJIeyp8UiTKQmzoTgHCQToeUtiy5LgnXdVy6sHM1yts10dVJO5b3Xq1D+W6xgUVQJ/4xCcol8sn/b5arWJZFh/72MeYmDguyx4fH6+Ho86jUqmQz+fr//7iF7/I5s2baW1tZcuWLXzhC18A4Ctf+Qrd3d0sX778gm87FUKhUH3GvHDW/MpQjh3DOR7bPsaKljj/4yfW8dO39BIPa6iKzOhcjY9/ZQ9//8wxapbLkekS39o9yfNH5+pJxTdwdfCNXRM8sX+a5wYy/Osro/XgxHkcmirxv755gN/8yh4+8+TheoL4O7Z08RMbO/jJLd00RDQs18O0XSSEJf6jN3dz85IGLNdDlmAkW6U9FeaBNW1XxYr+TCjUbMbzNSzb5akD07w4OMfAdJmxIOMN4N6VLYzlauyfKDGWq/LtvZNs62vkJzZ28sjmLu5f00q+avGV7WMMZirEQyotiRDv2tZDT2OUquUwkq3y2PZR9k0UefFYloGZMj+zrYcH1rTxM9t6F8Uz3MDFoxo4EaejGjd1ifzAroYwXekINdtlIl9DU4R/0stDczx3NEM8pPDQpk5mSxafe26Yzz47xFz5+NjfcjzG8zVKxukDlbIV8Tl4dTTP04dm66PfK4E9YwX+fec4UwUD3xdxP+2pMO2pMMWaXS/GXM9nrmxi2MeLmrtXNNOeCpOKaqzrSgXhz/D9g9P88fcO8eknDnN4ulS//3f2TvInTxzme3unrtjru1g4rveayLS7mrggDpDv+6c88bt27aKxsZFisUhfX199rDQyMsL69evrxQnA9PQ0jz76KK7r4vs+S5cu5Z/+6Z8A+Ou//mve//7384d/+Ickk0k++9nP1h93obedD2ZLBq8MZ3llKEtnQ4TmRIioJlM1JUrBrusfnxti92iBvuYoIh1HnJc7ljdf0N+8gYuD43o8sX+afNVGApa3xikbTj0dGuALLwxzaKrIaLbK04dneP5ohkdv7uGntvawvDXB8FyFXM2ujwuOzorgxR8emuXZoxkOTZXob47Rlgxj2B7ru1OMjZVOdThXBQMzZb69Z7J+QTg0XUKWJKaLpvA96RKdnZ7GKJYt+E2WC88OZHjwpnbuXnl8NzlTMrEDM7eIphBSFVoTYY7Olvn27knmKhYHJgtoioyuKoQ1hTeta78Re3KZMDwnzP5yVYtsxaIjcGmfLZus6UjSkQozWzJ55kiGvRMFlrXEgk4lmI7HitY4ZVNEVDy4XkRFfPnlUbIVC02ReCgImz0RMyVj0ahponDl/JheHc3R3iw6kY0xnarl1kd7VcvF8Xxcz+Ox7WPMlkzCmsKjW7poTYY5MFUkHdVJRwVfZixXxXXD/MvLY/VQ3prt8XtvX8fj+6f43LNDALxwbA5Vkbh/zbXPF7Jc+NrOcW7qarjah3Ld4rwKoHRaGFZJksTKlSsXFUGu61Iul/nwhz/Mn//5n/PUU0/VZfBr1qzhgQceWPRcS5cuZefOnaf8O6tWreL555+/pLedKyzX41imgu352I5J1XJY25HC8Xw838f1hI2/rsgMzJZJx3QkhBng7ILd1Q1cWYzmavXPo4/YuRZrNiFNrjuuFmo2uapFvmajyBJFw+HJ/dP81FZRqKuyTFRXKBmBsiuiUaja7BkvENUUdFVmNFelLRmmv+XaG/HsGMnVu5Aj2SqW4yFLEqbj1kdZ850Zx/Pq4xIJkaM0ka+hyhKtyTBtyTC6Kte5QvMXx+ePzuF4PhKCXNoY1QlpPtnKjc/+5UTNcjEcF8v18HzhOuwjCgF8H9v1qFgORcPGdX0OTpXAh4iuYjouh6aLrGhN1gMrB2bKdV6R7frsHM2fsgDqSEYWfQ56T3GfywXb9ZkuCVGCrsqs6UhwYFJsONZ0JNAUmQOT+UDu7hDRFLYP53hwfQcEHCHP89EUCQnIG3a9+AEYz4vx2uGpEq7n47geqiJzaKp0XRRAAOMLOrs3cP44rwLo05/+NL7v8wu/8At84hOfIJVK1W/TdZ2+vj5uv/12nnrqKb7//e8zMzOD53ns3LmT//f//h8A//AP/3BpX8Elxs1L0hTdEroicyxToWa5PHs0g+362G4QACcJMmxzXGf/eB4kESK4dcmN7LOrhaiusKI1znTRoGa55Ks2//t7B2mM6fzS61fQmgzj+x7Dc1Vs10dXJHRFWiSNbU2GaE+GOTqbIawqbOoRcSSyJKEqMus6kxi2xxvXtbG0OcZ00cB2LoxXcTkQDUjP86OBsuEwVRQ7+LCq8GdPHaErHeWWvjS39jeRqVhYjofj+nz/4AyvDOVIRjRu6Wvk9mVNvHFtGxP5Gk3xEOs6xYg4NO+7Ikm0JEJ0JCMkIxq9jVEM26VQs0lH9TP6s9zAucFyRMJ5KqLR0RBhXUeK6aLBoekSFcsloimkwhojuRrTJZOhuUqQo1apFyyr2+PkKzb5qg1IPLxZcMGi+mK5cPQ0EvFUVOOntnYzMF2mIaqztvPK2Rk0xjR62pL1Y93Sm2Y0W6v/N4hC/oVjGfJVh5Am09so+HztqQhzB2awXI9uNUJrIkxYVwSPaKKIqkjc2i/W676mGBP5EWxXFFrX4ubmdLj5Rv7iReG8CqB5lVZ/fz933HHHKcnOn/jEJ/j93/99tm7dSkdHx3U3o7x7ZQuOEuaZIxlAhPVVTDfIGxGZRb4vfDZs1yMeUlFkibAi8cpwlrtXttxY/K8C2pJh7lvdyrNHMuwYEX400yUJXZH51u5Jfv6ufrYPHSdxWq5PQ1TnfXf01Z9jrmzy3NEMcyUTSZb4zp5J3rGlm/vXtPLsQIaGqMYb17WjKzJ/++NBSoaNbuSu0is+GfeuasVyPF4ezJIMq2wvGniejyz57JsoUqhZDMwIefQdy5poSuh8e88kpuWyaySPrEis6UhiOy6DmTKZskVEV3jHliQHJosUag6bexoo1Gwm8zW6GyJ0paPoikxbMsTfPzOI5XgkIxo/fUvP2Q/4BhbBcjzyNVHw2K7Pl18epViziOoqj2zu4pb+NC8P5SibDpoiMTRXYSxbpWQ6tCZChDWFgZlyfa2yHI+DUyVSEZ1b+tJEdJUvvjTKT2/tZk1nilv7Gzk0XaIxpnPXitOP7lsT4avC6do1mqfs6/xy4wp83+drr07U872+9uoEH3hdP5N5g6G5qkipV2QGZgQ3VYRFS4DoXo3lqqzuSNIUC9HZEEGRJZriYpxmuR6piEbZFDmIZlA8Wo7HzpEcluuxobuBVOTClHTj+RoS0HkZ8uzGXmPqtiuNC+IA3XPPPXiex+HDh+tdnnn86Z/+KZ/73Of4uZ/7uUt2kFcSjit2XWFNxvNBlmQkyaVqu4usJzygbLrsmywGrWaFiXwNRZb42P0rbxRBVwGpsMrATImZosF00SAcjK3mJbBzFYt5RbsEvPPmLrYsOb6D2jteYLpo1tUyu8fzgOBQWI5HyXD48eFZjsyU2Dsu5Kdy9eoWQCVDkJ6bYiFaEiHuXN7MvvECiiz8X2zPAx9MHEZzNUqGQ0cqwlRLjEc2d/Pk/hlytkWmYuH5PjFdZa5sMlu2yFYtIprCjpEshaq4OGzoSqFrMh0NEcqmzUzRIKIrfGNXmbmKxdqOJK7n88pglmWxG6KAc8GR6RKHpkrsHi8Q0RSiukxrMswLRzMUDXHeexujPLi+g6Z4iGeOZDg8Xap7Vjmuj+14pGM6tuthOoKsL0kSmiy6nC8PZSkaDqmwxlCmwi/dv5xNvQ20JEI0RPVr0iQwU7Yoj+b53t5JHt7SUy9+AMqmg+P57B4rYAXkZ8/z2B+MyObKFlXLwQcKNTCC728irLKxpwEQ3CAQ4/JkRCMZFDjzVg/f3TfF0aCgOjRV4n139KEpMp9/fojH90/TFNP5+INrTgqNXogn9k/XxRYbe1K8fvWlHa3Nh7XewIXhggqgF154gXe/+90MDw+fJIv0fZ877rjjkhzc1UCxJtrFoviRiOgyvq8yh3VKCajliN/5posd8Tg8XWIoU2Zl+w3n2yuJYs3i//eVPRyZLmE6oliVJIlYSGXZfEv7hGbkdGmxV00yrGG7Xj3J2fPEQvvjI7NYjseu0Ty7xvKUTYey4dAY0ymbV09OW6jafPHlkcDtV2JNZ4JXR/Icni5RNmw0RWJe4KMpgreGJLFzNIciC/5IWJMBMQ60XNBkQXoenqsIKXy+hml7NCdClE2HI7MlGqI6ibDGgckSxZpNVFdQZAnPh2OZMqbtkSmbrIjf8AI6GwYzZb5/tMxYrspYrkZfc5SZokmuapGr2DQndMqmw/BcVbghawqW4yH5ULMEH8j3wXQ9XNelIaJTCBRSiiTRnAjjej65qo3leCiSxMGpIn/59FFSEY32VBhdUXjrho5rzt/MR2w+vrNvinduW8Kq9kS9mLipK4WmyDTGRLfM8YQwJ6KLjWc6qlI2XVzPpzEm0xBRSUc1fHz2jRdRZIm33CQMBG/pa+TV0TwlwyEZVutUhvFcjUJNJNv7fohizWZorsLfBvExkgT/61v7+fN3bznl8ZuOWz9egN1jBe5e0YJ6Bvn++eJMCr4bODsu6J348Ic/zNatW9m7dy/ZbJZcLlf/+djHPlbn+1yPiIc14iGVRFhFliXakmHuXNFMWzJMLHT6XZLj+cyUTKYKBk8cmFkkybyBy4+dI3lGs1Wqtovt+piuyMfzfZ+2pHDt1hcsPBKQjCyu/3VVJqwqyDIoskQirOF64gJTs4XqxPd9mmM6iizGa82xxYZzVxJHT3D7/eark2wfylEyHXJVcREMOK94PnSkwoRV0dkcydb4t+1jSMCylhjJiEYqooIkEdUVWpNhFFkipqukFpjoabJMb2OUmiUuLomwMFgUFyO9rrxrTYTqHi43cHpMB/mCavBGjWVrVC2XZFhDV2UMy6MxprOsNcb39k3zvX1TzJQMTNdDUyUkSXj/KBLkDZfxgoEiSXQkdDZ0J1jRFhdKveCzbzoeFcslX7UYy9UYy9bwfJ99E4XTHuPVhCxJdDcI4nVzTGMiX2MiX6MlLj6TYU0hGlIJqTIRTSYZEt/H2bJFVFeIhRR8H3JVG9PxkJBIRjTSUY2KLTpKG3sa+Pk7+3n05m5+/q5+1gceQgMzJb66c4Jv7Z7kh4dnSUY0DkwWyVdtCjWbYs1mYOb0KlBNlhd11iKaUiehXyqcZ/LDDZyAC+oAHTlyhMcee6zusfOrv/qri27/1Kc+xZNPPsmGDRtO4gl96lOfusBDvTLQVZmf2trNzpE8G7tTxMIqbckwpu3w5IGZ0+74fYRTdG9jlKrpMDBT5qau1CnvewMXh1PZMLSnwpRNYVAYNHComC6Oa7JjJM8b13Ug3iUBDwipi1cPXZVJx3R0S0aCuiP0LX2NPH80Q0gVF/94SKWzIUJXOkKHEuVzl/XVCpzqNTdENIqGTb4qeCLZqsVIroppu5i2Q80R7rkAjidUcKs7kuQChVzRsImEFDRFpimu0xQLEdUVmhI6jdEQVUtYVMgyjOcMVEXi5+/sp7cxysuDWVxPcCdGslUiusKt/U24nr/Ic+gGzozudIT9czZtSfH5VWUJy/VZ0hRFVSRSEZ0N3Sk29jTwzy+MMDhXYaZoMlMyiOkqmuyJtUcGw3IxbPGe267HdMUmMVdDlmXiYRXPFyaVybDG8tY4g5lqne9yrvyW01mgXA7EQgqdzVF+8b5lOK7HPzw7xEgwzv77Z4bY3NtIczyEH6jgJEmu83qSYRUJ4REUiwqn7Jrtcmy2zEzJRAJioeOv+eYlaW5esphQvGs0j6pI4As7gIHpMq2xEEawGZIkaeGSQqEqVGbtqTBhTUGWJd6+qZMfHxZRMvesarnk5y6i3qiALgYXVADdeuuti0JGT5Szz6e77927d9HvrxdCdENUP8lhU5cVKmcZdzieiBYo1hwe3HAjn+VSo2o5fO3VCaaLBv3NMd66vqPeTg5rCqmIxlTx+Hvk+sLr45kjs/DgGmr2YsXWscxiM8/uxii39Kc5Ml0mpMi8fo34DNy1opmtfWnKhsPAbJmornBTZwpZlhgbG7usr3mubPL1XRMUaw43dSUXyXNjIZWa5VI2XCzHZ6ZYI1+zMG0P31+0NgPiHL1lfTtf3zVJtmoHHQaXGi6261EyHZa1xklFNH5mW2/gvxIiHdWoBqojOdjBvm5lC+u7UxyYLPGmkFr3GMpWLL7+6jiFmsPq9gQ3cGZ0N0Z5dEuS0VyVd2zppisd4Zu7JxiZq/HGte28YW0b6aiOj7DaKFTFyCMZ1qiaLuHAo8l2HQpVp/6eW64Prk/NspAkURD0Nka5dWkTiiwTUmVkSaY7HWFVe4I7z+Jf5vs+Tx6YYf9EkVRE5e2buhYF5l4O/NGjG9i6vIuOhgg1y+XwdKlesB2eLuN6PuO5GjXLxfPBdHyG54R3FxIUDAfP85Eki46U6ALPP96Hk4xST8R8ITkfhaTIUHM8QqqEbwtxQSjo8AzPVfj6qxM4nk8yovGuW3qIhVRM2xVWBbAoUudSwbmCztyvRZxzAbR79+76f3/0ox/lv/23/8bU1BTr16/nM5/5zKL7btiw4dId4TWCV8fzyGcYGEpARJOxPQ/bc5nKG6xpT17ylud/ZLw4mGUqMGI7Nlth70SRTQGhsWw4xMMq4apM9YSFpr7onbBWnBiXkQxrfOjuZbw0mCWiK9y/gLAY1oTZX3Piyobg/ujIbCBhFhyCjlSY6ZKJKkuowYi2LRnm4GSRfM3GcY93fRZCkqAppnP3ihYcD54dmMW0veAcSCxriTNbMklHNd62oZNYSGV56/ECJhY6ealoiOrcvqxp0e8aYzrvv7Mf3/cZHx+/pOfitYqexugiD55HNnefstPy9o2djOdqTBcN2pMharZLIqySjuqMZStMFo9zrhZ+BCSEp05IU7h3VSs3L0kzWTBoiGiLjELPhKG5ap3Pkqva/PjILA9t6rrg13wuuGdlK8mkUE5JklBtzndvGoKx7LFMBV2V0RCvc96LbThTnbenxfV89o4Ved2qFlZ3JChUbVRZprfpzJ5G6zqTfP/gDJ7vs6QxSkdDhPG8QUTXkGXBu0sE+Xm7xwp1w8hizebobJl1nSm+/uo4h6fLSJLgE3709csvaSPgxE3dDZwfzrkA2rRpE5IkLSIC/8Iv/EL9v+dvkyQJ133t8V9qpothnf51yZLgAVVMh2xFLBCFms0jm7vqu2YAw3Z55kiGsumwsaeB/ubrx3PiSsJyPL5/cEaQaVvjNMVDvHBsjtmiSVc6gixJi6JHIrqCqgQGf4gLgITYtW3qbgBAVyRqzvHHjGZrvDSYZVv/cf+m5a1xVrRd/c6F7/tsH87x4mCWkbkqIVWmJRHiW7snURVZGDpWbVzPJxVRmS0ZKLIU5HGdXAGFVJn33dlPcyLMO7f28JNbutkxkuOJ/dNYrkcyrLGyLcHP3b6knrF0MeOO66Xbe63gxHN9qvPXnY7ye29fxytDOfaM52lJhImFVCYLNUbPJIf2RcEQVhUOTRVZ15k87bpjOR7PDmQo1GzWdSZZ0Zbg6GyZHxycYTRbrX/3vFN0HvaMFdg1licZ0XhgTWvdgPRCsfBaE9ZUHljbxq7RPCB4OyFN4cF17Ty+b4qq7aLKEq8L5PyGLZRwsiILk0PfJxnWuH91G88dnSOkyrxhrdjgeJ7PPzw3yOHpMqvb4rz/jn5kWWKuInhEHmA4LhXTob85xvrOJAOzFaIhpT4pmA8Snkc8pOJ6Po/vm2ayKDZtw3NV/ut9y1Eu4VdjSeON68fF4Jw/oYODg5fzOK55NCZ0BudkZETFLQfj3/kLrecLVU3NcpEkIcMcnqswV7FoWdA1ePLANEemxehlJFvlfbf3LSKZvtaxd7zAbMlkWUv8jDuwZwZm2TOWR1VkhjIVbNcjpMpkyiae77Otv6luzgeCrzOZM1jY1Y6HFBpjIR4IRllN8RATeQMP8f5VDJftw6IAGsxU+O7eKRzX484VzXWjtSsNw3bRFJlXR/P8+EiG6YLBaLZKOqojB8TrbMVicK5CRBWBmDXbwXYD4nZIwfU8PA/mT0VEk2mKh3jzuuMdLVmW2NrXyJqOBP/6yhhVy+WNa9vQggvGt/dMcnS2TFM8xMObOkmE/+N8Rq8kLMfjK9vHGM1V6UiFeWhT12kl6UdnywxlKrQlw/zcbX38yyujHJgUWWzzhHffB1UWYbjJkIrrCwPXdFSnYjr8+HCGr+4cZ3VHkp/Z1svm4HN+cKrIZN5gZK5KNnBLHp6r8kbP4/F9M7iecJoezFRY15nk9qWLR2YzRYOnDk7j+zBbMlEkibdeJA3gr54+yk397bzlpg5kWeJ9t/exvVPYTszzdVZ2JLilr5HRXJWGiM7rVrQAcNeKJr72qonj+nSkwqztEGvFxp4G1nelAvK4qET+/dVxHt87hefD0GyZxliIhzd3MVM069J7BYmSYdORipCM6LSnXBRZoiMlOlR3LGvCtF0yZYsVrXGWtsQpVC0mCwYlU3RwJwpgOS6RiywMFyKsXZ2NRsV02DGSQ5YktvSmiejXJxfpnN+JJUuWXM7juObhe4IMqkkyigRhVaJouosuuAS8i2LN4dVRYaCVq5qLCqB5jwkQBL1Cza4XQLYr5MOJQIn2WsOOkRw/PCQIgbvHCrzzlu76ArIQsyWTb+yaYCJv0BAVio3RbC2wtvfo7Erx01u7URaouuYqFo63uB3seL6IgQjk7uu6UhiOcIlWZYmiaTMUKJWe3D9ddzL+6x8e5c03tfPGte2nHP1cDvi+z+P7p9k3XggcyB1sV5jZNcY0muI6K1tj7J8sk68KEnMJG9f3cV1A8vFcUQiGNRVNlhjJV/E8cNwgwuUUXYUn9s+QC0Zs39k7xfvu6OPITKluKJcpmbxwLFvfLd/ApcWe8TwjWfH5nMgb7BjOCe+qOVEQ3ba0CVmWGJmr8o1dE8EYtwBr4T239vK3Pz7Gy4NZSqYIS/UlH02ViYdU1nYkCesKuqJwc18DLw/l2D9RxLBd8XcUma6GCNmqxbd3TzIwW+bAZImWuM7m3jRhTWE0V6t3exzXZaZo0pWKcOJkv2Q6i0bMZfPi5dkHp4rkHZWlzXHWdiaJhVTuXtmy6D5TBYPVHUlWBwXOXLC+djZECGsKluQRC6v1NfjFY3O8cCyLpkq8dX0HS5piDGcqTBYNLNsjpCl1orXhOHUunemKkddU0aS3KUp7Sqgk58foIVVhc2+aubJFT+BGrSA6R/MwLPes3Z+iYVM1XVoSoXOiT8yrCK8kfN/nKzvGmCuLcz2SrfIz23qv+HFcClzQ6v71r3/9lL+XJIlwOMzy5cvp7++/qAO7FjCfep2O6miKTERV6m7QBcOpq40AFg4e5ougXNXkK9vHuXeVxe3LxI5pVXuSzEAGgHRUo61OznP5l1fGyJRMNEXi7Ru7zjqjvt4wmT8epOj5IkvqVAXQ88fmiIVUXM8jVxFuxMLLxBL+NiN5vrV3ip/YcNxpPKRJaIqCj1d/HxxXFJg7R3K8744+bupMsX04hyJJxMIabYkQHUF457xz7qGpEp7vi//34NGbr0za+0TBYP9EkdmyyUzRwHBcshUx4qqaDq4Lmiyxsi3OXNlCliQyZZNyzQYJlEAB4/mQDCskwhqjObGQO56P5bhIkoTn+TwzkGGqYLCkKcpEQSi2PN9nJFtl73ge9QSym3sqUtEVQN/Hv3Xa24b+6K1X8EguH/wTKBxDcxWmi+KiNpKtEtYVtvSmmSzUMCyXkukwmq3ywrEM965qpTGqYziiaxjVZRwfVEkirCqUAq+qlkSYVEQH//h7qcgShu1RMR0GZytkKxZzgXS8aDgMz1VY25lkXWeSY7MVBmfL7BwpoMgSsyUT8PnNt66tH3dPOkpzIkSmZCJLUt1s8GJwdLbMjCEznq+eNoKjOx3FdNz6sc+PhPZPlIjoCooszG0PTpVY057kuSDN3rR9njowwy/c1U9EVyjWbDzPx3BcwoGJbVzXyMs2Hj4RXUWSJBqjgQVGcJ+WuFi/B2ZKfHP3JH5givvuW3uRZYlYSKFQFec8FlJOyc+bx+HpEt/ZM4Xn+3SlIzy6pfusRdAltBQ6Zxi2Vy9+QBShrudfl3zXCyqAHn744ZP4QLCYB3TXXXfx1a9+lXT6+swqqZgOX3xphJLhIEnQGA/RFNfJ1+y66mBh0aPI4Hrid67rU3IdDk6VKBsuAzMl0jGd1e1JtvU30pYUpnLLWuJ1Kfax2QqZklj4bNdnx0juNVcA9TRGODwtfDMUWaL7NNbwk9ka39kzhemI9OefvbWXr+2a5PB0CcfzmMhX+car43SnI/VRlWX7JCLaImJzSJWRZImK6bB3vMDXd03geT6qIuO6Hj2NUVZ3CL7Pvata+Lft42LxaYgQUpVFwYmXGobtiuMLCrj5pcN2RF5ZxXICDpOE6XgMzlXw8VnZnqCnMUo6qrF9OIcsS9iOi+n4pMIqIU1hZVu8npkkBz4xhuMxOFPiiYMzDGYqtCbCjOdrxEIKhu9zeLqEYbv84NAs6zqTdDVEGM/XSITVembSDVx65Gs2nu8jSxLpqEZ7KlIvgADywWcwEVbZM1FkplhjOFsjEVIYy9W4d1UL2/qbmMjXMGwH1xOE9bJhU7VdkhGdkKagyjK39jdxZKaM74uRWH9LjO8fnGH3WIH9k8IcMBXRaInrDM1VGc3W+P7BGd68rp2SYeN4PrIs+D+7Fhj8gbCQ+OmtPQzNlUlHRdF1sSjVbNAczDN4qsnBIuz5gvw/X7tP5KtM5sV3oFhzkBH3Gc1WOTRVRFVk7louCPySJLGkKUbVcojpav3LGNYV4f2FGC9GQwrpmM6ylhg/ODRLU0xny5IGAPaOFxnKVKiYDs3xEEOZCqvbk3Q3RHBcsRHpaYye0QTxlaFcvds2nqsxnqud9RrgXIXNSViTaU2GmAk+p11BtMj1iAsqgJ544gl+67d+iz/4gz9g27ZtALz00kv8zu/8Dr/9279NKpXiQx/6EL/2a7/G3//931/SA75SGJ6rUjLE/Nf3oScdIaYr/ODQLHJIXMDcoAhSZVHdOy6UFxCly4bLwKwwq/vnF4b5nw+vB2BJ02Li2nTR4OhsmbJpEw8dN/h6rWFDdwNhTWG2ZNLTKBb66aLJmo7EooXh2/smKBtivDNdNHhhMMtEoUbRsIOK0+bgVInH900hSxKbehqI6ArVIBFadDxEsGFEV1jVnuCJ/dPMVUxs1yMWUvF8n3WdSd6wtp294wVeODZHayLEXcub64vKPG/gUqJmOfzVD48xXTBY0RbnXdt6675Cm3ob2D9ZoGYL/x3T8UXH0QdNlRjJVmlNhOlpjPLkgWlaEiE0VSYZ0ehuCFOzPI5myhyZqdDVECaiK3UFnC7LfPXVCY5lKsyUTAzLpRhkSC1tiREPqSxriSNLEoemynz09cuo2R7hy2DedgPHcWiqRDgW4/WrW1nflWKuYnFgsohpuwxmqsKewHBojuusaoszXaihysc3XlN5g/vWtOL7PsdmK1QtB1WRCGnCAFANZi6NMY1js2XuWtFMvmJx14oWkhGN5wYyjOaqSJJITm+Iahi2h+/75IPNxBP7p9FUGUWSKBsOIVWmPRnG83wOBdL01W1xfnBoloNTovPy8KauM0ZEnAtM10M9i6HsSLZKSFPoTotCQXCUUkRDKhFdxfN84mGVqu3ieh6HZ0rkazYScGRGSOY39qR4eTBL2VSIh1U29YhNVcWwA66nhO2ITuxUwWD3WAHTdinULJ4ZmOPtGzsZylR4aTCL6XgkwioPbeoUeWOJEPmauI40x8Nn/C7Nu1jPI6yfvb1zJmHO5YIkSbx9Yyff2DWBLEm8dUP7FT+GS4ULKoA+9rGP8Td/8zeLIi/uv/9+wuEwH/zgB9m3bx+f/vSnF6nErjc0nEBMvnlJIz2NUQ5Pl7Fd4e1gWC4hTaIxFiamywwEX6h5+IiWsyyJQLyZknFSqOBEvsZj28cEH6jqENVVlrbEuHvlmX05rlesbEuwsi3BV7aP1Wfth6dLi0ZNhi1aaZ4LvuTz7MAs2aot5vG+CC9sjOlICP+NTT0N2K5HVFcpGsIMMRYSPj53LG1CVWWOzlToTEUYy1WRJVjRmuAt6zup2S5PHZip77xSUZU7ljUT0ZSTCtWLhe/7fO65oXp+z2RB8Cs2dAti5t0rWvj27kkSYZVC1aoH7/qIrqIX1MT9zTFhWqgpPDOQIVex6EhGSEU1mmMhKpb4HN3S38jRmTKyJNGVjtQT3KeLBoemyiQjKomWOMWqQ2syVFd/pSIqsiwTC93Is7sykMhWLWRZvD/vubWXpw/NUgsI8YemSoyFVHRVZnlrnOmSiYRE1RQZhZmSSTqq87qVEYYyFcZzBiva4zx4UweJsEZLPESuauF4IustqikYtosiw76JAmO5Kp4P6YjO7f1N1GyHb+89Pq62HI+GmEYyolJzXKRAbfj/XhoJxmHw9KEZ3GBHWLNEfMxPbb24QFzb9XGCAnAe86TkeY5kazKMJB23uGgLRtrLW+MUqqK7FlJlljTGyFYsYrpKRFMCawCxObi1r4npgsme8TwbuhvYGhCsJem4/YOmgOPCdKnGUwen6w7svi+KgaOzZQzbwXF9KqbPwGyZTb1pTNuvC2XmDRS10xCB7lvVyj9kBslVbB5Y23pOAbRXSwT/nb1T9U7lE/tneHjz5bVEuFy4oALo6NGjJJMn746TySTHjh0DYMWKFWQymYs7uquIzoYIb1jbxsGpEumoxp3Lm9FVmUdv7uKHh2aZKZpijm65TBcNXNfnVLW478N4vorpuOwZzVM0HUzboyGqMVkwGM/Vgswxha606ALct6r1FM90fcP3fV4eyjGardKeDDGSrTDfax7JVusdGxAX+EOBUg4fMiWLmu3Vv+yWI3J4NEWuJywbtoeuSDiu4ABpvsTwXJXmeIh7VrZwdKbCXSuaOTxdZk17gp/a2k00pJINQkDn4bg+qy9Djlu2YvHvO8f57t4pyqZDU0xnIl9jx7A4J3/5gwFWdyTQVQkJYep2Yns7oil0psK8cGyOY5kymiyTCGuENZmJgvCHiekKluvxwrE5GiIaucDw8Nb+RnzEhUuSJBRFLPCyJBRh9yxrZbJQQ5FlXncWU7xrAa81flBP+viooyGq09kQYTBTwbRd9k4UhOP2XBUPn2XNomM3UxLBvS8cy5KOafQ3C35Y0bQpGy6zJZPXrWhBkSVCmozpuAxmRIK8psiMZmuYrovteKiKhGm7/N0zx1jXmUSWJEKqjKYqbO5tIFuxBQcmotOWClMybHaP5escvnzVYjxfo1gTSfUNUR3P8/nBoRmGAkL3GwKV4bnCdnwqlks2IDa/NJjl2YA/eefyZrb1N9LVEOFtGzoZmCnTktDrI/H/cs8y/qB6gLmyxUObOulviWE5Lr1N0XpEy21LxQjs6GyZb+yaqGeure9KsbQlzm1Lm/jxQAbX81nZlqC9IcxLx+YoGw6VgNA8GXDochWDfNXBA1TLxXJc8EUBJwX9OmuBYqZiOnWj0XlDyZcGsxyZLmM6Hk8fmmXrksazijBar7AvGUDNcjkyXWI0W607bP/Exs7rslN8QQXQzTffzK//+q/zT//0T7S0CFb+7Owsv/Ebv8Ett9wCiLiMnp6L2wFcbdzUlTopzuLRLT2koxq7xwvYrofl+oLZf2r7FXzAcmGuYvPxf9vDqvYEsaBTsamnAcNxmcwb9b8zT6p7rWHfRLG+eI1kq1Qtt+4T0hDV6sUPiK7YQlROYfY1VTDoaAjXd2uyBLmaLUI5PZ+a7TFdNNg5kqerIcKD69vJlCzee3tfvWgCYdy3piPBgckSksRJ8t5LheePzlGs2bQlQxRnbGq2C0Fn5ulDs1RMh2LNpmy5RHTl5JBhBB/oR0dmaYiGaAs4PC3xEDMlA0nyqFluPQ08qinsmywGOV0SX9s1wed+fhv/vmOcFa1xVEXi0FSJTMliW38jW5ekUZWmUx/8DVw2bFmSZkV3C0tbFgeRru1Msne8wP5cFRDFSbZqE9MV4jGFpriOHRTIIgpDEiaJJZNkWCVTNtkxkuO2pU0cmSlxZLrM0ZkyVctlslAjVw3k3bIYabge2L5HvmpzLFPl1v5GblvayPruBnwf/vG5ITRFxgpGqhFdYUljVDhOI1RQWkDAkYJL/v7JIrvHBFeoWLNpiGrcsezcv1+KDBI+Y/kajuvxw8OCvwbC72tLbwOqIrpiJwa5bh/N056M0J6MMJKtUTJsEmGN/3LPMr78yiiJkMp7bhXKJRFxJM5HyXB48sA0H2yJ87aNXYzna1iOxxvWtol8NkVGkUVengyB7xYcmCrXN2iOL/g877u9H9v1KBsuSGIaoMoSRcPmSy+NUDGFlP6hTZ0saYrx5IGZerG3f6LIkekSm85ixzFwgpv9lYAmSwxmKos6c9dj8QMXWAD9/d//PQ899BDd3d31Imd0dJSlS5fyta99DYByucxv//ZvX7ojvUagqzJdDVFkSUKTJWqwSA12OjieR65qU6jalAwH1xP5NU2xEBFN8FQ6GyL1Qsh0XMqGQ0NUp2YLG/h4SGXlNWDSdzrMlU2ePTqH7/vcsayZppjOkekS2aq1iNgJsLk3XQ+AvHXp4gvvvCz7tPDFF65iunUScTxwxHVcT1jP+z7RwJsiX7VFV+c0o+o339TB1r5GdFUmeRn8bqYKBsdmy5QMmxWtCSK6yqq2BD5CbTYeSI1N20WWJWFweMJnSgIs2+XQZImt/TqxkMrKNkGinz1sEtEUlrfGcVwPw/GoBrJkKfBmsB1B7r65L82Lx7KAeA9uW9rEncubr9sF7HrH7cuaSCZPTmGPh1R+9rYlvDSY5fmjGfZPFYHjShtdFdEvhZpNVFd4y00d+D5ULIdSMAb2fdGh2DVawPc9RnI1ClWLbPX4hcv1hMtySJexHR/b8ykbFlNFg0zZ5gsvDDM0VyUVEWT4Y5kKbYkQ/+l1S1nWEueFY3MBd1EEki7zRTEWUmX+9ZVRDk2VWNIcpT0ZqY+NzhW6KhPWVZY2x5Akie/umWIs2Bx1N0T4b29YddrHHpstc2CyiOV4tKXCzJZMNEXmu/umCakKluvzvX3TPHpzN6mIStl0MG2HsKbWc9F2DGeRkZBlicPTJSqmQ09TjI09DQxlqoQ1mXtWiYJunms3j4mCgel4gf3E/Ln2sR2PozNlZksm+apNLKSyb6LIkqYYIU3G8wTvL6zJizaFp4N1FcLgHd+nLRFicLaMLEusak/8x1KBrVq1iv379/P4449z+PDh+u/e8IY3IAe7gIcffviSHeS1gkzZpGQ4JMJaQBZ0kCQ3uCALSatzisf5iJwwRfYZmquSCCusak8S1RUkCR7a1FUvfKYKBs8dnWX7cJ6mmE5jXBd5TbaH6bhs6U3zxnXXJuns33eO13cF47kaZdPmif3TqLLE+u4G4iGVRFhDlSU29zYsksAbtjCQDKkKHakwo2cI1PQQI6/5lHeA1kSY+1a18Nj2MWK6SiKskgxrJMIq957DSLH5MnXeRrNV/m3HOPmqxctDWZpiGjd1NwQkep+DU0Ucz8PxwKo5yBKnjbIwHA/T9TgwVWRdR5J1XSnWdiQpGsLaP1e1yJRNJAlaEzEMx2O2ZKKqgg8lSRK39DVSqNpMBjL4u5Y3L3Iqv4FrB7oqc2egVAqpMrmKRcVwyFdt3rohiSLLHJgs0pOOcvfKFizH48h0iacOzmA6LtFAxQRiU+EFvmML4QMxXaanMcpothp4YTlM5Gs8vm9S+Or44OJza38Tt/Q1ct+qFhqjOkXDYlV7gq++Oi46VBWLhqhORJPZM1bgyEyJ2ZJJpmzywFqdDYEj+6lQMR22DwuTw3Z1njQcYl1fE++/o5+iYTFdMuqd0emSQcmwSUVPHeWRKVv11zqWFbEYJcPBWECqzgSxGUuaokzma1RMEaezJFBe/eDgjFDNAWO5Gh+4aym9jVEeWNPG8FwVXZW5f7VYi9d1JnlxUBy/BDy6pRtVEZYB892l2ZKJosi4vs/e8WJ99L4q2NTe3t/I9iFBpO5IJVjWcnJhfCLak5c3j+1U0BWZ547OMVGoIQEyc9dl8QMXWAAByLLMm9/8Zt785jdfyuO5ZnFgssj39k3h+5AIKSxtjlEyBDG3NaGTqdgUqhaye2pimixBIqQSUhWiusb7bu9ldUcDYU2mIfgSFw2br+wYY994gbmKRTkZpmjYGLaH481LOEtEdZW7VlxbPI2FZMXBTJkj02WmiwaWK6IpqpbHfatbqVoOq9oSi0Z9Pzo8yxP7p4nqCm/d0EF7KoJM7rQEPwlIR1VyleOL+US+xpHZCktbYng+3LIkzetWttDREDmnheR84Xo+VUuELZ4Jg5kKnu8JU7eqxVzF5PB0md6mGCta41i2t8hM81RPF1Gleu7Qus4kSBIdDRFeHszy2Cuj9DRG8VXBEZrnL8VDKh9/cA1HZ0uoslx3/NUUmQfXX56g3vlzEtPVG0XVJYDn+VQsh9uXNXHXimY60xGeHcgQD2lM5A18H9JRHdsVmW7d6Sj3rm5lumQyWahxbLbC4akShZrNvokiBITghflRugLb+poZnBNk+WRYRVcVchWLYtWmZDngQzKiocgSNy9J8/ShGZ4/lkWRJXobowHxWKIpHuKBNW30NUf59X/dhSrLtCXCOJ7P61e1LjKEPRH/tmOMTOAtI1eFSOAL/+lWlnQ0I0kSuiMH/DVxf1mS6h3kU6EzFeZwSKFqufQ1R5ECm4HGmMbATBlVltnQLTadX35pjIol+Dtl0+XLL41y29JmxvI1yqYImJ0vHjsaIjyyuYt81SaiK3W17tKWGNuHcri+MMntSIWpWcIrbn5M5ng+tiMMFbvTEUHKDinEwuI5BmYrRDQVTfGwHI+ponFWIYbnXXka9EzRYCJfwwza1MPZKoblEr4O3aDPuQD60z/9Uz74wQ8SDof50z/90zPe95d+6Zcu+sCuNewey+P7MFMyeOaIkH72NcWoBKTmiOZRkmX80yQMe/VEYQlNldgzUeL2ZS2LKudMycRyvPrvSqZNR4OQmw4FKcdRXeXloSxb+9LnJJW3gjbs5bYqVxWZlW0JXhnOMl00Cati92m7HroiUzRsJvI11nQkGc3V2Dma55a+RmZLJn/8vUNM5KsgSUwVakR1BU0BM9isnUivSoRk0rHQIpLw0dkyVdNBkWUUYKYspL6XA4WqzWM7xijWbBQje8b7tiZDOK7PbMnE9nz8IJqgYjocmSlTtZxTUccA8bqTEZWWmC4k/SEVRZaJhzXGslWOBb4jY7ka/c2xRenWluvRngpftBT5XFE0bB57ZYxCzaY5EeKnrpCB5GsVhu3yr9uFMWoqovHozd2ENYXewOhvslBDkiTak2Fs12ffRJHudBRFEnYJ84aHPx7IsLotwcaeFAcmSpRMB91yxe0SdDVE2TsuLtyG5aAqotCwghR0xwNNkWiK6dy8JE1nQ4S/+VEeEAXvgckiqYhWX4tiIYWorrKkOUZuJI8sS7TFddacxsgQxBqVWWCslw02Nl98aZSlXTXesaWbiK6ypiNZD2Rd05EkegaC8LwZrev7zBRN4mE1KJpkbNfH80W0DsB0sYbngY/wEpoPXLYdoeDCF8WLG3RsJEk6KUR2z6jwFpJ9HySJHx/JcNfyZpIRte6anQirKIpMSzxEVzpS5yLOd8IHMxWKhjA/9X0RpzRfANUs95RdFk278hE1puNiL5jRW64XOHq+hgugP/mTP+E973kP4XCYP/mTPznt/SRJek0WQCILyWB4rhoQS2UM2+PmJY1MFQ1UWeLxfVOcyZi8HIzPehoiHJos8p//6RU8Hzb3pvjQPctoS4bRFInGmE7FcmiJh9m6pJHVHQn+7+OHALFgqbJ0xpZjzXKpWg6Zksnj+6dxPJ/NvQ3nNAq6GNyzspntw3PYrsvGnhQzZRO/JhYWVZaoWQ4V02ayYCJLPn1NUQ5OFBmYKWEEM/QfHcnw629cyfMDGYZzYiFSZYiGVNyA32K6oqW/ruv4orqsJU40pFANqqZV7Ze+6zOP7SNZikF7fa50ZrPE1e1JqmtEe1+WoGK5RDShnBjJVuvt8YXQZAkksYvVFJl8VbjcRkMa6zqTLGuN86PDGXRVpmIKOW9YE2OMeVnypkvgxHs+2DGcq48cMiWTPeMFOl57aS5XDHvGC3Vj1ELNZsdIjvVdKQZnKzieMDKc7ywA9VTyFW3xwGvLIB5SSYU15somU0WDYs1iaXOUcOCV1Z4MUbNc5ipCTWZ7YHuCRK9IwpdGkqAxFqKzIUJbIoSmSNiuR75q4XqiYDg2W6IrHeOeVcfJ3B++exlPHJimajq8+ab2M2bJ6apMZ0OYicApfuFoeyJvcGiqxKr2BLctbaQnLYqFrnSkLikfnqtwdLZMczwU5HxJ5AJ1px2oQvOBjcZMyaxL5fdNFLlrRQsbuoVDvHDR9usu1omISq5mBscoETvDJjKiBykBvo/ki25TWFd587p2vrN3Egl464ZONEWmOx0hGdZ4aTDLkuYo6wK/sflz63o+jufVpf4/ODTDjw/NEtYUbj+heRu/ClYVibAqfNcCTldMV+rUl+sNFxSG+h8xGPXeVS14vl+XxXu+z3jOoDGm8xMbO3lmIMMT+6cX7eZP5HP4iAv3zrG8yIWRxY5k33iBPWMFfubWXkzHo2w6rGpL8kv3L68H533s/pU8eUCEDd67qoXxXI2BmTLNiRAbu1N1MvDwXIVv7JrAdn0GMxX6mkT7d+eI8LhojF2+mfGHPr+dV4ZzOJ7H0dkKD21o58cDWWbLJqbjsWe8wOGpIiFdYaJQY894kbZEiJp9PL6iYjosa4vTloowE3RNwqqIITF8QdhMhBSWNEYYmClza7/gSHQ2RPil16/gmSMZmuL6ZfWlWHjhORuKhs3O0QLru1NBUSCjSDIePlMF45QjLy8gsGZKJiFNpmSKCISy6XBgsshEwWAkW6ElriPhEw9rJCOa4PNIosN4qoiRy4kTC/JrkRNwPUnnTzx/qizcit+1rYdnjmQCpSBkqxZNMZ2wKrNrNE9XOkJrIsRUvsZ43iAaUtg3Xqz7Y92abGJNh86usQKHp8sYloPtHQ93ng92lmWJsKbhmjbpqCY6uZoQCaSjGuP5GtmyFcSvSCTDGrf0HXcMT8d03nkePkAPb+5iT6AYa2TxqEyRxYbT8fw6N7At2BxM5mt86vHDZMomUV3h5+/s547lzUzkaozmariBZ5vluMRCYfwggkeVJTb0iBFYWFOQJAkpSDEIa+Ji3hDRmFGFaWoqrJ3RxXljTwOHpkt1o9VNPWl8X6xdy1viIEkET8uusTzf3TdJrmIxUaixujXBIzd309UQQUIIZhqDrm+2YvGXPxhgIi86fseGF3O4KlfBCDEdC6ErEvOud1FNQb9Kxr3/+zsHePLANAn5VOzbs+Oi9miWZTE4OMiyZctQ1df2di+qq7xtQydrO5J8b980nu/zc7f1sT6YIy9rifP/PT2AIh1XhS28uM0vLCXDoWKKxWZ+LCZLcHS2wv/4+n7aEqI9qsoSBydLbA5k3n3NMT7wuqWAaH9/+eXRuvmX64lOFMCLx7L19mShZpOv2aQDjpFyHhfu80XNEunAtuuBD4Wawzf3TFMyHBzPR5UFkbcKxDyfXLWM2xRbxAWSEKO0Ld1pWpMhMTJCkH+XNOkgScxVTGH1b7o4J0ilNvem61yXy4lt/Y1MFQymg2DEM2HPWIFizSaqq9za38TWvjRNsRDf2zfFS4Nzp3zM/MsyHA/bEw69jg+m4/CV7WOs7kiyvDVOIqxx3+pWdo0WKNYcvvbqBD+zrYfW5JUZey3ELX2NTOaNOrl6fVeK6cnK2R94A6fE+q4UI3NVhueqtKdCbA2+3y8P5eoBvmFN4f139PHN3RMcmBQRM57vkwiraKpMoya8fqqmgyKB7fkcnCygqQrZsonhHP/+nKg6dFyffNUiHlz4G6I6FdNltmyyqj0pZO4TRVRFomw6zFXE+J4L1BKEVIWtQQE1NiZe30S+xm3tTaxqS2A5Hqossz4Qi4hRlseLg3NMFUXnqGg4/OjILHcsbxbd1YCj6WOSLdusaBPRM/maCEOOaGr9nApfMbE+vhyQsaMhlVRUxfMhFog3xLkR/Jx4SK3zNztSYRqiGjXLpTWuEw0JP669k0Wygap132RJ+DYNZDgyXcZ2PRRJ4omD0zxyczdDcxUs10OWJCbyBrbrMZSpcGxW2BfIEuwuLZa9105hEXK5MZypUFrQuc7WbCo1m1jkyo7jvr17gn9+cQTH9fCs6gU9xwVVLdVqlY9+9KP84z/+IwCHDx9m6dKlfPSjH6Wrq4uPf/zjF3Qw1wOWtsT5xXtPHq9EdIXGuF63PYeTA1LnF5mFi818pljNcvB8EYsxVTA4MFEgV7XQVPkkL6LporkoeXmycNy1NaQd36X0N8eIhkSUwe3Lmuqp85cDYU2p5/HMQ+Qcif+e/54q8yfFFxk7ZdNBV4XVPEBHMoSmKaiyhCzJuJ4nnGcViY5kBE2RUGSZaEjh/jVXxzAyqqu8a1svvu8zPj5+xvueKGUNqcLy4MhMibimULVOv4C5Pniuv6irWLZcRnNViobIHFpohOb5PtNF86oUQGFN4Z239NSzAK8mztTpuV6gKTIPb+466Xwu/K4btjBhHVugmMxWLI7OlsmURShpWJOpWG7dVTxXs/E9ixM/djKgKiBJQoo9P9GQJDHWGc/X6E5HaIyF0INYjFREqyuZVrcnLnl3uWY7HJ4uMVexaI7r9ZEQCJK/Kkuko/oiJ+h5CftcxcJyxbFjw1xNyM7heMTNfOHU3hBGHZPqG9L5gOQ1HUlcz8f1PLrTUUKqgu16fP75YfaM54mHVH7u9j5WtiUYmC2Tr4q8tMmiyVygMPMXrfXiHxXLFSrQQCJfDsQjni984FzfJ6yKyKBYSKViunUxieYu7gCF1SvfeQnp0qJrmOv56OqV/87vGssLIYoP3gUWghdUAP3mb/4mu3bt4umnn16kAnvggQf4vd/7vdd0AXQmrGpNMJ6t1sm7IIqbqCZRtf1Tkl3nO0PFmoOmSlQtF8fzqNkKU0WDr+8aZ7poMFsyRUGkCH8IK0iAliSpnoAMcO/KVirmJEXD5taljdyxrPmSXpRGs1V+cGgG1/O5Z+XxmX/RcEhHdaYW+P2carzj+oI05yMR10V4ZyqiiUwbCdEuRrjhyhIgC+fiJeko//OR9fzoSAbbcbl9WfMiQ8OrgXM5p5t6GpgqiItUVzrC5t4GFFlidXuC5mSYuVN4/iz6GywmgEsIEnZjNER7MkzBOL4gakoQeXEVcbWLn8uBqzk6O/F89jVF6+aCibBKR0MYx/XYOZqnWLNJRTRKNYuSIbo+kiT8eeZ9aiTAPMW1QlNlGmManjfPKVOo2S6qLGM6PqvbY/WA05guMrNWtiXoa46xtCXG2zZ0XvLXPlUwKbhFdo7keOO6dh7e3LXICVqSJG5d2sTeiSJHZ8qkYzqPbhHkeyeIIJr/7jiOSyKsEl3AXWkPCp3/cu8yXhnMkq3aNMY0PnzvMgAevKmdSLCxW9ORJB3T2TtW4B+eHaRUEzlhNcvlj9+5iaMz5XqGn+f5HJwq86Z1HaxuT/D8sTkkROGlKjJN8RCqLON6LookkY6Jom1FW5xdo3kcz6cjqbC6I8ngrOj4zEfinPjtilyF0ZMmq0RUhWpgKRDVFPyTjuzyo6shSjBwOOW15lxwQQXQV7/6Vb785S9z2223LfqCrlu3jqNHj57TcxiGwbve9S72799PJBKhtbWVv/qrv2L58uXce++9DA8Pk0qJzsf73vc+fuVXfgWAmZkZ3vve93L06FFCoRB/+Zd/yd13333W264EQprC+u402YrJVLGGhNihGI6L4dj1D/HC96r+BfV8bMuv/65ouPzw0CyvjubIlC1Cqsyx2QrJsMpItkoirLKkKcb9a9qENDpAKqrx7sDhdB6X8qL07T2T9QXk23sm+dA9yxjNVvn4Y7uYKZ2JAi4gI3a3rckwPj63L21k12ge37fQVZnWQLW0qVfIbUuGyLXa2tdIQ1Tn7Rsv/UJ7OaEpMj8RHHPJsPnBwRkGMyW+smOCiXztrMWPokj4C7pAkiQCLjf3NtAUD7G8JcHKtjhzFYulLbHLyvG6gauP+1a1Eg+rOI7Php4UqiwjS2A5LvmqzXheuB6rsui6Vi0XTZUJKRI1x1809loICcE760iFyFZtPE/wjlRFomxYPH9sjj95/BCd6QjFmkMqouF6ft3F+HJgcK5M3JTrijZdketqVj3g44Q1hQ/fs4x81SIWUutqtCVNURFR5PskwhrdDYL8/ejN3ewcyRNSZbb1i5HbvvEiNVtEVhi2x8HJIstbE2zuTdPfHMNyvfqo/uXhLPmqRSD2YsdIHhDnWUKstT5QtWwkCbYP5zgwWQSoH3s8pKAEimBZlojrogCK64rImTQ9Ys2KUPD6vlCFBefkRMPFUvXMIozLgVhIZuElRZalM/KjLheMMyhozxUXVADNzs7S2nry+KFSqZzXxfaDH/wgDz74IJIk8ed//ud84AMf4OmnnwaE6uxUZoof//jHue222/jud7/Lyy+/zCOPPMLg4CCapp3xtiuB161o5l+3j+H60JGK0p4IcWS2TMV0kGUJ1z11FwhO7R1kez65is1zR2a4pb+JTNlk/0RRxD5UZUqGQ65qsX+iyC/eu+ysuTEXC88TC0T9+Fzx5fz6q+PsHi+cUxXuI0zdKqZww5aQ0FU5UGz4dav9vqYoTfEwIVX4bfS3XJ5F9krB9Xw+/eQRDkzk2TVWwHD8s3oICRK9v6gLNJ+BpqsyLYkQd61oJhXRWHHZX8HF4bUwlrra8H2f7+6b4tBUCV0Vqr+IJhQ4uYpNoSYy8xyP+s7Yx8dy3bNeKFzPp1izMByXhrBOY0zDdD1sV0TK2K7Pv+8cF+aEXcm6Yus7e6f40N1LL0vnz3Z9DMelULPxfZ9/2zlWH2NN5Gv8/J39gCgkmk4wMu1vitW7KamISluwsUpFNJY0RQmpcr1Y+tqr45QMO1iDbP59xzhv29jFRL7KZ58doma5PLKli5uXNNYVZI7nISHV1Xebe1OM5qrYjkc8rLKhq4Fs2WLfRKG+pu0ezVM1HUzHpyGmUzEddFWujxv/6YVhZgNV6QtDWfaOF1DlxZmAxgkFUMW6MPLvxWC2bImsswCGLQwmz2RLcDkwnjfOfqez4IKOeOvWrXzrW9/iox/9KHC8w/B3f/d33H777ef0HOFwmLe85S31f99222388R//8Vkf9y//8i8MDAwAcMstt9DZ2ckPf/hDHnjggTPediXw6M09LG2J89lnB0lHNQ5OlUmENSqmi+u69XnGQqL02eD5UDRFiGGuYmG7Lp4v2tS+LzgmL5tZ3Kc8+lviJCMa965qOSePoPOFLEts62/khSDNfFNvA2FN4eBU+bQ7yxOhyiIrR0UsXNGQQlRXaE9FUCQxu/d9n6rlclt/IzXbJawpi3wnrkc8f3SO549mGJgp1/OTzob2ZIii4WC7fn3nJ1yuNX7+rj560td3UXgt4Xoo0MbzNQ5NCbKz5Xg8ezTDz2zrZVV7gn/f6QWme4sfczpmhLzgNgkxYjFsH893mDCFjYasiLiZmu0RUiVsTxQko7kaiiQJMULgTH2iL86lQFwX6qJC1cJyvXrxA0LWbjkemiLxl08P8OpogY5kmP/2xlWkohoVy6ErHcF2PeIhjWzFpqfR5/PPD7FzJI8qS/zEpk5ev7ptUfCw5/sYgZ/W/338cGBk6jOcrfJn79rMyrY4SxpjTBZr6IrMHUGMz01dDXx3r1DpRnWFm7pThHUxRpzvYFVtcbxLGqMYtsjt83y/PoqrGG5dASpJMDBTJBnRFhWv7glrR8W88iowTWKReavrgXLRvZgLOI5LcIm7oALoD//wD3nwwQfZv38/juPwmc98hv379/Pcc8/xwx/+8IIO5DOf+QwPPfRQ/d8f//jH+Z3f+R3Wrl3LJz/5SZYuXcrc3By2bdPefjwKoq+vj5GRkTPediqYpolpHh/ZFIvFCzruE7G5N81orsbu0XxgaOVjOi4eIAWEZ/k8CiBZgu50BAmJnsYIU0WTmungBMVPWJWxXZcXB7MgiYwgw3YJqTJzFYvV7Ym6uuJisX04i6NEeOPaNtpSYZrjISzHpWSeWxtWliCiq2zsShEPabSnQixrjQd8HxsJ6Azk20ub47wylENVRLv1crg5XynYrsdsyQgK2HNfKEqmS2dDhJIhXMYVWaI9FWFTd4p09LUZmnsDp8fprAZaEyGWtcSoWQ6OC7LtIUkSxgJ7iZOfSxRBC8fykiw4JUaQCZbSFZSwUJm6nvDCiegKq1tjjOYNUhHBL5oXXliOd075VecK1/OJhlS2LEkTUhW605F6XMbNS9LoqswT+6d5Yv80ZdNhMl/jc88N8rEHVhLWFDJlS4goJIlYSGGqYPCV7WPMVSxkSaJgOLx+dRubulPsGc9juz66IrE5iOw4NlthpiS6DGXDEVL7kMrr17RSswQHc1W7iLE4MlOmIxXBdFxiusKesQL3rw7RnY7Ww5170lE8H/IBb89yPcKSQskU/07HdLIVEyTRVNjcm6ZQsxd1gE8cNaXjV37kHdYV5CB0GsTnULkKI7BzoVycDRdUAN11113s2rWLT37yk6xfv57HH3+cLVu28Pzzz7N+/fozPvapp57iqaeeYmZmpm7jvWvXLkZHRxkaGgLg85//PD09QlHyF3/xF7ztbW9j//79F3Kop8UnP/lJPvGJT1zS55zHW25qR/aF743vE4RSHv8Qn4qwXv/4BHcMqWB7EomwQrHmgOTguD4t8RByPEQqoqApCvmqje16VExXhGoCzwzM0hIPs7w1zmzJpCUROuOc3rBdBmbKhINAzdPhqQMzSHqEdFTnnbf08MT+aR57ZZQXj57ZDRlAV0SCcl9TjE29DYDE+u4UK1oT/Ke7+nli/xS6ovDg+nbhcJsK865tPYznarSnwlfc1+Z8MVc2GSrmaU+GF6mwnjowze6xArvG8hRq1nnuk3xWtycomWLUuaQxxvLWOHcsb16kiLmB/xjoSEXY2pdm50ieWEjl3lUtjGarPDOQIaTKWK4YI6ciOmFdpmK6lGoWlstJn7v5qYof/I+qyki+j4+MrnhoskTRdGiIaDTFJHxfuDzf1JniRwNz2K6I1VjRmkCWJL700giTBYOWRIh3bOkiqp/585mrWIzmqrQmTu9WbnkejuORjBx/rhMnbUOZMsdmKziekI/vmxAE8ULNphyMtTRFjNhzVZPJoontuCBJHAsIxg+sa2PvpIiqSUd17l/bBoiA5RnRcENTZRIRjbZkmDuWNbNnPC9sKAJzWd8T3WvwqQUb0FhY403r2utF27b+RkKawuGpojBA9QWnZ/+E+CNb+9IMZso4nk9LXKc1EWZ5S0LE/lRFTuCK1jjHFrz+bUsaznieLwdM21vUaZQkCce7SE+dC4B7CWJALuiY3/ve93Lffffx8Y9/nGXLlp3z4z7xiU/w+7//+2zdupWOjg4kSeLIkSOMj49z5513Eo0KT5X5hHlJkvjIRz7Cr/3arzE3N0dTUxOqqjI1NVXv9AwNDfH/Z++/wy07y/t++LP67u30OudM7zPqvSHAFAMGgwE7FBtwj5PYvhwc3tiO4/6LSxziOMTEJnFcKTbVgABJSEJlNCNN0/Q5ve/eVl/vH8/ae+ZMbzojiflel3TNKbuts9az7ue+v2V4ePiCPzsXfvVXf5Vf/MVfbH9dqVTar3u1+L9Pj/OlFwXJ1fd9oprI32nJLIPg7NZ0a4HSJNA0IV3vCP08bMcnlzBQZWHC5wU+kwXhQxEAqztjFBsOJ5bqYYyECMacKTUZysXaGV3nguP5/MOuSfKhFf2tI1nuO0+ExMGZClrUQ1OEWu25sQJ7p8qXdFPvTurct76bgUyUj963GkOV26PTO1d3tJ2LTx/ddScjdCdXXtJ9JfjHXVNo0TgQcM/aTjb0pKjZbluxs1gxUWRJkE0v8Tk7EwaOF7BtIIMqS3z4npELOurewGsf963rYqQjznePLvGtlxboiGthbIJIZJckERQc1xUSusJQNsLR+Sp1e/l51xqXaYqEEppn2i7ULIeoJtObiTBdFF40kiSRjqpEdZXjC1UatkfSUOlORTiZr7N3qtyW5y9WLZ4fL553DQFh9fHpJ04gITpKb9/R31aTno6EoeH6Ad8+tMRoZ5LJQqNtQjpZaGC7Pq4XhOMkId1vcWTGlhrY4YipajpMFJqs7oijSNDwAiQpaBOpb1mV4988vI6JQoNVHbG2l9gbNnXzlK5geT6b+1Jtvo/tesyVTdzT5kBDuSiqItG0PTJRo81J+tkH1/LCZAmAm4YzgBDLOF6A6/nIsoQe+g89P15EV2R0RaiCjy1UWdudIhvTsb1A2Broy0de46Wr74JcLiJnFLfCv23lVWC1azD+u6ICSNd1fvd3f5ePfvSj9Pf388ADD/Dggw/ywAMPsG7d+emYf/7nf85f/dVf8YEPfACAP/qjP2JiYoITJ06QzYqTznVd8vk8PT2iCv/c5z5HT08PHR1i1vqe97yHP//zP+c3fuM3eO6555ienuaBBx646M/OhGEYGMa1HyN4ns83D84DQhng+eLEDdMNiGhCkhqE187pPkGA8OfwPEChZrkYrkxEV5EIWKw5KLLIiJEQXYcAKDVsXD8gaagM5aI4YfaUIABqFxwfLdWsdvEDcGS+dsHFq/W5xvN1Xgxv7heDIsHm/gy3j+a4bSR3Tn7Sy8FZWkl4foASiEymk0sN1nQl2NSb5NhCFdcPmCo1qZ5Lf3wBRHWVm1dliBkahZrFZ54aY213kjdu7rkRNvp9Cs8P+NLeGaywjTxVFGaJ06UGDdsTncEAMjGD8XydxYp5ludPC34AhiLhA+WmuJnIElhuwELZRJGh0HCIaQqyJLFUs0NZvE9EEytW72V6Trmez2eeHOPFyTISwlftyHztnAVQw3aJqD4JQ0FXZfZOldg3LagK2wZS6KrMbNlsc+o8H+bCcVO+braNUk3Hp9JwSAyoKLLU9t+JG2LN8f2AmulSMR1qlhf6IEkkDJUj8+L6XdMZJ6opnFis8anHT1AxXVRZwvYC/u3r11Oo2WFBKbLGmiE5WZElDE0Wis7WNRvQTrUPgoAWr1lXJNzQ+DRuqNi+j+m6FJsiH8wnaEfdtPByC1/OBc/3l/EYTdcX47AVXsLnK9eJBP0Xf/EXAExPT/P444/z2GOP8Yd/+If81E/9FH19fUxNTZ3zcbZtc/fddwMwNTXFL/3SL7F69WoeeughQBQl3/72t3nrW9+KZVnIskxnZydf/OIX28/x+7//+3zgAx9g3bp16LrOX//1X7dVXhf62UphstjAcn1czyemK5iuuIA7ZYOG7VE1HZJRHct2qDvn7gXYHrieRwNBGlYsh4mCyL1JR9W2qsDzA7wAmo6FhCiECnUbXZOJagr3rM2RjWkcma+ysS+JEZpmlZs2z5woYDoefelomCnlsli1UCSxQCiyhO2KzB8rZPz3pAxsWSUdVdu7mgtBXPTQm4ryY3cM88DLnEV2vVGo28yVTfoyEWZKTb62fwbL8QRv6zKNuhQJ7hzN8dH71vDNg/PMhzvsl2YrrOqIsanv/OGSN/DaheP57eIHoFh36EoabB/I8sJ0iZolbszH5qsU6jYX0ybULH9ZN1oILIJwdx3guAFVz2976CQiKj0pA1WRuGdtBx+5dxRFljm+WGOubNKZNLgldK+fLTf56r45TMfjztU5blmVY7ZsUguLgyD8nY64xoGZMg3ba3NqAGQkUlGVzf0pmpbLsYUaZug9c2yhRtNymS8vdwCuhPyamK4KSXoQiJBSWaIQ5oNpqvAWq4a/+8zJPJ/53hgN2yOmK0RUmTtWd/A/HjveVrv9855pPnT3CMcWahxfrNN0XGRJIjEubqFV26Fmuliuj4ywMQH4yydO8q1D80jAG7b08tH7VmOG66kfCmJaxRCINTdA+AuNdiTwQuVtS30bl5Z3PVRp5cnHs6WzXZdt11/xOIz6OXIULxdXVT5ms1k6OjrIZrNkMhlUVaWr6/zdg49+9KP8zd/8Df/xP/5HBgcHT/vDL8euXbvO+xw9PT184xvfuOyfrQR2jRX47tElVuWiHJitkIporEsZ2F5Ad9JgtDPO947nkSQ4MFO+4DiktSg5PiJkD1HwNGyv7bh8+uETP4ea5ZJVdEoNm6/unSMZzdOTirB1IM36niR1y+W5sQLj+Qa5uM667gR3jHbwhT3TjOXrzFVM/uibR/iZB1bzj88L2alZXoDw9e3A53vHlpi9SAgoCBXTUEeczX2ps2SqrzUkogrfOyJ4PoYqcWKhRi3k7riXOarWZInOpM7Pv24twLJWu/j61a2Iey3hfOqxl8sgMaIpbB1It1PRV3fFqZouqT4NSYaG5bJUt5gsNC5a/MC5VWIB4AU+jidk9ASwWDXJxoWBnxOSk9f3JNFVMXJ//+3DWK6HoSoEQcCjhxf47K5JAkRw6aOHF1nTlRCO+XGd0c44pYbD6q44NcvjiWNCWfriZImHhsSNdCgXJZWMiE56EJCvW1jhh/LrPj4BwRmd0NaaGNNFCnsgSUjAQCaCpgpiuLh+gnYX48XJUtvbrGF7vDBZ4o7VHcyUzXYCfEuJG+BTtxxMx0eWoR7SC2aKJnXbxfehZLoU6xYNy+Ufd0+1u0H/sGuSD909Qr5mt1/b9WGyKCJjPF9sml0/IGGovDRbpSelYTqnbAyKzeVO0BPnKEZebhyfP7vzX6rbJFY4CuNaeC9eUQH0H/7Df+DRRx9lz549bNq0iQceeICPf/zj3H///e1R1rlgmiaf+tSneOSRR9i+fftZ3Zk/+qM/upK384rB4XlBZhvKxRnIxnj9pm7KTZfHjywS00Ub90fvGObLe2dQZBlNaS0yF0aLsOgFUL9AdEKA4E35QUDd8qiYTXSlyXi+LnZeprCWt8MbaqFu07A9Jot1Cg0LED48uyeKfHX/XFt2aoavOZSL8cxU85KKn4Qu8TMPjqIqYqE8M87jtYbZkknFdKmZbqgQka6o+DFkeGhjFz/z4FqycVE03jqSY6IgRhy96ciyXfINfP/hDZuF+akkQTam89nnp1isWvQkIxRVG9P16U1HqVu1S1abng4ZsN2gzVl0fJDlAM/zObZQIwgC4rrC53dPo6syr9so6AqtDvNzY0X+Zf8csxWTfM3mxGKdzqRBoWazujvB6zZ288Jkibiu8vrNPXxh96mJQdV0yVdFMRLR1La54ZmbPvFvia64gSKFkUIS5EJV1GAuSqFu4XgB2bgejpaEgWIrV+uUaWIcRV5qd75HOoVgJK4pNMPCSFMluhMGxxddbC80KPRO+fBYrofvC0NbxQ+oW25oaui2O00gNjN7z6AOLNWc8Dn8Nq/F9R2GcxFmys1lN4jgjPXEW3kbIDKxs8ee8evATWyY14kD9Hu/93t0dXXx67/+67zrXe9i/fr1l/S4vXv3snPnTgD279+/7GevBQv9jrjOQhgFoSkyw7k42bjOHatzLFQsMjGNZETj9tEcv/nlgxxfqDFXboor1xf+E74vvryShUtFkBrrltt+vO2B7XkcX6hx9+oOVOWU6SCIvK4jc6J9XTVdelIRUlHtnCZ95abNgZkL2wWoknCj/revX88H7hq5/A/xKsWjhxdYNMWCGjcU6pZ3WcVPyx5h+1CG33/PznamEUBX0uAn7h2lYQk7/xv8nxs4PQbmR28fpmq6RHWZbx9a4Mt7Z+lLR2hYDjMl67xeQOdFK1tLClVXgRBfzFWs9rrSsD2eHy9yx+oc3zm0wL7pMoVQJv78eBE39O1pmf2loxrjhQaruxNsH8ywPZSaA/SmIxTDzZauymTi4ra0bTBNJp0KU9IlDFXB88VNr9Vpun99F986tCAcrxWJ+9aKCcSm3iQHpiv4gU9UF/l75aYddmkCfEkoxQAe3NDFMyfzHF2osaE7wf3rOpe9r4CARESjI6nz5PE8rhe0MxxbOV51WxRGBKeI2HFDQ5Kg6XhISGRiEhFNWZbfBxALbQRq5inJexAEHJ2vs6kvuXxEeca6vLl35TeWg9mzA6BPz6BcKdjXwATyigqgPXv28Nhjj/Hoo4/yh3/4h+i63iZCP/jgg+ctiL7zne9c1Zt9peOhjd0YqkLFdNg+mGmbgxmqwlDu1EkzkI3x8Tdv4osvTrNYsRhfanBgtkzEUKmaDroi05PUGS+YIfntwpARkQmaLJGIqFRNF9k/FUwqIXYmT5/Ms7U/TSauocoyqzpiVEyXcsPh5uEsz40VCBASzIc3dvPCVJmTSzVSYeL5ZL5xwW7V2q4Yt67K8Y6bBrhrTecVH8dXI0zbw/NlJFkUv57nLzObOxMSYKhC7m86Prbrs74nyf/3nh3Lip8WNEUmHVv5ReYGXtlwPH9ZYfymrX0MZmN8Ze8MTVvwXazTZmFnZsudCTXMz9JUGVUWG7GoBo63PG/JD0TXY7rYpFh3mK80ObpQQ5YkXF/wlCKajI/GHaM5cnHjvEqhhzf1kIpo1G2P7YNpnIrI+xrMRrl3Uy/9mSieH9CbjrSDX3vTEeKGysObeji6WOOFiRJ96Qg/86AYG2diGpmYRsP26EtF8HyYDh8rhYVdy8342EKddFTn5uEssiRxfLHO1oE0jheQiQkjQk2RKNWddvdcCv9rFTuuD4Yi4xOgyhL5uk3TEYaSLfJzw3axXZ8HNnTyxLFFbE/whu8MzRQ9XxLvjVBaHgS8OFlcdqzOJE3cs2Hl19lS8+wJgOP6sMIsB/MaMAGuqADasWMHO3bs4Bd+4RcA4ePzx3/8x/zcz/0cvu8L1+OLoEWUHhwcvJK38IqEoSo8tPHSiL6jnXH+zcOiUPy9r77Ei9MlPFe4O2fjOm/d1sdfPDnebsGeDzKiPWuook3cImAHp0nuNUXETWRiGtm4zq+8aWP78d8+NM+LjTIdCYP+dJTudIRsXOfRI4u859YhoKf9t1qoXVhyedeaTj58z+ir2rTwSmG6HrKk4fki8+tihWtnUqcvFSET0/m3r1/HtsEM2nUwE7uBVy/yNYvP7Z6ibnn0pCL88C0DGKrw8pooNAgQY5/WWdVqsrfGRQTL3aAlROFQtVwShkpP2kBXZOKGyp4w86oFRRbZVkrovbNYE7yjmKagyDJ96Qib+lMQiA5mTzrCbecxZNUUmbvXnrqRT4VN5qWqzZPH8wx3xDFUmbVdiTbxdW1XAj8kOP/8Q+twPH/Z9XNgpio2DVGZxarFfKVJd9JAlqR2QdIa2eXrFjOlJhXTIRXRKIb5WlFdKHGDAKJJg2RUYyATwdBkLMdHkqAv9DDa2Jvg8GxFeCrJMresyuK6PqosoyviTi1LEp7vk4npxHQNLZTBp2NiozzaFaNs2vi+CJzd3JfC80Vnq52HFuaJSQjTylTkemT/vTI4iMPZKMfzzat6jisqgIIgYM+ePTz66KM8+uijPPHEE1QqFbZv335e2TmA7/v81m/9Fn/4h39IrSZMqJLJJL/0S7/EJz7xCWT5+/MGcNeaDv7pxWnKDQdFlhjMRnEDibiuCAWWD4QhmJ53WmdHAkMBRZbpSUVo2i5zoTRQlYT3TjyqsVSziWsqthu0/TpauG9dF5ois1ARafPZ8GKcKjZxPX+Z82jVdJFQz5tqf8tw5vuy+AGIqAo+IAeCg3Uh0VdElelPR4npCut7k6zqiN8ofm7gsvHcWLEdhTBfMTk0W2XHUIZ83SYXNzA0hYopAiN1BRRFRlcUYRooS2iSJKIaPB9ZkZCQ8AJBII4ZCjXT43239QvZuyrzxLE8tusjAQlD5ebhLKmYhq7K4QhXCtPOdXrSER7e1MNdqzuuyh26agr11+b+FPm6RYv5mq9by9ahM6+fnpTBicUafgCpqEbcEB2hTb1Jji/WkWS4e82pgmyq2BCxQ02nzTOSELEWAULCHvgBa7uSdCcilE0HRZLa3MbeZIRkVBCW01FBdcjEdbYOpDm+IO5163uSxAyNhuXRldSpW544dmFlOpKLc2CmgicJM8tsTGNNd4533TTI1/bPoikyP/PABj72xyKQNKLLbW+ilYShnt2hPo+e6WXFgxu7OP7kuZMeLhVXdPRyuRy1Wo0dO3bwwAMP8LGPfYz77ruPTCZzwcd94hOf4NOf/jS/93u/xz333APAE088wW/8xm9gmia//du/fSVv51WPvkyUd980yEuzZcpNl55UFFWRef/tg/z9c9M0Q3t1P4BMVMV0AkpNh4gmk43rqLLElv4UxxbqLNVsJEmE/q3uTvJ/P3oH/+7v97TlnBJQbjikY+Ik1hSZ+9Z1EQQBNculUBe7n86kcZbtuu+JXc+5Tva4LnHzqmsTufFqxPreFMdLHg3bwz1HB7TV/N8+kOLO1R2s6oyjyBK3jeRe8wq5G3h5oJ0xUmqNmLIxjb5MhIc3dvPksSVcP2AwG2UgHSGXiBDVFYLA59hCnXzdZmypTtVyiOsKnQmRP5eN6aFnmUc0zJ/LxXW8QBCFOxMGG3tTbBtI47g+3UmDiKaQimhs6E2ypT/NA+sv7Cd2qUhEVDwvQJVlekL+jCrLF/SeuW9dF6Yjwlz70lEGs2KMdttojlxcR1NldgyJ9UqSJLYNZqiZLomI2u6UZWI6g36AHwREdRXbD0jFNN60tZf5ikk8jOkAKDZdRk5z258tN5Ekid951zb+4bkpJAnee5sw2l3TkyQXN8iFv746DHperFvt8bcswVzFZE13kt9/93Z+/93bgVOTk85EhIHuJH2ZlTeK7c/FluVZqjKkYitfiB2dr131c1zRu/7rv/5r7rvvPlKpy/Mi+cxnPsNf/MVf8Pa3v739ve3btzMwMMDP/uzPft8WQOt7kty9tpOOlMFsyaQ3JZQ+d63uYE13kn85ME+p7rBUs0hGNbzAYSATwQdSEY1cTKfcdPF9H0UWpotxQ+WdNw8AsKU/TTamC9+KiIpyjlm8JEm86+YBdo0XkeCc7WpNlZHss3kEqgRv3T64jJj5/YYHN3TROFxirmzRsAIcX9jFe75YzFQZYobKSGec99w2xNruG0qu1zIuFK56rSTyd67uYLFqsVSzQuNNsR7HdJV33zLIvqkyG3qTzFcsUbQkDe5f28n+mQpRXcZQVY4uVJkoNNAUmYgqJOo1y4MgoNhwmC416UgY4X+icMjFdO4YzfHW7f30Z6JkYjr7psv0pKPtIuqhjVdf/HQkdbaNdLCmK0EQBPRno+w6KWJ31vTkLthZumk4S1fSoGqKwiSiiYxELwhv3J7g6gCs7U7w4mSJqKagylI7DugHtvbwpRdn8fyAHYMZBrMxFFni7rWd7J0qhcHTgvLw0IYuDs9V8QMhYb83NJPtS0f5N69fbg58+0iOj9w7yq6xIsMdMd4fFkbdyQilhoPvB+iqTOoCsvJ71nayYaj7ugQi5+IGH7t/NX/99DgAP/PA6usyvblvXRePH7t4DNOFcEUF0FvfemUXcKFQYOPGjWd9f+PGjRQKV/dBXu24e23nsjl4C2/fOcjbdw7y7MkCn989RbFhM5yL8YbNPUQ0hXLTYW1Xgr97bpKG7XHTUIa+TIQfv2eUzf2iPfsDW3r5+oE5LNfn3gvkSJ2ebXMu9GfiSE1hQBXRFG4ayrJjOMMbNnYz2p04q2P0/YR33zJEE4PZcpOFskm+bhM1FHIxHccTobgbeoVvSot7cAM3cDWIGyrvu/3cUT/dyQgPbxLdgbmySc1yGc7F0FWZVaHMe0Nviv/7vTFWdcRIRzWatkciovJT2/qomS5TxWa7O7mpL857bxvi4GyFdFTjnrWd7bHTzqFMO8rmWuJ9tw23N9meH5CL6W0D0I64jucHF4xgOFOtVDVdoqGPEkA15BMNZKK8//bhtolpZ/iZ33PLEDcPZ7Fcn3XdyTZ36P71Xdx/RnfrgQ0i5me80GDHUJrOxIU7M2/c0ssbt/Qu+95P3r+aP3nkKFXT4f71XWwbyJz38T9y2xCbV/Vc0/DZy8HH37yJj79503V57RZWdyfpjGuUm67YYV4BVrRvtWPHDj75yU/yp3/6p8u+/8lPfpIdO3as5Fs5Cy3i9tTU1GV3tlYCmmkRc8oYSgAuxJwIqzMJiMNcaRG5UWAkvOb64wopv8rUlPAlkoA3jbbIcjWmpi6vdTg5OQlAOijjyipdUUFs/IW7OsjENPAqzM9eWB7/WkXr2MzNTBG1G0Rtk1VRuKM3wtt29APw5RdnmC2bNIomDWBhVkZqvDoyzq4GrWMzMTGBGyp7bkCsMacfm4tRB64FIsDCXOms7//gmghu1cVxHYjC3WsTbOlXGctbzM4UCZMlKBIn2hlwSyeAzfzszMv2Xs91bDw/wKks0SWL3rNdbjAznbgsSwjX8wlqS+KGCXT1JpelFmQBs1RlqnTqMRFax6560eePABuSYJaWlj3HpUIHfuW+U4XVuRIVWsdGaxZYnLv6MNBXM6RGhSHDpE8LsJsWx+CSBFjLniM4nx3zy4DHHnuMt771rQwPD3PXXXcB8L3vfY/JyUm++tWvct99963UWzkLzz33HLfffvt1e/0buIEbuIEbuIEbuHI8++yz3HbbbZf8+ytaAAHMzMzw3//7f+fQoUMAbNq0iZ/92Z+lv79/Jd/GWSgWi+RyOSYnJ69bB+jkUo2v7p1rf92Z0nnvreducV9L1C2Xzzw11iY3xw2FD98z2v751NQUW7ZsWXZsPD/gfz1+oi3PVGSJj92/ut0mfmm2zLdfWmw/x1Auytt3Drzsn2Wl0To2Y+MT/P0LS+c8Hk3b438/cXLZ4376wTWnwhFfozjXeXMu7Jko8lQYhQCwczjDPecYB18q5spNPvf8dPvrREThQ3ePXuARK49LPTbfj3itHBvT8fj0d5df9z/5wOrLUnyWGjb/7+lTSie/tsT/7wM/cF2PTaXp8G/+bk/7fiFLEp/64C3XlQJRqVQYGhqiUChcMI3iTKw4dbu/v/8VSXZWFMHLSKVS1+3EWmvEyE42294/m4ZyK/JevLpFEx1VlUkYKrquLHvd1r9PPzam49FAQ1WFT4gkCUuD1kWwQY2wa8ZqB7duWtX5ql7MzofWZ5KNGHU0oobg+Jx+PJJBwKq+znZ68WA2Sjbz2o4GgXOfN+dCLOESiZ/ymIrEE6BHWaxadCcNkpdps2/E4nR11KmGLr0bB9KvuHPvUo/N9yNeK8cmBYz2V9vWIwOZKB3ZDCBMEWfLJh1xnUzs/F4+rmKL66H1tS+e63oeGyPqkU6nyddECHdnKkImnX5FONS37uOXipe9ANq7dy9bt25FlmX27t17wd/dvn37y/12rjtMx+PJY0vULJedQxlWnSadjBsq7711iENzVZIRlS39K1D8+AHfODhPxXSZKTUZ7YzzobtHLvgYx/P53G4RlHpgpkJ30uCj969etgPIxHR+5NYhji3UyMX113x+1T/umqRpS+ydKguS6JpOvCBA5ZTCbt90GVniguTG70dsG0hzeK7KYtVCkeH4Qo1/2jNNX1rEsrz31qHLsgowVIX33jbEgZkKUU1h22s8h+5ysNLhrSuB/dNlji/W6EwY3Lm64xXVWX3nzQPsC7O/WvEfVdPh756dpGa5qLLEO3YOMNxxdrwEQC6us3M4wwsTJVRZ4tbV199qxNAU1nTFODJXBQke2ND1iih+rgQvewG0c+dO5ubm6O7uZufOnUiSdM4UeEmSLpvA9GrEIy/Nt/0LJvINPnjXSNuTB4QL9F1rOlbs/SxWLWZKJsO5GH3pCOmo1lZanA9zZZOFikmhbpOJasiyxPGFGg+u71qW6daVNOhKfn943FiOTyYaQZMk4rpKzXJ57PBiW+kR0ZTzOuF+vyOiKfzo7cOUTYe/e3aCfVNl5iomVdNh20CGw3NV7l57eedRMqK1IwZu4LWLiXyDbx6cB+DEYh1JgrtfQTE8hqpw6xnX/bGFGrVQgeb6AftnyuctgAAe2tDNHaM5VFlmYe7lI59fKhqWy6HZWtsF+4XJcjtI9tWGl70AOnnyJF1dXe1/f7+jWD+Vo+L6AeWms6wAWmnEDAVFFlbrmiKTi1/cWj1uCDdoyxWOsjFdoWq6OF6Arr76LoJrBdP1kcLjAbRNJW/g4pBlEVFgOn47WLE1Cr7cEdgNfP+g0Fh+jb0arrkzz+fUJZzfMX3ljQbPhwDwg6Dd9fGvhw30NcLLflRXrVrV/vf4+Dh33303qrr8ZV3X5amnnlr2u69VrO9JslQThM9MTKMnfX07JKmIxlu29bJrrEhUv7Qss1xc5y3b+pivWFSaDiMdcVZ3xa+bJ8UrAbesyjLblLBdv931Wv8aH/tdayQNlYFMlCDMtEsYKreN5Ng68OrlgVwPXMiE8bWGkY7YsmyuDT2v/GtubXeCe9Z2cmyhRlfS4I5XwFjrciBCaLt56ngeSZJ4cEPXq7L7AytMgn7ooYeYnZ2lu3v5TbZcLvPQQw99X4zA7ljdQU8qwsHZClOFBn/6raPcPtrBPWs6zmLRz5SafPfoIhIS96/vojf98njHrO1OXpYz8Xi+zp6JEjuHMvi+yAu7f921sb2/EFzP5zuHF5mvmIx2xrl7Tceykdv1xJ1rOqgHGhKwVLO4fSSL4/o8P15g+0WCTicLDZ48toQsi8WkO/na9whqYe9UiX3TZRKGSl86Qn8mwqqOGG+L66ztTlyzv2/NcvnWS/PULJcdg5m2Gd4NvLqRien82O2rmCw26Ejo9KWvnRv9QtXkr783TsPx+KGd/Wy9Au7e+dbw20dz3D565YXPR//qOX7o9rX86J3Xp2nwobtHuWtNJ7Iksa771Zv/uKIFUBAE51zQ8vk88fjKW3q/XJjIN/jWoXk8P+DBDV1nFRe5hM6R+SovTJRoOh6H56qUmw5v33HKCsD3A/75hRlMx6Nmujw7lueHbx7k1pHcyxacWWrYLNUselKR844dTMfjSy/OYDo++6fLWI7H7as7+OzzU9y/vpMnj+UJENEQa7oSHJ6rMp6v05eOsm3w6m46z40V2T8tCIWLVYtcXL8oX2mlUGnafGbXFLIkYagyn/neOJv6koDE947nWd+TZCAbZUv/8mMwV27yn798kIbt0p+JUjNdfuLeV5Zk++XCQsXk24cWcFyfg7MVZAm2DmSIGwofuHPkqoufhYrJ/3z8BFXTIa6rpKIqIPHIS/P0pk85/t7AqxvpmEY6du0L2t/68kFenCoTBAEvTJT43x++7YLxFGfi9DUc4J9fmOYn71+NJEnsny7z/HiBkc4E96/rvOxzfbxQ588fP86WgRQ7hi5d9n2tsG+qxP964iQKwtJjc/+rc0OxIgXQu971LkAQnT/84Q9jGKcWHs/z2Lt3L3ffffdKvJUVwVf2zbZP+q/tm+OnH1ye9t2wPJq2RzP8Hdv1mcjXlz2H4/uYjoflehycq+D7AU8dz1M13bMs1K8Gtuvz5LElxvJ1xvMN0lGNiKbwvtuGyJ6DD2Q5Pqbj89xYgWMLVUBiIBulPxPlC3um2zEPX9s3y5u29vLVfbMAHJipIElc1c67ajrLvm4RCV8J+ONvHuGFOZuIprBzOE256eD5UGpYHFuo4Xg+B2YqqLK8TBH3jYPzlJvic43nG2QvIIl9reGluQr7p8vMlk2qTQdVkcMdvMFSzWIod35i6MUQBAF//M0jnFgS11WxbnP76hwdcYMgEN5XNwqgG7gQDs5UcT1h4zFbNpnIN9h6nk2c6/l870SepZrFuu4kWwfS7TW8habj4fkBxxer/P6/HMJ2xdiuZjq8dfvl++CJ56qteAHkuB4f//xe5ivCuuLEUp0v/et7XzHd+MvBihRA6bQ4aYIgIJlMEo2ealPqus6dd97Jxz72sZV4K9cMQRCwULUwVJlMTKdpe5SaNooMhbpFVFOQJAnXD0KC8anHdiUNRjvjHJytYLs+vekI3ckIs+Um6ahGTFcxVIXN/SmeOraE7wf0pAxkSWK+YlK3XCqmQ0fcuGrezeNHFtk3XebEUo2FitXu0hyZr3LHGSoa0/FwfQdNgaliA+H5F7B3uszq7gT+KSsXHC9gNkygb2GubF5WAXT65/TDcFHL9TBUhbihsP4VNO8/PFcDSafUtBnPNxgN096rpoumyliujyRJHJwtM5CJULVcMlEdSZLoiOvkQ/LmuewCqqZDzXLpShiviby1hYpJw/Z46liexapFqWHj+wHpmMZC1WIgG7skMv6FYLl+u7AE0FVZdFMth6FcjLiuMl8x6UoYyyS8luuF6kadqP79k9m2EuGtK42K6dCwPLqSRpuj0lq3dUVetsErNWzG83VGOuKkw01Id8rgxKI4h1JRlc7k+c/JZ04W2DVWBGBsSWwkh3IxNvenODgjYoK2DaRRFZk9E6W2P1oQwJ6J0mUXQKbj0ZtWr8ow9EqxWBPK4Zbp62SxgeX4RK7D9eJ6Pos1C99yLv7L58CKFEB/+Zd/CcDIyAi//Mu//KofdwVBwJf3znJsoYYkwU1DGQ7OVpkuNpgsNshENeq2x6a+FLeO5Ihoy08MRZZ4z61DbB1Ic2yhSiqqcXyhxt89O4muyvzwzYP0piO8cXMPqzvj/MOuKUzHC8MKNf7qqTFs1ycX13nvbUNnPf/loKWiaHVuTMcLxwVnt3r/3zPjoMWomg65uI6hehTrNpWmw0A6SncqwtMnBMH7tpEcq7vi7J4o4QcBkgQjnZf+d58pNfnCnmls1yemy+ydKrNUs5EIeO9tw/zA1t5XlDJisWaiRhQShsr2wTQ/8+Bado0VmSk2KNZtHj+yhK7KlBo2X98/x5ruBMmIxi3DGQo1i2LDYaQjxg+d4ZZ9YrHGV/bO4voBvekI775l8GUbga4Evn5gjoMzFY4uVDm5WEdTZVRZYqAjRn8mRn9GfMa4obJUE8XRQCZ22cWI6MRlefTwAp4fsKojTlfCoGF7TCw1+PSTJ9EVmZHOGO/YMYAsS1RNh79/bpKqKcJ+33Pr4I0u0asUR+erfG3/HJ4fMJCJ8q6bB1Bkia/sm+XovFi3H9zQzc6hDCcXa/zmlw/SsD3ihsKvv20LqzrivG1HH//7iTE83+fO0Rw9qfNz885UnxUbNkO5GG/c3NP2c2uFs67pTiBLUls9NXraujhfMamaLkO56AVDk03HR5EhFVn5jnEqoqHKUru7FdU1tOsggHE8n3/cNcV8xcRp1i/+gHNgRe8gv/7rv37Vz2GaJu973/s4ePAg0WiU7u5u/sf/+B+sXbuWhYUFPvjBD3L8+HEMw+DP/uzPuP/++6/BO1+OpZrNsQXh5RME8NV9s/Smo8yWTUzHR0vIrOmK8obNPW3zqzOhyBJbB9JsHUize6LIsycLzJZN0lGNVR0xdgxmyNdtsjGNhKFQatgEQLlht3cPhbrNkfnqeV/jUrCpN8V0sUlfOoImS6zvSbKuO3lObo1p+0Q0iOoqvekIz54oYLoeuqrz2ecn+dQHb2VTX5IgEH5G5YbDlv4UDdtl51D2gl4XZ+L0XdKLU2XG8w0ShkqAxK7xIu+8efCKP/PLgQBCGwCZ9942TDKiMZyLsVS3UWWJ6VITXZVJGiqW66Hm68R1jeFsjI/ctxrT8eiI62e1kXeNF3HDndZc2WQ832Dtq5R0WDUdnj1ZYLrY4NhCjXLTIRXV0FWZ9T1J7lzdyYMbuohoCscWqnxl7xx+EJCMqLz/9mHixuUtV2/b0UdUk4lqComoykszVXRV4YXJEp0JncFsjLGlBvNVk750lIMzlbZ7tOl47J0q8bqNPS/HobiBS8AXX5hmx+qg3ZXO1yyeOi42WHev6bigOeau8WK7QzFdajJVbJKMqG0PtiCAZ0/m2TmU4WsH5miElgt1ywtpC2uoNF0e3tSN5wfEdJVSwzknLQBE5/b4Yo0ggKiutA1uJUk6K5X+tpEcP37PCM+PFxnOxfiR24YAIQj47K4pLM9nXXecD941et4Ovx/ARMHkq3tn+OFbhy7peF4rqIrMuu44B2aEEeKGnuvT0BjP19vu+q2/9eVixbfQn/3sZ/mHf/gHJiYmsO3lVfPu3bsv6Tl+8id/kje/+c1IksQnP/lJPvrRj/Loo4/y8Y9/nDvvvJN/+Zd/4bnnnuOd73wnJ0+eRNOurY9IRJOXVfCthVlVxM1LU2QimkLvBXYMINQ/4/kGj7w0x+6JIoosUWzYfHXvbPtCXapZxA2V/owYGy7WLFT51EURu8q247bBNNm4RjmUs1/KTUaRJLoSOgG0dwKTxSb5msVgTlwMFdPhb56dwHQ8CnWLctPloY1d7cXAdDxenCwBsGMoc1YXK6qf+owRTUY5rTBIXOaNcCXQk4wQaAZruhPtxa9iOjRsjwBxgVqOT75uUagJsnk2pqMqErevznJ0vs5JRWLHYGbZohc947hc7d/7eiIIAo7MV7Fdn1LDwfV9opqCrsr81ANrlil49k9X2tdX1XQZy9fPIpCf+dz7pytUTIeN4Rjx87un2zyLQenUczcdl3xdKIiSEbV9jM/sMl1NZ/UGrh6ThSaL1nw4ThIcw1aBOl8x+ci9o+flnZx53UR1BUNTlq3b0bCDnDpjPWl1v6O6QsMWz6PI0gXpBut7kqQiGvm64K61vH3Ot85tGUgT01W6kka7o/vPL8xweF6kzs+Umty/rov1vecWeXi+j+W4bR7pSkIGCnUnvN9JFOou10MFfy2uzxW9k/zpn/4pn/jEJ/jwhz/MP//zP/PjP/7jHD9+nOeee46f+7mfu6TniEQivOUtb2l/feedd/Jf/st/AeAf/uEfOHbsGAC33XYb/f39PPbYY7z+9a+/pp8jGdF445Yenj6RJ6IpvP/2IXZPlNBVmaWqxWA2ys7hLN0XKIAmC40wTsLm0cOLuH6A4wVEVIWm4xEEAQEiaK7cdIhqCkEQcMuqLFXTZalms647cZbCrNSw+eq+OSqmw/aBNHdfwox4MBtj8BJ4dOt7EpQ8jZ6UgT8bkI5qVEJiciamUTU9/u/3xmjYHh0JA9PxWKyaHFsQ/KJC3eY9tw62CdNzYUbOiaU67799eejr3Ws625/zh1YNcnS+yjMnC3QlDT7yClRJxQ2VeDKyzIdkVUecVR0xnjlRwA8gpssEfoAfBDieT8P2SBoqf/XkGJIkIUsSk4UG7zqtu/Xghi4s16fSdNg2mG4Xwq9G+AGMdMSZKDRIRlU8P6AjYTCUjeJ6y3dw6ei5zeJczz+LB1Ws2/zpt49yZL5KbyrCmu4E2/rTmI6HLEkEgRjxbuxN8uxYgUxUx/MDTizW+Mi9q9tZTFv70yxULMYLDfrSkRvO3a8QFBs2velIu/gBLmq8+tDGbpwDc1RNlx1Dmfb46ge29vC943kMVeENm0V37923DDJZbHJyqc6argTvvkVcf2/a2ssjBxewXY+71nRedHPYm46cZVXy+d3T7S7FWL7Oe28bZq7c5A++doilmkXMUPnJ+1Zz22iOWvj5giDAcX0ajt9+Hs8PlhUZQQARXWVtz8p3g2uWS8V020alpYZ9XUxwB7Mx7lnbyf7pMp36la2LK1oA/dmf/Rmf+tSneP/7389f/dVf8Su/8iusXr2aX/u1X6NQKFzRc/7X//pfecc73kE+n8dxHHp7TymkRkZGmJiYOOfjLMvCsk6xdiuVymW9bsJQycQ0IqpCIqLxlm19Z/3ORL7B8xMFXpwsUbNcRjrifPTeUSK6ylSxSRBApemSjmrULBdDlYkZKht7kzw/UaRpiwV8VUeMvZMlKpbDTKnJm7f18YbNPRyZr3F0vsq60266jx0RPjnlpsPJxTrZaygVtz0f2/F4caLEscUq3akIuioT00TL9z9/5SAzpSY9KYPRjhgnlhocma8RBAFrexK4vs90qUlnwmgXPyBGO7brL9thRTSFd5zGh+lMGGTjOtnYhcMDrxdWd8VZtERh+/SJPLeuyoocn6EMR+drWK5H1XTJ120xygvACwJenC4xVWjgeAH9mQi227HMLiIZ0doL8qsdqYjGtsE0TdujYbl4gY8qC7+Vp44vMVpOtL1R7lnbyd6pEk8dzzOQiaArEl98cYbjCzXiusKW/jSdSYP1PQm+c3iBl2YrLFYtFioWQQDfO5ZnptxkOBdj20Ca3rCgsVx/mfNu5jQXdlmWeP3mGyOvVxIimsJIhzBZ7UkZPH50CYD713W214vPPT/JrrEiA9loe30lCBjLNyjWLVadNnrf2Jti4xldFUMTBrDri00Gs1G0sJPfnYzwo3cs35hdCJOFBks1i5GOONm4jul47eIHYKZk4ng+Tx3PMxd+v9J0+PahBW4bzbGlP8mzY3ks12ddd4LRsJP82OFFvrZ/lqiu8MZVp27ZuiJdF9WoIkvULRc73LTUbO+6dIAABrIRTi7VSKtXxtVb0QJoYmKiLXePRqNUq6Ld94EPfIA777yTT37yk5f1fL/zO7/DsWPH+Na3vkWz2bysx/7u7/4u/+k//afLekwLDdvln1+YplC3UWWZmuXy1u19FBsOmiwxW27y/HiJJ44uUDVdJotNYrrK2FKDhu3xCw+voz8TEYnhEZWIpnDP2k6ajsudIzmKTaH4OTRXRZYEEXaxJpQy08UmT58sMJCJsKE3RTamc9uIxb3rRKfHcnwWqiYnFgUp7HPPT/GvX7euHbdRatg88tICTcfj9pHcZYWU7p0s8+hJ4Vnk+gFRXeGOkRy3rMpQMUXIq+V4VJoOU8UmfhDg+j6eH1Cs2+TrNn1pUTR1JQ0Wq6IA7UqerWbL1yy+tn8Ox/PpTopjUbNcLMfjxGKNH7tjWCx0l4l8zcL2fHpTkWsq2wwCQUw8vljnU4+fYGZ7Hxt7kvzds5OcWKrRtD0kAElwYXxDxfZ86qZLvmGjSBInFuskDMFTyMQ1njme52S+TlfSENLa/tQVfeZXCvwgIBfTMB2XrpTB7vEixxfqeH7Aodkqq7sSLFYHeXBDNy/NVvjeiTy26zGWb/BbX36J1d0JvNAO4sWpEmu7k8yUM5QaDqWG0yZlPnMiT28mQlRTmCo2edOWXm4ZFi3O7pTB1/bNUjEdetNRupMGxxdqzFaajOTi9GWibcVQ0/YoNGw64vqNcdh1wOs2dbFpVS+piEYQBNQtF8dtcXVcgiDge8fz/MOuKUB0kg1V5mP3r+GT3znG7vEiXhBwYrHOSGecNV0JfN8XxpsRjTVdonuyb7rMt16ap9R0yM1pxA31gmpV03Z5aa5KZ9JgKBzpvzhR4n8+fpyG7dGTjvCrb95IJhxxvzBRAuDmVcIQNRXVqDTFeFxTT0XnTBabKJKEFhYY+bqF43v85ZMn26OuhTlBjfACKNYd9k2V2HCeMdnLBc/1MV0x2hfHI1zbVhjzFZMPffpZZsomqnt59/8WVnQ17e3tpVAosGrVKoaHh3n66afZsWMHJ0+ePGdA6oXwX/7Lf+Hzn/88jzzyCLFYjFgshqqqzM3NtbtAY2NjDA+fu4L/1V/9VX7xF3+x/XWlUmFo6NLIZFXTYf90hWKooCo1bZZqNtOlBvunK5QbNjXLxfF8fD/A9UUL0/Z8njq+RC6u877bh3n7jn7GC422lPGp40s8cmiRmVKTjphGXFdw/YAgEAF0ddvF9YI2GXqhavHmLb0cW6i2C6A7Vuf4zmERDpiMqBTrNn//3CRv3NLDQDbK1w/MMRPK0/9l/xx9mcglZdEA7JoosFj1ccM/ld10efTIYkjwVZAQZOAAaDo+BD4SYoynyjIPrT/FAXrXzQPsHi8BYmE4HeP5Or/5xQNMFBt0JyP0Z6JMFOoU6g7zFUEUf2muwq+/bctl5UQ9P17k8SOLgLCj/8HtfdesCIrrKnZZnA+m4/G55yc5sVhjrmJz+pmtIgoBTZYZykWZLDVxPR8vAMeTGFuq82v/vB9NlSg1HCzXZ7Zs0hnX2TqQ4fffve0VpX67VDiezz88N8k/vTDNdLGB6wXk6zYt7qLtmSSjGl/eO8uhuSpz5SZH52voqowsicev7k7QsF3qlkvcEDeN4ws1NvelRCivJDoGTdtDlWVUQyZuwHAuTsV0WKhaHJmrMl1q4ng+luvxJ48cYe9UmWLDpitp8P7bh3n3LUOUGjb/+PwUzVAZ9CO3Dr0iO4+vZWzqS7fXJscL+NzuKaaL4kY3XWryobtHmSotv/HNhp3l44v1dpdFV2Um8w1GO2L8py+/xKFZ4Un2jp0DvP/2YQ7OVPjmwXkcz0dTZNb0JM5bADVsl098YT/TxQaKLPGRe0d5/eZevnZglrmK6GTXLJenjud505ZegiAgFRUO8UEggsD7UhEUWaJhuyQkleHQ7+r4Qk2smwTk6w6zJRPXM2g6nlDSIjZwLfjA08fyvPvWS+9SXQvUXTGZkMKVTZbF5i++wurU//vUSY6FG33/Cj3hVnQlfd3rXscXv/hFbrrpJn78x3+cf/fv/h2f/exn2bVrV9ss8VLwR3/0R/zt3/4tjzzyCJlMpv3997znPfz5n/85v/Ebv8Fzzz3H9PQ0DzzwwDmfwzCMZYaMLfzL/ln0aJU7VufOb6seSNihQVbdctk7WWrzNObKTcpNF9v18AOIaBKSJH5fVxX6M1FemCxRbjr80E0DPLRBxIK8NFth5vkm06UmddNlqWqRjmkMZKLEdJV83aJque0bhuV62K7PYs1mS2jRbrkez5wsoCkyqiIR0RSmSk3SMY3f//ohBtJRJgrCoyaiKfhBQNP2LrkAkiWx8zgdjheQr1lYrriJW+HcuiuhUzEDHN9HBjb3J5fljMV0tV20nYnPPDXGnqkyjuuxWLVQFVEMzJaaeIHg0EwWmuyZKHL/+lPPWW46PHF0CcfzuX00dxZfZtfYqTHrsYUaxYZz1X4zLQzmohwteZiOh65IPDdWwvE8zizr/UCQCBuOR75u07RcwlMJ2Q+wXFHgTuRFt1AU0gEFbPZOlXj8yBJv2nrtjDAvhMNzVfZPl0lHNe5b33lBWe7FMJ5vMJavs1S1qFoepu3iBbSLZtsLWKxa9KQM8jWL+YqFGXLhorpCV0Ln+EKNp44vYbk+SCnWdCXoShrcOpLj9Zt7mK+YRDUFQ1V46sQSrhcw0hGnP2vw10+P43gBjx1ZRFNkbM9nvmIxURCbBV2RWahYfOPAHJWmi67IbY5D3fI4OFO5KJ/O8XyeOLZEoWazoTd5I27jKvHPe6bZscZn+2CGpuO2jfcA5isWTcflrtEOvrJ3ts33ujeM5EkaohD2A7EB60joHFusc2hWUB2CAL55cI733z7MiaUaxYaN4wZoqsTJxfNLqp89mWf3eIFy00VVJD67e4rXb+6lULM5vljH98U677g+ju/j+bQLnFY3/MhClVLDRpElLFeoDd9x0wDJqIZE6BsnS6SjKsMdMUzH48RiHVmGe/uX37MWq+a53ubLiq5khIShUgp9tpIR7bp4AD03Xrzq51jRAuhTn/oUvi9W+5/7uZ+jo6ODp556ire//e381E/91CU9x9TUFL/0S7/E6tWreeihhwBRzDzzzDP8/u//Ph/4wAdYt24duq7z13/915etADu+UCcSl5gpN/nIvaPnXPRjhsLW/hTjhQbHF2r4BEzk68iyMD50PR8/IExMD0gaCrmYxo7BDMWmS8NySUU0vrJ3lg/etYpMTOfFiSIvTJaomy5IEroqsaU/TU/K4I2be9k3U+Jvnh5nrmLh+bTlltsGUnQldY4tVDm2UOfEQo2BTJS5ssmR+SojHXEs16dQs8lGBYdmsthgXXeS4VyMrsvwORntiDNRqdI8rdgOgHzdRlckYoZG0/bwvICm45EyVHRFJqYrbOpL4foB57qHPnMiz988O4EsSXz47lUcW6ihytAMu2YzJZOYLqMoEp4b4HpBOD5c/rf9yt7Z9sz9XH+/mK605a6KLCIrrhXevqOfbSMBB2fLvDgpCPFB4Lfn5KcfL88HTZGoNF2ajt8ukvwAarbLfEWMD21PLJh+SIh3PB/bXRnVx0LF5Gv7Z2k1Zv0guGIH8plSE8WIhbwBn7iu4PsBgevhnuJ5EoTOuU+fyOP6Qv0X1RR2DAnFzPdOFHA8n4CA4wt1HljXyc7BNM+czDOYjVJq2BiqEoYzihZ5MqLxJ48cRQ7lyJmoynzFomGJ42ioMnXLRVNkHM+n2HBYqJoUGw5JQ12mCLoYnjy21B53TBQaZGLaWRLoG7h0TBWbLL20QDqq0ZeMkI5qbTf4ZEQjoSukozqfeMsmvrZ/lpuHMtwXbihzCYOuhIHpeuTiBoYqkw67hM2wWIqHG+B8zQ5J+AGuR9uU9FxYqlosVi28IEByxP0CwPUDfN/H9QM0RJivoSpENZkv7JkB4D23DqIqMuWGg+cHmI6HpsrUbLGgbu5NcmSuiu359GUidKcizJaamI6HIos1a/4Mc9mSufJu+EEggp5PLgo/pbXdyevCAeq5BpmJK1YAua7L7/zO7/ATP/ETDA4KUuf73vc+3ve+913W8wwODp53XNbT08M3vvGNq36vEEY+2P45C6BkROMt2/v4u2cnmK+ayMBS1aY/IzgFdctFkmj7t3i+jyzJnMw3qJkOhqaQiqpCIl4TzsH/95kJKk0H2xOtzoimkYyo5OIGG3qT3LO2g2dPFKmaBXEDVWVyMZ0Xpsp8ee8s+bpNZ0JHQiKiCxfq3lSEctMRMnJZIhVRMTSFm9IZ7lnbSX8muswF93R4fsA3DsxxYqmO1hSVtiRJJKM6VtVuj7oAHD9ULvgOXhAgB1AxPZSohCRLlJouT58ooMgy771taFnhYtoun/zOMaxwxv2n3zrG1oE0h+bETi2iKgxmo0wWGiiShOP51C1hvLhzKLPsPbdGkq2/X8Pylv393rS1j2+9NI/l+ty1puOyfWUuBFWRuWUkxS0jWZDGmCg0OLFYx3FtnNNO1wCxgJSbLqoMwRk/sx2PQt1BlsRYLarJFOsOnu+TjKjcvGplbO/LTWfZeys1rsxpFeALu6dJppKs605wZL5GzXLpTUWYr1kslE0CRCeo0HA4NFvFCs0vg/AHY/km6YgqzObCYtB0PQ7OVjk0f4RVuSgvTJXpCcelf/rtowxlo+yeKDFTapKNaTQdn4gqs7EvxaZ+iV1jBQo1G8fzUWWZXFwjFdEY7ogT01UiqkIyqqLKMkM54ct1MZx5jEoN55LUlTdwYRQbDqs64rx5ay+f2z0NwJu39qIoCks1kz/65mGKDYdd40ViEY1bVmWJqDKOL04gRRYKy+6kwZb+NI8eXkRVpLbH03A2RiqqYdoeEV1hOCs6x6bj8eknTlA1XT5wxzADuTi6qqApMq7jIUlSexTr+D4JQygbVUXG9jxc1+epE/nQ0kPiiWNL/MQ9I23bEccLkMNiDECWxWZRciRSEQ3L9amZgk8pNj4SJ+rLu1OKdGX+N1cDRZZo2h5muHuxXJ8ggJVOwjhTKXolWLECSFVV/uAP/oAPfvCDK/WSV4XBbDQMTzw3Nvam2NSX4tuHhNOs5/vYns8bN/fy1X0zLNZsvHAuKUkSZdOhI6Hj6Sqz5Qaf3z2NoUo8fmQR0/FORUsEoCpiRCRLEjuHMnSnItiuz0hnjBenxGhFmOs1ODIv1FhmOFIZzESZLTfpSUXY3JeiULfZMZgGJBaqFsmIyhu39F509HNgpsyhOUFSXwjn7MO5OJF4kieOLlKo28vGYW4AashX8gFdEruwqulSMx1OLtVRwiywO0+L2DBdn0LdptSwkYBcwuctnT24fquA9MnXLFRF3BCjmko6prJQtSg2bHLxUx2szX0pXgg9Nway0bMukI64zvbBDJbrtdvS1xKu5/P5PVMcna8xmI3Rk4zw4nSRicLyXVvLF8jzOWtE5oU5VZoi4wcu2ZjKSFeCnqTBYDaGGY4Ya5bLkfkqSUNdpgK8VhjMxkhG1Lb0eHP/1REtHS8gogne00ypyZa+FEt1m+8eXaLSdPACMWKtmQ6yIodcCOjQdGQJqpaLJks0HLHYJiIyXhBwYLrCRL5OoW4zXzbZP11iqWZzcEbBdERHqenIxA0N0/N56/ou7lrdwW988QBf3juDH0BcV/j5h9ayrifJ/3tmgtlyk95UhPfdPnzJ42GATX0pxvJ1ggDihnJZzuc3cDa+c2SBdQMBP3HvKE5IITjdLsPxfB49tEgxLDxt1+er+2a5ZVUWJImupFgbIpqCFwjOWdxQeet2odhtjY8e3NjFN1+ap6EJvtcDYRfpA59+mhcmygTAP+2Z5lu/+ACrclEiuhK620sMhmP2HQNpnjtZwPYCEhGFTX1pTNcXhYLjISHRtCVsVwglVEXGCwRXbSEc7U2XmsiSRFSTWapZNB2PmiXGwK4PkhTge6e1TKFNiVhJNG2XquW0rSuEp1eAvsJtoIVK46qfY0VHYA8//DCPPfYYIyMjK/myl4W37ugjEkuwujN+QYKs357TatiOjyx57RZrVzJCRFM4sVDD9kSrU5LErnqpZlGzPDzfJRXVyNdqoS9MgB9W0SIUMsLrN3Vz/3ox035xssSRuWrbSbhiukRVcUOYK5vEdAVNFjuNzoRB4Afsny7j+ZCNaXzkvtWkoxq6Il8S8dc540JrYTgXYzgbPcv6HUQ3Q0LIM3vTEWRJRG2Yjk/dcjm2KAJXTy+AorqCociiAyRJJHSFZ8aK6AoQiPcxWzbZ3J9kqWohy6DKMk3HPcs75qGN3Yx0xgVhtjN+VnfrkZfmORDm8uyfLvP+24evabbW3z83yRdfFO3ugIB13Ym2m/WZkBCFrn3GRMsPj6HgjMnoqsKD67vaKqS4oWA6Hn/37ES7OLlj1Lokv6fLQVRX+NE7hpkoNEhFtGviP/TIS4vUQh7b7okiO4ez7TGkBBCAL4FGQBBIYsxpqEwWm8QNhWxcx60Knk9HwmCy0GhL6stNJzymMroCVTPA9gJ8P8B0fAYyGj1Jg6PzNRwvYK5sto9pAOwaL5GK6kKFo8j4gegAXE4BtKE3SSamUaiLGIRXomHnqwkyMFexODBd5s7VHRia3OYYRjQFVZbIxDRcX3R7I5rc3rRu7E1iOoInmYlqZGOhki8IhFpMk9kZhohKksTqzjjHFmus6Tq17r84WWl38RcqFo8eXmBtT5INPQlmyxYRVW77sLVWotbSKkkBiYjaVu4CbIoKFaeEhO/7qLIgErshLURXxNjP8X0GM9HTPp/oehKAdMaa5rgrPwLTZJliwyEIxPsu1u2rzqS8IshX/5oreoW++c1v5uMf/zj79u3jlltuOSsT7O1vf/tKvp1zYqQjTip14R11EAR8ae8MxxfrYbK7i67JDGWjeEFAzXLI106NiVoXR9V0MB2/XewU685yhZAsDPWyMZ2K6fCtQwtULY83bOrm6wdnmSw2scN2o+36uB4ocmuhVhnKRQGJTV1J5iomU+GF98zJAi9MlnlgfRf3retqW8tfCJv70hyYqZCv2cQMcaLduirLM9NNZsrNs8jQAK17uesJ1+OWbN3zg3ZBtVQ1OTRXYSATJRnKW1MxjbSpI0sSiYhK03apmW7Ywoa65XB0vobn+9RDb6S+dITFqnWW2eToBXbdx08jNy7VbMpN54J2+peLY4tCotoq2qYKjfYCejpa58SZxc+ynwdQMV0Shkq5adOZSHDzqizdyQhPHltkMuSYgMTxpfo1L4BAdCHP9Ey5EmRiKh2ZCF/bP0vD9tqmnk+fyFNuOm2+XAD4vuAbyZLwFqmYHglDIV+z8RHnVjypCs5FENAR11msWUCA44PteXiqjISPLEsYqmjX1yybr+6bJRfT6c9GGc5FObZYw/XE700W6vzPx44TADcPZwCJ8bxQIV4OelKRC2ZG3cClI6arqLLETKmJLEu8bXs/j4UqzgfWdyFJEjuHM1SaLks1i4imhN1usRlyvYCq6bAj7KJ7nsd3jy1xfLGGBMQ1cft77PAi3zg4jx8WR+t7BIFdsM0EAoSqNhXRWKzZLFZNsZkM78EvTpVxPMF/rFsu+6crrO9OoipihAogI1zzdw6liRsaVctBUxRuC8fah+dqlJs2fhAwVRL3C8cV56cULrhn0j+8YOXJN7bvo0oSlVAOnI5ylo/bSkB/tRVAP/uzPwsIFdeZkCQJz1t5W+8rwVLN5sRinULDJh3T8PyAroTBc2NFauYClusTDXk3gSfalzXLE+qV4BTv46xbYyDmmp7vU6x7vDBRYv9UiU9/9wTFhoXl+Fiuj+OKIDxPKM1JGgpDuRhdyQjJiEq+blMzXfI1i0REo1gX46Uj81VM12O0K37R3WlUV/jR24epmC7lxQgfB9b1JPjc/jw168J/Jy8ImK+YZGI6miLhuAE1y0WRJb750gJ120NVJG5dlaMrqbNUtcjXhCqiNx1hfVdimQuq6wVto0hdkUlGVG5ZlWXPZIktl6G06U4aTBQa7c+XiFzb038kF+fxI4tUmg4g/u4EAQqnisPLgYQgI3/5xVnWdCeIagoT+QaH56scma/RmdBZ3ZW4LCL79cBgLsbumSqKJEYADdtFRnAJbM8/6zrww2skkGCu3ERTJVRVFnEiwGKY5r2+Jym8UlwfCantnm66QnmYiijkEjpV02W2bNF0fAp1h6mSyS2rMmzoSVJo2ERVGddvFcU2cV1lQ2/yFX9cvx8Q1RXuWCM6xkO5GP/qzlXLfv7ooUXhDi5LyBJ859AiD27ooWG5fPPgfDvs+abhLLsmRBe9HvrWfOPgHP/ph7by+NGF0G5EbEwfP7rIzz60jqQhU2yIK1dXoC8bY99UmYWKieX6uF7AM8fz8DDkaybFhuhuNmxP8Ik0BdfzKYdqqYShYqhyyDnTQYKYphAL1+KT+brg0yCcro8v1ulJR0IrEfF5z8y8ytevnJt3pVBlKZTmi6/rtn9dSNDjhSsLQD0dK1oAtRRgr1aUGjZVU+SvNGxXnAi2B5LI7Jotm/jhRdR0fLTTCtQgEDdyVT51Mrd2vi34CGfQuu0hBVBuCotxTUbcTCXRYpUkiUREoW4JmXUyqjKWb9CwXaKqghucsiefyDewXI+EoTFVbIYX7qX9HVRFXKiNovggT5/MM1tqUr9IART44BJQtVzc025wTcfHtF0WqybTJZP5sokiSyzVhNRdloTf0SOHF5Y9nySLRaVpe/gI0p0iy20CYgum7bJ3ukxXwmC062yL+Lds6+Ppk3ksx+eWVdmrknWf9ZmDANvz2NSb4qW5Cg3bEZ419pWf8wGCW1WzhQz7v1WOcf+6LvozUTb2JlmsWdyyKsOdq091f1oBgVvDrKFXAvZPVZivC2Wg5/nt8V6taXO+U7HVEfKCABWJwA9o2i6uR1gdQaVpkzA0inWn3S0EUThKkiBKe74YJyZ0hZrl4bhCWbdrrEhnwqAzaVBoOhiKTMJQkCQNRRYxCOfi8MyWBU/jzC5P0/ZYqlnk4vo1JdefDyMf/8rL/hrXG2/a2sMP3LS6bTZ4LsyWm21VlmxLnAi7sJ/4wn72TZfwffhf3z3B1v4UlbpFLWy7BsBCaMQa1VRRPIdzpmjYGdIUBU0Wa6ymKJi2y2ylSc10hckrUtuHaLLQbBcEXiA2mwBrexKUQ+Xa2m4RFL1Ut0KRgbgmWp36csNpd9Zt16fcsNnan8T3g/b31TMqDdtZ+QKoaXmCwB2+FcfzOaMxtSIoN86v1rtUXLcV0jRNIpFXT6v46HyVr+6bYzxfp9S06UlF2nLAw3PC18H3A1rreQCcfu8TiznokpBHy5IYebmnEWEVWSKiyjRtFx+wXVEUeZ6YhweBuABUGVxPkHrrthfGSoj2amdCpzNh0JEwyNctZFml1Dh1shqqfMWGbr4fULFcLnZL9xDv1w5vQO2Rj+uxUBEeL54fENEUqqZD3XIxHRFa2Z8WnI3TK0PLFcfq9M3PcC7Gw5tOxRY0bJf/8Pn97RvUh+8e4QfO8MuJ6krbd+law/UDapbHmu4EMUPmGwcWLv6gy4AXBCLeZKlGbypCKqox0hnn/vXdjC3VOThb4cRijSeOLhEgRjG/885t17zLdaWIqkp7J6zIEqoUcDHvMgkRvOu6wk6glfsojEV9ji3WkRCd0NPPSUUW5ne6IqEpkIvpNGwX1/fxfdCCAMv1KDVtIppCqwyOGyrre5JsGUifMz7m6wfmOBhyyG4dyXJf6DlTbjj8/a4J6paHocm8+5bByx6d3cDZyNccvvXSAu+7fei8xXxMV3HD7CxZltp2BccXa21VnmJLHJqvopzdcwdgS3+S74bXjRTAjgFBgYhoSpsPpCkSMV0lE9XwWqRkAvQwNuNsXqV4LdP26QiFGi03Z9PxMVQZ2/LRlVPhrKdbXARAzXSZK5vLuj71M8JPzeswNFFUicD320VZ4J/dxV0JXAvV2YoO7TzP4z//5//MwMAAiUSCEydOAPAf/+N/5NOf/vRKvpXLxp6JEn4QMBEmuE8UGsR0hWxMI24ouJd4EjTD8ZWmyng+y9qbjhcwX7VxfSEtP31RDxtAaKogMUd1mbihkjKUcFcQCBdSUzhGBwQYikJPMsJQNsZQLsbm/iR3nEZAvlz0pqPY4XjhYvBZXtz5/in/mwCRwZSJ6bi+uBl5gTAAKzVstg+kziqy2lxiSRSBwx2xZQTV58eLTBbqzJaajOfr/P2uc2fAvVzQFLkdK5KNGvSkDDrPM0K5kovOD0TnqxnuQke74rx9Rz+H5yr8j8eO88yJPF/dN9u2ApgPfXw+v3uqHdtyvSBLEo4f0JMy6E9HQoWjfNHrRVfFzaHp+qGHlOgSCm0g57xOZEkUTboiboQNWwTyzldPdZtsL0CRJBRZRpUlBnMx3nPrELeN5OhOGdx7Dj5VsW7zxRdmODhTYalmsXu81OZjHJyttLuiluNzYPrycgVv4Nz49uF5njq+xNF50dX5m2fGuON3HuGO33mEv3lmDAA3CERBrUiostTuREiSEJaIwtknG9PoTi3vJLVuoLsmSmiKFBbMEs+MlQDB+fEDQaTXVZmorlIxHSQk5PDxLVWmH5w6CwMEed8PxHq3WDVZqlpoYbHTkzQoNmxqpohwibfI+Gfc0b1ABCaftvRxJkvkfDzClxMSyzf3lsd1GYEN564+CHZFt4e//du/zWc+8xn+4A/+gI997GPt72/dupU/+ZM/4SMf+chKvp3zwvcDZspNdFVu7+RihkhpX6iauJ5QHbw4VUIijCxwgkuugr1wBwunCK8tBCzvdMgIsrskSSgyokPkePh+y4BQKL+CMGG89dCOmMH6Hp18zWZNVCOmK9y9tmPZuORyMdIRI6arxAyJmnV5Nb8kgaFIJGMaG3qSrOmOk43pWI6L74dFgSTGPbp27hIhAPBFt+cLu6dQJKnti9MR15kqNqhaLoEfsHvC45PfPsra7gQPb+pBWwGb9jdv7WVjbxIvCEhGVL5+YBblHO7ZVzoUCwLhgKsqNR5c382xhSp/v2uSI2FOWs0SxOGW/H/fdIlsTBRhxbrNh+8ZveLP1hr/9qYjl30s333rAMdLPs+PRzgyLzgYjUsYDTadAE0W4y4RJRAgyxLeuRj4IaKazIaeFB0JnV1jRWbL1rLrUkw5AoZzMUY74xiawkhngiAI2DVWoGGLLuXH37JpGU/uiWOLFBu2KNKbNpoi0bA94oZ6Fp9uJUZg3w+YK5lU3RKH5yps7o3zW1851M4C+62vHOI9Nw+QNFTiuoLlSqEqV3S3bTdoF8uyJKgKSUNtO4/Dqa7NQDrKAbmE54vuYW+YADBdMtsbr2Ldpta0CQJRbInH0yb+1s5oxRycq6HKEs9PFNsqMEkS3c8jcxUatovjgSt5HF0QBV5HXGe2ZIYjN4mBbIyeVARFOkWXiJ5nbVxJHJ9ZXuAHQMPySMVW9r31XgOxwYpeqf/n//wfPvWpT/Hwww/z0z/90+3v79ixg0OHDq3kWzkvWgqvVpjoPWs7uX00x2A2xpf3CmdcVREa7bol+ASVho3tecsurgvhYkt/6zlaRYGhik6THwh1VavY8QlwXcGE9gMIPKGM6UwYFBo281UT03ZRu5PcvCrDQxt62tLwxdDR9Fx+OeeHxO0jWb66zwQuT35pqLJIdI9q2J7PvWu7GMrFcD2fiC6KSwmJjpiOIslEVAnTPXU0dcQOSajsPJ48lsf1xWcd7oixrieJ7QU07VZh6fHSbAXHC8jEdO5c3cFUscG/7J/Dcn3uWdt5lpHi1UKSJFaH3KPaOpdnxvJoCnjXSKkqIYq/xarJ3z47QdPxGM83qFsOIOz+I6qCrso8vLGnbVU/WWiwa0wsrD+4vb/tj3KpOLZQ5St758S5lTT4kVsHL4s/1ZWMsGYgxequBJ96/ARDuRiPHVq4pOvFOf1iCdr/OydUWZiUaopMw/bOq5LVVJnhzhgDuRhN2+P20Sx/+cQYYyFfbqrY5KbhDD98y6lswKrp0ZUw2D1RJF+3KNUdms5BfvmNG9jSn2KpbjGRb9CXjoQqstcOLsQ3Gvu9t75sr1uzXCzZoVC3Wag67XghgMD1yDccRjsTxA0Nx7PRVIX1PeL6M9RwfBV2iBxfRJooEu0sw1bq+0/cM8K3Dy3geB4RReFj944AtN3CQWxiThbq7FyVIaopVDwHWZLbo9LWc7XQn9IoNizGlxrt9f7kYp2m5fDcRBHHO8Xx2z9TBuCh9d18bs8Unh/QnTS4Z21XW93m28JKJXZGca1dh85LxT27m9xwXFJcvTHh5WDhtFy0K8WKlmzT09OsXbv2rO/7vo9zHchc50Kx4bSLHxB+JSB8YxRJIm6oVJo244UGcUPG9TzKpiPGWec5GUWP5hQu9Zz1EZ46kiQk0cWGgxpGONiejxLKxmVZxlAldFWmULd56tgSB2fKHFuoMllssne6zL/sn2+nr4/n6/zNMxN8/cAc/++Z8Usej2TjOtuHMmQuMz9LlUQMhR6quPrSUXZPFCk1bIY74nQlDHIxnWxMY9tQmpuGs8vStxUJUGjLUiWEVDqmK7wwVQLECAzErq/lo/P8eJHnxgrUwkLgC3um+dKLM/zznik+/d0TbVv9lwPJiMp0sUlwDWSqyy9SiY6Ege35LNUs7FYwoQSO67O6K85btvXxrpsHOLFY5S+fPCEiLQgoNRy+c/jyuUm7w/EviCiAycKVGZCt7U4IV2bba6u/rtX6rcliNJqMqAzkIizWLILg7BtTylBY3RUnFdFQZZlkROOl2arYXJzGwTg0V+Xzu6f4yydPcnKxTjamsX9GhKb6PuQbFgemS/zhNw7zldB879aRLKWmsK8wnVeHovWVDD8Qqqf5ikl3OhKOkGiPljoSEfwgQAk7K7J0SibeH6qnWr5qG3uSbO5Po50m1c6FXMjHDi9gqDKGJqOrcvsaOV3VLQHdiQie5wu6QyC4L05YTWnq8vNMklVqzeV8SQ+xgZElaVkZL7cqdUmYckZCZVjNFJl0tidI0K5PuwN2PTHadTY/LmVcO0HJpeJa5KCtaAdo8+bNfPe732XVquVSxs9+9rPcdNNNK/lWzouIJqNIUptLIcsS5YbDWL7OVKHOVKGB43kYqszh2Vp4YxDunY0zBrLpiEJUF460Ncttt1NPP/lbl01b1n7G+3G9AD8QMl9JCmg6LomIRmB5YZie8BWSJFFIuoEY4VmuCOXUVWEyWLddGo5oRRyaq7ZvaJbjc3yxRi6eu+ixUWSJH755kG+9NMfYUuOiu/eIKj6sosjtUM+lmjCJMx2Pqunylm19vHFLLzOlJomIym0jOX5o5wBf2z/Lc2MFJEnM2z0/QJMkXN9HVeQ2BygRXngNS6jyFEnCDRUdddtlbKnO8aU6rwe+vn+u7dQ7V7F49mSehzctJ0p7fsBMqUlEUy67U3I6RnIxtNYu9CoogoYi8uWiioQWGiNu7U9zYqlOOhpmr4WZaaoiMV1q8nfPTvD4kQUeO7LUzqXbP13htpGO8xpcXghnjnhahFQ39DuK6col+Sk9dWwJ2xPnWyEkqF4teVKTT40YZUm4ne8eLzKUizPaEePgTIWm6+N5HomIRlcywt1rOlDlUxwkQ5W5d10nxxZrBIFwEZ8uNjkwU6HcdPjC7im29KeIaLLIMAv5dpPFJr3pCMcWahxdqFIz3bDwlZCgnZ1WMR1KdYfulLGssL+BC0OSWptKCXyfqKZiueK8iWoqUuAzGY69Xc9H4pTXl6oKQ0zXh4gsOuam66JIYkPWUtMC7J4sU2o4BIgO+wuTYsTTnYoI24wAEoZCJqbz9QPzbZdyL4C90yUALGf5mVyoW2eNQiUgoqls7kvynUOLeL5Yt4dzYuS2a7xI1fIgEGvQySXhfXb6NVs+Y9R2PdRXzXMwr2V55c/rq4nnaWFFC6Bf+7Vf40Mf+hDT09P4vs/nP/95Dh8+zP/5P/+HL3/5yyv5Vs6LqKYQ0WWOj9eYr5is70nyme+NMVtqMllsiiJHgsD12wQ0WQLJ85FlkEPJkyRBKqaztivB3ikhx2z9bhCcXQS1XKDPPKG9AIJw0Y1qYjdQbrqhb0Uo3Y2oIm6j6aBJgjxquuL9JAyNVFRlU1+K3pS40LJnqMCyMdG6LDVsnj5RQJLgztUd5xyNqYrMup4U3zi4eNFjqSoylhNg20LhRSCO3UypSU86Srkp0tjftqOfPRNF4obK6zeLMd0vvmE9v/WVlyg1bED4xwQEbc+lB9d305+JcvcawWnqz0TQVQUvcNq7LtP2cD2f8bxYFEtNGzuUQcuyxN89O0E6qnPriCj+fD/gn/ZMt72CHtjQxc3D2Yt+znN+dlXh3rWd1E2H+Yq1TKZ9OTA0Gdv0cHxI68Ik8+RSHcfz2dafFoW1G6B6Hmu64hxZqGM5Hifz9fZCDWKcoEhwz5rL54A9uKELx/OpNB22DKTpz0RxPZ/PPj/FbNlEkuD1m3oumn5+YqlOTFOwPf+8DtmnQ0ZcL+45Dp0ERFQJTVVC0r8gsPqha+6argRJQyEVFUaJEVVux8v87INrOb5Y54XJIpoqs1i1sFyfnUMZUoaG6Xo8c7LAQtWkabt4PkzkGwx1CHfnlpRaSPFdDsyUKdRtNEUmG9fZ0JNoK95mSk0+v3sKxxO8sDOz8G7g/JAlCUNVWNsdp2b54u8cngt126Xu+NRNh5opFH6W61MJu7pjS3WartgIlSyfhbqJ42m4wSneZT0MIZWhbV8iSwGt/owU/ifsR2Q0RSbAb/sFSZxar5MRtV3US0BvOoKuykRUuZ2ZFdFkVFVhfU+KnqRO2XSJaHJ7FN+wnTY3FE+450c0WRRrrSL/jJnN9TBgLptnj54aYY7aSiIZ1aB8dWOwFS2A3vGOd/ClL32J3/zN3yQej/Nrv/Zr3HzzzXzpS1/iDW94w0q+lfOi2HCoWx6D2Rj5us3B2QoLVYuxfB3HC9oXz+nNHj8AxxVJ53ao7EoaCpmoyrGFGjLCDbdiigtYlsXO1faWFz1nFkUB4ueaIuH5AXFdE1JJSSITEz5AuiqcRtd1J6g0HR4/toTvB8R1lXvWdDAYkj3vW9fVlojesiqL7frMV0xGu+KhP0XA53ZPhyZ+MFtqnkWa/eNvHmHJkhlbql0Sf8P1aCeZ+wH4nig8yqaD4/ltAvPWgfRZN88dQ1k+cOcqDs/XGM5F+OaBeZHUXbOIGqqQtG88FREhSRKeL2SlzfCO6YR5YrPF0PxQU9p/Q/yAdERj90SxXQAt1qx28QNC+XelBRDAzz+0lplSk8bJ/Fmu3y2cvkCeiYR2qkvh+QHlho3jBri+kMSPLdUZzEbp7hLRKy/NlGnaHk3HO8sx1g8gX7f47tFFnjmZD0nACXrTFycSxnSVd+wcaH89W27ypRdneH6syHBIjN8zWbpoAdSTivDksSVK4ci1Na6UOHeRE9FkOhI6juszHwbwtiCH5PKYIrp+EgGm65OOCJL9wZkyFdNl20CK7QNpJEmYt2WjGlOlJtsG02wbTPPCRIm/nZggCERhnYiomI5PwlA4sei2u0sl00Urm6ztTmA6HvNlk7LpcHC2gibLbB9MM1sxWapZ9KYMfmCLOBZ7p8o44ZNUTZcj8zWRVXUDF0Vcl4lHVHrCc/T0jWPr9HYD4Znj+AGKFGCGI6Jiw27zhVwv4MRcjddt6Vk2TtZDMn8qqrZJxn4A6dA6oh6atwaIbme+ZrGtP40qS2EWmMiMBBGDMlFoEiBG9juHMyiKRFfIxwToSBgoEnQlDTJxA0NT0RSp7WafMDR0RZznqiKTjuts6UuTiertqJeRjjjHT/sMsevgttC0z75Ym9dBjrapN86hudpVPceKyxXuu+8+vvnNb670y14yIpqQxtqeRylUfZiOF95Y3LNuYqdPfk8fcdVMj/3TVRRZXBCCoJek0nRY3Rlj30yVwPFwvQBZEkQ9SZLIxfV2GKbpiDFWR8IgAG4dyfH8WLE91/Z8C02RyddtRvyAd948yLreJLvGijy4vosP3j1yzqwrRZa4d93yToDl+u3iB0Qh6Hr+sse/OFliyVaomzaqLJ3KqDkHRFfrlEOoF4idVjqiMNqZYF13ggfCnLNzYf+0CGNt2C5TRfjVN2/mvz96FNvz8f2Ar+6bJaYrvOvmQUB0SkDmzFoiAI6E5mj5+ilFkBcIl+WbR5bnkimy1PbdSFzlXDsb1/nhWwapWw5PHc8ve28RBVZ1JujPRPne8aVlhG8JMFSJW0bF31uRBB/M9gI032em3BRFgx8wWWhQNd0wq0sKQ1aDc4Ykfv3APAdnKsiyzEAmSk8qwus2ddGfjuIHMJCJnpWfdiZcz+ef9sxQbNiUmg7NuSo3DWcv6Vg9uKGLJ44uMtIZb59fEU2hOxXB9TxO5pvt35VBFFeaSi6uUTtRWCYJBtpeUglDQ/C/JFJRLQzIFV3DuYpFX1pEX3xl3yyuF3B0ocb77xjmtpEcTx1f4rmTBVzfpzNhcP+6Tr5+YI6lmn1WN7ZmurxtWw9PnShxbLEmLCFkCdfzOZkXI0nfF/EcrfDYM8eHN/LBLh1dyQjpdBRVlomqUrtgBlEAx1SJI7OVdnfVC+DkYiP8+anzWJIgaqgkDZVcXDiDy5LULqwm8ss5bePhJkhT5fA6Ehu3iCZTMiEXN6iFI/d02E23XLHW+YGgTZi2GNkZmtz2/4lqMqoi/rt5OEOh7hA3lLbj+P3ru9pUgYFMlFW5GJoi8Z5bBnhxskxEl9mUcnnk9DcrrfzoacM54nH6MitfibVcuq8GN67GMxDTVd6yvY/P756iPx2hbotAvWLNPOvmCrSJnNIZcufWr7o+SIrolxiqzM2rsnQlDYqmy3zZFGoVIBfudJMRjZ5UhP50lHU9CSqmQ9P2BBdGkvjY/aM8dmSJuiX4BhFNoWG5TBaaPHU8j6bI3DScpen65Ov2JecSRTQRp9EiuK7uip9VPAWhV4+hKXTrajueoi8dIR3VePqEuMmrp83X3SBoB6RqigiPHc/X+e5RiVzCOG8RdGCmzN6pEo4nisObhjNkY3qbf+L5It25fbx9Ubz65xiKW+ECVG4sl2O9MFXm7nWnXj8V0XjT1l6ePVkgqik8vOnqTRM39aVY1ZHg0FyVStNFlqAzofPQxh5KDYcDM2UCJKKahOP6+IiOX1RX0BWZVEzDqngE7c64GOGpskxEE4TJlhTX9sS5er5pW6Fm4YYjRF2VmSk1OTBTRpYltvSnWN+T5O07+i8Ylmu5ImE9qgky8XzFYlVHdJkp5fmgKTJv2NxLZkwc34lCg20DGW4dyfDokUWs8JwNAojpMk3bo9x0qJgO6aiKLAVUrJbvimj/pyMqSrhZyMUNcnG9nUtkaEp4bUrsnznl1TNRaPA3z0ywWLV4fryIocr4TkDFdNgzURSO6Y5/llrT8nz+91PjpKM6bqjiUSWJqKFQC2+qqajGXOVUW/720Rw1Syj3Vncl2l5RN3Bx9GUi9HYKuwwXiaimYnunOEBOIIVjqzCIWTo1EtJOK+T9ALpTBjFd4+ZVOQ7NVdBkiTvCzY+hKaihR48iSeFmKlzTW8WVH5CKalRtj2woAtEUmZ6QJzhVbBVeout9aK5CzXSYLDbbhfR4voHluKzKxehNR8nFDSSJdqDqzqEsL06WqFsu63uSDGZj6IqM44n7SdPxz6Iv1K+DE2IyqtGZ0FmqifW3L22I/L5rJmm4NGSiV1++vOwFUDabvaT0cYBCofAyv5tLw5quBD/30FqyMZ3pQoNvHV6geR4ps4RYrBVJonKeiAiRMK2gyDKLVZvbRnMcmK6iKTYpQ3jjSIFokUc9n0w0wuquOD/z4FoKdZu/ffaUqV8QSPzxe3fSsFw+89QYB2YrNG1x8S5WLRIRlc6Ege36HJgpX1Yw4w/t7OfQXBVJ4pwhmGXTwQ2E38abtvaRi+voqky56QivDcdntiR4UjXLFeMpV2TiDGdjlE2HfN2m2HApNURUxOrOGEO5syMHGqHdOojFp1C3uWtNByeW6jRsj0xM49bTRgmKJFE1Xc7FOW45X59ZGGiqzLNjBX76tO/Nlpq8OFkiZqjcuSZ3xa7ZLQxkojy4oYuq6TBXEaqwbYNpHlzfxX/79jEyMY18XeSgZbIaCxURC6KESpGecKFpua0qkoSmy6zvSlJqOiQiKjcNZ6k0bQ7PVVEU6bx8I88XHYiq6eCFVgoRVSEV1ZgtmRyZq1FuOrxlWx/j+TpH52t0JQ3uX9/V9v6JGypruhMcX6jRnYzw+k09l1T8tHDvuk560wav29jNup4EMV1lqthgotCkWLfJxg1imsKxhRqWK2IzJDwyMZ2a7aHJflsaH4QLbkRVcDwfTZHY3JdkQ2+Kp0/kKTUcRjvjYZdI4aXZCkE4CkwYKofnqsxXTCKaUFV6fsALkyVMRwgMzqyAJGCxKnL1oposnM4DkY7dmdKJGYLb0yrCX5gUHjbZmM6P3DZ0TaNXvh8gh6rb1V1ifYhoSjs5vTX6vmN1B1/eO9MeoW7oFTL4irl8wd4zUeRt2we4d11nOzD5jlEx+n7/HcMcnK3i+SJi5723DwNCNt8ybg2CgIlCk1UdcSRoE5MHMmIElooIfpkI8pXoSooukev5bXWw6wc0bJeORJRNfSmeHy8wnIu1P9++6TJj+YYI6JUq5GsmuqqSiKjcPJxBliWKtfyyz3U9xIaJ0DHd8wVZfGNv6pyThpcbP/XAGr704twV5Sy28LIXQH/yJ3/ycr/Ey4KYrvJjd6ziU48fJxPVmZGa5+QpAFhuQNyQiYWcjdYOVOzUJTb0JJEViaFslO5UhEcPLxIgWu5V06EjbtAICwZFlsjEdKTwYjo8X+GFySKKLLG6M4Hj+2iKTDqmc/vqDgoNm0rToTNhMJCNLnPkjF9mFpSqyBfkcdw0nMGWRODqG7f00p00+PLeGV6arTCYFcqubFxHllws16cnFSGiCcfqrQNpJgsNTizWaIQy6Klik8lC85wF0K0j2TBTyyMX0xnOxbl9NMdoZ4LJQoP1PUn6w/k7iKgISTpHkaNIbB8UhZIqL/eViWgKqcipAme+YvL/ff0wU6UmmixRrFv86ftvvqxjeC7ctaaDFyZLlBoOiizxlq19bY6OIgt/JEKCsiQFPHk0TyKiUjVdvECiO6kzX7YICJBkicFMjLff1M+DG7rZPV7kZL7OXatzPD9RwnI9RDb62RjIRblnbSeH5yuUQ0uFqung+0JSr8kyu8YKTBfqzFdt8nURDCoh8brTumE/uK2PsXwdVRZqvMvBsycL7Jsuk45q7YV/MBvjo/eO4ng+uyeKyLJEOqaFHUalLfHPxjQUSRgQKrLEYDaKpihtTk3ddnlgQzfbBzPcNppjfKmOLEuMdsYpNcRufKLQIKoqrArf99aBNPumSiiyRLXhCPfygHbW0en8PF0VKsRiQzitR1SZVFRj51AG2/MZyMQIgoCedISDsxW+c0jIqWdKJpoi89DGbhYqJqbjE1whKf77CpJYP5dqNqtyUdb3xNumgeu640TUFodHoxHGkMQNcT2fyYEzFBlZlnjXTQOMFxoYqsxgmDH2th3CUPGRl+Z54+Ze7g/jcmK6TLUpxvyKKjzKJgr1UIkqnKdb47IH1nczUzKxXZ9kROeO0Q46EgbZuE6xIYrmbFwjFTU4vljjxckSqiwzUzJ59mSBe9Z28tSxJWRJmCsu1kxemCzzwIZuFFlCD4tn7Yw1PXkd+PQN28N0PCKqIGjXTAfPF536lUTN8tkykGKuYqLHAyav4Dle9gLoQx/60Mv9Ei8boroC0iluiHuGA21r9OWHvItkRNwsAoQqoD8bpT8dJW4oHF+s05+NoisKMU20zCOhIkZCtGEzUR0v9CO5a02H2JFOlIjrIuH9ZL7OB+5ahe8Lst8dozlBfg7VBB1xnadPFBgvNOhLRa452bIzHiESFzusVETlq/tncbyA9T1J/CBoy7MjmoIX+KSjGkO5KD//0FqiusqeySJ/+8wEY/kGQRCQiqjtHdSZ2DqQ5ge397c/S8tcbk13gjXdZ1ugd8QNMUI67U+Ui6kM5+K8KZQjJwyV4mmtvG0DKf7Nw6d8qQ7PlHlprtKOTHj08MWVbpcCXREKktVdohvx7FiBn7xvNfeu6+S5kwXW9yT4V3euYn1PkvFCg3LTpVi3iekqni/S0+OGQsP20MOA2vU9KWZLTT77/BSW67N3osSbNvewd7rMgZkyDcvDOu18VSS4f10nHQmDJ47ZNCxB9ldkmWLdwkMipimkoionFuvEdCHht12b58cLywogWT5l+Hg5mCo2ePLYEiBCfx87ssgPbu8HIJcweGhDN34gulx1S/xcVWS6kwYbe5M4bsBEsc5LczU6Ezr9mSgRTcEIuwGGppCKaMyWm3x+9zS26xPTFVblxHHPxnROLtbwgoB902XWdifYMZQhG9MwHZ/vHF5AkSTSUZWq6ZGLayL82PbCSAThRVV2hWTaD0B3PRZrNtsH0wxko+3u4d89M44iiwIJoNx02DVW4LtHxeePO+UrO5m+j3BkrsZ0TeL+dV2MdsZxvIBiqLRyvABZlqlZTjiuDLBcn2rY+elORZgMHZhVGUa6xJhJVWTWnHHuLlYtDs5V6UhEODBbZctAmo6EIUJRJUtYecgymbjGVFl0uOXQlLXlofYDW/uoWx5Vy6EnFWXrQBpDU/jNd2zlL74rIp9++oE1qIrcVgi20Pq6N9XyrwqIago96QgJQ+Ut23p54tgSMU1lc3p5xTN0jsDelx9ChNHiCwpxT4sMsnKo2y5RXWW0M4HTvLLXXnEO0PHjx/nLv/xLjh8/zn/9r/+V7u5uvva1rzE8PMyWLVtW+u1cFPev7eLzz08tu5m0ECDkiaoiQQB12yMThlRuG8jw3tuGcH2fuuWRjWsU6g596QgT+QZ/+u2jOK7P9sE0w7k43zu+RG86wvqeBO/YOcDGvhSLVZOq6TLaGWe4I0bSUElGNP73kyeZK5toisQ7bx5kfc8pXsH9FyAWXy1ardxMTGMwG21LmVvjkR+6qYsv753Dcjz60lH60ga5uEE25GYkIiq7x0sU6jbJiMYH71zFqvNcwJIkXdZnKTREPEFrAqbKsGMwyy0jWbYNia6W5frtS1SV4T23DjHalWBsqc6zJws8eWxxWTp5S9kwkW/w9Ik8mirx4PruNgfgUuG2C2SxeLXcvEc749Qtl4ShsqojTkfCQFNlNvYmw+JE4r51nXz2+SlemCyFHDFhFbBjKM2ffPNImA/mUWk6bB/O8O/ftIlPfuco82WTfdNlvFCum4nr/OY7tvJHjxzB9wOqZmil4PkYiohSqVkuM2WTvpSQ8DqeGDLVbZe/fXaC3nSE+9d1XfFO70xzwDOVIxv7UuyZLLJvqkK5abNzOEs2qpGN64x0xrl9JMfRhRqW47F3ukxUV7hzdY7HDi8xVzG5ZVWWVR0xHnlpoX1uNmyPIwtVGrZH3XKxvYB0VCMREYrKr++bZaZiMpCJMpyLMV1q0hHXUWQRs6LKMntC9+dyw6VqOlhhNhlA3RYF6pquBAs1q707TkQ0FiomJ5ZqLFQsbhvNMVM+RfKeLjW5gQujYbmgeUwVGxRrNkcXau3r9+hCjXIzVHq1T0ep3fkZ6YwxXzHxg4CorrKx9/wF+8GZMkfmqlRNl1RU5eBshfvWdeEHkIvroaGmzERB+D71pSPMVyx0RXDnQHD9Pnj3CPm6zapcrD06f8u2PnYOZpBk6AsjNtZ2J3j00AIzpSbJiNZ+jp973Vr+8BuHqVou967t5NZVYkQ3XTIp1h1qikuftnwuW7oGieiXC1WRWdedaKfYj3TELpnmci1xx2iOf9ozzZH5KuoVdlRXtAB67LHHePOb38w999zD448/zm//9m/T3d3Niy++yKc//Wk++9nPruTbuSjKDZsvvjiDpspthv+54HgBQeAR0WQsz2epZjNZFGGp3alTN/jh0GuwJxXhv73vJhZqJr3pKKmIxgfuXMVC1WwTOU3H42v75pgpNyk1HDb3p7h7bSfPnMgzXzE5MFPGD6DUdPixO1ZdVIJ8LfCeWwcJtBiDWbHzvmVVll1jRUBI6+9Y3clwR5wXJ0scmKkgh+OKZ07kefO2PibyDRzPJxXKTGcq5jVrncYNBcsL2muhFEBP2uD1m3vaC8/pF6kfgILICPry3hkcL8A7wyRQkcVN+59fnCZfs5GkgIl8g/ffPoyhKRTqNq5z8ZyLiKawczjDCxMlAG4byTJVanJ0voahCmn+E8eW+JFbh0hFNH7sjlXMlpvtc+Gm4SyPHprn24cXiekKPakIhqqQi+vULLe9CO4eL6LKwvxyuCMqCNYhWVhGQpZl6pboZgSBII4HgHmavtj3fTqTBjcNZZgsNsJRkMRc2WSubJIwVEY6ROHWnTJYqFjEDIXuZISK6ZCv2XQnjXPmYa3qiNObjjBXNlFlqW0/0EIurnPfui7myiYjHTEMTSERUXl4YzfJiMYX9kxRtzzSUY2feWANiYjKWL6BqkjhTclkqtgkYajULZfjizU8X3Qou5IGlaYIoIzowuTymeNLIoJFkjgyV+Vj940wmI2ja4JTtH+6QqlhM1cxMUMzUUkmPF9lJAJSEQ1DVZgpN5kqNFmqWizVbG4fydKdMngp5NQdmKlQqNvt7sNKjwtejVBCh3NVlUgYEjXLbZOSa5ZLTJVIGBpRXUZxhMgiGRJjFUlClQMcDwzJwz4ffwGYLZvMls32886F/17dFeelWcFzycR01nYn8IOAiHrKeqM1RmvaHs+PF1mqWZSbTlvc8eSxJZ49Kbitd67u4K41HZiORxBAVFfRQpNYEF3vv/zx29sUCoBC3Wb3uFhjHS/g6RPLOUDqdTiPIprCe28b4tHDi0gSvGFzz3U5nx0/YHVXnLihIjlNvnQFz7GiBdDHP/5xfuu3fotf/MVfJJk81bV43etexyc/+cmVfCsXRalh8/HP7eWFyRLFhoMmi9TbFhRCjmRoUiXLEnFDw3J9ig2bubLJTNlsezyciUxcXxYpkYpq7XY5wJH5Kvm6zbruBHXLY0NPkpuGs3z9wBwV020XYxISY/n6ihRAXckIqdSpndR967raWTit5PO+dJRi3eGl2Wr791qt0vF8g5lSs01ePTxXY6LQaJMSrwYtFZjlSO0b/IGZCr/9lZf492/ayNaBNH3pCGP5uiCuqjKPHJrHDYI22TpqqOghT0gC+jNRGpbL/ukyxbrNYs0mpiuUmw6m49OVNLDKlxYt8dCGbrb2p5El4QdyZpyEclpxFjfUtjKkhUPztbYC5OBMhTtGc/zoHat4dqyI4/l0xg1Mx2eq0KQvHWV8qY6mKviOkI7Lkihubh/JsX+6LFKtpSDMlQvC0EgwXUFk/5P37qTQEMTqPWHhBrB3ssyTx5bwvICpYpPBXBRFltg5mOHAbAXb9YnqCu+7bYgzoSky77llkKWaTdxQzmkImIy0ZOwC3UmD1V0JvnNooa3iKjeF/84dqzvazt6yJJLAJwoN7hjN8aUXZwBxrI/MV5gsqOyfqVBqOCR8n7WdUR49LNK2W7fGXeNlyqbPHaM57l7TiefDU8eXGM7FGFuqo6syCV1hU3+K6dAUtTcdIRPTefZkAcf1mSmbGKrMbMVktCNGpengBwEV02Vdd5zhXAzL9bm1q5tfv6Qz5/sXgSS4mKtycSxPcChnwuKkPx3BCSRGO2MkDZ2Sb6OrCuvD6+bwXJWmI+r6ghkwU6jB6o5zvk5vOkJPKkLVdEhFNLpT4vz7nXdu43999wQNy+N9tw+Rimq8EPLssjEdWZY4GBZITx1f4uSSMFzdPV5kIBNhOBdvFz8Az5zMc/tojqliE1kWlicg1sUt/afW71bxA2enrJ+R7IJ0HcjHAA9t7GHrQKa9nl0PzJVNPF/ce8z6lblCr+jR27dvH+985zvP+n53dzdLS0uX/Dy/8Au/wMjICJIk8cILL7S/f/ToUe6++27Wr1/PbbfdxoEDB674vT51LE+x4WCoMkEQ4PpCyp2JasR0GV0TmVyqLGSThio4GqosMZARN4WpK8xMAk5TjAglROtiuXN1B4PZKJIkdswdCf2apOJeKToTRrv4aWFDb5K13QkkCToSIogUoDdtLNspxA2FyDVKNzYdj/50lJiuoCkyEV0hqinYrs83D84BwoCsM2GQ0BVimoqmCG7WUC4Wvh+V3kyU/kyEvkyEdeFoURhf+tiuMBhcrFpMl8TftnkJqeYtdCWN9mIxlIuxfTCNJAm+2H3rL+zQbCxbFCU0RZDLP3LvKG/e2sf2oTRBEDBfNTm+UCMV1UgaKrHwOLQykHYMZXhgfRe3DOfoSgrjv3hERVNEa1tXhDGjpkj0paPsGMy0FTeaIgkvrACqlstMuUktNPd85KX59tipaXscmque/SEQr9GbjpzXDXmkI8aW/hSSJFLt713bedbnh1MqoDPP/Z5UBFWRWdURY/tghuFcjJrlsX9GEJ0NVUaWZNJxg/6WCWQAIDEYngd7JkvoqsybtvbyY3esIhXV6EgYjHTEuXttF++8aZBff/tW3rS1j51DWZE/lolSs100RaI3FWG0I44kSe3zXQJWdyX44VsG+dE7hq9J0f9ah4i6EXv0RETloY09bOlPs6U/zUMbe4jpqoguyURZ151kuCPWLh7K5inPtiCAF6Yr53kV2NyfYkNvku2DGdb3JtncJ4qR7lSET7x1M7/9rm1sG8yI5yJAkkRmmCpLtJzQmmeNd/2QvHzqvNUUMUnoSUWW5UZeSKmbiencvaYDWRLWGPeuXU4LUK/D6KmF09ez6/L6CeOqO2Ar2gHKZDLMzs4yOrrcYXjPnj0MDAyc51Fn493vfje/8iu/wr333rvs+z/1Uz/FT/7kT/LhD3+Yz372s3z4wx/mueeeu6L32pkUBUfcUMnGdSxHyLk7EgY10yEbE6nT02UTTZZIRTQ8AsoNN1QCqGwfuvKuzPqeBNOlNMcWanTEjXbkQzqq8a9ft463bOvjxGKdzoR+zVPNrxaKLPG2Hf34YeRECzcPZ/nAnSN848Bc6OTc3R5PXS0MTZBlHden1HRQTvMial2kP7C1lyeOLonss5hOVBcS8Lfv6OfZk3l0VaY3FeHQXJWIJvO6jT3EDJUt/Sn0eQnHEynNLYXfeL6OfgkjsPPh4U09PLSh+6LmgwBv2trLNw+KIuPuNZ3tEdMdozk0RYTglpungnzTUY1VYQdCkiSGcjFkWaY/E+UDd47whs297BrL88hLCyR1hd2h51J30mC0K9EOaMzGdd510wAvzVZY25Ng11iRk0t1dEUoQFqFVTauUzVFcndnwiAZUc+SkV8KJEnijVt6ef2mnmXH5ZaRLEt1m9mSkCK3Op6b+lL4QcBMyWQoF2W0M87YUp213Qn2TYsR4Ka+JKWGvSz0d+9kmVtHcgxlY2FOnyCegihIW9jQm+Rt2/v5/J4pkoZGNqahyhIJXeWetR1EdZVKw+HZsQJNx2OubLKmO4EkSWzoTfD6TT0sVC1yMe2GA/Rlomm5pE6LTfn3b9rA0yda4yQxPu1JR9g2mBYWIrrSJudnY6FrPiJsdHPv+dfi7mSEf3XnKharFt0pg9QFokrWdifYOZRhqtjAUBXesFFYQNw8nGU838B2fToSOut6EiiyxJu39rbFFK/b2I0kiQ3y23b0c2KxTlfSYMfghe8Td6zu4LaRHLIsMTU1texn966/eq+yVyuycZ133jzAS7NVVO/K7EpWtAB63/vex7//9/+ef/zHfxQyb9/nySef5Jd/+Zf54Ac/eMnPc//995/1vYWFBXbt2sU3vvENAH74h3+Yn//5n+fYsWPnTKC/GG4f7eCHbhrge8eXGHJ8NvUlOThXZWKpjiJLjBeauF5ANq4x3BFjVS5OKqpxdEEoVH7k1iGGzyHvvlRIksTrNvbwuo3n9lhZ05U4S83wSsOZN3ZJEg7UZ7pQXwuYjs/GvhTDHXFUGcbyYoEa7YzxvtuEr8c7dgyQimht8t5gNsqD67v48t4ZxkM32B1DaXYOZ4lqCtsGRIcmpqs0HZ9URKM/E6UjrrN/usRUsYlulq7qfV9K8QNitPjBu0bO+r4kSe0bazGUrcuyRFRTuHddJ08cXUJVJD58z6nHpmMacxWTxZrNuu4kSzWLd940wGLVoiNu8GN3Drd/t9Sw+cIL0zRt7//P3nvHS3LVZ97fyp27b85xcp7RJGWhRA5CJIPJNvZ6nfGubfa1sY1tsFnv2u9rHMBrYzIILAMCBAJlFGc0mpznzs25c+5K7x+nuubeCdJoNLojWD2fT8+d7q6uOnXqVNWvzu/5PQ+HpnK8dl07kiQ0Ujb1JMiW62q2On/5g6MUqxaxoMZ7dvTAi+Bnnt0vhqrw5k2d5122PivgOC7f2jPOhHd8N/eIz1uiBq1RUX5cNR0MTWI8XaavKURDROfGVa2saA1zeCovytVXLX7Kvnl1K8tbI4wmS+yfyPLIiXkOT+XoSgRZ1hLhbVu7qFg2TRGdtBcALmuNsHOgkYihMZYu0RkPsrnnlQDohSBZMsnPFZgvCGFJWZa5dvnia8eyFqEof3KuQHPE8Geb/79f2MJvfu1ZSjWL61Y0866dveesfyHiQe283odnIxrQ+MiNgxybzhMNqD6BuTMR5EPX9ZMrWzRFdL8wZLAlct6KyRd6/b7QdeItGzsueh0/j+huCNHdECKXu/AM33NhSQOgT37yk/z6r/86PT092LbN2rVrsW2b97znPfzRH/3Ri1r32NgYHR0dqKrYJUmS6O3tZXR09LwBULVapVo9o9h6vg58945e3r1j8Ynznn95klzFZNoj8FZNmfFUmcaQTjykcVVvA7+wvecFVwq9gheHrkSQ1qjBfKGGJMFv3rLiHF5UQ1j3rTPqSBaqfvAD8MMDU1gOhHSFxohOxFO8rq/L0ASROBESs0ol++UjbrehO85kVijPNoZ1NvUkhNWKLNESWTzNvm884xExhQL4mzZ1nMM7Ajg6nfertWqWw3CyuMgXrI6//fExIoZKUFPIlE3+149PcMfKpZ0eny9U/eAHhDP4zd4DxDXLmnlHusxkuszukTRtHndHkmBVW5SDk1kihsotq1vPS+DuaQxxbCbHruEUec9wcyYrqsdOzhYuKAb5Yqoy+//w+5f8258X2K4gEr9t67mcsjp2Djax8yx+z5rOGP/jDWvIVyw2Ps8MywtFY1jnmmXn8olCuuor1b/UkBDFDY8PpdjUd35u0yt4fixpAKTrOv/yL//Cxz/+cQ4cOEChUGDLli2sWLFiKZsBwKc+9Sn+7M/+7AX/LhZQSRergjjruhSrooqkIxGkKxHkzZu7XvH7uQLQVZl3be9lPF0iElBpjV4cLyqoK76vWb5isnskQ4snb///3X+CP3/LejRF8onS0YDmKw9bjnte640rhTUdMV9cszVm8MUnRqh6yo/37Jvkw9efST3HAioTC34bMc7/9LswHSTen7tcuSaqWizbIVexhIO66/DTE8lzln0pcbaX28JUhqbIvHNbD8PzRZBA9VJ8EnD/0ZlFvl9vOs9M00iyyDMjGco1UXVn2S4DnpDjc6VMft7xXEHa8F+94UWtW/K8vxLBF/4wef+RWU56ookzuQqt0YB/Xv+8wEUi+sq95kXhivReT08PPT0XjugvdZ1TU1NYloWqqkK6fHSU3t7zT31+7GMf46Mf/aj/PpfLXVSbrlvRzNB8kaCnUKtrMj0NIQxVIWyorwQ/VxC6Kr9ggb6QrvLGTZ08NZREUQR3oI5MsUZAU3jjxk6eHBIcoVetamUkWeSHh6YoVm1aQi+vm19L1KAlalCsWn7wA8JmxXVdnxd108pWbEdUVK3vil3QFX5tR4xMyWQ4WaQzHjyHx5KvmHz96TFs10VTZTRFKC+vbI2Snb/0IoBLQTSg8foNHezyvMZuXr2YH6EpMivaorxnZx+PnZhHkmCwOcwjJ84UYOQq568myVcsdEUmElD9kumoobGtv8FPg7yCywvJ81a7fX37C/5tfoEVhuuK8vafpwBIpOZlepteIdO/GCzp3fptb3sbO3bs4A/+4A8Wff7pT3+aXbt28c1vfvOS193a2spVV13Fl7/8ZT74wQ/yH//xH3R3d1+Q/2MYBobxwk+I8VSZ3sYQtuNSNm3CukJHIoAiS2xYglL0V3D5MdAcJhYQUviSJCrKApria3n0N4fpbw5zfCbPDw9Os288w1W9DRiqQubyiEVfdoQNlRVtEU7MiKfgjT3xRTpIQV3hDc/BH3hqKOnzKm5e1cp1y8/lbR2ZyvHdfZOcnisy0Bxm50ATEUMVru2uS+IKBIfLWyMsP49S+EJ0JYK80yvTr1kOh6fzzOUqjKRKmI7DoyfmuH5586L+GmwJE9QVRpMlJCTWdETpbw6zvnNxv76Cy4eooRIPaXAJs6wbu+P85EgF14XmqHFBxfmfVQhJBo222M9PUHclsKQB0COPPMKf/umfnvP56173Ov7X//pfF72eX/3VX+X73/8+09PTvOY1ryEajXLy5Ek++9nP8sEPfpBPfvKTxGIxPv/5z1/G1gsMJ4vM5CoUyiaSDNv6W+iMB3nv1X2v8H5exkgVRRVQZyJw3jz99w9MkSzU2NnfxGyhwi/s6GVr3xmhvmzJ5N4D00LTpSyqndZ2xHg53/vesKGDsa4ysizIgqWaxWSmQlNYf86xenK2wOOnRPpqNlclpCvcsGIxlyVVrHHfoRmKVYt0qYY777K6PcbqjihrOmJixqlg8NHzbeBlAMt2hC+YrvCubT388OAUpZrt+aGlaQjpizhkIV2UuidCGqbtkizW6DFtYZfzCs6LC6XHLjY15uJSqpqMJktcs+yFbXt9V5y2WIBC1aIrEVxUjv7zAF2RAIds5dKrUF/BEgdAhUIBXT/3wqtp2gticX/2s5897+erVq3iiSeeuOT2XQwUSSJXNqnaDnFDIx5Qhc/QK8HPyxbD80W+u28S23GJGCq/sKPnHC5LfcpcVWU6E6FzKjSKNcvn+ww0h5nNV2mPB9jYcPkr2i4XJEnyzUpzFZOvPz3qW2zcsbnrgkamdWXaOvLnucgWq6I/GkK6L+63vivO1YNNaIoweB2vpC//Tl0G2I7L3c9O+ITpa5c1EQ1oi8jP50uFVS2HzT0NQkzTcbl6sNHXI3oxeIXsfH7YjovrSr7z+gtFPR388wjhQylTqV0BO/ifIyxpALRhwwa+8Y1v8PGPf3zR51//+tdZu3btUjblkhHwrAgUWYhaIUlsepnp8LyCxTg0mVskoX9qrniOdtLmnoSv2jrYEj6nJLYtFqAzEfCdvX9xZy9behvO0eV4ueLUbMFXUrYdl4OT2QsGQMtbI+weTpGvWKiyxPrOc1O7HZ567kyuQldDkFtWCxf2nwUki4urxQ5MZHnTpk4OL1CyXtN+Lq9nU3eciXSZ1e0xGkIa1yx7+Qa/L2dcLHE6bKiEDIWuhvOP0/+bETJUogGV1gtw917BxWFJA6A//uM/5s477+TUqVPccsstANx///187Wtfe1H8n8sB2xY3h/HxcWKxC5Mau7QSklGhS3PRVIlbulU6tRLj40tL+FxKjI2NATA6OkoikbiyjbkEVDJJMnNn3LeLrS7jUmHRMn0BCHTKmI5DZ9xhYmLi7NWwsxWmDAhqCs1ykfHx4s9M3xTTRTJzM/77SqDC+PiFnx5f1a0wm7eIB1Xkcorx8dQ5y1zbDlNBwSdqpMD4+OI+fbn2TbFmkU/O+EGxETMwczq39KqkClVaogrF9CzFsyawgsAtPQr5qkV7TGZ+ZuqS27Cwb6zcxavg/7xjfHzc75vloSrdLVH0aprx8aU3/Xw5YmHf9Le4mNl5xq1L08D5eUI9g1S/j1803CXG9773Pffaa691Q6GQ29TU5N58883uQw89tNTNOAdPP/20ixDFf+X1yuuV1yuvV16vvF55/Yy9nn766Rd035dc92UkZHIFkU6naWxsZGxs7DlngC4V9x+Z4ahnEOq4LjO5CuGAyrGpPPmqSVhXOTado2a7/pOppsg0hjWuHmzi1GyR2XxFOJA7Lpoii6fzBY7ER6ezVE0HxxWjwXGFf5MqSygy/PmbN5y3pDRbMvnykyP+e02V+JUbz7AOx8fHWbdund83B8bTvP9fn8by3MTPxkJecP17TZFoCGqs6ohyx+YuTs4W/WX6m0O8YeNi7ZWnh1LsGj4z67C+O8Zgc4Tv7p30P2uK6r7K85VCvW9OD4/wtWfPPMm7rnAJuu/wFMPz5eahCLMAANLRSURBVEX9FNRkbNc9r0O1IsOKtgg3r2zjndu7eejYHN/ff2amYXlrmP/+2tULvOJevjh73CzEh//tafaMpXFcfGNfWQLXFX2gKaKkvjFsYNoOuUqNUtXBk2NC8pZXZMkfcEFNJqSr7Bxs4g0bOs8rVvdywXP1zXOhYtr866On/feSBL960zIOTGR4bIHu0mxeaN88fTrJ/vGsbxAMYOgyiixRNR0hlOn1nyyBrMg4jiv0lGwXQ5OoWi6qLOxUapZDPKgtUDoO8xu3rPC9Ci8FX31qhPQCM8udbXDrtVtfsmvxzzLq46br1/4d2QgR1CR2/dGrl7QNmVKN3/n6Xv+9JMFn37dtyYnmb/unxzg2LWadnWqJiX/6IKlUioaGi1dcX9IU2K5du3Ach507dy76/KmnnkJRFLZt27aUzVkERRE3lFgs9pKcdDtWaUwUxzFtl5CusK4/wKnZArNlmWZZolSzWaUFOTKdR3GFwF5bLEAkoNHd2sSmgQ6+8vQoNblKvmoTCag0RAw2dsd9a4dXrW/gwWOzvrt3Y0CoGGuqTEs0wBuuGiQQOPeQR6Mua/oqvpvx9v7GRX1Q/3+9b65bG6Oz9RQTmbJ/81qIiK7gAKblYLvC+z2oK4TDOttWdHPrxj6yz06Qr1gossTOVR2LXOYBtq4IcCJjUzFtdFVm+4pumiI6PdNV5vJVJAmuXtV6xS+Q9e03NiTYukLn0KSYil3bGUNTJOarMpPFKUyvo1SgMR7AcRySxRrWWfzOsKGwuqeN5d3NDHS0UJMD7J2uMpuvEtQUbljXTUvjz4alwtnjZiHef9MaDv/nASqmjeSCKkvIEliO6+tpOS7EozqGqtDpCt5S1RLjSVUkAt4FV1cVbMchoCkkQhrLu1rZuqKT2BU0anw+PFffPOfvgM3LKr7Z7IauOA2JOJsDIY4mLYpVYci8dWUnhydzbBjUGc45FKoWjitMZBvDOjXLwbAdjxfmokhgaAqqIlOu2SJId1wkWUK1HTRFIRYL0RkPelYiFQKawrYV7fS1N70oKYCdq7p9v6z2eIDBtjN9dKXP75cb6v0hGyFkI8T/fOf6Je+jWAy2ruhk/7igFWzvb6S5MbGkbQD4k7du5X3/tmvRw2X9Pn6xWNIA6Nd//df5/d///XMCoImJCf76r/+ap556aimbc9lxbDrPWKpEZyLI2s4Ypu2wezhNqWaxoTvOO7f38PCxOTErI4GLy86BRkK6wnCyxHyhSlBXKNcsVrRF6IwH2dSToGw6HJjIsrE7hutISBKMzBeRJImaaWGoErmyhapKfOSGflJFoQTsOBLDyQKaItMaM/g/j5/mzqu66TxLE0OSJN68qZPRVAlNlZ9TM2M2X+HfHztNVJNoCCpYjoSmQM22hS5FPEBvQ5htfQnufnaCZNEkFFAIqgpX9TawvDXM13eNETYUrl3WxIq2KKoi8eCxWRRJYlt/AwFVYWiugOU4KJLEjSuafaG+d27rYSJTJmwoF632XEfVstk9nKZq2WzpabjslXu3r21jTYc47kNzBR44Msv+8QyG7KKrsLYjTtV0mCtUWdEaZpMWZ9dImqolSnUHmiJcu7KFgaYwVdPh23snCGoKLVEdWZJY3R7BcRy+/MQIvU0htvc3/syUYbuuy38+O8HQXJGehiBNEYP3bO/h0RNzFGoWA81hEgGN8WyZiK5w65o2dg+nSZdNrhlsoi0WZMtMjh8emMTF5aq+RgZboqxoiXBqrsjpZIGGkM7qjigBVeXxk0lyFRNdkVnVHmVVe4QnTqUE+bsxxE0rW0iEdCqmza7hFJbtclVfw0X5QV1pvHZ9O+s6hU9dT2OImVyFrz09wsh8iVXtUW5d00YipJMvmzi2w9XLGhlJlgmoCitaw9yypo3T80UeH5pnIlmiULMJGwoNQR3XdQgFdFa2RuhoCDCbqxDQVCK6SmPEIGIoDM0VkREzQreuaWXPaIaDE1l0RWaj57/2QrClt4HORJBSzaa7IcjM1OTz/2gJ8VKqXb8YdAbhzVf1XZFtv+MqYZAsSxLv2t79/D94CXD9ylb6GjWGU+cXL70YLGkAdPjwYa666qpzPt+yZQuHDx9eyqZcdpycLfCDAyJVcWAiK4KUZJEjXtrr2Eye5ojORLrC0ekcU5kKhirjuC75ioUswXimguu6BDSZZNFkS69LqmRyeCpHqlCjbNpoikzFtMlXTFwX9k0AuMiSxMnZAj2NIa4ebCJdtDg8JWYj5gtVAqpCJKByZCrP375r8znTlbIs0d/83KqiVcvmUz8+wv1HZilWLT8dUUexWqNiudRsl5+enMdBKAVLWQlVkZgv1Hjo2BzxkJhCn8iUWdkW5e5nxkmXxCAeT5fpbw7xn3smGE2VkKS6imuA9ngAXZUZeJ52Xgj3Hpj2Z7lOzhb44LUDl3Xatu66/oP9U3zxiWH2j6UpLUhzPTMixoUkwUy+5pWyiu8mslWaosJX6sC4uJkMzRd9nZ2QrvLMSIqORBBZkljVHmUqW+ZdVzgFeLG4+9lx7to1Ts1ymMtXWN0e5ch0nlSxhu24ZMsWli3EEyUJ9v/kJGFdwXJcjk4XuGlFMw8cncV0RNrmwWPztMZC7B5Nc3K2iGk7nJgpMJoqEwmojHr+bgFNYWN3nIePKxyYyJErm2iKxGSmzC/fMMh3904ykREzqEPzRT54bb9Iq72MsVDeAOAT9xzi+EyBUs3i0GSOiuUgAVPZCo8cn6NkClVwRZZwcZnIVCiZNsdn8pQ8GxPyMCyVUWWJkK4wli6zvCVMzXIJGQp9jWH2jmcJagr7xjJoqsxYpsxIqkRAUzg5W0CS4NRcAQmJtS9QHbst9ko10wvFZBk+8Z0DfPwtG5Z0u7Zt89t37SNXFtfs3/jqs3znN65f0jYA/Ldv7HlRwQ/AkibtDMNgZmbmnM+npqZ8E9OfVczkKoveT+cqvmQ+QNV0GPasAfIVi1LNwnJcijWbsmmT93RVHNfFtF0qpk2hapMs1CjXbGq2g+24lGoWFdPGdlzBn3BcHEf8tV2XUs1maL7ga7lYjkO+YlHztDSSxeo5Oi8Xi1zZZDZXxbSc83J/HBfKpk2pZlOxHLGcC7bHPyjVbEo1y9f1yJYsJrJlP/gBMcM0lan4bazL2J/dv5eChcejWLV9U8vLjbF0iXzF9NNedTiIPnK918L/m5ZLzXJIFWsUqpa//+mSqH6pmDa2C1nvfaFiMZ2t8rNC4TvpKVKbthg7k1lhJlwfC5btYjqC/+Y4Yvxbbv29w1SmTNVycLyUoWk5ZEs1huaK/joqlkO6VKNQsahaNqbt4Lgu5ZrN6fmS36d1IcNi1WJ6wbjKlU2KtZ8tYbl8xWS+UKPm5VKLNYtkoUqmZFKoetcKW1xX6ur1M/kK+bKJ5Yjz0meQuoKfaNkO5ZpNumxSsx0KFYtizSJbNslXLaq2Q807tyczZTLemKyf45fjXH0FF4f7jswu+TanczU/+AHxgF2+AnpECy1sLhVLGgC9+tWv5mMf+xjZ7JmS5Ewmw//4H/+D22+//aLX84Mf/ICrrrqKzZs3s379er7whS8AMDs7y2tf+1pWrFjB+vXreeSRRy77PlwIvY0h/2lekqCvMUT/Ap+WaED1n4oSIY1oQENVJOJBlUhAJR7UPLKyhK7IhA2VRFCjIxEgFtAwVGH0GAtoRAIqmiIjy6AoYnZFUcRvY0GVjd1x4kHxJK0rMo0hDcOb6eiMB4mdhwd0MUiEdHqbQgQ0mfM9I8sSRDz+RthQMDQF2dsnWYJ4UCUa1NBVGVmSaI0Z9DWGFj399TSE6G8Ok/BSEbIskQhpdDW8eCn7/gVPzQ0h7SVLd6xoi9AQ1j211jNQJNFHklQn8J75f0CXCWkKrRGDxrDut030jUTYUNEUiWaP1xIPavQ1hX5mbBg29wjekqEKAu5AcxhNkXwyt65IGIosuECyRCSgokpibGuK8DwK6YrfZ4bHZVnfFffHdkhXaIkaxEMaQV3F0MT6wobKus6YP6YML80bDYg+rKM5ahBZIjfvy4VoQKO7IUhAE30QC2p0xoO0xgwSQY2woXiFEDKqIhHSVfqawt74lH3DUZkzpHJdFZ5nrRGDoMerihoqzRGxzoAqE9DEcVzeGvHHpOxdfy6kL/UKnh/9f/j9C77Oh/ddvfQzwO0xnZYF9IOuhtAVScW/bUvXi17Hkp7tf/M3f8ONN95IX18fW7ZsAWDv3r20tbXxpS996aLW4bou733ve3nooYfYuHEjw8PDrF69mjvvvJM//MM/5Oqrr+aHP/whu3bt4q1vfSunT59G0176vH5PY4i3b+1mPF2mMx7EdBwawxo3rGjGdlzWdMaI6CoHG7NcM9hEulRj71iaiKGypiNGslBjOFkkVajRFDVwXEFMfNtVPewZTfPtvRO0RQ3WdyWYzpZ5ajhJpeawuiOK67jM5mskQhq3rG1jS0+C+4/M0t8YojFssLw1TKZsEdBk3rixg+FkkbFUibChsqwlQthQOTFTIFs2mctXaIsH2DnQRKZUYzxdxs6LJ7qpbJk3b+ykMxbg67tGmc9X0VSJkCYCm2XNYYqWjevAu29dwe6RNJPek7skwZaeBtZ3Rjk1X6a/KcSta9qIBjTuvKqLgxNZZM9PTVNkilWLPaNp+hpDXLe82b/Ivhi8el07nYmgp1ocQ1UuX/x/fDrH8PEsqzti3LyqlbCh8B+7J9g9kmYqU0KWJLb2xchVHCYyFZa3hrl+WTPPjmWYL1TJlKoUaybXL2uipznM7uE0y1oi6KpMoWKiaworWiM4LhSqJu3xoO89N5YqkSrW6G8KC++klyFes76doK5wYjZPtmTiuC7b+xs5MJFlaK5IuWoiyy6GptIaDTDYEmYqUyFXtWmLaDREdP7LjQPsn8iTKlYZaA7T0xhioDlMUJM5PJVjY1eMle1RmiIGuqKQKdWYyVXoaQyxrjNGNKCSLtVY2xFne38jiizxhg0dHJjIYjsu67viyC/z9BfAydk85ZrDirYIAU3ht29dyVefGqFs2tyyqpXNfQ1UTZv7j86wrCXMqbkCqaJIg+cqFhFNPKTEAiqW61Cs2FiOS3NYR9cUmiM6A80RSlWLkWSJtrjBmvY47fEKubLJQFPIC3bEw4zjwObeBEFNYW1HnN6mENPZMk+cStIcNbhuWfM5/Zoq1hhLlWiNGXTEz/9ws/5PfoRsnD+YupLcm5cLblzeyK++asWSb1dRFP7kTav48L8/g4TEn7953ZK3AeAP3rCO+w5Pcyp56TOOSxoAdXV1sX//fr7yla+wb98+gsEgH/rQh3j3u9/9goIUSZLIZDKAEEBqamrCMAzuuusuTp48CcD27dvp7Ozk4Ycf5rbbbnspduccdDeE6G4I8cSpJE8OiZLUeFDjPTt7fcn8jd0JxlIlHjg6w4GJLCAIzNsHGpnMVmhPBHnqdBLTdlBlWdxAs1Vsx+HoVJ6pbIVj03kKNVHtMV+s0RbVyVcdSqbNw8fm+MH+KU7OFjBtkfffMdDEqvYo79jWzbOjGX5yZIZj03mCHj/CUBVm8xV+emIeVZFoDOvsW5HB9tJx2flpAO7ZO4URCvOt3WNM5qoAVGsuhZpJpiLSCaYtSvQPTOToawoynatSrFrIksRossSzY2GuHmxiJl/102gBTWFb/xnfrYMTWZ4ZSQMwk68SuExPF4r80ql2f/qHx5CMELIk8Vu3Luf4TIHT8wVGU0Wv0svlkZMZMQskSxycyBHSVVa2CDdy03YZSVX5/bv388ZNXUjAsZkCy1vCtMcDvHVDB63n4Ukcmsxy3yGRVg5oCu/Z2fuyJfLeuLKFfWMZnjqdomLaZMsmiZDGiZk8pdqZtGpYL/DsWAbLcZER57vluAw2hwloMqbtsn8iy2OnkgQ1xaumc3hGlukfz3Lz6lY+eO0APzgwRa5i8cRQkm8/O8GKtqg/a1F/YlUVmS29PxtVdQCPnphj97A4N/aMpnnNujbu2T+JoSkEdIWuxhBBTeE/9whe3US6RKZski7VGJotUKyJYEdCzEqvaI1SrAlPsxNzRbb0JDg5V2QsVWb/uOCsqYqELk+SCOskizVWtUVpjweYGUkLtXBF4uZVrXzougEUWWI6W+ZPvnOITNlEQvCCPnjtgL8P84UqX396FNN2kSR406bOc6xnXsFzQ5bgydNp9o+l2NjT+Pw/uMx45z8/hemlo9/8mZ9y7C9fv+Rt+PufHHtRwQ8scQAEEA6Huf766+nt7aVWE7nje++9F4A3v/nNz/t7SZL4xje+wZ133kk4HCadTnP33XeTz+cxTZP29jM6N/39/YyOjp53PdVqlWq16r9/IV5kz4cTs3n//1mPN7NwWvjkbIFkseaVkAs+wlNDKSIBFdtxmcqURYrMkEXFhZcKqNoOJ+cKlC0H13WxXcEBCCgyriTy/+PpEvmK4BNVTAddlZhMCwf7sVSZ4zN5kgXR72XTZjZXxXZdilWLimWjOBKOC0+eTrKxK4EsS4vMmKuWw1zhXFVW03b9C6ssu1Qtm3TRpFKzsWwXVREcjWSxRq5ioikyI8niefVDjs+c6b9yzWY8VWZt58vzpl6H5bhoCA7FI8fnyJat85a5255Ak+24DCdLTGTKvmwBQKZsMZUpE9AUXNclVazRFDEYmi+eNwA6OXtGfbli2oylSsQXmHi+3PDsmLh5l02bXNlElSUq5mJOWdl00FXBV1FlyU+3FqoWs3mbiKFi2g6lmkW5ZlO1PE6c7JKrWExlK17wKTh3qWKNZLHGMo+TNTRXPKcS8mcFx2fOHO9Usca+8azP/3FdMTsU1BWfV5cs1pjNC65Y2XQW8X5qluORmEVK2rQdTieLGKrCdLZE1bJRZBnLcSk4Froq47ouk9kyiiyI5NGAhmW7DM0VSZdqNEcMDk/myXgcERfYM5LhA9e4frr29HwR06ugcF04MVN4JQC6BFiOy1efHlvyAOjBI9N+8ANQtV2mMxXaE0tLZP/8Y8Mveh1LGgANDQ3x1re+lQMHDiBJEq7rLuIwXIyMtWVZ/MVf/AV33303N954I7t27eLNb34ze/fufUFt+dSnPsWf/dmfvdBduCg0Rww/yFBl6Zy0RHPEILRgViOoK3Q1BsmWTBRZImSovtBYNKhSqYnRpkjQGNIo1xws6jl7WZTOmw6aLBMLaMiSRMW0Pe6NTMzjAzVFdJojBmFDYb4gZkSiARUHFwkXRRbicwBdDcHzpgM0ReT/zeriYyXLon2OK/gEqteuQs1CdgTPQJElgppM0JsNu1BaqzliMOJV8dTb/bOE/uYw4+kSIV1FYrFYpKj8ElIGiZBGS9jgxEzB11MyVMFZOcNrEafohfqqKWwwNCcq2ySJFyVItxRojwU5NVfwhA5lAprii+7VUdcEUmXBXQERWNZ5cPXPNEVGkz2zTFnMrOmq4M+1xQJEAyr5ikVIVwh4fDT42RtPC9Ec0X0Cqq7K9DQEOTx55uGtKWIQ91JTNcshpCtEAyqVmnc9cEQQLnkctHhQxXZEX0qSRFNYp1C1iQc1koUaigyyJKHICromUzJtYgHVW++Z61oipBHxzGQ7EwF0RfYLL9rjgUXX+bPHckv0Z/d4XElIksS1g0vvR7elO7HouiZLLHnwA7CxJ8FDx18cEXpJA6Df/u3fZmBggPvvv5+BgQGeeuopUqkUv/d7v8ff/M3fXNQ69u7dy+TkJDfeeCMgUl3d3d3s378fVVWZnp72Z4GGh4fp7T0/SexjH/sYH/3oR/33uVyOnp6eF7Q/tuMym6+gSBLTuQqZUs1zINaZL4gno+6GICdn87RHA0iyhO24hA2Z65Y3E9QEsfW65c10NQQ4NVdkMlPhzi3dDM0VcFyX92zv4dhMgYNTWZY3R9g20Mh9h6Y5PJUjEdTYOdDAXF485S1rDbOmI47juDw7miZZqtEU0tk+0MT6rhhtsQCvWddO2FA4NJGjIayztU/o4ewZSdORCJEp1ehMBPmF7b2MpUsMzReRwiJFsKUvgSkH+J9v38iffvcwc4UqsgwNQY3uhhB9zWGOT+exXZdb17SBK2bDRGWTzabuGDsGmylUTeJeoDaXr9ISNZjNV5AlQfK9dlkTNdvh5Eye1phBYIHqcalmMe+lz5oihn/RrR8PUYYLnfEA2YpFQ0hfRNB7emie0XSJbT0J4uHAZdMCumNzJ6dyLitawrxjazepkkkipNMUMdg9nMS0HMKGSnsiiGU7xIIab97Uyc7BBsqWxVNDaQKazO/ctpKtfcJxfE1njYaQTmtUBK27hpOUqjbXL2/CdiWOTGWJ6ApNYR1ZluiMB6hZDmOpEoYm0xoVOi5i0sklpKtXPD32e69eyRceHyZZqBHSZbJli57GAIcnciRLJi1hnQ3dcSxHENUdb+ZCQhDs13VFOTyZZzZfZXlrhGhA5dhMgUyxhqHJrOmI8aZNXTSEdK5f3swDR2ZZ1hLh6oEmZvMVBprDdCaCjKdLuK5LLKhj1SssTZu5fAUZCRfoawph2i7NEWORXEK2bFKqWbRGA0teMn/bmjbutadwXMR1IxHEccWsiq7KzBfKfG9vgQ1dglfYFjXIV0xOJ0u0RHWOTOUpm2IWbW1nnOuWN1Gq2pyeL7Iz1Mia9hij6RKSC2s7Kgwni0QCKlcPNDGSLFGzHPqaQ6ztiFExHR45MUdDSOPdO86k+Vd3xPiVmwZ54OgsiZDGa9e1C3FF7zwcaA5z+9o2huaLtEUNrvJSkHP56gX3+xUshuPCqwYaePNlIAK/UCSiAX73tuX8/QOnkCT4w9euXvI2APz7h3ey4y/uY7ZgXnI115IEQK7rMjY2xuOPP86DDz5Ic3MzsiyjKArXX389n/rUp/it3/otnn322eddV09PD1NTUxw5coQ1a9Zw8uRJTp06xapVq3jHO97BP//zP/Onf/qn7Nq1i4mJCW666abzrscwDAzj0om1lu1w954JhuYLPDmUZCpToWza4ELIULzqHomqaaOpMmFdpTVmUK7ZSJKELEk0hDQGWyL85MgM0YDGTLbM0Zk8c/kqjusS1FWGk0UGWyL81Z0b6UwE+fKTw0xlKzSEdFoiOqfmSoJvJMGpuSLpkklnPEg8pNMcDSBJ0NsUYnlrFBCzTbevbef2tYstMc4nfhgPxVnfFWd8XDzJXbusmVgsxv/+8TFs1xWzFN6sT7ZiMZOtct0KEdg1hFSeOJVkMlNhIlOmKxEkXbJwXZcfHZwhWajyWXmIW1a2Yugy3sMiOwca6W4I8eiJeR46NosEfH/fNH/2lnXoqszXd42xZzSN47hs6W3gHdu66W4IYdkO//TwKR47OY/lOIQ0lU09CcKGytu3dtMcMfjzew7xzWeEFk1AV3j3th5es6HjHGf4S8EdV3UvUmRtjhi87+p+jk7n6W0Mc2K2QK5iUZot0BEPMJOr8rlHhvi3n0rMF6p+efjjp+b54HUDbPCc1U/NFfj+/in+5r5jnJotENJVVrVFaYkZHJrMMZEu0xDSkGXB3SrXxM1tQ3eckK7gunBkKkcsqNHbGOY169tYfR6n86VCU8Tg7Vt7+Ph3DnJiNu+L34UNDUUR/B7bhV+6YYCtfWJqv2Y5fP6x0zx8fJYfHZ6hXLOIeAH0u7b3UKo5OI7gq9kODCeL7B1NC+HF+SKaIoj6EW/m4q7d49iOsNVo9ALk+UKVEzN5IVFh2sQDGmFD5ZbVLXQmQrxrew8BTeHodI4fHZzBccXDzZ1XdS9ZEGQ7Lt8/MMVEuowsSRQqomR/fVecmm3zJ98+zLGZHLbrEjFUtvUlKNVcRpJFijWLQuWMdlepZhJJqQxmwswXa4Q0BUOVeezUPKOpEq4Ly1rCKLJEumjyyIk5fve2lTwxlKRYtTk0mWN1R5TGsI7rwu6RNG/YcGam54YVLWzuSfCNXWP84MA0AU3h7Vu7aYkafpvXL0jVPnx8jj0e76/HKC9Jf/6s46FTKR7aP8OrNrYt+bYfH0r5lYePnpznwzcMLnkbSjWLa5e3MpUt41YDjDz/T87BkgVAy5cvJxAIEI2KG3FzczOTk5OsWrWKvr4+jh07dlHramtr43Of+xzvfOc7kWUZx3H4zGc+Q29vL3/913/N+973PlasWIGu63z5y19+ySrAxtNlJjJl5vNVX7fGdYUIoO26vqy/LEHNdqlaDsWaJabxJcGHkT2xxFLNZnNPgqPTecYzZTRZEk+ZVYuGoMZIssSzoxlaowY/8XQfXNdl75ggKdZvnklTEKQbQwb7xjN+pcuu0yn/KevFIls2uf/wrK9FZHo6RLpqMZoqCpuPrjiPnEiSKlYFD8Z2SBarhA2VLz85gq7IvibLaKbIbK52pq3DaaZzFY5O5bC8qGi2UOG+w9P0NIaZypZ9zYnJTJlnRzN0N4QYT5fZN5bBdaFYsUnma/Q3h5EliQMeMfYHB6c9/SSXUtXi6HSexohxWQKg82HPSJrRZImZXMXnR1mOy1i6TGvUoFizMS2LiiVSEo7j8sSpFJZl+bpYz4ykvZmtIrYjeFYHJrK05XRSZQvbcZgv1DBUGdOysV0xyziZLjNfrLGqPUquYpGvCrXp3cPpKxoAAfz48DSz+YrQ+rEdJjNCE0iVJQxN5tRckadPp/0AaCRZ5NBk1tducl0X03I4NJXj3kPTtEQMX+OpuyHIT0/MIyH0mASPyiIjmfQoIaG27qXdAprCyUKepojBaEqQhSseSThfFdpZI6kyAU1USW7ojrN7OI3jkeLG02UmM2V6Gpem7HsyU2bCs71xXJfdIylWtYvr6b0HphnPlLAdwScr1Wz2T+QxVImKZS8KfgCqptf+bJnpbIV4UEdTQ+weTtMY1lFkicdOJokHNYK6QrFq85WnRn3JilLN5t4D0/77EzMF0svMRSnYI1MioATBT9s/nhEzw2fBtB0/+AE4MJ49Z5lXcH585Ou7ObFxaSvixlIFDk2cOUbPjKQpVCwilyivcql44lSSqaw4H6zzeTJdBJakxbIss2LFClRVZd++fQwMDLBz504+/elPo+s6n/vc5xgcvPgI8t3vfjfvfve7z/m8ra2N++6773I2/YKoT+dqiuzr3NicMWmUJfGZRF3/RUJVBNlQUSQUR/CfDFX2qiEkNE8jpT5DJHkvTZE8DRSJoKb4s0iqIqErEhk8oqiEz6lQFck3ObycGg2GKhPQFfGkJ0lIuJ66sadh5D0VBD2tH9VrhOLxNqIBjapp+xywoKZiqJbf1oAmL+LAgOi7WFAjpCs+NwpEdUqdTxRc8J3sacnUt13f/4CmLBI/DBvKZaswOx8SHvfr7HJ72asEk+pjpD6BIIGmyotEQev7p8oSto1flaMoCqosAkGxLrEd13Z9LoymyOjetjVZcDzq67uSiHkmvvXdFsfJRfK6qT7e6who4tjWf+MieRpXEhFD9fVrXM9TTFdlL6CSqSHsVCSPo6Z650Z967qq+HwkGcmbwXC9c1fyn3KDuvd3Qf9J3vm2VDh7WwvbEg1oLJSdkhD9o8oyEuJ8W1TNAMiI8aB6xrOqLK419fGoq0JrrI7EWenThelnRZYWnbNntw8ufB1SJBH4Vj1mra69/KUIXi6IGkt/PkcNFUWWsT1VUlWWWOLYBxC6dC8WS9bsv/qrv+JjH/sYw8PDAHziE5/gjW98IzfccANNTU184xvfWKqmXBa0xQLcuLKFvaNpXODwVJZsySSoyRiq6gcnOe8JqDMRpD0eIO/NFAU1laAms7w1iqHKZMomd2zu5NBkjr1jGboTARrCBooisWOgkW19CQB+9aZB/u2npzFtlzs2dzGbr3DvwSmqlsvK1gibexPoisxHrh/0KzpuW9sKiLTdhbRvnuu7hQhoCr9z23I+/cNjTGUrHiFSCB9u7W2kPW5Qsx1+6YZBHjgyw6GJLDMFldaowcbuBG/Z2M5nfzqM4Qmsre+Kc8dm4SujSBK3rWujMayTLta4e88ENdvi+uUtvGVzF64LqULNV6LeOdDIdcub/ePxnh29fGPXKM0RjeWtMUKaTE9TmK19Yvbrz9+yjv/n2wfJl016G0Nct6KF287zRHqpsGwH2bt7yLLEirYob93SxY8OTmHZDhXLJmJo3LSimZPzRdoRhNzT80Xhb6ar/MbNyyiWqxi6uNls70tQtRxuW9PK7uE0miLzjm3dhHSVHx2aYjwtvMLiQY2woVGqWhiqzIbuOMtaIgwnSyiKREBV6EoE/f292ON9ueE4Dq9Z287wfInHTs5TqFqsbo8QDmiMJktULYebVrbw6rVtuK5QO+9uCHLHli7+c88Eiiwxl68QCWjcubmTW9e28cjxObb3N2LaDm3xANcvbyZfMSnVTPaMZokHNDriAYpVi6awRmMkQKZUo2a79DWFkCVY3hrm2HSBqWyZUs0mEdLoaxRcl9XtUT+FfNuaNn50aJpC1WJLb8JP6SwFWqIGr1rVwjMjaWJBjVtXnxm7793Zy0S6xI8Pz1C1HPoag7xlUycT2Qr7xrO0Rm0xU10T5qcd8QA3Lm+mtzHEirYINdNBkiR+/ebl3Hd4Btd1+cWdvRyazDE0V2B5a4RfvXEZTw2nGE0W6UoE2dyX4KGj81Qtm2uWNRFeEBBZtsPajiiz+QrDyRLtsQDb+s5frSTLEm/c0MlDx8Xs9rXt7fzRS9qTPz/Y8yevXfJtJsIB/surBvn8T4eRJPiNm5dfESeHrX0NvH5DBz85Mk3XJYpvLlmr3//+91Mqlfi93/s9PvaxjxEMCs5JPB7Htm1uueWWpWrKZcPWvgYCmgheEhmddNFkJF3BdUS5qe2cYcofmsrTFQ9wx5YumiIGPzgwxXyhyr7xLGs6YrTHAly7rJmgrjKeFi7rN69uob85wteeHuXeA1NilkUSKrmdiSAT2RIRQ+NfP7hDkCEdh//5o+PsGU0TMZL83mtWsrYjju24fHffJKdmC8SDQniwHj3P5at8Z69wZl/TEeU169ovqC7sui4/ODDF/UdmmM5WyVeEvoihKvQ3hTg2k+Oe/QXPsmAI23GJhzSWtYQplC2++cwYPzgwxYauOH//7i1+G/73fcf4z2cnkCSwXJebV7dy955xnh3LgAu5ssWHrutnPFPmO/smKFQstg008oaNnYtmhB48NsuBiRwuLmOpCgFNpj0eZMdAA82RANevaOHh/37zSzIW7n5mnONpm9l8hTUdMfqaQkykK+Qrpkh1OS4gnrCR4D07eqlZNv/00CnSZZNYQFTqffLeY3zie0eIGCqW4wASiiw0ppoiBgPNIY5M5bEdh+6GMDsHmoiHNKazVXYPC7NP23E5NJnj9RvauXZ5Cx+4tt9vZ7Fq8eUnR5jLV+lqCHLH5q7L6of2XNg/nuHvfnKcUs1mR38jy9siPH4qyeOnUrREdUxbVCI+dTrJwYks8aBGPKRTtWxmcxUKVZstvQkGWsJ8d+8kn3v0NP/w0Cm/XwZbIjw7muGnJ+Y4OJGjWLVpjurs6G/k0RNznPAkA6KGSkcigGW75Mo1miMB4iGNd23v4fa1bf74f+JUUrRlMkdfU5g+T2jyndtfWLHE5UK2ZLJvLEO+YtESNRalHPZPZIkGNBrCOulSjbF0hW/umaAlqlOoWkxmy5iWK2ZqNIW5fI2v7R5HU2QawyqZkgVIRHSFlriBjMShyRySJAKvh4/Pset0CtWb2Q1qCjXb5d07ehZdL1zX5SdHZjk4kSViqLxlc+d5015no7cpxPuv6QdgfHz8cnfdK7jMePDoLPNFUen8wNFZ3usdu6VEpVLhr+49Qtl0cKulS1rHkgVAf/d3f7dUm1oyOI7LA0dmGUuVGEkWBdfDdnEusPx0rsLDx+cI6oogbrmC3yCe8Nu4e884oynxFAzww4MzNEfSTGcrpIsmmbJJWFdIhHQm0mVWd8ToSsg8dGyWX9zZx5NDKfaMilx6oWrx5SdG+OSdGzkxm+eUd/HPlk2eOJXkdRs6AHjs5Lyfpz8ylWdVe+yCZqND80UOjOfYP5ElW675+jWVms3wfJGRZJGgppKrCJ+hoCYzm7MpVEWKq+xpAg3NF/nm7jE+cuMy0oUadz87ITytXPjSE8OMpYocnsphe3ndyUyFT//wKI6L39Zdp1PsHk5xzTIxA/T06SQPHZvDchwKVVEptqItykyuwl27x/mvr1r+oo/3c2EiU2ZovuYf093Dabb3N7BvLM2hyRyWxz2az7vsH8/SEQ9y97Mi8DQth7lalYVp7GzlTFpQQqg9RwMaubKJ4dkQGKog2+8eSdPfFObZMUEOd1wYSRUZSZZwmWd1R5SYV7K8eyTtV9tMpMscmMj6M2QvNb70xAhFTz7hR4emSRZqOAi9n6G5ErGgBrgkC1V6G0OMZwRXKlcWhrBtsQD7xjI8cSpJsWqRq5j+/uYrJraDr16eLZtIEsznq/zns2Nky5bfv9mKRS1ZojGss2c0Q2ciyM6BRg5N5ljTEaOnUVRD1sVMq6bDA0dn+dB1AxfYs6XBE0NJX99naK7I0ekc6zrjzOQqPDuaYf9Ehvl8hZJ5xgxVcKNcKp7QpO24/vUFBMF8KltDk0V5fM2yMR2bsKHx5FBSpI3LJtmK8BarB18NIZ1Hjs+xvivO8tYzGj7j6TIHPX5IoWrxyIl53r71yriF/9+AtX/8Aw7/+dKKEA7PFdg9kvF9CB8+MU+tVkPXl1bO4CNf3kvZS5teqiPikgVAH/jAB5ZqU0sKd8Hr4pZ1z07F+wPp7HW4nAmmzv3uzIf19Tlnrdg+6/s6Ft5o3bPWfPY6zrPVcww43bP+nv1dXXht4Wf1NpzPVvXcJgjhwLMNyBYGmuf06cLlLpEg94Kx4DhceFyc+eRSjEzP28fPdQBY3DfnHLslNFM979i6UJu9fxx38Ri9UJ+Km/u5F0P37A/8L84/huttfK5z5srh7GN3/r+Xayv+ep+zHedvUx3PfT15BS8WLxcz5JoJSxz/XJbr+tKTABBTV7lcbtHrZxGyLCTguxuC9DSEaIkaGJqMobCIkFhHW9Tg+uXNvGZdG22xAJoi0dMQYlN3A7IkccfmTm5d0yoIiYrEbWvbuHNLF61RQ3ASmoK0xQM0R3S29CZoi4nt3bSyBYBrlzWxoVuUloZ0hfdd3QfAyrYo/c0iRxoNqFyzrMlv03XLmgl7RLoVbREGms4/+wMw0BRhTWeMjV0JYgENXZVQJEFc7mkKsa2/EUMTYoyJoIamCHPK9Z0x+hvDhAxVlGM3hHjbVqFf0RQxeNOmTp/4/a4dPbz3mn5WtkVRvJRfayzAf3vtSt69o9cnx27pTbB9Aadge38D1y5rEoTpgMpAcxhDlWmO6Lxj20ufsmiPB+hvDhMyFPqawtyxpRNFltjQlWB1R8wjJSs0RQw2dMXpTAT55RsGCAdUVFmiKaLTGhV9KkuC3Fg3nQzoCt2NQToSQTZ0xVjWEqE7IXyw2mMBblrVQthQ2did8Em9XYkQPY1Bdg42LtL+2dbf6AsBdsQD/nhZCrz36j6fyHvL6jauWdYkSPWqTF9TiGhAJRHSWdkWoSlisKo9yvKWCGs64gy2RJAl0Z/v2CbKqaMBjZBn0rmuI8bq9hjdDUHWd8VIhDQvvaPzhk1dDDSHveIEiOgKvU1BDFVhY3eC1R1RNEVmdXuUXq+iqyGss92zZ9FVmZtXtSxZP10IOweavFkyYb682qsAa48H2NgdZ31XjMawQdRQ0FSZtliAtZ1RWmMBDO0MyTlqKGhCwQJNkWiNCgkCQ5WJBzW64iFiAY0dAw2s74qzuSdO1NBoiRjC/DQcIKQrXLu8mcHmxQrOPY1B1nSckdy4YcXSC/X934SHf2v7km+zvyXCpu64X6RzzbJGIldAgPVf37+FgJe+v1Ta/JLNABWLRf7gD/6Au+66i2Qyec73F6MC/XLC8Zk8n/z+YfaMZpCBgZYQLRGdRFChMWQQDWq0xAzWdkQ5OZvnxGSB+bLJockcH752gK/+8k6Oz+TZM5bh2dE0Dx7L8NjJOd62tYt/fM9WNFVivlDjkRNz6KpMqlClYtl0x0Ns6IqzoTtBLCDKyv/n1DHeua2b65Y3c/3yZlrCOt1NZy6Qiizx1i3dVEwbQ5UX5exbYwF++fpBTMfxnbkvBFmWaInopEo1OhIBOhyNA1NFKqaDoUhs72/EdR2CqsK1y5qJhzSeHEozkS7R2xTkIzcNsrO/gXh4MXH0D167mv9y0yAKEAkKLZv37Ojh4XiAgK5wx5Yu7js0yxOnktiOw9UDjWztb/RVq0XbZN64qZNsuYbruqzpjKNIEv1NIZq8k3M8VeIfHjpJvmJx+9pW3rL53Kn5ew9O8f39U4R0hV+7aRkDFynR/45tPejBMIokUiwPH5tDBgKqTGciQNmyMCSJle0RxtJldj9yioAqc+2yRjJFE1mWWN0W5eh0nkMTaeJhg7XtUTRV5qFj8yTzVVYtj/GXb93oO3jbrsvTp1MMzRUZbAnzX24apGo6HJvJc9euUZ44nSJZNMmVTW5d04amyEQMlfdd3Ue6WOPRk/N8Y9cY67viNIV1Hj0xjyJL3LK6lVOzBU7MFmiJGty6pvV5x8bFYEtvA//15mXsGxUeUulSDV2RcLxTvyWiEQmoWBaoMixrjfAL23ppiuiYtsMDx2aZzlYoVi3AJaBJrGqN8sbNXbxlswioDVX2OGguli0qy6IBjWLF5FvPjHF6vshwsogqyTTHDVa1xdjUk2BdZ4xUscbXd43huC7XLWumbNo4rsvTp+d5+NgssaBKqiDI02s7Y/yP16+5LJUoF4tC1cJ2bEZTJTRFcHTG0yWeOp0iVawR1BR++YYBNEXmiaF5SjWH1qjB4ckslapJzXLY0JVg57ImJrNlfnp8FkmSCGgaXXFIl2tEdJ1tAw1c1SuEI3MVk1y5RiyoUjYtErpOT2OIX79lGV2Jcx+WJEnites7uHl1K5osXxFz2Qs5p8PPl4mqBLS2tl6Rbb9pYwfj8zkkSeJNGzqvSBsCgQCr26PsHc9yqYWDSxYA/f7v/z4PPvgg//RP/8T73vc+/uEf/oGJiQk++9nP8ld/9VdL1YzLgkLV4nMPn+Kp0ymRT3dh/3iOoK6gyhLDyQo9jcII9OhUgaihcmBaKDunSyZfemqEpqjBI8fnGUuXeOJUkqrpEAmofGPXOJ2JENcta+ae/ZNMpss8dHyOfNn0RMly2Ij0Vp2jAPDZR4bQVZlnRzMAHJ8u0BROc/XgmdmeC5XsyrKEIT//Da5QtfiHB08yPF/CdmymcoIE5wL7J/LCm8l0aQjpfHPPBJ3xAGPpMvmKSapUA0mipzF0TgAEEA+euZE8emKOR08mmfXsRL78xAgTmQo12/GqexzwVKPXdgpNm9lchS8+PkyyWKNQtTg6XeCmVa2UTYeGcJqdg0185sGTnJ4XthFff3qMdZ1xv7oHYCJT4ouPj/jT9n//wEn+97s2P2+/1FHv34ePTTGaKrFnJM18ocp8oUrFctAVidPJMrYrUnp1nkZQU2iNBtg/nqVcEzfdVLlEsmBSsWzKnvHt/Udm+M7ecd6xTaibn57J+8aYqWKNp4fT3LSyhS89OcJIskiqWGM6UyGsq0QDml8xJ0kST3qBEwhCo2mfCYA//9PTGN6+pIo1wobqzzK+GExmyjx+Molp2Xxn3ySlmtCncVyXTNVmLC3Mg8umQ1vUIFexSAQ13ndNP8cnCxydylO1bO7xfmvZDrlyjoaIQVPY4La1bYuOw0I8PpQiVbLYNZIhU6qhyBJHZwsYikymZNKdCHLPvkmfo/TZR07RFNZ5+Pgc01mhpl30tKvCukq2bPJvj53mo7evetH9cjGwbIfv7pvkWS94nMtXODVXRJMlTs4VmMlVaQxrpIo1f/+mshX2Wg65iolpC17QsxNZhlNlLMclWxZaR7hVJEnCcV3iQZcfHpxhPC3EVk/M5pnOCh2rUtUioFeoWA5ffHyEj71+7QXbezkC5lfw3HCB2/7mAX7y35a2gKhQsfh/HziJbYuo469/dJR37Ti/48JLif957xH2T2SF5t4lrmPJUmD33HMP//iP/8jb3vY2VFXlhhtu4I/+6I/45Cc/yVe+8pWlasZlQbkmnKwXcnccV+S/HVeQXU1bPIGathBAdF3XX75s2qSLNXFRqQlyZp3PUrUcilUL03Gomg4V08ZdwG1wcKmaNo7jkK+eOew1SwjiLUSpdqnD4kL7bXnGlWczh7zteTcP23GxbIdS1fKJzLan1FusPn+bSjXbN3gEyNcsbNf1c75VU2ynuGD/ijXb9x5yPLPR+jpKnnBinUAN4nili2c0gUBU2SzkLBQuoq3nQ/14m7aD6QibBbySbst2/LHigiDNu2I2x/QsGcD7znEwrcW8lon0Gffjs9tX8t4XFvS74wpj2rP7feFvbcf1b/wA2YrJQm7HxRyzi0F9PFre+BCifa5v1eG6wlQXV4iJui6+jET9WFue6W59LLgulGvOorFwPtT3oU4AtmxxPpY8EmW+YlGunRlz5ZqNbbvULM881Duv6+eg47pkSuY523mpYHnjuW4iatriOlAyba8vhCBp2bSpePvouC412/baL9ZjO2A6jn+u1MdhfdzXg/OKafvjsd7fLuA44vzKlC/vteUVXBqmc0tvH5IuVbDtM+eKZbu+KvlSYiT94hXDlywASqVSvthhLBYjlUoBcP311/PII48sVTMuC5ojOjevbiUa0JARKaawofimi/GgRjyo0RYTU3Qr26JC3E9TUGWZTd1xrupLsKItQk9DSKitakJccHV7lI3dCY+fEKctFqApYqCrYnYpoqssa40QCWi8du2Z8tLNPfFFfB5Dk1nfeXn5Hc0Rg219DZ6Sroy+YHpbVyQ29zagKjIhQ2GwOczGngQNIaEqGw/qDDaHfeXa58LmngRdiSCyJMTUdvQ10NMQJGyoKLLEMs8DauG6uhuCrO+MIwFhQ6WzIUg8qGFoMuu6xCzRq9e2+SJvvY0hNnQvVkRe2RplWYuY1pck/BmFF4qrehuQZYn2eIDOeJDGsI6hKQQ1hY5EkFhA6ERpiuzZQIjv+prCNEV0kZZQFdpiBivaor78QSyo8Z4FT1or26I+J0RXZTZ6ita3rG4lqIu+ao4YtHgaTIvbmPAtHOpVUHXcurqNmDcjpykSGy8TT6i3MUxL1CCoq/Q2hokFVAyPsxRUFcKGSktUJ2yoRA2NhrDGDd6s1Zr2mH+ODTSFCekKsiwTDagMtoSeV817c08CVZYYbA6jKTINIY1ESKM1YtAWC9DbFGJL75l1XL+8mVhIZ6A5RNA7r5tCOgFNQVdkEkGd161vv/AGLzMCmsL6rjjtcaG83BEPcFVfA/1NYSIBlbAurD5WtEbY2B1HVYQJcneD6CshaijTGtXpbwrT3xTyjZQNVaHR40wZikxXIsiq9qhf7dURC4jlNJlYUEVX5SXd91dwYfzDL25Z8m32NEZYuUBNfkN3fMlVoAH+2+0rfaHXC0m3PB+WrNWDg4OcPn2a3t5eVq9ezV133cWOHTu45557SCQSS9WMS0amWOF379rPfKHKa9e1s6w1wnt39LJ/IstktkxTSMNyoGoKu4ua62I5NpZlE9RVPvMLm/iPZyc5MVegZjoMJ0tUasKEsCWq09sYpCmk4eLyjadHyFYsKqbNbL5KRzzAq1a1srI1wobuOCfnipSqNqbjcNvqVgZbI9y8qgVZlnnf1f3MF6o0hvVFwmSXA5Ik8fuvXc26zijf2D1OSFMYS4s0yg2DjbTFg4Q0FVmR2Nwd5+Rcga4Gg+39CXobQ+iqwoNHZ1EVmeWtEVa2iQAmU6rx9OkUsiSxc7CRzkSQhpBGSJdpjwW42uMT/eezE7RFdTb1JHj12ja/tBuEIveta1rZM5pGdUUKZc9ImnVdMVqj4qZxzbImvn9gknTJ5LY1rQS0xf2jqjKfePN69o5nSAQ1lrc9f7C2EOlSja8+NUq+YrKuM0Zr1ECTJVZ2xPjB/kkePDqDIkus74wykixRsRyiAZWGkEamZNEZM1jdHmG+UCVdMDF0mY5Y0HP3lvjNVy2jWLP463uPoikSv3h1HzsHGnl2NE1vU4jWiM7jJ+eJBzRuWNHEdLbCoYksPzk8Q0dCaP7Ug57lrVE+cE2AXMUUAp1lk3zVQlckrl7WxPUrmpnLV0mEtEWu3y8GjmcRM5sv85p1bcwVahyfyqGpMr1NIVKFKgcmcrTHDK7qT/CBawaIBTR+dGia+XyFyXQZF/iLO9eTLZrMeOamuiJzZCrHfYdm0FWJYtUmUzZJFaqUajYdiQDdiRB9TSFeva6dqmXx1Ok0w/N5Yfwpwbd2j3M6WaRUNSlWbbLlKvGAzjXLmrh1dQs/OjxDsWqzrbeBRERH99SjXde95IvvC8Xta9tY3xUjWzKJBFQm0iW+8MQIhirzCzu6ODxZ4MBEhq54kHWdUSYzZfJli9ZIgpAhU6kJKfHZfIV0scbylhBrOmLUbIdUsUbZtKhZsKwpwqPHZ8mWLXYMNPIHr11NvmISUGVqjkNHLETPeYTnRpMlDk1miQU1tvc18NRwiu/tm8JxHV6zvoNbV7cuWV8tFa403+imVVcmEH3D+nb2j6aRJHjjFQqG+1sifOjafr6xe5SmeIjhS1jHkgVAH/rQh9i3bx833XQTf/iHf8ib3vQmPvOZz2CaJv/7f//vpWrGJeNXv7yHw5M5XAQ35JbVLUznqoynS5RrNsPJku9nVLNdNEV4MoV0lRWtYQ5P5kiXauQrJqPJErtH06iyxKm5Io7rehUZ4omsXLNRZKhaQrMjEdKYylZY1R4jU7Y4MVPg2HSOdMlkbWcM03U9EUVRefFSehNVLJu7nhnnwHiWXMXyEyX3n0jSOJEnHtSoWg7PjqYpVi3ChsbhyTzLWiKEdJXxTIkNXXGOz+QJakKd+D/2TJAri3TCVK5CsWLxo0MzJItVjk0XmcqUOTiVp2YJ/7FibQzLdvnQdf2+mnGmVOPj3z0kBPMqFk/WUjRHDEZSRaKGxm/dtoL//q19nPT0kD5171FWtcfoP0vzSFVltvWfX7H2+fD//uQ4R6byuK7Ljw/PcOPKFmIBjWMzBb7rCTjWbIfjswU/wzSRqfj2DEemhQVExaynaRwkKUs0oNIaC/DV3eNMZyv+90emcqzriuO6MF+ocXRKmIsensqRKVU5Nl2gYtroqsyn7z1Ka9TghhVnuDzxkEY8pGE7rq9JBPCdvRP84s6+yz6OvrV7nO8dmMR2XB47kcTQZEo1m4CqcHAyR6Eqgn5JkkgVa7RFgx7vrcYPDk7jui5tsQCfuf8kf/uuzUiSRMW0+fxjwwzPFzg1VyRbFnyXUs2mVLN8O5a2WIBrljWRq1jEgxpHp3I8OZTEdmH3cJqAJixpTMuhajmEDAVZktjW18CRqTyZUhVVkRlJldje30h/U5hHT8wDXPJ4uRR0xIN0xIMMzxf41L1HfePkvWNZHNfBsl2OTOV9GwrTS1WEdAUXiWLVomzaSMB0rsbpZImGkMF8oYJpu+iqzJ6RNLYrZkF/dGiGeFDnL9664TnblS7W+M7eCd+Tae9omieHkgzNFwGJofkiMUNlxwJO4it48XjL3z/Md37zpiXdZiZf4c+/f8SXmPh/vnOId1/dv6RtALj/8DRfeXoU13WZyFxaOmzJAqDf/d3f9f9/2223cfToUZ555hmWL1/Oxo0bl6oZl4zJzBnuheO6vlqz4MTgm2yadU6HI3gmpu1gOTBXKPs2Ca4L6ZKJrgg/rDN8DYegrlC1HAxVpmY7/velms1UpuT77RQ9XkupZlM1HbJl87J6fl0Ic/kqmZKJ5SxW8HFcKJsWhuedlCtbKLLYf1mSyHgBjusKnkJIV5kvVGmK6H7wA5AsVJnJVc5wFHCZyFSwPPdusW4hylas2cSDYnupYs1fj2k7uK7gTSiyxMm5PIBvmFlf5sRM/pwA6FJRsxxmvHy87QoORaEiFJ5PzubFuPD233ZF7rluz2Q7LrIk4Uqu4J54qtGCIyR4GKblkCrUSBfPHOeprFCdrj9Vj6ZKNEcMilWLqnmGA+W6UDJtpi9wkSib9iJ+1Fy++pLMbIykiv5xqdmCzyLLCI5SzVrAS3Gpmg7DySItEYOyaVOz6hwll7lClZpH2s5VTCqmTbFmYzkOVcvGtF0/WEYWfJma7VCs2cwXqtQswcEDfC5DwXbQVZmKJTgzVcshoMqki+KhpS45UrMc0sUa/Z5cRF1Ucqkxman4vAvB/bFQJMnn81QsB0UWD2GyLFE2He/hTJwbkuQt5/EM60Kdli3OMzECxTJ1Be3nQtLjNNYxli6T9XlC4niOpkqvBECXGYennv/YXG48O54559o/nanQnggsaTueGU2/aB2kK6YD1NfXx5133vkzEfwAPg+iPlOzuj1KJKASDwhjR12VUWUZwzMj1RTxCmgKqgybuhO+ho0iS/Q1hWiPBQSXRpHRVcXPo0YCKroiE6qvS5ZoDOusahcaMACNYR1ZlogHNBpC2iIX5pcSXfEgvY0hoUK84HPNKzeOGCqaItOZCCJLMkFN8DY64wESIR1VkYgaKpoi0dsY8meB6hhoDrO1t8E3oVRkmTWdUUK6iualbzoSQdpiAaILUnzt8QDdDWI9C4+BJEn+rMdCTlQ0oLG59/JxpHRVZm2HyIsrkkQipNMQEtVu1y0X3Ky6Sa6mSMiyuAnJkvhtnRMUD2qCZ6LKyJ7ZpqpIBHWF/mbB3ahjXWd8kY3FVR6HpTGsEzEEP0ZodUBjWFuUt1+IsK7QET9z8RpsibwkqYpNPQlhPOpx5lqiBrIk+Vo+IV0oWyuSRDyscVVvA52JoF/FJmY1JFa0Rvwqo4aQTmNYpyEk0lKxgEZEVwjpKoZnVBzUBb8oHtQYbImwrDVKRyLon7e6KtMaDaDKMjFvbEYMMYY6EkE64oEFZr4qfQu0sgYvUibhcmN5W4RWz4ldUSQaPH6SME+WSQQ1Ap4hriJLJEIahir7fSxJoMpivMUCqnedEuriunLmxqDKMjeufH4tn85EwOcfgpA8qPOVJEkiHtSWVHPq/xa8ddPSp59uXtO+SOdOU6QlD34AXr+hY5EV0qVgyWaAbNvmk5/8JP/8z//MzMwMx48fZ3BwkD/+4z+mv7+fX/qlX1qqplwS/uEXt/LH3z7ASLLEDcubKFsO4+kyLVGD5kiAiKGwvD3C4Ykclm2jKjLhgIqhKDRGdN60oZ0j0wWOTGUZT5cJqDKNIZ2IrlKyLPobw1y3vIn5oink/ysW6VKVfWNZTMvhw9f3s2OgGdm7eczlK5ycLYpSyDWtS+bnpKoyf/W2jfzLw6e498Ak2YpFezzIWzZ1Ua6Z3HtohkTU4HUb2jk6nSdVqHHr2ja29TeSLZvgwrGZPL2NIRIhQfi9Y4tnhipLrOmIocgS07kK3983gSRLrO9KsKU3wb8/NoyhKmzra2BlW2SRxkhIV/nzO9bz2YdPocoyy1vC7B7NsGOg0RdC/Ku3buB/fPsAs/kqv3nrMlqiwXP2r1i1ODqdJ6CJgOaFBAK/9qplNIR0JjIlXrehHdeV/H1a0Rrhk/cepVqz2NCVwPQqANtjBgFNYTIrvMtOThewXZeuRJCWiEHYUDk1V0DGpSMW5FWrWhhNlgnqCq/f0MGzo2lOzRU8LZs4J2cLpItVTs0VODlX4NB4jqCh8Lu3rWTDWUToEzN5smWTFa1R7ryqe9ExeCnwunXtlGoWp+dL7OhPMJauMJOrMNAUYn1XgkeOz/HQ8Vlihspbr+rmtrXt2I7LkakcqztiPH5yTqTJqhaf+O4h7riqi43dCW5c2czR6RwBVWKgOcZga5i5XJV9Y2lqtsvGrgTXLm9GliTmi1XCmsx7dvayY6CRVEFYkCSLVXBFhdRcTvikbR9oJBrQ6WoI8PCxOUZTJV67vo2OeIjDkzmSxSpHpnKYtkN/Y4gfHp7GBd6wvoPQZebfLcRsrsJoqsSHr+3jwePzRA2Vd2zv5uBEjmdHM5QqJiOpsngAUyUURcJ1JQxN/LVcF0USTvczuSoBVRbjKlXi1FyBzb0xbBtyFZN3bu9lW18DX35i2J9RigVVOhMhEiHNl5EI6So3rGzh289O0BkP8q7tPewcaOCh43NYtvD3W3aFgsWLwXNxeV6uiKjw6XdtvSLb/tf3beFXvvIskiTx1Y/suCJtWN+V4DduXsa/PnaatkSMI5ewjiULgP7yL/+SL3zhC3z605/mIx/5iP/5+vXr+bu/+7uXfQD09OkUjWGDuUKNH3tmf6LMWYi2BTWVYzN5ygumkw2vciuoyewfzxHSFUaTRZLFmp/2CusKuqZg2tCeCPIbt6zwt/nfv7XPL23/zt4prl4mZjL6msKcmiv4KZ179k3x3qv7liwIsmyH+47MMle0sB2HiukwnCpy78EpcmXBCzowmSMRUJFlmblilb7GMNsHGvnq06PM56tMZytkyyav39CBrspsWlDFc9fuMb721AjTuSou8L/uOw544naOx3eZzvPh6wa4wdOnKdUsfnhwmuZIgHzFZN94jv6mMHP5KkNzBQZbIvyfx04z5pVOfu6R0yxvidKwQJOoZjnctXvML2+eyVW4ZfXFV4Idm8kzV6iiqwqPHJ/n3Tt6aYoYzOWr/M19xxlPlzEth+n8DP1NQhk7VTI5PZ8hpKscmcr5ab7pbJWIoYAExarQATo4meetV3WxtiPGmzb18PTplK8F9djJeQaawyxvjfDDgwWeHEqxbyyDA/Q0BDk6nV8UAO0aTvFTj8OyeyTNe6/uW3QMXgo8eGyOibTgPN21e4JlLRExCxEyqFoOp+aL2A7kqjYHJ3NcPSjI7w0hnW/uHmPPaJqRVAnTcjE0mUdOzvNHb1jNvz02zL6xjMd3ytEdN0gWLfJV4RU2l69hOQ7T+SqlquC/3L6ujV+6fpAHjs5wz74pJjNlilWLbMWkPRpgtlDj1jXtXOX5pL1r+5nqu1LN4tBklieHUlRMm65EgLlCzU857h5O85d3rPf5aZcTs7kK39g1RtYTVV3eGsbQFGwH3rK5i6Cu8Pvf3EfNcqhYDomAStEUKfWKadMWM+hMhLiqN87Xnh7Hsm1qtsvJ2SJTuQoyLqfmiixvjXD72nYawhp/9r3D5Momp+eKJEIajgvLWyNs7E5wwwqTbf2NzOYqfPreo5RqNnvJ4LguH7pugA82v3yDnpcaFwqqLhc5umDBx+/eyyfu3HxZ1vdC8Otf348Y7i4f/PxuDvzp0rvS7xtN8/cPnsKyHdKZ4iWtY8lSYF/84hf53Oc+xy/+4i+iKGemSjdt2sTRo0eXqhmXjKE5kWvNFGukijWhkbFAWwOEyKGzQM+latkiv+6KCxdAqmT6ukGOCxXLoWra2I7DockzliDZUo3R5BmH26H5IpUFWid1ETsQBqep4mINoJcS+8ay5Mqm7700X6xxeDJHviJ4Go7HganaQjOoUnN4ZjRNvmIyv4AzURclPBt7R9Nky2cI1jXLpmq5ni+U0LAp1WyOTuf93wiFYLH9TNkTXkTwX4aTYjsHPJNGEByl4zOL8+dCRO4MH2lhH18MFi5v2oInBmKmZTZf8bRUBKcrXaxRqdlMZStUPV6JvSCd7SJ4KOXaGW0lx3U5NVtgvlAjWzYZmj/T/mLV9jlIQ/MFkoUaNdsRekw12ydn13F6QVvLNZupSyQRvhDU21s2bVLFGhVPz+n0fIHT80UydV6O4zJfqDKeEeP/dLJIqlSjWLM9A17Rh/mKycPH5plMV4SmkAum5TCVq1Komr5uT9m0OTKdJ1k4I9x5aCKHZTsMzRVJe2MlVaxhWkIjx3Zc9oykz7sf09kKycKZ9s8VaoymzpyrY6mSr190uTGcLHkihqYvrApnxt69B6axHaGjBJCrCk2qelvr7Xrg6By24/jcJmHO7C4IwMX16vGTQqS1WBWClTlPuHLWH2tiuwcnc77eFsCB8TPn2kuB/j/8/gVf/zfhrj0TS77NZ4ZTlM0zx7pYtUkVlu7+U8ePDk9jLdAjuhQs2QzQxMQEy5ef68jtOA6muXSCYpeKtliAqWyFsKFStWxBWkXclOtZkqCu4LqCeOu4+FLwigRhj98T0hWKVcv/jfD9kpElia6GMymZenl0/QLXHNEJ6GcOV2ssQL4ibiiGJi/ye3qpsaItQkBT/ItiRFfpSgQ5MSuqjmQkJBnqD8CaIjPYEiZsqIQNxQ9UWqLnKkKDmOEK6TIl7yQTpdviyiy5wu5AV2Wf8wPQFDZQZQnLcQnrqi8gCPhl8D2NQT9Q1L3S64Wo82/qN4s6x+Ji0RYL+FVmknRm/3oag0QCmk9alSSJcEDoqUQMlULVIqgpyNnFBq+qLOEiUbUcZFlweTriQjcoYqi0RgP+jUhTJBo9W4a2aIBIQOgAuS7ont7QwnReS8zwKycUWaIpcv5jcTnRFgswNFfEUAU3rE7ob40GaI2KdF+mbCIBEUOlxWtTa9QgrKsEVBlFEikcWZIIqELj6eBEltl8xd+XWFAlXxEK2i6iIrMzZpCp2P7Ma3dDEFWRaY0FCOsq5ZpN2FAo1Wx/5maw9fwE+aawQVgX3CzLdokFVL/vARpCggv30vSh6JO6xEXY4xW2ep9v7U1w/5EZr+BCzEKbXlWqaTsEPO7Umo4oU9kKkiT6IxbUyFUsJO+aVteXWt0e4/hMQahrS2I2Wywvtt/qjfFlLWH//AMx5l/B+XE5g7RN3S9Nuvq5sKEzsuhY64pMY2TpvcB29Dfx5SdHXxQReskCoLVr1/Loo4/S19e36PNvfetbbNmy9GJOLxQ3rGjGsh1iAZXRVImmsMFYukSxahEyFDpiAV6/oZP7Ds9QqIhKnYihoUgQMlS29TdQtRwUGb71zASlqk1QFyTmaFAnpMvguDx6fJYbVraSKpl8+PoBvrd/ClWW+MgNg0ykyzx9OklLRCceUFndHkWWJbb0JJakAqxQtTg5kuSJkyluWNHE3rEMFdPh9jVt3La2nZ7GIP+5d5KAKvPuHb2MJkvMFqrcsqqV163vAODtW3vYPZxCU2WuHhAVIRXTZiZXIRHSiQc1fum6AWzb5hu7J7Adh5vXtNIS0vnPfZNIwPb+Rl61qnWRUGE8pPHaDe18c9c4PY1B3rSpg2PTeZa3RVnfJciXv33rSv754VOkijXedlUXHfHFF+mgrvC2q7p4dixDUFPYOfjCypu39zeQKlZJF022DzQS0hWG5gqMp0rcvKqZvWNZ0sUaAUVidUeMlliQlqiB6z3Jr+mI8uiJeco1m46YZ1QquRybKuAgDGBvWd3G1r4GdI+3EdYVchWL9V0x4iGN03MFWiIGb1jfTm9DkOOzBQZbQrxr+2JD2BuWNxNQFbJlk7UdsZeURJ8tmWTMEq9a2UI8qDGVLXP1YBNDcwWG54vEjTgtUZ31XVESYQ3LdtjUHadqOZycyVOqWmzpEUUEqzuqTGWqhHSVt2/t5k2bO2mLGnz+sWEy5RqDzVG29MYZSRY5MJHDcV229jVy08oWchWTPSMZ2uIGd27p4tBEltaIwVs2d7BrOE1jWEeVJOaLNTZ0x/3051RWVHwGVJliVegKvWN7D5GAykyuyrXLGmlPBPjqk2O4LrxnZ+9Llo7uawrz+g0dnJ4vsKU3gaGKIG6Ll7587zX9jGfKPHRs1hM1DFCo2SQLNSIBlf6mEM0Rg0RQw3Ec9o7niAVUuuIBshWLyUyFlojBB6/tpyGi05UI0RjWeGYkw4aOGrmKRUNYZ+dAE01Rgx2egGZfU5jfuGU59+ydoj0R4FduGHhJ9v/F4udphiikwTf+yw1Lvl1d1/mbO9fxse8cRgL+31+4MvfvV61u5b07e/jG7nEaQ8GXNwfo4x//OB/4wAeYmJjAcRzuvvtujh07xhe/+EW+973vLVUzLhmn5oocnspz36EpxtNlJEk8pZZNkWuvmA5z+QobuuOcni+SL5uULaFx0hoLMNgSYUtvnU/Qx9/++BhPnEoylCyhKRUxley6fPmpUd6+tZvmqMGp2QJl02ZNR4yfHJnhC4+PkC4Jr6u+piD9TRH+4o71L3im4lLxmQeO893DGUpVGxe8SjeZbz4zzk+OzlI1bWqmjSbL7B3Lsqo9QmPEIFUymS9UaY4YNIZ1Xr3uTOVCoWrx9adHyVcsNEXiLZu7MG2HQs2hpzGIIsuENYW9E1kMVREkWMvBdt1FFQClmsWffucQw0lRat3VEOSaQaH7sqw5TGsswKHJHLLnITaWLrOl99xS79ZYgNesu7TKigeOznJkSqTlnjg1T9Izs62Ti+tlyKblcmyuRGNIZUVblFetamV9Z4wDE1k0RSbnWEzmKswdq7K5p4HmWIC1HTG6G0PctKrFr4DSFJlrl5+p0Lnv0DSff2wYx3VpiRoUqiYnZgocn8mTr9h8+u2b/GVVReaaZUtTkvy1p0fRgmHiQcHnmc5WefjYHHtG05RrNt96ZpyQodIS0ZnN1whoMg8enaMhpOPi+qmrjniQm1Y18+d3bPRnPE/OFnh2LMuG7gRNEZ0VrRGeHEoR1DXevrWHV69t465nxnn8VBJVlnjPzl56GkP8n0eH+MnhGVygKawz0BKmYjr0NYX4pRsGfYL9g8dm2TuaYS5fJVcxWdYSoTVm0N0QpFC1CRsqc4UaR6YLjGfK1CyHb++dxNDkRT5zlxOr2qMXVFQvVC2aIwY3LG/hgWOzDCVLgjSvydhZMSv9vf3T2I5DzXYJqDJjqTKHJ/O4QEiXcRH2M6k5k13DaRRZ4vUb2vncI0PMF2skSyavWt26aPzULIdTc0VaYgauCyOp8kWpvr+CS0fJhJ8eneT61UtvRvoXPzxO2dMj+5N7DnHr2qWvRjsxneffHhsRQp6XmMJfMg7QW97yFu655x5+8pOfEA6H+fjHP86RI0e45557uP3225eqGZeMvWNpHNdlLF32dExcZvNVylVBfrQdl2/vm8RxXKazFdIlwXeZL1Qxbcc3KQWo1CyeHEpRsx1qlsPIfBHbdn0vse8fmMKyHWbzVfIVi2LV4u49E5RqQijOdV3m84IH8uiJuSXrg0MTebF9REKqZrtYjkO2YjKdLZMs1vzPDk1mGU+JQVkxbY5M5c67zhMzeV+DxrRdDkxk2TeeYSYnhNkqps2ByRzj6TIFj88wNFfgxEyBXOVM6vSZEUGQBXExHporUrVE/9a5VXtGz3A6BPfj8qVeTdth/wLew6Mn5kkVa4ylij5/J1cRXmqO52uWr4gn89FkiXsPTjOXr/o+XnXfp8NTOWqWQ7JQYz5fZSx14RP9x4dnfE+n0WSJ/WNn2vPEqSS2bV/opy8p6mmn2XyFp08L0vbQXNHXiSmbNrmyCJKLVYtCRYj1TefKnvCjTdWySZdqjKXKHFvA/do3lvHXnyzUePDYrP/daKrEM6NpXx/Kclz2j2fJVUyeGkr6HLNDkzmy3lgYSZZILuAi7RvLAIIQP5evYjkOs7kqDx89c949fTrFaLLoazjN5CrsHXtpOTAXQv18Op0sejOros3Fmo1tO+wdF8UbdQ+6kqdj5iDO6VJNjM+7do356WLbcfnO3km/IMNxXe47PLNou2Ppkq+JJIQZz8+fegWXF79/96El3+b+sdQi38mpbPWKcID+7v7ji7TDLgVLFgB94AMfEAq5P/4xs7OzlEolfvrTn/LqV796qZrwohAxxBOnpshCRwN8W4F6NXYsoCFJErqnveG/JGkRJ0D39DgUb/bh7GqRsK76CrZ13aEGL0VR35bqCTEsBXejjmhQOWfGRELotpzR3RGfBzTZF0UELmjLET3LQyZiqEQMbVEKIeZ5gNX7y1CFTo6xYJmmiI7qdY4kgaZK/vv6thceA1WWfK2hywHV05upI2yo6Jrsz1JJSMjSGb4YiGMpyxKGJqQS6vsoefsgIdIugN8fz8UtiYfO8MBURVrU/0FNWVR8cCWgypJ/LAKa7GuJSJIkNHk8LpwsnRn3dX6c5I2xOm+qjrPHVcMCLo7i6WctRNgQ3KOgvvh8rPevsuA4KrLka3fV9XQUT1epYcF6Q7q6yIFetPHK9HW9b+rtrnsl1fs6oJ45R+DcG0D9+tJwVr+1nsXXS5zFOTx7XF5uG55XcH70X4Equ+6GiC/qC2LMXAkO0OBlELFdslGazWa57bbb6Ovr40Mf+hAf/OAH6exc+qm7F4pUoUZIhb7GEPO5Mjv6E5yaK+EAa9oiYkq4UKOrIcjH37iGJ4fSLG8No6sKsizhOi7tcYMd/Y3UajY2InX0a69axpeeGCVsaKxuj/Cjg9MkSybNYZ1/fu9VHJ7Oo2sKZY9z8L6re/jMA0PsG09TNh1WtUXYOdjMq1a1LllfvGFDB3lrnqH5ErIkgi/HdWmLGCxri5Ip1RhOFgkbKu+/po+wrnI6WWKgKcTqtijpYu2cC+vy1ig7B6scmcrTEQtw9WCTqCKrWUgI4cCbVjYzna1y/9EZarbLLatbuX1tm58KAljbEefD1w3wzWfGMFSZ29e0UbNdVrVFfIHA12/o4FvPjJItW9yyqu2C0f9YqkDUUEmEz6QWK6bt66CosrhJVy3Hf/KoWg63rWnlqaEUiizxls2dHJrMoUhw/5E5KqbFqvZG5nIVRtMVdFViWUuYNR1xrhlooCFsoEoSezWZ6WwZ24GWiM6rVrVSNm1aowG29J0RlyvXbAxVXqSF9MvXD/CZB06SrZjsHGhElWT+/YlhVFniD1+32t8HTZGxbGH5UCfzm3WiNdI5xoaupxgc0ORzAuCKafvBQX05Qbg980S2oi1CWTJY1RalLWbw05PzvGZdOy3RFMdmCvSHdDrjQa9ySazDdV0GmiNULYepTBnLdRhsFp53/U0hMavmOGzsjpMvm2TKNdZ1xulvDvPYyXkKVYvrljWxoi1KumgykiwSD2ls72/AUBX+683L+PfHhilULe68qhOQKFQsrl3eTMRQMW1RnXnTihaePJ3k6sFGaraDjMTWvgaaowaPnZynWLV448Z2JrNVFFmiXLO5qq+BGz3xzWLFpFizCesKIUO9KF2pC/W3aZocny3RlwhQcyWihoLlgiq5zBZMErpEzXLY1BklqClossRcocZcvortOIQDGresauX+IzNMZMQYawppzBXFLLWERHvCoLcxwiffup6JTJl9Y1mRUlvRTCyo8dMT87TEDN65rYea5fiBY1sswC2rhRdfIqRz8xJel/5vRUCV+eqvXLPk222M6Hzo2j6++OQosgS/euPgkrcB4Pdes5rHTiU5NJlDu8QHjiULgL797W8zNzfHl770Jb7whS/wJ3/yJ9x22218+MMf5o477kDTlq6K6WJQsxw+cc8h9o5lODFTWGTFIAG6ApPpEpIk0eop2n7pqRG++tQYpuWgKuIpUFhjuDSFhwhoCus6Y145r8NEukxDWKe7IcSOwSZGUyXiQY2woXHnVd3sGU3zyPE5koUaqZLFX739yqpmf/vZKYJGkF+7eRn/5cZlfO6RU3zlqVFOJYvMFqrkqxa5soWhSvzHngletbKFnJem+9efnsZyXLb3N/LR21f6N27TFoKSubKJ67oUqxaW43J6vsAzIxksx2EmXyEW0CibDmFD4drlTYvUeEHcNGq2I25epsNXnh7FdlzinqnpYEuEv/jeIb6zbwrbcfmXR4bY3t/IL90wyC2rz1ys3/25Jzg4kUWRZX7lxkH+683LefzkPE+dTjGSLBLxKn5cRHCmV8RU/5999xBpS2VNR4x3buthsCXCYEuEN23q5GOvh/lCla89NcrukTQdDSFuXNnCpu4EX3ximI9+c79fQtwQ0qhZNpoqM5Gt8OMjMxQqNiFdYe94BlxhDXJ6vkjYUHjrlm5aogbD80W+f2CKpohBY0jj3gPT7BnNUPEqFv/6h0e4ZXU7qizc3Z8YSnJgQohsZsomVdPGRaK7Icgv7OjhzZu6ABHg3L1ngplchYaQxtu2dvvmqPcdmubQZA5dlbl1dStPDiUZS5eZSJdZ1hqmWRIl0q9e104sJqpVyjVhNTGdq+Ai86aNXWztS/DMSJpnxzJYtkvG47kdmcoTD2noisyG7jiW4/DNZ8b5zt5JWqIGx2fyjKeFfo+hCeX0FW1RHBd6G0M8cmKen55Mki2bJItVYgGN8XSZ12/oYHV7jI3dcb67b5KvPDWG7lWYHZ8tcMvqVp44leSxk3NkSibNEYNNPXFS3sPOj4/MMNgc5nSyiOOl1XYMNqIpMkO5IgcmstiuS9W0+dITI8wXaiRCGr+4s5cPXDvwnATpimnzH3vGmc0JQ+O3be0mYqiMzBe54x8fo1C1cFzx9GuoMhu64jx6cp6qaZEqCqFRSYKepiDT2SoSwnqltzFIzbSZzJZIlWoosiSqWWUAMTMXC+j85VvXs3OgCcdxeep0ikypxr7xDPvG08wXarTHDPaN5/hv39xHIqTxe69exfquOK5nD5QpmVRMh1zFfGUW6CVGxXIYGxujp6fn+Re+zHjo2Jyffrr/6By/c/uqJW9DxUuV64qE7Fyacv2SWmG0tLTw0Y9+lH379vHUU0+xfPly3v/+99PZ2cnv/u7vcuLEiaVsznPioWOznJgtMJoqYS4IfsDTaLGhbAqOyXSuQqFq8dUnx7Asoa1RtRzSJdPzZRJ6L4Wqxa7hNKlClYMTZ8wfv/DEsK+Rki2bfH33GKbt8MjxOZ8X9NCxOZ/rcKVQ99h69MQ8x6ZzfGPXOJYjZmvGvIuf8GNy2Dua5glPpO/xU0mfV7FrOLVII+ToVJ4JTy8nX7F4ejjFE0NJ9oxlKVSFz9Px6TxPeesqVm2+/vTYOW0bS4nKF8eFsWyJmVwFxxV6Kf/w4EkAvrd/Csfrw5qn0/P1p0f9ddx3eJqDnlaQ7Th84YlhwRc5naJQtZjKVhieLzKcLPnLzXq8h6ms0KIZmivw0AIeSh1PDiV9mYBMyeTwZI67do/yzEiKYlVwRyzHJVmsUarZFCo2xarFaLIkdJ5KNaayFb781CinPE2qYtX2+/iRE3O+EN89+6eYK1QomyLYcByXg5N5LMvEcly+9vQoQ3NFLNvl+EyeTLHKfLFGulilVLX45u5xHE/j6eBElhlPwypdMnnG08aZzJR9blXNcvj6rjHSJZOxVIl0qcZsrsrweXSe9o0LQvHp+SLFqsV0rsJ3902xZzTjazbVRQkLVYuZXIV8xeKZkTRHpwokC1Vm81WeOJVkOFkiVzbJlk1yZZO5gtCjmslVmMyUOTKV4+BElmJVGAhPZSvULHFe5Ssm39s/5RnJVjk+ncdxXIbni3z5yRGOT+eYy1epmDapYpUHjs4ym6uSLplMZcs8O5ZmLFUSN6F0ie/snWAiU6ZcsxlJljgwnuU7eydJFWvYjvDqe/DYHIcvwIWr48BE1pc2SBVrfn//8XcOCtkJR2ggjSSFftHDx2bJlmqkSya2CzZguTCSLFP1jF1N22EsVSZTFrpJwt9MXHtG0mVKNYti1aZsWvzTQ6cAODVX8Hly09kKx6bzTGcr7B3PMp0rk6uYlGo2X31qBBB8q+MzgptVMW1fZPMVvLS48R/2L/k2T0znODkvik1cFw5OZskVlt4T7/88dppxj/dpOpemB3RFvMCmpqb48Y9/zI9//GMUReH1r389Bw4cYO3atfzt3/7tlWjSOVDkxbnyi8L5lvV5DuKvLJ39tYSMtOhAKB7/Y2GeVbrA6q8EBP9JXtA352+ZzOL2+58vmK08u38FB2Tx8menDeTzHBTBmZEWbOtMjy08lovWy+LjocuLT4f68gt/J0nScx6HOp/lbMiStGhfhQ/YuSml54LgW5293jPbXdiG861XVbUz3z/XRgDZ64sL9f3Z+3imHYv/no367+rrrR8D//fej/3+XrBPCz+RZc5ZZtEu1MfDAt5VfTnBxZLO+bz+H0WSkGRp0Tb9dUpnztlF62Rxm/2xtWAbknTu+X82zv6+/l456wt/rEuL23FB+Nef515eOc/xWfi7c4/7c3/+Cl5aXIkbuHZWtkkCjCuQwFHVFz/Glqz/TNPkP/7jP3jjG99IX18f3/zmN/md3/kdJicn+cIXvsBPfvIT7rrrLj7xiU8sVZOeEzevamFjd5z+JjHVvLCvJSCoCkJlUBdmno1hnf9606BnxClIiC1Rg1hAI6irdCdCNIR0rh5soiMRZGtfgnhQJ6Qr/NqrBlndIbRqWmMG797Ri6rI3LK6FdUjF9++rm0R3+NKwNAE1+P2tW2saIvwwWv7MVTheba8NUxTRBhSBnWV65Y3c9MqwYO4bW0rbbEAkiRx48qWRaakazpiDLaEkSQh9rhzsJHrljVz9WATiZDon03dcW5e3SLIpyGND1zTd07behpDvG5DO6oiMdASobcphCxBSyTAb9+6EoD3bO/1BQWDqkxfc4gPXjvgr+NVq1vZMdDoG0r+2k2DRAOaz3/oawqxrCXC8tYI2/qF/kldjLG3MYSuyqxsi3DrmnP5D9cua2JdR4ywodIU1tncneAXd/ZyzWAT8aCKqkjoikinxoM60YBKPKgz2BKhKaLTEjXoaQjxwev6PY8ySIQ0rlsmyuBvWd1KSFeQJYl3be/xzGI9c1FZYmd/A7IkiNEfvLafVe1RAprC+q44zbEAbdEAzVGDaGBx/27oitPTKAQj22IBtvU3AMJ89qq+BiRJnAfvu7qftliA3sYQbbEAbbEAK85TBr2pJ05XQ5DB5jCJkEZXIsjbtnZz9WATiiyxoi1Kf1OISEAjHtTo9M6tqwca2dQTpz0eoDMR5KYVLaxsi5AI6TRGhBlqVyLApp44vU1huhJBNvfE2drXQEhX2dAlthvUFW5e3UrEUHnH1m5URaItFmBTtzBqXdEW5cPXD7CuM0ZHPEDYUGmJGbxpUyediRBNEYPephBb+xtY0RYhqCksa4nwCztEeX08qNHfHGZrfwO/sL2XtlgATZFpihi8bn27b5h7IWzoSizu7z4xzj5153oSIQ1ZFuNkoClEayzAbWtbaYkGaAhpqDIoCI7h8pawT84OaDKDzSFaowa3rm6mPRYgGhRijYPNQp4gGlCJBTV+5zZhxSP4aVEawhp9jSHWdMToawqxpUdcE+uaXe+/tt8//zZ0xZEkUdhww0UYqL6CF49Tl8lW44WgvyXmH2tZgh39jRjG0hXj1PHL1/Wzoi2KJEm+wOcLxZIlaTs6OnAch3e/+908/fTTbN68+Zxlbr75ZhKJxFI16TkhyzI3r2qlMaSxqSdBvljlJ8fnyFdsdFlUfMWDGmu6Enzoun7G02VOzxd5+9YeAoqD6XpVS67D6VSFfKVGpmRxai5PoWrTGNIYbArSFhfqwbIM2/sS5Ko2//bT0xyczBHWZV6zrp23b+t5Sdy5Xyhes66dmqSzul2YhJ6czTOZLuG4sK49Qm/C4P5jSUzLYSZbIRHSyFdMehqj/M6tK5GQUM/iPwjCcBeO4y4K8H71pmV85IZBHMf1f2NZzjm/XwhZgpH5AlXLoSUSYFlziBXtcRrC4vHkmmVN/ODABFXT5nUbO/jU2zafs443berCdRxioQDXeN5rEUMjrItg4fa1bb6AouO4TE4KKfo/ffM6YrEYu0dSPHB0lt0jaV67vp1YQCNbMrnv0AyyLHHbmlamMmW+tmuUb++dIOml0FTJpT1mYDnCOXtbX4I/fuM6dFVhNFXigaMzSEAsoLNxQwOvWdeOJMFDx+c4NVugJWpw51VdPHB0lnzF4tduHOSJU0k+/9NTVEyXTNlkeL5AT2OIHxycoj0W4D07e1nfGeP+IzPULIerlzex1ju2Dx2b5aS33jdu7BAVWrJEplTjnn1Ct+mqvgZ+65YV/nEbaAn7x3Fh3yyEoSq8c1sPzlXdSJKYQh+aLzA0X2Q0WaBcc7xUq0M8ZHiO5hIjySJtsSC/e/sKBprOGOFalsOp+QLfeHqMyWyJ1e0x3rW9l3hQ85dZ2KaFY2z7QBOZkvDlu3FlK00RnR8fnuH4TJ5bV7dx/fJm/td9x5hMi3TfVb0JTM/e5tRcgfl8jYAm0RwW47whpPGObT3cvLLFH6d3XtXtb/diHmB0VebtW7vPaWtrNMgn37qRY1NpclWXqCFzcDLP8FyBaFClK26gayqrm3XuO55hJlvCUBUGGgNYrkLEUNBVhaZIgFvXthHRZY5MFyhUTHRVIaxJFGsO//NHx+hKBPnd21cylhKp3tZogA9d1097PIjjuPz748M8dmKWREgjuoDnc9vaNm5Z3fq8+7lnNM2ekTTRgMpr1rWTCC19BdEreHHY0d/IUY8GsGOg4Yq0QVVFtXS1Zp0zM37R67i8Tbow/vZv/5Z3vOMdBAIXFu1LJBKcPn16qZr0nBieL/LU6RQT6TJj6ZLvLA1QdWCuaJKp2ESCRf763qPsGGji6FSOTFnwVlTPDqNUs8hXLEqm0OGwHWERMZuroqsyLR6B2lBlaraDoSpMZso4rktjWOc/np3gqv7Gl4WT8lSmQiCs8uRQkqAm8bVdY4Ib5cKDx5NICD0RCdgzluFrT42wubeRI1N5miOGP2tyPpzvonn2TeO5gp/pbIV/ePAU2bJFzXJIlfJU7TBVO8vXnhrll24Y5BPfP0LJdAGZ7x+Y4S2bk1y9QMxtLFni67tGcV0oZsv844On+OSd6/nRoWmff/Wjg9N88LqBc9osyxIz+QqPnRScnHzF4sGjs7xlcxcPHBNVN2XTZt9YhlxFcFZKNdvXqHFdGElXUSSIBjSeGEpz1zPjvO/qPn5wcIqqJzr2/QNT/NpNy5BliaPTOfZ6+lL5isWRqZxfGXf/kTm+sXuUiiNhSy4nZgoUKhYjqRKaIpMpCrLqMyNpgt6c9v2H5xhoijCeLvu6VfmKxRNDSb+q5ydHZpnMCE7Qw8fm6GkILbI0qffJ890E/eDEdfk/j57mmZE08wVTVNYhxlCxViFTNnFciBgKlgOfffg0n15QDGC5Lv/57ASHPG7N46eSNEUM3rGt55xtnd2m7++f9G1Zfnhwmq5E0N+3+4/Msnc8w9BcCRD6VEen8/Q2hhhPlyibDookoSoSPzo8Q0ciyLa+Rg5OZGmvq3hz7hi+WJz9m8NTOcEPytcYmiviuA6TmQquC/mKSchQUWWJw1NCONLFxa4JDy9NkQnqCpoiM1+o+WX+42nBn8iVTTRVxrQcIgGNquXw598/7HORCtUin3/sNB97/Vp2j6T40aFp8XmyxL88OsTH37Tugu0+G7P5Cg8fExpK+YrF/UdmedvW7hfcP69A4Ka/vp+H/+DWJd3mfL7El58c8fOz//LoaX7z5mVLXsj09/cfZ/94BiSJXPXSvPeWLAX2vve97zmDn5cbKpa4MNbJVQtJ0IBHAHOxbHzDSstxcT0zVMcjL9fLgevkW9szaKz/tWzXM3gE0xLLmB673nHEduo3yZcTUsUaCznZdTE1/70L+QWDcqF53kuBimn7/eZ6/9SN8urtqC7wB3Ndl5ncYlHBfNVkoZ5WqWb5Ipd11NVPz4dybfE+1j3FyjXxm3p76mRly3F9UUnRJvz2O667SBSxjprl+B48lbPaUr+ZA1Qs8dv6Sl1v+/V11cd1YYFpp+OKbVXOOlaVBft1zncv8rhajthe3Sh2IVy888Z1/WNQqi2+0NW8cv4z63Mvqk31UvOFvysuWLflOJRrFvWj47qCpO64rn/uu64oCjBt1z934aUZ6/V1WvWHMNP12w1ge5/XzLownPhd3RS13n9V77pWOWt99d/Xlzv7mlPwxlb+LJPXwgu88VTPGrMv9XXh5x2pyyjmerHIlC1fcBXwBDWXvBlM51488fqKkKB/FjDYLCTv26IBDFVmeUvYF2iTEIJiEUMlZCjccVUXQV2hMxEkqKssb43QHNFpjxmsbIsIHkFER1XEjI+myLREDJrDOt2JAIMtIcK6ysr2CIYms7Yz5j+1rWiNsGEBZ+ZKoi6+2B4PcP0KoccCgASNIY2eRMAnTUYNlZs8m4aIITgYLyW6G4JcPdiE7HGmQrpCQ1hwiN64QfiQvXZtm59K7GoI8fqNi3WoVrdHWe1xNCQJ3rCxg0RIZ01H1P/sufzBehtDdCVEekyVJX/Ga8dAA4osxstgS5h1nTFkSaI9ZhDSFVHGKQtjy7rRa1sswBs2dKAqss8DAXwfMIBVbVEaPPFDQ5N506ZOX/xxfVeCtZ0xVEXQdaMBldZ4kNUdgvvTEQsSC2q8YWO7TwZe3R6lIayzsi3qCwgamuxbuIh9afQJuQv391JhqAq3rWmjNWpgqAqqZx6sSEIsMR7UiARUmiIGsiS0qBYiFtDYOdDoW2P0NAbZPvD8Hm6SJLFzwXIbu+Nct7zZ77+VbVFuX9MmSrkliURIZ63Hm4oGNFqiBoamENJF6f2KNjFDGwtqrO28/AaVazpixINiuxFDZX1XnLCu0hox0FWF1phBUFO4erABXZHRPNHGjngAQxN2PAFNYUN3nGhAZX1nTBjqxsR+tHvLNYd1dFXmw9cN+JpTmiLxJu9cuWZZE50LxvibN70wLbfORNDnOCmy5HuJvYJLw7++b8eSb3N5a4xlrWcyEms7ootEWJcKv3nLMl9q4VIpIq8INVwAuirzrm09zBeqHJzMMTSX57ETc0znqsznSoQMnfdfM8Cytgi5sslUtUJbROPEjMVQoUrFtGiLBXnfNX1s7kkwm6ty/P9v77zjrKjOxv+dub1uL2wHFpbeQUCavSWKMcZEI1EhtleNGhNijO1Vg8aCMfEXY4klMS/2Ho2KggIqoDTpnV3Yvnvv3b29nN8fs/eyl6UsbfcC58vnfthpZ545c+bMM+c8pbaFymYfwUgUh9nA2F6Z6FSVVTs9NLcGsRh0rKtxE4wICtJNZNrMnDM4n8XbmvhodQ2hSJShRen0zLEzpiwzabh5a4OXlVUunGYD48uzkoIEHinSrXqWVbewo9HLtsZWBvRwEIpoiTuvO6U3n62rZ/mOJtIsJm46rZw1NS2sqnIzuCjtoOb5I9EYizY38uH31bQGIowqy6Ta7WPu2jr0qmbEe0r/3ESWd9CiaT/6k2Es3abFfdGpCmt2eRhYkJZ4WH99VgW+cIR6T5AZE3omojRXu/0s3daMUa8ypMDOyioXaRZDIoDi2YN6MKJUe7Hs7zr0OpWLRhbR0BrEF4qyepebhRsbUBVYvr2JSpePbJuR4aWZjC7NZECBgy31LTz26SY8viBlWZoS9+2OZmpb/NwyZzkXjSogGNYi8VbkO5KmES1GHZeNLaXJG2ozttcxoMCJty0f1Gn98/jzpxvY2exleEkm5w7ugd2s5/udbnY0+YhEtfxN2xu9RGNg1Cl8uKqa8eXZXHpSSVK5cfrmOchPMxMIRcm2mzo1vfPByl0U5QXJdZj5eksDNe4AffOcGPRa4L6NdVoeqqJ0M2a9jmAkSn6aifOHFlHp8pNuNdAvX0vYuqXey7srdjG8eLexcNweamuDl4EFaTjNBt5bsQuTXgsq+cZ3O9HrFM4dlE+2w0yOw8TYnlmc1CuLvnkOIjGRmMa7ckLPRP0FQlHsJj2LtjZiM6j065FGaZaNwQVOfOEoX2yoJxQRTOqbQ588O25/mF0uP3PX1ibOsabaw+Z6zZZqSGEaizY3EojEKNQHOvcwtGE36fl5273+dG01y3a4ybIZ8IaiTOqdybTxPVlR5WbVLjcnlWfgC0YJBLTAq1Z9BJ2ixXjql+fg4zW1rKh0kWMz8vtz+1OcacHli5Bm0eH2R8l1mMh1msmym5i3vo6STM3o+59fbePjNbWYdApjyjLolWvnpF5ZbKprZfUuN06LZpS/vzhHOlXhR8MLaWgNYjbqcJpTK/7bscaY8u5RIG87oy93vvM9qqLwm7O6PgYQQH6alR+NLODdZdVkOFU2H0IZUgHaD3qdSl1LkFVVbhZtamCX20+tR5t390dCPDlvEz8dVUyTP8z2Rh/bG314g+FEUDuXP8wj/93AezdOoKrZT2Wzn421rTR6Q1TkO/h8XQMgaAlEWFfTgscfxhMIt8WFiVGYEWb2x+tBUQhFBI3eIOtrWpnUNweDTmFk28hAszfEeyt2tRvmjnH2oCOfnO6DFTU0hHX4Q1o8Ei27vRb19/dvrcZp1tEajOKPwP+btxmXL4zJoKOq2U+G1cgFwwo7dZ6Fmxt5bemOhB3KyspmaltDbdMO8JfPN+Lyh7mqnRIDbd5OvbJZX9PCf1ZVA7Ciyk2m3cSw4nT+PHcjVc3ai+fvC7bSM9dBjsPEW8t2EgzHcPtDvLeiGrNBpc4TYOYbq/j3L8cCJClb+0OnKmTZjLy9bCvN3hDLq1zUuALUtgSItcVnqXQFOXNAHu5AmFeX7KDOE0QIWFPTSpUrkGg/u5oD7PrUT2m2jb55dhpaQ5Rl28hul/7EoNNGi+JoqUT0RKIx3lpWRZXLjz8s+K7ShdmoY3hJBqt2ethS30pdS5CGliCBiBYvaHmli3MG5dHQGuTycWVJ5bbHaTYc1ItrW4OPLW4th1ZDa5BIW5C9TJuRcDTGqio3oUiUcEyLv+QwG2j2R2jwbmVCnxzCLQKT3ssud4DNdVoMpB2NXn4xvgyH2cCqnW7W7IonoW1k7to60iwGdrn8fLy6Rsu9JmBTnZfzBvcgx2FCr6qM6ZnZITJ5vP4APllby7JKF5WNPryhKLtcQYaVpDO8JJ2NO92JaaF56+soybISCEeZ12bfsqXeS0NLkM313sTygo0Nifa6sjk5l1ZnMOpV1tV4eG9FDXWeADtdfixGHY3eEP7oFpp9Ydz+EDub/Rj1Ki2BSFvAwxgmvcr2RoV1uzzUe0OoihZz7MEP1/HODRMoahvki1tO1XoCfLq2FiG0RNB//2IzcxZXEolqdkU7mv2co6q8v2IXO5r8iSmRSFRwxoC8/V6HqipdlsD5eOeiv37BGzdM6tJzBgIRZr65imDb9OXNr6xg8R2nd6kMAB+urOa1JTu1IJytHWOOdQY5BXYA4gH8WtvsA6KxuI0Gbca24YTthy8UaQtcSMK+xx+O0NAaSJTjS9iFRPGGIprC0LauJahNpMZtWQJhLSu6LxQlErfZaJtzb2yXfM7lDyfZqcSTGB5pokKzcYrFNDunuP1BKBLDF4wk2TA1+8KE28lU1dz5bL1N3iAe/27bgpa2QIHxaWdvMIovFE0oCnvS6E2eG25qW44H9APt3uxyaQlW43YJzd5wou4BGloObY7ZH9ZkiysWLcFIm+1I3HZHu+dacMxokg2QL6Qlu42/UFrD8bYVJSa0KMmdIRDRXlTxthkIRWkNRKh1BxIyAonIwpGYIBqL4QtGafKGDzm54D7lCUfxBMKJNuMNRvCForQEIprXFyTucSSqJeRsCUQIt9n3NHpDNLULthaOakEu49vihCKxhP1Ksy+UsHXS7PGieNuesSbvge9tozeEPxzdbdsXE/hDUZq8oaRnLBITePzhJDkAduyRuLZ9+2tvM3Qw7GgL/OYPx/sjkbQ+btcUv7/x9hyv97ixaPz21u+jjTd5Q0n2cBtrWpPbZZstUJXLn2QP0pl6lRw51ta2dvk5a1oDCeUHwB+KJNkSdhWrq92H3U8dcwpQY2Mjw4YNS/z69u2LXq+nqamJuro6zj77bPr06cOgQYP44osvDvt85Tl2dKpCYZpFs/0xqImAdNl2E8UZZjJtRhRFm99WFNqSgiqY9SrFGVaKM230zXOgKgrZNhM6VUnEQClKN5Nu0RJhlmRYURQFh1nLGZRm1lOUYaFHmgWzQUtEWpBuRlW0eClxeqSZcbZLTliRf3Q8xiwGzZPEqFdxmI1tCRcVnBYDhRkWbG05pBxmPf17OLC1Syo5tlfWfkpOpk+ug6IMSyIxZp7TjMWgSwRXK8qwUJhuSXLBbU95jj1hy6EqCuU5Wl2NLN1ty5JpM9K/h5MMq5Fcpzaikp9mTtjUAAxrmwI7WOwmvVYfJj0mvUpJhgWDqsVQUlWFbJsWQ2VMWSbFmdp1omjB/QrSTaiqil7VgiSWZJhRFS05pcOsT9hfHAibUUdRhpWstiSFmXYjJVk2BhVq9kdZNs2mpiDdgr4t6afdbCDNaqBvnv2Ih11wmA30zLYlRlcK0i1k242JetJiL2kJbM16zRaqV649kWS0Is9B33ZxhTKshsSoXPwZBc32LG6PU5xhTdgG6VQtvECW3YSiaDnoDkRFnoMsu2Zbo9ep2IyaLU1xppW+7Z6/dKuBXKeJnlm2xPSPomixn9ovt5++zDrE5JFje2llZliNKG3eo4qicO6gHhj1Kk6zPtHPKIqW2FVRFCx6FUVV6J1jTwTlVBSF8b33/lwWZ1oTCVUBzhuSryXUbWuX8enHMWWZSQmN29eL5Ohz3aSyLj9nWbY9aXS4KNPaIX9gV3D+0AKMh2nqoYgj/anXxTzyyCPMnz+f9957j6uuuoqSkhLuuecelixZwoUXXsjWrVs75Z7n8XhIS0tj/vfb8Akj2xp86HVa52o06HCYDOx0+ahs9vLsF1sJRWPkWA1kOMycUZEHKpj0Kh+trsHjD9Evz0nvPAc/P6mELY0+vtnaSCgSI81i1AKRmfWs3umm2R/C5QsTikSxGPVEYzHsZiMZVj25DgtTKnKIxbRUBya9QlGGTTPO3mMI2ReKsKXei8Os75An63CpqqqiuLiYZZur2NAUxaTXkW03sbW+lblra4lGY2Q7TDR6wzgteoYWZ3DWoHy8AS2fU/8eu42L98ZXmxp4ftE2cp1G/nDuAMxGPVXNPlZWunD5wgwrSSccifHK0koyrAbOHVJA3zxH0vQXwC6Xjz99tI4aT5B+uXZUvcrw4nR+OLSQjbVaKP/vtjfhDUUZXJhGUaaVIUXpuP1hPly1i0ZviMI0E19taSLXaea2M/uhqgreYITvd7oJRWMJg+LvdjSzuc7LkMwIl0wZwSfLtpKbnc6ggjRUVSESjbGhtpVwTDtmU10La3Z5KM60UpHvJNdhoiTTygerqnl6/kZqPSHKc23alEVYkJ9mZlhJOif1zEYgqGryYTLoGFiQljBOBc1+aWuDl1yHid45dtZUe3D7w1TkOfCHIzz80Xp84RjTJ5RRlGFlR5OPWrdfy0cXiaLXqRh0msJRnGnFYTbQJ9dOXUuQZZXNeAMRsuxGqt1+zAY9k/vmYDPpWVnlpr4loE236FTK82xUNgdoDYYZXpzBqLLMRLtZtGYHxflaYMtVVS6W7WimKMNK/3wHwajAGwwzd20tLcEo6WZDIrGpToHF25upyHNy0chC6ltCfLq2hlpPkCFF6eQ6jVS7AvTKsVOUYaG5rf3taPJR3xJkUEEaZqOOOYt3YNKppNsMfLmxgXSLnv49tPuUZTdiaJvGNRm0eDklmbbEC35zfStb671EYjEyrEZ8oTCvL91Joy9E31w7k/rmclLPTDbWtxKOxAhFomxv8jO0KI3BRem4fCEqm/xkO4z0SLOwqS0dijXsonfPMj5etgUfRupaAmTbTOSlaY4EAliypZGVO92U5zqYXJGDomgJZ6vdfjbVeWnxh9jR7Kdvjp1TB+QRjsRo8obxh8NUNgcQQtDQGiImojS0hjHrFVRVxR+MUNMSpGeWlWnje5Jl0+zL4qypdrNwUwNZNhN9ch3EhOYh1+IPs6G2lR5OM/0KNKPs4kwrOxq9fLmpgTynmdP65R628hxvN8U3v4pqsh5WWccT2x48L6lu7CYra7ohECLA8h1NXPuv71CBf84YQ+/cI2/43xk+WLmLZxdspdAa48krJ+F2uxN5BzvDMW8D9NxzzzFr1iwAXn31VUaOHMmbb76Jqqq43W6ee+45rr322k6XN29dPfO3tRKKaMPwuQ4zk/rmMKlvNmcP6sFJf/yURm+ISAyafRFo0IKkDSxIo9YToLE1hNWowxty8z+n9aHKFeChD9fhCYRp8mod+8CCNDbUeKhxB9jUlgMsJrTsvka9yqBCbZ/xvbMSRrdTh+/ffsZq1DxDjia9sh0M67W7cb35XRVVrgBVzT7C0RgGnYrVqCfa5n19+bjS/So+AOtr3Nzwf98l3LO31Pn499VjKcqwUpSR3PkNbeeNtCetwQgzXlzKprpWYkKweGsTpZkWVu/0UOcJ4glE2FjXQkNrEBWF9bUtDClKZ6fLT50nyOJtTTS2BmkNRshzmOmRbuXTtbWc2i+X15ZWUt8SZGWVG7tZT1NriI31LVgMOj72aHF/lmxrwlyvJcs8pV8uep2a5A00pCidH41IlvmtZVU8M38zWxr9RGMxvtnqQqcqWAwq3nCUX5/Vj57ZNlZWuVjfNtS9epeHn44pJtdhps4T4LWlVYlpkDynido219Bvtzbx4eqaxDTRwx+tZ3JFLnUtATbWtmIz6fAFo/QvcJLjMPHzsaUJu566lgD//Gobyypd+EIR3L4wZoMOp8XAqioXhRlW6jxBPl5Tk3Cvt5n0eIMRMqxG5q6t47YzK8hv610GFqbhdGoZ3DfUthKMCM02RtG8iKqafdhMRsLREN9VuuiTZ+f9VdXUeQIEIzGW7XCxvtaDQVX4Zmsz0ZiWz8ti0Dz9suxGJvXJ4cIRhfzzq+0JV227Sc+ZA/P57dn9+GxdLXe/sxqXL0QwEuOTNVogP0VRMBt0GPUqKlCWbaMs28ZFI4ooztQif8djcC3c1MA9765ll9tPNCZYUanlNatq9iVyhNV6AgwpSmf+hhCFGVYybcYkw/nyNoP8qirNZund5Tv5riaIw6SnJRhhQA8nnkAEIQTLq9zoFC333qLNDdiMenqkm6l2BUi3Gvl+pxubSceWOi873QH65jkYVZbBsh3a8/j9TjcZbfvpVIVqdwCjTrMJyrAZ+T7Ywj8WbGVgQRq+UIThJRlsrW9l1n/WJZ7HMwbkEYzsDp8woU82o9uNZHkCYd5fpcWpqvMEsRp1jO8to0B3BT7gmhe+5u9XjO3S84bDYa54fmkiJMUlf/+GpXee0aUyAOx0+ZizpBKjTmWXq/MmFu055qbA2rNo0SKam5v5wQ9+QGNjI+FwmHfeeYeVK1eyfPlyhgwZwgMPPLDXY4PBIB6PJ+kHJBJwxmOTxO1Jtjdqc+wuXzgp/g3sjv+i2QVoMUuCkShLtjWzcqe7LTaJ1oHUtQQIRqJsafTibzuHPxRFxETCpqWuJUg0Jqg8CLuZrqY1EElk4o7GNCNTLWZNlPoWTZFo6ESCvAWbGpLi3Gxp8CZiJh0MdZ4ANW32LfHwN562F+HS7U0AuH1hwhHRlghSiz+zobYlkUwzEhW0tmUYDkVi7Gjy4QlEaPaF8YWjhKJaUssaTyARPyUmkuOabG/svDHe+poW3AHNoDwW2x3/JyYEDS2hhF1H/H/Q6jiePLbK5U+y/YonJwWobw3R0M5ObEezn3AkirstYEd9SxCBFu8lGI4lbIMAdjb7cbfZlQXDMbyhCIG2e1TjDrCt0UujN0g4qtl+CSEStiSBtgSsKypdHa63pe0jICFTW11VthnRxpU1ty9MQ2sQb2i3HcuORj+73AEisVgi1k+8LLc/TLU7QJ0nmBSnpn29fbOlsS3CtGbPE4hodlr+UARvW1LiQES7v0JAZbtjd98vDy5/KGF3EIkKdrkDiQSnbr8WXDIUiRFuszE7EPFkuq1t1xp/5rc3+ZLiH+10+RFArUe7b1XNPgQCbyiKQFDXZl+0qspNNCZoabuOqmZfom6FEImYSa1tSlb8/PG6WrXLnfQ8fre9KWk53g8m5PcEkmL77GjsWG+So8dnGxq7/Jwb6nxJ8bg8gXCiX+lKVu/0JLXNQ+GYHgF67rnnmDZtGnr97ston0ojHA7vczh21qxZ3HvvvR3Wh1saibX6ENEYYQE2qwFXvZ6elnSqqsAYaCYYCNO+3vUGFV9zBGNQS3mhN+iJGHTk6rSRpICrnmgwTNAfxmiw4WuOkRb10OwLE2nxoghBWFEwqgrBsA6T0Y67IQoFKlVVh2bdfiSprNSyr+/YsSOpfq1hF4q3lWhrgFg0RlSnIvQqxkAUvyuG32Wkyrv/JlZi8hNrbUwYeRdk2/eaQuFABAIRrGE3zR5/IlidXjHha/YzMC0TV70PxddCuO1BDQd1+G0hcrOsNPtC4HUR8oVRghHCqglfcwiDwYGnQU/YU0/QFybk9mA16bCEQzS2+AgGdfibtMzvTXW7sNqdZOc5qKqq6pTMzqgHva9pd/3FABUiBhWzw4zibaSqKojqc+Gq15Q4VVEQhVq7EC0B3A01CWPVDLMeV30zADoEpmBzwhg2zWKgpcmI8AbxNfsw6lR83hjCYKdVtBDyGKgKa6HtRWuASEsDAVcLkVAE/GFEWI8voidPtWIxmQn6woQ9DSgCQggsOh1BfxSj2YAvrCcDK5WVbQpcW7uJRGOEPfWJgI2F6RaqqqpQfD5c9TUIfxhfcytCtWIKeGn1hgjGYqiKgt3qwICOsMeVMLo16nT4mn04LXpUnyDkMRF01yUCT2bk2BL3otDgJ9LSSDgQIhKJYTToIGrQok4bdESDCkJRUPRWXPUByIOqqmRXdXvEgyHQTLRVS5+h6FQs4RiZgKveDa0Bwq1BfM0hwjqVSIsuMdKzJ/FnSudvIuiOYjXq8YUi9NBr/UNGLEqdpxVVUYgJgdNgxdfsJ8dmpN4bwmZSqXH7sBh0BMJRjCYbrvooFfkONjS0EA1G8De3YDPpqHZ7MSAIe8MYdJoBttWs1/oagxVXfQzVlkFVlSA91krQXZ9QvAamZdDSuFvRLjNr/WCcoD9Ma1Nt4oOg2JRGVdXhfVfH6yboqsNg6f4I+KlCVVVVh7oZXuzsdH9zpLBFIij+pkTQTYtBR0tTLS1NXSoG9oiXsLuecEwQ8mvvyWj0IANrimOUlpYWYbfbxdq1axPrrFarqK6uFpdffrkoKioSBoNBPP3003s93u12i8rKysTvvffeiwfllT/5kz/5kz/5k79j7Ld48eKD0iOO2RGgV155haFDh9KvX7/EuosvvpinnnqKl156iSVLlnDmmWfy5ptv8stf/rLD8Y899theR4AqKysPyoiqPW98W0m1y897K6sJhqOay7sCJlXFF45pX18xLYVK/Cs1jkmvEImKpJQbKqBr84wJxUcI9oOCVnbcDT9pvQplWVZCEUHvXBv989MAuGR0MdkOE6FIjGe/3JIYTahtCZBj1/KUuepr+NPV51F43QsHZZSooqXHUNsykut1WpTf/DQL6RYjFw4v4ILhx3YeoKqqKgYOHMiWrdv5yfPL8QbDBEPa6OGByLUb8EeitAT2fmNVoCjTQq7DjCcQJhqLoSiaUbaWjNdIea6VNMvuuECn9c89oN1VVxGvmx/MeptdXm1a7kihQCIbdUxo7cukVyjOtPHateOP2HmOFvG6Odj+5t/fbKfZu3u6YUCBgxcXbU8s72jy0tgS4mgnmFCAgnQzvzunH6f0y2N7o5f3V1QntvdIN/OjEYf2bLdvNwazjR+PLGJCeSbnPLEgMf2oKAof3jSBe95by/wN9Ylj89NMfPiryZz9+PykkBHXTynHZtJz73trEut0qsKyu87kun8tZdmO5sT6kaUZPHnZKCY99BmhdiMKf/3pSN5btZO3lu1KrDMbVBbfcQYj7/skKSVLea6Nf/9yLP/z8neJPlVVFJ66fCTjZ31KYI8wCN/fexaD7v5v0rqKPCsTynN4buHu+xv2NFDzj+sP6z11POLxeCguLqa8vPygjjtmFaDnnnuug2Lz0EMPcfnll9OnTx+MRiNvvvkm5557Lo2NjWRlJbt73n777dx6662J5XgFOp3OQ25Ypw/tybsrdjG4p5ZMtTUUwaDT3HoVRZu/t+sUnGYDu1y+tsScmvITd+l2tc3dCyDLZsRh0uEJRonFYnj8EfYVPkSngMOkIyo0HxItbgygaMbVVpOOnEwtRUdpll2LelyURq/CnEQZZwwr48uNDQBcOrSUTXVeaj0BsmPpABgtVmKGzilAWsgAhXAUnGY94aigMN1EayhGdprmSnzhSX27xX3ySBJvK1mZ6fx8Uj+eX7gNxRhDH4sRjgj2pbMqwP+78iTmra/n6S+27PW+Di50almXa1qw2AWZNhMGRWVNjactnUEaPxlVxJebGvAGoxRnWhlRXoBelxqmffG6sdoclKWZCNe20hKI0NYs6YSO2AHNq7wtXYaqBaisbTNstxn13HR2/2PixRCX8WD7mzOH9eQ/q6oJRwWDC9OY2DebtY0Rlu1wYdKr/H5iBff/Zy3N3jCC3R8h+yKuQO5J+/ujoCkLkXY72owq4/rlc86I3hj1KgPtDrZ5BJvqWjEZVM4YVojTeWhpUuL1YTDb6F2Yw9STyrEa9Zw5rBdz12oBJE/rn0dJj1z+eImd855YSEsgjF6n8pvzBuJ0Orn+jME88skGotEYpVk2rj19IHq9nn8s1hITK4rC2QPzcDqd3DF1JFe9sARvMILNrOfOC0fhdDr56ckV/HvxDoQQDC3J4NRhZYzpU8C8rfNw+8OoisIVE3ridDqZceoAnpq/BYHWFz995Xjys9P50dg+vLdCU5guHF5IdmY6f79yPFe+tCxxvecOzMXpdDKwNIe1Nd5Enb91w2TsdjtzVjQmchD2L82lBhj/6Ff7/Bjd1k2eYamATndwbvHHvBt8e1wuFz6fj4ICLT/N22+/zQ033EBlZeUBXTPjbvAH60a3J+G2hJORtuBukVgMs16PEAJfWAtml2k1EIoIvt/lQgEG9EgjFI0RjgmsBj3bG71kWHQYDXrSrEZaApqhaqZVx+rqFgLBKFUuL7l2A/npdsIxQYbNgM1oRK/TAvrZTQq1nhB2o0pM0dEjzURLMEqm1dgW+C6G1dhR+YgnSTQbdAihGVk21lZTVlrCtp01bPUIdIpgfY2HXU1+hKrFVMl1WokJLZpyjzQro0uy2NTYSqHDjCsUIcdqxBeJ4TTrcQci5NiNqGpqvKgPh7hbarzd1LcEaPaGKM2ysqW+FVVRWbK1ltW73AwqdNI3L5stDR4uGJKfSA5c4/axpspFfrqN7yrr8foinNa/ByU5DvSqgicQRqco2Ex6bVQxJghFYzgtBvQ6lUg0RiASw9YW8yVViNfN9up6nE4nVoOOtdWakW5Zjp1AKMz/fb2D/DQjhek26lr8FGc60KlQ1xqiJNNCIBwlz2EhHIsRiETJsZsIhmMEozFy7SYCUYFNr1Ll9lOQZsXZDTmJDoU9283BoCXE3f38CiFo9IawGnRYTXrC4TDLqtykm4zoDAoWHSyr8pBjMeAJRSjJsOKLaPXqj0Rp8QWpdAUpdBqp94WpyLXREhTYjSrecAy9CnaTEYUY1S1BTDrIsFnokWbu8Ax7gxGMerVDiIpDqZuNlbX0KshOOse2Nq/ZsuzdtkHx663IS0vKSdXQqhnGD9gjl+I3WxrIc5gpy9ldRiAQYVNjK+VZdsztPsqqXX5aAmH65iffowUb6ynLslCUubuMGleAr7fWc3b/nKTE3y5fCAVIa+cN2NLSwtOLKvnB4GIqCnaHIFi6qYZvq1q4ZkqfpPO9t7ySkkwbWarvgCECTkQF6FDf38eVArR9+3Yuvvhi/H4/qqqSk5PDI488wrBhww547JFSgI5HDqezPt6RdbNvZN3sG1k3+0bWzb7pTIwkqQCdQHGA2lNaWsrixYu7WwyJRCKRSCQpzrE/ByGRSCQSiURykEgFSCKRSCQSyQmHVIAkEolEIpGccKSUDdDGjRv5/PPPqaurIxZLdt686667ukkqiUQikUgkxxspowA988wzXHfddWRnZ5Ofn5/kzqsoilSAJBKJRCKRHDFSRgG6//77eeCBB5g5c2Z3iyKRSCQSieQ4J2VsgJqbm7n44ou7WwyJRCKRSCQnACmjAF188cV8/PHH3S2GRCKRSCSSE4CUmQIrLy/nzjvv5Ouvv2bw4MEYDMkh7W+66aZukkwikUgkEsnxRsooQE8//TR2u5358+czf/78pG2KokgFSCKRSCQSyREjZRSgrVu3drcIEolEIpFIThBSxgZIIpFIJBKJpKvo1hGgW2+9lfvuuw+bzcatt966330fe+yxLpJKIpFIJBLJ8U63KkDLli0jHA4n/t4X7YMiSiQSiUQikRwu3aoAff7553v9WyKRSCQSieRoIm2AJBKJRCKRnHCkjBfYhRdeuNepLkVRMJvNlJeXc+mll1JRUbHPMgKBAD/96U9Zs2YNFouF3Nxc/va3v1FeXn40RZdIJBKJRHKMkTIjQGlpaXz22Wd89913KIqCoigsW7aMzz77jEgkwiuvvMLQoUNZuHDhfsu5+uqrWb9+PStWrOCCCy5gxowZXXQFEolEIpFIjhVSZgQoPz+fSy+9lL/+9a+oqqaXxWIxfvWrX+FwOJgzZw7XXnstM2fOZMGCBXstw2w2c+655yaWx44dyyOPPHJU5a5s9PH3LzezdpeHLLuRwnQrOhU21nnx+EO0BiLEAL2q0OQNoVcVQBCLgTccJRyJEY0JIkIrTwEK0kz4IzEQ0BIIE42BqoBOp5BhNZJtN9Inz0FJhpUMmxEB7Gz20xoIU9cSorbFj1GnY1hxGv16ODm1Xx45DlNC5lAkxty1tdR6AvRIt7CxtoWvtzTS6A1RkmnlnEE9uGBYQYcRuTlLdrBgWyvfV7lo8kf3Wy+qAgMLnDT5QugUhTMH5KNT4ZutzThMes4fWkAwGgNgct8cijOtR+6mdANT/vQZXoxYjDoGFTgx6HS0BiPoVYVtDV6qPcEOx6iAWQ+BCMQAgwqFGRbSLUZsZj0mvUpxpo1tDa18u72ZaExQmG5hbO8snGYDFoOOHukWsu1GtjV4SbcaOWNAHjZTyjzWHfj1q8v58PsaAuEoMbF7vYpWB3EUoCTTQprFQGswQiQm0KsqMSFo9oawmXUMKkjHEwjj9obY5Q4QA4YUOfnpqBL++vkmatwBVFWhONPCBUMLsBgNBCJREAJVUajIdzKudxYAm+pa+GpzIwadymn9k5+XVCUYifLJmlq+3tJIkzeE3aRneHE6MSH4anMTc9fW4I+IpGMselXrW9qwGlX65dkIR6HBGybPYcJp0WM26HH5QjjMBjKsRvr1sFPjDmLSqyzb0cym+laiMeiVbSUcE9hNBvKcJiryHbQGwqyvaWVdTQsZNgNXnFzG1GFFiXOuqGzm9W930hIIM7FPDpP65vDmd1Ws2ummJNPKlEIdAGNnfUqfwlzeuWECAC8u2sLTX2jx4q6e1JNfjO8FwPl/WcDWRi9Os54XrxpNea6T95fv5LY3VhCJQnmunY9ungTAdf9aymfr6tGrCredVcGVJ/ekqTXEuU98QbMvTKbVwAc3TSLTbuSB91fzj4XbEMDw4nTeuP5kAG55ZTlfbW7AbtLz6MVDGVqSwZpdLi57djG+UJTiDAuf/noKAM8v2MKzC7aiANef0odLTyohEAgw9qEv8ATCGHUqc345lmGlGayv8XD3O6vxBMKc3j+PW8/UZjv++30N767YidWo5/zy1G+XxxKKEEIceLejT05ODgsXLqRv375J6zds2MD48eNpaGhg1apVTJw4EZfL1akyL7/8cjIzM/nzn//cYVswGCQY3P1S8ng8FBcX43a7cTqdnSpfCMG9763hmy2NeAJh/OEouXYT7kCEWCyG2x8hKgSqAqGIQKdCNKZ17rEDlK2pSXvHYlBxmvXkOMz0zrFT2exDAVy+MHUtAXSqSjQWI8Nm5Iz++ZRlW7l8XFni+AUbG1iyrQmANdVudrkC1HkCBMJR0q1GeuXY+c1ZFQwqTAOgqqqK4uJibn5pIf9Z7yYYOZD0u69Bp4LFoENVFCxGHTpVQQiB2aDn5PIsMm0mzAYd107udUx6+8XrpuTmV1FNVgRg0isYVBW9TiEYieEPd66+4jhNOiJCkOsw4w9FafKFiEQFAk1RyLIbKc60YjPpKc+1s8vlp1++1mYr8h2cO7jHEb/OQyFeN/Fn6vP1tVz90reJa+kMBh0IobQ9D0J7fpS2oWtFIdNmoKE1REyATgGdqmAz6glGool6t5t1WA16zhiQh9sfptEbYmRpBqqicOHwQnKdJp79civRNo0sy25kWrvn5WiwZ90cCvM31PPx6hrWVnuo9QTJtBnIsRupbA7Q0Bqk2RfuVDkKWpuNCa3+FMBpMeANRrCbDRh1Kg6LHptRT7MvxLYGL0JATAjNREGvYDHq0etUSjOttATC7GjyAaDXqRRlWPjzT4fTK8dOQ2uQ2Z9sYFNdKwBpFgOlWVbW7PIQaav/CnuY+34+mV6/fg2dycrFo4q585z+jPvTZ0TaPpr0OpUlvz+Vu95dw+vfViWupVeOjY9unkz/Oz9Meu7+Z0ovxvTM5KoXvyX+yjPpVdbedw7n/2UB3+9yJ/YdWpTOW/9zMr1u/yBJSX/658NpDUa5+93ViXWFGVY+unkS4x+cS407kFh/wbACZl0wmLF/+oxom8wGncri35/KxX//mmWVu8+XZtaz4p6zuOyZr9nW6NXuiaLw8EVDKMu2ceurK4i1yWyLuHn+hnMobutv9sa2B8/rxF0/vvB4PKSlpR3085QyU2CRSIR169Z1WL9u3TqiUW20wWw2d/ol+cc//pFNmzYxa9asvW6fNWsWaWlpiV9xcfFByxyNCVqDmpITX47EYkSiMWJCU2CEELRXMQX7Vmw6ixDxcwkCkSjRmCAmIBzTzht/WCIxQTgawxtKHq3xhSKJvwOhKJFoLHENkVhMOyYYYU+8gTAHoy+LNlnbyxInFIkSimobA+FoovM7lolfQTQmiCEQQhA7hOuKtN3PeHtqX4YAIlFBNKrdp3AkRqidQrq3+5Yq1LgCCHFw7V+7dKH9a3ds/NmKxejwfAUjsaT9YlFBJCoIRWOEo0K7P2116g1FCIZjCeUHwBvc/+hmquALRghH4+1Dq4tAWGsXoU5+pMSJtevDBCSe1WhM6xuCYa1O4v/H+7Gk44TAH45o7bOtPoUQhKIi0S79oWiSbOFoDI8/nPT8t4aS23CtJ0BrJJJQfgAi0RiuQIS6luSR1fi9i+7x3G1r9LGlwZfUf8XP6fYnK4quNsVxz65uXU0Lu1yBpHXtr6s9Ne4gnmAkofzEz9cajNDoDSXtGx8Jb2337AohqPEEcPvDiTpufz7JkSFlFKDLL7+c6dOnM3v2bBYsWMCCBQuYPXs206dPZ9q0aQDMnz+fgQMHHrCsRx55hDfffJMPP/wQq3XvWvLtt9+O2+1O/CorKw9aZr1O5dR+uWRYjSiKQqbNSLbdTM9sKzaTDptRh9mgw6hXMRtUVEXBqFfRq9p0xz7LbfuS3dsuBhXMBh25TjM90syUZFopyrCQYTWQYzfhtBgwG1RMBh1F6RacFgOjSjOSyhhSlI5Rr5U+oMBJUYYFp9mATqeSYTXSO8dO/4KOWnRJpgW7ufPTKzajdr2KolCSaaU404qiKBj0OoYUpZNlMwIwrCQdgy5lmuIhoddpX86qAtl2E2kWAzaTgQybEd1BDGwZFLCbdDhMBhxmPQXpFjKtBlQl/qWuUphpJtthoiDNQqbNxIACbaROpyqM2ONepxLnDe5Bls3Y6U7HqAObUXt+THqtTZv0SttzpJBhM2I16bAadagKqIqC3aRjbK8MTHodqqqNCllNevr1sJNjN5GfZibfaUavU8m0aW093WqgPNeeOO+ostStw/YMKU4nP017Jm1GPXaznj55DiryHZRkWjpVhgKYDSp2kx5VUXBaDNhMevKdJox6HZk2I+kWA/17OLEadfTJdWA16tDrFHSKQprZgFGvI81iwGkx0D/fSX66mSy7CRQFk0HH4II0+uQ5AOiRZmZwYZrWLwAlmTbOHVxAfpoZAKfZwJh2bdioV7nx1HKy7ebEiDTAoMI0su1mbjy1PNGXqYrCT0ZpU22jSjOJfysbdQq3n9uXy0YX4jTvTrI9sIfWx107uRdq286qonDtFG1qrYdz93STSadw9YRSfnpSMWkWrd9SFIUfDtVGW6cO75EoQ68q3HZmX3LTzAxo148OKkwj3Wbmt2dVEO8SFODsAfkA/HDobrODPKeZM/rn0jfPTt+2ulMUmNgnp1P3VdI5UmYKLBqN8uCDD/LXv/6V2tpaAPLy8rjxxhuZOXMmOp2OHTt2oKoqRUVF+yznscce4+WXX+bTTz8lI6PzHdmhDqEBbG/0srPZR7bdjNOiJxITNLUGica0r5loLEam1ci2Bh8GvYLTZKAlFMXtDxKNCepbQrh9IardfkaXZdAj3aZNlwnYWteCJxjBadI6psIMC+lWI5k2Iw6zgVA0RrrZQF1rgGgM9CpsbfCSaTWSYTdiNerJtnecN24NRmj2hshxmPCHo2yqayEWE1iMevrmOTAbdIl948P163fUYLHZWbSpEY/Xx7ur6qhq8uEw6zm1fx5CCD5dW4NeVfn1Gf0oy7VR5wkSjgrG9spCCFi500WmzUhFviPxpZXnNB9UfacS8bp546v1bPfEGFyUTrbdRLrVQF1LsO1FofLuikpW7HCTYdPj9kex6BUmVuSQ77Cxsd7DtgYvY3tl0ivHgapotl4uf5iyLBv+UJTP19cSjsQYXJRGYYYFVVWJCU1BsJn01HoCOEwG0qyGAwvdRextmqc1EOLFRdsRQhtlrGzyM7ZXFm5/mE11LTS0BlEUmFSRy8AeaZiNeryhMK2BKNk2A+GoYFujl3SriVFlmWytb0VVYJcnQHWzn3OH9KAow8qizQ3saPTiMBvIspsYUZqB2xcmJsBq0uHxh8l1mBMvTyEE1e4ARr261+elK+rmUGgNRqj1BBIjWtl2E1aTjrW7PDR7g7y7fBdmvUo4FiPfaaE0x8a2Bh9rq11kWEyMKMtgckUO2xp8BCIRLAY9JZlWaj1BMqx6mn0R8tLMmPU6/KEISpsivnBzIw6TnqIMCwLNrrA400okJrAYdLh8ITbWtZBuNTKiJDNRz6CNzmxv8uIPRinNtmE36Wn2htjR5CPHYSLW2khxcTEPvfMtl4yvoDTbljj27WXadNfU4bvfAdsbvLy7YiejSjMYV75bQfjXV1v5fqeHG07rRVGGpkSEQiH+PHcz2U4zV57cM7HvN1sa+Gh1NT8YXMjIsszE+oc+XEudO8ADU/tjNmv9lMsb4P2VNfTJdXBSmw0ZwNw1NXy9pZGfjCqhT75jvzIv397Mcwu2cNqAXKYO3z37sKrKxU5XgAm9M7G3KVqRSIxVu9w4THrMYTfFxcVyCmwPDvX9nTIKUHs8Hg/AQXcM8U6lV69eOBxaAzSZTHzzzTedOuehKkDHO0eqsz4ekXWzb2Td7BtZN/tG1s2+ideNVICSOdT3d0q6ixxqoy8qKjooGxWJRCKRSCQnJt2qAI0YMYK5c+eSkZHB8OHD92vg/N1333WhZBKJRCKRSI5nulUBuuCCCzCZtPn2qVOndqcoEolEIpFITiC6VQG6++67Ac0A+pRTTmHIkCGkp6d3p0gSiUQikUhOAFLC91in03HmmWfS3Nzc3aJIJBKJRCI5AUgJBQhg0KBBbNmypbvFkEgkEolEcgKQMl5g999/P7fddhv33XcfI0eOxGazJW2X7pASiUQikZyYlP3ug31uW/n7iYdUZsooQPEkpueff36SN5hoyzcTT4chkUgkEolEcrikjAL0+eefd7cIEolEIpFIThBSRgGaPHkyLpeL5557jrVr1wIwYMAApk+fTlpa2gGOlkgkEolE0pXsb1rqWIhInTJG0EuXLqW8vJzZs2fT1NREU1MTs2fPpnfv3jIIokQikUgkkiNKyowA3XLLLfzwhz/kmWeeQa/XxIpEIsyYMYObb76ZL774opsllEgkEolEcryQMgrQ0qVLk5QfAL1ez29/+1tGjRrVjZJJJBKJRCI53kiZKTCn08mOHTs6rK+srExkdpdIJBKJRCI5EqSMAnTJJZcwffp0XnnlFSorK6msrGTOnDnMmDGDn/3sZ90tnkQikUgkkuOIlJkCe+SRR1AUhWnTphGJRAAwGAxcd911PPjgg90snUQikUgkkuOJlFGAjEYjf/7zn5k1axabN28GoHfv3lit1m6WTCKRSCQSyfFGykyBxbFarQwePJjBgwcfkvJz0003UVZWhqIoLF++/MgLKJFIJBKJ5Jgn5RSgw+XHP/4xCxYsoLS0tLtFkUgkEolEkqKkzBTYkWLSpEmHdfwna2qo9tWyvcnHhpoWXL4g9S0h/OEY4gjIpwACTfNU29TPWExbt2f5qgJ2o54sh5FAKIovFCXHYSTbbqJXjp3CDAtVTX4WbW7AH45RlG7BZtJR1xJkYEEat5xezr8XV9HoDVKYbqFvnoPx5dmkWQyHdQ2b61p5/NMNfLO5jjrv7hxtOgViQrtGm0mlJRjrcO0/GJLPTleAJm+QESUZXDiiiD9+8D1ra3yowK2nlzOhIpupT36913qLk2Uz8MCFgzm9fx56nVaRw//3I5p9HXPGLf3NONY1xbj33eVsrA8m1g8vTueBCwcRjMR4bWklDa0hlm1vot4bBuD2s/tyzZQ+fL6ulucXbcMXjFCQbiHXYWJttZu61jD97aFEeU3eEF9tbgSgT56dT1bX8OKibbQEI8REjKBm2oYKJNdM51CBgYVOch1mjHoVVYFFmxrwBiNk2ozkpplYWdWy12MVoFeOjRybgdqWEK2BMK3BCEaDjmhUJNpimsXIPT8cyJpqD5+tq8NsUCnLtrLLFSTTZmT6yWVsqGtlRaULtz9EtTuIUa8ypU8W32xz4QmEybYbOWNAPpmiNUmGQDjKos0NrK32UNnkx+ULYdSrrNvlYltTgFBUJK7TqAMUhVBEoCpae3IHOldrKnt/ntqjA/aVXVABDCqoqkIgIpLWd3hGgd65NtKtBtZVtxCOCvKdRs4ZXMgPh/Zg9icb2FDbSnGGmeunlNMSihIIRyk0BAB4f8Uutnp24guGsRj1DOjhYF1NC75QlFU73XgDYbIcZi4eUcCjn2ygxhPa63Udaps6EEr7v9sqQK8Dq1FHntPCT0YWEwNqPX7W17TQ7AvTK8fG3T8ciMWo48EP17G5vpWRpRncfFofVLXz39yD7v4vqsmaiCh83ux5rK71AjAwz8YHt0wBoPfvPkjcy1dnjGNMeSZ3vL6Ml5fuSpQVL+PMR+exoV4r45qJpdx+3iAaGhoY9cg3Hfa98rlFfL6xGQCnSWXlvecA0O8P/0m0i3vP68svJvbhi+/rmPavJR3KOPmPn7DTo/URZRlm5s08DYBLn/ma73e5ybaZeP3a8WTajbz17Q5ufW0VAihwmlj0+9MBGHbvf3H5tc7jzOL2d0RyuChCiCPxXk85ysrKePvttxk2bNhetweDQYLB3S9Dj8dDcXExd7+2mM+2tNDij+AJRIimYPXoVchzmvGHogQjUXwhresTtCkfRh2qqpDrMJJpM1HtDqAoCmcNzKM0y8bPxx7c6FhVVRXFxcW43W7MVjvX/nMpS7c34wlEDkl+owooCqqi0DPHwtpqb9L2znbmPZwmHr54GBP6ZPO715czZ+nOfe77q9P68Oe5GzusL8m0UJZto7LRR53Lh3ePt+K3fzidHz/1Fb5QmGZvBINOwaBT8AajmA0qIU89Gx6fhsvl4vVVTXj8YWIxwZpqD4u3NtISPPJJfA0qGPUqgXCM6EE2T50K0f1Url4FvU4h3WLEG4wQjsaICbCb9VgMOjKsRqwmPb5QhI21rRhUBUUBVVGIiXgLFIwozaCXJcQ9l07C7XbjdDr5cFU1S7Y18c3WJqpdfnQqNPsiRGKp94wdDgrgNOvIdZqpavYTjQl0qkKe08ykvjlkWI14m2u585JJTH96HmsbI1gMOvzhKNl2Ew2tQYLhGE2+EHpVK1GngD+SWvWkKuA06SnKtFDrCeL2h1EVBYdZz6iyTHqkmfnv6hoAFEXhlxN78ovxPQ9Ybry/Kb75VVSTFZMOFt06lpEPJ38UffubsUx5YkmHZ2zbg+d1SNGQaVG5+fS+3PXeugPuqwBb97L+mpOLWbi5ie9rkvurvZVh0sEXN43ipNlLO+x7x5sr+b8llYl1fXLt/PeWyR3KuGhYD9Isev7x1e59I54Gdv7tikTd7I2uTEHRlakwDpQNPi0tLdHXdJbjbgqss8yaNYu0tLTEr7i4GABfKEI0JghGoqSqbigEBMNRQtEY4ehu5Sf+vwCEEDT5wkSFICYE0VgMbzBCkze0r2I7hT8UpdEbOqy6ib+0Y0JQ7Qp22N7ZL1lPIEyjVzt+ydbmQ5LF7Q8TDMcIxwSBvegqlU0+QpFom9IgiMQEoUis7WW/m0hM4PFrI0fhWAy3L0jwKL2wYgJiMXHQyg9oo437QwgIRwSRSDSxHBMicZzHHyYQiuAPas9HJCYQgD+s7R//YGj2hjrUUZMvRCAcIxyJEY1px+65z/GAQGsPTd4Q8csTQtAaCOMPafUUbmsbLW0fEYGIVsHNbc9nvD7jumEgxZQf0NpGIBIlEIkRisYQQiDQ7mmNO0BVs7/dvoJtDb5DOk8wCh9v6Ph8f7yhmdZOfmA0+WO8v6qmU/vuq6Y/XF3Hpk5eQzAK/1rW0GH9u8sqWVOdPErb0Lr3Pvmbbc18sq6uU+eTHBrH3RRYZ7n99tu59dZbE8tut5uSkhKMsRAWQoR1IQK+MKKtg08VVAVQVew6BV00gp4orlAEITRtVlEVYijoVJVB+Wm0Bn3oIkEMOh0WEaLIbsHj8RzUOeP7ezwe7HZB3wyV7dV+YsFDGwEy6CEqFIx6hdEF2fx3ze6HXK+CyaDiDR5YDSrLclBk0+T65dg8fvvm6r3u5zCpBH2txIIdO68+PTLINESpjwawRH142l2SChRZIUMfpdbnR4QiWIwqRp1KKBLRFJCQ1sn7va30sAq21nsRQlDqVNlWE6LOd3gK554ogN6oogOMMQiEOz/xoQB6vUI4su82rVMVMq0GjIrAHwqhxARGBdRwmKjQUZ5nI0YYEQqjRvwYVIVoFLItBtz+APq2adAiuw01otVNvP0U2QQbRQCzCGKIBiAq0EWiRCNHZnq5O2k/PaYCZr2B/llOvt3RQjSqTXcXZzmxECLgjWJVtHaRbY5R2RTAZjbQGgzTM9fOxnov6XrweYOgQgyFAruByr18LHQnOhUyrWYcShgfIXzhEKigKDpG9NBGgL7fVk00JjDqdYwt7lzfE98n/rz2z7dx3sBcZr66LGm/8wbm8tpXm1laubtMg047fs9n/dS+mVw/uZQfr989Laa0nctBAHe7/ibDrNtrGbdP6ceyXQ089WVywF6Px4MI+pLa8KAeNn45vpg//3dN0r5TeqfR6EpnxZZqYkKgKAqjy514PB50YR/tH+frxpXhtBr4n3+vSKyLtsm0t76svTxdRVfK0ZlzHeyH+Qk7BbYn8WFXiUQikUgkxx6VlZUUFRV1ev/jTgG65ppr+OCDD6ipqSErKwuHw8GmTZsOeFwsFmPXrl04HA4U5cgZmsVtiyorKw9qbvJocSjyRKNRNm3aRHl5OTqdLmXkOlocjCxHq25SqT4OVZ4jWTepVh8Hw95k72zdpPp1Hw35otEoy5cv59RTT03Z6z4SHOozlSp1kyptMy7HmjVrqKioOChD++NuCuzvf//7IR2nqupBaY4Hi9PpTKkH+WDlGT169FGUZjepVE+dleVo1k0q1Qd0f7tJtfo4GPaU/WDqJtWv+0jLN3LkyKNSbipysNeYanWTKnIUFhYelPIDJ7ARtEQikUgkkhMXqQBJJBKJRCI54ZAK0FHGZDJx9913YzKZulsUIPXkiZNKcqWCLKkgQ3u6W57uPv/hcDiyp/p1Hy35Uv26jwSHeo2pUjfHgxzHnRG0RCKRSCQSyYGQI0ASiUQikUhOOKQCJJFIJBKJ5IRDKkASiUQikUhOOKQCJJFIJBKJ5JilufnQckEed4EQU4FoNMr8+fPZsUPLGVNSUsLkyZOPWhTlY0WWPUkV2VJFjlSju+ulu89/uGzZsiVJ9l69enWzREeGo3VfjvX7fSCO9evbvHkzM2bMYPv27UydOpU//vGPmM1mAMaNG8dXX33VJXIsX76cK664AlVV+ec//8lvf/tbPv/8c7Kzs3n//fcZMmRIp8uSCtAR5ssvv+TSSy+lsLCQ0tJSALZt28auXbt4+eWXmTRp0gkpS6rKlipyxEmVl2Z310t3n/9wWLt2Lb/4xS+orKykpKQEgB07dlBcXMzzzz/PwIEDD1hGqrSDPTla9+VYvt+d4WhdX9++fdmwYcORFHWfXH/99fz4xz9m7Nix/PnPf+a0007jo48+wuFwEAgEukQGgF/96lfcc889uFwuzj33XO6//34++OAD3n77bW677TY+/vjjzhcmJEeUwYMHiyVLlnRYv3jxYjFo0KATVpY9SRXZUkWONWvWiNGjR4v8/HwxZswYMWbMGJGfny9Gjx4tvv/++y6TI05310t3n/9wGDNmjHj99dc7rH/ttdfE6NGj93tsqrWDPTla9+VYvt+d4XCub8WKFfv85efnHy2ROzBs2LCk5QceeECMHj1auFwuMXz48G6Ro7i4OGnb0KFDD6osqQAdYfr06XNI244GqSTLwZy/K2VLFTkO56V5NOjueunu8x8Offv2PaRtQqReO9iTo3VfjuX73RkO5/oURRE9e/YUZWVlHX4Gg+FIi7pPKioqOqx7+OGHxciRI0V5eXmXydFeyZk2bVrStiFDhhxUWdII+gjTu3dv/vd//5e6urrEurq6Ou6991569ux5wsqyJ6kiW6rI4XK5uOiiizqs//GPf4zb7e4yOeJ0d7109/kPh+zsbP75z38Si8US62KxGC+++CJZWVn7PTbV2sGeHK37cizf785wONdXWlrKggUL2Lp1a4dfXl7e0RY9Qf/+/fnoo4+S1t12221ceumlbN68ucvkyMvLw+PxAPDiiy8m1ldXVydskjrNkdDIJLupq6sTV155pbDb7cJsNguz2Szsdru48sorRW1tbZfLctVVV6WELKkqW6rIMX78ePHSSy+JaDSaWBeNRsULL7wgxo0b12VyxOnueunu8x8OGzduFKeeeqpIS0sT/fr1E/369RNpaWnilFNOEevXr9/vsanWDvbkaPVvqdRvHg32vD6TydTp67vpppvEl19+uddt11xzzdEQd68EAgERCAT2uq2qqqrL5NgXLpdLbN++/aCOkakwjiJNTU0AZGZmdrMku2V59dVXufbaa7tZmmRSpZ66U45NmzZxzTXX8O2339KjRw9A+6IZMWIETz31FH379u1ymeJ09/3p7vMfKvX19VRWVgJQXFxMTk7OAY9J5XawJ0fyvrhcLtLT0494uamE1+vFaDTS0tJCJBJh5cqV9OvXj6Kiou4W7YRFKkBHmFRxFQR49913O6y7+uqreeaZZxBCcP7553eZLHtjT7fQ0tJSJk2a1O1u8N0lBxzaS/No0d310t3nP1wOx5MrldpBe45W/2Y0GjnnnHOYMWMG5513Hqp6fFlnvPTSS1xzzTVkZ2fz4osv8vOf/5yioiK2bNnCk08+ySWXXNLdIp6QSAXoCHPWWWdx/vnnJ1wFN2/enHAVHD58OMuWLesyWVRVZdy4cRiNxsS6r7/+mrFjx6IoCp999lmXybIncbfQgoICysrKgO51g+9uOeKkivtzd9dLd5//cFizZg1XXHHFYbnB742udHneF0erf6uoqODqq6/mueeew+VyMW3aNK666qqUGvE6HIYMGcJ7772H2+1m0qRJfPrpp4waNYpNmzZx0UUXsWLFiu4W8cTkSM/DneikiqugEEL84x//EOPHjxffffddYl1ZWVmXyrAvUsXtNVXkWL16dUq5P3d3vXT3+Q+Hw/HkShWX531xtPq39scuXLhQTJ8+XTgcDjFx4kTx4osvHnK5qUL7eistLd3nNknXIhWgI0yquArG2bZtmzj99NPFvffeKyKRiOjZs2eXy7A3UsXtNVXkSDX35+6ul+4+/+FwOG7wqeLyvC+OVv+2N+WptbVVPPvss+Lkk08+5HJThREjRojvv/9efPnllyI7O1ssWLBACCHE2rVrxeDBg7tZuhOX42uiNQVIFVfBOKWlpXz88cfYbDYmTpxIMBjschn2Rqq4vaaKHKnm/tzd9dLd5z8cDscNPlVcnvfF0erfxF4sMWw2G9OnT2fBggWHXG6qcN999zFp0iQuvPBC5syZwx/+8Af69evHSSedxB133NHd4nWKe+65h2HDhh3UMVOmTOHmm28+KvIcEbpbAzveSGVXwe+//1787W9/61YZ4qSK22uquFunmvtzd9+fVLkvh8LhuMGnisvzvjha/VtjY+MhH3ssEolExNKlS1O+LbenpaVFNDQ0HNQxjY2NwuPxHCWJDh9pBC3pdlLF7bU7QwWksvtzd9+f7j7/oZKqnlwSycEghCAajaLXH3+pQ+UUmKRb2Lx5M6eccgq9evXi/vvvx2q1JraNGzeuy+R49913E78FCxawYMEC7rnnHt577729hhE4WpSXlzN37lw2btzIyy+/zMsvv8zGjRv57LPPukX5SZX7A5rik5mZSXNzc5ee93BpaWnB4/Hg8XhoaWnpbnEkkgTBYJCbbrqJ3NxczGYzEyZMYMmSJQDMmzcPRVH48MMPGTlyJCaTKdEvtp8Ci0Qi3HTTTaSnp5OVlcXMmTP5xS9+wdSpUxP77DkFVlZWxh//+EeuuuoqHA4HJSUlPP3001101R2RCpCkW4hnFn7ttddoaGjgtNNOS7wkujKz8NSpU3nooYeYPXt24ud2u3nsscd4/PHHu0yOODk5OYwYMYIRI0YkRgy6QwHq7vvz5z//OfH31q1bGThwIAUFBfTs2ZNVq1Yd9fMfDmvWrGHMmDGcfPLJzJw5k5kzZ3LyySczZswYVq9e3d3iSST89re/5Y033uDFF1/ku+++o7y8nLPOOisx2grwu9/9jgcffJC1a9cyZMiQDmU89NBDvPzyyzz//PMsXLgQj8fD22+/fcBzP/roo4waNYply5Zx/fXXc91117F+/fojeXmdp5un4CQnKKkSLiBVQgWkmvtzd9+f9uf46U9/Kv76178KIYR4/fXXxemnn37Uz384pJpHn0TSntbWVmEwGMTLL7+cWBcKhURBQYH405/+JD7//HMBiLfffjvpuLvvvjspEWleXp54+OGHE8uRSESUlJSICy64ILFu8uTJ4le/+lViubS0VPz85z9PLMdiMZGbm9tttqnH36Se5JjA7/cnLf/+97/HaDQmjTR0BVdeeSWnnnoqM2bMYOLEidxxxx0oitJl548zbNgwysrK9uoN09jY2OXypMr9AW1E5f/+7/8AuOiii7jvvvu69PwHy/48+o4Vjx/J8cvmzZsJh8OcfPLJiXUGg4ExY8awdu1aRo8eDcCoUaP2WYbb7aa2tpYxY8Yk1ul0OkaOHJnk/bg32o8mKYpCfn5+krdnVyKnwCTdQiqFC0iFUAGp5v7c3ffH5XIl7LDC4XDStr0pianE4bjBH2leeOGFRI6t7mDbtm0oisLy5cu7TQbJoWGz2Y5KuQaDIWlZUZQDKk1HC6kASbqFOXPmcMopp3RYf+uttyY8Z7oSRVH49a9/zTPPPMOdd97Z5ec///zz2bJly163nXfeeV0sTfffn5KSEh577DFmz55NXl4eO3fuBLRYQO1Tu6QiL774Ii+88AKZmZn079+ffv36kZGRkVh/vHLFFVckGcCC5v1WXV3NoEGDukcoSQd69+6N0Whk4cKFiXXhcJglS5YwYMCATpWRlpZGXl5ewnAatNx933333RGX92gip8Ak3YLJZNrntsLCwi6UJJmBAwcecq6mw6G90e+ePPXUU10oiUZ335958+btdX1WVhbz588/6uc/HOIefe3d4EtKSsjOzu5myQ6NcDjc4au9s+h0OvLz84+wRMcG0WgURVFSLrGrzWbjuuuu4ze/+Q2ZmZmUlJTwpz/9CZ/Px/Tp0zudl+zGG29k1qxZlJeX069fP/7yl7/Q3NzcLSYEh0pq3ZnjkClTpnDjjTdy8803k5GRQV5eHs888wxer5crr7wSh8NBeXk5H374IbDbBfGDDz5gyJAhmM1mxo4dy/fff59U7jPPPENxcTFWq5ULL7yQxx57rFuHuiWSI8mWLVs49dRT6dWrF7feemvC80yn03Haaad1s3T757XXXgM0j76SkhKuvfZa8vPz0ev1ZGRk8IMf/CBpGrGqqoqf/exnZGZmYrPZGDVqFN98801i+3vvvcfo0aMxm81kZ2dz4YUXJrYFg0Fuu+02CgsLsdlsnHTSSftUHuO88847jBgxArPZTK9evbj33nuJRCKJ7Yqi8Le//Y3zzz8fm83GAw88QDQaZfr06fTs2ROLxUJFRUWS0n7PPffw4osv8s4776AoCoqiMG/evL1Ogc2fP58xY8ZgMpno0aMHv/vd75LOP2XKFG666SZ++9vfkpmZSX5+Pvfcc8/B3oYkXnrpJbKysjpMb0+dOpXLL7+8U/Xy2GOPMXjwYGw2G8XFxVx//fW0trYmtsenG999910GDBiAyWRKJDZONR588EEuuugiLr/8ckaMGMGmTZv473//S0ZGRqfLmDlzJj/72c+YNm0a48aNw263c9ZZZ2E2m4+i5EeYbjG9PoGYPHmycDgc4r777hMbNmwQ9913n9DpdOKcc84RTz/9tNiwYYO47rrrRFZWlvB6vQkL/P79+4uPP/5YrFy5UvzgBz8QZWVlIhQKCSGEWLBggVBVVTz88MNi/fr14sknnxSZmZkiLS2tey9WIjlCnHnmmeKvf/2rWLp0qbj88svF+PHjExFlUz15ZHsPthkzZoipU6eKZ599Vtx+++1iypQp4oc//KEYPHiwiEajoqWlRfTq1UtMnDhRfPnll2Ljxo3ilVdeEYsWLRJCCPH+++8LnU4n7rrrLrFmzRqxfPly8cc//jGp/PHjx4svvvhCbNq0STz88MPCZDKJDRs2CCGEeP7555P6hS+++EI4nU7xwgsviM2bN4uPP/5YlJWViXvuuSexDyByc3PFP/7xD7F582axfft2EQqFxF133SWWLFkitmzZIv71r38Jq9UqXnnlFSGEFiX4Jz/5iTj77LNFdXW1qK6uFsFgUGzdulUAYtmyZUIILVq01WoV119/vVi7dq146623RHZ2trj77rsT5588ebJwOp3innvuERs2bBAvvviiUBRFfPzxx4d8T3w+n0hLSxOvvvpqYl1tba3Q6/Xis88+61S9zJ49W3z22Wdi69atYu7cuaKiokJcd911ie3PP/+8MBgMYvz48WLhwoVi3bp1wuv1HrLMxxrRaFT07dtX/OEPf+huUTqNVICOMpMnTxYTJkxILEciEWGz2cTll1+eWFddXS0A8dVXXyUUoDlz5iS2NzY2CovFkuhsLrnkEnHeeeclneeyyy6TCpDkuKG73fAPh/ayDxkyREQikaTl+vp6AYhVq1aJv//978LhcOwzFcS4cePEZZddttdt27dvFzqdTuzcuTNp/WmnnSZuv/12IURHBei0005LUqCEEOKf//yn6NGjR2IZEDfffPMBr/N//ud/xEUXXZRY/sUvfpHkAi2E6KAA/f73vxcVFRUiFosl9nnyySeF3W5PpIHZs88UQojRo0eLmTNnHlCm/XHdddeJc845J7H86KOPil69eolYLNapetmT1157TWRlZSWWn3/+eQGI5cuXH5acxwrbtm0TTz/9tFi/fr1YuXKluPrqq4XBYBBr1qzpbtE6jbQB6gLau/3pdDqysrIYPHhwYl3cy6eurg6n0wkkR9vNzMykoqKCtWvXArB+/fqkYXCAMWPG8P777x+1a5BIupJUcsM/WAKBAKtWrUIIgaIobNmyhbvuuotvvvmG7du3U1ZWBsCOHTtYvnw5w4cP32eaj+XLl/PLX/5yr9tWrVpFNBrtECgzGAzu09tsxYoVLFy4kAceeCCxLhqNEggE8Pl8iYjfe3OBfvLJJ/nHP/7Bjh078Pv9hEKhg06OuXbtWsaNG5dkJ3LyySfT2tpKVVUVJSUlAB0C7/Xo0eOwXaV/+ctfMnr0aHbu3ElhYSEvvPACV1xxBYqidKpePv30U2bNmsW6devweDxEIpEO9WY0GvcaNPB4RFVVXnjhBW677TaEEAwaNIhPP/2U/v37d7donUYqQF3A3tz+2q+Ldwbd5Qq4N6ZMmcKwYcN4/PHHKSsr4+abbz5iWX0VReGtt97q4DFyrNC+biRHh7gb/tlnn51Yd9ttt6GqKrfddls3SnZg/H4/F1xwQcJd/5xzzqF37948/vjj/O53v+O1115j0KBBhEIhLBbLfsva3/bW1lZ0Oh3ffvstOp0uaZvdbt/nMffeey8/+tGPOmxrb7uxpwv0nDlzuO2223j00UcZN24cDoeDhx9+OMlW6UhyNFylhw8fztChQ3nppZc488wzWb16NR988AFw4HrZtm0bP/jBD7juuut44IEHyMzMZMGCBUyfPp1QKJRQgCwWyzFlBHw4FBcXJ3mSHYtIBShF+frrrxNfQ83NzWzYsCGhWVdUVCS5HwIdlo8kS5YsOWoxIY53rrjiClwuV6dCxEt2M2fOnL2uv/XWW7nkkku6WJqDY9u2bYm/Gxsbyc7O5vnnn2fkyJF88MEHCZd+0EY6nn32WZqamvY6CjRkyBDmzp3LlVde2WHb8OHDiUaj1NXVMXHixE7JNmLECNavX095eflBXdPChQsZP348119/fWLdnvGgjEYj0Wh0v+X079+fN954IzE6Fi/b4XBQVFR0UDIdCjNmzODxxx9n586dnH766RQXFwMHrpdvv/2WWCzGo48+mvDqevXVV4+6vJKji/QCS1H+93//l7lz5/L9999zxRVXkJ2dnRgxufHGG/nPf/7DY489xsaNG/n73//Ohx9+eNS+PHJycpKSYUqOLKFQqLtF6DI6e60mk2mfrvjdGSbhYMnIyCArK4unn36aXbt2sXXrVm699dbE9p/97Gfk5+czdepUFi5cyJYtW3jjjTf46quvALj77rv5v//7P+6++27Wrl3LqlWreOihhwAtR9xll13GtGnTePPNN9m6dSuLFy9m1qxZiZGNPbnrrrt46aWXuPfee1m9ejVr165lzpw5/OEPf9jvdfTp04elS5fy3//+lw0bNnDnnXd2+OgqKytj5cqVrF+/noaGhg4BLEHLMVdZWcmNN97IunXreOedd7j77ru59dZbu8Rd/NJLL6WqqopnnnmGq666KrH+QPVSXl5OOBzmL3/5C1u2bOGf//xnt4SnkBxZpAKUojz44IP86le/YuTIkdTU1PDee+8lAsCdfPLJPPXUUzz22GMMHTqUjz76iFtuueWQ3Q+9Xi/Tpk3DbrfTo0cPHn300aTtZWVliekeIQT33HMPJSUlmEwmCgoKuOmmm5L2ve+++/jZz36GzWajsLCQJ598cr/nnzlzJn379sVqtdKrVy/uvPPODp3n4bgCx91T33//fSoqKrBarfz4xz/G5/Px4osvUlZWRkZGBjfddFPSF+z+yo3FYpx77rmoqkpmZibZ2dnY7XbOPvtslixZwgUXXIDRaNyra3A8q/Kzzz5Lz549E/ft9ddfZ/DgwVgsFrKysjj99NPxer0JeZ599ln69++P2WymX79+/L//9/8S2+LuxnPmzGH8+PGYzWYGDRrUIWbO/lyQ33//fdLT0xN1sHz5chRF4Xe/+13i+BkzZvDzn/88sbxgwQImTpyIxWKhuLiYm266KUnmeHuYNm0aTqeTq6++er9t4XhDVVXmzJnDt99+y6BBg7jlllt4+OGHE9uNRiMff/wxubm5nHvuuQwePJgHH3wwMaU1ZcoUXnvtNd59912GDRvGqaeeyuLFixPHP//880ybNo1f//rXVFRUMHXqVJYsWZIYPd6Ts846i/fff5+PP/6Y0aNHM3bsWGbPnk1pael+r+Oaa67hRz/6EZdccgknnXQSjY2NSaNBoNnYVFRUMGrUKHJycvY6PVJYWMh//vMfFi9ezNChQ7n22muZPn36ARWwI0VaWhoXXXQRdrs9aQr+QPUydOhQHnvsMR566CEGDRrEyy+/zKxZs7pEZslRpFtNsCUdiHuBNTc3H9RxM2bM6OA50Vmuu+46UVJSIj799NOE273D4UgksSstLRWzZ88WQmieD06nU/znP/8R27dvF9988414+umnE2WVlpYKh8MhZs2aJdavXy+eeOIJodPpklxYAfHWW28llu+77z6xcOFCsXXrVvHuu++KvLw88dBDDyW2HwlXYIPBIM444wzx3Xffifnz54usrCxx5plnip/85Cdi9erV4r333hNGozHJ+25f5Y4ZM0Y4nU5xwQUXCL1eLwYMGCAURRFPPvmk6Nevn8jIyBATJkwQX3zxhTjttNOE0+kU48aNS7gG33333cJms4mzzz5bfPfdd2LFihVi165dQq/Xi8cee0xs3bpVrFy5Ujz55JOipaVFCCHEv/71L9GjRw/xxhtviC1btog33nhDZGZmihdeeEEIsdvbpqioSLz++utizZo1YsaMGcLhcIiGhgYhxIFdkF0ul1BVVSxZskQIIcTjjz8usrOzxUknnZSok/LycvHMM88IIYTYtGmTsNlsYvbs2WLDhg1i4cKFYvjw4eKKK65Iag9Op1M88sgjYtOmTWLTpk0H0TIlkiPPqaeeKm688cbuFkOSAkgFKMXorAL08MMPi+XLl4uNGzeKJ554QhgMhsSL6WBoaWkRRqMxKT5G3O1+bwrQo48+Kvr27ZuISbQnpaWl4uyzz05ad8kllyS5n+6pAO3t2kaOHJlYPhKuwEDSy/eaa64RVqs1oWAIIcRZZ50lrrnmmgOWW1JSIiZMmJBUbtxN94YbbhCA2LFjhxBCcw0+5ZRTBCAWL14shNCyKhsMBlFXV5co99tvvxWA2LZt216vs3fv3uLf//530rr77rtPjBs3TgixWwF68MEHE9vD4bAoKipKKJOdcUEeMWJEIsPz1KlTxQMPPCCMRqNoaWkRVVVVAkgoltOnTxdXX311kkxffvmlUFVV+P1+IYTWHqZOnbrXa5JIupKmpibx5ptvClVVxbp167pbHEkKIKfAjlEWL17MGWecweDBg3nqqad44oknmDFjxkGXs3nzZkKhECeddFJiXdztfm9cfPHF+P1+evXqxS9/+UveeuutpGipkOzCH1+Ou/DvjVdeeYWTTz6Z/Px87HY7f/jDH5IiqC5fvnyf0X/buwLb7fbEb/78+UlGmlarld69eyeW8/LyKCsrS/KWycvLS7ja7q9cv9+fcHWNlxt30427b8eNKwGcTifp6elJdVBaWkpOTk5ieejQoZx22mkMHjyYiy++mGeeeYbm5mZAm6LcvHkz06dPT5Ll/vvv72CI2r7u9Xo9o0aNSpz3QC7IAJMnT2bevHkIIfjyyy/50Y9+RP/+/VmwYAHz58+noKCAPn36AJpL9QsvvJAk01lnnUUsFmPr1q2Jc+wvq7RE0lUMHz6cK664goceemif/ZvkxEJ6gaUYU6ZM6VS26+7yQCguLmb9+vV8+umnfPLJJ1x//fU8/PDDzJ8//5DyBX311Vdcdtll3HvvvZx11lmkpaUxZ86cJDukI+EKfKBQBPF1cVfb/ZU7bdq0xLHx/+PHdtaQc0+vOp1OxyeffMKiRYv4+OOP+ctf/sIdd9zBN998kzBAf+aZZ5IU1fhxR5IpU6bwj3/8gxUrVmAwGOjXrx9Tpkxh3rx5NDc3M3ny5MS+ra2tXHPNNUk2YHHa26B0twfhwYZx2LZtGz179mTZsmUHHedGkrq0986TSEAaQZ/w9O7dG4PBkBTPI+52vy8sFgs//OEPeeKJJ5g3bx5fffUVq1atSmz/+uuvk/b/+uuv9xkca9GiRZSWlnLHHXcwatQo+vTpw/bt25P2ibsC7432rsDl5eVJv8NJwri/cveXjTzuyhtPgmk0GnG5XLhcrgNmWlYUhZNPPpl7772XZcuWYTQaeeutt8jLy6OgoIAtW7Z0kKVnz55JZbSv+0gkwrfffpuo+/79+/PVV18lKdh7uiBPnDiRlpYWZs+enVB24grQvHnzmDJlSuLYESNGsGbNmg4yHaiOupolS5YccePruGG9RCI5dpEjQCc4drud6dOn85vf/IasrCxyc3O544479jmS8cILLxCNRjnppJOwWq3861//wmKxJHmRLFy4kD/96U9MnTqVTz75hNdee22fbrl9+vRhx44dzJkzh9GjR/PBBx/w1ltvJe1z9913c9ppp9G7d29++tOfEolE+M9//pPwHou7Aj/66KMMHz6c+vp65s6dy5AhQzjvvPMOqV72V25jY+M+jxs6dCgAl112GY8//jg6nY6vvvqK0aNHU1ZWtlfXYIBvvvmGuXPncuaZZ5Kbm8s333xDfX19Qnm59957uemmm0hLS+Pss88mGAyydOlSmpubk9yqn3zySfr06UP//v2ZPXs2zc3NCXff66+/nscff5wbb7yRG264gfXr13dwQc7IyGDIkCG8/PLL/PWvfwVg0qRJ/OQnPyEcDieNAM2cOZOxY8dyww03MGPGDGw2G2vWrOGTTz5JHJsKtJ9qlEgkkjhyBEjCww8/zMSJE/nhD3/I6aefzoQJExg5cuRe901PT+eZZ57h5JNPZsiQIXz66ae89957SaH3f/3rX7N06VKGDx/O/fffz2OPPcZZZ5211/LOP/98brnlFm644QaGDRvGokWLuPPOO5P2OdKuwJ1lX+XuL9xA3L4mIyODSZMm8a9//YuMjAxWr169T9dg0OyEvvjiC84991z69u3LH/7wBx599FHOOeccQHM/f/bZZ3n++ecZPHgwkydP5oUXXugwAvTggw/y4IMPMnToUBYsWMC7775LdnY20HkX5MmTJxONRhOjPZmZmQwYMID8/Pwk24khQ4Ywf/58NmzYwMSJExk+fDh33XUXBQUFB1fRe3Cw7vidccVvH7V73bp1TJgwAbPZzIABA/j0009RFKVDsMotW7ZwyimnYLVaGTp0aCI2z7x587jyyitxu92J8AaHm61cIpF0A91shC05zmjvMSbpOvZMOnksczDu+J11xY+3yUgkIioqKsQZZ5whli9fLr788ksxZsyYJM/EeF3269dPvP/++2L9+vXixz/+sSgtLRXhcFgEg0Hx+OOPC6fTmch83t6bUCKRHBvIESCJRJJSpKWlMWzYsETQyXnz5nHLLbewbNkyWltb2blzJ5s2bWLy5MnMmjWLyy67jJtvvpk+ffowfvx4nnjiCV566SUCgUCHsj/55BM2b97MSy+9xNChQ5kwYUJSAsz23HbbbZx33nn07duXe++9l+3bt7Np0yaMRiNpaWkoikJ+fn7Ce1EikRxbSAVIIpGkHJ11x++sK36c9evXU1xcnGQgP2bMmL3K0D6rd48ePQAOOyO5RCJJHaQRtOSIIl1Nu4eysrJOhU84VuisO35nXfEPhfZhEuK2XYebkVwikaQOUgGSSCQpx77c8R988EGam5v59a9/DSS74neGiooKKisrqa2tJS8vD6BDUs/O0JnM5xKJJLWRU2ASiSTlaO+OH/dGmzRpEt999x0bNmxIKEUzZ85k0aJF3HDDDSxfvpyNGzfyzjvvcMMNN+y13DPOOIPevXvzi1/8gpUrV7Jw4cKEF1z7CNkHoqysjNbWVubOnUtDQwM+n+/wLlgikXQ5UgGSSCQpSWfc8Q/WFV+n0/H222/T2trK6NGjmTFjBnfccQfAfsMb7Mn48eO59tprueSSS8jJyeFPf/rT4V2sRCLpchRxPBkOSCQSyUGycOFCJkyYwKZNm5LyxUkkkuMbqQBJJJITirfeegu73U6fPn3YtGkTv/rVr8jIyGDBggXdLZpEIulCpBG0RCI5oWhpaWHmzJns2LGD7OxsTj/99KTkuxKJ5MRAjgBJJBKJRCI54ZBG0BKJRCKRSE44pAIkkUgkEonkhEMqQBKJRCKRSE44pAIkkUgkEonkhEMqQBKJRCKRSE44pAIkkUgkEonkhEMqQBKJRCKRSE44pAIkkUgkEonkhEMqQBKJRCKRSE44/j9YgLWKij+lEQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pd.plotting.scatter_matrix(Auto);\n" ] @@ -2962,12 +8096,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "6bbeca8b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:09.327848Z", + "iopub.status.busy": "2023-07-25T23:59:09.327740Z", + "iopub.status.idle": "2023-07-25T23:59:09.583864Z", + "shell.execute_reply": "2023-07-25T23:59:09.583496Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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mpgweight
count392.000000392.000000
mean23.4459182977.584184
std7.805007849.402560
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50%22.7500002803.500000
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" + ], + "text/plain": [ + " mpg weight\n", + "count 392.000000 392.000000\n", + "mean 23.445918 2977.584184\n", + "std 7.805007 849.402560\n", + "min 9.000000 1613.000000\n", + "25% 17.000000 2225.250000\n", + "50% 22.750000 2803.500000\n", + "75% 29.000000 3614.750000\n", + "max 46.600000 5140.000000" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Auto[['mpg', 'weight']].describe()\n" ] @@ -3004,12 +8248,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "id": "39bf4091", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:09.593932Z", + "iopub.status.busy": "2023-07-25T23:59:09.593794Z", + "iopub.status.idle": "2023-07-25T23:59:09.598241Z", + "shell.execute_reply": "2023-07-25T23:59:09.597941Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "count 392.000000\n", + "mean 23.445918\n", + "std 7.805007\n", + "min 9.000000\n", + "25% 17.000000\n", + "50% 22.750000\n", + "75% 29.000000\n", + "max 46.600000\n", + "Name: mpg, dtype: float64" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Auto['cylinders'].describe()\n", "Auto['mpg'].describe()\n" @@ -3029,6 +8298,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch3-linreg-lab.ipynb b/docs/source/labs/Ch3-linreg-lab.ipynb index db9b4c2..8cf09d3 100644 --- a/docs/source/labs/Ch3-linreg-lab.ipynb +++ b/docs/source/labs/Ch3-linreg-lab.ipynb @@ -23,9 +23,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ca5277a6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:10.810120Z", + "iopub.status.busy": "2023-07-25T23:59:10.809784Z", + "iopub.status.idle": "2023-07-25T23:59:11.236722Z", + "shell.execute_reply": "2023-07-25T23:59:11.236402Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -50,9 +56,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "675f24e6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.238770Z", + "iopub.status.busy": "2023-07-25T23:59:11.238614Z", + "iopub.status.idle": "2023-07-25T23:59:11.732369Z", + "shell.execute_reply": "2023-07-25T23:59:11.731999Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -77,9 +89,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "a0ee23c2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.734483Z", + "iopub.status.busy": "2023-07-25T23:59:11.734356Z", + "iopub.status.idle": "2023-07-25T23:59:11.737321Z", + "shell.execute_reply": "2023-07-25T23:59:11.737059Z" + } + }, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence \\\n", @@ -101,9 +120,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "b35eb887", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.738949Z", + "iopub.status.busy": "2023-07-25T23:59:11.738837Z", + "iopub.status.idle": "2023-07-25T23:59:11.834618Z", + "shell.execute_reply": "2023-07-25T23:59:11.834237Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -126,12 +152,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "961908f7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.836672Z", + "iopub.status.busy": "2023-07-25T23:59:11.836534Z", + "iopub.status.idle": "2023-07-25T23:59:11.839996Z", + "shell.execute_reply": "2023-07-25T23:59:11.839716Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['In',\n", + " 'MS',\n", + " 'Out',\n", + " 'VIF',\n", + " '_',\n", + " '__',\n", + " '___',\n", + " '__builtin__',\n", + " '__builtins__',\n", + " '__doc__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " '_dh',\n", + " '_i',\n", + " '_i1',\n", + " '_i2',\n", + " '_i3',\n", + " '_i4',\n", + " '_i5',\n", + " '_ih',\n", + " '_ii',\n", + " '_iii',\n", + " '_oh',\n", + " 'anova_lm',\n", + " 'exit',\n", + " 'get_ipython',\n", + " 'load_data',\n", + " 'np',\n", + " 'open',\n", + " 'pd',\n", + " 'poly',\n", + " 'quit',\n", + " 'sm',\n", + " 'subplots',\n", + " 'summarize']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dir()\n" ] @@ -154,12 +233,190 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "662caa15", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.841679Z", + "iopub.status.busy": "2023-07-25T23:59:11.841579Z", + "iopub.status.idle": "2023-07-25T23:59:11.844762Z", + "shell.execute_reply": "2023-07-25T23:59:11.844485Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['T',\n", + " '__abs__',\n", + " '__add__',\n", + " '__and__',\n", + " '__array__',\n", + " '__array_finalize__',\n", + " '__array_function__',\n", + " '__array_interface__',\n", + " '__array_prepare__',\n", + " '__array_priority__',\n", + " '__array_struct__',\n", + " '__array_ufunc__',\n", + " '__array_wrap__',\n", + " '__bool__',\n", + " '__class__',\n", + " '__complex__',\n", + " '__contains__',\n", + " '__copy__',\n", + " '__deepcopy__',\n", + " '__delattr__',\n", + " '__delitem__',\n", + " '__dir__',\n", + " '__divmod__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__float__',\n", + " '__floordiv__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__iadd__',\n", + " '__iand__',\n", + " '__ifloordiv__',\n", + " '__ilshift__',\n", + " '__imatmul__',\n", + " '__imod__',\n", + " '__imul__',\n", + " '__index__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__int__',\n", + " '__invert__',\n", + " '__ior__',\n", + " '__ipow__',\n", + " '__irshift__',\n", + " '__isub__',\n", + " '__iter__',\n", + " '__itruediv__',\n", + " '__ixor__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lshift__',\n", + " '__lt__',\n", + " '__matmul__',\n", + " '__mod__',\n", + " '__mul__',\n", + " '__ne__',\n", + " '__neg__',\n", + " '__new__',\n", + " '__or__',\n", + " '__pos__',\n", + " '__pow__',\n", + " '__radd__',\n", + " '__rand__',\n", + " '__rdivmod__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__rfloordiv__',\n", + " '__rlshift__',\n", + " '__rmatmul__',\n", + " '__rmod__',\n", + " '__rmul__',\n", + " '__ror__',\n", + " '__rpow__',\n", + " '__rrshift__',\n", + " '__rshift__',\n", + " '__rsub__',\n", + " '__rtruediv__',\n", + " '__rxor__',\n", + " '__setattr__',\n", + " '__setitem__',\n", + " '__setstate__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__sub__',\n", + " '__subclasshook__',\n", + " '__truediv__',\n", + " '__xor__',\n", + " 'all',\n", + " 'any',\n", + " 'argmax',\n", + " 'argmin',\n", + " 'argpartition',\n", + " 'argsort',\n", + " 'astype',\n", + " 'base',\n", + " 'byteswap',\n", + " 'choose',\n", + " 'clip',\n", + " 'compress',\n", + " 'conj',\n", + " 'conjugate',\n", + " 'copy',\n", + " 'ctypes',\n", + " 'cumprod',\n", + " 'cumsum',\n", + " 'data',\n", + " 'diagonal',\n", + " 'dot',\n", + " 'dtype',\n", + " 'dump',\n", + " 'dumps',\n", + " 'fill',\n", + " 'flags',\n", + " 'flat',\n", + " 'flatten',\n", + " 'getfield',\n", + " 'imag',\n", + " 'item',\n", + " 'itemset',\n", + " 'itemsize',\n", + " 'max',\n", + " 'mean',\n", + " 'min',\n", + " 'nbytes',\n", + " 'ndim',\n", + " 'newbyteorder',\n", + " 'nonzero',\n", + " 'partition',\n", + " 'prod',\n", + " 'ptp',\n", + " 'put',\n", + " 'ravel',\n", + " 'real',\n", + " 'repeat',\n", + " 'reshape',\n", + " 'resize',\n", + " 'round',\n", + " 'searchsorted',\n", + " 'setfield',\n", + " 'setflags',\n", + " 'shape',\n", + " 'size',\n", + " 'sort',\n", + " 'squeeze',\n", + " 'std',\n", + " 'strides',\n", + " 'sum',\n", + " 'swapaxes',\n", + " 'take',\n", + " 'tobytes',\n", + " 'tofile',\n", + " 'tolist',\n", + " 'tostring',\n", + " 'trace',\n", + " 'transpose',\n", + " 'var',\n", + " 'view']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A = np.array([3,5,11])\n", "dir(A)\n" @@ -176,12 +433,29 @@ }, { "cell_type": "code", - 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interceptlstat
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coefstd errtP>|t|
intercept34.55380.56361.4150.0
lstat-0.95000.039-24.5280.0
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interceptlstat
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interceptlstat
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OLS Regression Results
Dep. Variable: medv R-squared: 0.544
Model: OLS Adj. R-squared: 0.543
Method: Least Squares F-statistic: 601.6
Date: Tue, 25 Jul 2023 Prob (F-statistic): 5.08e-88
Time: 16:59:11 Log-Likelihood: -1641.5
No. Observations: 506 AIC: 3287.
Df Residuals: 504 BIC: 3295.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
intercept 34.5538 0.563 61.415 0.000 33.448 35.659
lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874
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Omnibus: 137.043 Durbin-Watson: 0.892
Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373
Skew: 1.453 Prob(JB): 5.36e-64
Kurtosis: 5.319 Cond. No. 29.7


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: medv R-squared: 0.544\n", + "Model: OLS Adj. R-squared: 0.543\n", + "Method: Least Squares F-statistic: 601.6\n", + "Date: Tue, 25 Jul 2023 Prob (F-statistic): 5.08e-88\n", + "Time: 16:59:11 Log-Likelihood: -1641.5\n", + "No. Observations: 506 AIC: 3287.\n", + "Df Residuals: 504 BIC: 3295.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "intercept 34.5538 0.563 61.415 0.000 33.448 35.659\n", + "lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874\n", + "==============================================================================\n", + "Omnibus: 137.043 Durbin-Watson: 0.892\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373\n", + "Skew: 1.453 Prob(JB): 5.36e-64\n", + "Kurtosis: 5.319 Cond. No. 29.7\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results.summary()\n" ] @@ -418,12 +1091,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "a0edf555", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.951447Z", + "iopub.status.busy": "2023-07-25T23:59:11.951309Z", + "iopub.status.idle": "2023-07-25T23:59:11.954106Z", + "shell.execute_reply": "2023-07-25T23:59:11.953845Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "intercept 34.553841\n", + "lstat -0.950049\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results.params\n" ] @@ -442,10 +1134,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "fdc5a3f3", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.955744Z", + "iopub.status.busy": "2023-07-25T23:59:11.955619Z", + "iopub.status.idle": "2023-07-25T23:59:11.959606Z", + "shell.execute_reply": "2023-07-25T23:59:11.959329Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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interceptlstat
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21.015
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" + ], + "text/plain": [ + " intercept lstat\n", + "0 1.0 5\n", + "1 1.0 10\n", + "2 1.0 15" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n", "newX = design.transform(new_df)\n", @@ -462,12 +1218,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "2c6acbf0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.961253Z", + "iopub.status.busy": "2023-07-25T23:59:11.961159Z", + "iopub.status.idle": "2023-07-25T23:59:11.963720Z", + "shell.execute_reply": "2023-07-25T23:59:11.963431Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([29.80359411, 25.05334734, 20.30310057])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_predictions = results.get_prediction(newX);\n", "new_predictions.predicted_mean\n" @@ -483,12 +1256,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "c472ef33", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.965332Z", + "iopub.status.busy": "2023-07-25T23:59:11.965233Z", + "iopub.status.idle": "2023-07-25T23:59:11.967788Z", + "shell.execute_reply": "2023-07-25T23:59:11.967530Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[29.00741194, 30.59977628],\n", + " [24.47413202, 25.63256267],\n", + " [19.73158815, 20.87461299]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_predictions.conf_int(alpha=0.05)\n" ] @@ -503,12 +1295,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "3e2ffc7a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.969435Z", + "iopub.status.busy": "2023-07-25T23:59:11.969317Z", + "iopub.status.idle": "2023-07-25T23:59:11.971777Z", + "shell.execute_reply": "2023-07-25T23:59:11.971501Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[17.56567478, 42.04151344],\n", + " [12.82762635, 37.27906833],\n", + " [ 8.0777421 , 32.52845905]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_predictions.conf_int(obs=True, alpha=0.05)\n" ] @@ -544,9 +1355,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "4e56a1d3", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.973521Z", + "iopub.status.busy": "2023-07-25T23:59:11.973393Z", + "iopub.status.idle": "2023-07-25T23:59:11.975425Z", + "shell.execute_reply": "2023-07-25T23:59:11.975172Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -572,9 +1389,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "7f43ffe7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.977054Z", + "iopub.status.busy": "2023-07-25T23:59:11.976936Z", + "iopub.status.idle": "2023-07-25T23:59:11.979018Z", + "shell.execute_reply": "2023-07-25T23:59:11.978751Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -606,12 +1429,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "3f7b67c9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:11.980623Z", + "iopub.status.busy": "2023-07-25T23:59:11.980502Z", + "iopub.status.idle": "2023-07-25T23:59:12.080197Z", + "shell.execute_reply": "2023-07-25T23:59:12.079809Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", + " scatter = ax.scatter(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = Boston.plot.scatter('lstat', 'medv')\n", "abline(ax,\n", @@ -653,12 +1501,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "b35a2fd3", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:12.082076Z", + "iopub.status.busy": "2023-07-25T23:59:12.081936Z", + "iopub.status.idle": "2023-07-25T23:59:12.186713Z", + "shell.execute_reply": "2023-07-25T23:59:12.186372Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = subplots(figsize=(8,8))[1]\n", "ax.scatter(results.fittedvalues, results.resid)\n", @@ -684,12 +1549,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "82673b80", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:12.188851Z", + "iopub.status.busy": "2023-07-25T23:59:12.188712Z", + "iopub.status.idle": "2023-07-25T23:59:12.286175Z", + "shell.execute_reply": "2023-07-25T23:59:12.285838Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "374" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NN3da/tJLL1VTU5Oee+65wGujRo3S0KFDtXjx4rCf8cYbb2jEiBH68MMPdeyxx2rTpk0qLy/XG2+8oTPOOEOSVFlZqQsuuEAff/yx+vXrF3O9GxsbVVJSooaGBhUXFzv56gAAwCXUIUY4VuO1lE7McejQIa1bt0633HJL4LWcnByNGzdOVVVVYf+mqqpKs2fPDnltwoQJevrppyN+TkNDg3w+n3r06BF4jx49egSCYUkaN26ccnJytGbNGl188cWd3qOlpUUtLS2BnxsbG618RQAAkAQTK8o0vtyvtVvrtXt/s/p0b0+ToGUYVqQ0IN6zZ49aW1vVt2/fkNf79u2r9957L+zf1NXVhV2+rq4u7PLNzc2aM2eOpk6dGngyqKurU58+fUKW69Kli0pLSyO+z8KFC7VgwQJL3wsAACRfbo5Po0/olerVQAZKeQ5xIh0+fFjf+MY3ZBiGHnjggbje65ZbblFDQ0Pg30cffeTSWgIAACCVUtpC3Lt3b+Xm5mrXrl0hr+/atUt+vz/s3/j9fkvLm8Hwhx9+qJUrV4bkjfj9/k6D9o4cOaL6+vqIn1tQUKCCggLL3w0AAGS/1jaDNI0skNIW4vz8fA0fPlwrVqwIvNbW1qYVK1Zo9OjRYf9m9OjRIctL0vLly0OWN4PhzZs368UXX1SvXr06vce+ffu0bt26wGsrV65UW1ubRo4c6cZXAwAAWa6yplZn3bVSUx96XTcu3aCpD72us+5aqcqa2lSvGmxKecrE7Nmz9dBDD+mPf/yjNm3apBkzZqipqUnXXHONJOmqq64KGXR34403qrKyUr/85S/13nvvaf78+XrzzTc1c+ZMSe3B8Ne+9jW9+eabeuSRR9Ta2qq6ujrV1dXp0KFDkqRTTjlFEydO1PTp07V27VqtXr1aM2fO1GWXXWapwgQAAPC2yppazVhS3Wna6LqGZs1YUk1QnGFSmjIhtZdR++STT3Tbbbeprq5OQ4cOVWVlZWDg3Pbt25WT8++4/Utf+pIeffRR3XrrrfrRj36kwYMH6+mnn1ZFRYUkaceOHXr22WclSUOHDg35rJdeeklf/vKXJUmPPPKIZs6cqfPOO085OTn66le/ql//+teJ/8IAACCjtbYZWrBso8LVrTXUXv94wbKNGl/uJ30iQ6S8DnGmog4xAADeVLVlr6Y+9HrM5R6bPoqqFylmNV5LecoEAABAJtm9vzn2QjaWQ+oREAMAANjQp3uhq8sh9QiIAQAAbBgxqFRlJYWKlB3sU/u00SMGlSZztRAHAmIAAAAbcnN8mndhuSR1CorNn+ddWM6AugxCQAwAAGDTxIoyPXDlMPlLQtMi/CWFeuDKYZpYUZaiNYMTKS+7BgAAkIkmVpRpfLmfmeqyAAExAACAQ7k5PkqrZQFSJgAAAOBpBMQAAADwNAJiAAAAeBoBMQAAADyNgBgAAACeRkAMAAAATyMgBgAAgKcREAMAAMDTCIgBAADgaQTEAAAA8DQCYgAAAHgaATEAAAA8jYAYAAAAnkZADAAAAE8jIAYAAICnERADAADA0wiIAQAA4GkExAAAAPA0AmIAAAB4GgExAAAAPI2AGAAAAJ5GQAwAAABPIyAGAACApxEQAwAAwNMIiAEAAOBpBMQAAADwNAJiAAAAeBoBMQAAADyNgBgAAACeRkAMAAAATyMgBgAAgKcREAMAAMDTCIgBAADgaQTEAAAA8DQCYgAAAHgaATEAAAA8jYAYAAAAnkZADAAAAE8jIAYAAICnERADAADA0wiIAQAA4GkExAAAAPA0AmIAAAB4GgExAAAAPI2AGAAAAJ5GQAwAAABPIyAGAACApxEQAwAAwNMIiAEAAOBpBMQAAADwNAJiAAAAeBoBMQAAADyNgBgAAACeRkAMAAAATyMgBgAAgKcREAMAAMDTCIgBAADgaQTEAAAA8DQCYgAAAHgaATEAAAA8rUuqVwAAAGSn1jZDa7fWa/f+ZvXpXqgRg0qVm+NL9WoBnRAQAwAA11XW1GrBso2qbWgOvFZWUqh5F5ZrYkVZCtcM6IyUCQAA4KrKmlrNWFIdEgxLUl1Ds2YsqVZlTW2K1gwIj4AYAAC4prXN0IJlG2WE+Z352oJlG9XaFm4JIDUIiAEAgGvWbq3v1DIczJBU29CstVvrk7dSQAwExAAAwDW790cOhp0sByQDATEAAHBNn+6Fri4HJAMBMQAAcM2IQaUqKylUpOJqPrVXmxgxqDSZqwVERUAMAABck5vj07wLyyWpU1Bs/jzvwnLqESOtEBADAABXTawo0wNXDpO/JDQtwl9SqAeuHEYdYqQdJuYAAACum1hRpvHlfmaqQ0YgIAYAAAmRm+PT6BN6pXo1gJhImQAAAICnERADAADA0wiIAQAA4GkExAAAAPA0AmIAAAB4GgExAAAAPI2AGAAAAJ5GQAwAAABPIyAGAACApxEQAwAAwNMIiAEAAOBpjgPiV199VVdeeaVGjx6tHTt2SJL+3//7f1q1apVrKwcAAAAkmqOA+K9//asmTJigrl27av369WppaZEkNTQ06I477nB1BQEAAIBEchQQ//SnP9XixYv10EMPKS8vL/D6mDFjVF1d7drKAQAAAInmKCB+//33dfbZZ3d6vaSkRPv27Yt3nQAAAICkcRQQ+/1+ffDBB51eX7VqlY4//vi4VwoAAABIFkcB8fTp03XjjTdqzZo18vl82rlzpx555BH993//t2bMmOH2OgIAAAAJ08XJH918881qa2vTeeedpwMHDujss89WQUGB/vu//1s33HCD2+sIAAAAJIzPMAzD6R8fOnRIH3zwgT777DOVl5frqKOOcnPd0lpjY6NKSkrU0NCg4uLiVK8OAAAAOrAarzlqITbl5+ervLw8nrcAAAAAUspRQHzxxRfL5/N1et3n86mwsFAnnniiLr/8cn3hC1+IewUBAACARHI0qK6kpEQrV65UdXW1fD6ffD6f1q9fr5UrV+rIkSN6/PHHNWTIEK1evdrt9QUAAABc5bjs2uWXX65//etf+utf/6q//vWv2rJli6688kqdcMIJ2rRpk66++mrNmTMn5nvdf//9GjhwoAoLCzVy5EitXbs26vJPPPGETj75ZBUWFurUU0/VCy+8EPL7J598Uueff7569eoln8+nDRs2dHqPL3/5y4FA3vx33XXX2doGAAAAyA6OAuLf/e53uummm5ST8+8/z8nJ0Q033KAHH3xQPp9PM2fOVE1NTdT3efzxxzV79mzNmzdP1dXVGjJkiCZMmKDdu3eHXf61117T1KlTNW3aNK1fv15TpkzRlClTQj6nqalJZ511lu66666onz19+nTV1tYG/t199902tgAAAACyhaOA+MiRI3rvvfc6vf7ee++ptbVVklRYWBg2zzjYr371K02fPl3XXHONysvLtXjxYnXr1k2///3vwy5/3333aeLEifrBD36gU045RbfffruGDRumRYsWBZb5r//6L912220aN25c1M/u1q2b/H5/4B+VIgAAALzJUUD8X//1X5o2bZruuecerVq1SqtWrdI999yjadOm6aqrrpIkvfzyy/riF78Y8T0OHTqkdevWhQSuOTk5GjdunKqqqsL+TVVVVadAd8KECRGXj+aRRx5R7969VVFRoVtuuUUHDhyw/R4AAADIfI6qTNxzzz3q27ev7r77bu3atUuS1LdvX82aNSuQN3z++edr4sSJEd9jz549am1tVd++fUNe79u3b9jWZ0mqq6sLu3xdXZ2t9b/88st13HHHqV+/fnr77bc1Z84cvf/++3ryyScj/k1LS4taWloCPzc2Ntr6TAAAAKQnRwFxbm6ufvzjH+vHP/5xIDDsmHJw7LHHxr92CXLttdcG/v/UU09VWVmZzjvvPG3ZskUnnHBC2L9ZuHChFixYkKxV9ITWNkNrt9Zr9/5m9eleqBGDSpWbEz3NBgCQ/ri+I9PENTGH1DkQtqp3797Kzc0NtDCbdu3aJb/fH/Zv/H6/reWtGjlypCTpgw8+iBgQ33LLLZo9e3bg58bGRg0YMCCuz/WyyppaLVi2UbUNzYHXykoKNe/Cck2sKEvhmgEA4sH1HZnIUQ6xJP3lL3/RN77xDY0aNUrDhg0L+WdFfn6+hg8frhUrVgRea2tr04oVKzR69OiwfzN69OiQ5SVp+fLlEZe3yizNVlYW+UQtKChQcXFxyD84U1lTqxlLqkMulpJU19CsGUuqVVlTm6I1AwDEg+s7MpWjgPjXv/61rrnmGvXt21fr16/XiBEj1KtXL/3rX//SV77yFcvvM3v2bD300EP64x//qE2bNmnGjBlqamrSNddcI0m66qqrdMsttwSWv/HGG1VZWalf/vKXeu+99zR//ny9+eabmjlzZmCZ+vp6bdiwQRs3bpQkvf/++9qwYUMgz3jLli26/fbbtW7dOm3btk3PPvusrrrqKp199tk67bTTnGwO2NDaZmjBso0ywvzOfG3Bso1qbQu3BAAgXXF9RyZzFBD/5je/0YMPPqj/+Z//UX5+vn74wx9q+fLl+t73vqeGhgbL73PppZfqF7/4hW677TYNHTpUGzZsUGVlZWDg3Pbt21Vb+++nyS996Ut69NFH9eCDD2rIkCH6y1/+oqeffloVFRWBZZ599lmdfvrpmjRpkiTpsssu0+mnn67FixdLam+ZfvHFF3X++efr5JNP1ve//3199atf1bJly5xsCti0dmt9p5aDYIak2oZmrd1an7yVAgDEjes7MpnPMAzbj2rdunXTpk2bdNxxx6lPnz5avny5hgwZos2bN2vUqFHau3dvItY1rTQ2NqqkpEQNDQ2kT9jwzIYdunHphpjL3XfZUE0e2j/xKwQAcAXXd6Qjq/Ga46mb6+vbn/COPfZYvf7665KkrVu3ykF8DQ/p073Q1eUAAOnB6nW7d1FBgtcEsM9RQDx27Fg9++yzkqRrrrlGs2bN0vjx43XppZfq4osvdnUFkV1GDCpVWUmhIhXf8al9NPKIQaXJXC0AQJw+bWqJvZCk7z/xVsTBda1thqq27NUzG3aoaste8o2RNI5SJtra2tTW1qYuXdqrti1dulSvvfaaBg8erO985zvKz893fUXTDSkTzpmjkCWFDL4wg+QHrhxGaR4AyCCtbYbOumtl1BxiU6RrPeXakAhW4zXbAfGRI0d0xx136Fvf+paOOeaYuFc0UxEQx4cLHwBkj6otezX1odctL++T5C8p1Ko5Y5Wb4ws0lHQMSGgoQbysxmu2J+bo0qWL7r77bl111VVxrSC8bWJFmcaX+5nJCACywO79sVuGgwVXnBgxqDRquTaf2su1jS/3c49Awjiaqe68887Tyy+/rIEDB7q8OvCS3ByfRp/QK9WrAQCIk9OB0Lv3N9sq18Y9A4niKCD+yle+optvvlnvvPOOhg8frqKiopDfX3TRRa6sHAAASH/mgOm6huawLb2R9OleaLl12W4rNGCHo4D4u9/9riTpV7/6Vaff+Xw+tba2xrdWAAAgY+Tm+DTvwnLNWFItnxQzKDZziEcMKrU8UQflOJFIjsqumVUmwv0jGAYAwHsmVpTpgSuHyV8SPXA1s4DnXViu3Bwf5TiRFhy1EAdrbm5WYSFPbQAAeF3HAdPb9hzQY2u3q67x3+kO/g4VhaK1LncMnoFEcVSHuLW1VXfccYcWL16sXbt26Z///KeOP/54zZ07VwMHDtS0adMSsa5phbJrAADE1tpmWKooRDlOJELCyq5J0s9+9jP98Y9/1N13363p06cHXq+oqNC9997riYAYAADEZrWiEOU4kUqOAuI//elPevDBB3XeeefpuuuuC7w+ZMgQvffee66tHAAA8A7KcSJVHA2q27Fjh0488cROr7e1tenw4cNxrxQAAACQLI4C4vLycr366qudXv/LX/6i008/Pe6VAgAAAJLFUcrEbbfdpquvvlo7duxQW1ubnnzySb3//vv605/+pOeee87tdQQAAAASxlEL8eTJk7Vs2TK9+OKLKioq0m233aZNmzZp2bJlGj9+vNvrCAAAACSMo7JroOwaAABAurMarzlqIf72t7+tf/zjH07XDQAAAEgbjgLiTz75RBMnTtSAAQP0gx/8QBs2bHB5tQAAAIDkcBQQP/PMM6qtrdXcuXP1xhtvaPjw4friF7+oO+64Q9u2bXN5FQEAgFe0thmq2rJXz2zYoaote9XaRmYnEs+VHOKPP/5Yjz32mH7/+99r8+bNOnLkiBvrltbSLYfY6tSYAACkK6ZvhtsSOnVzsMOHD+vNN9/UmjVrtG3bNvXt2zfet4RNXEAAAJmusqZWM5ZUq2MrXV1Ds2YsqdYDVw7jnoaEcZQyIUkvvfSSpk+frr59++qb3/ymiouL9dxzz+njjz92c/0Qg3kBCQ6GpX9fQCpralO0ZgAAWNPaZmjBso2dgmFJgdcWLNtI+gQSxlELcf/+/VVfX6+JEyfqwQcf1IUXXqiCggK31w0xxLqA+NR+ARlf7id9AgCQttZure/UsBPMkFTb0Ky1W+s1+oReyVsxeIajgHj+/Pn6+te/rh49eri8OrCDCwgAIBvs3h/5XuZkOcAuRykT06dPV48ePfTBBx/o//7v/3Tw4EFJEnN8JBcXEABANujTvdDV5QC7HAXEe/fu1XnnnaeTTjpJF1xwgWpr2/NUp02bpu9///uuriAi4wICAMgGIwaVqqykUJGS+3xqHyw+YlBpMlcLHuIoIJ41a5by8vK0fft2devWLfD6pZdeqsrKStdWDtFxAQEAZIPcHJ/mXVguSZ3uaebP8y4sZzxMCmV7fWhHOcR///vf9X//93865phjQl4fPHiwPvzwQ1dWDLGZF5AZS6rlk0IG13EBAQBkkokVZXrgymGdyoj6KSOacl4o7+ooIG5qagppGTbV19dTbSLJuIAAALLFxIoyjS/3M9FUGvFKfWhHAfF//Md/6E9/+pNuv/12SZLP51NbW5vuvvtuffnLX3Zz/WABFxAAQLbIzfFRGSlNeKm8q6OA+O6779Z5552nN998U4cOHdIPf/hDvfvuu6qvr9fq1avdXkdYwAUESD9MqQ4gk3mpvKujgLiiokL//Oc/tWjRInXv3l2fffaZLrnkEl177bX66U9/qgcffNDt9QSQJgjyrPFCzh2A7Oal8q4+w8XiwW+99ZaGDRum1tZWt94ybTU2NqqkpEQNDQ0qLi5O9eoASUGQZ02knDvzsSFbcu4AZLeqLXs19aHXYy732PRRadtCbDVec1R2DYD3mEFex+4zc2BFZU1titYsvcTKuZPac+6yrWQRgOzjpfKuBMQAYiLIs85Ozh0ApDMv1YcmIAYQE0GedV7KuQOQ/czyrv6S0Flv/SWFWZX+ZWtQ3SWXXBL19/v27YtnXQCkKYI865hSHUC28UJ5V1sBcUlJSczfX3XVVXGtEID0Q5BnnZlzV9fQHDbFxKf2lpVsyLkD4B3ZXt7VVkD88MMPJ2o9AKSx4cf1VGlRvuqbDoX9PUHevzGlOgBkHnKIAURVWVOrc37+UtRgWCLIC+aVnDsAyBaOJuYA4A2R6ukG81OHOCwv5NwBQLYgIAYQVrRSa6bSojy9/INzld+FzqZwsj3nDgCyBXcxAGHFKrUmSfVNh7Xuw0+TtEYAACQGATGAsCi1BgDwCgJiAGFRag0A4BXkEAMIi3q6ADJZa5vBoFZYRkAMICzq6QLIVJU1tVqwbGPIOIgyKuIgClImAEREPV0AmcYsF9lxUHBdQ7NmLKlWZU1titYM6YwWYgBRUU8XQKaIVi7SUHvv1oJlGzW+3M81DCEIiAHERD1dAJkgVrlIQ1JtQ7PWbq3nmoYQpEwAAICsQLlIOEVADAAAsgLlIuEUATEAAMgKZrnISNnBPrVXm6BcJDoiIAYAAFnBLBcpqVNQTLlIRENADAAAsgblIuEEVSYAAEBWoVwk7CIgBgAAWYdykbCDlAkAAAB4GgExAAAAPI2AGAAAAJ5GQAwAAABPIyAGAACApxEQAwAAwNMIiAEAAOBpBMQAAADwNCbmAGBba5vBDFAAgKxBQAzAlsqaWi1YtlG1Dc2B18pKCjXvwnJNrChL4ZoBSCc8OCOTEBADsKyyplYzllTL6PB6XUOzZiyp1gNXDiMoBsCDMzIOOcQALGltM7Rg2cZOwbCkwGsLlm1Ua1u4JQB4hfngHBwMS/9+cK6sqU3RmgGRERADsGTt1vpON7hghqTahmat3VqfvJUCkFZ4cEamIiAGYMnu/ZGDYSfLAcg+PDgjUxEQA7CkT/dCV5cDkH14cEamIiAGYMmIQaUqKylUpDHiPrUPmhkxqDSZqwUgjfDgjExFQAzAktwcn+ZdWC5JnYJi8+d5F5ZTVgnwMB6ckakIiAFYNrGiTA9cOUz+ktDWHX9JISXXAPDgjIzlMwyDoZ4ONDY2qqSkRA0NDSouLk716gBJRcF9ANFQhxjpwmq8RkDsEAExAACR8eCMdGA1XmOmOgAA4LrcHJ9Gn9Ar1asBWEIOMQAAADyNFmIAltD9CQDIVgTEAGJigAwAIJuRMgEgqsqaWs1YUt1pOta6hmbNWFKtypraFK0ZAADuICCGba1thqq27NUzG3aoastetbZRqCRbtbYZWrBso8LtYfO1Bcs2cgwAADIaKROwha5zb1m7tb5Ty3AwQ1JtQ7PWbq1nNDkAIGPRQgzL6Dr3nt37IwfDTpYDACAdERDDErrOvalP98LYC9lYDgCAdERADEvsdJ0je4wYVKqykkJFKq7mU3vKzIhBpclcLQAAXEVADEvoOvem3Byf5l1YLkmdgmLz53kXllOPGACQ0QiIYQld5941saJMD1w5TP6S0H3rLynUA1cOYzAlACDjUWUClphd53UNzWHziH1qD5DoOs9OEyvKNL7cz0x1AICsREAMS8yu8xlLquWTQoJius69ITfHR2k1ACnD9PFIJAJiWGZ2nXesQ+ynDjEAIIGogY9E8xmGQZ0sBxobG1VSUqKGhgYVFxenenWSiqd0AECymDXwOwYr5l2HsQyIxmq8RgsxbKPrPP3x0AIgG8Sqge9Tew388eV+rnGICwExkGXoWgSQLZg+HslC2TUgizC9NoBsQg18JAsBMZAlmF4bQLahBj6ShYAYyBJMrw0g2zB9PJKFgBjIEnQtAsg2TB+PZCEgBrIEXYsAshHTxyMZUh4Q33///Ro4cKAKCws1cuRIrV27NuryTzzxhE4++WQVFhbq1FNP1QsvvBDy+yeffFLnn3++evXqJZ/Ppw0bNnR6j+bmZl1//fXq1auXjjrqKH31q1/Vrl273PxaQNLRtQggW02sKNOqOWP12PRRuu+yoXps+iitmjOWYBiuSWlA/Pjjj2v27NmaN2+eqqurNWTIEE2YMEG7d+8Ou/xrr72mqVOnatq0aVq/fr2mTJmiKVOmqKamJrBMU1OTzjrrLN11110RP3fWrFlatmyZnnjiCb388svauXOnLrnkEte/H5BMdC0CyGZmDfzJQ/tr9Am9uJbBVSmdqW7kyJE688wztWjRIklSW1ubBgwYoBtuuEE333xzp+UvvfRSNTU16bnnngu8NmrUKA0dOlSLFy8OWXbbtm0aNGiQ1q9fr6FDhwZeb2ho0NFHH61HH31UX/va1yRJ7733nk455RRVVVVp1KhRltbdyzPVIb1RhxgAgHZpP1PdoUOHtG7dOt1yyy2B13JycjRu3DhVVVWF/ZuqqirNnj075LUJEybo6aeftvy569at0+HDhzVu3LjAayeffLKOPfbYqAFxS0uLWlpaAj83NjZa/kwgmSZWlGl8uZ+Z6gAAsChlAfGePXvU2tqqvn37hrzet29fvffee2H/pq6uLuzydXV1lj+3rq5O+fn56tGjh633WbhwoRYsWGD5c4BUcmN6baZ/BgB4BVM3W3TLLbeEtE43NjZqwIABKVwjIHFIuwAAeEnKBtX17t1bubm5nao77Nq1S36/P+zf+P1+W8tHeo9Dhw5p3759tt6noKBAxcXFIf+AbMT0zwBSrbXNUNWWvXpmww5VbdnLDJtIuJQFxPn5+Ro+fLhWrFgReK2trU0rVqzQ6NGjw/7N6NGjQ5aXpOXLl0dcPpzhw4crLy8v5H3ef/99bd++3db7ANmI6Z8BpFplTa3Oumulpj70um5cukFTH3pdZ921kodxJFRKUyZmz56tq6++WmeccYZGjBihe++9V01NTbrmmmskSVdddZX69++vhQsXSpJuvPFGnXPOOfrlL3+pSZMmaenSpXrzzTf14IMPBt6zvr5e27dv186dOyW1B7tSe8uw3+9XSUmJpk2bptmzZ6u0tFTFxcW64YYbNHr0aMsVJoBsZWf653hzlAGgI7OHquMjt9lDxUQcSJSUBsSXXnqpPvnkE912222qq6vT0KFDVVlZGRg4t337duXk/LsR+0tf+pIeffRR3XrrrfrRj36kwYMH6+mnn1ZFRUVgmWeffTYQUEvSZZddJkmaN2+e5s+fL0m65557lJOTo69+9atqaWnRhAkT9Jvf/CYJ3xhIb0z/DCBVYvVQ+dTeQzW+3G9rgC8DhGFFSusQZzLqECMbVW3Zq6kPvR5zucemj6KFGICrEnH9YYAwrMZrKZ+6GUD6YPpnAKnidg8VA4RhBwExgACmfwaQKn26F7q2HAOEYRcBMYAQEyvK9MCVw+QvCb3p+EsKGdACIGHc7KGyM0A4HpSHyx5MzAGgk+Dpn+saDqq+6ZBKjypQSdd8tbYZtBADcJ3ZQzVjSbV8Ukjrrt0eqmQMECY/ObsQEAMIKzfHp4aDh3T3/73PBR9AUpg9VB0DTX+Y60606hFupl+EQ3m47EOVCYeoMoFsF+mCb7bNcMEHkCixSqXFap1tbTN01l0rVdfQHDaP2Kf2IHvVnLG2e7zM946UkhHPe8N9VJkA4BgDUgCkUm6OT6NP6KXJQ/tr9Am9OgXDsapHJHKAcLLyk5FcBMQAOuGCDyAd2XlYT9QAYSYwyk7kEAPohAs+gHRkd3r54AHCbs1Ul+j8ZKQGATGATrjgA0hHTh7WzfQLt5jl4WLlJzOBUWYhZQJAJ8xYByAdpcPDOhMYZScCYgCdcMEHkI7S5WGdCYyyD2XXHKLsGryAwvMA0o1ZZUIKP3lHMgPSWOXhkHpW4zUCYocIiOEVXPABpBse1mEVAXGCERADAJA6PKzDCqvxGlUmAABAxnG7egS8jUF1AAAA8DQCYgAAAHgaKRMAACArkWcMqwiIAQBA1qESBewgZQIAACRNa5uhqi179cyGHarasletbe4XuzJrFQcHw5JU19CsGUuqVVlT6/pnIrPRQgwAAJIiGa22rW2GFizbqHBhtqH2CTwWLNuo8eV+0icQQAsxAABIuGS12q7dWt/pM4IZkmobmrV2a70rn4fsQEAMAAASKlarrdTeautG+sTu/ZGDYSfLwRsIiAEAQEIls9W2T/dCV5eDNxAQAwCAhEpmq+2IQaUqKylUpOxgn9rzlkcMKo37s5A9CIgBAEBCJbPVNjfHp3kXlktSp6DY/HneheUMqEMIAmIgCySjjBEAOJXsVtuJFWV64Mph8peEBtj+kkI9cOUw6hCjE8quARmO4vMA0p3ZajtjSbV8UsjgukS12k6sKNP4cj8z1cESn2EYNCU50NjYqJKSEjU0NKi4uDjVqwOPMssYdTyJzcs9LSEA0gkP8Eg2q/EaLcRAhqL4PIBMQ6st0hUBMZCh7JQxGn1Cr+StGABEkZvj45qEtMOgOiBDUXweAAB3EBADGYri8wAAuIOAGMhQFJ8HAMAdBMRAhqL4PAAA7iAgBjIYxecBAIgfVSaADEcZIwCAFa1tBveKCAiIgSxAGSMAQDRMihIdKRMAAABZzJzVtGPt+rqGZs1YUq3KmtoUrVn6ICAGAADIUrFmNZXaZzVtbQu3hHcQEAMAAGQpO7OaehkBMQAAQJZiVlNrCIgBAACyFLOaWkNADAAAkKWY1dQaAmIAABC31jZDVVv26pkNO1S1Za/nB2mlC2Y1tYY6xAAAIC7UuE1v5qymHfeRn30U4DMMg0c4BxobG1VSUqKGhgYVFxenenUAAEgJs8Ztx2DCbG9kGvn04cWZ6qzGa7QQAwAAR2LVuPWpvcbt+HJ/WgdeXgkUmdU0MgJiAADgiJ0at+kaiJHuAYlBdQAAwKFMr3HLlMYwERADAABHMrnGLVMaIxgBMQAAcCSTa9wypTGCERADCEEtUQBWZXKN20xP94C7GFQHIIDBJQDsytQat5mc7mGXV6poxIOAGICkyLVEzcEl1BIFEMnEijKNL/dnVNBlpnvUNTSHzSOWpNKiPNU1Nqtqy960/z6R0NBhDRNzOMTEHMgmrW2GzrprZcR8Op/aW3tWzRmbkTcEAAjHbAiQFDEoNmViEMmkKdbjNXKIATC4xCXkXwOZxUz38JfETotwWootVdcFN6toeOHaRsoEAAaXuIBuSSAzBad71DUc1O3Pb1J906FOyzmZeS+V1wW3Jk3xyrWNFmIAnhpckggU9wcymzmlsb+ka9hg2GSntyzV1wU3GjpS/R2SiYAYQEbXEk01ivsD2cOt3rJ0uC7E29CRDt8hmQiIAWR0LdFUI/8ayB5u9Zalw3Uh3oaOdPgOyURADEBS5MEl/pJCT4xEdor8a3hRtg6ycqu37MWNdZY+L5HXhXgbOrx2bWNQHYCATKwlmmrkX8NrsnmQlRlEzlhSLZ9CS7FZ7S1rbTP01IYdlj4v0deFeCZN8dq1jYAYQAhzcEmiZNuMSbGK+5s1nMm/RjaIVNe2tqFZ1y2p1rQxAzWu3J/R53W8M++t3Vqv+qbDMT+nV1F+Uq4LThs6vHZtIyAGkDTZ2LLkRosSkAmiDbIy/W71Nv1u9baMP6/j6S2zmkIweWi/pF0XnDR0eO3aRg4xgKTI5vI95F/DC2INsgqWDee1GUROHtpfo0/oZTnws5pCML7cH8/qJYWXrm20EANIuFjle+wWu09H5F8j29kZPJUt57UTsVINpMwqY+mVaxsBMYCEc2vGpHSX6PxrIJXsDp7KlvParmxMNfDCtY2UCSREtpbkgTNeK98DZKNYJckicfu8zoT7i5dSDbIFLcRwXTYOnEJ8rLYsbdtzIMFrAsCpaC2f0bhZlsvt+4uTqjdW/8YrqQbZwmcYRvo9WmWAxsZGlZSUqKGhQcXFxalenbQRqSSPefrzZOxNrW2Gxty5QnWNLVGXKysp1Ko5Y7lhAGmqtc3QopUf6OHVW7XvYPTSYmZZLrfOabfvL06Caxp8Mo/VeI2UCbjGa/Oew7rcHJ+mjjg25nLZNA0okG0qa2p11l0rdc+L/wwEw93ycyUlfsp3t+8vTqreZHOlHBAQw0Vem/cc9gzsXWRpOfKIgfQTKRg8eKhVklTSLS/kdbdzZd28vzgJrmnwyX7kEMM1DJxCNNk8DWi2zb4HBLNSNrGwS44e+fZI7fmsJSHngJv3FydVb7xSKcfLCIjhmmwOeBC/bJ0GlJxCZDsrwWBdY4tyfD5NHto/Ievg5v3FSXBNg0/2I2UCrolVksenzCpGDneZI9SlxOcbJgs5hfCCdAgG3by/OAmuafDJfgTEcE02BjxwVzbV5iSnEF6RDsFgbo5PFw0pi1rqzer9xUlwbaUGc2lRnuoam9O2NjKiI2UCrjIDno5dyH66kPG5bKnNmYqcQnKVkQrpkO5UWVOrB1/ZGvH31549yPL9xclMclZqMNc3HdasxzdIIm0qE1GH2CHqEEfHjRvZ7pkNO3Tj0g0xl7vvsqGu5FWSq4xUMtODpPABZCJ7eFrbDJ1118qoD6BOapi7VYc4HGrvpw+r8RotxEgIL8x7Dm9LZjdypAkJzFxlbrpItFT2/sXqjZGs98Z0bKx5+Qfnat2Hn1puvAnu4aprOKjbn9+k+qZDnZYzq28sWLZR48v9NAhlAAJiAHAgWd3IVkpecdNFMqQq3cmtQX3RWoTt9OKYDT5VW/aGDYZNlGLLLAyqgyWtbYaqtuzVMxt2MGAAUPIGkTLhDdKJGQxOHtpfo0/olZSHMDd6YxJRESYdqm/APbQQIyZyF4HwktGNzE0XXhdvb0yielnSofoG3ENAjKjIXQSiS3Q3MjddeJ2TqhDBElURJh2qb8A9pEwgIuqsIps5TQMK93eJ7EZmwhsgvhrmieplofZ+dqGFOMMks5wZc7cjWzlNA0pF+lC8rWNAtnDaG5PIXhZq72cPAuIMkuybMbmLyEaR0oBqG5p13ZJqTRszUOPK/Z1utKlMH8qWm26sB3rql6eHdN4PTkp6Jjq1IVsmG/I6JuZwKNkTc0S6GSey+HfVlr2a+tDrMZd7bPooWoiREawU+DcFP2zG+jvzhmp3YgC70jlQiSXWAz2Dd9NDtu6HVE4sgtSyGq8REDuUzIA4VTdj83NjPVUnOggA3GL1IU8KvVF2L8zTFb9dE/Nv3Hg4TJeg1831iPVAf+3Zg/TgK1uT+sCPzlLR8JJM2RrsIzpmqssiqcrlJXcR2cZOeo9ZjunmJ9+R1WaDeNOH0uWG7XQ9wgXRkmIOzn3o1c7BcPDvmXgk8bwwAQypDYiGgDgDpDKXN1tyFwHJ/qAZQ9K+A4cT9v7B0qXEodP1iBREX3bmgJgpKrEKfDB4N/G8MojaSQ6yVenSuwNnCIgzQKrrkPJUjWwRa3CNU/EOykmX1jmn6xEtiL7nxc2urNvyjXUZHYilOwZRxyddenfgHHWIM0A61CFNxXSdyG6pmA48Wt3QeMWTPpQu0zM7WQ8r9crd8MyGnWptM5hGPkG27WmytBwTwHSWiGmhkXy0EGcAcnmRbVLZmhIpDcipHt3ydOclp8a13unSOudkPWIF0VZ0vK6Fs7fpkBat/EBL39hOK5zLKmtqY7bkM+taeOnSu4P40UKcIeKZpQdIJ+nQmjKxokyr5ozVY9NH6VtjBkpy3mJ8/9T4z79Up0XFsx52gvRws3n5JI09+WhLf3/Pi/+kFc5lZkBnBQ0vnaVL7w7iRwtxBiGXF5kunVpTzDSg0Sf00ohBpZ0HjhYXqPlImxoOHI5adnCUC3mtiZ44IJbWNkOvb9mr1Vs+0VEFufqspTXscuHWw2oQfdN5g/Wn1z9UfdOhwGvm4NySrvla8d4njtadShTxsdrCf9O4kzo9+DGILH16dxA/AuIMk8gRsnAPN4rw0nUke6SHzeUb65KSqpTKtKgX3q7VD//6tj5rORJ1uUjrYSWYL+mWp6VvfBQSDJcW5WnupFMCE5/EO9gxGyogpILVQG1g724hPzOIrF269O4gfqRMAC6rrKnVWXet1NSHXteNSzdo6kOv66y7VtKlq/RuTQk3cDSZqUqpSIta+MJGfffR6pjBcLT1iDZQ0Qzu9x04rLrG0H36adNhXf/oelXW1MZ8D6uWb6yzsTQkZwFdOqQ9pYt0GPQOdzBTnUPJnroZmSHbZ3qKl9WZ4h6ZNlJjBvdOwhpZk8wW/2R91gtv79R3H10fc7keXfN0/xXDNOr46NVlwrUYmmknkWo5d5ztMlotYyvl23oV5Wvtj8fRG2NDrBlJpfZ9YO6jdJnGPJ0wLXR6Y6Y6IMnSKT82XVmtA/z9J97S/IvSp+vVbD02g9Xn3t6ZsGC1Y1qUWWbMzQC5tc3Qrc/UWFp238HDyvH5Yn5muLSTNsOIOuW1mSLzh9Vb1bt7gfp0L9TLPzhX6z78tNNsd3+s2qb6puiTpOxtOkTahE3B6TqRXDSkLLD/0zXtyW12HkyZwCo7pEVAfP/99+vnP/+56urqNGTIEP3P//yPRowYEXH5J554QnPnztW2bds0ePBg3XXXXbrgggsCvzcMQ/PmzdNDDz2kffv2acyYMXrggQc0ePDgwDIDBw7Uhx9+GPK+Cxcu1M033+z+F4QneOVGYXLSkhktVzbYrsbkzs5mRSJyJmNtw0Tlaa7dWh8zuAxmNYWlYzD/zIYdlv7u9uc3Bf7f/H6Th/YPWebiof31u9XbXFtX/NvEijJde/Yg/e8rW8P+/sFXtur0Y3tqYkVZWqc9ucXJeceg98yX8hzixx9/XLNnz9a8efNUXV2tIUOGaMKECdq9e3fY5V977TVNnTpV06ZN0/r16zVlyhRNmTJFNTX/bu24++679etf/1qLFy/WmjVrVFRUpAkTJqi5OfQE/clPfqLa2trAvxtuuCGh3xXZzQs3ClM8edJma0rf4oKIywRXDkiHiRcSkTMZaxu+8HatrktQnqbdY9DpgCAnfxfp+40r9yfsM72utc3Qs29FP57MczHbB5HFc64zgVVmS3lA/Ktf/UrTp0/XNddco/Lyci1evFjdunXT73//+7DL33fffZo4caJ+8IMf6JRTTtHtt9+uYcOGadGiRZLaW4fvvfde3XrrrZo8ebJOO+00/elPf9LOnTv19NNPh7xX9+7d5ff7A/+KiooS/XXhQKbMTJXpNwqr29mN4HBiRZl++Y2hUZdJl/qdVmZisxu4x9qGP3v+Xc18LHwXtvH5v5v/+o5Wf7DH0flg5xiMZ0BQrAFH4UTapgxeShw7vVvZvB8Sca4nQ6bcI9NdSgPiQ4cOad26dRo3blzgtZycHI0bN05VVVVh/6aqqipkeUmaMGFCYPmtW7eqrq4uZJmSkhKNHDmy03veeeed6tWrl04//XT9/Oc/15EjsUdap5IXD/pMqtiQyTcKq9vZzRvGns9aLK1bqlvU3S68H2sbGpIeenWbYm3CfQcP64rfrnF0PpjHqhXxlHtzOlV2uG1qpRIFE0c4Y6d3KzfHp7mTyiOW2JMydz9k4iQbmXSPTHcpDYj37Nmj1tZW9e3bN+T1vn37qq4ufPmcurq6qMub/431nt/73ve0dOlSvfTSS/rOd76jO+64Qz/84Q8jrmtLS4saGxtD/iWTFw/6TCvtk6k3bDvb2c0bRjq2qId76HQ7FcaNqY6DOTkfzGM12pFYVJCrxS7kcEcqJ2dFx23KjJ2JYedcrKyp1e3Ph5/ZLtX7Id5Go0xLe8u0e2S6S4tBdakwe/bswP+fdtppys/P13e+8x0tXLhQBQWdcxsXLlyoBQsWJHMVAyKV8jIP+my8EWRqxYaJFWW6//JhuvWZmrAzcqXbfrK7nd28YaR6draOopX8ssJqUOH2zdTp+RBpZHxRQa6mnzVIN5x3kmvnVscBR7sbW/SzFzbF/Ltw25TBS+6zei5+2nRI1z/a+V5kMidaSQU3BqCm40N6JOl2j8yGyahS2kLcu3dv5ebmateuXSGv79q1S35/+AEUfr8/6vLmf+28pySNHDlSR44c0bZt28L+/pZbblFDQ0Pg30cffRT1u7klU3Oa4pWJXVeSAq0nkWbkSjd2t7ObN4x0alGP1tJyz4ub1aNbnmupMIm4mTo9HyZWlGnVnLF6bPoo3XfZUD02fZTenjdBN43/QsLKyRV0ydHvVv0r6rKxtimDl9xl5VycO+kU3f58+HuR6UdP1TjOa4+HWy2lmZT2lk73yGzpwU5pQJyfn6/hw4drxYoVgdfa2tq0YsUKjR49OuzfjB49OmR5SVq+fHlg+UGDBsnv94cs09jYqDVr1kR8T0nasGGDcnJy1KdPn7C/LygoUHFxcci/ZEjkQZ+InGS33jPTuq6kyBfl4Bm50o3d7ez2DSMdusCttLSY3AjcnQw0s8rJ+ZDM4NI8R+oaI+ePp3N6UTaLdS72LCqImeoTT167U242GqXTQ3os6XKPzKa0jZSnTMyePVtXX321zjjjDI0YMUL33nuvmpqadM0110iSrrrqKvXv318LFy6UJN14440655xz9Mtf/lKTJk3S0qVL9eabb+rBBx+UJPl8Pt1000366U9/qsGDB2vQoEGaO3eu+vXrpylTpkhqH5i3Zs0anXvuuerevbuqqqo0a9YsXXnllerZs2dKtkMkiTroE1Hf1M33zKSuKyn9uq+ssrudo9URdnrDSHUX+Ov/2hvzoXPfgcOaNe4kLX1je9yF961MhOBUupwP4UQ7R4Kla3qRF0Q7F63WlJaSm87ndv33TJlkIx3ukZl634sk5QHxpZdeqk8++US33Xab6urqNHToUFVWVgYGxW3fvl05Of9uyP7Sl76kRx99VLfeeqt+9KMfafDgwXr66adVUVERWOaHP/yhmpqadO2112rfvn0666yzVFlZqcLC9gOjoKBAS5cu1fz589XS0qJBgwZp1qxZIXnF6SIRB30icpLdfs90yy+NJVMn5XCynRNxw+g4oYObouW2VdbU6ua/vmPpfQ63toadRc3Jhd7chjf/9R3tO2h9goxI0u18CMfqYMJffG1IWk3b7TWRzkU795hwwVCickwT0WiU6od0K9LhHpmp971IUh4QS9LMmTM1c+bMsL/7xz/+0em1r3/96/r6178e8f18Pp9+8pOf6Cc/+UnY3w8bNkyvv/66o3VNNrcP+kQ80SXiPRPREplI6dJ9ZZfT7ZwJNwwpfK9Fj655umbMQA3u0z3qAKGOFr20RX+t3hF2FjUnJlaUqXthXtSpjYP16JanfQcOZ8T5EI7VY39Pk7VyfG7JhsFAUuK/h9Vp103BwVDDwUMJmXFRSlxLaSIf0t2QDvfITL3vRZLyiTkQnds5TYnISU5UnnM65JdalQ7dV0453c5Wck9TWTs7Um7bvoOHdc+Lm3X9Y9aDYVOty3lxo47vFTOfuEfXPD3y7ZFad+t4Lc6Q8yEcq8f+5l2fJe1YyYTBQFbOoWR8D6c1pZdvrEtojmkmDYRzW6rvkZl83wvHZxhGdpUnSJLGxkaVlJSooaEhKQPs3MrPfWbDDt24dEPM5e67bKjlVrBEvGcwKy0fqW7laW0zdNZdK2O25K+aMzZtW5/c3oaJyFO3ytwfbtb7DVbm4r6MlG5k+s7Zg3TLBeWBn1N9rDsV6xzpKNaxEu92iLTdzXdIh4cMK+dQsr9HuHWKprQoP6TqTjC3rovmNpDCt5Q+cOWwjOjRcipV14RMue9ZjdcIiB1KdkAsuXPQV23Zq6kPxU4XeWz6KMvdRYl4TztSGXh1XI9YF+VU32CTJdXBhtVjMh5uHs8LX9io/31la9jf+RR9e2VSgBzpHAkn2rFi5ZyPtl1iPTClw43cyjk0vtyfku/R2mbo9S17df2j1RFz4H2Sehblqb4pdo68G+dStGNCUlrcI7JRJtz3rMZraZFDDGvcyGlKRCK+ldyy0qI8DT/OegUPqzf5dJq0JFNGJydaKkcem8fN35LQ5e1WXlxrm6Fn34q+vpG2V7o8DFoV6RwJJ9KxYuWcl6IHQOk+GMjqOdS9MC8l3yM3x6cxg3vrzq+eGjUYunhof/1u9baY7+fGuRRpXIOZspEO94hslE33PQLiLBMrkExEIn609zTVNx3WOT9/ydIJYvUmn44lXzJlsFkipSrYsNuVGy+38uKcbq90ehi0I/gcWbX5E93/jy0Rl+343a2c8zc/+Y4aDhzutExtQ7OuW1KtaWMGqmu+tVtfqgYDWT0mqrbstfR+ifoesYKhkq75lgJit86ljo1G6XiPyEbZct8jIM4AdlpLrQSSbj3RdVyv+y8fptufjxyQWLlR27nJp2srT7qPTk60VIw8jpWH67ZeRfmuDdJxsr0y/Uafm+NTw8FDemTNdkvLm9/dyjm/70D0LnorAZopVYOBrJ8b1o74RH6PaMFQa5sRd4+kmZ5R9a89ktqvraOOtzaBTLreI7JRNtz3CIjTnNUg125rUbxPdJHW68dfOVm3LdsYdhBFrBu13Zt8tpV8yRbJHnl86EibfvRUjWvBsM8nxRpZcfvkCtcCTSfbK9Nv9HYfYMzvnqxzOdV1na0eE6OP762/Vu9Ieb32SMFQvD2SlTW1uvnJd0Iecha99IF6dMvTnZecGrPxhnsE7KDsWhqzOiWi06krnU7ZGm29Zi7dEHFEsbk+kUqw2S3flm0lX7JFMssgVdbUatTCF6Mec3ZdUFEWtazUd84epAtOcy8Vwcn2StaNPlHTu1uZsU7q/N2TcS7HU8PVre1l9ZgYdUKvtJ9q2GlpsMqaWl23pDpsi/++A4d1nYWSbdwjYActxGnKSmvp/GffVffCPFVt2ZO01iIrwbcV4W7Udm/y6TBTDzpLVsF4u62MXfNydPBwW8zlXninVteePUjPvlUbcl6VFuXpp5MrdMFp/RyucXhOtpfVG3jvogJVbdnrai9QvANlrM5YZwr+7nYnh3DC6WAgN7eXnWMiEwY12e2RbG0zNP/Zd2O+b6y0IO4RsIOAOE1ZaS2ta2yxPMuV5E63kN2bWSThbui9jyqw9bduBl6ZVLoqEyT6Jm2nldH0nbNP0L0rNlta9tm3al2bptkKK9sr+BjtXVQgf3GhdjVGvtGXdMvT9594S3WN9gO0RA7Ys3odCtctbmUArxMzzz1Bg/t2d7yfE7G97JxDTlLgkn3Ns5NjunZrveoaY89YGKmhJ/i7XXbmsbr3xX9m7AyPSB4C4jSViJwmN7qF4l2vSE/klTW1MVsEwv2tG4FXppWuyhSJGHls3uhWf/CJ5Qczn6S+xQVa+sZHlpY3e1TWffhpUvNvo22vsFNQd8sL9BZ1vNH/e3BZaHezlQAt0QP2rF6H7p86TGMG9+70eqRzvkfXvIg1cWMZc+LRjvd1IreXnXPITsCZ7tc8O/eZjstGOlek0AGX6dSCjvRAQJym3MxpstotZKXFwM56WX0it9L1He1pPp7AK1NLV2UKN0cex1NWbeqIY3XPi9Zah02pGGgTbntFOkYbPr+5l3TLC7nR9y0uUPORtrC5l1YCtEQP2LPajT0qynuHO+fbDMNWj1nwZ8XTZZ7o7eX26P10vuaZ96DNu/Zb/pvge1K0c8WQNGvcYA3sXUQvIMIiIE5TbuXKWe0WstpiYPVmNndSeacSbOGeyK12fcd6mo9204gU6Ntt2SGtIn5Ot6HTsmq9ivL1s4sr1HIkdu5wR+kw0MbKMVrYJUePfHuk9nzWYikwjBWgJXrAnlupTuFqztq5ZrrVZZ6I7WXnPLG7bLqW63PywFtWUqjhx/VU1Za9qms4qNuf3xT1uy1946OUTyOM9EVAnKbcypWz0i1kp8XA6s1sYkWZJlTEbrW1mpP8i68NCdt9Gku0QL+ka77llp2Gg4fSuosxEzjtpnWSLyy1D4KruuU85XfJ0X0v/tPy36XTQBurYwlyfD5NHtpfkvTMhh2W3jtSgJaMkfmJyDG3e810q8vc7e1l5zwJt6y/uEBTRxwbtiU0Xcv1OX3grehfrHN+/pKle0g8343GEG8gIE5jdqY57WjmuSdqzIm9LQ2ssNtiYPVmZqWrz2qryZ6m2AMsOooV6H9rzEBL77N8Y50eXr0t4sxXi0mriCmeblqnAzkvHtpf6z78VMOP66nH1lqbACLdBto4aX2MN0BL1sj8ROSYR7o2lZUUau6kU9SzqMD1oMbu9ooWXNk5TyIu29gSkh4UHEynY11epw+8krR8427bf2P3u6V7vnUmyJQHCgLiNGfeNO5Z/k8teukDS39TVlKoWeNPSuhMPm7dzKzevPfsb1Frm2H5/a0E+k9ZbEl7esPOqBfrm598J21nBEsH8XbT2r2B5fikNqN9RrLfrd6m0qI81TdZG2yVbgNtnAS38Qa0ySqbZ36W2y2RyZ5G1s72ihZcjS/3Wz5P9Pn/Wwkig4PpdKzL61blIqvsfLd0zrfOFJn0QMHEHBkgN8enMSdaTxewc7OKp8XAysQesQrVxypAb7r9+U06666VMQuxm6wE+vVNh1ValB+1+H17MBV90od9Bw5r0UprDyteZHfClY6s3sC+UtEeKHScC8FqMDzz3BO0as7YtLpIO5m0wwzQzN93XF6KfY1wOplCunA66ZBTVrZXrImWFq3cbPk8sRNEBk/ONPy4nkmbNMeqZM4+aOe7OZ3wKhGT2WQqq5OLpQtaiDOElUF2OT5p0VR7N6tEthhYeTK0k/dn56nc6kV2ytB+enj1togtOxcP7a/frd4W830efm2rZo49kVbiMOLtprXS4tm3uEDrt+9zvI5Se+mtdNt/TltrraY1RevKjNTSKsnxZB/ZLFrLtJVekoctXGek9vPEbowVXEowWa3/VqXr7INOek8zqTU00dJ5AGckBMQZwkrguGjq6banlE1UvqCdriarudJ2TiKrF9nx5X6NGFQaMXAo6ZpvKSDed+Bw0geiZIp4H7qsBIVOyqoFv0e6DKILx+kAtFipA1YfWIOP6Wy/4ceb6xgpBcRKcGW1hvLWT5r0u9VbLa9TsN37mzV5aP+0mtluxKBS9ehQOtBtTr6b3Qd5O/e8TMmpjUe6DuCMhoA4g0QbMJKIkdlOWwycDtQbX+7XH1Zv1e3Pb4r43lZPIjuBfm6OL2rLjtWC/6moW5sJ3HjoihUUOimrZn62lD6D6CJxmhcbKUBzkhvpVj5lMoMBO58Vb7Af7bMsz87XNU8NBw9HPE+65udanm0xHPOhM9l51tEs31iXkGC4tChPc//zi/IXJ3Z8S5/uhbbuecs31mX1Q6UpHQdwxkJAnGHCXciGH9dT6z78VM9s2OHowuZ2CSSnT4a5OT717m5t+uZYJ5HdQD9S4JCb49M1YwZaan3cvOszVW3ZGxJMp8MNJ9XceuiKdhOv2rLX0rqUFuWH5ISn2yC6aNwagObkgdWt7s9ktjDbLV8WT7Afdna0rnm6ZsxAzRw72HJwdc2YQRGnGTYkHTjUaul9Ogr30JmIAY12mcdVNN3yc3Xw8+9tp770HRefGtcxZedB3uo9b9HKD3Tvi//0xCC9dBzAGQsBcQYKvpBV1tR2qsPo5AbjZotBPE+Gbp5EbgX6M8cO1sOvbYvZirHopQ+06KUPVFZSqIuGlOnZt2qzvhXAKrf2RfCxH/zA0buoQP7iQu1qjH7zevkH52rdh596+iHFyQOrG92fiRyx3/Hh89OmQ7r+Uevd1/EE+5G+176Dh3XPi5v18GvbdMeUCkvB1cyxJ+oL/qM6nSd9iwu0v+WImlrsB8Tp3AtiZXDggUOtmjXuJC19Y3un62m466xbD7l2HuSt3vMeXr3VchWRTG9MSVb5RjcREGcwt28wbrUYxBPUun0SuRHo5+b4dOclp1ouHF/b0Kz/faVzjl82tgLY4eZDV9gWuW55gRtLpJtXfpeclLeKWeGkd8Hq3zh5YI23+zORA2zCHQs5vvCtieE+K55g30oN3X0HDuv6R9fr2rMH6cFXtsYMrtyaltqUzr0gVo+rgb27adWcsWGP7x9OPCVhwaPVB3mr97xoqXfBrcjhgv903YeRJLN8o1sIiDNUOo/gjCeoTcRJ5EagH88kKaZU75dUcTt1JNKDYMPnLfglHQbopHNAEI6TtAI7f+PkgTXenptEDbCJdCxEq8JgftYfVm9V7+4F2rzrM0ufFS54s1r+zJD07Fu1uv/yYZamtO94zbI6+2Cwq0Yfp69UlKV166Kd4ypaWlsiH3KtPMhbqQJV0rWLGg4eifl594SZVTNTG1Mi3Td7FuXpp5Mr0u67EBBnKCc3mGTltMYKag1Jl505QM+9vTPseiRiWlenOm4zs8t99QefaNFLW2y/XzqOrE0kt3NGrTwIFnbJ0SPfHqk9n7VkXHdjMga7Wbl59+iapzbDCEyGE2/PTV2j+wNs4pnhTFLUwbvhhAve7KxvbUOzehblR2zpjGbbniZb6ypJX6koS8trjJNUp1R3q8cKuq1UgToSRz3iTG5MmVhRprY26dZnagLjN+qbDuv25zcp5/MekXRBQJyhnJSEiRaYuB0sRwpqS7rlSVLEqUWD/z7Vo6CjbbPBfbvH9d679zdn/aC7ROSMWnkQrGtsUY7Pp8lD+9tf6c+lYt8ka7Bbbo5PFw0pC5vWY9p38LCu+O2akHPTbs+NuQ2Xb6zTE+s+trQN7AywSdYMZ9GCMrsDgnbvb7bdotnaZlieelxKnyAyHKepTplwXTTveTc/+U7Y8SZO8r+DudGYkorrWmVNreV8/lQjIM5QdrqaYgUm1549KCEDwDoGtdv2HLA1wjbajcPKiR1PHubyjXX6fZj6w+a63jRusKVtEMm2PQd01l0rMz5PLJJEpfQ4zWVNZvktp5I12K2yplYPRgmGg3U8N6323ITbhtE4CeKSUa4pVlBmtpxb/Z7mddvO8bh2a73qGltcWd9UyvZUJ6m9rv38ZzdKij4AO1LPqRVOj/tUXNfSObUzHALiDGW1C3P4cT11zs9fijr9ZCIHgJlBbWubobPuWun4xAi+gWzbc0CPrd0e0g3b8cSOdPLPnXSKehYVWJ6oINK6PrZ2e9Suvkh8ar/wp2vpHbdaEBKVM+oklzWZ5bfikcjBbnUNB1W1Za/qGg7q9uc3WT5mO56bVnpuIm3DSJwGccko1xQrKDO7yq9bUh31fYIDfruBiZ0AKF2DyGxPdTK1P7zE3l89O5R/7PgwEI3TWWNTcV3LtMk5CIgzlNXBZ+s+/NRRt2KsINVu4BTPiWElUA0+sSWFPflrG5r13UfXh7xm3ogi/U2kda1rbNGscSeFHQARSfB+SuYTs9V95WYLQqKKslvJfy0Lamm0O3tUKlszEjnY7fbnN4XcgO3oOAgt2jHkJK+3tChfP7vY/gAbq9PZ203dnHnuCRrct3vM65p5XrUcadOscSfp4dVbw1YRCL4eL99YZzswsbqP5046Rd8cMygtg8hkpTqlmtXr2dxJp8hf0jWQR/39J96SlVZlJ6kwqbyuZdrkHATEGcxKF6aT0cmmSEGqk8DJ6YlhtbXJPLHnP/uuJJ/lG3JdQ7OuW1IdyGOzY2Dvbpo1brDlKYP9JYW67MwBUZd3+4nZ6r5yuwUhUUXZgx8EI7loSFlgYhQ7N4JUt2Y4GbhmJSiU5DgYDhY8CC3S+e4kr/fWSackbJbNRVNPD/QIvfrPPfpLdexc5jEnHh22vFrHOsedqkUUF+g/B5dp1eY9IYGxeT0eX+531Etm9bhI12BYyrzAyCmr1zN/SdfAMVa1Za+lVmVDzlJhrF7XXt+yVzmf11R2q4U+0ybnICDOcLG6MN040IIvUk4DJycnht3WJrOVwQ7zvZ1MHWpn28489wTNGv8FPff2TkvLu3FjsLqvEtGCkMii7BMrynTt2YMiDgp78JWtOv3Ynirpmm8rwE31TdtJyUEro9sTIdL57mTb+Eu6Rv19tB4Oq3nNlTW1MYPhSMek1XzoXY0tev7tWt1/+elh07Kqtux1PINnouq5JmuQVaYFRk45ue5ZPWe+NWagowdHq+9//aPVIQ9ybuQXZ9rkHATEWSDa4DOrLUjRBA8EcRo4OTkxkjWK3K7gdV27td7S34w58Wjl5viSdmOws68S0TKa6Jv4s2/VRl1mwbKN+uHEky29n3nDcPrQlozqLNFyQyP9TWlRnuqb7D/oWWHuz/nPvhtyvts9bkuL8lTX2Bwy5XkwKz0csRoFrEwPbOp4TNrJhzbPq9uf36RVc8Z2+i5OHriC0zJuGndSp7ET8eQMJ3OQVaYFRlaFO/9jXffmTgqdSKT3UQWWPsucwc7OuuTm+CyX7OuY7hP80Bt8fvUuKpB8spTrnWmTcxAQZ7l4WpA6XqTiCZycnBjp2H3WcV3tXugTcWMIdyG0s68S1TKaqHrSVr9b/WfWegvMIM7uvnESUFgJoJ2UHDT/5vUte1X1rz1qTxsydL+DWtl21DW2aNHKD3Tj51VX7D6A1zcd1qzHN0gKPzDWam9UtEYBqw/WN407KWS/OcmH7ngNDN7fe/bbOx7DHV/+4gLNGjdYA3sXxT3TYzIHWWVaYGRFtPM/0nXvoiFluv35TZ32aY9ueWo4cDhyXfBueVHvCZHWJVZ5xWjMB7ybn3xH85/dGDGtI9Y1L53mFYiFgNgDIh2Q5gljlmCKdZGKt+SV3VYOu61NPkl9iwsk+WxXf7Cq47ravdC7fWOIdCG8oCJ6a4LJDLiscNJqHS5QG31CL4063nkOrtXjsLQo31aAG+vh0VD7hDKS8wk0rAbQTmbfWr6xLq6ZFHt8XiPcbvrQPS/+U1/wHxUI4r9S4dfvV2+z/QDesUXKSU3mcA8RdqYHDhZPD9Xu/c0Rp5SONMivYyWKcMfXrsYW3fviZj1w5bC4atGmYpBVJgVGsVg5/ztOwPJp06Gw9Xh3NbbEPE/2HTis5Rvrwm6jSOtS29DsOBg2GTKvB5GvCVYeotJhXgErCIg9ItoBefqxPS1dpNwqeWW1lcNOa5P51/Mv+qIkuZ5T+c3Rx2lChGlQ7V7o3boxRLso/y5MDeVwzO2fyO7MjoHaopc+iKtr1s7AFbsPH7Gm6L7nxc16bO12NR9psxVQxLqBRso7tcpuqTNTaVGe5v7nF+UvDu0JMls0rc7mFq4VyeeTjKAVKi3K0+Qh/fTMW7VhB/kFb7vuhXm2eqOiPWxYPV727G8JzMwnxddDFanmerRgWFKg4k0iA9ZUDh7NlMAoGjsPFMGzxI65M/KASil6LeJI+zze2RrdYPWYTPQU224gIPaQSAek1YuUky7leFo57KR7dAwmowU1Tjz7dq1GndAr4sluJY8x+Hfjy/1x3RhiXZSl9tYowwi/3YL3VSK7MxPRNWu13NanTYd0wWnOcnLHl/u1aOUHYcvqxRq4Ga7bPNa+mvnY+pBgyc4Dg5Oborkn77j41E6fEXwT/+2qrZbOoXCtSOb3mTZmoMaV+wOpPA+/9mHE9zG3XdWWvZa+h9kSG+thw8qD9e3Pb9JvV20NbHenefyl3fL02NrtUT+rY0tx8PHodPBdsGipOekweDTdA6NgHbdlm2HY3j+LVm6OWUki2vESaZ+nyzgbc/3+sHprWlc7iYWAGJKsXaTsBE5udctFbE0tLtDUEcdGbGWOFdSYivJz1XQo9pSa9U2HYgZwkbZhIgavWLkQmjdcK0FuIrozE9U1a6X0WpvRPmr6gZxhjlullr5hfbrccMyAws6+Mtl5YHByU7SyX61OOhGNT9Lz79Rq7Cl99dzbO7V512cW/9JaeP+vT5r05zc/inqM3f78Js2dVK7rH439YN0xbcPJgOTjenXT+o8aoi7TZkj/NepYDTuuNNA6H0/AamXiInNSos279lt6/0yv+OCGsFNNd82z9Lfm/qmsqbVcmtPqe0b6OdU6PlRmGgJi2GI1cHKzWy6ebrZYQU1+lxxLAbHJbgCXqMErVi+EY08+Wptq91tO5XCzOzORXbMTK8p0/+Wnd2pZ7Sh4f9n5DDdaXsyAwslNy84Dg9X3tzrhREdFBblqarF+jgQz1N6ifsVv19j6u5EDe+mRbtv1aYx85v/3+raolTTMY6xnUb6lXiNzu89/9l11L8xzlA8dKxj+97pv14ubdnfqfbGbmmalLFy4SYkiydSKD26LdO0ON/lKOH26F9qqcGL1PaP9nA5i3duSVerPCQJiRBTpwLUSOLndLRcpoIl2clkJaj49cFilRfn6tOlQzBue3QAukYNXrF4IV773iX4TIT810rZzqzsz0V2zPYsKogbD8QTc8ba8lBblafhxPSU5v2lFW38n1QvCTTgRjdO85Hj41D6N7Q/++nbMYFiS5bJyu/c3a/LQ/hpf7tcfVm+NmhsdLojvmA/tltqG9omBZo0brJljB9uuXOP2PsrUig9uiyc3N3j/uJXSEOkhxeo4G6fjaYryc3XgUKvtSiuR7m3JLPXnBAFxFnHzySvWgRsrcEpGvd1IA/bMVAqr3bNThvbTwxYHoUn/DpZa24yw1ROsBuTxBGzmhdDKxfZHT9Xo/iuG6T9P6xcyyCvRF6ZEHwOJDLjjbXmpbzqsc37+UmCGsnhqgYebvdFp9QKrUjFYx7xpxxrV7oS5P3NzfOrd3Vrd12Dmtj3v5KO14r1P3Fw1SeZgzY80/6L2889KapoUefCdU05SpOzcdyItm+pWQ7t5wpF0fKBwI6Uh2kNKbo5PcyeV67uPdk5rMpe89uxBevatWkffJ69LjnSo1XZAHe7eluxSf04QEGeJWMGhnYuMGwduoisXRFzHxhbb+VrjPx/w86On3rHU4tSne6Eqa2p185PvhJSoWvTSB+rRLU93XtI+UCnegC3aTcJOfue+g4d1xW/XBAJeSUm5MCX6GEhkwG1l3Uu65amwS27EwTLB2zOe2eQ6Vm0Jt++sVC+wE2BYbdlys5JL3+ICNR9ps132LVoPT7hjzOnDjk/Sxtr9uuZLA/Xwa9ss/43V7VPXGHr+xUpNizX4zo6Z556oMSf2dlTdxOqDdbRauR0DtmS2GsaTJ9yja17YabrN9XYjpSHaQ0plTa1ufz58Skbw3/1w4ilRc8wj2XfgsGaNO0lL39ju6FgLbjxKRak/uwiIs4DV4LC0KE8XD+0fGPEd7sBz68B1WrnASkuBW61XHSstjD25r0YtXBG2JJSpR9c8rfnXXt27InzQve/AYV23pFqLrxwWV8BmdYauaWMGWi6xVvd592yPbnlRKx50nH3MqUQX409kwG1l3e+85NSox0zw+bJqztiwAY6dll0rx3206gV2WH2Yi/ccDM5rbjMMW7nG5vaJNGAu0jHmdPZOs9XrmJ7Rp5o2OQ0kzOtrrNQ0NwdUDe57lO1eKjsNJ3Zr5Sar1TDePOH7Lx+mnM9bgsPdr+KZKbaksItmjh2s3t0LVNI1P6QkYLR1N82ddErEiWtmjj1R9yz/pxa99EHM9RjYu1tITeXeRQXaWNuon70QuySjeW9LZak/O3JS9slwhZ3gsL7psH63epumPvS6zrprpSprOk9/a+fAjcVs5fCXhAZ8/pLCiBMXnHXXSk196HXduHRDxPV0Iy8r3M0yv0uO7ri4Qr6g33e07+DhiMFwsAXLNmr4cT1VVlIY8b18ag9yOwZs5oWu43c0bxLB22NcjOk8g5nHSKwWOHP2MTfYPQbsMINWqfP+iifgbm0zVLVlb2Aimb7Fkdd93YefRn2ACj5fJlaUadWcsXps+ijdd9lQPTZ9lBZNHRb2eAu3/larVcyddErg/VfNGZvQWs/xGnPi0Zo8tL9Gn9BLeyzOLCiFToPbsyhf14wZqJ5F+SHLRDrGoh03VpgTvsQ6r2eOPVGr5ozV3EmnWH5v83h5/fOyc2YgY24jJ4PvrLD7XlZKCS5YtlGtbYbjGf+C3yMR4s0TLisp1KgTekXcP1J8x1pD8xH97IVNmvV453thrHU3q6tE2na5OT6NObG3pfXo070w5DgcM7i3vnXWIFv3tlSX+rOKFuIM5zQ4jPQE7vaBa7VygZ3WBjdOGruTZthV29CsdR9+aruF1G4LfTwtENGYs4913D5Ocv0SWYzf7XJxdieSsXu+hMu9fyDH2vpb/aze3Qs0eWh/S8tG4sZxlchUhkjT4EbqBQtXB9zpeW53wpdvjhmk367aamtbXv9ote64uCLqZC12xhFE4rQXxW7DiZN1THSrodN7p5WH7Y7H2/2XD9Ptz8d3Twm+F5Z0zY+7xTWeHja7vX/JGFPkBgLiDOc0OIyU/uD0wI2V7xrtgmY3CHR60lgtOxU83fD1j1Zb7j7ryBzZHu7G2/fz/O6WI22q2rI3sD52u5bsTF5iV7jZ1pwOxEtkMX63Am4nE8m4caG3uv7JvKm4cVyZg1XtpDKUFuVZyuO/9IwBum/F5k7r9WnTYf1+9TadGbT9oh23HbuCv//EWxGnfe+YYmX1QcxK3eyO9h083KlMWsdzLd460U4CO7vTYbvReJGoVkOr7xsrT7ijSMfb3EmnqHtBnmY8uk6fOShjaB6TP3rqHd16Qbmlv4n2HeNNabPTGJHo8SRuISDOcPHc/MI9Re7d3xK1xFC4AzfeigV2g0CnrVd5ubmaPLS/pVbO3ByfcnJ8joNh6d/7pmPAYw5qCM7vNrdXy5E2S+8dfKFzq1W7o45T46ZyhHCsfRZvwO00d96tC72V9U/2TSXScWU1aDUHq1ptvc/N8eniof0t5cT/4bVtlvbV8o11EXNXO5Y7k6T5F1kPEOw8iJnbcv6z78ac6TCScOfaxIoy/cZCPe4eXbuoMK9LyEAqp4Gdnemw3Xg4S1SrodX3jZUnHCzadfK7j65Xj255joLhYPVNhzVv2buWlt2867OQBpeO4u1hs3oOJHo8iVsIiDNEpIDAja5NM7ha+MLGsAMcOgo+cOMNlFrbDK3+YI+l9axrOKiqLXu1e3+zLjvzWN374j9ttV7d++I/deDQ4U4jmiNV44inZaJjbrAZ8FTW1OreF/8ZcXvdNG6wpffveDG32qrtk9T189qSVuze3+w4WHSrlFIySsQ5HfSRzAu9G59ld5+Eu+ENP66nzvn5S5YC89wcn63W+3HlfksBcbQH1eA83Fg5oh3LndkNEKw8yJjbvOVIm3759aF6Y1u9pXEI4b5XuHPtgtP6aZF8UUtv3fnV02zth3inw+74cObkHpXoVkOrD5ijgnKDo50/VvKq7VZQiWR/8xFLyy166QMteumDqNfLeHvYrPQAm8f/TeNO6lThIp7ZUN3mM4xElBvPfo2NjSopKVFDQ4OKi4sT+lmxAgLz4iU569p8bPoofdrUEnMmoxyftGjqMF1wWvuB29pm6Ky7VkYMJMwLyqo5Y8OeXFZmWApWWpQfMoCpR7f20jhuXWRM5rYt6ZqvqQ+97ug9Fod5ELCyvWKVnoq1TSVFPB7MpW8aN9hyabrHpo+SJEvb4bHpo0JqTroRxEa6MZvfxa2W6Wc27NCNSzfEXO6+y4aGzc8N9317duui0cf30vFHd+9UozoeTretnb+LFTjHOsac7hfzHIla7q5DF3YkM889QYte2mLpc30KXedwNcbPHFiqdR9+aitoiFZq7PE3PnbcAxV8rsX6LCd1ha1c183qHlLsY8DpPSrcddRNdo7jWNu3astex/eLeFhpFHL7emmVm+Vg42E1XiMgdihZAbHVgMBucGm+h7+kUC//4FyNWviipW7Q4Aux1QvAY9NHBWbtMW8mnzYd0vWPujPD0gUVfXX80d1V23BQf63eEff7mdv2/stP1+3Pb7LVsnFUQRf94uunhVx0zOBi9QefWL5BR/Oby0/XBaf1i7pMtIv3+HK/xty5ImbXbdnngfdzb++0HCz+52n9tGjlB7rnxX92+r3di7LdB654WqTtHMuRWkPMz1++sU5/fvNjfdYS2orTLT9X3zn7+JAueqfMz6prOKj6pkMqPapA/uLI3zlWiabgY8pqYJWolvtoQYoh6WvD+usvFs7zi4f201Mbdlr6zI7HkpXJT2J911jX7hvPO1H3rnBWzSXSg5kbvTJ2zoWGg4fiqkMcTY9ueVp36/iEB0tWjmMr9+GWI22WrpNu69hQFImVxhQ3Jasxwwqr8RopE2nMTld1x26PrZ806U+vfxjxRAnuYm0vHWV9CtRw/x/N8o11mv3nDZ1uLm49ib1Qs0vSLsvF1GMxt+1PntvYnppho3vz9slfjBk0xOsnz21STo4v6sUkVjfY/Iu+GHMwjtn9bjXXbtueA1EDbTt1rCV7aQx2bszhuJGfm5vjU8PBQ/p9hC7/A4dadc+Lm/W/r/xL3zn7eM348om2Wxw7ftbd//d+zO9spbzUzMfWa5F8ysmJPGmLmXNrtuyML/cnpHpIpLSFks97hKwEw5IsB8NS52PJyuQn0dLCrFy7l77xkfzFhREH8UUT6Zx0Y/CqnQFz5nTYVvOozWWtNA7sO3A4oXVpg7vyf/G1IZJP2t3YHHjANGv/SpFnBAy+pv3i60MSsp6xzJ10ivwlXWNu02TW+s2UiTg6IiBOY04qDph5qo+/+VFIMNxxoFxw3s4zG6y3qgZfiLftabL0N+ECBKulJY8qyLU8CCGeAXAdGWqvx2s3189f8u+i/bFa5Zyqa2wPTqaNGRh1khUzx9y8Wa3dWh9YdmJFmRZfOazTbHuS1LNbnhZ+PtueZH3WtnC50R2FuyjHO5J9+cY6Pby68yArOwP+3MrPnf9s7MEuZmB874rNIedkj655umbMQEstyHZy963WL/7uo7EnbQk3EDQRrTzhBqJaOb7i9fw7O/XCO3WWPifajd3KtbuusUU3nTfY1jUmGaPxrV7Xg6fDthpgmcumui5t2NnpwqTglZUU6rIzB1i6D8twlisdL39J17TYpsEyZSKOjgiI05iTAzzSjdK88YYLoqy2APYqyg9ciCtrai3loXbsZrQrnZ4eo+l4o3JrNr1ofrd6m363epvtqVLNZYMH4gXnSnbMdY0VLJo/2/mu5jHrxkj2P7/5sSstEfGOuF67td5WBYGOyWr7Dh7WPS9u1sOvbQtM/x2O3dYXOzdAO/n4ia4wYgZPZupMMoKMJa9vt7V8pBv7ixvrLP39HyxOAR0s3kGasQaGPbY29jYIN6GQHYmoUmE1XSTi7HRhjv26hmbL4y32NLU4KlVoNihEmwY+kuD9kE61fv/+rrXjP9UTcXREQJzG7B7gVoKwpzbs0JyvnOKowPvtkysCuZoLloWfP72jeCcZajhobTRtKoVrQXRjNj2r7EyVGq7re9QJvTRmcPRZi6IFi5edOcDyTcPUp3uhpZHs/uKCmEFmx1zdYHZbIuIZce3WxX3fgcNRA027rS+JugGawff8Z99V98I87fmsJSGDZJJ5LjnVsVHC6nTqdnq1enTLi/qgZEWsh2SrD3WXnXlsXPt4xKBS9eiWF3PwsNWg22o+u92GCju3rz7dCzX6hF5hr5Pmd43U+3TnJafaSicxXVDhD/T8uZH2FfxQ0buoQPLJ9nn9wts7LT/opXoijo4IiNOY3QPcyo2jvumwhv90ub4x/JiQlmLzyTbSBWD6fwwMVJdI9g2qpGueGg8eTmo3lB3+z4uul3TN1zMbdqhP90LbT/rxCG4VHHtyX72xtV43//UdW13f7VPgRp4VS4ocLD73tvVcTfOYNUt3RWvlvP35TbZzuCOxE6zazcM0byKbd+13smphGYrcsm2358iNGc0iMbv+r/jtmsBrpUV5+unkipgDP61Kt1akcDo2SiTC9V8+sdNEPnZEegA1azIv/nxgmBUDe3ez9dkdLd9YF7U3wpD1lnC304ec+vTzFMVI18nlG+ti9j6Zdfb/Wr3DUupFx17CuZPKo5bfi7ZNY413sVrJJla1KlNwj3O6ICBOY3bzGq3eOPY3Hwnb3R5tcofn3q7T8ONKNbGiLOk3qNY2IxAkpUtQHDzr3adNhzpNy1laZG2A35BjSvTWxw1xr4/ZKjhq4QpLI46D1X5eND5YpItfuGDRzlO+eaNb9+Gnllo5X9tirUZ1LG63RJhluZas2aZXN++Ju9h+OJFatu32HOXm+DR30imWb1Txqm9qn2XtOx/v0y0WZ9SKJt1akcIxg6FEBVw+n/SzFzYFfrabv22lZfTmJ9/R/ZcPs/R+8ewTKw8NPbrlaXy53/J7WU0fsprK4sTtz2/UhIr2zwl3nXQyiYVVZs+fmQfdUcnn4xPCbdPWNiNiZaBgtTFSpOw+DE4e2i/tUiJzUr0CiM4MVP0loRcgf0lhpwPTyUXKbB144e2dmlhRprmTwt/AzKftyprapN+gmj7vEi+JcLKnwpgTj9bkof3VcLC9fFzHm6DVqh1uBMOhn2svGI4keH9H09pmqK3NUFF+rqX3Paqgi8aX+y0/VK3d9qml5aIpLcrT8ON6xv0+psqaWg3/6XJd8bs1+lvNroQEw6Zw28nsbo6mtChPO/cd1O9e/ZeeWr/DcpB2VEEX+fTvB+54/O8rW/XC29GPHyvMFm63b53d8qwds1bc/vxGtbYZCWss6JhvbvX8NFkJ1PcdOKwn130sf3Hkbe1T/PnDVtdl7db6uN8rOH2otc3QUzYGkNtlfk40ZqA8eWj/wED4cCZWlOnaswdZ/uxYE3+Y4xPOumtlyDFTWVOrMXeuiBkMB3/O/GffDVTfCGb3YdDKA0+y0UKcAaw+WcYza931j67XDXX79ec3Pw77e/P9fvTUO1o957ykjqY1n/QjziedRMFpKskYOJcqVgakOSkp91nLEa3dWp/Uh6r6psM65+cvuVIRobKmNma5OjcFd8UHV1yINfitvumwvv/EW7Y/7xtnHBN2umWnbn7ybY0r76v8Ls7bXqL1lDlhHsnfOed427nvkdQ2NOue5f9UzyQ9tFs5P4NrVb/yz08sve9f1+9Qt/zcsD1y5s+XnTlAz72903G+uFvVEOzMcmpW2bHaUOGUWw9ErW2Gnn0r/ofJjoLTY6TwJRZjqWts0a9XbNaIgaVaveUT7dzXrP49u2rLbmvVSaT4H6oShYA4Q0TKa+w4svY/TyvTQ69utf3+hqRfr4xdJL6+6bBG3PGiRgzsmdQ8YkPSviQPsAt38zXUXvcxN8enqi17036wTzyiDUiLp6Tc7v3N+s/T+kUdVOO24BuB06DYalm1YCMG9tT67ft02Obo0uAHr0TUso5kfLlfo0/oFbbkmWQ/GG1sPqLhP12uuy451VFOcaxpX2Pplp+r/C45IceZmbc5vtyvpW985NqD/aKX2q+fsSrr9OzWRZJPn8Z57Mc6P50eM4Fp3TtcAM0eunhL77lRDcHu9+vTvTApqX5uPegnepzOnL++ra55XRwf9/fFOa5j7iR3prN3GwFxBkvmjTLY/uYjWvGetRaHTDVr3Ela+sb2sNv29ufbJ8awOgAl03W8kcTbMt6ne2HMQTV2+ST16Z6vw23R00ZufvIdx8Xg7ZZVk6SK/iV6w2baR/D4gOUb6xJSyzqc4Fabjg/gX/AfpfnPvmv7+0vt1wsnOcWRpn2dNW6wDre2WRqJ/9B/naFRJ/SK2LtmN1fTiljPPp8eOKJuFlOMrOh4frpV/zy4VGdx1/ywdaCdlN6LtxqCne8X/F5WUjCccrs+dKKD94aDR1JawalnUX7KPjsacogzlHlRyIQWyqIC9y7+8bIaBg3uc1TMfGqrBewzXcdWj3haL8o+rzDh5kh8c59ePvK4mDnU+w4c1iILPSHhOLlJPb1hp+3AxBwfML7cn5SUHDNnONoI9IkVZfrlN4bG9Tl2coojXd92Nbbo3hc3q7ysOGpesZnrOipowqJweZt2czXdEmiFdcG2PQcC/5+INK7n36nVY2u3R61as2DZxrB5peGYKTBS5+txrGoIdr5fx/cyA/FEibc+dLBMGEgaj3StHENAnIEyKXf1W2MG6rIzBqR6NSRJ3QtzLW+znzz3rn7yXPigzXyPx9Zul784ey9ckQbQOL2YmUFXrAoTdpkB5MDeRZaWf/i1rZZv3sHs3qRKi/JsDXLs0TVPj3x7pFbNGRuoCZuMB95wA3TD2fOZ/dbhjuY+UxNz28eqHCC199KYD6x2g6qOn5WIXM14+dTeGt63e0HMZZe+sT2wTRNxzNQ1tkRNUwlO3bDKzmDxYHa+X8f3Cg7E3XbTuJNcnZwm0cF7qqVrwE9AnIEyoVC9aXy5X+PSZDTp/mbrrTJWbgJ1jS2aOuJYF9Ys/UQLKpxczHp0ywvcnNxqHTjnpN56bPqoQABpdb2sjmDvaMSgUvmLYwcoUvv2u3hof8vv7ZN051dP1ZgTe9suoxiPuZNOCWy/WNy4ie1tOhRz21utHNCzKN9RUGXns1LBPNvmX/RFXT4y9vUlOBhNZcub3c+eWFGmVXPG6rHpo3TfZUNDzuV4P2PmuSeEfa+JFWWaNW6wrfW0It66zB2ZwXv6ZdnGL10H1EnkEGekdO1uCNYxpyqeqhQ9uubZmtEpmQb27qbfXH66Zj62Pu5Z+VLJ5wst4hFtuuIRg0o/b/20vk/unzosMBueW60D151zYkie64hBpZaPFSfnUG6OT/Mv+mLMKhM9u+Vp4SWnqqRrvqUZy3oV5etnF1d02tbJaEXp3b3AcjdvPFVsgsXa9naqEEwe2t/xzIJ2PiuROh6zweee1XEK5vdIZcubk8+2OwmO1c8Yc+LREY+BmWMH67G1H7k6eVIitnusuQEylZupJW4jIM5ATk++WKOf3RKuddFJ2aQeXfN0/xXt5WGCZ8KKR2lRvmu1eqV/T9e5SL6IMwQZas+jbkpgvdpgN503WIOOLtKe/S26/flNsf9A0vfGDtao43tZCipyc3z66eQKSxM9mA9GozoErvEGVuFaGXJzfLpmzEBLpbScnkMTK8q0+MphuvnJdzoNCizKz9W1Zx+vmWMHB6Y4j/U9S4vyVHXLeWHLktndTk6OMTvbwa3yZ7E+08nEI3aCKieflUj3Xz5MOTm+sOee3W3h9qyEPkl9iwsk+bSr0fmUwG5xY3ri9gfbfw+mDFdazmoFnER/9+CSq39/t1YPv/ZhQj4nWWa5nFriNlImMpDdQvXmoJnp/zEo5t+Yv49V+D+acF2WkXLGIq1DcBfyqON7xfy+/uICSwXlfzq5wtLnW30/80J4wWntgVLHvC9/SaEWXzlMP//qaTE/1w2/ufx03TT+JE0e2l/fHDPIUhd/j255+t55gy0VjDddcFo/fcfiYKSOLQLRBtXEEmsA2Myxg6Meu25MLDCxokzrbh2vR6aN1MxzT9DMc0/UI98eqbfnT9CN404KrFeswUM+SXdcfGrEGr1WBh/NGjc40N28fu75ls9bp9sh0nlcVlKo6f8xMObfW/nMWNc3N/ah1c9KpOCBf5HOPbvbwm5Xu0+KWPEiOHVj/kXx52u7IZ4BecGi5TAvvnKY1t06PpDKMWvcSWEnq0nWdzcf+OZdVKHFVw6LeY4X5eemZaqFv7hAM8eemOrViMpnGGkw20EGamxsVElJiRoaGlRcXJz0zzdHYUuxW2qCa0Vana9csla023yinjVusAb2LorZutixbnK4aY/D1baM9H3NT3kgqNB4tGXMbRCuhc/p+0X7fsHbYuELG/W/r9irER2pxaJja3+keqBWJpKIpzbvC2/X6tZnasK2useqURrrWCzMy1GOzxcyIt9K3dNIZZmi7bdECvc97dRvtfP3VkpSubEdIh3n0Y5xn43PtHK+u7UPY11LO6YTucHO93CyLayW5PzN5adrQkWZFq38QA+v3hqSutHxGIv3OHaTW+sS7XqdiM9zgznVcsf9VVqUp59OrlBOji/q8dyzW56+ccYxevDz8zTWoe0vLtDUEccG7u+fNh3Sj54Of/8MJ1XX3WBW4zUCYodSHRBLkU/SuZNOUc+igognefBFoHdRgeRrH0Hecdlw79/x5uDGRcHNi5LVC1eki0qibwLhAshoqSzmZ3XMkxx+XE+t+/BTSykOkR4AzFzXeC9SwTNi1TcdUulR7a3rVnI5Yx2Lkhzlh6bTDUyyfoy78fdWH3oTtR3CHeNOPjOZ+zDcZ/XomqdrxgzUjC+fGDjXtu050GlykHABQ8eHfKsPsHbWL9Z7mMfM39+t1V+qd2h/85Gof2vlGIv3OHZTstclnb57rPWJdjybKV1O4wfzs1/fsjdkprovndBbDQcOW2rgSjYC4gRLh4BYSvxJ2vH97QRiieD2RTsVN4Fo2zTaA0o8zAtY1b/2SGrvght1fOzUiEyWbjewZLL60JuMz4/nM5O5D61+lpNrhhvXzXi2hZfPBa9K1QNOOh5rBMQJli4BMQAAAMKzGq8xqA4AAACeRkAMAAAATyMgBgAAgKcREAMAAMDTCIgBAADgaQTEAAAA8DQCYgAAAHgaATEAAAA8jYAYAAAAnkZADAAAAE8jIAYAAICnERADAADA0wiIAQAA4GkExAAAAPA0AmIAAAB4GgExAAAAPI2AGAAAAJ5GQAwAAABPIyAGAACApxEQAwAAwNO6pHoFMpVhGJKkxsbGFK8JAAAAwjHjNDNui4SA2KH9+/dLkgYMGJDiNQEAAEA0+/fvV0lJScTf+4xYITPCamtr086dO9W9e3f5fL6Ef15jY6MGDBigjz76SMXFxQn/PLiL/Ze52HeZjf2Xudh3mS1d9p9hGNq/f7/69eunnJzImcK0EDuUk5OjY445JumfW1xczIUhg7H/Mhf7LrOx/zIX+y6zpcP+i9YybGJQHQAAADyNgBgAAACeRkCcIQoKCjRv3jwVFBSkelXgAPsvc7HvMhv7L3Ox7zJbpu0/BtUBAADA02ghBgAAgKcREAMAAMDTCIgBAADgaQTEAAAA8DQC4gxw//33a+DAgSosLNTIkSO1du3aVK8SJL3yyiu68MIL1a9fP/l8Pj399NMhvzcMQ7fddpvKysrUtWtXjRs3Tps3bw5Zpr6+XldccYWKi4vVo0cPTZs2TZ999lkSv4U3LVy4UGeeeaa6d++uPn36aMqUKXr//fdDlmlubtb111+vXr166aijjtJXv/pV7dq1K2SZ7du3a9KkSerWrZv69OmjH/zgBzpy5Egyv4onPfDAAzrttNMCBf9Hjx6tv/3tb4Hfs+8yx5133imfz6ebbrop8Br7L33Nnz9fPp8v5N/JJ58c+H0m7zsC4jT3+OOPa/bs2Zo3b56qq6s1ZMgQTZgwQbt37071qnleU1OThgwZovvvvz/s7++++279+te/1uLFi7VmzRoVFRVpwoQJam5uDixzxRVX6N1339Xy5cv13HPP6ZVXXtG1116brK/gWS+//LKuv/56vf7661q+fLkOHz6s888/X01NTYFlZs2apWXLlumJJ57Qyy+/rJ07d+qSSy4J/L61tVWTJk3SoUOH9Nprr+mPf/yj/vCHP+i2225LxVfylGOOOUZ33nmn1q1bpzfffFNjx47V5MmT9e6770pi32WKN954Q//7v/+r0047LeR19l96++IXv6ja2trAv1WrVgV+l9H7zkBaGzFihHH99dcHfm5tbTX69etnLFy4MIVrhY4kGU899VTg57a2NsPv9xs///nPA6/t27fPKCgoMB577DHDMAxj48aNhiTjjTfeCCzzt7/9zfD5fMaOHTuStu4wjN27dxuSjJdfftkwjPZ9lZeXZzzxxBOBZTZt2mRIMqqqqgzDMIwXXnjByMnJMerq6gLLPPDAA0ZxcbHR0tKS3C8Ao2fPnsZvf/tb9l2G2L9/vzF48GBj+fLlxjnnnGPceOONhmFw7qW7efPmGUOGDAn7u0zfd7QQp7FDhw5p3bp1GjduXOC1nJwcjRs3TlVVVSlcM8SydetW1dXVhey7kpISjRw5MrDvqqqq1KNHD51xxhmBZcaNG6ecnBytWbMm6evsZQ0NDZKk0tJSSdK6det0+PDhkP138skn69hjjw3Zf6eeeqr69u0bWGbChAlqbGwMtFQi8VpbW7V06VI1NTVp9OjR7LsMcf3112vSpEkh+0ni3MsEmzdvVr9+/XT88cfriiuu0Pbt2yVl/r7rktJPR1R79uxRa2tryIEjSX379tV7772XorWCFXV1dZIUdt+Zv6urq1OfPn1Cft+lSxeVlpYGlkHitbW16aabbtKYMWNUUVEhqX3f5Ofnq0ePHiHLdtx/4fav+Tsk1jvvvKPRo0erublZRx11lJ566imVl5drw4YN7Ls0t3TpUlVXV+uNN97o9DvOvfQ2cuRI/eEPf9AXvvAF1dbWasGCBfqP//gP1dTUZPy+IyAG4GnXX3+9ampqQvLgkP6+8IUvaMOGDWpoaNBf/vIXXX311Xr55ZdTvVqI4aOPPtKNN96o5cuXq7CwMNWrA5u+8pWvBP7/tNNO08iRI3Xcccfpz3/+s7p27ZrCNYsfKRNprHfv3srNze00QnPXrl3y+/0pWitYYe6faPvO7/d3Ghx55MgR1dfXs3+TZObMmXruuef00ksv6Zhjjgm87vf7dejQIe3bty9k+Y77L9z+NX+HxMrPz9eJJ56o4cOHa+HChRoyZIjuu+8+9l2aW7dunXbv3q1hw4apS5cu6tKli15++WX9+te/VpcuXdS3b1/2Xwbp0aOHTjrpJH3wwQcZf+4REKex/Px8DR8+XCtWrAi81tbWphUrVmj06NEpXDPEMmjQIPn9/pB919jYqDVr1gT23ejRo7Vv3z6tW7cusMzKlSvV1tamkSNHJn2dvcQwDM2cOVNPPfWUVq5cqUGDBoX8fvjw4crLywvZf++//762b98esv/eeeedkIea5cuXq7i4WOXl5cn5Ighoa2tTS0sL+y7NnXfeeXrnnXe0YcOGwL8zzjhDV1xxReD/2X+Z47PPPtOWLVtUVlaW+edeSof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coefstd errtP>|t|
intercept33.22280.73145.4580.000
lstat-1.03210.048-21.4160.000
age0.03450.0122.8260.005
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coefstd errtP>|t|
intercept41.61734.9368.4310.000
crim-0.12140.033-3.6780.000
zn0.04700.0143.3840.001
indus0.01350.0620.2170.829
chas2.84000.8703.2640.001
nox-18.75803.851-4.8700.000
rm3.65810.4208.7050.000
age0.00360.0130.2710.787
dis-1.49080.202-7.3940.000
rad0.28940.0674.3250.000
tax-0.01270.004-3.3370.001
ptratio-0.93750.132-7.0910.000
lstat-0.55200.051-10.8970.000
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coefstd errtP>|t|
intercept41.52514.9208.4410.000
crim-0.12140.033-3.6830.000
zn0.04650.0143.3790.001
indus0.01350.0620.2170.829
chas2.85280.8683.2870.001
nox-18.48513.714-4.9780.000
rm3.68110.4118.9510.000
dis-1.50680.193-7.8250.000
rad0.28790.0674.3220.000
tax-0.01270.004-3.3330.001
ptratio-0.93460.132-7.0990.000
lstat-0.54740.048-11.4830.000
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vif
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chas1.071168
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coefstd errtP>|t|
intercept36.08851.47024.5530.000
lstat-1.39210.167-8.3130.000
age-0.00070.020-0.0360.971
lstat:age0.00420.0022.2440.025
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coefstd errtP>|t|
intercept17.71510.78122.6810.0
poly(lstat, degree=2)[0]-179.22796.733-26.6200.0
poly(lstat, degree=2)[1]72.99085.48213.3150.0
age0.07030.0116.4710.0
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df_residssrdf_diffss_diffFPr(>F)
0503.019168.1286090.0NaNNaNNaN
1502.014165.6132511.05002.515357177.2787857.468491e-35
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" + ], + "text/plain": [ + " df_resid ssr df_diff ss_diff F Pr(>F)\n", + "0 503.0 19168.128609 0.0 NaN NaN NaN\n", + "1 502.0 14165.613251 1.0 5002.515357 177.278785 7.468491e-35" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "anova_lm(results1, results3)\n" ] @@ -1004,12 +2620,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "9a5ec13f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:12.392629Z", + "iopub.status.busy": "2023-07-25T23:59:12.392494Z", + "iopub.status.idle": "2023-07-25T23:59:12.503315Z", + "shell.execute_reply": "2023-07-25T23:59:12.502922Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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coefstd errtP>|t|
intercept6.57561.0096.5190.000
CompPrice0.09290.00422.5670.000
Income0.01090.0034.1830.000
Advertising0.07020.0233.1070.002
Population0.00020.0000.4330.665
Price-0.10080.007-13.5490.000
ShelveLoc[Good]4.84870.15331.7240.000
ShelveLoc[Medium]1.95330.12615.5310.000
Age-0.05790.016-3.6330.000
Education-0.02090.020-1.0630.288
Urban[Yes]0.14020.1121.2470.213
US[Yes]-0.15760.149-1.0580.291
Income:Advertising0.00080.0002.6980.007
Price:Age0.00010.0000.8010.424
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 6.5756 1.009 6.519 0.000\n", + "CompPrice 0.0929 0.004 22.567 0.000\n", + "Income 0.0109 0.003 4.183 0.000\n", + "Advertising 0.0702 0.023 3.107 0.002\n", + "Population 0.0002 0.000 0.433 0.665\n", + "Price -0.1008 0.007 -13.549 0.000\n", + "ShelveLoc[Good] 4.8487 0.153 31.724 0.000\n", + "ShelveLoc[Medium] 1.9533 0.126 15.531 0.000\n", + "Age -0.0579 0.016 -3.633 0.000\n", + "Education -0.0209 0.020 -1.063 0.288\n", + "Urban[Yes] 0.1402 0.112 1.247 0.213\n", + "US[Yes] -0.1576 0.149 -1.058 0.291\n", + "Income:Advertising 0.0008 0.000 2.698 0.007\n", + "Price:Age 0.0001 0.000 0.801 0.424" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "allvars = list(Carseats.columns.drop('Sales'))\n", "y = Carseats['Sales']\n", @@ -1116,6 +2937,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch4-classification-lab.ipynb b/docs/source/labs/Ch4-classification-lab.ipynb index 0161f78..e5ab894 100644 --- a/docs/source/labs/Ch4-classification-lab.ipynb +++ b/docs/source/labs/Ch4-classification-lab.ipynb @@ -41,9 +41,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "71523a7f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:13.693335Z", + "iopub.status.busy": "2023-07-25T23:59:13.692982Z", + "iopub.status.idle": "2023-07-25T23:59:14.692387Z", + "shell.execute_reply": "2023-07-25T23:59:14.692045Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -65,9 +72,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "901ed7c5", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:14.694539Z", + "iopub.status.busy": "2023-07-25T23:59:14.694353Z", + "iopub.status.idle": "2023-07-25T23:59:14.710282Z", + "shell.execute_reply": "2023-07-25T23:59:14.709950Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -94,12 +107,210 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "8b56ee0f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:14.712197Z", + "iopub.status.busy": "2023-07-25T23:59:14.712071Z", + "iopub.status.idle": "2023-07-25T23:59:14.723241Z", + "shell.execute_reply": "2023-07-25T23:59:14.722941Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume \\\n", + "Year 1.000000 0.029700 0.030596 0.033195 0.035689 0.029788 0.539006 \n", + "Lag1 0.029700 1.000000 -0.026294 -0.010803 -0.002986 -0.005675 0.040910 \n", + "Lag2 0.030596 -0.026294 1.000000 -0.025897 -0.010854 -0.003558 -0.043383 \n", + "Lag3 0.033195 -0.010803 -0.025897 1.000000 -0.024051 -0.018808 -0.041824 \n", + "Lag4 0.035689 -0.002986 -0.010854 -0.024051 1.000000 -0.027084 -0.048414 \n", + "Lag5 0.029788 -0.005675 -0.003558 -0.018808 -0.027084 1.000000 -0.022002 \n", + "Volume 0.539006 0.040910 -0.043383 -0.041824 -0.048414 -0.022002 1.000000 \n", + "Today 0.030095 -0.026155 -0.010250 -0.002448 -0.006900 -0.034860 0.014592 \n", + "\n", + " Today \n", + "Year 0.030095 \n", + "Lag1 -0.026155 \n", + "Lag2 -0.010250 \n", + "Lag3 -0.002448 \n", + "Lag4 -0.006900 \n", + "Lag5 -0.034860 \n", + "Volume 0.014592 \n", + "Today 1.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Smarket.corr()\n" ] @@ -165,12 +552,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9f075144", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:14.735695Z", + "iopub.status.busy": "2023-07-25T23:59:14.735602Z", + "iopub.status.idle": "2023-07-25T23:59:14.838151Z", + "shell.execute_reply": "2023-07-25T23:59:14.837685Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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LinearDiscriminantAnalysis(store_covariance=True)
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QuadraticDiscriminantAnalysis(store_covariance=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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"outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.5317460317460317" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "knn3 = KNeighborsClassifier(n_neighbors=3)\n", "knn3_pred = knn3.fit(X_train, L_train).predict(X_test)\n", @@ -1352,12 +2895,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "98fef4ed", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.127395Z", + "iopub.status.busy": "2023-07-25T23:59:15.127266Z", + "iopub.status.idle": "2023-07-25T23:59:15.145746Z", + "shell.execute_reply": "2023-07-25T23:59:15.145440Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "No 5474\n", + "Yes 348\n", + "Name: Purchase, dtype: int64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Caravan = load_data('Caravan')\n", "Purchase = Caravan.Purchase\n", @@ -1377,12 +2939,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "1af72c0b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.147502Z", + "iopub.status.busy": "2023-07-25T23:59:15.147373Z", + "iopub.status.idle": "2023-07-25T23:59:15.149578Z", + "shell.execute_reply": "2023-07-25T23:59:15.149324Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.05977327378907592" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "348 / 5822\n" ] @@ -1397,9 +2976,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "9ea841e4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.151176Z", + "iopub.status.busy": "2023-07-25T23:59:15.151061Z", + "iopub.status.idle": "2023-07-25T23:59:15.153606Z", + "shell.execute_reply": "2023-07-25T23:59:15.153341Z" + } + }, "outputs": [], "source": [ "feature_df = Caravan.drop(columns=['Purchase'])\n" @@ -1439,9 +3025,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "6e26b14f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.155200Z", + "iopub.status.busy": "2023-07-25T23:59:15.155088Z", + "iopub.status.idle": "2023-07-25T23:59:15.156876Z", + "shell.execute_reply": "2023-07-25T23:59:15.156624Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1472,9 +3064,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "bd178f38", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.158484Z", + "iopub.status.busy": "2023-07-25T23:59:15.158369Z", + "iopub.status.idle": "2023-07-25T23:59:15.164862Z", + "shell.execute_reply": "2023-07-25T23:59:15.164529Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1494,10 +3092,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "18929b37", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.166628Z", + "iopub.status.busy": "2023-07-25T23:59:15.166522Z", + "iopub.status.idle": "2023-07-25T23:59:15.172686Z", + "shell.execute_reply": "2023-07-25T23:59:15.172394Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "MOSTYPE 1.000086\n", + "MAANTHUI 1.000086\n", + "MGEMOMV 1.000086\n", + "MGEMLEEF 1.000086\n", + "MOSHOOFD 1.000086\n", + " ... \n", + "AZEILPL 1.000086\n", + "APLEZIER 1.000086\n", + "AFIETS 1.000086\n", + "AINBOED 1.000086\n", + "ABYSTAND 1.000086\n", + "Length: 85, dtype: float64" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "feature_std = pd.DataFrame(\n", " X_std,\n", @@ -1521,9 +3148,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "9d439d0b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.174341Z", + "iopub.status.busy": "2023-07-25T23:59:15.174217Z", + "iopub.status.idle": "2023-07-25T23:59:15.178004Z", + "shell.execute_reply": "2023-07-25T23:59:15.177680Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1549,12 +3182,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "9b8c0171", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.179661Z", + "iopub.status.busy": "2023-07-25T23:59:15.179563Z", + "iopub.status.idle": "2023-07-25T23:59:15.228740Z", + "shell.execute_reply": "2023-07-25T23:59:15.228255Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.111, 0.067)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "knn1 = KNeighborsClassifier(n_neighbors=1)\n", "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n", @@ -1585,10 +3235,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "f31b6e26", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:15.231007Z", + "iopub.status.busy": "2023-07-25T23:59:15.230866Z", + "iopub.status.idle": "2023-07-25T23:59:15.236660Z", + "shell.execute_reply": "2023-07-25T23:59:15.236308Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept-27.20686.715-4.0520.000
mnth[Aug]11.81814.6982.5150.012
mnth[Dec]5.03284.2801.1760.240
mnth[Feb]-34.57974.575-7.5580.000
mnth[Jan]-41.42494.972-8.3310.000
mnth[July]3.89965.0030.7790.436
mnth[June]26.39384.6425.6860.000
mnth[March]-24.87354.277-5.8150.000
mnth[May]31.13224.1507.5010.000
mnth[Nov]18.88514.0994.6070.000
mnth[Oct]34.40934.0068.5890.000
mnth[Sept]25.25344.2935.8830.000
hr[1]-14.57935.699-2.5580.011
hr[2]-21.57915.733-3.7640.000
hr[3]-31.14085.778-5.3890.000
hr[4]-36.90755.802-6.3610.000
hr[5]-24.13555.737-4.2070.000
hr[6]20.59975.7043.6120.000
hr[7]120.09315.69321.0950.000
hr[8]223.66195.69039.3100.000
hr[9]120.58195.69321.1820.000
hr[10]83.80135.70514.6890.000
hr[11]105.42345.72218.4240.000
hr[12]137.28375.74023.9160.000
hr[13]136.03595.76023.6170.000
hr[14]126.63615.77621.9230.000
hr[15]132.08655.78022.8520.000
hr[16]178.52065.77230.9270.000
hr[17]296.26705.74951.5370.000
hr[18]269.44095.73646.9760.000
hr[19]186.25585.71432.5960.000
hr[20]125.54925.70422.0120.000
hr[21]87.55375.69315.3780.000
hr[22]59.12265.68910.3920.000
hr[23]26.83765.6884.7190.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", + "
" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept -27.2068 6.715 -4.052 0.000\n", + "mnth[Aug] 11.8181 4.698 2.515 0.012\n", + "mnth[Dec] 5.0328 4.280 1.176 0.240\n", + "mnth[Feb] -34.5797 4.575 -7.558 0.000\n", + "mnth[Jan] -41.4249 4.972 -8.331 0.000\n", + "mnth[July] 3.8996 5.003 0.779 0.436\n", + "mnth[June] 26.3938 4.642 5.686 0.000\n", + "mnth[March] -24.8735 4.277 -5.815 0.000\n", + "mnth[May] 31.1322 4.150 7.501 0.000\n", + "mnth[Nov] 18.8851 4.099 4.607 0.000\n", + "mnth[Oct] 34.4093 4.006 8.589 0.000\n", + "mnth[Sept] 25.2534 4.293 5.883 0.000\n", + "hr[1] -14.5793 5.699 -2.558 0.011\n", + "hr[2] -21.5791 5.733 -3.764 0.000\n", + "hr[3] -31.1408 5.778 -5.389 0.000\n", + "hr[4] -36.9075 5.802 -6.361 0.000\n", + "hr[5] -24.1355 5.737 -4.207 0.000\n", + "hr[6] 20.5997 5.704 3.612 0.000\n", + "hr[7] 120.0931 5.693 21.095 0.000\n", + "hr[8] 223.6619 5.690 39.310 0.000\n", + "hr[9] 120.5819 5.693 21.182 0.000\n", + "hr[10] 83.8013 5.705 14.689 0.000\n", + "hr[11] 105.4234 5.722 18.424 0.000\n", + "hr[12] 137.2837 5.740 23.916 0.000\n", + "hr[13] 136.0359 5.760 23.617 0.000\n", + "hr[14] 126.6361 5.776 21.923 0.000\n", + "hr[15] 132.0865 5.780 22.852 0.000\n", + "hr[16] 178.5206 5.772 30.927 0.000\n", + "hr[17] 296.2670 5.749 51.537 0.000\n", + "hr[18] 269.4409 5.736 46.976 0.000\n", + "hr[19] 186.2558 5.714 32.596 0.000\n", + "hr[20] 125.5492 5.704 22.012 0.000\n", + "hr[21] 87.5537 5.693 15.378 0.000\n", + "hr[22] 59.1226 5.689 10.392 0.000\n", + "hr[23] 26.8376 5.688 4.719 0.000\n", + "workingday 1.2696 1.784 0.711 0.477\n", + "temp 157.2094 10.261 15.321 0.000\n", + "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", + "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", + "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X = MS(['mnth',\n", " 'hr',\n", @@ -1825,9 +4115,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "064b24d1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.142403Z", + "iopub.status.busy": "2023-07-25T23:59:16.141745Z", + "iopub.status.idle": "2023-07-25T23:59:16.146332Z", + "shell.execute_reply": "2023-07-25T23:59:16.145086Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1846,10 +4142,378 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "27c1aa54", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.150801Z", + "iopub.status.busy": "2023-07-25T23:59:16.150164Z", + "iopub.status.idle": "2023-07-25T23:59:16.223980Z", + "shell.execute_reply": "2023-07-25T23:59:16.222963Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept73.59745.13214.3400.000
mnth[Jan]-46.08714.085-11.2810.000
mnth[Feb]-39.24193.539-11.0880.000
mnth[March]-29.53573.155-9.3610.000
mnth[April]-4.66222.741-1.7010.089
mnth[May]26.47002.8519.2850.000
mnth[June]21.73173.4656.2720.000
mnth[July]-0.76263.908-0.1950.845
mnth[Aug]7.15603.5352.0240.043
mnth[Sept]20.59123.0466.7610.000
mnth[Oct]29.74722.70011.0190.000
mnth[Nov]14.22292.8604.9720.000
hr[0]-96.14203.955-24.3070.000
hr[1]-110.72133.966-27.9160.000
hr[2]-117.72124.016-29.3100.000
hr[3]-127.28284.081-31.1910.000
hr[4]-133.04954.117-32.3190.000
hr[5]-120.27754.037-29.7940.000
hr[6]-75.54243.992-18.9250.000
hr[7]23.95113.9696.0350.000
hr[8]127.51993.95032.2840.000
hr[9]24.43993.9366.2090.000
hr[10]-12.34073.936-3.1350.002
hr[11]9.28143.9452.3530.019
hr[12]41.14173.95710.3970.000
hr[13]39.89393.97510.0360.000
hr[14]30.49403.9917.6410.000
hr[15]35.94453.9958.9980.000
hr[16]82.37863.98820.6550.000
hr[17]200.12493.96450.4880.000
hr[18]173.29893.95643.8060.000
hr[19]90.11383.94022.8720.000
hr[20]29.40713.9367.4710.000
hr[21]-8.58833.933-2.1840.029
hr[22]-37.01943.934-9.4090.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", + "
" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 73.5974 5.132 14.340 0.000\n", + "mnth[Jan] -46.0871 4.085 -11.281 0.000\n", + "mnth[Feb] -39.2419 3.539 -11.088 0.000\n", + "mnth[March] -29.5357 3.155 -9.361 0.000\n", + "mnth[April] -4.6622 2.741 -1.701 0.089\n", + "mnth[May] 26.4700 2.851 9.285 0.000\n", + "mnth[June] 21.7317 3.465 6.272 0.000\n", + "mnth[July] -0.7626 3.908 -0.195 0.845\n", + "mnth[Aug] 7.1560 3.535 2.024 0.043\n", + "mnth[Sept] 20.5912 3.046 6.761 0.000\n", + "mnth[Oct] 29.7472 2.700 11.019 0.000\n", + "mnth[Nov] 14.2229 2.860 4.972 0.000\n", + "hr[0] -96.1420 3.955 -24.307 0.000\n", + "hr[1] -110.7213 3.966 -27.916 0.000\n", + "hr[2] -117.7212 4.016 -29.310 0.000\n", + "hr[3] -127.2828 4.081 -31.191 0.000\n", + "hr[4] -133.0495 4.117 -32.319 0.000\n", + "hr[5] -120.2775 4.037 -29.794 0.000\n", + "hr[6] -75.5424 3.992 -18.925 0.000\n", + "hr[7] 23.9511 3.969 6.035 0.000\n", + "hr[8] 127.5199 3.950 32.284 0.000\n", + "hr[9] 24.4399 3.936 6.209 0.000\n", + "hr[10] -12.3407 3.936 -3.135 0.002\n", + "hr[11] 9.2814 3.945 2.353 0.019\n", + "hr[12] 41.1417 3.957 10.397 0.000\n", + "hr[13] 39.8939 3.975 10.036 0.000\n", + "hr[14] 30.4940 3.991 7.641 0.000\n", + "hr[15] 35.9445 3.995 8.998 0.000\n", + "hr[16] 82.3786 3.988 20.655 0.000\n", + "hr[17] 200.1249 3.964 50.488 0.000\n", + "hr[18] 173.2989 3.956 43.806 0.000\n", + "hr[19] 90.1138 3.940 22.872 0.000\n", + "hr[20] 29.4071 3.936 7.471 0.000\n", + "hr[21] -8.5883 3.933 -2.184 0.029\n", + "hr[22] -37.0194 3.934 -9.409 0.000\n", + "workingday 1.2696 1.784 0.711 0.477\n", + "temp 157.2094 10.261 15.321 0.000\n", + "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", + "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", + "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X2 = MS([mnth_encode,\n", " hr_encode,\n", @@ -1888,10 +4552,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "dd0114c8", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.228972Z", + "iopub.status.busy": "2023-07-25T23:59:16.228601Z", + "iopub.status.idle": "2023-07-25T23:59:16.238595Z", + "shell.execute_reply": "2023-07-25T23:59:16.237798Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4.090076132857841e-19" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n" ] @@ -1907,12 +4589,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "292585a1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.243655Z", + "iopub.status.busy": "2023-07-25T23:59:16.243153Z", + "iopub.status.idle": "2023-07-25T23:59:16.252786Z", + "shell.execute_reply": "2023-07-25T23:59:16.252026Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n" ] @@ -1933,12 +4632,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "e05e0575", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.257683Z", + "iopub.status.busy": "2023-07-25T23:59:16.257311Z", + "iopub.status.idle": "2023-07-25T23:59:16.267187Z", + "shell.execute_reply": "2023-07-25T23:59:16.266452Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "mnth[Jan] -46.0871\n", + "mnth[Feb] -39.2419\n", + "mnth[March] -29.5357\n", + "mnth[April] -4.6622\n", + "mnth[May] 26.4700\n", + "mnth[June] 21.7317\n", + "mnth[July] -0.7626\n", + "mnth[Aug] 7.1560\n", + "mnth[Sept] 20.5912\n", + "mnth[Oct] 29.7472\n", + "mnth[Nov] 14.2229\n", + "Name: coef, dtype: float64" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", "coef_month\n" @@ -1954,12 +4681,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "9f04a25e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.272068Z", + "iopub.status.busy": "2023-07-25T23:59:16.271356Z", + "iopub.status.idle": "2023-07-25T23:59:16.283167Z", + "shell.execute_reply": "2023-07-25T23:59:16.282005Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "mnth[Jan] -46.0871\n", + "mnth[Feb] -39.2419\n", + "mnth[March] -29.5357\n", + "mnth[April] -4.6622\n", + "mnth[May] 26.4700\n", + "mnth[June] 21.7317\n", + "mnth[July] -0.7626\n", + "mnth[Aug] 7.1560\n", + "mnth[Sept] 20.5912\n", + "mnth[Oct] 29.7472\n", + "mnth[Nov] 14.2229\n", + "mnth[Dec] 0.3705\n", + "dtype: float64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "months = Bike['mnth'].dtype.categories\n", "coef_month = pd.concat([\n", @@ -1982,10 +4738,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "20c7c054", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.292541Z", + "iopub.status.busy": "2023-07-25T23:59:16.292061Z", + "iopub.status.idle": "2023-07-25T23:59:16.444617Z", + "shell.execute_reply": "2023-07-25T23:59:16.443792Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig_month, ax_month = subplots(figsize=(8,8))\n", "x_month = np.arange(coef_month.shape[0])\n", @@ -2006,9 +4780,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "3f83f4bf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.449734Z", + "iopub.status.busy": "2023-07-25T23:59:16.449373Z", + "iopub.status.idle": "2023-07-25T23:59:16.458325Z", + "shell.execute_reply": "2023-07-25T23:59:16.456791Z" + } + }, "outputs": [], "source": [ "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", @@ -2028,10 +4809,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "86e9a741", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.463694Z", + "iopub.status.busy": "2023-07-25T23:59:16.463309Z", + "iopub.status.idle": "2023-07-25T23:59:16.589630Z", + "shell.execute_reply": "2023-07-25T23:59:16.589275Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig_hr, ax_hr = subplots(figsize=(8,8))\n", "x_hr = np.arange(coef_hr.shape[0])\n", @@ -2056,9 +4855,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "a9448278", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.591638Z", + "iopub.status.busy": "2023-07-25T23:59:16.591489Z", + "iopub.status.idle": "2023-07-25T23:59:16.661619Z", + "shell.execute_reply": "2023-07-25T23:59:16.660835Z" + } + }, "outputs": [], "source": [ "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n" @@ -2074,9 +4880,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "ec8505be", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.666429Z", + "iopub.status.busy": "2023-07-25T23:59:16.666119Z", + "iopub.status.idle": "2023-07-25T23:59:16.691878Z", + "shell.execute_reply": "2023-07-25T23:59:16.690343Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -2102,12 +4914,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "id": "0b8a5edf", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.696339Z", + "iopub.status.busy": "2023-07-25T23:59:16.696023Z", + "iopub.status.idle": "2023-07-25T23:59:16.992754Z", + "shell.execute_reply": "2023-07-25T23:59:16.992369Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_33380/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", @@ -2134,10 +4971,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "id": "d487c7ed", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:16.994758Z", + "iopub.status.busy": "2023-07-25T23:59:16.994651Z", + "iopub.status.idle": "2023-07-25T23:59:17.107603Z", + "shell.execute_reply": "2023-07-25T23:59:17.107255Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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3338f6enpGDNmDK5du4bs7Gw4OTnB09PT4J6AgABkZ2cDALKzsw0Cse687pwpy5cvh0ql0n9169bNvh+MiIiIyAaZBVq7jfXw6BAkvTQZ3Xxc7TZma9AqyyduvfVW/Z8HDBiA4cOHIygoCF999RU6dOjQaM9dvHgxnn76af33xcXFDMZERETU7IK87RNgFQLwwu1hdhmrtWmVK8X1eXp64oYbbsD58+cRGBiIiooKaDQag2tycnL0NciBgYENulHovjdWp6zj7OwMDw8Pgy8iIiKi5tbDzx2RoX5wEARZ47zcTgMx0EZCcUlJCS5cuIBOnTph8ODBUCqV2L17t/58SkoKsrKyEBERAQCIiIjA6dOnoVar9dfs3LkTHh4eCAtrv38ZiIiIqPVaERWOUb18ZY0R2dvfTrNpfVpl+cSiRYswbdo0BAUF4cqVK3j55Zfh4OCAqKgoqFQqREdH4+mnn4a3tzc8PDwQGxuLiIgIjBgxAgAwefJkhIWF4YEHHsBbb72F7OxsvPDCC4iJiYGzs3MzfzoiIiIiafalqJF8SYNB3b0wJtQP66OHIT2vFBn5pQj2ccPN7+6T/IKdZwclQnzdGnnGLVerDMWXLl1CVFQU8vPz4efnh9GjR+Pw4cPw86ttG/Lf//4XCoUCs2bNQnl5OaZMmYLVq1fr73dwcMCPP/6IBQsWICIiAm5ubpg3bx6WLl3aXB+JiIiISLLM/FJMX5WAQm2l/piuY0SIr5s+3H4RPQz3fHRE0pjh3VSNMtfWQhBF0YqOdVRXcXExVCoVioqKWF9MRERETSZ86Q6DQKzj5apE0kuT9d/fvuI3nLksrYXshuhhbbIvsdS81iZqiomIiIjai30paqOBGAAKtZX47a8d7dJySyQH4o7OijYZiK3BUExERETUiiRf0pg9fyKrEABw7oq0QOzlqsRPcWPlTqvVa5U1xURERETt1cCunmbPD+ruBQD49GCGxbHevmsAZg/hngsAV4qJiIiIWpWxvf3h5ao0es7LVYkxoX5Iyy3BscxCi2MdScu39/RaLYZiIiIiolZma8zoBsFY130CkL7t8/ncErvPrbVi+QQRERFRK5KWW4LMAi2+fXwULhVqcSKrUN+nOC23BHtS1FAXlUkaa3KY6Z182xuGYiIiIqJWQKOtQFx8Mvb/1V0CACJD/bAiKhwiRMxdm2hwTorHx/ey9zRbLYZiIiIiolYgLj4ZCefzDI4lnM9DbHyS/s/W+O/dN9ltbm0BQzERERFRC5eWW2J0FbhaFK1eHQZqV5hnDOpqj6m1GXzRjoiIiKiFk/rinFQrosLtOl5bwFBMRERE1MIFebvadbwd57LtOl5bwFBMRERE1ML18HM325s40sotmhMuWFd/3B4wFBMRERG1cCt3p6JQW2n0XKG2EvcN7wbBivFG9fS1z8TaEL5oR0RERNRCnb6kwYzVB1FVI5q97vEvTsD8FX9zVAjc2tkIhmIiIiKiFmr6qgRUS0i7Uq4BAIUAbI0ZJW9SbRRDMREREVELo9FW4O4PDlkMuwoANRLHdFQA55dNlTu1Nos1xUREREQtTFx8MlLVJRavC+vsIXnMV6b1lTOlNo+hmIiIiKgF0W3UYakiItjHFeN6S+86seOcWt7E2jiGYiIiIqIWROpGHRcLruOroxclj7s/NRfpeaW2TqvNYygmIiIiakGkhrNqUYS6pMKqsTPy208oLi0thShK7cnBUExERETUomQXlzXa2ME+bo02dkui0WgwYcIEPPHEE5KDMbtPEBEREbUA+1LUSL6kwQd7L9h9bAdBwKhevgjxbfuhuLCwEJMnT8axY8eQmJiIykrjm57UJ4jWrCuTgeLiYqhUKhQVFcHDQ/rbn0REREQ6mfmlmL4qweSOdfYQGeqHFVHhUJnYKrqtKCwsxM0334zjx483OGcpr3GlmIiIiKgZNXYg3hozCgO6eTba+C1FQUEBbr75Zpw4ccLgeEBAAHJycizez5piIiIiomayL0XdqIEYAN7Z8Wejjt8S5OfnY+LEiQ0CcefOnfHTTz9JGoOhmIiIiKiZJF/SNPoz2nortry8PEycOBHJyckGx7t06YK9e/eiV69eksZhKCYiIiJqJgO7ekq+1t1Jgd7+7jY9p622YtMF4pMnTxoc79q1K/bu3YvQ0FDJYzEUExERETWTsb394SXx5beSihqkSNj62Zi22IotNzcXEyZMwKlTpwyOd+vWzaoVYh2GYiIiIqJmsikxC0ODvBr1GZGhfm2uFZtarcaECRNw+vRpg+Pdu3fH3r170bNnT6vHZPcJIiIioiZ2+pIGM1YfRFVN43bGjejhgxVR4Y36jKaWk5ODCRMm4Ny5cwbHg4KCsGfPHoSEhNg0LkMxERERURNrzEDs4ijguVv6YPyN/m1uhbimpgZTp05tEIiDg4OxZ88eBAcH2zw2yyeIiIiImtCmxKxGXSF2UTriodEhbS4QA4BCocDy5cvh7OysPxYSEoK9e/fKCsQAQzERERFRkzqUnt+o42uuV+K31NxGfUZzuvnmm/H999/D2dkZPXr0wN69exEUFCR7XIZiIiIioiYUEeLT6M84kVXY6M9oTlOmTMG2bduwd+9edO/e3S5jMhQTERERNaF7hnWHo0Jo1GcM6t64HS1agokTJ6Jbt252G4+hmIiIiKiJbY0Z1Whje7kqMSbUr9HGbyqXLl3Ct99+22TPYygmIiIiamKnLxc1yrherkpsjRndKGM3paysLIwdOxazZ8/Gxo0bm+SZbMlGRERE1MTs/bJdF08XvHh7GG7p18mu4zaHzMxMjB8/Hunp6QCABx54AAqFAnPmzGnU53KlmIiIiKgJZeaXYsfZHIvXOTsI+OC+QbghwN3itZ8/PKJNBOKMjAyMGzdOH4iB2t7Eb7zxBiorKxv12bJWitevXw8AmD59Ojw8PCTdU1JSoq8PmTt3rpzHExEREbU601clQFtRbfG6wcHeKLpeidScEovXZuSXtvq+xLpAnJmZaXA8LCwMu3btglKpbNTnC6Io2tw9WqFQQBAEnD59GmFhYZLuuXDhAkJDQ6FQKFBVVWXro1uE4uJiqFQqFBUVSf6hgIiIiNqvfSlqzFt31O7j7lk0rlWH4vT0dIwbNw5ZWVkGx/v27Yvdu3cjICDA5rGl5rVmK5+QkcWJiIiIWqXvki/bfcx+XTxadSBOS0vD2LFjGwTifv364ddff5UViK3R5C/aVVfX/rrA0ZHv+BEREVH7cCA1F//4/DhKyi2XTVhr2Yz+dh+zqVy4cAHjxo3DpUuXDI73798fu3fvhp9f07WWa/JkmpKSAgDw9vZu6kcTERERNSmNtgJx8cnY30jbLkeG+mFAV89GGbuxnT9/HuPGjcPly4ar5wMGDMDu3bvh6+vbpPOxKhTv37/f6PGjR48iLy/P7L3l5eW4cOEC3nnnHQiCgIEDB1rzaCIiIqJWJy4+GQfON04gHhbshRVR4Y0ydmNLTU3FuHHjcOXKFYPjAwcOxK5du+Dj0/hbYddnVSgeN24cBMFwW0JRFPHQQw9JHkMURQiCgMcee8yaRxMRERG1Kmm5JVavELs4Ctj02EjcuSrB7HX9u3jgq3+MlDO9ZpOSkoLx48fj6tWrBsfDw8Oxc+fOZgnEgA0v2omiqP8ydszSV9euXbFq1SpMnz7dnp+DiIiIqEXJLNBafc/iW/vgwXWJFq87fbkY6Xmltkyr2e3atatBIB40aFCzrRDrWLVSvGfPHv2fRVHEhAkTIAgC1q5di5CQEJP3CYIAFxcXdOrUCd26dbN9tkREREStRJC3q9X3fH3iEgq10japaK29iWNiYnDt2jUsXrwYADB48GDs3LkTXl5ezTovq0Lx2LFjjR4fNmyY5D7FRERERO1BDz93RPTwwaE06Vs6n75cLPnaYJ/WF4h1/vnPf6KmpgbfffcdduzYAU9Pz+aekrzuE7ot+Lp06WKXyRARERG1FScvFuLUJY3dx1UAGB3q1ypXiev617/+hWeeeQbOzs7NPRUAMkNxUFCQveZBRERE1CY0dhu20aF+rabrRHV1NRwcHEyebymBGGjGHe2IiIiI2iJb27B5uSpNnuvo4oiV94Zjz6JxWB89DCoz17YUp06dQt++fZGcnNzcU5FE0krx0qVL9X9+6aWXjB63Rd2xiIiIiFo7W9qwAcDArp5INlNq0cvXDbcP6CxjZk3r5MmTmDhxIvLz8zFx4kT8+uuvuOmmm5p7WmYJYt3eaiYoFAp9f2LdNs31j9ui7litUXFxMVQqFYqKiuDh4dHc0yEiIqJmtidFjfnrjlp9XxdPF1zWlJkfe9G4VlFHnJycjIkTJ6KgoEB/zMfHBwcOHMCNN97Y5PORmtckl0/U701c/7gtX0RERERtiS1t2ABYDMRAbQu2lu7EiROYMGGCQSAGgLCwMHTt2rWZZiWNpFBcU1Oj/zJ13JYvIiIiorakh587IkP9GmXslt6C7fjx45g0aRIKCwsNjo8dOxY//fQT3N3dm2lm0vBFOyIiIqJ60nJLsCdFbfWucRptBa6VVTTKnF7+/iyKJG7s0dSOHTtmNBCPGzcO27Zta/GBGLCiJZtu97pPPvmErdiIiIioTTLWTi3yrxZoUjo+xMUn4+SlokaZW8L5PMTGJ2F99LBGGd9WR48exc0334yiIsPPPWHCBPzwww9wdbWtpKSpSV4p3rt3L/bu3YvSUuM/MaWmpqJHjx7o2bOn3SZHRERE1JTi4pORcD7P4JgujFqi6zxRI/G1qRv83XDypcmIDPWDg4TGBdWiiP2puVavXjemI0eOYNKkSQ0C8cSJE1tVIAbsWD5RUVGBjIwMZGRk2GtIIiIioiajC7XV9ZoBSA2jmQVaq57Xr4snTl3WYEVUOEb18pV8X0t54e7w4cOYPHkyiosNt6a++eabW10gBmTuaEdERETUVlgKtRn5pWZbomXklVj1vG+TLuPbpMtwd3bA3UO6YnhIb1SJNeikcsFzX582eV9LeOHu0KFDmDJlCq5du2ZwfPLkyfjuu+/QoUOHZpqZ7RiKiYiIiGC5nZqpMLovRY3tZ69iY+Ilm55bUl6NTxIyDY55uSpRfL0S1XUWrR0EAaN6+TZ7r+KzZ89i8uTJKCkx/CHglltuwZYtW+Di4tJMM5OH3SeIiIiI8Hc7tfr1vQ6CgMhQvwZhNDO/FOFLd2DeuqM2B2JTirSV8Ohg+GLfqF6+WBEVbtfn2OKGG27AzTffbHDs1ltvbdWBGOBKMREREZHeiqhwxMYnGXSfMBVGp69KQGEjtUirAVCorcSG6GGoqhER7OPW7CvEOkqlEl9++SXuuecefPfdd5g6dSq++eYbODs7N/fUZGEoJiIiIvqLylWJ9dHDkJ5Xioz8UpNhdF+KutECcV1VNSLG9/Zv9OdYy8nJCZs2bcJ///tfPPnkk60+EAM2hGJBQssQIiIiotYsxNf8ymzyJY1N4woAJHZsA9AyXqozxcnJCc8//3xzT8NurA7FkydPhlLZsHl1ZeXfPy316NHD4jiCIODChQvWPp6IiIio2Q3s6mnTfVIDcUt5qe7gwYPo3bs3fHx8mnUeTcHqUHz58mWT53SryFJ6FXPFmYiIiFqrsb394dlBCc31ximhaAkv1e3evRvTpk1D7969sXv3bnh7ezfrfBqb5FDcvXt3BlkiIiIi1G704ap0sFsonhHeGd6uTugV0BEjevg0+wrxrl27MG3aNJSVlSE5ORmTJk3Crl272nQwFkRRtKa0heooLi6GSqVCUVERPDw8mns6REREJFFabgkyC7RWd3XQaCsQF59s0J3C3iJD/bAiKhwqVyX2paiRfEmDQd29MCbUr9GeWdeOHTtw5513oqyszOD4kiVL8PrrrzfJHOxJal5j9wkiIiJqN4yF2roh1JK4+GQknM9rzCki4XweHll/FKnqEoMOF16uSmyNGY1uPo23ffL27dtx5513ory83OD4Pffcg1deeaXRntsScPMOIiIiajeMhdqE83mIjU+yeG9abgn2p+aiupF/yV4tikjMKGzQ8q1QW4k7Vh1otOf+/PPPRgPxnDlz8Pnnn8PRsW2vpTIUExERUbtgKtRWiyL2p+YiPa/U7P2ZBdrGnJ4khdpK/NYIpRs//fQTpk+f3iAQ33vvvdiwYUObD8QAQzERERG1E5ZCbUa++VAc5N14ZQvWOJFVaNfxfvzxR8yYMQMVFRUGx++//36sX7++XQRigKGYiIiI2glLodbSRhk9/NwxJMjLnlNCvy4ecKjX3UthodnXoO72m8PWrVsxc+bMBoH4gQcewKeffgoHBwe7PaulYygmIiKidqGHnzsiQ/0ahFAHQUBkqJ+kLhTn1SV2nZNSEDC8h2Gbs9G9/KDqYHx11stVabcuFN999x3uuusugw3YAGDevHlYt25duwrEALtPEBERUTuRlluCe4Z0xfWKKhzN/LsEQepGGT+dvmL3zTpOXS7GqF6+2LNoHDLyS/Ut4i7ma3HHqgNGu0/Yw5YtW3D33XejqqrK4Pj8+fPx0UcftbtADDAUExERURtnrA3b0GAvzBsZjL6dVZL7FP/75z/sPjfdS34AML63v/54Nx9XJL00Gb+l5uJEVqFd+xRXV1fj1VdfbRCIo6OjsWbNGigU7bOQoH1+aiIiImo3jLVhO5GpwVdHL0kOxLWbfVxvjOkBMP2S35hQPzwx8Qa7btzh4OCA7du3o0+fPvpjDz/8cLsOxABDMREREbVhctuw6TR2OzZLL/nZW0BAAH799Vf07t0bjz76KD788MN2HYgBlk8QERFRGyalDZul1eLM/FI8IWFzD1s4CAJG9fK1aqtpewkMDMTBgwfh6enZ7gMxwFBMREREbZjcNmwAMH1VAorLqixeZwupL/k1Fm9vb8sXtRN2D8XFxcW4du0aqqurLV7bvXt3ez+eiIiISO9aWSXcnR1QUm6YSxQARktow7YvRd1gu2U5hgZ74a27bjLoNAHoapa1Bsfs4YsvvsCOHTvwySeftMuOEtawSyjeuXMnVq9ejQMHDqCgoEDSPYIgNHjrkYiIiMgejHWcqEvlqpS0Qpt8SSP5mQIA0cx5L1clPp47FCpXpT74GptnZKgfVkSFQ+WqlPxsYzZs2IAHH3wQNTU1EEWxXfYetobsApK4uDjccsst2Lp1K/Lz8yGKouQve3jzzTchCAKefPJJ/bGysjLExMTAx8cH7u7umDVrFnJycgzuy8rKwtSpU+Hq6gp/f388++yzDOlERERtRFx8Mg6cNx6IAaBQW4kCbYXJ8zoDu3pKfqa5ZDM0yAt7F41vEHSNdcZIOJ+HWJk1zJ999hnmzZuHmpoaALUB+aGHHtJ/Tw3JWineuHEjVq5cCQBwcXHB9OnTMXjwYHh7ezdJwfbRo0fx4YcfYsCAAQbHn3rqKWzbtg2bN2+GSqXCwoULMXPmTCQkJACo7c83depUfYH51atXMXfuXCiVSixbtqzR501ERESNR9dxwhIpL9mN7e0PL1el7BKKt2bf1CAQm5pn3c4YtpRSfPrpp3jooYcaLED6+PhAECzsId2OyQrFH374IQCgW7du+PXXX9GzZ0+7TEqKkpIS3Hffffjoo4/w+uuv648XFRVh7dq12LhxIyZMmAAAWLduHfr06YPDhw9jxIgR2LFjB86dO4ddu3YhICAAAwcOxGuvvYbnn38er7zyCpycnJrscxAREZH9aLQViPtS2iqrlJfsNNoKhPp3RGKGtPJQUz47mI4bAz0wvIePPujaozNGfZ988gkefvjhBoH4mWeewdtvv81QbIas5dxTp05BEAS8/PLLTRqIASAmJgZTp07FpEmTDI4fP34clZWVBsdvvPFGdO/eHYcOHQIAHDp0CP3790dAQID+milTpqC4uBhnz541+czy8nIUFxcbfBEREVHLERefjHNXzP/3WYHaul0pgfPxL07IDsQA8OnBTPzz29MY/85e3PvRYRRpK+3SGaOujz/+GNHR0Q0C8bPPPstALIGsUFxZWfurhPDwpm0l8uWXX+LEiRNYvnx5g3PZ2dlwcnKCp6enwfGAgABkZ2frr6kbiHXndedMWb58OVQqlf6rW7duMj8JERER2YuuHKHGwmtLo/96kU3KeAcv5Js8/9asASbPmXPwQr6+ZrhfZ48GYcxBECSHdp01a9bgkUceaXD8+eefx7///W8GYglkheLg4GAAtaUMTeXixYt44okn8MUXX8DFxaXJngsAixcvRlFRkf7r4sWLTfp8IiIiMu2XM1fNng/y6YCtC0dhffQwSZ0djqSbDsQAUCOKGBrsZdUcdfan5mLCf/bhzJVi1H/1zdrexR9++CEee+yxBscXL16M5cuXMxBLJCsUz5w5EwCwe/duu0xGiuPHj0OtVmPQoEFwdHSEo6Mj9u3bh//9739wdHREQEAAKioqoNFoDO7LyclBYGAggNodXOp3o9B9r7vGGGdnZ3h4eBh8ERERUcvw1vY/zZ7/dP5wDLCim0RtkzXTRAB/5thnYVAhAP26eGDPonGSQzsAvP/++/jHP/7R4PiSJUvwxhtvMBBbQVYofuaZZ9C9e3f83//9H/744w97zcmsiRMn4vTp00hOTtZ/DRkyBPfdd5/+z0ql0iCop6SkICsrCxEREQCAiIgInD59Gmq1Wn/Nzp074eHhgbCwsCb5HERERGQ/K3enmj0f4uNq9Utrw0PM7/amEICi6/bZ2KNGBM5ctu5dpVWrVuHxxx9vcPzFF1/Ea6+9xkBsJVmhWKVSYfv27QgICMDIkSOxevVqFBYW2mtuRnXs2BH9+vUz+HJzc4OPjw/69esHlUqF6OhoPP3009izZw+OHz+O+fPnIyIiAiNGjAAATJ48GWFhYXjggQdw8uRJbN++HS+88AJiYmLg7OzcqPMnIiIi+0u4kGf2vK+79Z2levi5I6KHj9FzET18kF1cZvZ+d2frN8rIyC+VdN0HH3yAhQsXNjj+yiuvYOnSpQzENpDVkq1Hjx4AAK1WC41Gg9jYWMTFxcHX1xeurubfqBQEARcuXJDzeJP++9//QqFQYNasWSgvL8eUKVOwevVq/XkHBwf8+OOPWLBgASIiIuDm5oZ58+Zh6dKljTIfIiIialyjevriUJrpLhHjbwwwec4UjYnNPUb29MH79w1G8kXzC4H1t5aWQmrHifDwcHh4eBh0wnr11Vfx0ksvWf1MqiWIMraWk7NBhyAIqK62/i9LS1JcXAyVSoWioiLWFxMRETWjX05fxT++OGHyfMabU60aLy23BHFfJuHclWKDbhYK1HavWB89DAAQvnSH5I09Qv3dsWbuELz8/VkknM9DdZ0I5iAIGNXLVz+uFIcPH8bkyZNx7do1vPbaa3jhhRck39ueSM1rslaK582bJ+d2IiIiIlk02grExSeb3cFu0yMj7DZeDWCw29zWmNG4Y9UBScE4VV37Ut6KqHDExicZPMPajhMAMGLECGzfvh0JCQlYtGiRVfdSQ7JWits7rhQTERE1r7lrE/Hb+VyYSzNv3zUAs4dI21tg7trEBqu4xqybPxTje/vrv/8tNRer95w3W8JR/770vFJk5Jci2MfNpu2cSRqpeU3Wi3ZEREREzUW3WYel5T1LL+HVH89SIAYa1v6OCfXDP8Za3t237n0hvm4Y39vfYiA+ccJ0WQjZD0MxERERtSppuSXYk6JGYrq07ZdH9fSVdF1mgdbiNcZ2m9PNRxAEuDqZjlZDg72QkV+K9DxpHSYA4M0338TgwYOxcuVKyfeQbWTVFBuTk5ODM2fOoKCg9i+qt7c3+vXr12BbZSIiIiJr/HT6Cv798x/ILLhu1X1SSyeCvM13zgIMa3+l1DPruDgKOJpRiPnrjgIAIv/aatrcJh3Lli3DkiVLAACxsbEQBAExMTFSPgrZwC41xaIoYs2aNVi5ciXOnTtn9JqwsDDExsbikUceaTO981hTTERE1Pgy80sxfVWC5C4P9e1ZNE5yza6xmmIFgLDOHlhx7yCDcaTUHzs7CCivbni+fheL+l5//XW8+OKLDY6fOnUK/fv3l/RZqFaT1RQXFhYiMjISjz/+OM6dOwdRFI1+nTt3DgsWLEBkZGSDLZiJiIiITJETiAHpG2IAtZ0hRvUyLLcYHeqHLx4eYRCI96WoJdUfGwvEgGEXi/qWLl1qNBC/9957DMSNSFb5hCiKuPPOO5GQkAAA8PHxwd13343hw4cjMDAQAJCdnY3ExER89dVXyMvLw8GDB3HnnXdi37598mdPREREbdq+FLWsQAyY3hAjLbcER9ILIAAY3sMHIb5uULkqsT56mMnOENaUTEiRkV+qH18URbz66qt49dVXG1y3cuVKlk40MlmheOPGjThw4AAEQcC9996L1atXo2PHjg2umzt3Lt58803ExMRgw4YNOHDgAOLj4xEVFSXn8URERNTGJV/S2HyvQgBG9/JrUDqh0VZgwecncCgt3+C4bqc6lasSIb7G26TFxScj4by0bhZS6AK7KIp4+eWX8dprrzW4ZtWqVXj88cft9kwyTlb5xMaNGwEAY8eOxYYNG4wGYh13d3d89tlnGDt2LERRxOeffy7n0URERNQOBHZ0sfne0b38jG6IERef3CAQA8DBC/mIjU8yOZ41LdukGBrkhRBfN4iiiBdffNFoIH7//fcZiJuIrJXiEydOQBAELFy4UPI9sbGx2LdvH5KSTP+lIyIiIgIAf5X1odjd2QEbHx6BAd08G5zTBVtT9qfmYtWeVFTViBjU3QtjQv3056S0bKtL1UGJouvGSz+8XJX4eN5QiKKIJUuWYPny5Q2u+fDDD/Hoo49a9UyynaxQrGu7FhISIvke3bW6e4mIiIhMkdImrT5TgRiQFmzf3v6n/s9erkqsjApHRY0IdVGZpOeH+rtjzdwh8HZ1woIvjuPgBcNV6fBuKnw6fzg8Ojhi8eLF+Pe//91gjI8++ggPP/ywpOeRfcgKxSqVCvn5+bhy5QrCw6Xt13316lUAYAszIiIisuhoegH8OzpDfa1c8j352gqT56wN2YXaSty3NtGqe1LVJQAAlasSGx8ZgfS8UhxJy4cIYMRfL/SJoojnn38eb7/9tsG9giDg448/xkMPPWTVM0k+WaG4X79+2LdvH9atW4epU6dKumfdunX6e4mIiIiMOX1JgxmrD6Kqxvr6XVPdJgDgYoEWnVUuuCJx1ddWdbtKGHtpLzEx0WggXrt2LebPn9+ocyPjZL1od9ddd0EURWzZsgWvvPIKLO0D8tprr+Gbb76BIAiYPXu2nEcTERFRG2ZrIB4a7GW0a0RmfiluenUH5q072uiBGDAfzAFg+PDh+PDDD/XfC4KAdevWMRA3I1k72lVWVmLAgAFISUmBIAjo27cvHnzwQQwfPhz+/v4QBAE5OTk4cuQIPvvsM5w5cwaiKKJPnz44efIkHB3tvst0k+KOdkRERPa3KTELz3972ur7Ojo74sDzE4xunRy+dIfsfsdSRZrZqa6+Dz/8EDExMVi3bh0eeOCBRp5Z+yQ1r8lKpUqlEj///DMmTpyI9PR0nD17Fs8++6zJ60VRRI8ePfDzzz+3+kBMREREjeNQesN2aZZ4uDjit+eMB2IpG4AoULvLnFx9OnXEoik3SL7+sccew8SJE9GrVy87PJ3kkL3Nc3BwME6dOoVnnnkGKpXK5DbPKpUKixYtQnJyMrp3726PuRMREVEb1MvX3arrhwZ5mQzEgLQNQMI62/4b3w5KBUL9a+f8+9VruGNlAuauTUSRxJVpBuKWQVb5RH0VFRU4fvw4zpw5o2+55u3tjX79+mHw4MFwcnKy16NaBJZPEBER2U9abgkyC7T45lgWfjydI/m+DdHDDPoJ1/fT6St4/Avz+yPsWTQOQO0LcnnXynGl6Dp6+3fExsSLkrZ0FgDUDVQOgoBRvXzx2UND8eyzz+KOO+5AZGSkhE9D9iY1r9k1FLc3DMVERETyabQViItPlhQ+jXnq5lA8MdF0ycL0lQeQfKnI5HlLNcDpeaV4b/ef+C7pilXzEsUajMndii/WfQw3Nzf88ssvGD16tFVjkHxS85rs8gkiIiIiOeLik5FwPs/m+wd19zJ6XKOtwOwPDpoNxP27ehjdCrquEF83RIT4WDUnUaxBwfbV+GLdxwCA0tJS3HrrrUhISLBqHGo6fNuNiIiImo2lbZct8XJVmiydiItPxrGMQrP33zssyGQtMlAbrBd8fgKH0qS//CeKNSj4ZSVKTu0wOH79+nVkZ2dLHoealqRQvH79ev2f586da/S4LeqORURERO3P0fQCm+91c3LA1hjj5QhSw7ZQ757MAi2Cff7ebCMuPtnqQJz/8wqUnt5pcNzR0RGbNm3CzJkzJY9FTUtSTbFCoYAgCBAEAVVVVQ2O2/TgemO1RqwpJiIikmfAK9tRXGZbHlg3fyjG9/Y3em5Pihrz1x21OMaeRePg5apsUNMc5OOKh0YF4+Wt5yTPR6yprg3EZ3YZHHd0dMTmzZsxffp0yWOR/di9T7Gp7Mz39IiIiMgW+1LUNgdiwPyucV4dTJdE6Izs6YMQXzfMXZvYoKY5M19rQyD+H0rP7DY4rlQqsXnzZtx5552Sx6LmISkUp6enW3WciIiIyBIp/YNNMbWdM1BbBzz/U/OrxJGhflgRFS67phn4KxD/9H8oPbvH4LhSqcQ333yDadOmyRqfmoakUBwUFGTVcSIiIiJLHGBbCaaHiyM+njvU5PmHPztmdge7D+4bhFv6dwIAnLho/kU8S8SaauRv+y9Kz+01OO7k5IRvvvkGt99+u6zxqemw+wQRERE1i2rYVoL5efRwkx0j0nJLcCzTfNB1dnLQv1TnYFsuB1AbiPO2vQvtuX0GxxWOSmzZsgW33Xab7YNTk2MoJiIiomYxsKunTfflaytMnsss0Fq8f/We8zhap1Wbl6sSGm2lVRFdrKlG3o//gfb3/QbHFY5O+GrzNwzErZDszTu0Wi20WtN/AVesWIExY8agT58+uO222/DDDz/IfSQRERG1Ymm5JdiTokZ3HzeoOli/PmfuBTtLwcbDxREnMjUGx4qvV8LTTK9iowQBCqWLwSEnZ2f89ONWzJrOkonWSNZK8Q8//IDp06fD3d0dly5dQseOHQ3OP/TQQ/jss88A1Hap+PPPP7F9+3a8/vrrWLx4sZxHExERUStjbDtnF0fr1udMvWCn0Vbg8S9O4OAF0z2FPVwcjXa7qBaBQm0l/jP7Jry89QxKyqstzkMQFPC+ZSFEsQalp3fB2cUFP2zdiptvvtmqz0Mth6yV4u3bt0MURdxxxx0NAvGBAwfw6aefAgBcXV0RHh4OFxcXiKKIl156CWfOnJHzaCIiImpljG3nXFZVI/n+js4OJl+wi4tPNhuIhwZ5YfmM/mbH93Z3woHnJ+AGf9Mr0XUJggI+t8bBfeCteH31egbiVk5WKD58+DAEQcD48eMbnFuzZg0AoHPnzvj9999x/Phx/PHHH+jWrRtqamrw4Ycfynk0ERERtSK61mfVMvY3OPD8RKMv2ElpqzZrcFeUVJjviezt6oTx7+zFn+pSyXMSBAV8psRg1jTWELd2skKxWq0GAPTu3bvBuV9++QWCICA2NhZdu3YFAHTr1g2xsbEQRRH79u1rcA8RERG1TXK2c9YpMPGCnZSX6/757Wk8/81po+cUQm3f4te3nTPayk2srkR1ifGOFg6CgMhQP5M9k6n1kBWKc3NrfyqrXzpx9uxZ5OXV/nqk/g4uQ4YMAQBkZmbKeTQRERG1Iq/+cFb2GBn5xldwg7xdZY1bIwInsgoMOlLoiFWVyP1uObI3Poeqa3kNzo/q5YsVUeGynk8tg6xQ7ODgAAAoKDD86e/AgQMAAD8/vwaryF5eXgCAsrIyOY8mIiKiViAzvxT9Xv4F2krptcOmmOo60cPPHZGhfrLGNvZyXW0gXobr5xNRVXgVOfH/QtU1w7rlRyJDTPZMptZFViju0qULACA5Odng+LZt2yAIAsaMGdPgnqKiIgCAr6+vnEcTERFRK3D7it8kdXMwR0qJwoqocIzs6SPrOXWJVRXI3fIGrl/4e7voqsIryP32NYh16qJPZMnbEY9aDlmheMyYMRBFEStXrtSXSxw9ehS//PILAGDKlCkN7vn9998BAIGBgXIeTURERC3cvhQ1rpXJC8SAtBIFlasSGx8ZgT2LxuHNmf3x9M032Pw8saoC6i1v4HraMYPjglMHeE18FILw9zZ4g7p72fwcallk9Sl+/PHH8emnnyI9PR09evTADTfcgHPnzqGqqgre3t645557Gtzz66+/QhAEhIWFyXk0ERERtWAabQViNp6w+X5nRwXenX0TwrqorHqJTRRFBKhckFdSDv+Ozsi9Vm7dTnVVFVB/+zrK0g3nLjh1gP/spXDp2kd/zMtViTEyyzao5ZAVigcNGoS3334bzz77LEpKSnDiRO1fIKVSiY8++qjBC3hFRUXYtm0bAGDcuHFyHk1EREQtlEZbgfHv7JVVNvHPW3pj6k2dzV6TlluCzAItgn3c4OWqbLAxiLVqKsuR++3rKMtIMjguOLki4O6lcO5yo/6Yl6sSW2NG2/wsanlkhWIAeOqppzBp0iR8/fXXyM7ORqdOnRAVFWW0TdvevXsxdGht0+3bb+cWiERERG3RA2uPGG1tZo1xNwaYPGdsZzwvVyWKzDzTQQA+fWgYFnx+3GhYr6ksQ+43r6MsM9nguODkioB7XoNz5954bkpvVNbUYFB3L64Qt0GyQzEA9O/fH/37m98lBqhtz1a/RRsRERG1HWm5JTh9uVjWGBE9fMyWTBjbGc9SCK8WgX1/qM0E4tdQlnnS4Ljg7Fa7Qty5dqGvsqYGT0y0vVaZWja7hGIiIiIijbYC8z89avlCM3oHdsQH9w82eV7K7nWmHLjQsM9wTUUZ1N8sRXnWKYPjCmc3+N/zOpw7heqP8aW6tq3RQ3F5eTkOHDiAvLw8hISEYNiwYY39SCIiImoGcfHJyMq3vLucOQEdXcz2/ZWye50po3v64o/sEv33tYH4VZRnGe50p3Bxrw3Egb30x/hSXdsnqyVbZmYmnnvuOTz33HPQaDQNzh8+fBg9e/bE5MmTce+99yIiIgJDhgzhbnZERERtzL4UNfan5lrV6cGY/am5SM8zvnMdYPvudY4KAS9M6wuvOoFbs/8zyYGYL9W1fbJC8bfffot33nkHu3btgqenp8G54uJiTJ8+HVevXoUoivqvEydOYOrUqaislFeAT0RERM1Po63A3LWJmLdOXtlEXaa2cwb+3r3OoU6vYEscFQK2xowCAGyNGa0Pxp6j74NTnfIIhUtHBMx5Qx+IO3u6YEP0MCS9NBndfORtJU0tn6xQvHPnTgiCYPTluTVr1kCtVgMA4uLi8P333+Pxxx8HULuBx2effSbn0URERNQCPLL+GA6ct70NmjGmtnPWWREVjlG9pO2M+9yU3ji/7DaEdVEBqH1Z7t17BuLtuwbgmWnhGLnwXTgFhkLRwQMBUW/AKaCn/t4rmjJ09WIYbi9k1RSnpaUBAIYMGdLg3FdffQVBEDBz5kz83//9HwBg2rRpyM3NxebNm/HNN9/g4YcflvN4IiIiaiYabQUe/uwYjmXab5tjB0HAqF6+FjfrULkqsT56GNLzSrH15GX8d2eqyWv7dPYAAPx0+gr+/fMfyCy4rj8XGeqHBZMH4Lz6NVRfy4eTX1CD+zPyS63aPIRaL1mhODe39ifDTp06GRwvKirSb+Tx4IMPGpybM2cONm/ejJMnDdueEBERUesRF5+ME3YMxIC07ZzrCvF1gwPMl1FUVFYjfOkOoy3bEs7n4XplFRxc3OHg4m70fkur1tR2yArF165dAwBUVxv2/Dt48CBqamrg6OjYYOe6bt26AQAKCgrkPJqIiIiaiZy2aMYE+3TAuvnDrVqRPX1JgxmrD6KqxvyrfTEbk1BxvQRFhzbDc/R9EBz/ftGuWhRxNKMQQ4O8cCJLg2rx77GkrlpT2yGrplj3ct2VK1cMju/duxcAMHDgQLi5Gf/L5OLiIufRRERE1Ez2pajtOt7dQ7qbDZ9puSXYk6I26EohJRADQIX2GnI2vYjiI18j97tlEKsarhg/ODK4QY2ytavW1PrJWinu27cv9u/fjy1btuhftquurtbXE9dfJQaAy5cvAwACAkxv30hEREQt19Iff7freI+P72X0uLHtnCND/TD+Rj9JgbimrAQ5X72Iiqu1NcfXLxxF7vfL4Td9MQSHv1eMw7qosP6mzkjPK0VGfimCfdy4QtwOyQrFM2bMwL59+7BhwwYEBARgzJgx2LBhAzIzMyEIAu6+++4G9xw7dgzA32UURERE1Dpk5pfi1vf2y+5FXNemR0aYPGdsO+eE83n442qRxXGry0qg3vQiKrINX8KruJqKqmv5UHoGQgAwJtRPH4BDfBmG2zNZ5ROPPfYY+vTpA1EU8c477+DOO+/E119/DaC204SxrhRbtmyBIAgYMcL0vwRERETU8kxflQBtRY3dxrt/WDcM7+lj9JyubrlunS9QWwesLqkwO2719WtQf7mkQSB2cPdGQNRyKD0DAQBDgr1YIkF6skKxs7Mzdu/ejZkzZ8LR0RGiKEKpVOKBBx7Ahg0bGly/f/9+nDt3DgAwZcoUOY8mIiKiJrQvRW20g4McU/p3MnnO1u2cq68XI+fLJajIuWBwXB+Ifbrqjx3NKERsfBKK7Py5qHWSVT4BAIGBgfj6669RXl6OgoIC+Pj4wMnJyei13bp1w549ewAAo0dzu0QiIqLW4nBavl3H83JVYkyon8nz3nW2Y5aqWluEnE0voFKdbnDcwd0HAVHLoPTu0uCehPN5iI1PwvroYVY/j9oW2aFYx9nZuUG/4vpCQkIQEhJir0cSERFRE/k++bLdxnJ1csDWGPOLY//ZYXpDDmOqtUXI+XIJKnMzDI47dPStDcRenY3fJ4rYn5qL9Dxu0tHe2S0UExERUdtz+pIG01cnoNpOpcQKAOeW3mL2Gmv7IJsOxH4IiFoGZ+9OsNSsgjvXkd1CcVFREb7++mscOnQI2dnZ0Gq1WLduHYKC/t4y8cqVK9BoNHBxcUGPHj3s9WgiIiJqJDNWH7RfIBaAHxdaLp+0pp64ulRTG4jzMg2OO3j46V+qC+vsgTOXi82Ow53ryC6heOXKlViyZAlKSkoAAKIoQhAElJaWGly3d+9e3H///XBxccGlS5fg7e1tj8cTERFRI9iUmCWpH7BUT0wKRVgXlcXrgrxdJY1XXVqInPglqMzPMjju4OFfWzLxV5eJFVGDAACxG0/g3JVi1M343LmOdGR1nwCAl19+GU888QSuXbsGJycnDB482OS1c+bMQWBgIMrLy/HNN9/IfTQRERE1op2/59h1vEHdvSRd18PPHZGhfnAQBIPjDoKA8K4qOCpqj9eUlaD6uuEKsIOHPwLv/bvtWuRffYhDfN3wxcMjMLrey33cuY50ZIXi48eP4/XXXwcA3H///cjOzkZiYqLphykUmD17NkRRxM6dO+U8moiIiBrZwQt5li+SyFK3ifpWRIUb3Xr504eG4/yy2/D2XQPQJagnAua8DoVr7eqzgyoAgfe+CUdV7a65I3v6GARelasS66OHYc+icVg3fyj2LBqH9dHDoLKh0wW1PbLKJ1auXAlRFDFy5EisX79e0j0RERFYsWIFTp8+LefRRERE1Ij2pajttlGHgwB8ET3cqnt0AdbU1suDg7ygLqmAk18wAua8gfyfV8Bv+vNw9PAHAGyIHmYQwtNyS5BZoNWPw3IJqk9WKN6/fz8EQcDChQsl3xMcHAwAuHzZfq1diIiIyL6SL2nsOJqAN39JsakXsCgar2mu+zKek18wAh94B0KdcouqGhFpuSU4e7UY6w9m4GhGof5cZKgfVkSFI7+03CAoU/smKxRfvXoVANC7d2/J97i4uAAAysvL5TyaiIiIGpGyXj2vHLb0AtZoKxAXn6xvzVZTrsW4fkFYERWO5IuF2Jp8xeB6od58V/96HkczC2HM/tRcjPr3bpSUV+uP6YIySynaL1mh2MnJCeXl5dBoNJLvycmpLdr39PSU82giIiJqBBptBR7/4gQOXrDvDnaAdb2A4+KTkXC+tqa5qjgXOfH/wg/9xuPghXtRZaaqw0EQ4NHBESeyNGbHrxuIAe5sRzJftOvevTsAIDVV+q4zv/76KwDrVpeJiIioaTyw9kijBGJAei9g3eYd1aKIqmI1cuIXo0pzFZoDG5H3W7zZewcFeaJQW4lqE2UXptRdzab2SVYonjhxIkRRxAcffCDp+suXL2PNmjUQBAGTJ0+W82giIiKyo8z8Ugx4ZTtOW9jkwhYOgqBvjSbFkfTaUF5VpEbOxsWo0mTrzxUd+ALXkn4yee/1ymqT56TIyGcobq9kheKFCxdCqVTi5MmTeO2118xem5KSgltuuQVFRUVwdXXFY489JufRREREZEfTVyWguKyqUcaW2gtYo63AXe8fxOJvz6CqKAfZ8YtRVWTYK9nRuys6hI4wOca5K/JCPXe2a79k1RT37NkTb7zxBp577jm88sor2LZtG2bOnKk/v3nzZiiVSiQkJGDHjh2oqamBIAj4v//7P/j5Se9VSERERI1nX4oahdpKu4w1JSwAc4Z3h6NCQFWNKLmzg0ZbgfHv7EWhtrI2EG9cjOpitcE1Sp9uCJizDA7upjcB0W3AJwCwpoCCO9uR7G2eFy1aBFEU8cILLyAxMRFHjx7VvwG6dOlS/XWiKMLBwQHvvPMOoqOj5T6WiIiI7ORwmv1qiDt5umB8b3+r73tk/TEUaitRqclGTvxiVBfnGpxX+nRHQNQbcHCTtited+8OyCy4Lvn53NmOZG/zDADPPvsskpOTMX/+fPj6+kIURYMvDw8PREVFISkpCU888YQ9HklERER28luq/Xaum9gnwOp70nJLcDSjsDYQbzQSiH2DEBC1THIgBoDHx/WyeI2DAnh71gDubEcA7LBSrNOnTx+sXbsWAJCVlQW1Wo3q6mr4+PigR48eUCjskr+JiIjIjtJyS3BGZh2ujqqDo1VbOescSc9HZeFV5MT/C9XX6gXiv3asc/hrK+e6uni64GpRmb5kAqjdPc+jgxLPf2t559zqGmBIiDdLJgiAzFA8YcIEAMADDzyA+fPn6493795d366NiIiIWiaNtgKPbThml7G8XJXYGjPa6uc//sUJ7Dt2+q9AbLhibS4QA8Di2/rgq6OX9Bt8ALWBuMiK+mhreidT2yYrFP/222+oqanBiy++aK/5EBERUROo+2KbXG/fNQCzh3Sz+r64+GTsO3qqNhCXGNY1K/17IGDO63Do4GHy/r6dVVgf3RnpeaXIyC+FgwDM/eSoVXNgtwnSkVXT4O9fW0jP3emIiIhaF92Lbfbg29HZ6nvSckuw7/fLyNn0YoNA7BTQ02wgrt/3OMTXDeN7+6PainYT1vZOprZPVii+6aabAAB//vmnXSZDREREje+X01dxNKPQbuPZstqaWaCF4OgErwnRgPB3HHEK6An/e8yvEA8K8jTaKSLI21Xy89ltguqTFYoffvhhq3a0IyIioual0VbgH1+csMtYtq62puWW4OxlDQDArfco+N7xHCAo4BTYC/5z3oBDh45m7398fC99p4i03BLsSVEjPa8UFwu0Zu97bkpvrJs/lN0myChZNcUzZ87E/fffj88//xwPPfQQVqxYATc3/hqCiIiopZr1/kG7jWXtaqtGW4G4+GSDF+MAwO3G0VA4dYBz595QuLhbHCfYx83oWN6u5mNNZU2NTT2UqX2QFYrXr1+PiRMn4tSpU/jss8/w/fffY9q0aRgwYAC8vLzg4OBg9v65c+fKeTwRERFZ4UCqGhdyS2WN4eHiiGUz+6NvZ5XVK8Rx8clIOG+8J3KHHoMljaFbmZ67NrHBWAVa89tUD+ouvc8xtT+CKIrW7IJoQKFQ6HevA2p3rav7vdkHCwKqqhpnj/WmUlxcDJVKhaKiInh4mK59IiIiagnCXvoF2opqm+/vE9gRXz4aYVPZwc/7E/Hgip/QoedQm58/sqcP3r9vMPJLyzHhP/usvn/PonF8sa4dkprXZG/eUT9Ty8jYRERE1Ej2pahlBWIAeO7WGxsE4rTcEmQWaBHs42YycJ4+fRpR029DUXER/GYsgavEYLwhehguFV6HAGB4Dx+IoogTFwuRU1Rm0/zt1ZNYymem1kdWKE5PT7fXPIiIiKiRaLQVeOn7M7LHqdtlwlhNb2SoH1ZEhRsE55MnT2LixIkoKqxtu5a75Q34z3gBHXoOMfkchQCM7uWn3x3PVC2ynPnbQupnptZJVigOCgqy1zyIiIiokcTFJyOz4LrN9+tCat1VUWP1wQnn8xAbn4T10cMAAMnJyZg0aRLy8+v0Ia6uQvHR7+DSY7DJkssaEaisrkGRthIqV6XZWmSphgZ7yV7VlfKZqfWS1ZKNiIiIWraTFwtlr7D27eyBe4Z0RXpe7Ut6abkl2J+ai+p6JZPVooj9qblIzytFUlISJk6caBiIAfiHDoTfzCX6QOzmZPyl/CNp+Xj4s6Mmn2UNL1clPp5rey0zIO0zU+vWKkPx+++/jwEDBsDDwwMeHh6IiIjAzz//rD9fVlaGmJgY+Pj4wN3dHbNmzUJOTo7BGFlZWZg6dSpcXV3h7++PZ599ttW/+EdERFTfos0nZd3voABOXy5GTHwSxr+zF3PXJuLc1WKz9+zYfwgTJ05EQUGBwfGbho7E7h2/YN+/bsW6+UMxNMgL103UOdcAOJpZiMc2HJc1//6dPbB30XjZ5Q2ZFnogZ+QzFLd2rTIUd+3aFW+++SaOHz+OY8eOYcKECbjzzjtx9uxZAMBTTz2FH374AZs3b8a+fftw5coVzJw5U39/dXU1pk6dioqKChw8eBCfffYZPv30U7z00kvN9ZGIiIjsSqOtwLQVvyFVLS+sVdcYfp9wPg+fHcwweX159nk899BsFBYa7pjn3L0/CkY/jds/OIaXvz8Lb1cljmYWosbEODrnc0tsm/hfnp7S2y71vpZ2y5Nbr0zNT1ZLth49elj/QEGAi4sLVCoVQkNDMWLECNxzzz3w9va2dRoAAG9vb7z99tu466674Ofnh40bN+Kuu+4CAPzxxx/o06cPDh06hBEjRuDnn3/G7bffjitXriAgIAAA8MEHH+D5559Hbm4unJycJD2TLdmIiKilmrs2UXbZhDlDg7xwIktjUE5QmZ2KvM0voUJ7zeBal6Cb4DfrRSiULgBqd8Lr07kjzlw2v+JclwKwGKCNsWcbNl1v5Lqf2UEQMKqXL2uKW7AmacmWkZFh8L0gCCZbshk7d+TIEXz++ed45pln8OKLL2Lx4sVWz6G6uhqbN29GaWkpIiIicPz4cVRWVmLSpEn6a2688UZ0795dH4oPHTqE/v376wMxAEyZMgULFizA2bNnER5ufHee8vJylJeX678vLpb+LzMREVFTOZCa26iBGAAeHBmMDk6X9M8pv5KC/K9fRuV1w5Xd+oEYqK3DtSYQA0BYZw+cuWLdPfZ4ua6uFVHhiI1PMvhna+2uftRyyQrF8+bNAwCcOnUKSUlJEEURPj4+GDhwIPz8atuo5ObmIjk5Gfn5+RAEAQMHDkS/fv1QXFyMM2fO4MKFCygrK8MLL7yAq1ev4n//+5+kZ58+fRoREREoKyuDu7s7tmzZgrCwMCQnJ8PJyQmenp4G1wcEBCA7OxsAkJ2dbRCIded150xZvnw5Xn31VUnzIyIiai7/+FxeHa4UYV1UWH9TZ6TnleLnX/fj+dWvNgjEg0dGQj3iCSiUzkbH6NfZA79fvSbpJboV9w4CUFu7+0R8EorLzL8HZI+X6+pTuSqxPnoY0vNKkZFfyj7FbYysmuJ169Zh/PjxOHv2LHr06IHvv/8eOTk52LlzJzZu3IiNGzdi586dyMnJwXfffYfg4GCcPXsWY8eOxZYtW5CamoojR47gpptugiiKWLVqFQ4fPizp2b1790ZycjKOHDmCBQsWYN68eTh37pycj2PR4sWLUVRUpP+6ePFioz6PiIjIWl8dzUJJubxNOsxRCH9vtQwA2amn8M+H70HJNcNV3Jtvvhnr4782GYgBYNmM/hjVy9fiM71clQjxrQ2gCsBiIB4a5GWXl+tMCfF1w/je/gzEbYysUJyUlIRHHnkEAQEBOHz4MKZNmwaFouGQCoUCd9xxBw4fPgx/f38sWLAAx44dAwAMHToUu3btQqdOnQAAa9askfRsJycn9OrVC4MHD8by5ctx00034b333kNgYCAqKiqg0WgMrs/JyUFgYCAAIDAwsEE3Ct33umuMcXZ21ne80H0RERG1FBptBZ7/5rRdx/TsYBgs6/YQBoBffvkF164Z1hBPmTIF33//PcK6+yEy1A8O9foROwgCIkP9MKCbJ9ZHD8OeReOwfGZ/k3Mo1FbqW54lX9KYne/8kcHYvGCk3QNxWm4J9qSo2XqtDZMVit99911UVVVh8eLF8PW1/JOen58fFi9ejMrKSrz77rv64z4+PliwYAFEUcSBAwdsmktNTQ3Ky8sxePBgKJVK7N69W38uJSUFWVlZiIiIAABERETg9OnTUKvV+mt27twJDw8PhIWF2fR8IiKi5nb/2iOwvZvv3xQC0K+LB/YsGocBXT0bhIXE9ALExicBAF555RUsWrRIf+6WW27Bd999hw4dOgCorcOtvxpcvw43xNcNgSoXmPPDyct4b/efcDSx4YfOhD7+lj6eVTTaCsxdm4gJ/9mH+euO6tvS6X4ooLZDVk3x/v37AQBDhpjeqrG+oUNr63vqh9/Ro0cDMF/Tq7N48WLceuut6N69O65du4aNGzdi79692L59O1QqFaKjo/H000/D29sbHh4eiI2NRUREBEaMGAEAmDx5MsLCwvDAAw/grbfeQnZ2Nl544QXExMTA2dn0r3mIiIhaqrTcEqtfXjNldK/arYvzS8uNvrBXd8OKEF83vPXWW6ipqcEff/yBb775Bi4ufwdcc3W4abklyCzQwkEQkF1kfse9d3emWpy3l6tSvzW0vXAXu/ZDVijWrbTW7chgie7a3FzDf8m8vLwAQNIGGmq1GnPnzsXVq1ehUqkwYMAAbN++HTfffDMA4L///S8UCgVmzZqF8vJyTJkyBatXr9bf7+DggB9//BELFixAREQE3NzcMG/ePCxdulTy5yAiImpJ9qSoLV9kxpsz+yNA5WIQWr8+kWX2noz82lAsCALeeecdVFVVQak0XragqwkGaldf4+KT7dohw8tVia0xo2WPowvqwT5uEP8K//XV/6GA2gZZodjX1xdXrlzBzp07MWrUKEn3bN++XX9vXUVFRQCg71phztq1a82ed3FxwapVq7Bq1SqT1wQFBeGnn36y+CwiIqKWzF4Bc3gPH4uhVayphqD4e1vmuhtWCIJgMhDXlZZbgrgvk3DOyvZqxjw3pTcqa2owqLuX7BViY5+5Xxfz7w7pfiigtkFWTfH48eMhiiL+85//6F+cM+fo0aN49913IQgCxo8fb3Du1KlTAKB/4Y6IiIgse/yLE7ICse6lt7rhLi4+GQfqjVmWeQpXPl6AyoLLAGo7PFgKhHVfTqtbm3vmcjFqLBQ/L5/ZHzPCu5i9prKmBk9MvMEuJRPGyiQsBXfuYte2yFopfv7557Fp0yZotVpERkYiLi4O999/P/r27Qvhr0J4URRx9uxZbNiwAStWrEBZWRmcnJzw3HPPGYy1ZcsWCIKAyMhIOVMiIiJqNw6kqnHwQr6sMXr4uWF8b199KUBabkmDkH098yRyv14KsaocOfH/QsC9y/GgmQ0rjK26erkqUXxd+stpXxzOtLhZx6DuXpLHM8fYZwagD+71d9PT7WLHVeK2RdY2zwDw+eefY/78+aiurtYHYWdnZ/22zQUFBfo6YlEUoVAosG7dOjzwwAP6MS5cuKDfgW79+vUYM2aMnCk1GW7zTEREzanPiz/jeqUtmx8bF9HDB/eN6I6FG5P0x65nJCP3m6UQqyr0xxw6+mL/gQSMHHCD0XGMbYdsLQEw20nDy1WJpJcm2zx+XXtS1Ji/7qjJ8/3q7aYXGVr7ImJj9UEm+2qSbZ4B4P7770ePHj0QExODkydPAgDKyspw5cqVBtcOGDAAq1atalB/3LNnT6Snp8udChERUbvx351/2DUQA8ChtHxcK/97Nfd6ehJyv33NIBADQJcbBmBonxCjY5hadbWWpUBsj5fqdLwthNu6u+lxF7u2S3YoBoCRI0ciKSkJiYmJ2LVrF86cOYPCwkIAtV0l+vbti4kTJ+pbohEREZHtMvNL8d7uC40ytq6t2/W041B/+zpQbVjyEDRkEo7t+t7kS3WZBdpGmZfOosk3YOGEULuO+Z8dptu91a23Zhhu2+wSinWGDRuGYcPYs4+IiKgxjX97b6OObyoQ33LHTPzwzSY4OpqOD5ZWXY2Z3McfO36X1lJu6oDOVo9vjqWV7UVTjJeIUNsjq/sEERERNZ3M/FL0eeEn2LdowtD1C0eh/va1BoE4KirKYiAGzK+6mtK/m6ek67xclXZfrbW0sp1fWmH2PLUddg/Fly5dwrFjx7B//35cv25+dxoiIiKSRqOtwMT/7MP1Knts5Gyc9nwi1FveAKoNN9KaNG0W1q9fbzEQ21JP7KgQMLW/tHashdpKpOeVWjW+JUHermbPs+1a+2GXUHzt2jW8+OKL6NatG4KCgjB8+HCMHz++wctzX375Je6++2488sgj9ngsERFRu5CWW4Jb39uPKkvNfWXQnj+C3C3LGgRit77j8f5Hn5gNxLp+xInpBVY/t6pGrG3JKrHXcEa+6VBcty+yVD383BEZ6geHvzpo6Rjr30xtm+ya4tTUVNx2221IS0tD3e5uQr2/XAAwYsQI3H///RBFEfPmzcPo0fZ7c5SIiKit0Wgr8PBnx3Ass7BRn6NNPYy8798EagwDsXu/ibgz7jX0CjDexspeu+mduVKEqhppRSHGVm6NzcNU27S62zjrAu+KqHDExicZ3D+qly9WmOnFTG2PrFBcVlaGqVOn4sKFC3Bzc0NMTAwiIyNx++23G70+ODgY48ePx6+//oqtW7cyFBMREZmg0VYg8q09KC6rsnyxDGUXzyD3u+VATbXBcbf+k3Bn7FKsum+IyXuN7QJni/UHM3AiU2PxOlMrt8bmkXA+D7HxSVgfXdsAwFJwXh89DOl5pWy71o7JCsXvv/8+zp8/Dzc3N/z2228YOHCgxXtuvfVW7N69G4cOHZLzaCIiojarqQIxADgFhsI9eABK0v7esOO2u+7F/1Z9gJ7+HU3eZ69+xEOCvHA0w/JK+IAuHkZXbk3No1oUsT81V79Tn5TgHOLLMNyeyaop/vbbbyEIAp544glJgRgAbrrpJgC1ZRdERETU0MOfHWuSQAwACqUzvKYvQeCNtSvCDz/8MH7YtMFsIAbs0494ZE8fzB8ZbPaaUT19sGfROGyNHWN0B7kjFuqYM/JL9cG5/g57dYMzkayV4t9//x0AMHmy9G0WfXx8AAAajUbOo4mIiNqkkxcLG72GuD6F0gUuUxdjyMAD+PDDd6BQWF4zs9S1wZL5I4PxxKRQPPzZMbPXvT6jv9HVW6n1zME+bmZfzgNqgzNXiEnWSnFJSQkAwN3dXfI95eXlAGByJxwiIqL2LO7LJMsXSaBUNHzh3RzR0Rm5QRORWSCtnaqprg1STejjj7j4ZCRlaYyet9T9wVI9c9372XaNpJAVinWrvhkZGZLvOXv2LAAgMDBQzqOJiIjalMz8Ugx4ZTsy8+3T47/SSPu262nHUVNuvuzB0qpqXSuiwjGql6/Vc/NyVaKLZwejJQ06g4I8TXZ/MFUOUVfd7hFsu0ZSyArFgwYNAgDs379f8j3r16+HIAiIiIiQ82giIqI2ZfqqhEatIy45uwfqr1+F+utXUFNhOnhbs2qq69qwZ9E4rJs/FEODvSze4+WqxNaY0TiSnm/2usfH9zJaQwxYrmeePzIYr97ZF/ml5fq+xcYCPNuuUV2yaorvuusubNu2DWvWrMHTTz+N7t27m73+//7v/7B//34IgoCoqCg5jyYiImozvjqahUJtpeULbVRydg/yt/0XEGtQfukc1Jtfgf/sV6Bw6qC/RgFgtI2rpiG+bhBF0WwXibsGdcGd4V3Qv4tKci2wKZbKIdYdzMC6gxkGx3Tt1wq0FWy7RkbJWil+4IEHMGDAAJSVlWHcuHH4+eefG2zgIYoijh49ivvuuw/PPPMMBEHAmDFjcOutt8qePBERUWt3+pIGz31zutHGLzmzG/k/vguIf2+OUX7pLErP7Da4Lqyz8ZZnluxLUeO93X9i5a/nzV439abOGBPqZ1UtsCm21DPr2q+F+LphfG9/BmJqQNZKsUKh0G/CkZGRgdtvvx2urq763ezGjRuHa9eu6V+uE0URPXv2xFdffSV/5kRERG3AnasSGm3sktO7kf/T/wEwrL3tOOROuIdPNTi24t5BJssVjMnML8X0VQmSV7iDfdwk9TaWWtJgbBc6c+r3LSaqT9ZKMQB0794dycnJiIqKgkKhQGlpKURRhCiKyM3NRVlZmX71+O6770ZiYiL8/f1lT5yIiKi125SYBSPvw9lFyamdRgOxx7AZ8JrwsH4By9aXzaQG4rrjW6oFXj6zP9ZHDzMaztNyS/T1wYBhPfOM8C6S523Ni4TUvshaKdbx9vbGF198gWXLlmHbtm04duwY1Go1qqur4ePjg/DwcEybNg033HCDPR5HRETU6mXml2LJd41TNnHt5A4U/LIC9QNx8Pg5GH3vk/itTvmCLS+b7UtRS14hrju+pVrgET18Ghwz1o94aLAX5o0MhmcHJWLjk6yqx2b7NTLFLqFYJygoCI8//rika8vKyuDi4mLPxxMREbUKmfmlGPv23kYZ+1ryLyjYvrLBcY/hd6Fm6H1YOr0fAMh62WxPilrSdfNHBuPlO/rqv9fVAieczzNop+YgCBjVy9foXIzVIB/NKJS0NTSRNWSXT1irtLQUb775JoKDg5v60URERC3Cbe/91ijjmgzEI2bDc+w8CIKg373NlpfNNNoKzF2biE8PZkq6fkKfhuWS1rRGk9KP2FosnyBT7LpSbI5Go8F7772HFStWoLCQP90REVH7tC9FjdKKaruPey3pJxTsWN3guEfEPfAcc7++htjW8oG03BLEfZmEc1eKJV3v5arEmFC/Bsd1tcDpeaUWV6st1SDbguUTZIpNoVitVmPv3r24ePEilEolgoODMWnSJLi6NqwV0mg0eOutt7B69Wpcu3ZN/9Kdbjc8IiKi9iIzvxQPrz9m93GvndiGgp3vNziuGjkHqtH3QRAEsyUK5hir6bVEt0GHOSG+lks3LNUgW8PWz0/th1WhuLKyEk8//TTWrFmDqirDXXc8PT3x+uuvY8GCBfpj7733Hl599VUUFRXpw3BgYCCefvppg+uIiIjaOo22AhPe2YtqO3ebqKkoQ3Hitw2Oq0ZFwXP0ffrvbd29zVJf4fqemXwDYieEWv0cY0zVINuCu9eRJVaF4qioKGzZssVggw6dwsJCLFy4EB06dMC9996LWbNm4aefftJf2717dzz33HOIjo6Gs7OzfWZPRETUCmi0FRj971/tHogBQOHkgoCoZcje+E9UF9eu5qpG3QvP0feif2cP/GNsT4R1Udm0Qiqlr3B9tw/obPVzzLG2H3Fdof7u+NfUPty9jiSRHIp37NiBb7/9FoIgwNHREXPmzEH//v3h5OSE33//HV988QVKS0vx4osv4uDBg9i2bRuA2jD80ksvYe7cuXB0bLISZiIiohbjkfXHUFJu/zpiHUdVAAKiliNn42K43zQZnqOiAABnrxRj07FLWH+TbUHVmppehQCM7mXbNtHm1K1BPne5CJ8ezMDRTGnvJqWqSxiISTLJKXXDhg0AgA4dOuDAgQMYOHCgwfklS5ZgzJgxuHjxItauXQtBEPDggw9i5cqV6NChg5ERiYiI2r4DqblN0j5M6RmIzg+tgMLFXX+sBpC1i5s1Laps3SZaKl0N8qhQXzyy/pjkf6a6bhtElkj++3706FEIgoCnnnqqQSAGgG7dumHZsmX6comxY8di7dq1DMRERNRuabQVmPtJol3HNFbCqFM3ENdlaxuyGiuuXRFl3TbRtoqLT8aJTI3k69ltgqSSHIqvXr0KABg5cqTJa8aMGaP/82OPPSZjWkRERK2bro7Ynts4Fx35GoW7PjQbjI2xNRgqFYKk64YGeTXJaqy1fYtt2b6a2i/J5RMlJSUAaleETenatav+z7169ZIxLSIiotbt4c/sW0dcdHgzNPs+q/1GEOA18VF972Fz5ATW2Pgki9d4uSrx8byhNo1vrSPp+ZKvHRrkxW4TZBXJoVgURQiCAIXC9OJy3X85WTZBRETt1W9/qnFM4stgUhQd+gqa/ev13187/gMgKOA98RGz96k6ONocWDclZqFQW2n2mqHBXvh47tBGL5uQ2it5ZngXhPi54fYBnblCTFZjOwgiIiI7W/DFCbuNpTn4JYp++7zBcUePhlso19e3s8rqwKrRVmDB5ydwKM38quyDI4Pwyh39rBrbVpZ6Jes25nj3noFNMh9qm6wOxUePHkVenuUm3lKui4yMtPbxRERELdq+FLXdyiY0CfEoOvBFg+NeEx+Fx5A7LN5/8EK+1Z0n4uKTLQZiAJjYJ0DymHJI6ZXMjTnIHqwOxQ899JDZ87oSCinX1d8Vj4iIqDU7ebEQcfH2WSXWHPgCRQnxDY57TXoMHoOnSR7HmpZkUjfr8HJVYkyon+Q5GHtOZoFWUg9hS72SQ/3dsD56mM1zIdKxKhRb+7YrERFRe6DRVuDxL07g4AXpL4KZIooiig5sRNHBhoHY++Z/oOOg260az5rOE1I263B3dsDWmNFWzQGoDcJnrxZj/cEMgx7DkaF+WBEVbrLMI8jb1ey4qepSm/swE9UlORS//PLLjTkPIiKiVisuPtl+gfi3z1F0aFODcxMeWowLfqNM3isIQN21K12drTVh0VIABYAfYsegm4/l63QsvSSXcD4PsfFJJld7e/gZ771cFzfoIHtgKCYiIpJBasmBJaIoQrN/PYoPb25wzntKjNlADAC9/NyRqi7Rf29LnW0PP3dEhvqZ/Dy29P219JJctSia3HVPo63A3R8csvgMbtBB9sDuE0RERDJIKTmwRBRFaPZ9huIjXzc45z1lIToOvMXiGKnqEgwN8sKDI4MR1kVl88rpiqhw/OPz4w1ethvZ08fqkG3NDwzGVnvj4pMNgr4x/h2duUpMdsFQTEREJIO6qEz2GJrfPjcSiAV43xKLjjdNljzOiSwNOjhdwvqbOts8F5WrEvGPjkB6XikOp+VDADC8h49NwXPDoUzJ19Zf7ZUaqGPG95Q0vjUv91H7xFBMRERko9OXNHj+29Oyx3Hp3h/Xjm6BWFXx1xEBPrfGwn2A9EAMmC9FsFaIr+3h8fQlDWasPogqiXtc9+vioX+WLrzmSPhhw1EhYN7IELPXGKtptvRyH7VPDMVEREQ22HHmKh793D7t1zoED4TfzBeg/uY1oLoKPrc9Aff+k2wer7lfPLMmEAPAshn9Je9ap+MgAFtjzNdZA8Zrmi293EftE0MxERGRlTTaCrsFYp0OIYPgP/MFVGs1cO83UdZYzfni2abELMmBWAFgdKgfBnT1xNy1iTggIRALAEID3LHjqbEWrzVVgmHPFXVqOxiKiYiIrGDPFeL6OvQYLOt+W9qw2duhdOmt6Ub/VcaQnFUoeYV4zF/3SGHpJcjmXlGnlkXR3BMgIiJqLU5f0sgKxKIoovxKiuTrverVvDo7mv/PdkvY7jgixMfiNf06e2BrzCisjx4GlasSL3x/RvL4r97ZV3ItsKW+y2zlRnUxFBMREUk0bWWCzfeKooiCnR8ge8MilJzdI+megI4uuCu8CzqrXAAA5VU1Da4ZGuyFlfeGY8+icfqQ2ZyGhnhDIZi/5ver1/DOjj8B1JY4nLlcLHn8jPxSydfq+i47CIYTchAEm3ouU9vG8gkiIiIJVv2aavO9oliDgp0foCTpJwBA/rb/QhAEuIWNM3vfHznX8EfOtQbHBQB9u3hgRdSgFhPsrHlRTlfTu//PXCRdLLR4fV3Wru6uiApHbHySwbxawoo6tTwMxURERBb8cuYK3v5rZdNaoliDgu2rUXLylzoHa5D303tw7tYPjh19rR8TsGp1tSnExSfjwHnDQCygdq6mzP0kUfL4upfyrP0hQOWqxProYUjPK0VGfin7FJNJDMVEREQmaLQVePyLEzh4QfrLY3WJYg0KflmJklM7DE8ICvje/oxNgbiulvKi2MmLxl+Uk96UzbKwzh6yVnfl9F2m9oGhmIiIyIRH1x9DYoZ1v97XEcUa5P+8AqWndxqeUDjA947n4Nbbco9dS1rKi2KLNp8ye97SirEUK+4d1Oz10tS22S0U5+fn49ChQ0hLS8O1a9dQXV1t8Z6XXnrJXo8nIiKyq02JWbYH4prq2kB8ZpfhCYUD/O58Hq43jJQ9v6FBXs2+8qnRVuCR9ceQqi4xe53cQMyX4qgpyA7FarUaTz31FL7++mtUVVVZdS9DMRERtTSZ+aW4c2UCNNcrbbq/NhD/D6VndhueUDjCb/o/4Ro6QvYcvVyV+HjeUNnjyBUXn4zjmdb94KAQACs2u8PInj58KY6ahKxQXFhYiNGjR+PChQsQRXtWDhERETWP6atkBuKf/g+l9VuuKRzhN30xXEOHy57f0GAvfDx3aLOXEpjaLc4SqYF4/shgzB0ZzBViajKy+hS/+eabOH/+PERRxOTJk/HLL78gNzcX1dXVqKmpsfhFRETUkuxLUaNQKyMQb/tvw0Ds4Ai/Gf+ySyDeED0Mm/8xstkDMWB5tzi5GIipqclaKf7+++8hCAKmTp2KrVu32mtOREREzSL5ksam+8SaauRtexfac/sMTzg4wm/GErj2lFfqoNu+eUyon6xx7MnSbnG2aglbVVP7JGulOCsrCwAQExNjl8kQERE1F422At8ev2TTvdfTjhsJxEr4z3jBqkD84MggbF04CpH1wm9L3WyiXxcPs7vXebkq4WBhd7v6WupnpbZP1kqxu7s7ysvLERAQYK/5EBERNTmNtgKjlu9CaaVt78e49hoGz3EPQrP309oDDkr4z3wBHXoMtmqcTw9mIiW7BB/cPxgF2ooWudmEpZ3rhgZ7Yd7IYPTtrIKDANy5KsFiScqbM/sjQOXS4j4rtS+yQnH//v2xd+9eZGZmYuDAgXaaEhERUdM5ebEQc9YcxnUbA7GOavhdgCiiKCEefjNfQIeQQTaNcygtH7HxSVgfPaxFBsS4+GQknG8YiN2dHbDx4REY0M1Tf2zu2kQUX7fcmWp4D58W+VmpfZFVPvHYY49BFEVs2LDBXvMhIiJqEhptBeauTcSdqw7ieqV9Xv5WjZiNzg+/b3Mg1tmfmov0vFK7zMme9qWosT81F9VGfn4oKa/Gaz+eQ1puCfakqLH/T9215n/YGBrc/P2WiQCZK8V33303fvjhB2zcuBFvvvkm/vnPf9prXkRERI0qLj4Zv9nQUswSR5V9SgpbyhbOgOWSCZ2jmYWY8J99Zq+pb97IYBkzI7IfWaF4//79iI6ORnp6OpYsWYJvv/0W9957L2688Ua4ulp+KzUyMlLO44mIiGxia49dsboS+b+sgseQaXAK6NkIM/tbS9nCGTBdMmEPfTurGmVcImvJCsXjxo2DIPz9Wunx48dx/PhxSfcKgmD1DnhERET28NOpq1bfI1ZVIvf7N3H9/BFcP38EAXPegFNAj0aYXcva1tjWHyCIWhtZNcUAIIqizV9ERERNSaOtwL0fHcY7O/+06j6xqhK53y3D9fNHAAA1ZdeQ8+USVKjT7D7HiB4ta1vjxt6kI3bjCRTZuGEKkT3JWines2eP5YuIiIhaiAWfn8ChtHyr7hGrKpC7ZRmupx0zPF5diZqK6/acHjZED2tRG3QAjbdJh865K8X6bhtEzUlWKB47dqy95kFERNSo0nJLbArE6i1voCzNsDRQcOoA/9mvwKVrX7vNr38XjxYXiAHA280JHi6OKC5rnJLHGvzdbaOllIxQ+yS7fIKIiKil02gr8NiGY5YvrEOsqoD629dNBOKldg3EjgoBn0ePsNt49vTwZ8dwzUQgFgB0UTnb5TkZ+S2vBR21L7JWiomIiFqD+esSkaqWHrpqKsuR++3rKMtIMjguOHVAwN1L4dylj93m5uHiiG2xY6ByVdptTKnSckuQWaA1upOcRluBR9Yfw7HMQpP3iwAuF5XbZS4tqdsGtU92DcXHjx/Hrl27cObMGRQUFAAAvL290a9fP0yaNAmDB1u33SUREZEcGm0FHv7sGJIuFkm+p6ayDLnfvI6yzGSD44KT61+B+Ea7ze/tuwZg9pBusscxF26NMdZ3ODLUDyuiwvXhPC4+GcfNBGJ7GhrEDTyo+dklFJ8+fRqPPvooEhMTTV7zr3/9C8OHD8eHH36I/v372+OxREREZi34/ITZlc76agPxayjLPGlwXHB2qw3EnXvbZV4KAKND/WQHYinh1pjavsN5BscSzufpX3izdxs2d2cHlJRXmzz/IDfwoBZAdk3xrl27MGzYMCQmJupbrTk6OiIgIAABAQFwdHTUHz98+DCGDRuG3bt322PuREREJiVnFVr1Yl1NRRnUXy81Hojvec1ugRgAwjp72KXtmrlwa4ou8NbffrlaFLE/NRf7/1Tjh1NXZM8NAGYP6oIN0cOwdeFos9eFdeEGHtT8ZK0U5+XlYfbs2SgvL4dCoUB0dDQeeeQRhIeHw9Gxdujq6mokJSXho48+wieffILy8nLMnj0bqamp8PHxscuHICIiqkujrcC9Hx+WfH1NZRnU37yK8qzTBscVzm7wv+d1OHcKtev8Vtw7SHYN8b4UtdHVXF24NdXNwVLf4bmfHJU1L1UHRzwW2ROPj+9lcDwy1A8J5/MMwriDIGBUL1+E+LpZXQJCZG+yQvF7772HoqIiODk54fvvv8eUKVMaXOPg4IAhQ4ZgyJAhmDVrFqZNm4aioiK89957WLp0qZzHExERGfXwZ8egraiRfL2gcISDS0eDYwoX99pAHNjLxF3WqxsCbWWsZMKYjHzjoVhO32GFALgoFWb/2RZdr8Kt/Ts1OL4iKhyx8UkG8x7VyxevT++LuWsTrS4BIbI3WeUT27ZtgyAIWLhwodFAXN/kyZMRGxsLURSxbds2OY8mIiIyKi23xKo6YgAQHBzhe8dz6HBDBABA4dIRAXPesGsgBmpDoLVlE0u3nsUt/7cPr/94DoDxkgljTHVz6OHnjshQPzgIglXzAIDRvfwwR0IdtLH2aipXJdZHD8OeReOwbv5Q7Fk0Duujh+GF785aXQJC1BhkrRSnp6cDAO644w7J99xxxx149913kZZm/60xiYiofdNoKzB/nW2//hccHOF3x3PI374KHoPvgFNAD7vNa3KYPxbfFmbVCvGOM1fx6Ocn9N//kV2Cjw+kW7xPymq0sVVbc566ORR33NQFIb5u2JSYZfF6c+3VQnz/Lo8w9UKfpRIQosYgKxSXlZUBANzcpP+F1V1bXm6fvoZERERpuSX46fRVvLvzT9SIlq83RXBQwve2J+02L50HIoKtDnd1A7E1pKxG61ZtZ79/EMczC2Gp0OSOm7rg3JUiPLguEZn55muSI0P9zH7WfSlqJF/SYFB3L1RZ+B/LVAkIUWOQFYoDAwORlZWFpKQkyT2Ik5Jqfx0SEBAg59FERETQaCuw4PMT1nWZKNdCrK6Eg2vTdDzwclVavX3z0q1nbXrWhuhhkp+1L0WNoxbKTBQC0MvfHXeuPCBpm+ehQV4mA3lmfimmr0pAobZSf8zDxXwM4YYe1JRk1RSPGTMGoijizTffRHFxscXrr127hn//+98QBAFjxoyR82giIiLExSdbGYhLkfPVi8j5cgmqr1v+75ZcHi6O2Bpjvh2ZMQfTLNcM1+UgCIgM9ZMUiLeduoIx//4V8ySUmdSIwJ85JRYD8UOjgrFn0ThsXjDS5Mtx9QMxABSXVcFRITSob9Z9Hq4SU1OSFYofe+wxALW1xZGRkTh2zPS+8seOHcPYsWNx4cIFg3uJiIhsYe0GEzVlJcjZ9CIqrqSgMjcDOV++gOrr1xptfv07e+DUK1PQzcf6bg/dvczf00XlbPC9lJKJzPxShC/dgZiNSbhYeN3iHKx5DW/MDZZLJuoHYp2qGhFhnQ07f9jyQiKRXLLKJ0aNGoXHH38cq1evxunTpzF8+HD07dsXw4cPh7+/PwRBQE5ODo4cOYKzZ//+VdDjjz+OUaNGyZ48ERG1X5b67dZVU1aCnK9eRMXVVP2xSnUa8n58BwGzX7X73Pp19sDnD4+w+j5jJQbGJCyehPS8UmTkl5rt61u39+/M1ZbHrcua0mxLZQ7JlzRmz08KC8D/ogZZ/DxEjUn2Ns8rVqyAq6sr3n33XdTU1ODMmTMGARgAxL8adSsUCixatAhvvvmm3McSEVE7J/VXndVlJVBvehEV2akGxxVunvAe/7D9JwbbN+eQEojXzh0CwLCLQ31SexnLpRBq27RZCrEDu3qaPT+ou5fZz0PUFGRv8ywIAt566y0kJydjwYIFCA0N1W/rrPsKDQ3FggULkJycrK8pJiIiskVabgn2pKjxw0nLWxFXX78G9ZdLGgRiB3dvBEYth9LXcs9da9laC2uuxAAAbukbgIw3p2JimOUX1aX2MpZrdC8/SWUOY3v7w8vEDwm2vIhI1BhkrxTr9OvXD6tWrQIAVFRUoLCw9o1WLy8vODk52esxRETUTlm7+ll9/RrUm15ARc4Fg+MO7t4IiFoOpXcXu88xooePzbWwlkoM+nT2kDSOtbXWtvrP7Jswa3BXyddvjRmNO1YdMAj+Xq5Km15EJGoMsleKjXFyckJAQAACAgIaJRAvX74cQ4cORceOHeHv74/p06cjJSXF4JqysjLExMTAx8cH7u7umDVrFnJycgyuycrKwtSpU+Hq6gp/f388++yzqKqy3HKGiIiaXlx8Mg5IDsTFyPlyiZFA7GOXQOyoMPyNZ6i/G7bGjEL8oyNs3ppYSomBFNbUWttCEGpXw60JxADQzccVSS9NxoboYXjq5lBsiB6GpJcm2/QiIlFjsNtKcVPat28fYmJiMHToUFRVVeFf//oXJk+ejHPnzuk3B3nqqaewbds2bN68GSqVCgsXLsTMmTORkJAAAKiursbUqVMRGBiIgwcP4urVq5g7dy6USiWWLVvWnB+PiIjqSc4qlL5CrC1CzqYXUKk23P3NoaMvAqKWQenVWdZcPFwc8dtzE1CgrbDLi2F1X4bzclUaLaGwpsSgoKTM5rlIMUZCycSmxCwcSs/HqJ6+mF1vW+gxElvHETU1QdS9BWeDyspKpKbW1mn17NkTzs6GLWLKysqwZMkSfPXVV8jLy0NISAgWLFiA2NhYebOuJzc3F/7+/ti3bx8iIyNRVFQEPz8/bNy4EXfddRcA4I8//kCfPn1w6NAhjBgxAj///DNuv/12XLlyRb+RyAcffIDnn38eubm5kla4i4uLoVKpUFRUBA8Pab/WIiIi692+4jecuWy5r3C1tgg5Xy5BZW6GwXGHjn5/BeJOdpnPnkXjbA7Cuh3dbvB3R3ziJYOwPzTIC6nqEmiuNywxkLqiGvzPbTbNy5J+nT2wbEZ/DOjmafKa05c0mLH6oMFOdY4KAVtjRiGsS9NslkJUn9S8JmuleMuWLYiKioK3tzcuXbrU4PyMGTOwY8cOffeJP/74A08++SRSUlKwcuVKOY82UFRUBADw9vYGABw/fhyVlZWYNGmS/pobb7wR3bt314fiQ4cOoX///gY7602ZMgULFizA2bNnER7O/ohERC1BWm6JtEBcqqkNxHmZBscdPPxqSyY8A+02J1u2H5bSbu1ElgajevnikcgQnMgqxKDuXlatqlq7E56DAFRLWBqTulNe/UAM1PYhvmNVAs4vu82quRE1NVk1xdu3b4coipg+fXqDVeJt27Zh+/btAICuXbtixowZ6NKlC0RRxPvvv4+DBw/KebReTU0NnnzySYwaNQr9+vUDAGRnZ8PJyQmenp4G1wYEBCA7O1t/Tf2tpnXf666pr7y8HMXFxQZfRETUuKTWyBbs/MBIIPa3eyAGbNt+WEq7tWpRxP7UXHT1csUTE2+QHIg12grMXZuITw5mWLx2aJAXVkWF4+VpfSQF4n6dPSTNY1NiVoNArFNVI2LzsYuWH0bUjGSF4hMnTkAQBIwdO7bBuU8++QQAcMMNN+Ds2bP45ptvcObMGfTp0wcA8PHHH8t5tF5MTAzOnDmDL7/80i7jmbN8+XKoVCr9V7du9m/lQ0REhoK8pZUNeN/8GJQ+f///soOHPwLvtW8gtnX7YUvt1urLyC+1anypLyHOHtwVmxeMxNSbOuPTg5kWrweAZTP6S7ruULr57bYTLjR+izgiOWSFYrVaDQDo1auXwfGamhrs3r0bgiAgNjYWHTvWbt+oe+FNFEUcOnRIzqMBAAsXLsSPP/6IPXv2oGvXv9+CDQwMREVFBTQajcH1OTk5CAwM1F9TvxuF7nvdNfUtXrwYRUVF+q+LF/lTLxFRY0rLLcGPpyz3IwYABzcvBMxZBkfvrnBUBSDw3jfhqLLc09cUNycFRvb0MThm6/bDltqt1WfNSrTuJcQaCdduPn4Jc9cm4uRFDTLzza/A67pMmKshrisixMfs+VE9fSWNQ9RcZNUU5+XV/tTXoUMHg+PJyckoLi6GIAiYOnWqwTldiYOcQCmKImJjY7Flyxbs3bsXISEhBucHDx4MpVKJ3bt3Y9asWQCAlJQUZGVlISIiAgAQERGBN954A2q1Gv7+/gCAnTt3wsPDA2FhYUaf6+zs3KBMhIiI7C85qxAvfH9GUi1xXQ7uXgiIWgbUVMHRw1/WHAZ09cLGR0ZI2k7ZHI22Aj9K2GgEqF2JHtXL16rnvPD9Gavmk3A+DwWl5RavM9dlom7HDN1c7xnWHUu+O2O0hMJRITToQkHU0sgKxc7OzqiqqtKHY539+/cDqK0lDgoKMjinWzWurq62+bkxMTHYuHEjvv/+e3Ts2FFfA6xSqdChQweoVCpER0fj6aefhre3Nzw8PBAbG4uIiAiMGFG7F/3kyZMRFhaGBx54AG+99Rays7PxwgsvICYmhsGXiKiZ2GN7Ykd3b7vM5VBaPtLzSmVvPxwXn4wLamnlENauREt9CbGualHEmSvm7zG1MYex/30iQ2vDs8pVia0xo3DHqgSj3SeIWjpZ5RO6wHvkyBGD4z/88AMEQUBkZGSDewoKCgAAfn629yh8//33UVRUhHHjxqFTp076r02bNumv+e9//4vbb78ds2bNQmRkJAIDA/Htt9/qzzs4OODHH3+Eg4MDIiIicP/992Pu3LlYunSpzfMiIiJ5Hll/zGIgrrqWh8K96yDW2L64IpW1tb316XaXs1TasHxmf+xZNA7ro4dJ3vyjNqAm2Ty3fl084CAYbkIiABga7GVyYw5jtcsJ5/MQ+9c8wrqocH7ZbXj7rgGYHt4Zb981AOeX3cZ2bNQqyFopHj9+PM6ePYsVK1ZgxowZ6NOnD7Zu3Yq9e/cCAG67rWH7lTNnan/N06mT7b0ipbRWdnFxwapVq/RbTxsTFBSEn376yeZ5EBGRfWi0FXj4s2M4lllo9rqq4jzkfLkYVYVXUX0tHz5Tn4KgcGi0ednSZQL4u7wgp8j8RhoKARjdyw9Rw7pb/Yy4+GScs7Dia86yGf3xzvY/DX4IEQEczSjE3LWJ+tVfnW2nrhj9gUXXMUO3qg4As4d0Y7kEtTqyQnFsbCzWrFkDtVqNfv36wcvLC4WFhRBFEV27dtXX89a1Y8cOCIKAAQMGyHk0ERG1IXHxyThhMRDnIif+X6jSXAUAlJ7bCwgCfG570u7B2Jba3rTcEpy7UozPDmbgqIXPotO3s4fkcom6dbyJafk2l5joPtuArp5YHz0MM1YlIOmixuCa/am5WPDFcWx8ZITkkhZbejcTtSSyQnFoaCg2bNiAhx56CKWlpfrSCE9PT8THxzfYFS47Oxs7d+4EAEyYMEHOo4mIqI3QlRiYU1Ws/isQG/aRL7+aipqyEji4yvv1vKNCMKiDtaa2V04d9H3DgyyWS8its+7f2QOn66wo1/1sabklDQKxzsEL+diUmIXV+84jM/+6xefYuqpO1FLICsUAMHv2bIwdOxbbtm1DdnY2OnXqhDvuuEO/u1xdp06dwr333gvAeGkFERG1P18fN9+NqKpIjZz4xagqMmyj6ejdFQFRy2QHYt02ylWiaFOXibj4ZCSct60Hr4S9M2weX7civD56mMkOGkcs9BZ+/tvTkp7Vr7MHV4mp1ZMdigHA398f8+fPt3jd5MmTMXnyZHs8koiIWjkp2x5XFeUgO/5fqK4XiJU+3RAwZxkc3L1kzeHtuwYY1L6aC3bG2pBJWeU2Z0QP87195Yxfd0XYdAcNwcgx60nd4IOoJbNLKCYiIrKWpUBcqclGTvy/UF2sNjiu9OmOgKg34OAmLxAPC/aW9DKYuTZkUregNia8m8ri6qqt42+IHtZga2ZjoX54iLz2dboXBaVu8EHUkjV6KM7JycGPP/6IvLw8hISE4Pbbb4erq7QtO4mIqG2ytO1xbSBejOpiw1VSpW/32hViN09Zzx8W7I2P5g6RdK2x8gVdG7JX7jC+2ZMlHi6O+HT+cIvXqS10r6hPVzJRNxBrtBW4f+0Rg37GulAPADcGdsQf2deseo7OaDMbfBC1NrJC8e+//46XX34ZgiDgww8/hKenp8H5rVu34t5778X1638X6Hft2hXff/89Bg4cKOfRRETUSmm0FVhspla1svBq7QrxtXqB2C8YAXPekFVDLAD4PmaU5JVNU+ULujZkR9ILMDTYCycyNaiW0C5UZ/mM/mZfsJNSWmJM/RcEM/NLMfE/+xrsMvdbai7GvbPH6vHrevWOMMwbGWL5QqJWQlYo/u677/D1119j1KhRDQKxWq3G/fffD63W8Fc/Fy9exLRp0/D777/D3d1dzuOJiKiV0WgrMObfv+JaufGNNyoLr/wViA1XZu0RiD1cHLEtdgy6+Uj/baWlPsC6cO/lqrQqYFrazMKaQOzh4ogXp4ZhSIh3g3KMaSsOGN12WQRkBWIvVyUDMbU5sna02717NwRBwO23397g3OrVq1FSUgJHR0e8++67OHnyJN566y0oFApcuXIFH330kZxHExFRK5OcVYiRb1oIxBsXNwzE/iGyArGTg4AN0cNw6pUpVgViAPj0YIak64quV+KGAMvdFxwEAZGhfgYv6u1JUSM97++d8yyVlug8ODJI/7lmD+3WIBBvPpaF4rIqSfO3hq5bB1FbI2ulOCsrCwAQHt6wnuibb76BIAiYO3cunnzySQBA//79kZqaio8++ghbt27FU089JefxRETUCpy8WIglW87gjIVV15qyEtRUGPbDVfr3QMCc1+HQwcPm5386fxhG9vKVfP2+FDWSL2ng6eJocYc9nRoR+DPH8pbQuvIGcy/vJV/SSHrm2N7+DV6mq+vF785KGsca9bt1ELUlskKxWl37RrC/v7/B8by8PJw9exaCIOj7Euvccccd+Oijj3Du3Dk5jyYiohbO2k0nnDvdgIB7XkPOphchVmjhFNAT/ve8DocOHW16vgBgTKif5EBsax2vVHU7Qsxdm2jy5b3o0cGSxjO3Wca+FDXKqmpsnmt9uhf4GIipLZNVPqF7ga6szPDt2AMHDgAAnJycMHq04a9YOnXqBADQaDRyHk1ERC1cXHwyDpy3rseuc+feCLh7KZy7D5AViIHaQGxNZ4TGCsS6kgldINa9vFf/xTzdy3vdfdzgZeYlvPolGMZIWW12EBp+b+q51uzwR9RayVop9vb2hlqtRlZWFkaMGKE/vnv3bgDAkCFDGmz1XFVVW9/El+yIiNouOZtOOHe5EQFz3oAg2LaxxDOTb8DtAzpbtcOa1DpeWzToCGGh93DsxhP4Ino47lt7xOicpATUgV09zZ7voHTA0GBvg/+NRv3VXq1AW4GM/FL91tfW7vBH1FrJCsU33XQTdu7ciY0bN+Luu+8GULt6vHnzZgiCgAkTJjS4JzMzEwAQEBAg59FERNSCWQp+NWUlULiYXhyxNRB7uSoROyFU8vW6DS12nM226jlhnTri3FXTvX03RA8zGSiDvM2/7HfuSjHe/CUFSS9Nxm+puTiRVYjOqg7w7ehsdDzdZ3AQBFSLtc8c29sfbk4OKK0w/lLj9cpqPBIZglfv7Ntg+2eVq5IhmNolWaF4zpw52LFjB3744QfMmTMHo0ePxqZNm6BWq6FQKBAVFdXgniNHjgAAgoKC5DyaiIhaIF1AO3e5yOQ1FbkZyPnyBahG3gOPwdPs9mxruiJYW+9cV78uHvgxdgxmv38QxzMLUbdy19jmGfX18HNHZKgfDpzPhZFuaagBsD81F+l5pRhTp+zCms8QGeqHGQM74/PEiybncSKrEGMslGEQtSeyQvHcuXPxySef4MCBA9i8eTM2b96sPzd//nzceOONDe759ttvIQgCRo4cKefRRETUgkgNmbWBeAlqtEUo3PUhBEFAx0EN23qa08XTGe/fNwT52grkXSvHlaLrGNTdy2wQrc/YLnVSLRzfCwDw8byhiI1PqleCIK32dkVUOO5be9hgl7n6MvJLzQbWuPhkHDDxzzvhfB4KOpmvx+7tb3u9NlFbJCsUKxQK/Pzzz3j55ZexefNmZGdno1OnTpg3bx5efPHFBtf/+OOPyMjIgCAIuO222+Q8moiIWpC4+GT8ZikQq9NrA/H1v4Ngwc4PoHD1hNuN0vvevj5jgOQd6YzZl6K2ud4ZAP7x+Ql9+7T10cOQnlfaoATBEpWrEv+bE44J/9ln8hpz3SXWHUgz+xmqRdFiC7yNiRdxS/9OlidL1E4IomjFvpQyFRYWori49l/StlA+UVxcDJVKhaKiInh42N5Dk4ioNTt5sRB3rjpo9pqKnDTkbHrBIBADgHOXMPjPfgUKZ+mbauxZNM6mX/nLKZmoT1cmsT56mKxxdK3Z6naiMDe2vdvG2frPkqg1kZrXZLVks5aXlxeCgoLaRCAmIqJaS7acMXu+IudCgxViAHDu2teqQCylFZk5ckom6tO1T6u7E50tVkSFI7y7p8ExUyUYabklmPq/3+zaJSMjX978idoSWeUTRETUvqXllpj9NX159nmoN72AmrISg+PO3frB/66XoXDqIPlZcnrlymkRZ46lul9zdCvXdXfNGxrshRVR4VDV6RdszxXu+syVaBC1NwzFRERks3OWAvGXS1BTbrga6dy9P/xnvQyFk4ukZ0zq448lU8Nk/ZrfUos4WzkqbGsdBxhfuT6RqUFsfJJB6cT9Hx+xWB9sLd1ufyydIPqbpFCs6zcsCIJ+Y466x21RfywiImp9Pj2YYfR4+dU/od70opFAPAD+s16SHIgBYN7IYFnhLS23BPtT7L/KCgBVxnqqSWBq5bpuWYZCAG77328oLTfea1gOa3f7I2oPJIXivXv3AmjYTH3v3r0QBAHWvKunu97WxuxERNQyJGcVGvzqX6f8SgpyvnoJYr1A7BI0AH6zXoJCKT0Qd3R2tKrVWl0abQUe/+IEDl7It+l+KWwtP7C0cn32chGe3JRsc+g2xd3ZAWseGIKRvXztOi5RWyApFEdGRhoNsaaOExFR25aWW4InNiU3OF5+JQU5m16EWGEY+lyCBsJv1gtWBWJHhYCf4sbYPMe4+ORGDcRDg71sXsG2tKvde7v/tFsgFgB09+6A12f0t/kHDKL2wKqVYqnHiYiobTL30lf19WKov3qpYSAODoffzBegUDpLfs4NAW7Y/NgogxfOrHHyYmGjvJim4+WqxMdzh9p8v35Xu9TcBjviDeiqQtJFjcUxvF2VcHQA1NfMd6PQlUrY+s+SqL1o0pZsRETUupnbpMOhgwc8x0cbHHMJGWR1IB4a7IUdT42TFeIstYmzxo2Bhju/DQ3ywt5F42XNT6OtQFVNjUEgBoBhId7IvVYuaYwCbaXFQAwAr97Zl4GYSAJ2nyAiIkmktDXreNNkQKxBwfaVcAkZDP+ZSyA4Okl+xtAgL6tXYNNyS5BZoNXvKGepTZy13r9/MADgcFoeAAEjevjIDplx8ck4klZgcEwhAIkZ+aiun5RlktM2jqg9aZRQXFVVhcLC2pcvvLy84OjI7E1E1NpJbWvWceAtcOzoA5egm6wKxGGdPLB5wUjJ1xsr5YgM9cPdQ7tKHsMc3c5yXq5KPLL+GI5m/P1SYaSMkgRTP1zUiAAaYY9Z9iImksZu5RO///47YmNj0adPH7i4uCAwMBCBgYFwcXFBnz59EBcXh3PnztnrcURE1ITScktwNF36S2sdeg61KhB3dHbE4+N7WrVDnLE+vwnn8/CZiTZx1hrVyxevT++L8e/sNQjEtc/JRWx8kk3jyumZHD0qWPK1cncAJGpv7LKEu3jxYrzzzjuoqalp0J5NFEWkpKTgzz//xPvvv49nn30Wy5Yts8djiYiokWm0FQ1WSQGgLOs0AMCle3/Zz3BzcsC18ios3FgbMqWswprr81t/rtbq19kDK+4dhBBfN9z1/kGj2ypXi9D3E7Y2dHrbWHoxNNgLpy4XSb5ezg6ARO2R7FAcGxuL1atX68Nwnz59MHz4cAQGBgIAsrOzkZiYiHPnzqG6uhr//ve/UVpaivfee0/uo4mIqBFptBUY/87eBqGwLPMU1N+8CgDwn/0qXLr1s/kZHRyA0grDzSkOpOY22NWtvsbaoe4Gfzd88fAIqFyVSMstMdqHuS5r63X3pajx0tazNs3NUtgP9XfDS9P6oqpG1NdXE5F0skJxQkICVq1aBUEQEBYWhjVr1mDkSOP1YIcOHcI//vEPnD59GitXrsQ999xj8loiImp+9398pEEgvp55ErlfL4VYVdshQb35Ffjf/Spcuva1amw3JwUCPDogzUi5RA0sr8Ja6vNrC4UA7Hh6nP57KcFbar1uZn4ppq9KMLrqbC9PTryBfYiJZJBVU/zhhx8CAEJCQpCQkGA25EZERGD//v3o0aMHAOCDDz6Q82giImpExjo4XM9IRu7Xr+oDMQCIlWUoObndqrEFAKUVNUYDcV0Z+abP9/BzR78uHlY915IP/+oyoWPP4N3YgRgAwrqoGnV8orZOVij+7bffIAgC/vnPf0Klsvwvo0qlwvPPPw9RFPHbb7/JeTQRETWSHWeuYsJ/9hkcu56ehNxvlkKsqjA47tp7FHxuibNqfKkNFiytwo4Jtc9WxQJq65hv7htocLyHnztu7NTR+E1/eW/3nyb7NuvsS1E3aiBurBfq0nJLsCdFbdXLj0StmazyiezsbABAeLj0Qv5BgwYBAHJycuQ8moiI7CwttwRH0guw+NvTBsevpx2H+tvXgWrDYOd64xj43v4MBAf7t90cGvT3FsrG+hDrvrcH3Y5vxnRwdDB773dJV/Bd0hV4uSqxNWY0uvk0XF1OvqSxek5Dg70kvzBo7xfqTLW646541NbJ+n8yFxcXVFRUoLRU+k+RumudnaXvbkRERI1Ho63Ags9P4FBaw5ZrJgNxn8jaQKwwHxpt4eWqxMfzhhoNZx4ujiguq9J/rwAa7Apnjf/MvgmzBhvva5yWWyJpu2UAKNRW4o5VB5D00mT9vZkFWni7KrHrnOVFoMhQPyyacgPySyv04X/u2kQknM9DdZ2uTrreya/e2RcZ+aWN8kKdqVZ3ll5+JGrtZIXikJAQnDx5Ej/88AMiIyMl3fPDDz8AgL62mIiImldcfLLxQHzhKNRb3gCqqwyOu/YZC9/bn26UQDw0uHZHO5WrUh8K66obiAF5gdjLVakPxHVXn0VRRGaBFjlFZVaNV6itxM+nryA+8ZLFnf903J0d8EPsGKPBdkVUOGLjkwzG0q0Kq1yVjdJdwlyrO1tb0BG1FrJC8W233Ybk5GSsWLECt9xyCyZOnGj2+j179mDFihUQBAG33XabnEcTEZEd7EtRGw1B2gtHkWskELuFjYPP1KcaJRAvn9kfUcO6A5C2pbRchdpKnLxYiP/sSLXbs978+Q9cLLwu6VoPF0dsix1jtOQCAFSuSqyPHob0vNJGWxWuz1LHDW4ZTW2ZrBftnnzySXh4eKCyshK33norFi5ciBMnTqCm5u+f3WtqanDixAksXLgQt9xyCyoqKuDh4YEnn3xS7tyJiMhGGm0Fpv1/e/cd3nS1/wH8nbTpSNt0D8pqS8vetCB7XlARWQ5ALkMBRcCFPy5wGaIi3ouigojXcQHHLYgKOBFkFGUXWilTsGWX7r2bnN8ftbFpRpM2q8379Tx9pN+Vk2/a+s7JOZ+z4RdM33xKa1/x1RPI+FpHIO401GKBGADuifBXt+2Zeq4WZ6p/7jyn1RvdENezS6qWazbCO5N76A3ENYUHeGBouyCLh9HkjEIkpxcaPIZLRlNT1qCe4oCAAHzxxRd48MEHUV5ejk2bNmHTpk1wcXGBn58fJBIJsrKyUF5eNVtZCAEXFxfs2LED/v7+ZnkCRERkGn2LcgBA8ZUTyNi1BlDVCsSdh8H/vmctEoirx8lWh76nPz+jVQ6uvib0CMXXCXf07q/P4zhLJag0NvkaYC8BU9fYbV24ZDQ1dQ3qKQaAkSNH4vjx44iOjoYQAkIIlJWVITU1FXfu3EFZWZl6e3R0NE6cOIERI0aYo+1ERFQPs7bG6y0RVpJyRkcgHmGxQAxoVk9Y8Hk8jv6hPb65vv7WKQS+OiomSAGT6hyvmdAFm2fG4OCLQ3Bw4RCta3q4Gn9vLFVCzRQ1y63pmliny4uj2lqhZUS2Y5Y6Ot27d8fJkydx6tQp/Pzzzzh37hyys7MBAH5+fujcuTNGjBiBmJgYczwcERHVU11LF/v97UlAWYHCs3sBAB5d/gb/+xZAImlwH4paz5Y+mNSnJVLzStGzlS+a+7jjo1+SseHgVbM9BlA1kS72xE3kl2i/AfCWy7B6XGeM3XjUqGvdE+GvEWITVozEL1cycOZGDnq28sWyXedQVGbc0tPmLqFmCmN7hXXJKiqv+yCiRsysxSVjYmIYfImI7FhdE6kkEin87p0PIVTqf5szEAOAk1SCRV8m1X1gAyjcnDFrYDjW/vS7zv05xRVQuLtgUFSgVtmzmmoP7ahpYFQgBkYFVlWuyDJ8X1c92BGt/D2sMllOn+SMQjyzLQHnb9dvaIq9DPcgspR6heLvv/8ee/bswfXr16FUKhEaGoohQ4bgkUcegUzGwt5ERPbot5s5eGn3+TqPk0ik8L/vGfW/ze2UgZ5qU7k6S/HMsCjc37UZbuUU4+jVTOy/lIbf04r0BuJq17KKdJY9q6l2r27thUSAut9oeLo6YXq/cBOfmfk0pHcYMPzGgKgpMSkUp6WlYdy4cTh58qTWvv/+979YsWIFdu3ahS5dupitgURE1DC5xeV4+vMzWmN1hbICEifdHRmWCMPmtvahrng4uqX6e1+5DM/EJhi9pHKYv4fOsmcAtEqgGVrlrbWf4QoSH/w92tSnZlbGjhnWx5bDPYisyehQrFQq8eCDD+LUKe3yPdVSUlIwatQonD17FgEB5lmTnoiIGuaZ2EStQFx0IQ65R2IR/OircFY0rr/XEgBRQR4I8HLVWEzC0ATCmnT1fIYHeGh9X1Ndq7zpGoYhlQADIgPRL9J297e+9Z5jwnwxvV8YOoV6s4eYHIbRofiLL77AqVOnIJFI0KZNGyxZsgS9e/eGTCZDUlIS3nzzTRw/fhxpaWl48803sWbNGku2m4iIjKArFBVdOITM79YBQoW0bUsQPHkNnL0aTzAWAH5PL8LMP2ssD4oKxMKRUQYnENbUJ8LPpJ5PY1Z50zUMY0BkoE16WLefvIFjKVno3yYAAV6uRp3TOVSBDVN6Wm2RECJ7ZFIoBoCwsDCcPHkSPj4+6n1t27bFuHHjMGLECMTFxWHHjh0MxURENlB7ueJvz2rW6C08fxBZ378FiKpFlipzUpEWuxQh09bByc3TFk1usCNXM5FdXGb08c5SKbx1lGnTx9hV3iy5+pyuscy1Jd3Kxfj3jqprKO9KuAMnI0fBvDa+i1ZvOZGjMToUJyQkQCKRYOHChRqBuJqTkxNWrVqFIUOGICUlBQUFBfDy8jJnW4mISA9jJlMVntuPrO/fRlVf61/cI6IhdW28YUgpBM6ZUFGhunfX2ABY15jhmlUZzB0sDY1lrhnst5+8gcVfJ6F2DQ3lnwvMOkkkOitsVA/x6NrSx2xtJmqsjJ5JkZFR9QsZHa1/wkDNfZmZ5ls2k4iIDKsa82ogECfpDsRe0WPhO3w2JBKJZRtoBTKp8c/hWlaR0cdGBHpiUFQgnGrdI2sswmFoLDNQ1TscufQH/ENHIK6pTZDuYG+rIR5E9sjonuKSkhJIJBJ4eur/eE0u/+uXrrS0tGEtIyIio9Q1marw7D5k/bgeWoE4Zhx8hz7RJAIxAFSYsPSyqTV3dY0ZtnRVBmPGMtccLmFIx1Bv/OfvMbiWVaRepppjh4k0mXXxjpqEnkLoRERkXobGvBb8thfZezagdiBW9J4AnyEzm0wgNkV9end1lW6zdKCsayzzP3b8ZlQgBoD+bQI4ZpioDhYLxUREZB3pebo/mStI3IPsn97V2q7oMxE+g2c4ZCDu18a/Qb271gyWdY1lPmlktQ2pBBr1nIlIN5ND8XvvvYegoCCzHLdixQpTH56IiP50PasI4zYe0VmbV28gvuch+AyabneB+M2Hu+FWbjHe2nfF7NcO9XbFM8Pbok+Ef6PqKa0ey2xoGWpjrHqwkxlbRdR0SYSR4xykUqnZ/4gqlUqzXs/a8vPz4e3tjby8PCgUCls3h4gcTI+X9+oOxAk/IHvve1rbFX0fgc/Av9tdIO7SXIFvFwxEckYhhr0ZZ/brfzOvf6OtrpBXXGFwGWpjbJ4Zg6Ht6u7MImqqjM1rJq3jKYQw2xcREdVf3OV0nYG46NKvOgOxd99H7TIQO0sl+OyJewBU9YzGhPma7dpSSdX4YXsMxMkZhTh4OR0pmYarYHjLZVg/uTtiWtf/vpg6qZDIURk9fOLgwYOWbAcRERkpt7gcS3Ym6dznHt4DLs3aojz1d/U2736T4T1git0FYoWbM75fMBBZRWU4czMHYf4euK9zM5y6ZtxY2brYY7kxXXWHOzdX4LXxXdC1hY/W8ckZhXhmWwIu3DG+DnM1XctZE5F+Rg+fIG0cPkFE1vbbzRxM/uA4iitUeo9RlRYi7YvlKE+9Au/+U+AzYIoVW2iYh4sTHo5uAX9PVwyKCsCbe69oBMSoIE9cSS80+brVi1CsGtvJbpcq3n7yBt7cdxnpBeU699dclOOHpDv414+XcD27pN6Pp2uRDyJHZGxeYyhuAIZiIrIWY1asq0lVWoiiy0fg1W2UhVtmPC9XZ7QP8cIpI6sm6OPtLkO3Fj51rvJmL2ovv6yPFEDP1r74Pa0A+aWVJj/OM8MiERPuxxrERLUYm9dYko2IyM5Vf4RuylLGUjdPuwrEzRSuaOErx+kbDQvEEgDfzR+Alv5yq9YMrq/kjEKM23gESiO6n1QA4uvxhqFtsCd2PNnPLt8QEDUmDMVERHaqrt7hoku/Qh7ZGxJnFyu3zHSp+WVIzS9r8HUEgMo/P+C058UoTO3ZN5UUQMdQBTZM6Wm394CosWEoJiKyU3M/O4NjyVk69+Wd+BK5h7bAPSIageP/CYmz4/QSXssqsusguHB7Ir797TbK9Q/7brABdjxchKixYigmIrJDcZfT9Qfi4zuQG7cVAFCSHI+MXa8hcNxShwnG1i4xlpxRiOvZxXUO09h5+iae33HWom3pzN5hIothKCYisiO5xeWY/Um83rJkece+QO7hTzS2lfxxCiXJpyBv288aTayTBFXDHMzN2iXGdA2BMDShz9KBOKa1Lz6aHsPeYSILMWnxDiIispzc4nIMXntQbyDOPbpNKxADgO+wWXYTiAHLBGIA6B8ZYNW6w8/EJuLI1UyNbUeuZmJBbILGttzicvR8ea9F29KluQI75nIyHZElsaeYiMhOTP3oBPJKdJfiyj0Si7xfP9fa7jt8NhTRYy3dNC0dmykwtG0ANsYlW/RxJAA62WDIQHJGoc5JckohcPhKBlIyq8Y15xaXY+gbh3SuLmgufSP88f7UXha7PhFVYSgmIrKx6iET5/SsWpb76+fIOxKrtd13xJNQ9Bpj6eZpcJdJ8faj3bH46ySLB2IAGGijCWXXs4sN7v/ut9vo3MIH6/ZetlggjgrywAfTYjh+mMhKGIqJiGzs6c/P6BwyIYRA3q//Q95R7UDs97en4NXzAWs0Ty0mzBfLRnfEpA+OocTAinr10TfCDxN6tsDD0S3tov5waz+5wf1v7rti0cev7h3mcAki62EoJiKyobjL6Tj6h3aVCSEE8n75DHnHtmvt8/vbXHj1HG2N5gEAOjdXYMl97fGfuBSM3XjEIo/x2oSu6gBszfrD+ipLRAR6YlBUII5czYTSCgu/zugXhvYhXhAA7onwZ+8wkQ0wFBMR2cBvN3Pwj6+ScOlugdY+IQRyf/kU+ce+0NrnN2oevLrfZ40mok2gBz6aXvXx/cPvH8XpBi7PrIu1K0pUM6ayxKvjOuGBDb/Wa8nl2ro0VyDJwIqEwzsEYWBUYIMfh4jqj6GYiMiK6lrpTAiB3MNbkX/8S619fqPmw6v7vZZuIgDAx12GdY90x/nbeVjwvzN6xzubyl0m1Rh6Ye2KEtUMVZb45IneyC0ux9iNRxoUiHu09MYTAyPQKdQb4QEe6PHyXp3jj33lMgZiIjvAUExEZEWzP4lHvJ6SawCgKs5FYdLPtbZK4HfvAnh1G2nZxv2pXbAX/D1dLDJUYvucvvByl9l0zLAxlSVe2J5Y7wl0UcEeePPh7ujawkdj+zfzBuDBjb9qXNdXLsM38wbU63GIyLwYiomIrCC3uBwzN59Cws1cg8c5efgieNJrSItdAlVxHgAJ/O9bAM+u1gnEMa19IXOW6hzn3BBSCTAgMhBdW/oAgE3HzNZVWeJ4cladr1NNUgC9Wvvi6WGRBoN+S385ElaMxC9XMnDmRg56tvJlDzGRHeHiHUREVvD052eMDlouAa0QPGk1pB4+8L//WasFYl+5DMse6Gj2QAxUBWJbDJPQpa7KEvEp2SZdr9efK80NbRcEIQQOXk5HSmaR3uMHRgXi2eFtGYiJ7Ax7iomILExfhQlDXALD0Hz2B5C6Gg5w5pRTXIG39l02y7UGRQXixZFtkVVcbtZhEtXVIpwkgFJA77X1VZUA6q4s8VXCbaPbExPmix1P9UNucTmmfXzS6CWhicj+SISwQq2ZJio/Px/e3t7Iy8uDQqGwdXOIyM4YM6kOACQSiTWbZTFdQhV4anAbdGzubfbhEYbuZc3waUxVCQDIK67AgtgEva+NMfq18cemx6pqCU/7+KRWyK6urPHJE73r/RhE1HDG5jWG4gZgKCYifeIup2PFN+dwI6sEuv7ICiGQ8/N/IHXzgs/Ax6zePnPwdHXCB1OjUaZSWXTS3PaTN/Dmvt+RXlCmc3/N8GlKOE3OKMSwN+NMaktEgAfmDIpAnxq1hOu6zsEXh7DuMJENGZvXOHyCiMiMrmcVYUwdtW2FEMje9z4KE76v2iCRwGfAFCu10Hy+XTDQomEv6VYuxr93FJUqw3031VUjDv+eUWdViZrtrWvCXW01e5yTMwpx8HI6wvw96rzOtawihmKiRoChmIiogarHr/rJZZjw3lEoDWQ4IVR/BuIf1NvyjvwPkErh02+SFVrbcNZacMOYQFxTwk3Di4vUDqd1Tbir6dMnemNgVKDOscPRrX0Nnhvmz0BM1BgwFBMR1VNdY4ZrE0KF7L3voTBxj+YOiRQy3+YWaKFlWGrBjbjL6Ui8lYtm3m64eKfApEAMAD1amhZOqyfcGXr9qt8AVFeK0LXoR8KNXPjKZcgvqdQ5bIO9xESNA0MxEVE9PRObiF9NCcQ/bUThbz9p7pBIEfDgIni0t+8FHHq08MasgREWmUR3PasI4zYeqfdiGdXhc1DbQJ1VJQyF04Uj2xoMxT1b+ajfABha9COnuAIxYb44VWNhFlut1kdE9cNQTERUD/oCki5CqJC9510Unt2ruUPqVBWI2/W3QAvN582HumFidAuLXHv7yRv4564kVKrqPlafmuFzw+QeWlUlDIXT7OJyg9d+elikumpFXWOHnx5atXiHLVfrI6L6YygmIqoHYydpCaFC1o/rUVR76WapEwLH/gPytv0s0DrzkKBqoQlTArGu+sC1tyVnFCLu9wy88u0F1DcLSwB0aq7Ahsk9NcKnt1yGT57ojZTMIqPCaV3jimsOuTDm2PAAhmGixoqhmIioHoyZpCVUSmT9uAFF52oHYmcEjlsMedQ9FmqdeUS39jX6439d46t7tPRBaYUSF+8WqLf5ymX1HiZR08A6FsYwNpzqW8hD15ALU44losaHoZiIqB4iAj0R5OmC9ELdH79XBeJ3UHTugOYOqTMCxy2BPKqPFVpZPxJJVSDe8ZTxvdg6J6DpWNa6voF4RIcg9Gzliw6hCrMPTTBlyIWpwzOIqPFolKH48OHDWLt2LU6fPo3U1FTs3LkT48aNU+8XQmDlypX48MMPkZubi/79+2PTpk2IiopSH5OdnY0FCxbg22+/hVQqxcSJE/HOO+/A09PTBs+IiBqbuMvpaO3voTMUC5USWT+8jaLzBzV3ODkjcNxSyCPte4WzgZGBJoU8U8ZXm0oqAQZEBuKj6TEWuT5g2pALU4dnEFHj0ShDcVFREbp164bHH38cEyZM0Nr/73//G+vXr8fWrVsRHh6O5cuXY9SoUbhw4QLc3NwAAI899hhSU1Oxb98+VFRUYObMmZgzZw7+97//WfvpEFEjkZxRiFPXsrH6+4sGF+fI+nGD7kA8/p+Qt7FcuGuo9iFe+PfEruja0sek80xdBMMUA0wM6A1hynhgjh0manoa/TLPEolEo6dYCIHQ0FAsXLgQL774IgAgLy8PwcHB2LJlCyZNmoSLFy+iY8eOOHXqFKKjowEAe/bswf33349bt24hNDTUqMfmMs9ETVd1zdyerXzRpbm3SfWIiy8fRcbu1wHx5zQyJxmCxv8T7m2iLdji+mvtJ8eWx3ubFPJqTp67kVWE6ZtPmbVNnUMVeG18F5MDOhFRbQ67zHNKSgru3r2LESNGqLd5e3ujT58+OHbsGCZNmoRjx47Bx8dHHYgBYMSIEZBKpThx4gTGjx+v89plZWUoKytTf5+fn2+5J0JENqGrZq6zVAKVCQtJyNv1Q8CDi5D5zb8BqROCJiyDe0QvSzS3wbzdnfHN/AF6J6zVlltcjqkfn8C523/9/Qvzdzdbe1aN6YhB7YLYC0tEVtfkQvHdu3cBAMHBwRrbg4OD1fvu3r2LoKAgjf3Ozs7w8/NTH6PLmjVrsGrVKjO3mIjsia5FJExdWQ2AejEOqYu73QbimDBffDQtxuhAvCP+Bv7xZZJWGbVrWSUNbkvbYE/seLKf0W0hIjK3JheKLWnJkiV44YUX1N/n5+ejZcuWNmwREZlT3OV0s5QLq2Zvq9SF+cuxeWZvkyaIGTuOuj7WPtQVAV6unKxGRHahyYXikJAQAEBaWhqaNWum3p6Wlobu3burj0lPT9c4r7KyEtnZ2erzdXF1dYWrq6v5G01EdiHxVq5JxwtlBcrv/gHX5u0t0yAz8nSRYve8qmESugJo7QU2dA2TMKdBUYF4OJqdCkRkP5pcKA4PD0dISAj279+vDsH5+fk4ceIE5s6dCwDo27cvcnNzcfr0afTqVfWx5oEDB6BSqdCnj/3WDiUiy+rewsfoY4WyAhm7/4WSP+IRON6+y6zFtPbFR9N1D5PQtehGcx833M4ttVh7+rXxZ11fIrI7jTIUFxYW4urVq+rvU1JSkJiYCD8/P7Rq1QrPPfccXn31VURFRalLsoWGhqorVHTo0AH33nsvZs+ejffffx8VFRWYP38+Jk2aZHTlCSJqega3C9K74ppUAlQPLRbKCmTseh0lV08AADJ2vfZndQn7KrfWIcQL703tBSEEztzM0TlMQdeiG5YKxFIJsH/hEA6VICK71ChDcXx8PIYOHar+vnqc7/Tp07FlyxYsWrQIRUVFmDNnDnJzczFgwADs2bNHXaMYAD7//HPMnz8fw4cPVy/esX79eqs/FyKyD8kZhTifmg+5sxQ5OvarA3FlBTJ2r0HJ1ZN/7VRWIvO7dWj+1MeQuta9/LM19I3wx78mdsGyXec1eoEH1Vge+eVvzlts0Y3anKUSfDOvPwMxEdmtRl+n2JZYp5io8dM1fEAfUVmBjF2voeQPzZq8Epkbgh5+CW4tO1uqmUaLCvLEm490g6erM56JTcCFO/ka1SKcJBJ0au6Fs7esU1IyyNMF84ZGYnr/cKs8HhFRbQ5bp5iIyBSztsbjzHVdfcOaRGU5Mna+hpLkeI3tEhf3qkDcopOlmlin5gpXLB3dER2be8NXLjMY8pVCWDwQd2ymwNND26BTqDd7homo0WAoJiKHlFtcjtmfxCPeyECc/vVqlKac1theFYhfhluLDpZqpkHBXi6Y2jcMgZ6u6Ni8KoA+/P5RnDbiOZmLFECvMF9M6NkCEgB9IvwZhImoUWIoJiKHk5xRiCc/O40raYV1HquqKEPG16+i9FqCxnaJizuCH3kZrs1tE4gVbs5IKyjHm3t/V2/zkDmhqEJp1XYMqDFGmYioMWMoJiKHYcr4YcBQIJb/GYitW5/YQ+aEhaPa4d0DvyO7WHshDUsHYqkE6NXaF/9+qJtJC4AQETUGDMVE1OTUXoiietszsQk4f8e48bSqilJkfPUKSq//prFd4upRFYhD25m93YbEtPbFlN4t8fyOs1Z93JpUAjh1rWpoxtB2QTZrBxGRJTAUE1GToasnuG+EP9ILSvFHRpHR11GVlyL9q5dRdkMzgEpcPRD86CtwbdbWbG02Rms/OR4fEI65n5+x6uPqcy2riD3ERNTkSG3dACIic9G1EMWx5CyTAjEAFF2M0wrEUlcPBD/6qlUCsRSA3OWvP8/Xs4utEoilAAa3DcCiUYZ7wcP87TsQJ2cU4uDldKRkmva6E5FjY08xETUJyRmFZluIwrPrSFTm3EH+ia8AAFI3TwQ9+ipcQyLNcv26qAAUl6vqPM6cerT0wZaZvdUT5o4nZ+PI1Uwoa5Syd5JI0D8ywG57iXV9UjCIEwGJyEjsKSaiJuF6drHZriWRSOAzeAYUvSdA6uaF4EmrrRaIrcldJsXKMR1x8MUh2Dmvv0Zw3DC5B/pHBmgc3z8yABsm97B2M42m65OCI1czsSA2Qc8ZRER/YU8xETUJrf3Mu7yyRCKBz5CZ8Or1IJwVAXWf0MjEhPnio2kxentQveUyfPJEb6RkFjWKShP6PilQCoHDVzKQkslx0ERkGEMxETUJEYGe6ByqwDkjq0sYQyKRNIlA7CeX4b7OIbi3SzNUqoRJATc8wL7DcLW6Ping5EAiqgtDMRE1GUPaBpgUilVlxcj64W34DJoGmX8LC7bMNjqEeOFfE7uia0sfWzfF4ur6pMDeJwcSke0xFBNRo5d0Kxfj3zuCShPmpqnKipD2xQqU37mMsjuXEDx5DWR+zS3XSCuTSoBAL7cmHYhr1qOOCPTEoKjARjc5kIjsB0MxETVayRmFOJGSjaVfJ0HUfbiaqqwIadtXoDz1MgBAWZiNtNglTSoYqwSa7FhafVUmVo/rjH/uOqex3d4nBxKR/WAoJqJGJ7e4HHM/O4NjyVkmn6sqLazqIU79XWO7ECoIlWWXSbaFpjiWVl+ViX/uOteoJgcSkX1hKCaiRqe+gVhZWoj07ctRfveKxnYnD18ET3oNsoCW5mqi3WhqY2mNrTLBMExEpmKdYiJqVJIzCusXiEsKkL59mXYg9vSrGjbRxAKxk0SCQVGBTS4cGlNlgoioPthTTER2reZkqvAAD5xIyTb5GupAnPaHxnZ1IG4i44hrauhY2tr33V6wygQRWQpDMRHZJX2TqYa0M61usLIkH2nblqEiPVlju5OnP4Inv9YkAnF1hYVVYzs1eCytvS+VzCoTRGQpHD5BRHZJ12SqX69kYEf8LaOvoSzOQ9q2f2oHYq8ABE9pOj3E1b3C4QEeGNouqEHB0JSlkpMzCnHwcjpSMq07ZKExLkFNRPaPPcVEZHf0TaZSAbh4t8Coa6gDccY1je1OXoFVPcS+zczQUuvwlcuQU1yh/n5QVCBeHNkWWcXlZh3eYOwkNlv3Jje2JaiJqHFgKCYiu1PXZKq66A3EisCqMcQ+IQ26viXpGwphjQBo7FLJhnqTP3mit0XapgurTBCROTEUE5HVVU/icpIASgF10Ku5vSEkMjc4yX1QUWObkyKoqofYhoHYwxlY9mAXdApV4I2fftfZK9s/MgALR7bVCsDWCIDGTGIztjeZiKixYSgmIqvR9bF7NYWbM/JLK83yOFKZKwInLkPGV6+g9PpvcFIEIWTKGjh7B5vl+vXhK5fh0ItD1cMLan787yyV4FZOCW5lF2PvxbsYu/GI+jxrDkswZhLbwcvpBq/RFBcLISLHwFBMRFaj62P3auYKxNWkMjcETlyO7L3vw2fAFDh7B5n1+qaICfPFR9NitIKtEALJGQX434mb+CND92Q1aw9L2DC5BxbEJuhdKpkl0YioqWIoJiKr0PexuyVJZW4IGP2cVR+zWkyYL6b3C0OnUG+NntPkjEJcuJOPt/b9jj+MqNpQe1iCpesH1zWJjSXRiKipYigmIqto6OQ5fSoLsyGVuUHqargH01qigjwxa2A4LqYW4My1bBSUVuKzo9fw86V0pOeXoqRSVa/rnr+dh5W7z1ut4oOhMcx19SYTETVGEiFqvNUnk+Tn58Pb2xt5eXlQKBS2bg6R3cotLsfUj07g3J18s163siATabFL4ST3QdDDL9k8GMukACRAhdL8145p7YszN3J19s5as+JDTSyJRkSNgbF5jaG4ARiKiQyLu5yO4ylZ2HrkGoor6tdDqk9lfibSti1BZU4qAMC1RaeqYOzibtbHqU0CoG8bfzhLpVYbDlLXJMSDLw5hKCUi0sPYvMbhE0RkdtezijBu4xGNBSfMqTI/A2mxS1GZm6reVnbrPLL3/cfiY4gHRgViQvdQPLfjN4s+Tk11TUJkxQciooZjKCaiBtE18cuygTj9z0B8V2O7s19z+Az6u0UeEwD85M4IVrjhwu1cq08YrAsrPhARNRxDMRHVi76lfsd0DbFcIM5LR1rsElTmpWlsd/ZrgeDJr8HZ088ijwsA2cWVyC4utNj16xIT5osz13WPKWYvMRFRw0lt3QAiapx01Rz+9UoGlu46Z5HHq8xLw109gThk8poGBWKZtIFL6FmQk0SCQVGB+GhaDPpHBmjsY8UHIiLzYU8xEZlMX81hFQCV0vxzdyvz0nD3f0ugzNdcTU3m3wrBk1fDycO3QdevUFl/vnErX3f0j/JH7MlbBo+rDr511Q8mIqKGYSgmIpNZquawLhW5d5EWuwTKfM0QLgtoheBJDQ/E1iaTAlsf74N+f/b63s4p01oIQyoBOoYqsGFyT63ga6h+MBER1R9DMRGZrK6lfs2lIicVabFLoSyoFYgDwxD86Ktw8vCxSjvqSwqgXxt/hHi7wdPNGcM7BGNgVKDGMboWwhgQablFOYiISDeGYiICoLuKhK5jTqVkY/UPFy3enoqcO38GYs1xy7LAsKoeYrm3xdvQUB/PjMHQdkEGj+GwCCIi+8BQTOTgdFWR6NxcgYFRAejXJgDNfdxxPjUfnxy9hlPXcqzSJiEEsn54RzsQB4VX9RA3gkAMmFYqjcMiiIhsiyvaNQBXtKPGqmav8Mrd57XGtNqDyrx03I1dAuWf1SZkQREInvQqnNzt43dt1ZiOmN4/HNM+Pql1/2y9/DIREf2FyzxbAUMxNTa6eoXtWVXVicVwclcg6NFX4eTuZesmoWOoArGz7lGP980rrtAaEzwoimOCiYjsBUOxFTAUU2Ojq1fT3lXmpUHiIrdJIG7t64Z2zbxRWFaBDiEKTO0bpneIA8cEExHZJ2PzGscUEzkIfbWF7Z2zd7BNHveb+f3RtYWP0cdzTDARUePGFe2IHIQ1awubojzzBnLitsJePrSSomr4gymBmIiIGj/2FBM5gOtZRXg2NsHWzdBSnnENadv+CVVxHkR5MXxHPAWJxLZLLg/4czwwERE5FoZiIgcwbuMR5JdW1nmcBIC1+mtrBmIAKDjzPSCRwnf4HKsF45gwX0zvFwZvdxkqVYLjgYmIHBhDMVETF3c5HTnFFUYda7VAnJ5SFYhL8jW3p16BqCyDROZmscdWuDlj+eiOiA73YwAmIiI1hmKiJu7gZfuaXFeenoy0bcu0ArFr844IevglSBsQiCUAmvu6w9ddhqQ7f12/S3MFBvy5GEntZZbNLe5yOg5eToe/pyu6tfCBUrAHmoioMWAoJmqicovLMWtrPOKvW2cVOmOUpyVX9RCXFmhsd23REUEPvQSpq7xB1x9Yoz6wpUukJWcU4kRKFgAJmvu44eytXGw8+AdKKlQ6j2ftYiIi+8Y6xQ3AOsVkSzVXpRNCqP8dHuCB3OJyDH3jkNHDJqyh7O5VpG9fBlVpocZ21xadqnqIXdzrfe2VD3TAkPbBZg+/tYPvp8eu47ebOSiuUKGwTGnStbjKHRGRbbBOMVET9dvNHLy44zdcSS/SuT8mzBe3sosbRyBu2RlBD62sdyCWSoABkYGYOSCi3m2Lu5yOxFu56NnKFwcvpuPQ7+kI9HJB0q18FOvp9a0PpRA4fCUDKZlFHEpBRGSHGIqJ7FjNnsqOzRR4/cdLOJacZfCcU9fsZ7gEAJSlXqkKxGWaId61VRcETVwJqYv+McQeLk7498SuUMhluJJWiNiTN3Al/a9gPSDS+PJptXvWT6VkY9V351Fcrh18kzMtV9P5WhZDMRGRPWIoJrIDNQNb9fCHmZtPIeFmrq2b1iBlqb8jbftyiFqB2K11VwROXGFwUl1MmC8+mhajHoM7MCoQjw8Ir3OscM17+enRa9h/OQ15xRXILam7JJ01hPkzEBMR2SOGYiIbyi0uxzOxiRrLL/eN8MfF1HzkltjP8If6KLtzuSoQl2v2urq17obAict1BmJ3mRTvTe1lcHKcruWUt5+8gbjfM3AiORNZxfYRfmurHlPMXmIiIvvEUExkQ8/EJuLI1UyNbXUNj2gsVBWlgEozoLqF9UDc3u/xyp6rSLqdr3VO51BvDG0XpPN6cZfT8enx6yguU6J/pD/8PF1w7nYePj9x0yLtN7f+kQFcKY+IyI4xFBPZSHJGoUYPcVPj3robAieuQMZXL0NUlsMtrAcCJyzDHzkVOgMxAJy6noOUzCLcyCpC4q1cNFO44URKFnaeuYOaI3+P2vkbhxCFKyb2bIF72vhzpTwiokaCoZjIRq5nW24yl71wD+uOwAnLUZD4AwLH/B8kzi6oawHn+945jFIzVn2wJFcnCZ4dHoUuLX1wO6cEAsA9Ef4MwEREjRBDMZGNtPZr2EIVjYV7eA+4h1cNG+gcqkDvcD+Dx9t7IHaWAB1DFfi/e9tbfHU8IiKyHoZiIhPUrhKhb5sxIgI90bm5Auf0DCVoTJTFeXCSe9d53LyhkTiRkg1XZynKKu07/AKA3EUKbzcZfOQydAr1xrxhUQgP8FC/5qw5TETUdHBFuwbginaOQ1+VCIkEOPrHX+Nbo1v7Yma/MHRs7m0wLP16JQOPbzmJctMWRbNLpTeSkP7Vy/D721x4dh6m9zi5i1RnTWB74iQBWvnK8fSwSPRq7VtjNTt3JN7Mwe93C3DuTj6uZf019IXLNxMR2Tdj8xpDcQMwFDuOaR+fxJGrmVCa8OvSpbkCr47rjOziCvWCEedT8/HfX5KRcDPPgq21ntIbZ5H+5SqIijIAEvg/8AI8Ow21dbOMonB1gpNUAk+3qjBbUl6JIC83BPu44mRyDoqMfMfC5ZuJiOwbl3kmMpP6VolIup2PsRuPWqBF9qHk+m/I+PJliMqyP7cIZH3/Fpy9/OHWqqtN22aM/LKq0JtTY1GPzKIKXLhbYNJ1uHwzEVHTwFBMpEPNccKOUCXCVCXXEpHx1Ss1AnEVedQ9cG3e0Uatsi0u30xE1LgxFBPhrxDsJ3fBm3t/1+gZjgnztWHL7E9VIK6qPVyTvG0/BDy4CBInx/yzwuWbiYgaN8f8vxfRn3RNoKvtzPVc+MplyC+pNGlMcVNUknIGGV+/qh2I2w1AwJgXHTIQSyXAgMhA9hITETVyUls3gMiWnolNxK9XDY8XVgqBnOIKdGzmZaVW2aeS5NNI/+oV7UDcfiACHvw/hwzEQFUg5vLNRESNn2P+X4wcVs2xwvkl5SZNoEu6k48gLxekF5TXfXATU/JHPNJ3rgaUFRrb5R0GIeCBhZBInWzUMtvq0dKHVSeIiJoIhmJqdPQtoFFdU1bXMru6hkm4OZv+QYljBuJTfwbiSo3t8o6DETD6BYcNxACQcDOXVSeIiJoIhmJqNHQF2/YhXnBxkuBsrVXh+kb44/2pvdQLKjwTm4gjVzM1jiltBCuq2Vrx1ZPI2PWaViD26DQU/vc/59CBuBqrThARNQ0cU0yNhq5ge+lugVYgBoBjyVlYEJsA4K86w44+Sc5UFbl3GYiNwKoTRERNA0Mx2ZXkjEIcvJyOlMwire2mBtvDVzKw/dQNrD9wxdzNdAgynxD4DJymsc2j83AG4j85SSQYFMWqE0RETQWHT5Bd0DU0IirIE28+0g1dW/jgwh3t3mBj/OOrJHM10SF595kAQIXcQ1vg0WUE/O9d4JCBWArAWy5DTvFfEw37Rwaw6gQRURMiEYKfKdeXsWtpU92mfXwSv17JgK5Rvt1b+CC/tALJtXqPyXpK/jgFt4hekEgc88OlQVFVZdeyi8txLatIY5InERHZN2PzGnuKyeYSb+QYLI2WeCvXeo0hndzbxNi6CRancHPCuG6h8Fe4oWcrX7TwlWsFYG+5jGGYiKiJYiimBtFVHs3U45ftPmfpZlIdii4fgbN3MFxDIm3dFJvJL1XiWnYpXh7fVb2NAZiIyHEwFFO96BoDXP0Rc3UZNGOOXziyLc7pqB5B1lN0IQ6Z370JqascwZNWwyW4ja2bZDOHr2Sw7jARkYNyzAGC1GC6yqMduZqpLoNm7PH/3MmJcLZUdOEQMr97ExAqqEoLkbZtGcrTkm3dLJu6lsWx60REjog9xWSy6vJotSmFwOErGTj8e1XptOohEoaOP1fPqhLVnCSAklNF66Xw/EFkff8WIP6a3qgqLUDRpV/gEhxhw5bZFusOExE5JoZiMtn17GKD+6f996T634OiAjG6a4jF2sJAXD+F5w4g64e3NQIxAHj1GgOfQdN0n+QAWHeYiMhxMRQTAOMnzOUWl2PjgatGX/fI1Uwk3swxRxPJTAqT9lcFYmi+o/Dq9SB8h8+GRCKxSbtsLaa1L+sOExE5MIZiB2dowlxWURlOpGQBkOCeCH+EB3jgmdhEJNzINfr6SiGQX1pZ94FkFYVJPyPrh3egFYijx8J32CyHCsRtgzzQPzIQfp4ueKBrKHuIiYgcHBfvaIDGtniHrt7gaR+fxK9XM6Cq8VMglQAeLs4oKNMMs91beCPxVp41m0xmVHh2L7J+3IDagVjRewJ8hsx0qEBc7eCLQxiGiYiaOC7eQWqJN3KwbPc5jdJnMWG+6B3up3MCnEpAKxADYCBuxAp++wnZezZobVf0mQifwTMcMhADVZUmGIqJiAhgKG7ScovLMfuTeJy6pj2m99S1HJ3bqekpSNyD7J/e1dquuOch+Aya7rCBGGClCSIi+gtDcSOnb4JcbnE5Bv37IMfzOriCxB+R/dNGre2Kvo/AZ+DfHTYQO0kk6B8ZwF5iIiJSYyhuRGoGYF+5TGuCXOdQBV4b3wWt/OUMxAQhBEqvn9Xa7t33UXgPnOqwgRgA+kcGsNIEERFpcPhQvHHjRqxduxZ3795Ft27dsGHDBvTu3dvWzdKgq0KEr1yGvOIKjePO3cnHgxuPQOHmzEBMkEgkCHhgITKFCsWXjwAAvPtNhveAKU0+EDtLgFb+crQN9kKHUAV6tvJFC185rmUV1Vl2kIiIHJNDh+Lt27fjhRdewPvvv48+ffrg7bffxqhRo3D58mUEBQXZunlqupZIzqkViGtiIKZqEidnBIz5P2QKAVlgGHwGTLFpe7zdndVryztLJcgvrUSFUsBJCkilEkglEkgBlCtVkEqASiUACSCRACoV4CQFnJ2kkEklkLs6w8VJCpmTBD5yF6iEQKCnG/7erzUGRgXqfHyGYSIi0sehS7L16dMHMTExePfdqklIKpUKLVu2xIIFC7B48eI6z7dGSbbkjEIMezPOItcmxyFUSkikTnUe1zlUgflDI+Hq4gRnqQSVqqrlum/lFOPMjRyEervjUmo+9l64iwqlClFBXnCTOSG7sBy384ohc3LCPRH+uJ1bjJvZxQj0dMPQDkGsA0xERDbDkmx1KC8vx+nTp7FkyRL1NqlUihEjRuDYsWM6zykrK0NZWZn6+/z8fJ3HmVNdSyqT9fnKZQZ76m0lRC7F3BHt8dvNPBSWVqC4QolLqVU/o0Pbt0BrfzkyC8vQKdQbl1LzcT27GKM6hSA6zK/OYQXhAR4ava/Lx3SyynMiIiKyFocNxZmZmVAqlQgODtbYHhwcjEuXLuk8Z82aNVi1apU1mqfW2k9u8cfo0cIbpZUqXLxboPcYX7kMG6f0xGs/XMS5O9pvBnzlMuSXVEBZ43MHJ4kEPVv7oKRcqfOcxiimtS8+mh6D7OJyXMsqgrNUgqtphfjfyeu4kl6kPi7YU4be4f5o10yBbi19cDunBAJVQwYu3MlHfmkFhKi6b5HBXnCWSnD+Th6cJBL4erggu6gcQgAdQxUI8HJVlw77/uwdZBaWo1OoAnfzS5GSUYTwQA/c/fVLfPfVNox+ej+m9ws3+XmxF5eIiBydww6fuHPnDpo3b46jR4+ib9++6u2LFi1CXFwcTpw4oXWOrp7ili1bWnxFu2kfn8SRq5lQ1nipnCSAwl13j6UUgHcdvZkxYb6Y3i8MnUK91YHo7K1c/OPLs1rhuEcLb2x5vA+85TIAQEpmkToQVn+87id3wYLYBJ3LRQPA3M9P4+gfWfW+B9Va+Lrj+RFtsTvxjsZj9WjpA2epBKeu66+93CbAA1PvaQV3F2c093XHp8eu42p6AdoGe0ElBG5mF0MikaCsQgkXZyla+Xmg+pZ3aeld5xCA6vti7Ylca9euxaJFiwAA3bp1w/79++Hv72+1xyciIrJnxg6fcNhQXF5eDrlcji+//BLjxo1Tb58+fTpyc3Oxe/fuOq9hrWWe84or9AbO69lFWLozSWO1uup9NXszK1VCI8TWFe5OJGdBALgnwt+kgGcoGFZfN7OwDAJAoKcrmvu6q9sEQN3e3Qm3cTWjENGt/RAZ5KmzLboeKyWzCMeTsyAB0NzXHbdySiAB0MfE59FY/Otf/9Ia/969e3ccP34crq6uNmoVERGR/WAoNkKfPn3Qu3dvbNhQtfytSqVCq1atMH/+fLuZaFdTXYGT5aYcy5o1a7B06VKt7W+88QYWLlxogxYRERHZH060M8ILL7yA6dOnIzo6Gr1798bbb7+NoqIizJw509ZN0yk8wPBEKIZhx7F69WosW7ZMa/u6devw/PPP26BFREREjZtDh+JHH30UGRkZWLFiBe7evYvu3btjz549WpPviOzJK6+8ghUrVmhtf/vtt/Hss8/aoEVERESNn0MPn2goaw+fIFq1ahVeeuklre3r16/HggULrN8gIiIiO8fhE0RNiBACL730El5++WWtfe+++y7mzZtng1YRERE1HQzFRHZOCIGVK1filVde0dq3ceNGPP300zZoFRERUdPCUExkx4QQWL58OVavXq21b9OmTXjqqads0CoiIqKmh6GYyI5t2LBBZyD+z3/+gzlz5tigRURERE2T1NYNICL9Jk2ahI4dO2ps+/DDDxmIiYiIzIyhmMiOBQUF4cCBA+jQoQMkEgk+/vhjzJo1y9bNIiIianI4fILIzgUHB+PAgQP45Zdf8PDDD9u6OURERE0Se4qJGoGQkBAGYiIiIgtiKCayA0IIfPrpp6isrLR1U4iIiBwSQzGRjQkh8Oyzz2LatGmYNm0agzEREZENcEwxkQ0JIbBgwQJs3LgRABAbGwupVIqtW7fCycnJxq0jIiJyHOwpJrIRlUqFefPmqQNxtdjYWJw4ccJGrSIiInJMDMVENlAdiDdt2qSx3cnJCf/73//Qr18/G7WMiIjIMXH4BJGVqVQqzJ07Fx988IHGdicnJ8TGxrLKBBERkQ0wFBNZkUqlwpNPPomPPvpIY7uzszO2bduGiRMn2qhlREREjo2hmMhKVCoVZs2ahc2bN2tsd3Z2xvbt2zFhwgQbtYyIiIgYiomsQKlUYtasWdiyZYvGdmdnZ+zYsQPjxo2zSbuIiIioCkMxkYUplUo8/vjj+OSTTzS2y2Qy7NixA2PHjrVRy4iIiKgaQzGRBSmVSsyYMQOfffaZxnaZTIavvvoKY8aMsVHLiIiIqCaWZCOyoOTkZHz77bca21xcXPD1118zEBMREdkR9hQ3gBACAJCfn2/jlpC9Cg4Oxs6dOzF27FgUFBRAJpPh888/x6BBg/hzQ0REZAXV/7+tzm36SERdR5Bet27dQsuWLW3dDCIiIiKqw82bN9GiRQu9+xmKG0ClUuHOnTvw8vKCRCKxdXPsRn5+Plq2bImbN29CoVDYujkOh/ff9vga2Bbvv23x/tseXwNNQggUFBQgNDQUUqn+kcMcPtEAUqnU4DsOR6dQKPjLaEO8/7bH18C2eP9ti/ff9vga/MXb27vOYzjRjoiIiIgcHkMxERERETk8hmIyO1dXV6xcuRKurq62bopD4v23Pb4GtsX7b1u8/7bH16B+ONGOiIiIiBwee4qJiIiIyOExFBMRERGRw2MoJiIiIiKHx1BMRERERA6PoZjqbfXq1ejXrx/kcjl8fHx0HnPjxg2MHj0acrkcQUFB+L//+z9UVlZqHHPo0CH07NkTrq6uiIyMxJYtWyzf+CZs48aNCAsLg5ubG/r06YOTJ0/auklNwuHDhzFmzBiEhoZCIpFg165dGvuFEFixYgWaNWsGd3d3jBgxAleuXNE4Jjs7G4899hgUCgV8fHzwxBNPoLCw0IrPovFas2YNYmJi4OXlhaCgIIwbNw6XL1/WOKa0tBTz5s2Dv78/PD09MXHiRKSlpWkcY8zfJNK2adMmdO3aVb0YRN++ffHjjz+q9/PeW9frr78OiUSC5557Tr2Nr0HDMRRTvZWXl+Phhx/G3Llzde5XKpUYPXo0ysvLcfToUWzduhVbtmzBihUr1MekpKRg9OjRGDp0KBITE/Hcc89h1qxZ+Omnn6z1NJqU7du344UXXsDKlStx5swZdOvWDaNGjUJ6erqtm9boFRUVoVu3bti4caPO/f/+97+xfv16vP/++zhx4gQ8PDwwatQolJaWqo957LHHcP78eezbtw/fffcdDh8+jDlz5ljrKTRqcXFxmDdvHo4fP459+/ahoqICI0eORFFRkfqY559/Ht9++y127NiBuLg43LlzBxMmTFDvN+ZvEunWokULvP766zh9+jTi4+MxbNgwjB07FufPnwfAe29Np06dwn/+8x907dpVYztfAzMQRA20efNm4e3trbX9hx9+EFKpVNy9e1e9bdOmTUKhUIiysjIhhBCLFi0SnTp10jjv0UcfFaNGjbJom5uq3r17i3nz5qm/VyqVIjQ0VKxZs8aGrWp6AIidO3eqv1epVCIkJESsXbtWvS03N1e4urqK2NhYIYQQFy5cEADEqVOn1Mf8+OOPQiKRiNu3b1ut7U1Fenq6ACDi4uKEEFX3WyaTiR07dqiPuXjxogAgjh07JoQw7m8SGc/X11d89NFHvPdWVFBQIKKiosS+ffvE4MGDxbPPPiuE4M+/ubCnmCzm2LFj6NKlC4KDg9XbRo0ahfz8fHXvwrFjxzBixAiN80aNGoVjx45Zta1NQXl5OU6fPq1xP6VSKUaMGMH7aWEpKSm4e/euxr339vZGnz591Pf+2LFj8PHxQXR0tPqYESNGQCqV4sSJE1Zvc2OXl5cHAPDz8wMAnD59GhUVFRqvQfv27dGqVSuN16Cuv0lUN6VSiW3btqGoqAh9+/blvbeiefPmYfTo0Vr/3+RrYB7Otm4ANV13797V+OUDoP7+7t27Bo/Jz89HSUkJ3N3drdPYJiAzMxNKpVLn/bx06ZKNWuUYqn+edd37mj/rQUFBGvudnZ3h5+enPoaMo1Kp8Nxzz6F///7o3LkzgKr76+LiojW/ofZrUNffJNIvKSkJffv2RWlpKTw9PbFz50507NgRiYmJvPdWsG3bNpw5cwanTp3S2seff/NgTzFpWLx4MSQSicEvBiwisqV58+bh3Llz2LZtm62b4lDatWuHxMREnDhxAnPnzsX06dNx4cIFWzfLIdy8eRPPPvssPv/8c7i5udm6OU0We4pJw8KFCzFjxgyDx0RERBh1rZCQEK3KB9UzYUNCQtT/rT07Ni0tDQqFgr3EJgoICICTk5PO+1l9v8kyqu9vWloamjVrpt6elpaG7t27q4+pPeGxsrIS2dnZfH1MMH/+fPUkxRYtWqi3h4SEoLy8HLm5uRq9ZTV//o35m0T6ubi4IDIyEgDQq1cvnDp1Cu+88w4effRR3nsLO336NNLT09GzZ0/1NqVSicOHD+Pdd9/FTz/9xNfADNhTTBoCAwPRvn17g18uLi5GXatv375ISkrSCAL79u2DQqFAx44d1cfs379f47x9+/ahb9++5ntSDsLFxQW9evXSuJ8qlQr79+/n/bSw8PBwhISEaNz7/Px8nDhxQn3v+/bti9zcXJw+fVp9zIEDB6BSqdCnTx+rt7mxEUJg/vz52LlzJw4cOIDw8HCN/b169YJMJtN4DS5fvowbN25ovAZ1/U0i46lUKpSVlfHeW8Hw4cORlJSExMRE9Vd0dDQee+wx9b/5GpiBrWf6UeN1/fp1kZCQIFatWiU8PT1FQkKCSEhIEAUFBUIIISorK0Xnzp3FyJEjRWJiotizZ48IDAwUS5YsUV8jOTlZyOVy8X//93/i4sWLYuPGjcLJyUns2bPHVk+rUdu2bZtwdXUVW7ZsERcuXBBz5swRPj4+GrONqX4KCgrUP+MAxLp160RCQoK4fv26EEKI119/Xfj4+Ijdu3eLs2fPirFjx4rw8HBRUlKivsa9994revToIU6cOCF+/fVXERUVJSZPnmyrp9SozJ07V3h7e4tDhw6J1NRU9VdxcbH6mKeeekq0atVKHDhwQMTHx4u+ffuKvn37qvcb8zeJdFu8eLGIi4sTKSkp4uzZs2Lx4sVCIpGIvXv3CiF4722hZvUJIfgamANDMdXb9OnTBQCtr4MHD6qPuXbtmrjvvvuEu7u7CAgIEAsXLhQVFRUa1zl48KDo3r27cHFxEREREWLz5s3WfSJNzIYNG0SrVq2Ei4uL6N27tzh+/Litm9QkHDx4UOfP+/Tp04UQVWXZli9fLoKDg4Wrq6sYPny4uHz5ssY1srKyxOTJk4Wnp6dQKBRi5syZ6jeRZJiuew9A4+9FSUmJePrpp4Wvr6+Qy+Vi/PjxIjU1VeM6xvxNIm2PP/64aN26tXBxcRGBgYFi+PDh6kAsBO+9LdQOxXwNGk4ihBBW754mIiIiIrIjHFNMRERERA6PoZiIiIiIHB5DMRERERE5PIZiIiIiInJ4DMVERERE5PAYiomIiIjI4TEUExEREZHDYygmIp3CwsIgkUgwY8YMWzeFyCpmzJgBiUSCsLAwWzfFZl566SVIJBJIJBJbN4XI6hiKiZqoQ4cOqf/n9tJLL9m6OQ6n+t7X/nJxcUFwcDAGDx6M1atXIz093dZNpSZoy5Yten8GdX0dOnTI1k0msjmGYiIiK6qoqEB6ejoOHz6MZcuWoUOHDti7d6+tm0VklCFDhkAikWDIkCG2bgqR2TnbugFEZJ+uXbtm6yY0CdHR0di8ebP6+4KCAly9ehXvvfcejh8/juzsbEyYMAFJSUkIDw+3YUtpy5Yt2LJli62bYXavvvoqxo4da/CY6p+9l156iZ8skcNiKCYisiAPDw907txZY1vfvn0xdepUPPLII/jyyy9RVFSEN998E++++66NWklNWfPmzbV+BolIG4dPEBHZgEQiweuvv67+/ueff7Zha4iIiKGYiHQyVH2i5iS+6gk6X3zxBYYPH47AwEC4u7ujXbt2WLRoEbKzs416vF27duHhhx9Gq1at4ObmBh8fH0RHR2PVqlXIyckxeO7x48exbNkyDBkyBCEhIXBxcYFCoUDHjh0xd+5cXLhwweD5tasOpKam4h//+Ac6deoELy8vi01EioiIgIeHBwDg5s2bBo+9evUqnn/+eXTp0gXe3t5wd3dHREQEZsyYgfj4+Dofq7KyEuvXr0fv3r2hUCjU9/ett95CeXk5rl27pn5NdQ0hqM89UiqV2Lp1Kx544AGEhobC1dUV/v7+GDBgANatW4eSkhKDbT59+jSeeOIJtG3bFh4eHnBzc0PLli3Rq1cvzJs3D9988w2EEFrnlZaWYv369RgyZAgCAwMhk8ng5+eHdu3a4b777sO6det0Dg8ytvpEUlIS5syZg6ioKMjlcnh5eaFTp054/vnnDQ470nWP9+3bhzFjxiAkJASurq4IDw/H3LlzcevWLYNtsBR91Seq701cXBwAIC4uTmuyniNX7aAmQhBRk3Tw4EEBQAAQK1euNPn81q1bCwBi+vTpBq+9f/9+MXXqVPX3tb8iIyNFamqq3sfJzs4Ww4YN03s+ABEUFCSOHTum8/zNmzcbPBeAcHJyEhs3btTbhunTpwsAonXr1uLYsWMiICBA6xoHDx406f5Vnzd48GCDx3l7ewsAwtvbW+8xa9euFTKZTO/zk0gkYvny5XrPz8vLE/fcc4/e83v37i0SEhLU32/evFnrGqbeo+vXr4tu3boZfF0iIyPF5cuXdbZ53bp1QiqV1vnaFhQUaJx3584d0bFjxzrPW7hwocHnqM9rr71msF2urq5i69atOs9NSUnRuMeLFy/We53AwEBx4cIFve2oS83fC12vpz4rV65Un1dT9b0x9GXovhE1BhxTTEQNsnz5chw9ehTjxo3DtGnT0Lp1a6SlpWHjxo34/vvv1T2csbGxWueWlZVhxIgROHPmDJycnDBlyhTcf//9CA8PR0VFBQ4fPox169YhPT0d999/PxISEtC6dWuNa1RWVsLX1xdjx47FoEGDEBUVBQ8PD9y5cwdnzpzB+vXrkZmZifnz56N9+/YYNmyY3udSWFiIiRMnorS0FP/85z/xt7/9DXK5HElJSWjWrJnZ711qairy8vIAQG8v29q1a7Fo0SIAQNeuXTF37lxERUXBx8cHly9fxrvvvotjx47hlVdeQUBAAJ555hmta0yaNAnHjx8HAPTv3x8LFixAZGQkMjIy8Nlnn+Hzzz/HU089ZVSbjblHWVlZGDBgAG7evAlXV1fMnj0bgwcPRlhYGAoLC7F371688847uHr1Ku677z6cOXMG3t7e6sc4e/YsXnzxRahUKoSHh2P+/Pno3r07/Pz8UFBQgMuXL+PgwYPYvXu3VvsWLFig/mRg6tSpmDBhAkJDQ+Hk5ITU1FTEx8frPM8Y7733HpYuXQoACAwMxD/+8Q/0798fSqUSP//8M9auXYuioiLMmDEDAQEBuP/++/Ve68MPP8TRo0cxePBgPPnkk2jbti1yc3PxySef4JNPPkFGRgYef/xxHDt2rF5tNbfVq1fjxRdfxMyZMxEfH681gRQAXFxcbNQ6IjOxdSonIsuwVk8xAPHqq69qHaNSqcTIkSMFAOHs7CzS09O1jlm6dKkAIHx8fER8fLzOdly7dk00a9ZMABBTpkzR2n/r1i1RVFSk93nk5uaKrl27CgBiwIABOo+p2Qvm6ekpEhMT9V7PWNXXM9RTvGDBAvVxr7zyitb+8+fPq3uIV65cKVQqldYxSqVS3VPv6ekpsrOzNfbv2rVL/RgTJkwQSqVS6xpvvPGGxutpqKfYmHs0ZcoUdc9hcnKyzmPOnDkjPDw8BACxdOlSjX3Lly8XAISHh4e4e/eu3sfJzc3VeD4lJSXq+6WrJ7imrKwsvc9RV49nenq6kMvlAoAIDQ0VN27cMPicmjdvLsrLyzX21+wpBiBmz56t8zWdNWuW+pgzZ84YfB761OwpfvXVV0VSUpLer5ycHPV5+nqKqw0ePNioT0CIGiOGYqImylqhuFevXjr/xy6EEHv27FEft3v3bo19BQUF6qEDGzZsMNiW9957TwAQMplMFBYWmvxcagbDzMxMrf01A9/LL79s8vV10ReK8/PzxenTp8WMGTOERCIRAERUVJRWmBVCiMcff1wAENHR0XrvsRBC5OTkCFdXVwFAfPDBBxr77r33XgFAuLu763xjIkTVG5iePXsaHYoN3aOUlBTh5OQkAIhvv/1W73FCCLFo0SJ1yKxp9uzZAoDo0aOHwfNru337tt6fN2MYCsX/+te/1Nfetm2b3mu8+uqr6uO++OILjX01Q3GzZs1EaWmpzmtcunRJfdw777xj8vMQwrhhRbpeb4ZicmScaEdEDTJlyhS9S8L26tVL/e/k5GSNfXFxceqhAw899JDBxxg0aBCAqoUvTp8+bfDYoqIiXLt2DefPn8e5c+dw7tw5yGQy9f7ffvvN4PmPPfaYwf2mqj0hSaFQoFevXuqJVmPHjsWhQ4fg6+urde63334LAJg4caLBZXd9fHzQpUsXAND4uL2yslI9Meree+9FYGCgzvMlEgn+/ve/G/2cDN2j77//HkqlEnK5HPfdd5/B61S/rnfu3MGNGzfU26uHYVy4cAEnT540ul3+/v7qj/A//fRTVFZWGn1uXaqrg/j4+GDChAl6j5s1a5bWObo89NBDcHV11bmvXbt28PT0BKD9e0NElsNQTEQN0r59e737/Pz81P8uKCjQ2FezYkKzZs0MLkFbs8bq3bt3tR4nMzMTS5cuRbt27eDl5YXw8HB07twZXbp0QZcuXTB69GiNY/Xx9PRERESE4SdsRqGhoXjuuecQGhqqte/69evIyMgAACxZsqTOZXqr72fN+/PHH3+oKzzUfIOiS3R0tFFtruseVbejuLgYzs7OBtv8wAMPqM+r2e7JkydDJpOhrKwM/fv3x5gxY/D+++/j3LlzOqtNVHN1dcWjjz4KAPjyyy8RGRmJRYsW4YcffkBubq5Rz0+fc+fOAQB69uyp8SartuDgYPX48OpzdDH0ewNA/Sap9u9NfWzevBmi6pNhnV+6KswQOSKGYiJqELlcrnefVPrXnxilUqmxLz09vV6PV1xcrPH96dOn0b59e6xZswa///67wdAEwGAZMB8fn3q1yZDo6GgkJSUhKSkJZ8+exd69e7F8+XJ4e3vj9u3buPfee/HLL79onWeO+1OzlJ2+XmJj91er6x6Zo93t27dHbGwsfH19UVlZie+++w5z585Fly5dEBQUhL///e867xkAvPvuuxgzZgyAqjcWa9euxejRo+Hv74+YmBisXbtW/QmFKapLCwYFBdV5bEhIiMY5uhj6vQH++t2p/XtDRJbD6hNEZBM1/2d/5swZg71vNbVo0UL97/LycjzyyCPIysqCTCbDggULMHbsWLRt2xa+vr7qj6eTk5PRpk0bADAYmp2cnOrzVAyqvaJdly5d8Le//Q2PPPII+vXrh4KCAjz22GM4d+4cFAqF+ria92fFihV4+OGHjX48S6rrHlW3OyAgAAcPHjT6urWXuJ44cSJGjBiB7du346effsIvv/yCjIwMZGZm4rPPPsNnn32G6dOn47///a/Gmy+FQoFvvvkGJ0+exBdffIFDhw4hMTERSqUS8fHxiI+PxxtvvIFdu3ahb9++JjzzKoaGsRBR48ZQTEQ24e/vr/53YGCgRtg11oEDB9RjLt977z2N8Zw1GbuAiDV17twZr732GhYsWICbN29i7dq1eOWVV9T7a94fmUxWr2V6a45Trh6KoU9d+41V3e6CggJ06NChQW80vL29MWfOHMyZMwcAcPHiRezevRsbNmzAnTt3sHXrVvTo0QPPPvus1rm9e/dG79691W05dOgQtmzZgq+//hrp6emYOHEi/vjjD7i7uxvVFj8/P6SmpiItLa3OY6uHgtQcPkRE9o/DJ4jIJnr06KH+95EjR+p1jfPnz6v/XT2WVBdjVnyzhSeffFLdQ/rWW29pjHeOiIhQ1+6t7/1p06YN3NzcAKDOCYrmukfVr2tZWZnZ73uHDh2wePFiHD9+XN0j/sUXX9R5npeXF8aMGYOvvvpKXcc5NTUVv/76q9GPXf2m5MyZMwYn8KWnp+P69esa5zQl7CmnpoyhmIhsYsSIEepxlevXr69zLLAuNcNJUVGRzmNUKhU+/PDD+jXSwmQyGRYvXgygqv1vvfWWep+Tk5N68Ye9e/fi4sWLJl/f2dlZXeFhz549enuDhRD49NNPTb6+LmPGjFEHp7ffftss16ytZcuWaNu2LQDDEyd1GT58uPrfppw7YsQIAEBubi6+/vprvcd9/PHH6p/l6nOakuo3WWVlZTZuCZH5MRQTkU34+Phg/vz5AICjR4/i+eefh0ql0nt8WloaPvroI41tUVFR6n9XlzirbcmSJThz5kzDG2whM2bMQPPmzQEAGzdu1JgEtmTJEjg5OUGlUuGhhx7CrVu39F5HqVTi888/1zrmySefBFA1wfCpp57SeY/XrVtntnvUrl079fjnbdu2Yd26dQaPT0lJ0VrtcNeuXQarRdy8eROXLl0CoDkWOTk5WV2CTp+9e/eq/117HLMhM2fOVL+JW7hwIW7fvq11zG+//YbXXnsNANC8eXOMGzfO6Os3FtXl8pKTk+v1RpbInnFMMZEDSExM1Bsaaxo2bBhatWpl+Qb96eWXX0ZcXBxOnDiBd955B4cOHcLs2bPRvXt3eHh4ICcnB+fPn8fPP/+MH3/8EV26dNEYNzxq1CgEBQUhPT0dy5Ytw7Vr1zB+/HgEBATg6tWr+PDDD7F//37079+/3kMQLM3FxQUvvvginn/+eeTl5WH9+vVYvnw5gKpJeW+88Qaef/55XLhwAZ07d8acOXMwbNgwBAcHo7S0FNeuXcOxY8fw5ZdfIjU1FUlJSRrjsydMmICRI0di7969+PrrrzFo0CA888wzGss8f/bZZ+jdu7e6JnBDPyLftGkT4uPjkZycjIULF2L37t2YNm0aOnXqBFdXV2RlZeG3337Dnj17cODAAYwfPx6TJ09Wn//222/jsccew+jRozFs2DB06NAB3t7eyMnJQXx8PDZs2KCuIlJzeeobN25g6NCh6NixI8aPH4/o6Gj1G46bN29i+/bt6uEW3bt3R58+fYx+ToGBgVi7di3mzZuHW7duoVevXli8eDH69euHyspK9TLPhYWFkEgk+OCDD4yePNqY9OvXD5s3b0Z6ejpeeOEFTJ06VT3MRyaTaS3DTtSo2GLFECKyvNpLMRvztXPnTvX5xq5od/DgQYPtqD5O36p6+fn5YsKECUa1b+jQoVrn79mzR7i5uek9Z8iQIeLcuXNGrdamayWz+qp+PGNW/ioqKhIBAQECgPD39xcFBQUa+z/44AP1EsOGvlxcXMSVK1e0rp+TkyN69+6t97wePXqI+Ph4gyu2mXqPUlNTxcCBA416XWfOnKlxbvWqaYa+pFKp1tLYxv7Mt2/fXufy08Y8x9WrVwupVKr32q6urmLr1q06z625op2un8OaDP3+GaPminZ1PVZNda1oV1BQICIiInQ+d3P+/hDZAodPEJFNeXl54auvvsIvv/yCWbNmqRfgcHZ2hp+fH2JiYjBv3jz88MMP2Ldvn9b5o0aNQnx8PKZOnYrQ0FDIZDIEBgZi8ODB+OCDD7B//36LlylrKLlcjueffx4AkJWVhU2bNmnsnz17NpKTk7Fq1Sr0798fAQEBcHZ2hoeHB9q2bYuJEyfi/fffx+3btxEZGal1fR8fH/z6669466230KtXL3h6esLLywvdu3fHmjVrcPToUY0qEdU9fw0REhKCw4cP47vvvsNjjz2GiIgIyOVy9evTr18/LFy4EHFxcfjvf/+rcW5sbCw++OADTJkyBd27d0dISAicnZ3h6emJTp06Ye7cuUhISMCyZcs0zhs4cCAOHTqEJUuWYOjQoYiMjISXlxdkMhmCg4MxcuRIvP/++0hMTDRp6ERNS5cuRUJCAmbPno02bdrA3d0dHh4e6NChA5599llcunQJ06ZNq/d9s3eenp44evQonn32WXTo0KHOestEjYlECA4KIiJydJ999pl6qeerV6+q6zoTETkK9hQTEZF6sltgYKBVl7omIrIXDMVERE3c7du3DS5v/dFHH+GHH34AAEybNo21aInIIXH4BBFRE7dlyxYsWrQIkyZNwpAhQ9C6dWuoVCr88ccf2L59O3bt2gUACA4Oxvnz5zVW0yMichQMxURETdyWLVswc+ZMg8c0a9YM33//vcZKg0REjoShmIioicvMzMSXX36Jn376CRcuXEBGRgYKCgrg4+ODDh06YMyYMXjqqafg5eVl66YSEdkMQzEREREROTxOtCMiIiIih8dQTEREREQOj6GYiIiIiBweQzEREREROTyGYiIiIiJyeAzFREREROTwGIqJiIiIyOExFBMRERGRw2MoJiIiIiKH9/9Si6eIzDNwnQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(M2_lm.fittedvalues,\n", @@ -2174,6 +5029,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch5-resample-lab.ipynb b/docs/source/labs/Ch5-resample-lab.ipynb index b499676..86162e6 100644 --- a/docs/source/labs/Ch5-resample-lab.ipynb +++ b/docs/source/labs/Ch5-resample-lab.ipynb @@ -24,9 +24,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "60fad148", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:18.516196Z", + "iopub.status.busy": "2023-07-25T23:59:18.515741Z", + "iopub.status.idle": "2023-07-25T23:59:19.379955Z", + "shell.execute_reply": "2023-07-25T23:59:19.379486Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -50,9 +56,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2478aeb4", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.382153Z", + "iopub.status.busy": "2023-07-25T23:59:19.381970Z", + "iopub.status.idle": "2023-07-25T23:59:19.384142Z", + "shell.execute_reply": "2023-07-25T23:59:19.383878Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -88,9 +100,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "99c95faf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.385847Z", + "iopub.status.busy": "2023-07-25T23:59:19.385721Z", + "iopub.status.idle": "2023-07-25T23:59:19.391245Z", + "shell.execute_reply": "2023-07-25T23:59:19.390937Z" + } + }, "outputs": [], "source": [ "Auto = load_data('Auto')\n", @@ -109,9 +128,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "41b0717d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.393063Z", + "iopub.status.busy": "2023-07-25T23:59:19.392958Z", + "iopub.status.idle": "2023-07-25T23:59:19.396896Z", + "shell.execute_reply": "2023-07-25T23:59:19.396495Z" + } + }, "outputs": [], "source": [ "hp_mm = MS(['horsepower'])\n", @@ -132,10 +158,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d7ea3c0d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.398586Z", + "iopub.status.busy": "2023-07-25T23:59:19.398485Z", + "iopub.status.idle": "2023-07-25T23:59:19.403011Z", + "shell.execute_reply": "2023-07-25T23:59:19.402741Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "23.61661706966988" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X_valid = hp_mm.transform(Auto_valid)\n", "y_valid = Auto_valid['mpg']\n", @@ -158,9 +202,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a02a2d05", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.404921Z", + "iopub.status.busy": "2023-07-25T23:59:19.404788Z", + "iopub.status.idle": "2023-07-25T23:59:19.408021Z", + "shell.execute_reply": "2023-07-25T23:59:19.407664Z" + } + }, "outputs": [], "source": [ "def evalMSE(terms,\n", @@ -194,10 +245,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "51d93dea", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.409619Z", + "iopub.status.busy": "2023-07-25T23:59:19.409514Z", + "iopub.status.idle": "2023-07-25T23:59:19.420772Z", + "shell.execute_reply": "2023-07-25T23:59:19.420495Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([23.61661707, 18.76303135, 18.79694163])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "MSE = np.zeros(3)\n", "for idx, degree in enumerate(range(1, 4)):\n", @@ -220,10 +289,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "83432f06", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.422551Z", + "iopub.status.busy": "2023-07-25T23:59:19.422418Z", + "iopub.status.idle": "2023-07-25T23:59:19.433976Z", + "shell.execute_reply": "2023-07-25T23:59:19.433683Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([20.75540796, 16.94510676, 16.97437833])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Auto_train, Auto_valid = train_test_split(Auto,\n", " test_size=196,\n", @@ -285,12 +372,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "bcfc433f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:19.435742Z", + "iopub.status.busy": "2023-07-25T23:59:19.435616Z", + "iopub.status.idle": "2023-07-25T23:59:20.107186Z", + "shell.execute_reply": "2023-07-25T23:59:20.106844Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "24.23151351792924" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hp_model = sklearn_sm(sm.OLS,\n", " MS(['horsepower']))\n", @@ -336,12 +440,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "f951ffc8", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.109173Z", + "iopub.status.busy": "2023-07-25T23:59:20.109038Z", + "iopub.status.idle": "2023-07-25T23:59:20.813810Z", + "shell.execute_reply": "2023-07-25T23:59:20.813473Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([24.23151352, 19.24821312, 19.33498406, 19.42443033, 19.03323827])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cv_error = np.zeros(5)\n", "H = np.array(Auto['horsepower'])\n", @@ -376,10 +497,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "e3610b5a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.815663Z", + "iopub.status.busy": "2023-07-25T23:59:20.815525Z", + "iopub.status.idle": "2023-07-25T23:59:20.818253Z", + "shell.execute_reply": "2023-07-25T23:59:20.817958Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 7],\n", + " [ 7, 9],\n", + " [11, 13]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "A = np.array([3, 5, 9])\n", "B = np.array([2, 4])\n", @@ -398,12 +539,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "1627460d", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.819903Z", + "iopub.status.busy": "2023-07-25T23:59:20.819780Z", + "iopub.status.idle": "2023-07-25T23:59:20.842970Z", + "shell.execute_reply": "2023-07-25T23:59:20.842686Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([24.20766449, 19.18533142, 19.27626666, 19.47848403, 19.13720581])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cv_error = np.zeros(5)\n", "cv = KFold(n_splits=10,\n", @@ -446,12 +604,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8a636468", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.844695Z", + "iopub.status.busy": "2023-07-25T23:59:20.844573Z", + "iopub.status.idle": "2023-07-25T23:59:20.850336Z", + "shell.execute_reply": "2023-07-25T23:59:20.850052Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([23.61661707])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "validation = ShuffleSplit(n_splits=1,\n", " test_size=196,\n", @@ -473,10 +648,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "746aeccd", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.852176Z", + "iopub.status.busy": "2023-07-25T23:59:20.852041Z", + "iopub.status.idle": "2023-07-25T23:59:20.873556Z", + "shell.execute_reply": "2023-07-25T23:59:20.873271Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(23.802232661034168, 1.421845094109185)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "validation = ShuffleSplit(n_splits=10,\n", " test_size=196,\n", @@ -529,9 +722,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "daa53d0c", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.875275Z", + "iopub.status.busy": "2023-07-25T23:59:20.875145Z", + "iopub.status.idle": "2023-07-25T23:59:20.878569Z", + "shell.execute_reply": "2023-07-25T23:59:20.878269Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -557,10 +756,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "578c9564", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.880246Z", + "iopub.status.busy": "2023-07-25T23:59:20.880127Z", + "iopub.status.idle": "2023-07-25T23:59:20.883007Z", + "shell.execute_reply": "2023-07-25T23:59:20.882752Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.57583207459283" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alpha_func(Portfolio, range(100))" ] @@ -578,12 +795,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "5754d6d5", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.884729Z", + "iopub.status.busy": "2023-07-25T23:59:20.884605Z", + "iopub.status.idle": "2023-07-25T23:59:20.887853Z", + "shell.execute_reply": "2023-07-25T23:59:20.887569Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.6074452469619004" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rng = np.random.default_rng(0)\n", "alpha_func(Portfolio,\n", @@ -604,9 +838,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "8320a49c", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.889568Z", + "iopub.status.busy": "2023-07-25T23:59:20.889470Z", + "iopub.status.idle": "2023-07-25T23:59:20.892197Z", + "shell.execute_reply": "2023-07-25T23:59:20.891914Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -643,10 +883,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "e656aa1f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:20.894076Z", + "iopub.status.busy": "2023-07-25T23:59:20.893953Z", + "iopub.status.idle": "2023-07-25T23:59:21.209418Z", + "shell.execute_reply": "2023-07-25T23:59:21.209114Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.09118176521277699" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alpha_SE = boot_SE(alpha_func,\n", " Portfolio,\n", @@ -691,9 +949,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "c5d14195", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:21.211286Z", + "iopub.status.busy": "2023-07-25T23:59:21.211158Z", + "iopub.status.idle": "2023-07-25T23:59:21.213339Z", + "shell.execute_reply": "2023-07-25T23:59:21.213042Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -720,9 +984,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "7e0523f0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:21.214975Z", + "iopub.status.busy": "2023-07-25T23:59:21.214858Z", + "iopub.status.idle": "2023-07-25T23:59:21.216686Z", + "shell.execute_reply": "2023-07-25T23:59:21.216408Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -747,12 +1017,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "32836e93", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:21.218240Z", + "iopub.status.busy": "2023-07-25T23:59:21.218130Z", + "iopub.status.idle": "2023-07-25T23:59:21.234117Z", + "shell.execute_reply": "2023-07-25T23:59:21.233834Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[39.88064456, -0.1567849 ],\n", + " [38.73298691, -0.14699495],\n", + " [38.31734657, -0.14442683],\n", + " [39.91446826, -0.15782234],\n", + " [39.43349349, -0.15072702],\n", + " [40.36629857, -0.15912217],\n", + " [39.62334517, -0.15449117],\n", + " [39.0580588 , -0.14952908],\n", + " [38.66688437, -0.14521037],\n", + " [39.64280792, -0.15555698]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rng = np.random.default_rng(0)\n", "np.array([hp_func(Auto,\n", @@ -772,12 +1068,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "14ce3afa", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:21.235834Z", + "iopub.status.busy": "2023-07-25T23:59:21.235710Z", + "iopub.status.idle": "2023-07-25T23:59:22.536815Z", + "shell.execute_reply": "2023-07-25T23:59:22.536478Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "intercept 0.848807\n", + "horsepower 0.007352\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hp_se = boot_SE(hp_func,\n", " Auto,\n", @@ -803,12 +1118,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "6b1213ac", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:22.538715Z", + "iopub.status.busy": "2023-07-25T23:59:22.538582Z", + "iopub.status.idle": "2023-07-25T23:59:22.627465Z", + "shell.execute_reply": "2023-07-25T23:59:22.627170Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "intercept 0.717\n", + "horsepower 0.006\n", + "Name: std err, dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hp_model.fit(Auto, Auto['mpg'])\n", "model_se = summarize(hp_model.results_)['std err']\n", @@ -856,10 +1190,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "af99b778", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:22.629295Z", + "iopub.status.busy": "2023-07-25T23:59:22.629160Z", + "iopub.status.idle": "2023-07-25T23:59:24.704262Z", + "shell.execute_reply": "2023-07-25T23:59:24.703883Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "intercept 2.067840\n", + "poly(horsepower, degree=2, raw=True)[0] 0.033019\n", + "poly(horsepower, degree=2, raw=True)[1] 0.000120\n", + "dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "quad_model = MS([poly('horsepower', 2, raw=True)])\n", "quad_func = partial(boot_OLS,\n", @@ -878,10 +1233,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "0206281e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:24.706236Z", + "iopub.status.busy": "2023-07-25T23:59:24.706087Z", + "iopub.status.idle": "2023-07-25T23:59:24.716168Z", + "shell.execute_reply": "2023-07-25T23:59:24.715845Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "intercept 1.800\n", + "poly(horsepower, degree=2, raw=True)[0] 0.031\n", + "poly(horsepower, degree=2, raw=True)[1] 0.000\n", + "Name: std err, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M = sm.OLS(Auto['mpg'],\n", " quad_model.fit_transform(Auto))\n", @@ -894,6 +1270,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch6-varselect-lab.ipynb b/docs/source/labs/Ch6-varselect-lab.ipynb index f433c1e..925821d 100644 --- a/docs/source/labs/Ch6-varselect-lab.ipynb +++ b/docs/source/labs/Ch6-varselect-lab.ipynb @@ -19,9 +19,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "564883b2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:25.902673Z", + "iopub.status.busy": "2023-07-25T23:59:25.902240Z", + "iopub.status.idle": "2023-07-25T23:59:26.930903Z", + "shell.execute_reply": "2023-07-25T23:59:26.930443Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -47,12 +54,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "9aa8d3ee", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:26.933137Z", + "iopub.status.busy": "2023-07-25T23:59:26.932936Z", + "iopub.status.idle": "2023-07-25T23:59:28.365522Z", + "shell.execute_reply": "2023-07-25T23:59:28.365174Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: l0bnb in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (1.0.0)\r\n", + "Requirement already satisfied: numpy>=1.18.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.21.6)\r\n", + "Requirement already satisfied: scipy>=1.4.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.10.1)\r\n", + "Requirement already satisfied: numba>=0.53.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (0.57.1)\r\n", + "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from numba>=0.53.1->l0bnb) (0.40.1)\r\n", + "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\r\n", + "\u001b[0m" + ] + } + ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.decomposition import PCA\n", @@ -93,10 +120,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "05371e33", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.367608Z", + "iopub.status.busy": "2023-07-25T23:59:28.367471Z", + "iopub.status.idle": "2023-07-25T23:59:28.374856Z", + "shell.execute_reply": "2023-07-25T23:59:28.374563Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "59" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Hitters = load_data('Hitters')\n", "np.isnan(Hitters['Salary']).sum()\n" @@ -114,12 +159,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "aa5bc5b8", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.376501Z", + "iopub.status.busy": "2023-07-25T23:59:28.376391Z", + "iopub.status.idle": "2023-07-25T23:59:28.379413Z", + "shell.execute_reply": "2023-07-25T23:59:28.379133Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(263, 20)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Hitters = Hitters.dropna();\n", "Hitters.shape\n" @@ -138,9 +200,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "10494837", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.381043Z", + "iopub.status.busy": "2023-07-25T23:59:28.380935Z", + "iopub.status.idle": "2023-07-25T23:59:28.383115Z", + "shell.execute_reply": "2023-07-25T23:59:28.382819Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -164,9 +232,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b8173cef", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.384748Z", + "iopub.status.busy": "2023-07-25T23:59:28.384661Z", + "iopub.status.idle": "2023-07-25T23:59:28.397967Z", + "shell.execute_reply": "2023-07-25T23:59:28.397658Z" + } + }, "outputs": [], "source": [ "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n", @@ -185,9 +260,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "0cc8d7bd", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.399611Z", + "iopub.status.busy": "2023-07-25T23:59:28.399511Z", + "iopub.status.idle": "2023-07-25T23:59:28.401406Z", + "shell.execute_reply": "2023-07-25T23:59:28.401144Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -217,9 +298,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "440b4c50", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.403063Z", + "iopub.status.busy": "2023-07-25T23:59:28.402971Z", + "iopub.status.idle": "2023-07-25T23:59:28.404782Z", + "shell.execute_reply": "2023-07-25T23:59:28.404512Z" + } + }, "outputs": [], "source": [ "strategy = Stepwise.first_peak(design,\n", @@ -241,10 +329,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b33a8617", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:28.406436Z", + "iopub.status.busy": "2023-07-25T23:59:28.406315Z", + "iopub.status.idle": "2023-07-25T23:59:29.209115Z", + "shell.execute_reply": "2023-07-25T23:59:29.208717Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Assists',\n", + " 'AtBat',\n", + " 'CAtBat',\n", + " 'CHits',\n", + " 'CHmRun',\n", + " 'CRBI',\n", + " 'CRuns',\n", + " 'CWalks',\n", + " 'Division',\n", + " 'Errors',\n", + " 'Hits',\n", + " 'HmRun',\n", + " 'League',\n", + " 'NewLeague',\n", + " 'PutOuts',\n", + " 'RBI',\n", + " 'Runs',\n", + " 'Walks',\n", + " 'Years')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hitters_MSE = sklearn_selected(OLS,\n", " strategy)\n", @@ -262,10 +386,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e7f554cf", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:29.211384Z", + "iopub.status.busy": "2023-07-25T23:59:29.211235Z", + "iopub.status.idle": "2023-07-25T23:59:29.694768Z", + "shell.execute_reply": "2023-07-25T23:59:29.694467Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Assists',\n", + " 'AtBat',\n", + " 'CAtBat',\n", + " 'CRBI',\n", + " 'CRuns',\n", + " 'CWalks',\n", + " 'Division',\n", + " 'Hits',\n", + " 'PutOuts',\n", + " 'Walks')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hitters_Cp = sklearn_selected(OLS,\n", " strategy,\n", @@ -301,9 +452,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fb7091b8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:29.696711Z", + "iopub.status.busy": "2023-07-25T23:59:29.696579Z", + "iopub.status.idle": "2023-07-25T23:59:29.698718Z", + "shell.execute_reply": "2023-07-25T23:59:29.698431Z" + } + }, "outputs": [], "source": [ "strategy = Stepwise.fixed_steps(design,\n", @@ -322,12 +480,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "56adc728", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:29.700418Z", + "iopub.status.busy": "2023-07-25T23:59:29.700293Z", + "iopub.status.idle": "2023-07-25T23:59:30.144070Z", + "shell.execute_reply": "2023-07-25T23:59:30.143731Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(263, 20)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "full_path.fit(Hitters, Y)\n", "Yhat_in = full_path.predict(Hitters)\n", @@ -350,12 +525,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "387267d9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:30.146111Z", + "iopub.status.busy": "2023-07-25T23:59:30.145965Z", + "iopub.status.idle": "2023-07-25T23:59:30.281524Z", + "shell.execute_reply": "2023-07-25T23:59:30.280654Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mse_fig, ax = subplots(figsize=(8,8))\n", "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n", @@ -402,10 +594,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "0047ba88", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:30.285990Z", + "iopub.status.busy": "2023-07-25T23:59:30.285674Z", + "iopub.status.idle": "2023-07-25T23:59:32.494537Z", + "shell.execute_reply": "2023-07-25T23:59:32.494207Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(263, 20)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "K = 5\n", "kfold = skm.KFold(K,\n", @@ -436,10 +646,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "27b0eb63", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:32.496480Z", + "iopub.status.busy": "2023-07-25T23:59:32.496340Z", + "iopub.status.idle": "2023-07-25T23:59:32.499706Z", + "shell.execute_reply": "2023-07-25T23:59:32.499405Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(20, 5)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cv_mse = []\n", "for train_idx, test_idx in kfold.split(Y):\n", @@ -460,10 +688,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "a45fe587", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:32.501370Z", + "iopub.status.busy": "2023-07-25T23:59:32.501275Z", + "iopub.status.idle": "2023-07-25T23:59:32.592962Z", + "shell.execute_reply": "2023-07-25T23:59:32.592640Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ax.errorbar(np.arange(n_steps), \n", " cv_mse.mean(1),\n", @@ -487,9 +734,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c71c2079", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:32.594717Z", + "iopub.status.busy": "2023-07-25T23:59:32.594588Z", + "iopub.status.idle": "2023-07-25T23:59:33.030365Z", + "shell.execute_reply": "2023-07-25T23:59:33.030053Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -515,12 +768,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "2ff34edf", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:33.032304Z", + "iopub.status.busy": "2023-07-25T23:59:33.032173Z", + "iopub.status.idle": "2023-07-25T23:59:33.128471Z", + "shell.execute_reply": "2023-07-25T23:59:33.128143Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ax.plot(np.arange(n_steps), \n", " validation_mse,\n", @@ -550,9 +821,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "845f3fa0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:33.130280Z", + "iopub.status.busy": "2023-07-25T23:59:33.130150Z", + "iopub.status.idle": "2023-07-25T23:59:33.142290Z", + "shell.execute_reply": "2023-07-25T23:59:33.141982Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -573,10 +850,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "e9f943e7", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:33.143916Z", + "iopub.status.busy": "2023-07-25T23:59:33.143817Z", + "iopub.status.idle": "2023-07-25T23:59:35.350804Z", + "shell.execute_reply": "2023-07-25T23:59:35.350454Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocessing Data.\n", + "BnB Started.\n", + "Iteration: 1. Number of non-zeros: 1\n", + "Iteration: 2. Number of non-zeros: 2\n", + "Iteration: 3. Number of non-zeros: 2\n", + "Iteration: 4. Number of non-zeros: 2\n", + "Iteration: 5. Number of non-zeros: 3\n", + "Iteration: 6. Number of non-zeros: 3\n", + "Iteration: 7. Number of non-zeros: 4\n", + "Iteration: 8. Number of non-zeros: 9\n", + "Iteration: 9. Number of non-zeros: 9\n", + "Iteration: 10. Number of non-zeros: 9\n", + "Iteration: 11. Number of non-zeros: 9\n", + "Iteration: 12. Number of non-zeros: 9\n", + "Iteration: 13. Number of non-zeros: 9\n", + "Iteration: 14. Number of non-zeros: 9\n", + "Iteration: 15. Number of non-zeros: 9\n", + "Iteration: 16. Number of non-zeros: 9\n", + "Iteration: 17. Number of non-zeros: 9\n", + "Iteration: 18. Number of non-zeros: 17\n", + "Iteration: 19. Number of non-zeros: 19\n" + ] + } + ], "source": [ "path = fit_path(X, \n", " Y,\n", @@ -593,12 +905,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "657ac171", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.352837Z", + "iopub.status.busy": "2023-07-25T23:59:35.352703Z", + "iopub.status.idle": "2023-07-25T23:59:35.355490Z", + "shell.execute_reply": "2023-07-25T23:59:35.355210Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'B': array([0. , 3.25484367, 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0.67775265, 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. ]),\n", + " 'B0': -38.98216739555551,\n", + " 'lambda_0': 0.011416248027450178,\n", + " 'M': 0.5829861733382012,\n", + " 'Time_exceeded': False}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "path[3]\n" ] @@ -634,12 +970,435 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "e576ad54", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.357157Z", + "iopub.status.busy": "2023-07-25T23:59:35.357034Z", + "iopub.status.idle": "2023-07-25T23:59:35.424501Z", + "shell.execute_reply": "2023-07-25T23:59:35.424223Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120446, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.685659595, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.02159835, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.30893663, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435659, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.34364891, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107168, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.92275897, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.09233121, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.277090184, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.98847021, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376922, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.251295805, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761593, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955906, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546297, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.6204245, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.32609803, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.002990715, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601913, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047491, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683641, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.273268215, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.981010474, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539254, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087247, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.15213543, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450393, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126286, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247456, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.0816231, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213055, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.950182974, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.08142715, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032734, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.375795275, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.65542218, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721766, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.65921827, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01861009, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520982, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.66025478, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.54819732, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147845834, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362815, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553557, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499264, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408341, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.272819825, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300109, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638604, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "data": { + "text/plain": [ + "(19, 100)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Xs = X - X.mean(0)[None,:]\n", "X_scale = X.std(0)\n", @@ -678,10 +1437,408 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "3ef15192", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.426658Z", + "iopub.status.busy": "2023-07-25T23:59:35.426532Z", + "iopub.status.idle": "2023-07-25T23:59:35.436847Z", + "shell.execute_reply": "2023-07-25T23:59:35.436537Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AtBatHitsHmRunRunsRBIWalksYearsCAtBatCHitsCHmRunCRunsCRBICWalksLeague[N]Division[W]PutOutsAssistsErrorsNewLeague[N]
negative log(lambda)
-12.3108550.0008000.0008890.0006950.0008510.0009110.0009000.0008120.0010670.0011130.0010640.0011410.0011490.000993-0.000029-0.0003900.0006090.000052-0.000011-0.000006
-12.0782710.0010100.0011220.0008780.0010740.0011500.0011350.0010250.0013460.0014040.0013430.0014390.0014500.001253-0.000037-0.0004920.0007690.000065-0.000014-0.000007
-11.8456860.0012740.0014160.0011070.0013550.0014510.0014330.0012930.0016980.0017720.0016940.0018160.0018300.001581-0.000046-0.0006210.0009700.000082-0.000017-0.000009
-11.6131020.0016080.0017870.0013970.0017100.0018310.0018080.0016320.0021430.0022360.0021380.0022920.0023090.001995-0.000058-0.0007840.0012240.000104-0.000022-0.000012
-11.3805180.0020290.0022550.0017630.0021580.0023100.0022810.0020590.0027040.0028210.0026980.0028920.0029140.002517-0.000073-0.0009900.0015440.000131-0.000028-0.000015
............................................................
9.784658-290.823989336.92996837.322686-59.748520-26.507086134.855915-17.216195-387.77582689.573601-12.273926476.079273257.271255-213.12478031.258215-58.45785778.76126653.622113-22.208456-12.402891
10.017243-290.879272337.11371337.431373-59.916820-26.606957134.900549-17.108041-388.45840489.000707-12.661459477.031349257.966790-213.28089131.256434-58.44885078.76124053.645147-22.198802-12.391969
10.249827-290.923382337.26044637.518064-60.051166-26.686604134.936136-17.022194-388.99747088.537380-12.971603477.791860258.523025-213.40574031.254958-58.44168278.76123053.663357-22.191071-12.383205
10.482412-290.958537337.37745537.587122-60.158256-26.750044134.964477-16.954081-389.42341488.164178-13.219329478.398404258.967059-213.50541231.253747-58.43598378.76123053.677759-22.184893-12.376191
10.714996-290.986528337.47064837.642077-60.243522-26.800522134.987027-16.900054-389.76013587.864551-13.416889478.881540259.321007-213.58486931.252760-58.43145478.76123553.689152-22.179964-12.370587
\n", + "

100 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " AtBat Hits HmRun Runs RBI \\\n", + "negative log(lambda) \n", + "-12.310855 0.000800 0.000889 0.000695 0.000851 0.000911 \n", + "-12.078271 0.001010 0.001122 0.000878 0.001074 0.001150 \n", + "-11.845686 0.001274 0.001416 0.001107 0.001355 0.001451 \n", + "-11.613102 0.001608 0.001787 0.001397 0.001710 0.001831 \n", + "-11.380518 0.002029 0.002255 0.001763 0.002158 0.002310 \n", + "... ... ... ... ... ... \n", + " 9.784658 -290.823989 336.929968 37.322686 -59.748520 -26.507086 \n", + " 10.017243 -290.879272 337.113713 37.431373 -59.916820 -26.606957 \n", + " 10.249827 -290.923382 337.260446 37.518064 -60.051166 -26.686604 \n", + " 10.482412 -290.958537 337.377455 37.587122 -60.158256 -26.750044 \n", + " 10.714996 -290.986528 337.470648 37.642077 -60.243522 -26.800522 \n", + "\n", + " Walks Years CAtBat CHits CHmRun \\\n", + "negative log(lambda) \n", + "-12.310855 0.000900 0.000812 0.001067 0.001113 0.001064 \n", + "-12.078271 0.001135 0.001025 0.001346 0.001404 0.001343 \n", + "-11.845686 0.001433 0.001293 0.001698 0.001772 0.001694 \n", + "-11.613102 0.001808 0.001632 0.002143 0.002236 0.002138 \n", + "-11.380518 0.002281 0.002059 0.002704 0.002821 0.002698 \n", + "... ... ... ... ... ... \n", + " 9.784658 134.855915 -17.216195 -387.775826 89.573601 -12.273926 \n", + " 10.017243 134.900549 -17.108041 -388.458404 89.000707 -12.661459 \n", + " 10.249827 134.936136 -17.022194 -388.997470 88.537380 -12.971603 \n", + " 10.482412 134.964477 -16.954081 -389.423414 88.164178 -13.219329 \n", + " 10.714996 134.987027 -16.900054 -389.760135 87.864551 -13.416889 \n", + "\n", + " CRuns CRBI CWalks League[N] \\\n", + "negative log(lambda) \n", + "-12.310855 0.001141 0.001149 0.000993 -0.000029 \n", + "-12.078271 0.001439 0.001450 0.001253 -0.000037 \n", + "-11.845686 0.001816 0.001830 0.001581 -0.000046 \n", + "-11.613102 0.002292 0.002309 0.001995 -0.000058 \n", + "-11.380518 0.002892 0.002914 0.002517 -0.000073 \n", + "... ... ... ... ... \n", + " 9.784658 476.079273 257.271255 -213.124780 31.258215 \n", + " 10.017243 477.031349 257.966790 -213.280891 31.256434 \n", + " 10.249827 477.791860 258.523025 -213.405740 31.254958 \n", + " 10.482412 478.398404 258.967059 -213.505412 31.253747 \n", + " 10.714996 478.881540 259.321007 -213.584869 31.252760 \n", + "\n", + " Division[W] PutOuts Assists Errors \\\n", + "negative log(lambda) \n", + "-12.310855 -0.000390 0.000609 0.000052 -0.000011 \n", + "-12.078271 -0.000492 0.000769 0.000065 -0.000014 \n", + "-11.845686 -0.000621 0.000970 0.000082 -0.000017 \n", + "-11.613102 -0.000784 0.001224 0.000104 -0.000022 \n", + "-11.380518 -0.000990 0.001544 0.000131 -0.000028 \n", + "... ... ... ... ... \n", + " 9.784658 -58.457857 78.761266 53.622113 -22.208456 \n", + " 10.017243 -58.448850 78.761240 53.645147 -22.198802 \n", + " 10.249827 -58.441682 78.761230 53.663357 -22.191071 \n", + " 10.482412 -58.435983 78.761230 53.677759 -22.184893 \n", + " 10.714996 -58.431454 78.761235 53.689152 -22.179964 \n", + "\n", + " NewLeague[N] \n", + "negative log(lambda) \n", + "-12.310855 -0.000006 \n", + "-12.078271 -0.000007 \n", + "-11.845686 -0.000009 \n", + "-11.613102 -0.000012 \n", + "-11.380518 -0.000015 \n", + "... ... \n", + " 9.784658 -12.402891 \n", + " 10.017243 -12.391969 \n", + " 10.249827 -12.383205 \n", + " 10.482412 -12.376191 \n", + " 10.714996 -12.370587 \n", + "\n", + "[100 rows x 19 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "soln_path = pd.DataFrame(soln_array.T,\n", " columns=D.columns,\n", @@ -702,12 +1859,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "40b67c85", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.438537Z", + "iopub.status.busy": "2023-07-25T23:59:35.438413Z", + "iopub.status.idle": "2023-07-25T23:59:35.665568Z", + "shell.execute_reply": "2023-07-25T23:59:35.665151Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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Pipeline(steps=[('scaler', StandardScaler()),\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n", "scaler = StandardScaler(with_mean=True, with_std=True)\n", @@ -814,12 +2093,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "a89989c3", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.695813Z", + "iopub.status.busy": "2023-07-25T23:59:35.695707Z", + "iopub.status.idle": "2023-07-25T23:59:35.698261Z", + "shell.execute_reply": "2023-07-25T23:59:35.698013Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "160.4237101772591" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.linalg.norm(ridge.coef_)\n" ] @@ -844,10 +2140,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "3eb41945", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.699892Z", + "iopub.status.busy": "2023-07-25T23:59:35.699805Z", + "iopub.status.idle": "2023-07-25T23:59:35.707120Z", + "shell.execute_reply": "2023-07-25T23:59:35.706784Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([134214.00419204])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", " test_size=0.5,\n", @@ -876,12 +2198,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "635bc363", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.708905Z", + "iopub.status.busy": "2023-07-25T23:59:35.708772Z", + "iopub.status.idle": "2023-07-25T23:59:35.716331Z", + "shell.execute_reply": "2023-07-25T23:59:35.716004Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([231788.32155285])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ridge.alpha = 1e10\n", "results = skm.cross_validate(ridge,\n", @@ -908,10 +2255,242 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "e117267f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:35.718146Z", + "iopub.status.busy": "2023-07-25T23:59:35.718021Z", + "iopub.status.idle": "2023-07-25T23:59:36.172012Z", + "shell.execute_reply": "2023-07-25T23:59:36.171718Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "param_grid = {'ridge__alpha': lambdas}\n", "grid = skm.GridSearchCV(pipe,\n", @@ -933,12 +2512,1043 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "0037e9e4", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:36.173832Z", + "iopub.status.busy": "2023-07-25T23:59:36.173705Z", + "iopub.status.idle": "2023-07-25T23:59:39.246833Z", + "shell.execute_reply": "2023-07-25T23:59:39.246526Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "grid = skm.GridSearchCV(pipe, \n", " param_grid,\n", @@ -960,10 +3570,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "71f0f5c5", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:39.248632Z", + "iopub.status.busy": "2023-07-25T23:59:39.248534Z", + "iopub.status.idle": "2023-07-25T23:59:39.354727Z", + "shell.execute_reply": "2023-07-25T23:59:39.354370Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ridge_fig, ax = subplots(figsize=(8,8))\n", "ax.errorbar(-np.log(lambdas),\n", @@ -986,10 +3614,1079 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "fb265379", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:39.356640Z", + "iopub.status.busy": "2023-07-25T23:59:39.356501Z", + "iopub.status.idle": "2023-07-25T23:59:42.482886Z", + "shell.execute_reply": "2023-07-25T23:59:42.482602Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/html": [ + "
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
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+       "                                        ElasticNet(alpha=10000000000.0,\n",
+       "                                                   l1_ratio=0))]),\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", + " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('ridge',\n", + " ElasticNet(alpha=10000000000.0,\n", + " l1_ratio=0))]),\n", + " param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n", + " 8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n", + " 3.45506012e+04, 2.73807606...\n", + " 4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n", + " 1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n", + " 7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n", + " 2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n", + " 1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n", + " 4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "grid_r2 = skm.GridSearchCV(pipe, \n", " param_grid,\n", @@ -1007,12 +4704,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "79616f48", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:42.484749Z", + "iopub.status.busy": "2023-07-25T23:59:42.484621Z", + "iopub.status.idle": "2023-07-25T23:59:42.586564Z", + "shell.execute_reply": "2023-07-25T23:59:42.586230Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "r2_fig, ax = subplots(figsize=(8,8))\n", "ax.errorbar(-np.log(lambdas),\n", @@ -1039,10 +4753,2089 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "11e55883", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:42.588593Z", + "iopub.status.busy": "2023-07-25T23:59:42.588455Z", + "iopub.status.idle": "2023-07-25T23:59:42.930873Z", + "shell.execute_reply": "2023-07-25T23:59:42.930547Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795326.355502333, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795268.885511458, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795196.367825005, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795104.862821113, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794989.399687696, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794843.706650957, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794659.87071198, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794427.908521358, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794135.22526347, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793765.932449568, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793299.98803079, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18792712.112872534, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791970.425932087, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791034.72591697, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18789854.32913581, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18788365.350956466, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18786487.290938053, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18784118.748442672, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18781132.05553399, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18777366.566605024, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18772620.289297033, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18766639.479676694, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18759105.758860495, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18749620.243803147, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18737684.132153213, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18722675.157982755, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18703819.37168406, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18680157.84067929, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18650508.189617783, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18613421.503628485, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18567136.14871325, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18509531.699850053, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18438088.608600505, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18349862.649110064, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18241487.557216965, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18109224.25083878, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17949079.523028806, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17757018.994714484, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17529294.98190815, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17262895.457700975, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16956091.882983487, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16609021.736273043, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16224194.650997939, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363884, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15364525.127389485, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14907268.75187378, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14446023.624531085, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13991857.160644894, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13554773.727504015, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13142847.182203237, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12761747.456957739, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12414679.232309299, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12102642.724649917, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11824874.692517474, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11579334.50630629, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11363143.416383019, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11172936.696242273, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.92643167, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10856105.032984463, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10722381.625233045, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10600721.735570516, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10488247.552619573, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10382531.68105097, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078632, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10184320.545404715, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089716.55059902, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9997617.850835908, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9908230.155360885, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9822083.085401118, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739888.930170696, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9662401.666184625, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9590296.226307327, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9524082.854699288, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9464062.902306747, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9410323.196208755, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991764, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9321112.753117379, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285016.290065145, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9254029.627395952, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9227672.214767914, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9205447.27460862, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9186860.578098293, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133288, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9158722.527650403, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396464, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9139831.50202173, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055235, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9127409.145408802, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9122944.972944392, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9119363.705526328, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9116497.490587894, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834428, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.008592516, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9110927.575648237, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829819, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108850.148759764, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108119.08491204, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107538.538969103, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962065, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9106712.046135923, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005651.632865302, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005578.608102243, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005486.463074774, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005370.192059726, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005223.47917251, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005038.355660334, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004804.76767336, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004510.03120046, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004138.144828446, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003668.923421204, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003076.906345215, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21002329.98203154, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21001387.655909717, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21000198.8704182, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20998699.26312138, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20996807.72107362, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20994422.05552329, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20991413.57989597, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20987620.324921425, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20982838.567338496, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20976812.283196613, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20969220.065253027, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959658.970863715, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20947624.701018073, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20932487.468798272, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20913462.923603535, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20889577.599545892, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20859628.61984418, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20822137.913488373, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20775302.126054227, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20716940.917180095, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20644448.64953633, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20554757.795455974, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20444326.815649558, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20309170.5956441, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20144956.94257016, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19947196.308887925, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19711550.604615457, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19434276.168588594, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19112791.023677077, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18746315.49762964, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18336483.416578818, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17887774.82963546, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17407607.14883928, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16905965.499829993, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16394560.80209675, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15885645.94315279, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15390736.734407002, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14919517.25785277, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14479140.715843389, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14074002.01810337, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13705921.512677444, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13374594.126102015, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13078142.079861483, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12813645.639316088, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12577583.791150972, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.387483226, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175587.27845306, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12002182.958268248, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11842589.470659975, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11693840.031875866, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.60800361, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11419454.0438313, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11290441.388440857, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11165501.742342338, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11044168.420816425, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926319.289729377, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10812069.210340973, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10701669.403435929, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595426.714498514, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10493648.013477515, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10396608.203056702, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10304536.713966034, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012153, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876721, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059761.060749074, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9989004.697692012, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593688, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9863986.795334544, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9809627.884194935, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9760531.052715844, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9716491.487344079, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9677258.06531545, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642549.951165989, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835175, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134583, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908714, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681038, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9526251.156759001, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9512226.472533092, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319627, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9490849.431706948, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9482895.334901826, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9476399.71781569, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471123.439398324, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9466857.004635958, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9463420.20844639, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9460660.409301298, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484353, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9456683.22035802, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331946.25629055, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331864.018678214, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331760.248581372, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331629.308755428, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331464.086506005, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331255.607747704, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330992.550247405, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330660.62979839, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330241.82628314, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329713.40806704, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329046.702501133, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22328205.546983715, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22327144.338416774, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22325805.578253012, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22324116.784799173, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22321986.613041975, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22319299.9839329, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22315911.97874348, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22311640.198869713, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22306255.226839963, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22299468.750693016, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22290918.833475478, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22280151.72747749, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22266599.559077755, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22249553.162800502, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22228129.35292585, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22201232.036903117, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22167506.872833706, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22125289.76574775, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22072550.542125095, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22006834.845984127, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21925209.906269174, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21824223.56629905, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21699890.94922881, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21547729.124614064, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21362866.213577304, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21140255.446179498, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20875023.13975618, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562967.32341789, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201195.56502676, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19788844.32939185, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19327763.89751004, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18823001.04313301, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18282896.08461045, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17718660.4886989, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17143422.40324079, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16570887.230051238, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16013892.090309372, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15483171.861886727, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14986579.129588084, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14528848.289413737, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.239774454, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13735069.935277399, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13396407.639332836, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13092660.916831579, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12820093.900344713, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12574781.90922219, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.175078167, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12150651.369793259, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11964850.854771722, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11792543.263225015, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11631302.416094316, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11479227.7561279, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334963.041737791, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11197685.003164051, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11067056.224580359, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10943139.511030385, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10826278.220752902, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10716956.341549171, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10615659.18706467, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10522756.819315987, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10438426.844454892, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10362623.27115231, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10295087.38179537, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414705, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10182978.74114095, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10137247.95260475, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10097567.748922419, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10063321.789749103, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10033922.656392608, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10008819.486834103, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9987500.645290056, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9969494.453323793, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9954369.32479691, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9941733.515465437, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9931234.335989065, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9922556.777457738, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9915421.679110043, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9909583.627876062, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904828.718921196, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900972.216495434, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9897856.106707212, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9895346.540855931, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755055, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948297, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9890425.865192825, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9889395.604661888, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116646, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.674489552, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887400.660169175, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225193.80408011, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225110.813517075, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225006.093373984, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224873.954836704, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224707.22016197, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224496.83322094, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224231.36831536, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223896.410779048, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223473.77603032, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222940.525154293, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222267.72434169, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22221418.88207672, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22220347.981225494, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22218997.002387535, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22217292.809172478, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22215143.234477364, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22212432.168317866, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22209013.40126823, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22204702.922219783, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22199269.304569546, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22192421.741654057, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22183795.21258787, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22172932.17909693, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159260.14304964, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22142064.35203175, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22120454.95809202, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22093328.06334204, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22059320.403233726, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016758.03845356, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21963600.508906867, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21897383.65478575, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21815166.968429696, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21713495.123522323, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21588388.30984886, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21435381.888817236, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21249641.65996918, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21026184.505123127, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20760231.655652393, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20447708.31167379, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.018901825, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19674021.850113407, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19214128.3053442, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18711289.424232315, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18173771.014405873, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17612557.62934444, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17040407.03219554, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16470567.131662391, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15915425.819018744, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15385384.27148134, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14888160.528800251, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14428585.410549453, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14008814.608291918, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13628800.43657365, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13286857.361730725, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12980197.224087035, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.410345197, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12458600.903357476, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.77957509, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12033913.49389702, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11848733.596731743, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11677333.403468838, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11516981.070353946, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11365424.704670552, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073826, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11082243.920528807, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10948669.457422748, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819942.016223667, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10696213.317397818, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10577957.546131799, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929628, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10360715.842557454, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10263264.873279892, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10174122.558233691, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10093678.084935276, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10022055.90958463, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252601, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904471.381944738, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9857566.988895562, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9817713.479318874, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9784158.875562938, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9756135.4293958, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9732897.547924208, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.933154197, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9698053.28484727, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9685251.610393653, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9674853.346299471, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9666438.328081315, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9659650.291029936, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654190.159247063, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649808.977198932, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646301.012972398, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643497.331329834, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9641259.984843817, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9639476.879484767, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691978, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636928.172691077, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636030.684258943, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635317.743812287, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914779, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158696, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182535.705905367, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182443.31748153, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182326.738805104, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182179.636849403, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181994.021044992, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181759.809716668, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181464.28327085, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181091.39464482, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180620.89990636, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180027.262331244, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22179278.27131426, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22178333.30250969, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22177141.126954924, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22175637.153777506, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22173739.962460298, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22171346.945451487, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22168328.83898362, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22164522.86814139, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159724.170510717, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22153675.090679448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22146051.856030278, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22136448.05522982, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22124354.250566233, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22109132.97552027, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22089988.320511386, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22065929.327634364, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22035726.555516478, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21997861.52451259, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21950469.43740475, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21891276.769023754, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21817537.260214396, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21725972.80421009, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21612729.92224466, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21473368.08108258, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21302902.69437747, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21095932.158423126, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20846882.286273133, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20550398.911674757, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201904.639180813, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19798303.254432607, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19338763.60317261, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18825451.629099563, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18264026.303933263, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17663705.112665202, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17036766.85903686, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16397496.223623449, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15760744.92149185, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140415.226936534, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548197.66197113, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13992801.316187331, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13479749.374918321, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13011650.716625076, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12588761.536151327, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12209637.009462353, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11871722.016013274, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11571805.12738007, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11306328.206388844, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11071585.798533637, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10863860.419768564, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10679529.96999051, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10515164.967978276, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10367617.395431116, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10234094.932508666, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112213.557229094, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10000024.23575536, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9896012.57105465, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9799072.614562154, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9708457.890308099, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9623714.619163413, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9544604.25348744, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471024.212762302, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9402936.228999598, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9340310.144654753, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9283087.29859646, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9231162.854348717, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9184382.359520858, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9142546.024753645, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890352, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093456, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9044152.703403218, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019396.685310023, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8998109.575324507, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8979944.333787149, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8964556.387394711, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.3695335, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8940797.002727017, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8931817.822045382, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8924407.976697778, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8918327.548498938, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8913363.779400796, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909330.473245148, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906066.743651448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8903435.248435475, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8901320.0564094, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8899624.301448012, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.772619218, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8897184.565959448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152896, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895633.08348321, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895085.869018715, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('ridge',\n", + " ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n", + " 8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n", + " 3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n", + " 1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n", + " 5.37495461e+03, 4.25955961e+03,...\n", + " 1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n", + " 7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n", + " 2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n", + " 1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n", + " 4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n", + " cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", + " l1_ratio=0))])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n", " l1_ratio=0,\n", @@ -1063,10 +6856,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "d107f961", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:42.932916Z", + "iopub.status.busy": "2023-07-25T23:59:42.932770Z", + "iopub.status.idle": "2023-07-25T23:59:43.043431Z", + "shell.execute_reply": "2023-07-25T23:59:43.043097Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tuned_ridge = pipeCV.named_steps['ridge']\n", "ridgeCV_fig, ax = subplots(figsize=(8,8))\n", @@ -1092,10 +6903,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "21d65d1f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.045221Z", + "iopub.status.busy": "2023-07-25T23:59:43.045098Z", + "iopub.status.idle": "2023-07-25T23:59:43.047578Z", + "shell.execute_reply": "2023-07-25T23:59:43.047310Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "115526.70630987917" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.min(tuned_ridge.mse_path_.mean(1))\n" ] @@ -1113,12 +6942,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "11373de9", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.049360Z", + "iopub.status.busy": "2023-07-25T23:59:43.049242Z", + "iopub.status.idle": "2023-07-25T23:59:43.051656Z", + "shell.execute_reply": "2023-07-25T23:59:43.051383Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-222.80877051, 238.77246614, 3.21103754, -2.93050845,\n", + " 3.64888723, 108.90953869, -50.81896152, -105.15731984,\n", + " 122.00714801, 57.1859509 , 210.35170348, 118.05683748,\n", + " -150.21959435, 30.36634231, -61.62459095, 77.73832472,\n", + " 40.07350744, -25.02151514, -13.68429544])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tuned_ridge.coef_\n" ] @@ -1157,9 +7007,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "72181964", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.053342Z", + "iopub.status.busy": "2023-07-25T23:59:43.053221Z", + "iopub.status.idle": "2023-07-25T23:59:43.055713Z", + "shell.execute_reply": "2023-07-25T23:59:43.055414Z" + } + }, "outputs": [], "source": [ "outer_valid = skm.ShuffleSplit(n_splits=1, \n", @@ -1177,12 +7034,2037 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "4c3054b5", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.057432Z", + "iopub.status.busy": "2023-07-25T23:59:43.057306Z", + "iopub.status.idle": "2023-07-25T23:59:43.365804Z", + "shell.execute_reply": "2023-07-25T23:59:43.365508Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.893047336, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721532, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898538, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.16890147, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324104, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170538, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507718, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836109, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040235, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.727331886, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.28977405, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442707, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647634, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416634, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094172, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783943, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788342, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472542, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502639, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835295, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395373, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356627, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495732, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501963, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.320705947, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581451, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469924, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.97009871, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491088, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622943, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.961932577, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781398, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964379, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216344, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326858, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333132, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243072, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988702, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148528, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599191, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660212, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.23862632, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.88143778, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592549, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.88091306, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576018, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592053, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559342, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773343, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569963, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.312997252, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.88124088, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431445, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573516, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192497, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409387, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950285, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093235, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631114, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589174, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.107432563, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020331, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113679, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072118, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745581, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013418, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.8293056125, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206969, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.645216511, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947152, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247044, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469164, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564693, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.1469736025, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.63533624, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616933, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404535, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.48376607, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.76970095, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858365, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.911777701, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.571027264, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528801, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.79244657, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319955, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039895, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.05095333, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534292, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908728, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345776, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998693, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629447, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630652, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791553852, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512218, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783063, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.892213011, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162482, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967933, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.82487654, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.33151807, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703294, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.51311625, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649913, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059805, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268812, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.193566969, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.866617758, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748763, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.00720005, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.090500388, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.71354342, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771877, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.213598879, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179863, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224856, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.11903821, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821936, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191706, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135195, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.6946986, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.628850497, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.241777299, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980713, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.850599289, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.41286841, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066573, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110113, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.208955988, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870235, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844737, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850953, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151276, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.41690654, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015966, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016687, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940995, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934752, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094303, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.205683708, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702371, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.573228309, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217315, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401602, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.578529166, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698365, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.230672905, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233683, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070565, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535858, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.19434187, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689679, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287674, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.280234471, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660187, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690647, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.1056430675, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.35524954, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.53846852, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275469, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.535335509, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.802084766, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684168, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666606, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521793, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162683, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117484922, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.041596897, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674365, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695727, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456673, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.012726546, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405474, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238734, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036072, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610088, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073966, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.4663270125, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661856, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.977539104, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.2920428645, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171555, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603645, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569127, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.97498893, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.207170451, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867455, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.853912757, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852352, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345377, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501478, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.94700243, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652601, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999971, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776321, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791689, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780785, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.242584623, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469946, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.14527111, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.128467944, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.892430544, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207122, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140417, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968959, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532348, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307216, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220387, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893765, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962447, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599917, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.060097354, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588472, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383827, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.042096594, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194367, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.47325135, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912459, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906962, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558565, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.990428165, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220897, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036042, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371142, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116722, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098895, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258615, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385439, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455775, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.07465526, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592412, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.27386727, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.36384661, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940228, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143818, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873377, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990832, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361462, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.79937819, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668119, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.36369123, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169304, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702794, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200799, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.2675956, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.64783793, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.819731826, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.688773429, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.61072085, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433397, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351493, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835387, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905487, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109628, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262948, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199782, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107317, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355204, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804332, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980087, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847987, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.81877588, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.491724883, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.53569288, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006125, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576015, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131106991, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931618, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364969, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578147, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267373, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337014, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.53934286, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.0266303, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473388, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602925, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.407961924, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412938, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325930048, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782039, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061372, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284237, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612171, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205515, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.584421616, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672326, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096559, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096591, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784204, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014641162, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080780019, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668608, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092072, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.805305656, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.844425442, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219734, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.655993313, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938994, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082258, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.13980752, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748439, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.08077331, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848666, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612368, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286435, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.12581003, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.894413952, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859002, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.98172164, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.01686267, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.406945651, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966294, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.31968939, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396805, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.257695232, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583746, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.49455343, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.749109622, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.02436845, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.03569032, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086988, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015218, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622873, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156208, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.48980598, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488008, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455977, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389356, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404455, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886174, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820404, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.752768412, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278118, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379244, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.98420337, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464427, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969216, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900381, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.138994666, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742979, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.239570614, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329573, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594905, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591967, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.99395707, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983279, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.9445123505, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915861, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.8174845725, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.0523840925, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150196, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.436922483, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368484, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584399, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.9732326735, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369866, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.567763316, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.2241215855, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985473, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757254, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128507, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388530005, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167879, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628094, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966857, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857503, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839442, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273544, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385814, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983606, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225937976, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329608, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.873515937, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701696, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567688, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.7780512385, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079299, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.176477653, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794392, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473216, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.3336993465, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.074242126, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697735, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028902, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897053, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972435, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142942, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.8736711275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658319, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447501, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.200043006, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596577, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871313, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543608, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935372, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587788, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986472, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.55809336, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.663752725, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.060052717, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.779778486, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.168518286, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996723, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918179, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036688, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.47434148, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294181, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470121, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017829, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.46580684, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.35332688, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018394, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450969, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.476241706, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872495, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394275, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596483, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.11469827, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060235, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340755, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206709, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.420748936, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182043, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.908593165, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.453317674, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.177746587, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.082835246, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678281, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817537, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286602, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.869677957, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.659031235, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043081, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912804, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176824, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656613, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610261, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946363, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.25861056, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.101396974, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097185, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072414, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051512, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382325, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.9531656, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803495, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990706, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955583, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532658, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.8422521455, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908956, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898104, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651163, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854576, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079197, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818847, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832884, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831903, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035702, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235791, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998971, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001433, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484867, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.93844572, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.96370539, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.339338534, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895556, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319144, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.533593078, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.495343714, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579567, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622955, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.7733192, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915036, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645212, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.662147184, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.581406483, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.34181063, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.2530382, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.7760819215, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037627, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491712, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.90595999, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146216, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.23751574, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.038791819, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992944, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([132393.84003227])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results = skm.cross_validate(pipeCV, \n", " X,\n", @@ -1218,12 +9100,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "72bb0c1d", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.368185Z", + "iopub.status.busy": "2023-07-25T23:59:43.368043Z", + "iopub.status.idle": "2023-07-25T23:59:43.419802Z", + "shell.execute_reply": "2023-07-25T23:59:43.419498Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.1472370031649866" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lassoCV = skl.ElasticNetCV(n_alphas=100, \n", " l1_ratio=1,\n", @@ -1237,9 +9136,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "69adc7ac", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.421483Z", + "iopub.status.busy": "2023-07-25T23:59:43.421361Z", + "iopub.status.idle": "2023-07-25T23:59:43.431280Z", + "shell.execute_reply": "2023-07-25T23:59:43.431010Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1265,12 +9170,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "2aa75789", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.432981Z", + "iopub.status.busy": "2023-07-25T23:59:43.432885Z", + "iopub.status.idle": "2023-07-25T23:59:43.617766Z", + "shell.execute_reply": "2023-07-25T23:59:43.617396Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "path_fig, ax = subplots(figsize=(8,8))\n", "soln_path.plot(ax=ax, legend=False)\n", @@ -1291,10 +9213,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "e38c9bed", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.619681Z", + "iopub.status.busy": "2023-07-25T23:59:43.619553Z", + "iopub.status.idle": "2023-07-25T23:59:43.621973Z", + "shell.execute_reply": "2023-07-25T23:59:43.621711Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "114690.73118253548" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.min(tuned_lasso.mse_path_.mean(1))\n" ] @@ -1309,10 +9249,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "53c42724", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.623732Z", + "iopub.status.busy": "2023-07-25T23:59:43.623610Z", + "iopub.status.idle": "2023-07-25T23:59:43.735692Z", + "shell.execute_reply": "2023-07-25T23:59:43.735334Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+fLi2bt2qDRs2SJKuvvpqmzYHDx5UZGSkvL29NXPmTI0ZM0aGYejqq6/Wa6+9poceeshsGxUVpcWLF2vMmDGaPn26GjRooHfffVexsbFmm3vvvVcnTpzQhAkTlJKSomuvvVZLly61eQjz9ddfl4eHh/r166ecnBzFxsbqzTffrOi3BQAAAC6uSszzXdU5cp5ver4BwPVYrVbVqFFDkpSRkaHAwEAnVwTgz6rVPN8AAACAOyB8AwAAAA7CmAIAAFxcYGCgGCUKVA30fAMAAAAOQvgGAAAAHITwDQCAi8vOzlb//v3Vv39/ZWdnO7scAHYgfAMA4OIKCgr0+eef6/PPP2d5ecDNEb4BAAAAByF8AwAAAA5C+Ea5ZObmK/KpxYp8arEyc/OdXQ4AAIBbInwDAAAADkL4BgAAAByE8A0AAAA4CMvLAwDg4gICApSRkWFuA3BfhG8AAFycxWJRYGCgs8sAUAEYdgIAAAA4COEbAAAXl5OTo+HDh2v48OHKyclxdjkA7ED4BgDAxeXn52vOnDmaM2eO8vNZawFwZ4RvAAAAwEEI3wAAAICDEL5x2VhyHgAA4NIQvgEAAAAHIXwDAAAADkL4BgAAAByEFS4BAHBxAQEBOn78uLkNwH0RvgEAcHEWi0V169Z1dhkAKgDDTgAAAAAHIXwDAODicnJyFB8fr/j4eJaXB9wc4RsAABeXn5+vN998U2+++SbLywNujvANAAAAOAjhGwAAAHAQwjcAAADgIIRvAAAAwEEI3wAAAICDEL4BAAAAB2GFSwAAXJy/v78OHjxobgNwX4RvAABcnIeHhyIjI51dBoAKwLATAAAAwEEI3wAAuLjc3FyNGzdO48aNU25urrPLAWAHwjcqXGZuviKfWqzIpxYrM5dlkAHAXnl5eXrllVf0yiuvKC8vz9nlALAD4RsAAABwEMI3AAAA4CCEbwAAAMBBCN8AAACAgxC+AQAAAAchfAMAAAAOwgqXAAC4OH9/f23fvt3cBuC+CN8AALg4Dw8PtWzZ0tllAKgADDsBAAAAHISebwAAXFxubq5eeuklSdIzzzwjHx8fJ1cE4HIRvgEAcHF5eXl64YUXJEnjxo0jfANujGEnAAAAgIMQvgEAAAAHIXwDAAAADkL4BgAAAByE8A0AAAA4COEbAAAAcBCmGgQAwMX5+flp48aN5jYA90X4BgDAxXl6eqpTp07OLgNABWDYCQAAAOAg9HzDYTJz89ViwjJJ0s5JsQrw4csPAMojNzdX06dPlyQ98cQTrHAJuDHSDwAALi4vL09PPvmkJOmxxx4jfANujGEnAAAAgIMQvgEAAAAHIXwDAAAADkL4BgAAABzELcP35MmT1alTJwUFBSksLEx9+/bVnj17bNpkZ2crPj5etWvXVo0aNdSvXz8dO3bMps2hQ4cUFxengIAAhYWFady4ccrPz7dps3r1arVv316+vr66+uqrNXv27FL1zJw5U5GRkfLz81OXLl3MhRAupRYAAABUfW4ZvtesWaP4+HitX79eCQkJysvLU69evWS1Ws02Y8aM0cKFCzV//nytWbNGR44c0d13320eLygoUFxcnHJzc7Vu3TrNmTNHs2fP1oQJE8w2Bw8eVFxcnHr27KmkpCSNHj1aDz74oJYtW2a2+fTTTzV27FhNnDhRW7duVdu2bRUbG6vjx4+XuxZXkpNX4OwSAAAAqi6jCjh+/LghyVizZo1hGIaRmppqeHt7G/Pnzzfb7Nq1y5BkJCYmGoZhGEuWLDE8PDyMlJQUs81bb71lBAcHGzk5OYZhGMaTTz5ptGzZ0uZe9957rxEbG2t+3LlzZyM+Pt78uKCgwIiIiDAmT55c7louJi0tzZBkpKWllau9PX4/YzUaj19kNB6/yNjy6ylzvzUnz9xvzck7777L2Q8AuLD8/Hzju+++M7777jsjPz/f2eUAKEN585pb9nz/WVpamiQpNDRUkrRlyxbl5eUpJibGbNOsWTM1atRIiYmJkqTExES1bt1a9erVM9vExsYqPT1dO3bsMNuUvEZxm+Jr5ObmasuWLTZtPDw8FBMTY7YpTy1/lpOTo/T0dJuXo9QMODd37Nc/HnHYfQEA5+fp6akePXqoR48e8vT0dHY5AOzg9uG7sLBQo0eP1vXXX69WrVpJklJSUuTj46OaNWvatK1Xr55SUlLMNiWDd/Hx4mMXapOenq6srCydPHlSBQUFZbYpeY2L1fJnkydPVkhIiPlq2LBhOd+NirXwp6PKKyh0yr0BAACqIrcP3/Hx8dq+fbvmzZvn7FIqzNNPP620tDTzdfjwYafUcdqaqzV7Tjjl3gCAc/Ly8jRz5kzNnDlTeXl5zi4HgB3cOnyPGjVKixYt0nfffacGDRqY+8PDw5Wbm6vU1FSb9seOHVN4eLjZ5s8zjhR/fLE2wcHB8vf3V506deTp6Vlmm5LXuFgtf+br66vg4GCbl7N8sfU3p90bAFAkNzdXo0aN0qhRo5Sbm+vscgDYwS3Dt2EYGjVqlL766iutWrVKUVFRNsc7dOggb29vrVy50ty3Z88eHTp0SNHR0ZKk6Oho/fzzzzazkiQkJCg4OFgtWrQw25S8RnGb4mv4+PioQ4cONm0KCwu1cuVKs015anFlK3cd1xkr3+gBAAAqgpezC7gc8fHxmjt3rr7++msFBQWZY6dDQkLk7++vkJAQjRw5UmPHjlVoaKiCg4P1+OOPKzo6Wl27dpUk9erVSy1atNCQIUM0ZcoUpaSk6Nlnn1V8fLx8fX0lSY888ohmzJihJ598Ug888IBWrVqlzz77TIsXLzZrGTt2rIYNG6aOHTuqc+fOmjZtmqxWq0aMGGHWdLFaXFWz8CDtTjmrhT8d0T0dGlz8BAAAAFyQW4bvt956S5LUo0cPm/2zZs3S8OHDJUmvv/66PDw81K9fP+Xk5Cg2NlZvvvmm2dbT01OLFi3So48+qujoaAUGBmrYsGGaNGmS2SYqKkqLFy/WmDFjNH36dDVo0EDvvvuuYmNjzTb33nuvTpw4oQkTJiglJUXXXnutli5davMQ5sVqcVV9r43Qy0v36IstvxG+AQAAKoBbhm/DMC7axs/Pz3w45XwaN26sJUuWXPA6PXr00I8//njBNsXj8OypxRXFtamvV5bv1bbf0vTL8QxnlwMAAOD23HLMNxyjdg1f9WgaJkn6Oul3J1cDAADg/gjfuKB7OlwhSVq47aiTKwEAAHB/bjnsBI7Ts1mYagZ46/jZHGeXAgDVlq+vrxYtWmRuA3BfhG9ckK+Xp+5oG6EPEn91dikAUG15eXkpLi7O2WUAqAAMO8FFMdMJAABAxSB846JaXxGiq+oGVtr1M3PzFfnUYkU+tViZufmVdh8AcFd5eXmaPXu2Zs+ezfLygJsjfOOiLBaL+l57hbPLAIBqKzc3VyNGjNCIESNYXh5wc4RvlMsd10aY29t/T3NiJQAAAO6L8I1yqRt07un6jzcccmIlAAAA7qvc4fuNN97QG2+8oTNnzth1w8OHD+vuu+9Wv3797LoOnGfJz0d1gqkHAQAALlm5pxocPXq0LBaLYmJiVKtWrVLHd+zYodatW8vDw0P5+ed/aC49PV0LFiyQxWK5vIrhdHkFhj7ZeEgPdotydikAAABupcKHnRiGUdGXhAv6aP2vys0vdHYZAAAAboUx37hkdYN8dfxsjhJ2HnN2KQAAAG6FFS5xyQZ2aqh/r/pFH65n1UsAcARfX1999tln5jYA90X4xiXr37GB/rPmgH76jSkHAcARvLy81L9/f2eXAaACMOwEl6xODV/d3ra+s8sAAABwO4RvXJYR1zHTCQA4Sn5+vubPn6/58+dfcEYxAK6PYSe4LK0bhKhdw5r68XCqs0sBgCovJydHAwYMkCRlZGTIy4sf34C7oucbl+3+6MbmNtMOAgAAXNwl/+r89ddfa/PmzaX2//777+b2Bx98cN7zS7aDe4tpHmZuL92RooGdGjmxGgAAANd3yeH72WefPe+x4lUrR4wYcfkVwW14e577w8nCpCOEbwAAgIu4pPDN6pU4n/UHT+tURo5q12D+WQAAgPMpd/ieNWtWZdYBN1dQaOjb7Sm6v2vjizcGAACopsodvocNG1aZdaAKWLjtCOEbAADgApirCBVmY/JppaRlK9ifLysAqEg+Pj7mX6B9fHycXA0Ae5CSUCHaN6qprYdStfjnoxrUuaHd18vMzVeLCcskSTsnxSrAhy9VANWXt7e3hg8f7uwyAFSASk0027Zt0y+//CKLxaIrr7xS1157bWXeDk7Uu3W4th5K1cJtRyokfAMAAFRFlxS+9+7dK0mqWbOmwsLCzttu1apVeuyxx7Rv3z6b/Y0bN9brr7+uO++88zJKhSvr1SJck5fsVtLhVP12JtPZ5QBAlZKfn69ly4r+GhgbG8sKl4AbK/cKlz/99JOaNWum5s2ba+nSpedtt2zZMt16663at2+fDMOweSUnJ6tfv36aO3duhRQP11E3yFddr6wtSVq6PcXJ1QBA1ZKTk6Pbb79dt99+u3JycpxdDgA7lDt8L1++XJIUEhKiQYMGldkmMzNTDzzwgPLz82UYhkJDQzVkyBCNHz9eN998sySpsLBQo0aN0unTpyugfLiSPm0jJElLfiZ8AwAAlKXc4Xvjxo2yWCyKi4uTt7d3mW3mzp2ro0ePymKxqFWrVtq+fbvmzJmjyZMnKyEhQe+//74kKS0tTR9//HHFfAZwGbe2DJeXh0W7U846uxQAAACXVO7wvWvXLklS9+7dz9tm/vz55vYbb7yh8PBwm+PDhw9X7969ZRiG2ZOOqqNWoI+6Nanj7DIAAABcVrnD92+//SZJat68eZnHCwsLtW7dOlksFjVo0EA9evQos92AAQMkSdu3b7/EUuEOioeeAAAAoLRyh++MjAxJUnBwcJnHd+zYIavVKkm68cYbz3udZs2aSZJOnTpV7iLhPm5pUU8+XuX+sgIAAKhWyp2S/Pz8JElnz5Y9nnfDhg3mdocOHS56nezs7PLeGm4kyM9b3Rl6AgAAUKZyh+/69etLkpKSkso8/v3335vbXbt2Pe91zpw5I0mqUaNGeW8NN9O7dX1z2zAMJ1YCAFWDj4+PZsyYoRkzZrC8PODmyh2+O3bsKMMwNGvWrFLHrFarFi5cKEkKCgpSx44dz3udPXv2SJIaNGhwqbXCTfS4pq65vf1IuhMrAYCqwdvbW/Hx8YqPjz/vjGMA3EO5w3fx3N4//vijHnroIaWnF4Wq1NRUDR8+XKmpqbJYLLrnnnvk6el53uusXbtWktSyZUt76oYL8/c59/8/YccxJ1YCAADgWsodvvv06aPrr79ehmHo/fffV926dXXFFVeoTp06+vLLLyUV/WY+fvz4814jMzNTCxculMVi0fXXX29/9XB5y3ceY+gJANipoKBAq1ev1urVq1VQUODscgDY4ZKmpfjiiy/UqlUrGYahvLw8HT16VIWFhTIMQx4eHnrzzTfVpEmT854/Z84cc9aU2NhY+yqHWzh0OlN7jrHoDgDYIzs7Wz179lTPnj2ZsABwc16X0jgsLExbtmzRO++8o2+++UaHDh2Sj4+P2rdvr8cee0ydOnW64PkJCQnq0KGDGjRocMGQjqpl6fYUNQsve4pKAACA6uSSwrdk+9DHpSoenoLqZen2FI2OucbZZQAAADgdq6GgUnl5WLQ75awOnrQ6uxQAAACnI3yjUnWOCpVU1PtdETJz8xX51GJFPrVYmbn5FXJNAAAARyF8o1Ld0qKeJGnpjooJ3wAAAO6s3GO+J02aVOE3nzBhQoVfE67lpmZhmrRop7YdTtXRtCxnlwMAAOBU5Q7fzz//vCwWS4XenPBd9dUN8lWnxqHamHxaK3Ydd3Y5AOCWvL29NWXKFHMbgPu65NlOKmrBlIoO8nBdsa3CtTH5NKtdAsBl8vHx0bhx45xdBoAKcMnh29/fX3feeaeGDh2q5s2bV0ZNqGJubRWuFxft1JZDZ5xdCgAAgFOVO3zffPPN+u6775SVlaVPP/1Un376qTp06KAhQ4Zo4MCBqlu3bmXWCTd2RU1/tWkQop9+S3N2KQDglgoKCrR161ZJUvv27eXp6enkigBcrnLPdpKQkKBDhw7pX//6l7nE/ObNmzV69Gg1aNBAd9xxhz777DPl5ORUZr1wU7e2Cnd2CQDgtrKzs9W5c2d17tyZ5eUBN3dJUw1GRERo3Lhx2rZtm5KSkjRmzBjVq1dPeXl5WrRokQYNGqTw8HA9/PDDWrt2bWXVDDd0a0vCNwAAwGXP892mTRu9+uqr+u2337R06VLdd9998vf3V1pamt577z317NlTUVFRmjBhgvbu3VuRNcMNXVm3hpqE1XB2GQAAAE5l9yI7Hh4e6tWrlz766CMdO3ZMc+bMUc+ePWWxWPTrr7/qn//8p5o3b65u3bpVRL1wYzF/LLgDAABQXVXoCpeBgYEaMmSIVqxYoUOHDmnSpEny9fWVYRjasmVLRd4KbqhXifBtzWFpeAAAUP1c8lSD5ZGYmKgPP/yQBzBh45p654adrN17Qv06NHRiNQAAAI5XYeF7//79+uijj/TRRx/pwIEDkooW5PHz89Mdd9yhoUOHVtSt4KZKLqy0bMcxwjcAAKh27ArfZ86c0bx58/Thhx9qw4YNkooCt8ViUbdu3TRkyBD1799fwcHBFVIsqo61+04oMzdfAT6V8scXAKhSvL29NXHiRHMbgPu65OSTl5enhQsX6sMPP9S3336rvLw8c8n5a665RkOGDNGQIUPUqFGjCi8WVUd2XqFW7zmh21rXd3YpAODyfHx89Pzzzzu7DAAVoNzh+4cfftBHH32k+fPnKzU11QzctWvX1sCBAzV06FB16tSp0gpF1bPk56OEbwAAUK2UO3x3795dFotFhmHI19dXd9xxh4YMGaJbb71VXl4MHcClW7X7uLLzCpxdBgC4vMLCQu3atUuS1Lx5c3l4VOhkZQAc6JJTs7+/v2JjYxUYGKgvv/xSX3755WXd2GKx6L333rusc+H+6of46WhattbsPaFuTerYfb3M3Hy1mLBMkrRzUixjyQFUKVlZWWrVqpUkKSMjQ4GBgU6uCMDluuSEkp2dra+//rpCbk74rr56taynOet+1bc/H62Q8A0AAOAOLunvVoZhVOgLrifAx0vJL8cp+eW4Su09jm0RLklaseu4cvMLK+0+AAAArqTc6aqwkICEitOmQYjCg/2Ukp6tdftPOrscAAAAh+CJDTiFh4dFt7Yq6v1etuOYk6sBAABwDLcM32vXrlWfPn0UEREhi8WiBQsW2By3WCxlvqZOnWq2iYyMLHX85ZdftrnOTz/9pG7dusnPz08NGzbUlClTStUyf/58NWvWTH5+fmrdurWWLFlic9wwDE2YMEH169eXv7+/YmJitG/fvop7M9xY8TSDq3Yfd3IlAAAAjuGW4dtqtapt27aaOXNmmcePHj1q83r//fdlsVjUr18/m3aTJk2yaff444+bx9LT09WrVy81btxYW7Zs0dSpU/X888/rnXfeMdusW7dOgwYN0siRI/Xjjz+qb9++6tu3r7Zv3262mTJlit544w29/fbb2rBhgwIDAxUbG6vs7OwKflfcT4fGtVQ3yFdns/OdXQoAAIBDuOV8bL1791bv3r3Pezw8PNzm46+//lo9e/bUlVdeabM/KCioVNtiH3/8sXJzc/X+++/Lx8dHLVu2VFJSkl577TU9/PDDkqTp06fr1ltv1bhx4yRJL774ohISEjRjxgy9/fbbMgxD06ZN07PPPqs777xTkvTBBx+oXr16WrBggQYOHHjZ70FV4Olh0a0tw/Xh+l+dXQoAuDRvb2/9/e9/N7cBuC+37Pm+FMeOHdPixYs1cuTIUsdefvll1a5dW+3atdPUqVOVn3+uBzYxMVHdu3eXj4+PuS82NlZ79uzRmTNnzDYxMTE214yNjVViYqIk6eDBg0pJSbFpExISoi5duphtypKTk6P09HSbV1XVu3XZv/wAAM7x8fHR1KlTNXXqVJufSwDcj1v2fF+KOXPmKCgoSHfffbfN/r/+9a9q3769QkNDtW7dOj399NM6evSoXnvtNUlSSkqKoqKibM6pV6+eeaxWrVpKSUkx95Vsk5KSYrYreV5ZbcoyefJkvfDCC5fx2bqfLlG1FRroo9PWXGeXAgAAUOmqfPh+//33NXjwYPn5+dnsHzt2rLndpk0b+fj46C9/+YsmT54sX19fR5dp4+mnn7apLz09XQ0bNnRiRZXH08OimOZh+mzzb84uBQBcVmFhoQ4dOiRJatSoEcvLA26sSv/r/f7777Vnzx49+OCDF23bpUsX5efnKzk5WVLRuPFjx2ynwCv+uHic+PnalDxe8ryy2pTF19dXwcHBNq+qLLblufcir4D55AHgz7KyshQVFaWoqChlZWU5uxwAdqjS4fu9995Thw4d1LZt24u2TUpKkoeHh8LCwiRJ0dHRWrt2rfLy8sw2CQkJatq0qWrVqmW2Wblypc11EhISFB0dLUmKiopSeHi4TZv09HRt2LDBbAOpU2Qtc3v9gVNOrAQAAKByuWX4zsjIUFJSkpKSkiQVPdiYlJRk/klOKgq58+fPL7PXOzExUdOmTdO2bdt04MABffzxxxozZozuv/9+M1jfd9998vHx0ciRI7Vjxw59+umnmj59us1wkCeeeEJLly7Vq6++qt27d+v555/X5s2bNWrUKElF842PHj1a//jHP/TNN9/o559/1tChQxUREaG+fftW3hvkZrw8z30Zfrv9/GPhAQAA3J1bjvnevHmzevbsaX5cHIiHDRum2bNnS5LmzZsnwzA0aNCgUuf7+vpq3rx5ev7555WTk6OoqCiNGTPGJliHhIRo+fLlio+PV4cOHVSnTh1NmDDBnGZQkq677jrNnTtXzz77rJ555hk1adJECxYsUKtWrcw2Tz75pKxWqx5++GGlpqbqhhtu0NKlS0uNQUeRlbuOKye/wNllAAAAVAq3DN89evSQYRgXbPPwww/bBOWS2rdvr/Xr11/0Pm3atNH3339/wTb9+/dX//79z3vcYrFo0qRJmjRp0kXvB+lsdr7W7j2p66+ubfe1MnPz1WLCMknSzkmxCvBxyy93AABQhbjlsBNUbYt+OuLsEgAAACpFuboC/7wyZEWwWCzav39/hV8X7m/FzmOa2KeFs8sAAACocOUK38XT712MxWKRpFJDQsraX7wPKKl+iJ+OpmVr7d4Tzi4FAFyGl5eXHnvsMXMbgPsq17/gYcOGXfB4UlKStm3bJsMwVLNmTbVr185c1fHYsWNKSkrSmTNnZLFY1LZt23JN/Yfq6dZW4Zr1v2RmPQGAEnx9fTVz5kxnlwGgApQrfM+aNeu8x95//33NnTtXDRo00Kuvvqq77rqr1G/lBQUF+vLLLzVu3Djt3LlT8fHxGjlypH2Vw6ECfLyU/HJcpd+n9x/hew093wAAoAqy64HLzZs365FHHlGdOnW0fv169e/fv8w/h3l6eqp///5KTExUaGioHnvsMW3evNmeW6OKahkRrEahAcrOY6VLAChmGIZOnDihEydOXHS2LwCuza7w/frrr6ugoEDPPPOMIiIiLtq+fv36euaZZ5SXl6fXXnvNnlujirJYLLq9TX1nlwEALiUzM1NhYWEKCwtTZmams8sBYAe7wnfxHNhdunQp9zldu3aVJP3www/23BpV2O1tLv6LHAAAgDuyK3yfOFE0LjcnJ6fc5xS3LT4X+LPm9YMUVSfQ2WUAAABUOLvCd926dSVJ3377bbnPWbJkiSSpTp069twaVZjFYlHvVuHOLgMAAKDC2RW+b7rpJhmGoddee03/+9//Ltp+3bp1ev3112WxWHTzzTfbc2tUcb1bnwvfaVl5TqwEAACg4tgVvp966in5+voqJydHN998s0aPHq2kpCSbJ7ENw1BSUpLGjBmjm266SdnZ2fLx8dFTTz1ld/Gouq6qW8PcXraDOb8BAEDVYFf4btasmebMmSNPT0/l5ubq3//+tzp06KCAgABdccUVatCggQICAtShQwe98cYbys3NlZeXl2bNmqVmzZpV1OeAKu6bpCPOLgEAAKBC2L1G7YABAxQVFaXHHntMW7ZskVT0UOXRo0dLtW3fvr3efPNNde7c2d7bohrZeihVySetiqyghzAzc/PVYsIySdLOSbEK8GGpZgCuzcvLy1xtmuXlAfdWIf+CO3XqpE2bNmnz5s1asWKFfv75Z50+fVqSVKtWLbVu3VoxMTHq1KlTRdwO1dCXP/6usbdc4+wyAMApfH19NXv2bGeXAaACVOivzx07dlTHjh0r8pKAJOnLrb9p9M1NnF0GAACAXewa8w04QqCvp347k6WNyaedXQoAOIVhGLJarbJarSwvD7i5Cg/fv/32mzZv3qy1a9cqKyuroi+Paii2ZdG0g19u/c3JlQCAc2RmZqpGjRqqUaMGy8sDbq5CwvfZs2f13HPPqWHDhmrcuLG6dOminj176uDBgzbt5s2bpwEDBuihhx6qiNuimuh7bdFy80t+TlFWboGTqwEAALh8do/53rdvn2677TYdOHDA5k9hFoulVNuuXbvq/vvvl2EYGjZsmG644QZ7b49qoH2jWmoY6q/Dp7O0cvcxZ5cDAABw2ezq+c7OzlZcXJz279+vgIAAPfnkk1q0aNF520dGRqpnz56SpG+++caeW8MFBPh4KfnlOCW/HFep0/V5eFh0V7sGkqSvf2TObwAA4L7sSkxvvfWWfvnlFwUGBur777/Xtddee9FzevfurZUrVyoxMdGeW6Oa6df+Cr2xcp8SD5xydikAAACXza6e7y+//FIWi0VPPPFEuYK3JLVt21ZS0XAVoLwa1w5Ux8a1VMhD/gAAwI3ZFb537dolSerVq1e5z6ldu7YkKTU11Z5boxrq16GBs0sAAACwi13DTjIyMiRJNWrUKPc5OTk5kiRvb297bo1q6LbW9TXxmx3KzS90dikA4FCenp665557zG0A7suunu/iXuzk5ORyn7Njxw5JUnh4uD23RjUU4u+tm5uFObsMAHA4Pz8/zZ8/X/Pnz5efn5+zywFgB7vCd/v27SVJa9euLfc5H3zwgSwWi6Kjo+25NaqpO/6Y81tShfeAZ+bmK/KpxYp8arEyc/Mr9NoAAACSneH7nnvukWEYeuedd3To0KGLtp82bZoZ1AcNGmTPrVFNXX9VbXN7zd4TTqwEAADg0tkVvocMGaI2bdooOztbPXr00LfffltqoR3DMLRp0yYNHjxYf/vb32SxWNStWzf17t3b7uJR/Xh5nvuS/Wrr706sBAAcx2q1ymKxyGKxyGq1OrscAHaw64FLDw8PffPNN7rhhhuUnJys22+/XQEBAebqlj169NDZs2fNhywNw9BVV12lzz77zP7KUe19/8tJHT+brbAgxj8CAAD3YFfPtyQ1atRISUlJGjRokDw8PGS1WmUYhgzD0IkTJ5SdnW32hg8YMEAbN25UWBgPzcF+BYUGvd8AAMCtVMia4KGhofr444/10ksvafHixdq8ebOOHz+ugoIC1a5dW+3atVOfPn10zTXXVMTtANNnmw/r4e5XOrsMAACAcqmQ8F2scePGeuyxxyryksB5+Xl7aP8Jq348nKpm4UHOLgcAAOCi7Bp2cujQIR06dEgFBQXlPqewsNA8D7BHrxZFc8XP3/ybkysBAAAoH7vCd2RkpK688krt2bOn3OccPHjQPA9VU4CPl5JfjlPyy3EK8KnQP67YuKtd0Zzfi7YdUVZu+X8BBAAAcBa7k1HJqQUdcR5QrFNkqBrU8tdvZ7K0YtcxZ5cDAJXG09NTt912m7kNwH1VXrfkeRSHbg8PuydaQTXn4WHRPR0aaNqKffqSWU8AVGF+fn5avHixs8sAUAEcnoCPHj0qSQoK4gE52O+eDg1ksUgbDp52dikAAAAXVSHhu3hRnQvJy8vT7t279c9//lOS1LRp04q4Naq5BrUCdF2JJecrWmZuviKfWqzIpxYrMze/0u4DAACqh0sadlLWODPDMNSqVatLuqnFYtE999xzSecA59O/Q0P975dTzi4DACqN1Wo1F6g7fvy4AgMDnVwRgMt1SeH7fA9JXurDkwMGDNDo0aMv6RzgfGJbhquGr5cycuiZBlB1ZWZmOrsEABXgksL3xIkTbT5+4YUXZLFY9Mgjj1xwyXiLxSI/Pz/Vr19f1113na666qrLqxYog7+Pp25rHa7PmO8bAAC4OLvDtyTFx8erRYsWFVcVcInu6dDADN+nMnIUEOrwiXwAAAAuyq6EMmvWLElSgwYNKqQY4HK1uiLE3P5y6+96IuYaJ1YDAABQNrvC97BhwyqqDqDCfLr5sEbd1MTZZQAAAJTCSjeoco6kZmvV7uPOLgMAAKCUCh0Ye+bMGW3btk0nT55UVlbWRWdBGTp0aEXeHjB9uP5XXX915c3/DQCO5OHhoRtvvNHcBuC+KiR8r169WhMnTtQPP/xQ7nMsFgvhG5XCYpHW7j2h5FNWZ5cCABXC399fq1evdnYZACqA3eH7rbfe0uOPPy7DMC55vm9ULwE+Xkp+Oa7S79Pt6jpau++kPt10uNLukZmbrxYTlkmSdk6KVYAPs6sAAICLs+tvV7t27dJf//pXGYah1q1ba8GCBVq8eLGkop7t/fv3a9OmTXrrrbfUvn17SdINN9ygHTt26MCBA/ZXD5RhUOdGkqSvtv7u5EoAAABs2RW+//3vf6ugoEB16tTR999/rzvuuEONGjUyj0dFRalDhw76y1/+ok2bNmncuHH64Ycf9Pjjj6tx48Z2Fw+U5YYmddQw1F/p2ax4CaBqsFqtqlu3rurWrSurlSF1gDuzK3yvWbNGFotFf/3rXxUUFHTBthaLRf/6179000036bvvvtP7779vz62B8/L0sGhwF365A1C1nDx5UidPnnR2GQDsZFf4/u23ohUFi4eUSEUhu1heXl6pcx5++GEZhqGPPvrInlsDFzSgY0P5eDEjAAAAcC12pZPs7GxJUkREhLkvMDDQ3D5z5kypc66++mpJ0s6dO+25NXBBoYE+6t0q3NllAAAA2LArfIeGhkqSzfizunXrmr3fe/fuLXVO8Z/MUlNT7bk1cFGDOjc0t09bc51YCQAAQBG7wnezZs0kSfv27TP3BQQEqEmToqW9v/nmm1LnfPXVV5KKQjpQmVpfEWJuf7LxkBMrAQAAKGJX+L7hhhtkGIa+//57m/133323DMPQG2+8oVmzZslqter48eOaMmWK3n33XVksFt100012FQ5cTMnnDz5c/6sycpj9BAAAOJdd4fv222+XJC1YsMAc/y1Jf/vb3xQaGqq8vDw9+OCDCg4OVv369fX000+roKBAfn5+euqpp+yrHLgE6Vn5+mRD5fd+Z+bmK/KpxYp8arEycwn7ACqGh4eHOnbsqI4dO7K8PODm7FqWr0uXLpo1a5by8/N15swZ1a9fX5JUu3ZtLVu2TAMGDNDBgwdtzgkLC9MHH3yg5s2b23Nr4JK98/0BDYlmCkIA7sff31+bNm1ydhkAKoDda2IPGzaszP0dOnTQ7t27tWrVKu3YsUP5+flq0qSJYmNjFRAQYO9tgUsSHuynlPRsfb7lN93d/gpnlwMAAKopu8P3hXh7eys2NlaxsbGVeRvgokbcEKnJS3br7TX7dUfb+s4uBwAAVFMMHEO1cE/7Bqod6KPfzmRpyfYUZ5cDAJckMzNTkZGRioyMVGZmprPLAWCHSu35BsojwMdLyS/HVeo9/H089cANUZq6bI/+u/ZApd4LACqaYRj69ddfzW0A7qtc4fuDDz6olJsPHTq0Uq4LlGVIdGO9vWa/9p+wXrwxAABAJShX+B4+fLjNnMkVwWKxEL7hUMF+3hoWHakZ3/3i7FIAAEA1Ve4x34ZhVPgLcLQR10fKz5tHHQAAgHOUK4UcPHjwvK+tW7eqU6dOkqRWrVpp6tSpWrNmjXbv3q3du3drzZo1euWVV9S6dWtJUqdOnbR161YdOHD5427Xrl2rPn36KCIiQhaLRQsWLLA5XtxTX/J166232rQ5ffq0Bg8erODgYNWsWVMjR45URkaGTZuffvpJ3bp1k5+fnxo2bKgpU6aUqmX+/Plq1qyZ/Pz81Lp1ay1ZssTmuGEYmjBhgurXry9/f3/FxMRo3759l/25wz61a/iqf4cGDr0nC+8AAIBi5QrfjRs3LvNVv359Pfjgg9q8ebMmTZqkbdu26W9/+5u6deuma665Rtdcc426deumsWPHKikpSS+++KI2bdqkhx56yFyQ53JYrVa1bdtWM2fOPG+bW2+9VUePHjVfn3zyic3xwYMHa8eOHUpISNCiRYu0du1aPfzww+bx9PR09erVS40bN9aWLVs0depUPf/883rnnXfMNuvWrdOgQYM0cuRI/fjjj+rbt6/69u2r7du3m22mTJmiN954Q2+//bY2bNigwMBAxcbG2qwICscacX2Uub3hwCknVgIAAKobu2Y7+fe//62tW7dqwIABevbZZy/Y1mKx6P/+7//0888/a/78+Zo+fbrGjRt3Wfft3bu3evfufcE2vr6+Cg8PL/PYrl27tHTpUm3atEkdO3Y0P5fbbrtNr7zyiiIiIvTxxx8rNzdX77//vnx8fNSyZUslJSXptddeM0P69OnTdeutt5qfx4svvqiEhATNmDFDb7/9tgzD0LRp0/Tss8/qzjvvlFT08Gq9evW0YMECDRw48LI+f9gnPMTP3J6+8hf1aBpW4c80AEBFslgsatGihbkNwH3ZNfh17ty5slgsGj58eLnPGTFihAzD0Lx58+y59UWtXr1aYWFhatq0qR599FGdOnWuhzMxMVE1a9Y0g7ckxcTEyMPDQxs2bDDbdO/eXT4+Pmab2NhY7dmzR2fOnDHbxMTE2Nw3NjZWiYmJkoqG66SkpNi0CQkJUZcuXcw2ZcnJyVF6errNC5Uj6XCqVu0+7uwyAOCCAgICtGPHDu3YsYNVogE3Z1f43r9/vySpXr165T4nLCzM5tzKcOutt+qDDz7QypUr9a9//Utr1qxR7969VVBQIElKSUkx6yjm5eWl0NBQpaSkmG3+/HkVf3yxNiWPlzyvrDZlmTx5skJCQsxXw4YNL+nzx6V5ZfleFRbyADAAAKh8doXv4hlLLuUBwuK2lTnbycCBA3XHHXeodevW6tu3rxYtWqRNmzZp9erVlXbPivT0008rLS3NfB0+fNjZJVVZNXy9tOtouhb/fNTZpQAAgGrArvDdvHlzSdK0adNUWFh40faFhYV6/fXXbc51hCuvvFJ16tTRL78Uze8cHh6u48dthxrk5+fr9OnT5jjx8PBwHTt2zKZN8ccXa1PyeMnzympTFl9fXwUHB9u8UDmGX9dYkvR6wl7lF1z8axgAnCEzM1MtW7ZUy5YtWV4ecHN2he+hQ4fKMAxt2LBBffv2veBQimPHjunuu+/Whg0bHL7Azm+//aZTp06ZM6xER0crNTVVW7ZsMdusWrVKhYWF6tKli9lm7dq1ysvLM9skJCSoadOmqlWrltlm5cqVNvdKSEhQdHS0JCkqKkrh4eE2bdLT07VhwwazDZxraHSkagV468BJq77Z5tjeb6YgBFBehmFo586d2rlzJ+tkAG7OrtlOHnnkEc2bN08//PCDFi9erCuvvFK9evVSp06dFBZWNIPEsWPHtGnTJi1fvlw5OTmSpOuvv16PPPLIZd83IyPD7MWWih5sTEpKUmhoqEJDQ/XCCy+oX79+Cg8P1/79+/Xkk0/q6quvVmxsrKSiXvdbb71VDz30kN5++23l5eVp1KhRGjhwoCIiIiRJ9913n1544QWNHDlS48eP1/bt2zV9+nSz516SnnjiCd1444169dVXFRcXp3nz5mnz5s3mdIQWi0WjR4/WP/7xDzVp0kRRUVF67rnnFBERob59+17254+KU8PPS4/1uFr/XLJLM1n5EgAAVDK7wreHh4e+/fZbDR48WN98842ys7O1cOFCLVy4sFTb4t/U+/Tpo48//lgeHpff6b5582b17NnT/Hjs2LGSpGHDhumtt97STz/9pDlz5ig1NVURERHq1auXXnzxRfn6+prnfPzxxxo1apRuvvlmeXh4qF+/fnrjjTfM4yEhIVq+fLni4+PVoUMH1alTRxMmTLCZC/y6667T3Llz9eyzz+qZZ55RkyZNtGDBArVq1cps8+STT8pqterhhx9WamqqbrjhBi1dulR+fuemu0NpAT5eSn45ziH3GhLdWO/+cEBH05h7HQAAVC67wrckBQYGasGCBVq8eLHeeustrV69utR4NH9/f/Xo0UOPPvqobr/9dntvqR49elzwz27Lli276DVCQ0M1d+7cC7Zp06aNvv/++wu26d+/v/r373/e4xaLRZMmTdKkSZMuWhOcw8/bU4/f1ETPLth+8cYAAAB2sDt8F4uLi1NcXJwKCwu1f/9+nT59WpJUq1YtXXXVVfL09KyoWwEVbkDHhnp7zX79dibL2aUAAIAqrMLCdzEPDw81adKkoi8LVCofLw+N6nm1nvryZ0nSqYwcBYRW+D8PAABQzdk12wlQldzepr65/cYq5z18ySwoAP7MYrGocePGaty4McvLA26O8A38wcPj3A+0L7b8pp1H0p1YDQCcExAQoOTkZCUnJ7O8PODmyvV39QceeEBS0W/e7733Xqn9l+PP1wJcSaEhTVq0Q+8N6+jsUgAAQBVSrvA9e/Zs889cJQNzyf2XwjAMwjdcmq+Xh9YfOK0Vu45fvDEAAEA5lSt8N2rUqMyQfb79gLsbcX2k3l5zQFOX7XF2KQCgrKwsde/eXZK0du1a+fv7O7kiAJerXOE7OTn5kvYD7m7kDVH66sffmXoQgEsoLCzU5s2bzW0A7osHLoEyBPp6afytzZxdBgAAqGII38B59L32CrW+IsTZZZiYghAAAPdH+AbOw8PDomduO9f7vf33NCdWAwAAqgKW8INbCfDxUvLLcQ67X9uGNc3tCV/v0MLHb5C3J7+zAgCAy1Ou8H3llVdW+I0tFov2799f4dcFKsvulLN6Z+0Bxfe82tmlAAAAN2XXbCf2YIpCuKPpK/YptmW4Imr6ObsUANVMnTp1nF0CgApQrvA9bNiwyq4DcHndmtTR9/tOavwXP2n2iE7OLgdANRIYGKgTJ044uwwAFaBc4XvWrFmVXQfg8p6/o4Xu+Pf/tOXXM/pk4yFnlyOpaAaUFhOWSZJ2TopVgA+PcQAA4Mp4cgwop/oh/nrqtuaSpNdX7HNyNQAAwB0RvoFLMLhzI3WOClVWboGzSwFQjWRlZalHjx7q0aOHsrJYeRdwZ4Rv4BJ4eFj0r35t5OvFPx0AjlNYWKg1a9ZozZo1LC8PuLkKHyCanJyskydPKisrS4ZhXLBt9+7dK/r2QKWLqhOoUTddrVeX75UkHUnN0tVhQU6uCgAAuIMKCd979uzRSy+9pG+++Ubp6enlOsdisSg/nyWy4Z6GRTc2w/e4+T/ps0eiWXwHAABclN1pYcGCBWrfvr0++ugjpaWlyTCMcr8Ad+VVImj/eDhVryXsdWI1pWXm5ivyqcWKfGqxMnP5JRcAAFdhV/g+fPiw7r//fmVlZSkiIkLTpk3TO++8I6moZ3vlypWaP3++xo8fr4iICEnSDTfcoBUrVmjVqlX2Vw+4iLdW79fqPcedXQYAAHBxdg07eeONN5SZmamgoCBt2LBBERER2rFjh3m8Z8+ekqR+/fppwoQJGjlypD799FO99957+vjjj+2rHCghwMdLyS/HOeXegzo31CcbD2vsZ9v05aPRTqkBAAC4B7t6vlesWCGLxaLHHnvM7Nk+H39/f3300Udq166d5s2bpy+++MKeWwMu48nYpmpRP1inrbka9/lPzi4HQBUVEBCggIAAZ5cBwE52he/k5GRJ0nXXXWfus1gs5vafH6j08PDQX//6VxmGoffff9+eWwMuw9fbUzMHt1egj6c2JZ9xdjkAqqDAwEBZrVZZrVYFBgY6uxwAdrArfFutVklSw4YNzX0lfytPS0srdU7Lli0lSdu2bbPn1oBLiaoTqJfubu3sMi6KBzEBAHAuu8J3SEiIJCk7O9vcV7t2bXN7//79pc4pDuQnT56059aAy7nz2it0T4crzI+PprEKHQAAsGVX+G7atKkk6cCBA+a+oKAgNW7cWJK0fPnyUuckJCRIkmrWrGnPrQGX9HTv5ub2Xz9JUnYey9ADsF92drbi4uIUFxdn0+EFwP3YFb6jo4tmdli/fr3N/ttvv12GYWjq1Kn67rvvzP2fffaZpk+fLovFouuvv96eWwMuyd/H09zecSRdT3/5M3PaA7BbQUGBlixZoiVLlqiggF/qAXdmV/i+7bbbZBiGvvzyS5tvBuPGjVNAQIAyMjIUExOjunXrKigoSIMGDVJ2drY8PDw0btw4u4sHXJmnh0Vf/fi7Pkz81dmlAAAAF2FX+O7Ro4cmTpyoESNG6Pfffzf3N2rUSPPnz1dISIgMw9CpU6dktVplGIZ8fX313//+V127drW7eMCVPXlr0bCsqctda/VLAADgPHYtsmOxWDRx4sQyj/Xu3Vv79u3T559/rh07dig/P19NmjTRgAEDdMUVV5R5DlCV3N+lkfaknNWXW3+/eGMnyszNV4sJyyRJOyfFKsDHrm8LAADgAir1p2zt2rX1l7/8pTJvAbgsi8Wil+5qrb0pZ7X9SLqkoqBLuAUAoPqya9gJ4MqKl5xPfjnOaYHXz9tT0wdda3785Oc/q6CQBzABAKiu7ArfXbt21YwZM3TixImKqgeocuqH+Jvbq3Yf1/Pf7GAGFAAAqim7wvfGjRv1xBNP6IorrlDv3r310UcfmateAijNYpE+XP+r/rP2wMUbA8AfAgMDZRiGDMNgeXnAzdkVvps0aSLDMJSfn6/ly5dr2LBhqlevnu677z4tXryYuUiBPxkfWzQDysvf7tain444uZoLYyl6AAAqnl3he8+ePdq0aZPGjBmj+vXryzAMZWZm6tNPP9Udd9yh+vXra9SoUVq3bl1F1Qu4taHXRWrkDVGSpGe+2u7kagAAgKPZ/cBlhw4d9Oqrr+rw4cNasWKFHnjgAXN+75MnT+qtt95St27ddOWVV+q5557Trl27KqJuwG39323NFde6vvILGPcNoHyys7PVv39/9e/fn+XlATdXYbOdWCwW3XTTTXr33XeVkpKiL774Qv369ZOvr68Mw1BycrJeeukltWrVSu3bt9drr71WUbcG3IqHh0WvDmir9o1qmvt+O5PpvIIAuLyCggJ9/vnn+vzzzxnSCbi5Splq0MfHR3fddZfmz5+vY8eO6b333tPNN98sDw8PGYahpKQklpdHtebn7akZ97UzPx72/iYdOuUeAZyx4AAAXL5Kn+c7KChII0aM0PLlyzV79mzVrFmzsm8JuIWaAT7m9tG0bA18J9FtAjgAALg8lR6+t27dqr///e9q2LChhg4dqrS0tMq+JeB2ImsH6EhxAD9NAAcAoKqqlGX/Dhw4oI8//lhz587V3r17JclcVCQwMFB9+/bV4MGDK+PWwEUVr3zpSuY80FkjZm/SgRNWDX9/k7PLAQAAlaTCwveJEyc0b948zZ07Vxs3bpR0LnB7eXmpV69eGjx4sO68804FBARU1G2BKqFukK/mPdRVg/67XvtPuN9CVZm5+WoxYZkkaeekWAX4VMrv9QAAuD27fkJarVZ9+eWX+vjjj7Vq1SrzCezi0B0dHa3BgwdrwIABqlOnjv3VAlVYWLCfPnm4qwb+Z70OnCwK4IdOZ6pZeLCTKwMAABXFrvAdFhZmzjdaHLibNWumwYMH67777lNUVJT9FQLVSFiQn2aN6KQbp66WJA2ftUmfPRytRrX5axFQnQUEBCgjI8PcBuC+7ArfWVlZkqSIiAgNHDhQgwcPVrt27S5yFoALqRvka26npGVr0H/Xa97DXVW7hs8FznJNDEcBKobFYlFgYKCzywBQAez6SThixAgNHjxYPXv2lMViqaiaAPwhqk6gDp60auA76zV7RCdnlwMAAOxk11SD7733nm666SaCN1BJZo/opCvrBOr31CwNm7XR2eUAcJKcnBwNHz5cw4cPV05OjrPLAWCHSpnnOzk5WTfddJNuvvnmyrg8UG3UDfLVJw93VVSdQB1JzXZ2ORWGVTKBS5Ofn685c+Zozpw5ys/n3wzgziolfFutVq1evVqrV6+ujMsD1Uq9YD998lBXNQo995DVL8cznFgRAAC4XJW+wiXgLooX30l+Oc7lHgwMD/HTnAfOjfke8t5G/XjojBMrqnj0hgMAqgPCN+Am6gX7mdtpWXka/O4GrfvlpBMrAgAAl4rwDbih666qrczcAj3y8VZnl1Lp6BGvGs73/5H/vwCqG8I34IbeHNxeca3rK7/AcHYpTkNoc00V8f+F/7cAqjLCN+CGfLw89MagdhrQsYG577Xle1VYWH3DeDGCW8VzlV5r/t8CqAoq5amysLAwTZw4sTIuDeAPnh4WTezTQp9t/k2S9O4PB/Xr6UxNu/daMfW+LVbaLB/eJ9cVEBCg48ePm9sALsyVv59VSiV169YlfAMOUHKBKx8vDyXsPKZ+b63Tv+9r58Sq3Icrf3OuKOf7HKvD516VWCwW1a1b19llAA5R1venqvS9jGEnQBUxZ0Qn1anhq90pZ3Xvf9Y7uxy3VtbwBmcMebiU4R7VeUhGdf7cAXfgKkPXXEWlh++FCxdqyJAh6t27tx577DFt3Vr1Z2dA1eHKc3//WduGNfXNqOvVon6wTltznV1OtVERAbm6/gBC+eXk5Cg+Pl7x8fEsLw+XwPezy2dX+P7uu+8UFhamRo0aKTU1tdTx5557Tn379tXcuXO1fPly/ec//1HXrl314Ycf2nNbAOcRUdNfnz8arVtahJn7/u+r7XwDBNxcfn6+3nzzTb355pssL49KQZh2HLvC95IlS3Ty5El16tRJNWvWtDn2008/6aWXXpJhGDIMQzVr1pRhGMrPz9df/vIXJScn23NrAOcR4OOl1wdca3781Y+/644Z/9PulHTnFQU4AaEB1R2B2jXZFb5/+OEHWSwWxcTElDr21ltvyTAM1apVS1u2bNGpU6e0ceNGhYaGKicnR2+//bY9twZwAR4e5x7ErBvkq1+OZ+jOGf/T/M2HnVgVAKAyEKbdi13h++jRo5Kkli1bljq2aNEiWSwWjRo1Su3aFc280LFjR40aNUqGYWjFihX23BpAOX312HW68Zq6yskv1MRvdjq7HMCpCClwF/RaV112he8TJ05IUqkhJ/v379fvv/8uSbrrrrtsjnXr1s1sc7nWrl2rPn36KCIiQhaLRQsWLDCP5eXlafz48WrdurUCAwMVERGhoUOH6siRIzbXiIyMlMVisXm9/PLLNm1++ukndevWTX5+fmrYsKGmTJlSqpb58+erWbNm8vPzU+vWrbVkyRKb44ZhaMKECapfv778/f0VExOjffv2XfbnDlyq0EAfzRreSU/1bibPEj3iB09anVgVAFQ/zFQEyc7wbRhFq+mlpaXZ7P/+++8lSSEhIbr22mttjtWuXVuSlJmZedn3tVqtatu2rWbOnFnqWGZmprZu3arnnntOW7du1Zdffqk9e/bojjvuKNV20qRJOnr0qPl6/PHHzWPp6enq1auXGjdurC1btmjq1Kl6/vnn9c4775ht1q1bp0GDBmnkyJH68ccf1bdvX/Xt21fbt28320yZMkVvvPGG3n77bW3YsEGBgYGKjY1Vdnb2ZX/+wKXy8LDokRuv0ocPdDb33fuf9fpu93EnVgUA7o3eaVwOu+ZOCw8P16+//qpdu3aZPdqStGxZ0WTn119/falzrNai3rZatWpd9n179+6t3r17l3ksJCRECQkJNvtmzJihzp0769ChQ2rUqJG5PygoSOHh4WVe5+OPP1Zubq7ef/99+fj4qGXLlkpKStJrr72mhx9+WJI0ffp03XrrrRo3bpwk6cUXX1RCQoJmzJiht99+W4ZhaNq0aXr22Wd15513SpI++OAD1atXTwsWLNDAgQMv+z2AcxVPQehurm1U09zOyMnXA3M26YmbmzivIMBFuONCHagcVWkxF7gmu3q+u3btKsMw9NZbb5k92QcOHNDXX38ti8WiW265pdQ5e/fulaTzht7KkJaWJovFUmp4zMsvv6zatWurXbt2mjp1qs30TYmJierevbt8fHzMfbGxsdqzZ4/OnDljtvnzw6axsbFKTEyUJB08eFApKSk2bUJCQtSlSxezTVlycnKUnp5u8wIq2oCODWQY0rQVDIMCXJ2/v78OHjyogwcPyt/f39nluBV6p+Fq7ArfDz74oKSisdGtWrXSPffco65duyo7O1v+/v667777Sp2zdu1aSdI111xjz63LLTs7W+PHj9egQYMUHBxs7v/rX/+qefPm6bvvvtNf/vIXvfTSS3ryySfN4ykpKapXr57NtYo/TklJuWCbksdLnldWm7JMnjxZISEh5qthw4aX+mkDF/X8HS31z7tayavEOPDkU4wDB0pylYDm4eGhyMhIRUZGysODxanPx1X+fwEXYte/4JtuuklPPPGEDMNQcnKyvvrqK508eVKSNHXqVNWpU8emfXZ2ttkr3r17d3tuXS55eXkaMGCA2Ttf0tixY9WjRw+1adNGjzzyiF599VX9+9//domVw55++mmlpaWZr8OHmR4OlWNwl8aaNaKT+XG/txL12abD5vMcAOBM9FqjKrJ7wNLrr7+um2++WfPnz1dKSorq16+voUOH6qabbirV9ptvvlFwcLBCQkLUp08fe299QcXB+9dff9WqVatser3L0qVLF+Xn5ys5OVlNmzZVeHi4jh07ZtOm+OPiITPna1PyePG++vXr27T584OoJfn6+srX17d8nyhgpw6Nzz1/kZVboCe/+Emr9x7Xs3HNnVgV4LqcMfY3NzdX//d//ydJ+uc//2kzJLKqYEw1qosK+dvV7bffrjlz5mjZsmWaPXt2mcFbkgYMGKDk5GQdPHhQjRs3rohbl6k4eO/bt08rVqwwZ1i5kKSkJHl4eCgsrGhZ7ujoaK1du1Z5eXlmm4SEBDVt2tR8WDQ6OlorV660uU5CQoKio6MlSVFRUQoPD7dpk56erg0bNphtAFcy9pYm8vKwaMnPKbr7zfM/lwDAsfLy8vTKK6/olVdesfm55I7otUZ155a/VmZkZOiXX34xPz548KCSkpIUGhqq+vXr65577tHWrVu1aNEiFRQUmOOrQ0ND5ePjo8TERG3YsEE9e/ZUUFCQEhMTNWbMGN1///1msL7vvvv0wgsvaOTIkRo/fry2b9+u6dOn6/XXXzfv+8QTT+jGG2/Uq6++qri4OM2bN0+bN282pyO0WCwaPXq0/vGPf6hJkyaKiorSc889p4iICPXt29dxbxhQTg92u1I9m9bTE/N+1IES84Bn5RbQCwVcBD23tng/gLI55F/C/v37dfLkSUVGRpZ6+PBybN68WT179jQ/Hjt2rCRp2LBhev755/XNN99IUqmhHd9995169OghX19fzZs3T88//7xycnIUFRWlMWPGmNeRimYlWb58ueLj49WhQwfVqVNHEyZMMKcZlKTrrrtOc+fO1bPPPqtnnnlGTZo00YIFC9SqVSuzzZNPPimr1aqHH35YqampuuGGG7R06VL5+fnZ/T7A9bjrFIQltW4QokV/vUETvt6uz7cULZZ158z/6cW+rdSzaZiTqwPcT1UKoUzDB9jPrn8dx48f1+effy5JGjx4sEJCQmyO//LLL7r33nuVlJQkqagn+M4779S7775r1zzfPXr0uOADYRd7WKx9+/Zav379Re/Tpk0bc8Gg8+nfv7/69+9/3uMWi0WTJk3SpEmTLno/wFUE+Hhp0p2tzPD925ksjZi1Sbe1Dte42KZOrg5wf64UVsuqxZXqA6oau8Z8f/nllxo1apSmT59eKnjn5OSod+/eSkpKkmEYMgxDhYWFWrBggbngDAD3MPy6SHn+MRb89n//4OxygCqrIsZDM0MI4NrsCt/Lly+XxWLRXXfdVerY7NmztX//fknSHXfcoenTp6tPnz4yDEP/+9//9Omnn9pzawAO9OStTbVw1A1q16imrDkF5v7Ve44zLSHgAH8O0cX/JUwD7seu8L1nzx5JRStd/tncuXMlFc0FvmDBAj3++OP6+uuvFRMTI8MwNG/ePHtuDcDBWkQE64tHrtPEPi3MfY99/KMG/CdRPx4648TKAABwH3YN4jpx4oQkqUGDBjb7s7KytH79elksFpsHFCXpgQce0IoVK7R161Z7bg3ACTw8LLq3U0O9sHCnJMnXy0Obks9o8LsbnVwZULX5+/ur/gMzzW2gOiksNGTNOffXnX3HzsqQRVm5BUrNyjX3z9t0WIWFhrJyC5Se7bpTctoVvlNTUyWp1FK369evV15enjw8PBQTE2NzLCoqSlLRw5pAdVAVZkA5n2+f6KZ31h7QZ5sPq/CP0Sd/+2yb4nterVZXhFz4ZADl5uHhIZ+6jc1twB0cTctSfoF0MiPb3Pf5lt+Um1+ojJx8nck8F5zjP96q7LxCZeYVKKNEcG43KUE5+YU2171z5roy7zfpj44hV2dX+K5Ro4bS0tLMebSLrV69WpLUokWLUrOaeHt7F93YiyenAXcXHuKnl/u10eCujdTn3/+TJH27PUXfbk9RtyZ1NPy6SOcWCAC4ZLn5hbLm5CgtK0/H088F53kbDykrr1Dp2Xk6Yz0XnO/77wZZc/KVkZNv0+N886trS117wtc7yrznd3tOlLn/z8FbkkIDfeTv7akAH0/5enlo+5F0SdItLcJUw9dbft4e8vKw6MP1h8r3CTuYXQm4WbNm2rBhg5YuXarbbrvN3P/FF1/IYrHoxhtvLHVOcVCviPm+AbiGq+rWMLfj2tTX0u0p+n7fSX2/76S5Pze/UAFVb0VswCFyc3OV+sPHf2z3ZOo/XFRBoaHUEj3LP+w7qez8QqVl5upERo65f9TcH5WRk2/T9tpJCWVec9KiXWXuTzqcWuZ+b0+Lgv28FeDrqcOnsyRJPZvWVbC/t2r4esnXy0Pv/y+56Np3tlSIv7cCfbzk6SGNmL1ZkpQwprtCA4t+eHT4x4qiz2V8zzLnmJ8+sJ3N/ioZvuPi4rR+/Xq98847at68ubp166bZs2dr586dslgsuvvuu0udUzzW+4orrrDn1gBc1NR72uipW5vp3e8P6NPNh5WdV9Rr0X3qd+p77RW6p0MDXVU30MlVAu4lLy9Paf/75I/tt5xcDRzttzOZys03lJaVp2MleqJfW75X1twCnbaeC9Oxr69VWlaezubkq+RkVA9/uKXMa6/aff5hwEF+Xgr289bvqUXB+ZYWYaoV4KMgP2/5e3toxndFs9q9MfBa1anhqyA/b3l5Sr2nF01Ju21ir1Lzxs8c3N4mIBeH73s6NLDZX+yKWv7mNaoKu8L3qFGj9Oabb+ro0aMaNWqUzbHo6GibVSiLLVy4UBaLRZ06dbLn1gBcWMPQAL1wZys91P1K3fCv7yRJ6Vn5+iDxV32Q+Cvhu5opLDSUknYuMHyy8ZAysgt0JjNXJ86e2/+3z7Yp2M9bPl4Wc993u4+rbcOauqImDxnCPaVm5urE2RylZ+XreImv91n/S1Z2XoFOlRi+MeS9jbLm5Otstu3wjV6vl73g37s/HCy17/CZrDLbNg0PUq0Ab9X091Ggr6e+2Fq0iNqEPi0UFuQrPy8PPfhBUUBf91RP1Qv2l6eH5YI9y8XhO6ZFvTKDM8pmV/gOCQnRihUrNGTIEJvZS7p166ZPPvmkVPtt27Zp06ZNslgsuuWWW+y5NeD2qvKDmMWK/1QoSf8d2kELtx3Vsh0p2n/Cau6/9z+JurVVfXW/po4zSkQFyi8o1K6j6ebHD3+wWUdSs/XbmSzlFpwbt/nief50/e32lFL74uf+KEkK8vVSk3rnhjelZeUx9AIOUVBo6Gx2no6mnQu1i346ouy8Qp04e67HOX7uVllzCpSelafUrHPB+bqXvyvzulOX7Sm1b8uvZU/b6uftoRB/b4X8MVxj66FUSdKQro1Uu4av/H08NXnJbknSxw92Vr1gf4X4e8vb02IOIfnqsetsAnJx+B7YqWGpnuWaAT7y9LAIlcPu71zNmzfX5s2bdfDgQaWkpKh+/fqKjIw8b/tZs2ZJKpr/G0D1cf3VdXRLi3ClZ+fpq62/aeI3RU+l//x7un7+Pd3mB9H6A6d03VV15Oft6axycRGGYZh/ipak+9/doJ1H081hRpL0wy+nzG1PD4sK/pgS55YWYapTw0+hgUXjO6f88f/+mduaKa+gaJzqf78v6tFrWq+GDpy06mxOvhk4JKnblO/U45q66tM2QtddVbsyP1VUIelZeTqenqPTmbk6VuKvMdNX7FNmboFSs/J0usR46K6TV+pstu3wDUl68vOfS137u91lPzAoSYE+nuY4533HMyRJfdrWV2iAj/x9PPX2mgOSpNcGtFXdoKLhGz6eFt32RtHwja3P3VLmGOenb2tuBufi8N2uUS16oV1chXUbREVFmdMInk/btm3Vtm3birolADcU7Oet/h0bmuH7hTtaaPWeE/rhl5PKKyj6CffA7M3y9fJQp8hQdYqsdaHLwcHeXrNfO4+kK+lwmk6WCCnFwbiGr5cy/piPd9KdLXV13RpqGBqgEH8vtXmhqAfuz3+6Lg7f93dtbAaJ4vD9Vfz18vLw0IGTGdp2OFXjvygKPfkFhlbsOq4Vu47Lv8QvaZm5+fSIV0P5BYU6kZGjgyX+qvavpbt1MiNXR0oMw+g6eVWZ5/9n7YEy96dnnQuvAT6eyswtWuG3S1SoQgN9VMPXS/O3/CZJev6OFqpTw1ch/t7y8fLQvf9ZL0naNvEWhfgX/RWwZHD+V7825td7cfi+tVU4wbka4DsUAKfq37Ghhl0XpePp2er80kpJUt0gX504m6MffjmpH345N2PKsPc3qk2Dmmp9RYiuDmPceGXa8usZ7T2WoZ9+S7WZyeCNlb+Y214eFuX/0Zv9z7taqUtUqMKD/dTq+eWSzv8A1aXy8fJQs/BgNQoNMMP316Ou14qdx/TNtiP69VSm2fb6l79TtyZ11atlPV1Pj3iVciQ1SyfO5urw6Uz9ciLD3H/TK2t0IiPH/MtKsTnrfi3zOoE+nqoZ4KOaAd7a8ccUdfd1aWQG5wAfDz395XZJ0qLHr1d4iL+C/byVX1hoBudZIzqZwbk4fA/o2LDMr3dvT+Zlh60KDd/Hjh3T6tWrtX37dp0+fVqSFBoaqlatWqlHjx5MLwjgvGr4nft2tPrvN+poWrZ+2HdSa/edNJ/G35R8RpuSS4+JnPj1DjWvH6ym4UFqUIsH8y7Vuv2ndOBEhn76Lc3cN+S9slctva11uNo3qqV2jWoqqk6g2r9YNPXXXe2ucOiMBE3Caqhtg5oae8s12ph82uxlzMkv1Ipdx7Ri1zGVHLJ64ESGWkaw8JMry8krMLfnrEtWSlq2Dpw815Md81rpOaMlKeWP2T+8PCwKC/bVkdSij0dcH6mGtQIUGuit0Z9ukyT9+FyMagX6SrLthX42rrlNcC4O31fWrWHuz88tPd80cDkqJHwfPXpUY8eO1Zdffqn8/LK/8Xp5ealfv3569dVXVb9+/Yq4LYAqymKx6OqwIF0dFqQBnRqaPyD/eVcr7TuWoZ9/T9POI+nK+uOHdXHP0589/eXPuqpuDUXU9HNY7a6mZKD5YutvOpGeo9/OZCn51LlQ8+CczaXOCwvyVZsGNdW2QYiuCa+hv3xY9FD9K/3butSfxS0Wi1qXWE31q8eu09q9J7V8Z4rZqylJt//7f2pcO0Ddmrjng71+fn4KH/qaue2uCgoNHTp97i8VLyzcqd/OZCr5ZKaOlHig8V9LSz+M6OVhUYNa/mpUO1ARIX6at+mwJGnew110ZZ0aql3DVzn5Beb3i3GxTUv8QlgUvn15jgQuwO7wvW3bNsXExOj06dMy/vxEQgl5eXn69NNPtWLFCq1cuVKtW7e299ZAlVQdZkG5XMW9q5J0NjtPrf8Y3vDojVfqwEmr9h3LUPIpq7nU/ddJR0pdo8tLK3VFTX81qOWvsCBfc//m5NNqFBqosGDfUue4goLCojl+z2bn2cywsHDbEeXkF+pUxrnpyh7+YLNOZuQqJT1bqZnnZl14bkHZK8s1rh2gVhEhujqshqav3CdJWj2uh0uF7PJqGh6kdo1q6YmYJtp3/Kxu+aO31NvTol9PZerXU+cW3Xjmy591d/sGbvHApqenp3zrX2Nuu7qCQkMHS/Ra/+2zbTp40qqDJ602KxZ++keA/rPYlvUUVaeG6of4ms+H/DjhFgX5Fa2SnZmbb4bvNg1qMs4fbsWur1ar1aq4uDidOlX0RHtMTIweeughdenSReHh4ZKKVrTcuHGj3n33XS1fvlwnT55UXFycdu/erYCAAPs/AwDVUslpsB6/uYn5w/e0NcccCvHEzU30e2qWDpzIMB8IPJudr90pZ7U75azN9Ya+v8ncruF77lvjE/N+VN0gPwWV2Jew85hqBhQtb2yxnOt0OHjSKl8vDxUatoF1c/IZWSxFMy0Um7fpsAoKDGXmFigt61xw/usnPyorr0AZOQU6W6J98S8af1Y8BrqkkrOMlHT9VbXVuE6gGtTyV70gX/1t/k+SpG+f6Gb2EBaH76qg5Nzg6566SVsPpSphZ4o+31I0xdqCpCNakHREdWr4KLZluLPKdHtnrLnacujccLB+b63T/hNW5ZYI2SWnkfTx8jCP/aX7lWpSL0hRdQIUFuSrblNWS5Jev/da82uyOHwz9R2qCrvC94wZM3TkyBF5eHjoP//5j0aOHFmqTaNGjdSoUSPdc889ev/99/XQQw/p999/18yZMzVu3Dh7bg8ApZScnvAvN15ZanW1b0Zdr1PWXP1+Jku/nrKas2o0rh2g4+k5fwTfc8E5YWfp1d+emJdU5r3j/pgW7M+Gvl96/PSkhTvLbLti1/lXm5OKgkuQr5e5MEfXK0NV64/pyr78Y97ef/RtpUahAaoX7Kdgfy9F/zHDw3+HdbTpzS4O39VBoK+XYluGq1uTOmb4HtipoZbtSNHJjFx9vOFcj/hLS3bptlb11Skq1FnllpKbm6u0DV/8se2c5eUNw9CREtNLxs/dqj1Hz+pIiSn7JGnX0aJfbP29Pc2hYX/rdY1a1A/W1WE1FBroY/4y+URME7f8CwtgD7v+9X799deyWCwaPnx4mcH7zx544AGtW7dO77//vr766ivCNwCHuzqshtqU+GFfHL6/faKb/L09lZGTr0OnM80g/Wxcc2Xk5Ot4erbmbiz6M3e7hjWVk1+o7LwCZeYWmA98Bft5ycPDIg+LRRbJDMiRtQPk5+0pb08P/fx70UONMc3DipZo9vGUt6fFnJlhQp8Wqh3oo0AfL3l7WjRsVlGP/A/je6pukK98vTxtfpl4f/i5WReKw/fd7a8g0JTDhD4t9GLfVvph30l9sfU3LfrpqCTpo/WH9NH6Qwrx93aZxZ/y8vKUunrWH9uvV/79SiyK9K+lu7U3JUM7j6YrrcRfY0rOa90oNMAcy/3GoGvVtkFN1Q70MWe+GXlDFF+TwB/sCt979+6VJA0cOLDc5wwaNEjvv/++eS6A8mEseOWzWCwK8vNWVJ1z0xje16WRGW6Lw/fHD3Upc8GL9c/cXOb+JSWGdRTve2OQ7VzXxeG7eLW54v3FQgN95Ovl+mN93Y23p4d6NgtTlytDzfB9V7srtGbvCZ225mrhtqNm2z7//kGdo2qrbYOqNWuKtcRfep5bsF17j2Voz7Fzw7JKTtlXcnrJp29rpnYNa6l5/SB5eljMr+2Y5vUcOvMN4G7sCt8ZGUXzbIaGlv9Pc7VqFS2YYbVaL9ISAADH++ddreTr5aktv57Rkp+Pava6ZEnS/hNW7T9h1SclRhE99tFWtW3ofnPPz1mXrD0pZ/Xz72k20/kVLzle0v1dG6lNg5pqGRGsK2r6m8uVD/ljUSSJ3mzgUtgVvuvWrasjR45o165dat++fbnO2b27aPnTOnVc4095AAD8maeHRZ2jQtXqimAzfP97UDv9/HuaNhw4pW1/zIm+eu8Jrd5belnxFxft1FV1ayiydqDqhThvBp11+0/pl+MZ2nEkXT//lmruL2sqP6lo5qC2DYvmcI+d9r0k6ZnbmhOygQpkV/ju2rWrvvjiC7322mu699575eV14cvl5+frtddek8ViUdeuXe25NQAADnVz8zD1aRthM3zomduaaU9Khrb/nqZ9x8+a01x+srHsKfT6v52oiJr+qlPDx9y3Zu8JRYT4KzTQR/4+l7ca4u6UdP16KlO7U85qZ4n5zcuaw12SbmoWpmv/6LG/KixQ3f+YZaR45iBCNlB57ArfQ4cO1RdffKGkpCTFxcVp1qxZioiIKLPtkSNHNHLkSG3dutV8SBMAAHd2f4mhF6cyctThH0XTXD7c/Ur9nlo0o87Bk1ZZc4pm/dhxJN1m8R9JevSjrWVe+44Z/1OIv7eC/bzk4yGF9oqXxctHYz7foax86bT13BSVd7+ZWOY1GoUGqHWDELWMCFaTsBp66IMtkqQZ97WjNxtwErvCd58+fdS3b18tWLBAK1as0JVXXqlevXqpS5cuCgsLk8Vi0bFjx7RhwwYlJCQoN7foG8Vdd92luDgeHAPsxUOYgOvw9zn3QOzoElPoWXPy1HJi0awfM+9rp9OZefrtdKb+s/aAJKl5/SClZubplDXXZm7sX45n2Fw/qF1vSdIP+8/oz4L9vNQsPFhNw4MUVTfQnMpy6ehuhGzAxdg9Uegnn3yioUOHav78+crNzdXixYu1ePHiUu2KV7/s37+/PvjgA3tvCwCAW7BYzi0O07NZmDmsozh8f/HodQrw8ZJhGDqZkaNO/1wpSXpvWEflFRQqPStfJ85mafL8H2QUFuofQ25S/Zr+8vP21PA/pqJMfPomBfqeW/3xfPPIA3A+u8O3r6+vPv30Uw0dOlRvvvmm1qxZo8zMTJs2AQEBuvHGGxUfH6/bbrvN3lsCAFDlWCwWBZZYSTX6qto2vdZTlxetPnpv50alxmWXDPgAXFuFLZEVFxenuLg4FRQU6MCBAzp9+rSkomkIr7zySnl6Mj8t4CgMRwEAwDXZFb5vuukmSdKQIUM0YsQISZKnp6eaNGlif2UAAEBS0QqXZ7cu+mP7JskJy8sD5VFW58/5OoQuZX9FXMNV2PWv9/vvv1dhYaGee+65iqoHAAD8SW5urk4nvP3H9stSoL+TK4I7qK7h1tXZFb7DwsKUkpKimjVrVlA5ACoT3ywBoPI5OvTyvd292BW+27Ztq5SUFO3du1ft2rWrqJoAAACcgtCLymZX+H7wwQe1dOlSvf3227r33nsrqiYAAIBLVhE9y0Blu7x1bP9w99136/7779eaNWv0wAMPyGq1VlRdAAAAZSoOzskvx5nTMQLuwq6v2A8++EA333yzfvrpJ82ZM0dff/21+vTpozZt2qhWrVoXnV5w6NCh9tweQAWg9weAq+L7E6oiu8L38OHDbSb2P3PmjD788MNynWuxWAjfAABUMwwDQXVn999qipeNP9/HAADAPr6+vqp7z0Rz2x0QpoGy2RW+Dx48WFF1AHAx/OAEXIeXl5cCrupkbgNwX3b9C27cuHFF1QEAANwUv6wD5cevzwAuCT9kAcfLy8tTxs8r/th23vLy/PsH7Ef4BgC4FAJeabm5uTq1ZNof28+zvDzgxi4pfH/77bf6v//7P0nS3//+d913333lPnfu3Ll65ZVXJElTpkxRTEzMpdwaAODGKmKGC0K54/BeA5Wn3OHbMAyNGTNG+/btU0xMzCUFb0kaNGiQZs+erRUrVuhvf/ubtm3bdsnFAnBN/KBGMWd8LfD1Zx/eP8Cxyr3C5apVq7R37155eHjo9ddfv+QbWSwWTZs2TZ6entq+fbvWrFlzydcAAKC8WAURgCsq93ejL774QpJ0yy23qEWLFpd1sxYtWig2NlbffvutPv/8c914442XdR0A7oEetarNHf//umPNFaU6f+6AKyl3+N64caMsFov69Olj1w1vv/12LVmyROvXr7frOgAAx6gOoa06fI4AXEO5w/evv/4qSWratKldN7zmmmskScnJyXZdB4D7IujAXbjj16o71gxUJ+UO32lpaZKk0NBQu25YfH56erpd1wFQtRAYnI//B+XjjPfJ19dXde58ytx2Vh0A7Ffu8B0cHKwzZ84oNTXVrhsWnx8UFGTXdQBUDwSMysH7WvEuZTrFS516MTjATycWTK64YgE4TbnDd926dXXmzBnt3LlTPXr0uOwb7tq1S5IUFhZ22dcAAMJj+fA+AYBrKXf47ty5s/bs2aOFCxfqscceu+wbfv3117JYLOrUqdNlXwMAzqc6hM1L6UlF1ZCfn6+vvvpKknTXXXfJy4upEwF3Ve5/vb1799aHH36o5cuX64cfftANN9xwyTdbu3atli9fLovFot69e1/y+QBwOSpidcXK5Or1wflycnI0YMAASVJGRgbhG3BjFsMwjPI0zMvLU9OmTZWcnKx69epp7dq1atKkSblvtHfvXnXv3l0nTpxQZGSk9uzZwzePckpPT1dISIjS0tIUHBzs7HIAAA5mtVpVo0YNSUXhOzAw0MkVAfiz8ua1cq9w6e3trVdeeUWSdPz4cXXo0EHTp0+X1Wq94HkZGRmaNm2aOnbsqOPHj0uSXn31VYI3AAAAqp1y93wXe/HFFzVx4kRZLBZJUmBgoLp166YOHTooLCxMgYGBslqtOnbsmLZu3arvv/9eVqtVxbeZNGmSnn322Yr/TKower4BoHqj5xtwfeXNa5ccviVp1qxZevzxx5WZmVl0kT+CeFmKLx8QEKAZM2Zo+PDhl3q7ao/wDQDVG+EbcH0VPuykpBEjRmjv3r0aO3as6tSpI8MwzvuqU6eO/va3v2nv3r0EbwAAAFRrl9Xz/Wc7duzQtm3bdOrUKZ09e1ZBQUGqXbu22rZtq5YtW1ZEndUaPd8AUL3R8w24vvLmtQp56rFly5aEbAAAKomPj49mzZplbgNwX0w5AgCAi/P29mboJlBFXNaYbwAAAACXjp5vAABcXH5+vpYtWyZJio2NZa0MwI3xrxcAABeXk5Oj22+/XRLLywPujmEnAAAAgIMQvgEAAAAHIXwDAAAADuKW4Xvt2rXq06ePIiIiZLFYtGDBApvjhmFowoQJql+/vvz9/RUTE6N9+/bZtDl9+rQGDx6s4OBg1axZUyNHjlRGRoZNm59++kndunWTn5+fGjZsqClTppSqZf78+WrWrJn8/PzUunVrLVmy5JJrAQAAQPXgluHbarWqbdu2mjlzZpnHp0yZojfeeENvv/22NmzYoMDAQMXGxio7O9tsM3jwYO3YsUMJCQlatGiR1q5dq4cfftg8np6erl69eqlx48basmWLpk6dqueff17vvPOO2WbdunUaNGiQRo4cqR9//FF9+/ZV3759tX379kuqBQAAANWE4eYkGV999ZX5cWFhoREeHm5MnTrV3Jeammr4+voan3zyiWEYhrFz505DkrFp0yazzbfffmtYLBbj999/NwzDMN58802jVq1aRk5Ojtlm/PjxRtOmTc2PBwwYYMTFxdnU06VLF+Mvf/lLuWspj7S0NEOSkZaWVu5zAABVR0ZGhiHJkGRkZGQ4uxwAZShvXnPLnu8LOXjwoFJSUhQTE2PuCwkJUZcuXZSYmChJSkxMVM2aNdWxY0ezTUxMjDw8PLRhwwazTffu3W2W8Y2NjdWePXt05swZs03J+xS3Kb5PeWopS05OjtLT021eAIDqy8fHRzNmzNCMGTNYXh5wc1VuotCUlBRJUr169Wz216tXzzyWkpKisLAwm+NeXl4KDQ21aRMVFVXqGsXHatWqpZSUlIve52K1lGXy5Ml64YUXLv7JAgCqBW9vb8XHxzu7DAAVoMr1fFcFTz/9tNLS0szX4cOHnV0SAAAAKkCVC9/h4eGSpGPHjtnsP3bsmHksPDxcx48ftzmen5+v06dP27Qp6xol73G+NiWPX6yWsvj6+io4ONjmBQCovgoKCrR69WqtXr1aBQUFzi4HgB2qXPiOiopSeHi4Vq5cae5LT0/Xhg0bFB0dLUmKjo5WamqqtmzZYrZZtWqVCgsL1aVLF7PN2rVrlZeXZ7ZJSEhQ06ZNVatWLbNNyfsUtym+T3lqAQDgYrKzs9WzZ0/17NmT2bIAN+eW4TsjI0NJSUlKSkqSVPRgY1JSkg4dOiSLxaLRo0frH//4h7755hv9/PPPGjp0qCIiItS3b19JUvPmzXXrrbfqoYce0saNG/W///1Po0aN0sCBAxURESFJuu++++Tj46ORI0dqx44d+vTTTzV9+nSNHTvWrOOJJ57Q0qVL9eqrr2r37t16/vnntXnzZo0aNUqSylULAAAAqhEHzb5Sob777jtzyqWSr2HDhhmGUTTF33PPPWfUq1fP8PX1NW6++WZjz549Ntc4deqUMWjQIKNGjRpGcHCwMWLECOPs2bM2bbZt22bccMMNhq+vr3HFFVcYL7/8cqlaPvvsM+Oaa64xfHx8jJYtWxqLFy+2OV6eWi6GqQYBoHpjqkHA9ZU3r1kMwzCclvxRLunp6QoJCVFaWhrjvwGgGrJarapRo4akor/+BgYGOrkiAH9W3rzmlsNOAAAAAHdE+AYAAAAchPANAAAAOEiVW+ESAICqxtvbW1OmTDG3AbgvHrh0AzxwCQAA4Np44BIAAABwMQw7AQDAxRUUFGjr1q2SpPbt28vT09PJFQG4XIRvAABcXHZ2tjp37iyJeb4Bd8ewEwAAAMBBCN8AAACAgxC+AQAAAAchfAMAAAAOQvgGAAAAHITwDQAAADgIUw0CAODivL29NXHiRHMbgPtieXk3wPLyAAAAro3l5QEAAAAXw7ATAABcXGFhoXbt2iVJat68uTw86DsD3BXhGwAAF5eVlaVWrVpJYnl5wN3xqzMAAADgIIRvAAAAwEEI3wAAAICDEL4BAAAAByF8AwAAAA5C+AYAAAAchKkGAQBwcd7e3vr73/9ubgNwXywv7wZYXh4AAMC1sbw8AAAA4GIYdgIAgIsrLCzUoUOHJEmNGjVieXnAjRG+AQBwcVlZWYqKipLE8vKAu+NXZwAAAMBBCN8AAACAgxC+AQAAAAchfAMAAAAOQvgGAAAAHITwDQAAADgIUw0CAODivLy89Nhjj5nbANwX/4IBAHBxvr6+mjlzprPLAFABGHYCAAAAOAg93wAAuDjDMHTy5ElJUp06dWSxWJxcEYDLRfgGAMDFZWZmKiwsTBLLywPujmEnAAAAgIMQvgEAAAAHIXwDAAAADkL4BgAAAByE8A0AAAA4COEbAAAAcBCmGgQAwMV5eXlp2LBh5jYA98W/YAAAXJyvr69mz57t7DIAVACGnQAAAAAOQs83AAAuzjAMZWZmSpICAgJYXh5wY/R8AwDg4jIzM1WjRg3VqFHDDOEA3BPhGwAAAHAQwjcAAADgIIRvAAAAwEEI3wAAAICDEL4BAAAAByF8AwAAAA7CPN8AALg4T09P3XPPPeY2APdF+AYAwMX5+flp/vz5zi4DQAVg2AkAAADgIIRvAAAAwEEI3wAAuDir1SqLxSKLxSKr1erscgDYgfANAAAAOAjhGwAAAHAQwjcAAADgIFU2fEdGRprj40q+4uPjJUk9evQodeyRRx6xucahQ4cUFxengIAAhYWFady4ccrPz7dps3r1arVv316+vr66+uqrNXv27FK1zJw5U5GRkfLz81OXLl20cePGSvu8AQAA4LqqbPjetGmTjh49ar4SEhIkSf379zfbPPTQQzZtpkyZYh4rKChQXFyccnNztW7dOs2ZM0ezZ8/WhAkTzDYHDx5UXFycevbsqaSkJI0ePVoPPvigli1bZrb59NNPNXbsWE2cOFFbt25V27ZtFRsbq+PHjzvgXQAAAIArsRiGYTi7CEcYPXq0Fi1apH379slisahHjx669tprNW3atDLbf/vtt7r99tt15MgR1atXT5L09ttva/z48Tpx4oR8fHw0fvx4LV68WNu3bzfPGzhwoFJTU7V06VJJUpcuXdSpUyfNmDFDklRYWKiGDRvq8ccf11NPPVWu2tPT0xUSEqK0tDQFBwfb8S4AANyR1WpVjRo1JEkZGRkKDAx0ckUA/qy8ea3K9nyXlJubq48++kgPPPCALBaLuf/jjz9WnTp11KpVKz399NPKzMw0jyUmJqp169Zm8Jak2NhYpaena8eOHWabmJgYm3vFxsYqMTHRvO+WLVts2nh4eCgmJsZsU5acnBylp6fbvAAA1Zenp6duu+023XbbbSwvD7i5arG8/IIFC5Samqrhw4eb++677z41btxYERER+umnnzR+/Hjt2bNHX375pSQpJSXFJnhLMj9OSUm5YJv09HRlZWXpzJkzKigoKLPN7t27z1vv5MmT9cILL1z25wsAqFr8/Py0ePFiZ5cBoAJUi/D93nvvqXfv3oqIiDD3Pfzww+Z269atVb9+fd18883av3+/rrrqKmeUaXr66ac1duxY8+P09HQ1bNjQiRUBAACgIlT58P3rr79qxYoVZo/2+XTp0kWS9Msvv+iqq65SeHh4qVlJjh07JkkKDw83/1u8r2Sb4OBg+fv7y9PTU56enmW2Kb5GWXx9feXr61u+TxAAAABuo8qP+Z41a5bCwsIUFxd3wXZJSUmSpPr160uSoqOj9fPPP9vMSpKQkKDg4GC1aNHCbLNy5Uqb6yQkJCg6OlqS5OPjow4dOti0KSws1MqVK802AABcjNVqVWBgoAIDA1leHnBzVTp8FxYWatasWRo2bJi8vM518u/fv18vvviitmzZouTkZH3zzTcaOnSounfvrjZt2kiSevXqpRYtWmjIkCHatm2bli1bpmeffVbx8fFmr/QjjzyiAwcO6Mknn9Tu3bv15ptv6rPPPtOYMWPMe40dO1b//e9/NWfOHO3atUuPPvqorFarRowY4dg3AwDg1jIzM20mBgDgnqr0sJMVK1bo0KFDeuCBB2z2+/j4aMWKFZo2bZqsVqsaNmyofv366dlnnzXbeHp6atGiRXr00UcVHR2twMBADRs2TJMmTTLbREVFafHixRozZoymT5+uBg0a6N1331VsbKzZ5t5779WJEyc0YcIEpaSk6Nprr9XSpUtLPYQJAACAqq/azPPtzpjnGwCqN+b5Blwf83wDAAAALobwDQAAADgI4RsAAABwkCr9wCUAAFWBh4eHbrzxRnMbgPsifAMA4OL8/f21evVqZ5cBoALw6zMAAADgIIRvAAAAwEEI3wAAuDir1aq6deuqbt26LC8PuDnGfAMA4AZOnjzp7BIAVAB6vgEAAAAHIXwDAAAADkL4BgAAAByE8A0AAAA4COEbAAAAcBBmOwEAwMV5eHioY8eO5jYA90X4BgDAxfn7+2vTpk3OLgNABeDXZwAAAMBBCN8AAACAgxC+AQBwcZmZmYqMjFRkZKQyMzOdXQ4AOzDmGwAAF2cYhn799VdzG4D7oucbAAAAcBDCNwAAAOAghG8AAADAQQjfAAAAgIMQvgEAAAAHYbYTAABcnMViUYsWLcxtAO6L8A0AgIsLCAjQjh07nF0GgArAsBMAAADAQQjfAAAAgIMQvgEAcHGZmZlq2bKlWrZsyfLygJtjzDcAAC7OMAzt3LnT3Abgvuj5BgAAAByE8A0AAAA4COEbAAAAcBDCNwAAAOAghG8AAADAQZjtBAAAF2exWNS4cWNzG4D7InwDAODiAgIClJyc7OwyAFQAhp0AAAAADkL4BgAAAByE8A0AgIvLyspSp06d1KlTJ2VlZTm7HAB2YMw3AAAurrCwUJs3bza3Abgver4BAAAAByF8AwAAAA5C+AYAAAAchPANAAAAOAjhGwAAAHAQZjsBAMAN1KlTx9klAKgAhG8AAFxcYGCgTpw44ewyAFQAhp0AAAAADkL4BgAAAByE8A0AgIvLyspSjx491KNHD5aXB9wcY74BAHBxhYWFWrNmjbkNwH3R8w0AAAA4COEbAAAAcBDCNwAAAOAghG8AAADAQQjfAAAAgIMw2wkAAG4gICDA2SUAqACEbwAAXFxgYKCsVquzywBQARh2AgAAADgI4RsAAABwEMI3AAAuLjs7W3FxcYqLi1N2drazywFgB8Z8AwDg4goKCrRkyRJzG4D7oucbAAAAcBDCNwAAAOAgVTJ8P//887JYLDavZs2amcezs7MVHx+v2rVrq0aNGurXr5+OHTtmc41Dhw4pLi5OAQEBCgsL07hx45Sfn2/TZvXq1Wrfvr18fX119dVXa/bs2aVqmTlzpiIjI+Xn56cuXbpo48aNlfI5AwAAwPVVyfAtSS1bttTRo0fN1w8//GAeGzNmjBYuXKj58+drzZo1OnLkiO6++27zeEFBgeLi4pSbm6t169Zpzpw5mj17tiZMmGC2OXjwoOLi4tSzZ08lJSVp9OjRevDBB7Vs2TKzzaeffqqxY8dq4sSJ2rp1q9q2bavY2FgdP37cMW8CAAAAXIrFMAzD2UVUtOeff14LFixQUlJSqWNpaWmqW7eu5s6dq3vuuUeStHv3bjVv3lyJiYnq2rWrvv32W91+++06cuSI6tWrJ0l6++23NX78eJ04cUI+Pj4aP368Fi9erO3bt5vXHjhwoFJTU7V06VJJUpcuXdSpUyfNmDFDklRYWKiGDRvq8ccf11NPPVXuzyc9PV0hISFKS0tTcHDw5b4tAAA3ZbVaVaNGDUlSRkaGAgMDnVwRgD8rb16rsrOd7Nu3TxEREfLz81N0dLQmT56sRo0aacuWLcrLy1NMTIzZtlmzZmrUqJEZvhMTE9W6dWszeEtSbGysHn30Ue3YsUPt2rVTYmKizTWK24wePVqSlJubqy1btujpp582j3t4eCgmJkaJiYkXrD0nJ0c5OTnmx2lpaZKK/qcCAKqfkqtbpqenM+MJ4IKKc9rF+rWrZPju0qWLZs+eraZNm+ro0aN64YUX1K1bN23fvl0pKSny8fFRzZo1bc6pV6+eUlJSJEkpKSk2wbv4ePGxC7VJT09XVlaWzpw5o4KCgjLb7N69+4L1T548WS+88EKp/Q0bNrz4Jw8AqNIiIiKcXQKACzh79qxCQkLOe7xKhu/evXub223atFGXLl3UuHFjffbZZ/L393diZeXz9NNPa+zYsebHhYWFOn36tGrXri2LxeLEylxTenq6GjZsqMOHDzMsxw68jxWD99F+vIcVg/fRfryHFaO6vI+GYejs2bMX/QW5SobvP6tZs6auueYa/fLLL7rllluUm5ur1NRUm97vY8eOKTw8XJIUHh5ealaS4tlQSrb58wwpx44dU3BwsPz9/eXp6SlPT88y2xRf43x8fX3l6+tb6nPAhQUHB1fpf9SOwvtYMXgf7cd7WDF4H+3He1gxqsP7eKEe72JVdraTkjIyMrR//37Vr19fHTp0kLe3t1auXGke37Nnjw4dOqTo6GhJUnR0tH7++WebWUkSEhIUHBysFi1amG1KXqO4zf+3d/9RNd9/HMCft9YPQqskLIm0NjVlRYUdwuRsa36LQ90crJpfycR2kLGZHTMs8nOrZA4lLWOx6YTtlKgkv+KIloqkRKLidr9/dPp8b8u93cT9dPN8nHPPud37/rw/r8/nXMezT6/7/tTPoa+vD2dn5wZjamtrkZSUJIwhIiIiotdLmwzfX3zxBU6ePIm8vDykpKRg3Lhx0NXVxdSpU2FsbIyZM2ciODgYycnJyMjIwIwZM+Du7g43NzcAwKhRo9C3b1/4+Pjg/PnzOHbsGJYtW4Y5c+YIV6QDAgJw48YNhISEICcnB+Hh4YiJicHChQuFOoKDg7Fz505ERUXhypUrCAwMRGVlJWbMmCHKeSEiIiIicbXJtpOCggJMnToVpaWlMDc3x5AhQ3D69GmYm5sDADZs2AAdHR1MmDAB1dXV8PT0RHh4uLC9rq4uDh8+jMDAQLi7u8PIyAhSqRSrVq0SxvTq1QtHjhzBwoULsWnTJlhaWmLXrl3w9PQUxnh7e6OkpAQrVqzAnTt34OTkhKNHjzb6Eia1jIGBAUJDQxu16lDz8Dy+HDyPLcdz+HLwPLYcz+HLwfPYUJtc55uIiIiIqDVqk20nREREREStEcM3EREREZGGMHwTEREREWkIwzcRERERkYYwfFObY21tDYlE0uCxdu1ascvSStXV1XBycoJEIkFWVpbY5WidTz/9FFZWVjA0NES3bt3g4+ODoqIiscvSGnl5eZg5cyZ69eqFdu3awcbGBqGhoaipqRG7NK3z7bffYtCgQWjfvj1v2tYMW7ZsgbW1NQwNDeHq6troBnyk2qlTp+Dl5YXu3btDIpHgt99+E7ukVoHhm9qkVatW4fbt28Jj3rx5YpeklUJCQpq8TS4p5+HhgZiYGFy9ehVxcXHIzc3FxIkTxS5La+Tk5KC2thbbt2/HpUuXsGHDBmzbtg1fffWV2KVpnZqaGkyaNAmBgYFil6I19u/fj+DgYISGhiIzMxOOjo7w9PRscAM+Uq2yshKOjo7YsmWL2KW0KlxqkNoca2trBAUFISgoSOxStFpiYiKCg4MRFxcHe3t7nDt3Dk5OTmKXpdUOHTqEsWPHorq6Gnp6emKXo5XWrVuHrVu34saNG2KXopUiIyMRFBSE8vJysUtp9VxdXTFgwABs3rwZQN1dqnv06IF58+Zh6dKlIlenfSQSCeLj4zF27FixSxEdr3xTm7R27VqYmZmhf//+WLduHZ49eyZ2SVqluLgYs2fPRnR0NNq3by92OW1CWVkZfv31VwwaNIjBuwUePHgAU1NTscugNq6mpgYZGRkYOXKk8JqOjg5GjhyJ1NRUESujtoDhm9qc+fPnY9++fUhOToa/vz/WrFmDkJAQscvSGnK5HH5+fggICICLi4vY5Wi9JUuWwMjICGZmZsjPz0dCQoLYJWmt69evIywsDP7+/mKXQm3cvXv3IJPJGt2R2sLCAnfu3BGpKmorGL5JKyxdurTRlyj/+8jJyQEABAcHY9iwYejXrx8CAgKwfv16hIWFobq6WuSjEJe65zAsLAwVFRX48ssvxS65VWrOZxEAFi9ejHPnzuHPP/+Erq4ufH198bp3+zX3HAJAYWEhRo8ejUmTJmH27NkiVd66vMh5JCLxseebtEJJSQlKS0tVjunduzf09fUbvX7p0iU4ODggJycHdnZ2r6rEVk/dczh58mT8/vvvkEgkwusymQy6urqYNm0aoqKiXnWprVpLPosFBQXo0aMHUlJS4O7u/qpKbPWaew6LioowbNgwuLm5ITIyEjo6vG4EvNhnkT3f6qmpqUH79u1x4MCBBj3KUqkU5eXl/AvWC2DP9/+9IXYBROowNzeHubn5C22blZUFHR0ddOnS5SVXpV3UPYc//fQTvvnmG+HnoqIieHp6Yv/+/XB1dX2VJWqFlnwWa2trAeC1/ytMc85hYWEhPDw84OzsjIiICAZvBS35LJJq+vr6cHZ2RlJSkhAWa2trkZSUhLlz54pbHGk9hm9qU1JTU5GWlgYPDw907NgRqampWLhwIaZPnw4TExOxy9MKVlZWDX7u0KEDAMDGxgaWlpZilKSV0tLScPbsWQwZMgQmJibIzc3F8uXLYWNj81pf9W6OwsJCDBs2DD179sQPP/yAkpIS4b2uXbuKWJn2yc/PR1lZGfLz8yGTyYR1+/v06SP8G6eGgoODIZVK4eLigoEDB2Ljxo2orKzEjBkzxC5Nazx69AjXr18Xfr558yaysrJgamra6P+a14qcqA3JyMiQu7q6yo2NjeWGhobyd999V75mzRp5VVWV2KVprZs3b8oByM+dOyd2KVolOztb7uHhITc1NZUbGBjIra2t5QEBAfKCggKxS9MaERERcgDPfVDzSKXS557H5ORksUtr1cLCwuRWVlZyfX19+cCBA+WnT58WuyStkpyc/NzPnVQqFbs0UbHnm4iIiIhIQ9g8R0RERESkIQzfREREREQawvBNRERERKQhDN9ERERERBrC8E1EREREpCEM30REREREGsLwTURERESkIQzfREREREQawvBNRERERKQhDN9ERERERBrC8E1ERA1ERkZCIpFAIpEgLy9P7HLUUlNTA1tbW0gkEhw4cEDpOLlcjk6dOkFHRwcWFhbw9vZGfn5+k/PPmTMHEokEUqn0ZZZNRK8hhm8iItJ6mzZtwvXr1+Hg4IAJEyYoHZebm4uKigrI5XLcvXsXMTEx8PLyanL+JUuWQF9fH9HR0cjIyHiZpRPRa4bhm4iItFpFRQW+//57AMCyZcsgkUiUju3WrRsuXLiAo0ePonfv3gCA7OxsnD9/XuU+rKysIJVKIZfLsXz58pdXPBG9dhi+iYhIq23duhWlpaWwsrLCpEmTVI41MjKCg4MDPD09sXr1auH1rKysJvezaNEiAEBiYiKvfhPRC2P4JiIirSWTybB582YAwNSpU6Gjo/5/a+7u7sLzixcvNjnezs4O77//PgAgLCysmZUSEdVh+CYiIq31119/4datWwCAadOmNWtba2trGBkZAVAvfCvuIzY2FhUVFc3aHxERwPBNREQvoKamBuHh4fDw8IC5uTn09fXRtWtXfPTRR9izZw9qa2ubnKO0tBQhISGws7NDu3btYGFhgQ8//BDx8fEA1Ft1JSYmBgBga2uL9957r1nHIJFIYGNjA0D98F3/Zc7Hjx8jISGhWfsjIgIYvomIqJny8vLg6OiIOXPm4MSJE7h37x6ePn2K4uJiJCYmwsfHB0OHDkVZWZnSOS5cuAB7e3usW7cO165dQ1VVFe7evYvjx49j/Pjx8Pf3V6uW5ORkAICbm1uzjyM1NRUXLlwAABQUFODBgwdNbtOzZ0907doVQF3vNxFRczF8ExGR2h49eoQRI0YgJycHADB27FgcOnQI6enpiI2NxdChQwEA//zzD7y8vCCTyRrNUV5ejtGjR6O4uBgA4OPjg8TERKSnp2Pfvn1wd3fHjh07sG3bNpW1FBQUCFfEBwwY0KzjePbsGQICAiCXy4XXLl26pNa2AwcOBACcPHmyWfskIgIYvomIqBm+/vpr3LhxA0Ddsn7x8fHw8vKCs7MzJk6ciOTkZKEvOiUlBTt27HjuHEVFRQCAjRs3Yvfu3Rg9ejScnZ3h7e2Nv//+G2PGjEFaWprKWlJSUoTn/fv3b9ZxbNiwAdnZ2Q1eU7f1xNnZGQBQWFgo/AJBRKQuhm8iIlJLdXU1du3aBQCwt7fHypUrG42RSCQIDw+HmZkZAAgrkSjOERkZCaDuavWCBQsazaGrq4vt27fD0NBQZT0FBQXC8y5duqh9HP/++69Q+6BBg4TX1Q3fivuq/0WEiEhdDN9ERKSWjIwMlJeXAwD8/Pygq6v73HGdOnXC5MmTAQCXL1/G7du3hffS09OFOaZPn650XxYWFvD09FRZT0lJifDcxMREnUMAAMydOxePHz+GsbExYmNj0bFjRwDqh29TU1Ph+Z07d9TeLxERwPBNRKS16lcCacmj/iq0OhTDqaurq8qxiu8rbqf4vL59QxkXFxeV7yt+oVPd8H3w4EEcPnwYALB27Vp0794dDg4OjWpTRXFflZWVam1DRFSP4ZuIiNSiGHabavOoXxHkv9vdv39feG5ubq5yjqbeV2xLefLkicqxQN1t6OfPnw+grt2kfkWV+iUKS0pKcPfu3SbnUdyXnp5ek+OJiBS9IXYBRET0Yq5cudLiObp16/ZC20kkkhbvu6UUw3lZWZnQPqLM8uXLUVhYCD09PezYsUM4BsX1wS9evIjhw4ernEfxl4k333zzBSonotcZwzcRkZZ65513NLo/xV7n4uJivP3220rHKvZCK26n2LJRUlKicg7Fnu7nUQzf9+/fR8+ePZWOzczMFL78GRISAnt7e+G9fv36Cc/VCd+KV++trKxUjiUi+i+2nRARkVrqe6MBNLkM4JkzZ567nWLozcjIUDlHenq6yvcVr1hfu3ZN6bja2lr4+/tDJpPB1tYWy5YtUzqPOn3f9fsyMDBAnz59mhxPRKSI4ZuIiNTi7OwstFlERUUpvYV8RUWFcNv3vn37NmhtcXFxgbGxMQBgz549SvdVXFyMY8eOqazHxcVF6Ps+e/as0nFbtmwRgvy2bdsaLWFoYmKCt956C4B64bt+X/3792fPNxE1G8M3ERGpxcDAALNmzQJQF1JXr17daIxcLsfcuXNx7949AHXL+ikyNDSEr68vgLoQu2nTpkZz1F+prqqqUlmPvr6+sKqK4pV2RUVFRcKVbqlUqrSlpP7qd1N3uayurhZuzjNq1CiVY4mInofhm4iI1LZixQr07t0bALBy5UpMnDgRR44cQWZmJuLi4jB8+HDs3r0bAODu7o7PPvus0RwrV64UVkMJCgqCr68vjh07hszMTMTExOCDDz5AQkKCcBt3QPkXPMeMGQOgLnxXVFQ0en/BggV4+PAhOnfujPXr1ys9rvq+74cPHyI/P1/puFOnTuHp06cAgHHjxikdR0SkDMM3ERGprWPHjkhKShK+7BkXF4dPPvlEuL38iRMnAACDBw/G4cOHn3sjHlNTUxw9elT4wmR0dHSD28unpKTAz89PWAoQgNK7Xfr6+sLAwABVVVWIj49v8N4ff/yBAwcOAAB+/PFH4a6bz6Nu3/fevXsB1PWuOzk5KR1HRKQMwzcRETWLtbU1zp8/j82bN2Po0KEwMzODnp4eLCwsMHr0aERHR+PUqVMNVjn5L0dHR1y+fBmLFi2Cra0tDAwM0LlzZ3h4eGDv3r2IiIjAw4cPhfH1feL/ZWZmhvHjxwP4fzAG6tbirm95GTFiBHx8fFQekzrhu6qqCgcPHgQAfP755yrnIyJSRiKXy+ViF0FERPRfs2bNws8//wxLS0vcunVL6bi0tDS4ublBV1cXubm5KpccbIk9e/bAx8cHZmZmyMvLQ4cOHV7JfoiobeOVbyIianWePHmChIQEAICbm5vKsa6urhg/fjxkMhm+++67V1JPbW0t1qxZAwBYvHgxgzcRvTCGbyIi0rjc3Fwo+8OrTCZDYGCgsGKKVCptcr41a9bgjTfeQEREBAoKCl5qrQAQGxuLK1euwMrKSrhFPRHRi+AdLomISONWr16NM2fOYMqUKXB1dUWXLl3w5MkTZGdnY+fOncjMzAQAjBw5Eh9//HGT89nZ2eGXX35Bbm4u8vPzYWlp+VLrlclkCA0NxfDhw9GuXbuXOjcRvV7Y801ERBrn5+eHqKgolWMGDx6MhIQElauUEBFpG4ZvIiLSuKtXryIuLg7Hjx9HXl4eSkpK8PTpU5iZmcHFxQXe3t6YMmUKdHTYHUlEbQvDNxERERGRhvCSAhERERGRhjB8ExERERFpCMM3EREREZGGMHwTEREREWkIwzcRERERkYYwfBMRERERaQjDNxERERGRhjB8ExERERFpCMM3EREREZGGMHwTEREREWnI/wAqXLcbFycuOQAAAABJRU5ErkJggg==", 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GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('pca', PCA(n_components=2)),\n",
+       "                                       ('linreg', LinearRegression())]),\n",
+       "             param_grid={'pca__n_components': range(1, 20)},\n",
+       "             scoring='neg_mean_squared_error')
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" + ], + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", + " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('pca', PCA(n_components=2)),\n", + " ('linreg', LinearRegression())]),\n", + " param_grid={'pca__n_components': range(1, 20)},\n", + " scoring='neg_mean_squared_error')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "param_grid = {'pca__n_components': range(1, 20)}\n", "grid = skm.GridSearchCV(pipe,\n", @@ -1460,10 +9513,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "5e0c5a96", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.891415Z", + "iopub.status.busy": "2023-07-25T23:59:43.891279Z", + "iopub.status.idle": "2023-07-25T23:59:43.997785Z", + "shell.execute_reply": "2023-07-25T23:59:43.997406Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOIXwDAAAADiF8AwAAAA4hfAMAAAAOKZfhe8KECerUqZOCgoIUFham3r17a8eOHS41GRkZGjZsmKpXr64qVaqoT58+Sk5Odqk5cOCA4uLiFBAQoLCwMD3xxBPKyclxqVmyZIk6dOggX19fNW7cWFOmTCnUn7feekv169eXn5+foqOjtWbNmsvuCwAAACq+chm+ly5dqmHDhmnVqlWaP3++srOz1aNHD6Wnp9s1o0aN0ldffaWZM2dq6dKlOnz4sO688057e25uruLi4pSVlaWVK1fqo48+0pQpU/Tss8/aNfv27VNcXJxuuukmJSQkaOTIkXrwwQc1b948u2b69OkaPXq0xo0bpw0bNqhdu3aKjY3VkSNHit0XAAAAVBKmAjhy5IiRZJYuXWqMMSYlJcV4e3ubmTNn2jXbtm0zkkx8fLwxxpivv/7aeHh4mKSkJLvmnXfeMcHBwSYzM9MYY8yTTz5pWrVq5dLWvffea2JjY+3X11xzjRk2bJj9Ojc310RGRpoJEyYUuy+XkpqaaiSZ1NTUYtUDAADAWcXNa+XyyvevpaamSpJCQ0MlSevXr1d2dra6d+9u1zRv3lxRUVGKj4+XJMXHx6tNmzaqVauWXRMbG6u0tDRt3brVrjn/GAU1BcfIysrS+vXrXWo8PDzUvXt3u6Y4ffm1zMxMpaWluSwAAAAo/8p9+M7Ly9PIkSN13XXXqXXr1pKkpKQk+fj4qGrVqi61tWrVUlJSkl1zfvAu2F6w7WI1aWlpOnv2rI4dO6bc3Nwia84/xqX68msTJkxQSEiIvdStW7eY7wYAAADKsnIfvocNG6YtW7Zo2rRppd2VEjN27Filpqbay88//1zaXQIAAEAJ8CrtDrhj+PDhmjNnjpYtW6Y6derY68PDw5WVlaWUlBSXK87JyckKDw+3a349K0nBDCTn1/x6VpLk5GQFBwfL399fnp6e8vT0LLLm/GNcqi+/5uvrK19f38t4JwAAAFAelMsr38YYDR8+XJ9//rkWLVqkBg0auGzv2LGjvL29tXDhQnvdjh07dODAAcXExEiSYmJitHnzZpdZSebPn6/g4GC1bNnSrjn/GAU1Bcfw8fFRx44dXWry8vK0cOFCu6Y4fQEAAEDlUC6vfA8bNkxTp07VF198oaCgIHvsdEhIiPz9/RUSEqKhQ4dq9OjRCg0NVXBwsEaMGKGYmBh17txZktSjRw+1bNlSAwYM0Msvv6ykpCQ9/fTTGjZsmH3V+ZFHHtGkSZP05JNPasiQIVq0aJFmzJihuXPn2n0ZPXq0Bg4cqKuvvlrXXHONXn/9daWnp2vw4MF2ny7VFwAAAFQSzky+UrIkFblMnjzZrjl79qx57LHHTLVq1UxAQIC54447TGJiostx9u/fb3r27Gn8/f1NjRo1zJ/+9CeTnZ3tUrN48WJz1VVXGR8fH9OwYUOXNgr885//NFFRUcbHx8dcc801ZtWqVS7bi9OXi2GqQQAAgLKtuHnNMsaY0ov+KI60tDSFhIQoNTVVwcHBpd0dAAAA/Epx81q5HPMNAAAAlEeEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIcUO32+++abefPNNnTx50q0Gf/75Z915553q06ePW8cBAAAAyhuv4haOHDlSlmWpe/fuqlatWqHtW7duVZs2beTh4aGcnJwLHictLU2zZ8+WZVm/rccAAABAOVXiw06MMSV9SAAAAKBCYMw3AAAA4BDCNwAAAOAQwjcAAADgEMI3AAAA4BDCNwAAAOAQwjcAAADgEMI3AAAA4JBiP2SnwBdffKF169YVWn/o0CH7848//viC+59fBwAAAFQmlinmU3E8PDxK7KmUxhhZlqXc3NwSOV5Fl5aWppCQEKWmpio4OLi0uwMAAIBfKW5eu6wr3zy9EgAAAPjtih2+J0+efCX7AQAAAFR4xQ7fAwcOvJL9AAAAACo8ZjsBAAAAHEL4BgAAABxy2VMNXo6NGzdq9+7dsixLDRs21FVXXXUlmwMAAADKtMsK3zt37pQkVa1aVWFhYResW7RokR577DHt2rXLZX29evX02muv6fbbb/8NXQUAAADKt2IPO9m0aZOaN2+uFi1a6Ntvv71g3bx583TLLbdo165dMsa4LPv371efPn00derUEuk8AAAAUJ4UO3x/9913kqSQkBD169evyJozZ85oyJAhysnJkTFGoaGhGjBggMaMGaNu3bpJkvLy8jR8+HCdOHGiBLoPAAAAlB/FHnayZs0aWZaluLg4eXt7F1kzdepUJSYmyrIstWrVSt99953Cw8Pt7VOmTNGQIUOUmpqqTz/9VCNGjHD/DAAAAIByothXvrdt2yZJuv766y9YM3PmTPvzN9980yV4S9KgQYPUs2dPGWPsK+kAAABAZVHs8H3w4EFJUosWLYrcnpeXp5UrV8qyLNWpU0c33nhjkXX33HOPJGnLli2X2VUAAACgfCt2+D59+rQkKTg4uMjtW7duVXp6uiTphhtuuOBxmjdvLkk6fvx4sTsJAAAAVATFDt9+fn6SpFOnThW5ffXq1fbnHTt2vORxMjIyits0AAAAUCEUO3xHRERIkhISEorc/v3339ufd+7c+YLHOXnypCSpSpUqxW0aAAAAqBCKHb6vvvpqGWM0efLkQtvS09P11VdfSZKCgoJ09dVXX/A4O3bskCTVqVPncvsKAAAAlGvFDt8Fc3v/8MMPeuihh5SWliZJSklJ0aBBg5SSkiLLsnTXXXfJ09PzgsdZtmyZJKlVq1bu9BsAAAAodyxjjClucdeuXbVixQpZliUvLy/VqFFDycnJ9hMsfXx8tHnzZjVp0qTI/c+cOaPw8HClp6frjTfe0PDhw0vsRCqytLQ0hYSEKDU19YI3vAIAAKD0FDevFfvKtyT973//U+vWrWWMUXZ2thITE5WXlydjjDw8PPT2229fMHhL0kcffWTPmhIbG3s5TQMAAADlXrGfcClJYWFhWr9+vd577z19+eWXOnDggHx8fNShQwc99thj6tSp00X3nz9/vjp27Kg6depcNKQDAAAAFdFlDTtB6WDYCQAAQNl2RYadAAAAAPjtCN8AAACAQwjfAAAAgEOKfcPl888/X+KNP/vssyV+TAAAAKCsKvYNlx4eHrIsq0Qbz83NLdHjVVTccAkAAFC2FTevXdZUg5JUUpOjlHSQBwAAAMq6yw7f/v7+uv322/XAAw+oRYsWV6JPAAAAQIVU7GEnv/vd77R48WLl5eXZV607duyoAQMGqG/fvqpZs+YV7WhlxrATAACAsq24ee2yHrJz+PBhffrpp/rkk0+0efPm/ANYlry8vBQbG6v7779ft99+u3x9fd0/A9gI3wAAAGXbFQnf59u0aZM++ugjffbZZ0pKSso/mGUpODhYd999t+6//35df/31v633cEH4BgAAKNuuePgukJeXpwULFujjjz/W7NmzdebMGXtYSlRUlAYMGKD7779fTZs2daeZSo3wDQAAULY5Fr7Pl56erlmzZumjjz7SkiVLXMaHX3vttfr+++9LqqlKhfANAABQthU3r5XoEy4DAwM1YMAALViwQAcOHNDzzz8vX19fGWO0fv36kmwKAAAAKHcue6rB4oiPj9d//vMfzZgxQ5mZmVeiCQAAAKDcKbHwvWfPHn3yySf65JNPtHfvXkn5D+Tx8/PTbbfdpgceeKCkmgIAAADKJbfC98mTJzVt2jT95z//0erVqyXlB27LstS1a1cNGDBAd999N+OUAQAAAP2G8J2dna2vvvpK//nPf/TNN98oOzvbfuR806ZNNWDAAA0YMEBRUVEl3lkAAACgPCt2+F6+fLk++eQTzZw5UykpKXbgrl69uvr27asHHnhAnTp1umIdBQAAAMq7Yofv66+/XpZlyRgjX19f3XbbbRowYIBuueUWeXldkfs2AQAAgAql2PN8e3h4yLIs+fn5KTY2VlWrVnWvYcvSBx984NYxKgvm+QYAACjbSvwhOwXhuyTl5uaW6PEqKsI3AABA2VbcvHZZ40VK8GGYAAAAQKVT7PCdl5d3JfsBAAAAVHgl+nh5AAAAABdWLsP3smXL1KtXL0VGRsqyLM2ePdtlu2VZRS4TJ060a+rXr19o+4svvuhynE2bNqlr167y8/NT3bp19fLLLxfqy8yZM9W8eXP5+fmpTZs2+vrrr122G2P07LPPKiIiQv7+/urevbt27dpVcm8GAAAAyo1yGb7T09PVrl07vfXWW0VuT0xMdFk+/PBDWZalPn36uNQ9//zzLnUjRoywt6WlpalHjx6qV6+e1q9fr4kTJ2r8+PF677337JqVK1eqX79+Gjp0qH744Qf17t1bvXv31pYtW+yal19+WW+++abeffddrV69WoGBgYqNjVVGRkYJvysAAAAo64o920lZZVmWPv/8c/Xu3fuCNb1799apU6e0cOFCe139+vU1cuRIjRw5ssh93nnnHf31r39VUlKSfHx8JElPPfWUZs+ere3bt0uS7r33XqWnp2vOnDn2fp07d9ZVV12ld999V8YYRUZG6k9/+pP+/Oc/S5JSU1NVq1YtTZkyRX379i3WOTLbCQAAQNlW3LxWLq98X47k5GTNnTtXQ4cOLbTtxRdfVPXq1dW+fXtNnDhROTk59rb4+Hhdf/31dvCWpNjYWO3YsUMnT560a7p37+5yzNjYWMXHx0uS9u3bp6SkJJeakJAQRUdH2zVFyczMVFpamssCAACA8q/CP5ryo48+UlBQkO68806X9Y8//rg6dOig0NBQrVy5UmPHjlViYqJeffVVSVJSUpIaNGjgsk+tWrXsbdWqVVNSUpK97vyapKQku+78/YqqKcqECRP03HPP/YazBQAAQFlW4cP3hx9+qP79+8vPz89l/ejRo+3P27ZtKx8fH/3hD3/QhAkT5Ovr63Q3XYwdO9alf2lpaapbt24p9ggAAAAloUIPO/n++++1Y8cOPfjgg5esjY6OVk5Ojvbv3y9JCg8PV3JysktNwevw8PCL1py//fz9iqopiq+vr4KDg10WAAAAlH8VOnx/8MEH6tixo9q1a3fJ2oSEBHl4eCgsLEySFBMTo2XLlik7O9uumT9/vpo1a6Zq1arZNeffxFlQExMTI0lq0KCBwsPDXWrS0tK0evVquwYAAACVR7kcdnL69Gnt3r3bfr1v3z4lJCQoNDRUUVFRkvJD7syZM/XKK68U2j8+Pl6rV6/WTTfdpKCgIMXHx2vUqFG6//777WB933336bnnntPQoUM1ZswYbdmyRW+88YZee+01+zh//OMfdcMNN+iVV15RXFycpk2bpnXr1tnTEVqWpZEjR+rvf/+7mjRpogYNGuiZZ55RZGTkRWdnAQAAQAVlyqHFixcbSYWWgQMH2jX/+te/jL+/v0lJSSm0//r16010dLQJCQkxfn5+pkWLFuaFF14wGRkZLnUbN240Xbp0Mb6+vqZ27drmxRdfLHSsGTNmmKZNmxofHx/TqlUrM3fuXJfteXl55plnnjG1atUyvr6+plu3bmbHjh2Xdb6pqalGkklNTb2s/QAAAOCM4ua1cj/Pd2XAPN8AAABlG/N8AwAAAGVMscZ8N2zYsMQbtixLe/bsKfHjAgAAAGVVscJ3wfR7l2JZliTp1yNZilpfsA4AAACoLIoVvgcOHHjR7QkJCdq4caOMMapatarat29vP9UxOTlZCQkJOnnypCzLUrt27Yo19R8AAABQ0RQrfE+ePPmC2z788ENNnTpVderU0SuvvKI77rhDXl6uh83NzdWsWbP0xBNP6Mcff9SwYcM0dOhQ93oOAAAAlDNuzXaybt06XXvttapZs6bWrl2ryMjIi9YnJiaqY8eOOn78uFasWKGrr776tzZdqTDbCQAAQNnmyGwnr732mnJzc/WXv/zlksFbkiIiIvSXv/xF2dnZevXVV91pGgAAACh33Arf33//vSQpOjq62Pt07txZkrR8+XJ3mgYAAADKHbfC99GjRyVJmZmZxd6noLZgXwAAAKCycCt816xZU5L0zTffFHufr7/+WpJUo0YNd5oGAAAAyh23wvfNN98sY4xeffVVrVix4pL1K1eu1GuvvSbLstStWzd3mgYAAADKHbfC91NPPSVfX19lZmaqW7duGjlypBISElwepmOMUUJCgkaNGqWbb75ZGRkZ8vHx0VNPPeV25wEAAIDyxK2pBiVpxowZuv/++5WTk2M/tdLHx0ehoaGyLEvHjx9XVlaWpPwg7uXlpY8//lh9+/Z1v/eVBFMNAgAAlG2OTDUoSffcc49WrFihjh07yhgjY4wyMzOVmJiow4cPKzMz017foUMHLV++nOANAACASqlYT7i8lE6dOmnt2rVat26dFixYoM2bN+vEiROSpGrVqqlNmzbq3r27OnXqVBLNAQAAAOVSiYTvAldffTVPrQQAAAAuwO1hJwAAAACKp0SvfEvSwYMHlZSUpDNnzqhTp07y9/cv6SYAAACAcqlErnyfOnVKzzzzjOrWrat69eopOjpaN910k/bt2+dSN23aNN1zzz166KGHSqJZAAAAoFxx+8r3rl279Pvf/1579+51md+7YNrB83Xu3Fn333+/jDEaOHCgunTp4m7zAAAAQLnh1pXvjIwMxcXFac+ePQoICNCTTz6pOXPmXLC+fv36uummmyRJX375pTtNAwAAAOWOW1e+33nnHe3evVuBgYH6/vvvddVVV11yn549e2rhwoWKj493p2kAAACg3HHryvesWbNkWZb++Mc/Fit4S1K7du0k5Q9XAQAAACoTt8L3tm3bJEk9evQo9j7Vq1eXJKWkpLjTNAAAAFDuuBW+T58+LUmqUqVKsffJzMyUJHl7e7vTNAAAAFDuuBW+C65i79+/v9j7bN26VZIUHh7uTtMAAABAueNW+O7QoYMkadmyZcXe5+OPP5ZlWYqJiXGnaQAAAKDccSt833XXXTLG6L333tOBAwcuWf/666/bQb1fv37uNA0AAACUO26F7wEDBqht27bKyMjQjTfeqG+++abQg3aMMVq7dq369++vP/3pT7IsS127dlXPnj3d7jwAAABQnljm/LT8Gxw4cEBdunTRwYMHZVmWAgICdObMGUlSjRo1dOrUKfsmS2OMGjVqpBUrVigsLMz93lcSaWlpCgkJUWpqqoKDg0u7OwAAAPiV4uY1t658S1JUVJQSEhLUr18/eXh4KD09XcYYGWN09OhRZWRk2FfD77nnHq1Zs4bgDQAAgErJ7Svf5/vpp580d+5crVu3TkeOHFFubq6qV6+u9u3bq1evXmratGlJNVWpcOUbAACgbCtuXivR8I0rg/ANAABQthU3r3m500jBDCe1a9eWp6dnsfbJy8vTwYMHJeUPWQEAAAAqC7fCd/369eXh4aFNmzapZcuWxdpn3759atKkiTw8PJSTk+NO8wAAAEC54vYNl7911AqjXQAAAFDZuB2+L1dB6PbwcLxpAAAAoFQ5noATExMlSUFBQU43DQAAAJQqt8Z8F7As65I12dnZ2rNnj/7v//5PktSsWbOSaBoAAAAoNy4rfBc1o4kxRq1bt76sRi3L0l133XVZ+wAAAADl3WWF7wvdJHm5N0/ec889Gjly5GXtAwAAAJR3lxW+x40b5/L6ueeek2VZeuSRRy76yHjLsuTn56eIiAhde+21atSo0W/rLQAAAFCOufWESw8PD1mWpc2bNxd7nm9cPp5wCQAAULY58oTLyZMnS5Lq1KnjzmEAAACASsGt8D1w4MCS6gcAAABQ4fGkGwAAAMAhJTLPd4GTJ09q48aNOnbsmM6ePXvJWVAeeOCBkmweAAAAKNNKJHwvWbJE48aN0/Lly4u9j2VZhG8AAABUKm6H73feeUcjRoyQMeay5/sGAAAAKhO3xnxv27ZNjz/+uIwxatOmjWbPnq25c+dKyr+yvWfPHq1du1bvvPOOOnToIEnq0qWLtm7dqr1797rfewAAAKAccSt8//Of/1Rubq5q1Kih77//XrfddpuioqLs7Q0aNFDHjh31hz/8QWvXrtUTTzyh5cuXa8SIEapXr57bnQcAAADKE7fC99KlS2VZlh5//HEFBQVdtNayLL300ku6+eabtXjxYn344YfuNA0AAACUO26F74MHD0qSPaREyg/ZBbKzswvt8/DDD8sYo08++cSdpgEAAIByx63wnZGRIUmKjIy01wUGBtqfnzx5stA+jRs3liT9+OOP7jQNAAAAlDtuhe/Q0FBJUnp6ur2uZs2a9tXvnTt3Ftrn2LFjkqSUlBR3mgYAAADKHbfCd/PmzSVJu3btstcFBASoSZMmkqQvv/yy0D6ff/65pPyQDgAAAFQmboXvLl26yBij77//3mX9nXfeKWOM3nzzTU2ePFnp6ek6cuSIXn75Zb3//vuyLEs333yzWx0HAAAAyhvLuPFknNWrVysmJkahoaE6ePCg/Pz8JEnHjx9Xs2bNihzzbYyRv7+/1q1bpxYtWvz2nlciaWlpCgkJUWpqqoKDg0u7OwAAAPiV4uY1t658R0dHa/LkyXrppZdcgnb16tU1b9481a9f337yZcESFhamzz//nOANAACASsetK9+Xkp2drUWLFmnr1q3KyclRkyZNFBsbq4CAgCvVZIXElW8AAICyrbh57YqGb5QMwjcAAEDZ5siwEwAAAADFR/gGAAAAHOJVnKKPP/74ijT+wAMPXJHjAgAAAGVRscZ8e3h42E+tLLGGLUs5OTklesyKijHfAAAAZVtx81qxrnxL+fNzAwAAAPjtijXme9++fRdcNmzYoE6dOkmSWrdurYkTJ2rp0qXavn27tm/frqVLl+of//iH2rRpI0nq1KmTNmzYoL179/7mTi9btky9evVSZGSkLMvS7NmzXbYPGjRIlmW5LLfccotLzYkTJ9S/f38FBweratWqGjp0qE6fPu1Ss2nTJnXt2lV+fn6qW7euXn755UJ9mTlzppo3by4/Pz+1adNGX3/9tct2Y4yeffZZRUREyN/fX927d9euXbt+87kDAACgHDNuyMzMNB07djQeHh7mb3/7m8nLy7tgbV5envn73/9uLMsyV199tcnMzPzN7X799dfmr3/9q5k1a5aRZD7//HOX7QMHDjS33HKLSUxMtJcTJ0641Nxyyy2mXbt2ZtWqVeb77783jRs3Nv369bO3p6ammlq1apn+/fubLVu2mM8++8z4+/ubf/3rX3bNihUrjKenp3n55ZfNjz/+aJ5++mnj7e1tNm/ebNe8+OKLJiQkxMyePdts3LjR3HbbbaZBgwbm7NmzxT7f1NRUI8mkpqZe5jsFAAAAJxQ3r7kVvv/xj38Yy7LMvffeW+x97r33XuPh4WFefvlld5q2XSh833777Rfc58cffzSSzNq1a+1133zzjbEsyxw6dMgYY8zbb79tqlWr5vJLwpgxY0yzZs3s1/fcc4+Ji4tzOXZ0dLT5wx/+YIzJ/4UjPDzcTJw40d6ekpJifH19zWeffVbscyR8AwAAlG3FzWtuTTU4depUWZalQYMGFXufwYMHyxijadOmudP0JS1ZskRhYWFq1qyZHn30UR0/ftzeFh8fr6pVq+rqq6+213Xv3l0eHh5avXq1XXP99dfLx8fHromNjdWOHTt08uRJu6Z79+4u7cbGxio+Pl5S/nCdpKQkl5qQkBBFR0fbNUXJzMxUWlqaywIAAIDyz63wvWfPHklSrVq1ir1PWFiYy75Xwi233KKPP/5YCxcu1EsvvaSlS5eqZ8+eys3NlSQlJSXZ/Sjg5eWl0NBQJSUl2TW/Pq+C15eqOX/7+fsVVVOUCRMmKCQkxF7q1q17WecPAACAsqnYs50UxZybAWXXrl1q3759sfYpuNnQXMHZU/r27Wt/3qZNG7Vt21aNGjXSkiVL1K1btyvWbkkZO3asRo8ebb9OS0sjgAMAAFQAbl35btGihSTp9ddfV15e3iXr8/Ly9Nprr7ns64SGDRuqRo0a2r17tyQpPDxcR44ccanJycnRiRMnFB4ebtckJye71BS8vlTN+dvP36+omqL4+voqODjYZQEAAED551b4fuCBB2SM0erVq9W7d++LDqVITk7WnXfeqdWrV8uyLEefbnnw4EEdP35cERERkqSYmBilpKRo/fr1ds2iRYuUl5en6Ohou2bZsmXKzs62a+bPn69mzZqpWrVqds3ChQtd2po/f75iYmIkSQ0aNFB4eLhLTVpamlavXm3XAAAAoPIo1hMuLyQvL0833nijli9fLsuy5Ovrqx49eqhTp04KCwuTZVlKTk7W2rVr9d133ykzM1PGGHXp0kVLliyRh8dvy/6nT5+2r2K3b99er776qm666SaFhoYqNDRUzz33nPr06aPw8HDt2bNHTz75pE6dOqXNmzfL19dXktSzZ08lJyfr3XffVXZ2tgYPHqyrr75aU6dOlSSlpqaqWbNm6tGjh8aMGaMtW7ZoyJAheu211/Twww9LklauXKkbbrhBL774ouLi4jRt2jS98MIL2rBhg1q3bi1Jeumll/Tiiy/qo48+UoMGDfTMM89o06ZN+vHHH+Xn51es8+UJlwAAAGVbsfOau9OqnD592tx+++3GsixjWZbx8PAocinYftttt5lTp0651ebixYuNpELLwIEDzZkzZ0yPHj1MzZo1jbe3t6lXr5556KGHTFJSkssxjh8/bvr162eqVKligoODzeDBgwv1a+PGjaZLly7G19fX1K5d27z44ouF+jJjxgzTtGlT4+PjY1q1amXmzp3rsj0vL88888wzplatWsbX19d069bN7Nix47LOl6kGAQAAyrbi5jW3rnyfb+7cuXrnnXe0ZMkSnTlzxmWbv7+/brzxRj366KO69dZbS6K5SoUr3wAAAGVbcfNaiYXvAnl5edqzZ49OnDghSapWrZoaNWokT0/PkmymUiF8AwAAlG3FzWtuTTVYFA8PDzVp0qSkDwsAAACUe27NdgIAAACg+AjfAAAAgEOKNexkyJAhkiTLsvTBBx8UWv9b/PpYAAAAQEVXrBsuPTw8ZFmWJCk3N7fI9ZfDGCPLslyOhQvjhksAAICyrURvuIyKiioyZF9oPQAAAIDCihW+9+/ff1nrAQAAABTGDZcAAACAQwjfAAAAgEMI3wAAAIBDCN8AAACAQ4p1w2XDhg1LvGHLsrRnz54SPy4AAABQVrk124k7mKIQAAAAlU2xwvfAgQOvdD8AAACACq9Y4Xvy5MlXuh8AAABAhccNlwAAAIBDCN8AAACAQwjfAAAAgEOKNeb7cuzfv1/Hjh3T2bNnZYy5aO31119f0s0DAAAAZVaJhO8dO3bohRde0Jdffqm0tLRi7WNZlnJyckqieQAAAKBccDt8z549W/3791dGRsYlr3QDAAAAlZlb4fvnn3/W/fffr7Nnz6p27dp64oknFBAQoIcffliWZWnBggU6ceKE1q1bp//85z86fPiwunTpovHjx8vT07OkzgEAAAAoFyzjxuXqJ554Qq+88oqCgoK0bds2RUZGauvWrWrTpo0sy1Jubq5de/bsWQ0dOlTTp09X37599emnn5bICVQGaWlpCgkJUWpqqoKDg0u7OwAAAPiV4uY1t2Y7WbBggSzL0mOPPabIyMiL1vr7++uTTz5R+/btNW3aNP3vf/9zp2kAAACg3HErfO/fv1+SdO2119rrLMuyP//1DZUeHh56/PHHZYzRhx9+6E7TAAAAQLnjVvhOT0+XJNWtW9deFxAQYH+emppaaJ9WrVpJkjZu3OhO0wAAAEC541b4DgkJkSRlZGTY66pXr25/vmfPnkL7FATyY8eOudM0AAAAUO64Fb6bNWsmSdq7d6+9LigoSPXq1ZMkfffdd4X2mT9/viSpatWq7jQNAAAuw5msHNV/aq7qPzVXZ7J4zgZQWtwK3zExMZKkVatWuay/9dZbZYzRxIkTtXjxYnv9jBkz9MYbb8iyLF133XXuNA0AAACUO26F79///vcyxmjWrFku0woWzPd9+vRpde/eXTVr1lRQUJD69eunjIwMeXh46IknnnC78wAAAEB54lb4vvHGGzVu3DgNHjxYhw4dstdHRUVp5syZCgkJkTFGx48fV3p6uowx8vX11b///W917tzZ7c4DAAAA5YlbT7i0LEvjxo0rclvPnj21a9cu/fe//9XWrVuVk5OjJk2a6J577lHt2rXdaRYAAAAol9wK35dSvXp1/eEPf7iSTQAAAADlhlvDTgAAAAAUn1vhu3Pnzpo0aZKOHj1aUv0BAAAAKiy3wveaNWv0xz/+UbVr11bPnj31ySef2E+9BAAAAODKrfDdpEkTGWOUk5Oj7777TgMHDlStWrV03333ae7cuS7TDwIAUNnxoBsAboXvHTt2aO3atRo1apQiIiJkjNGZM2c0ffp03XbbbYqIiNDw4cO1cuXKkuovAAAAUG65fcNlx44d9corr+jnn3/WggULNGTIEHt+72PHjumdd95R165d1bBhQz3zzDPatm1bSfQbAAAAKHdKbLYTy7J088036/3331dSUpL+97//qU+fPvL19ZUxRvv379cLL7yg1q1bq0OHDnr11VdLqmkAAACgXLgiUw36+Pjojjvu0MyZM5WcnKwPPvhA3bp1k4eHh4wxSkhI4PHyAADHMeYaQGm74vN8BwUFafDgwfruu+80ZcoUVa1a9Uo3CQC4BEIoAJSOK/qES0nasGGDpk6dqmnTpikxMfFKNwf8ZmeyctTy2XmSpB+fj1WAzxX/7wEAACqZK5Iu9u7dq08//VRTp07Vzp07JUnGGElSYGCgevfurf79+1+JpgEAAIAyq8TC99GjRzVt2jRNnTpVa9askfRL4Pby8lKPHj3Uv39/3X777QoICCipZlGBcOUZqBz4v1758G8O/MKtr/709HTNmjVLn376qRYtWmQ/VKcgdMfExKh///665557VKNGDfd7C1RQ/GCqfPg3B4DKya3v9mFhYcrIyJD0S+Bu3ry5+vfvr/vuu08NGjRwv4cAAABABeFW+D579qwkKTIyUn379lX//v3Vvn37EukYSgdX41CZ8PUOAHCaWz9pBg8erP79++umm26SZVkl1ScAAACgQnIrfH/wwQcl1Q8AAIASx1+4UNZckYfs7N+/XzfffLO6det2JQ4PAAAAlEtX5Ne/9PR0LVmyhKEoAAAAcFxZ/ovHFX+8PAAAAIB8hG8AACq4xNSzevW7nfbrFbuP2VMEA3BW2bkGD6BUlOU/zQFwT8LPKfpg+T59vTlRuXm/hO2HPl6v6xpX15hbmqttnaql10GgEuKnLAAAFUhObp6++zFZHyzfp/U/nbTXd6pfTWv357/29rS0Yvdx3TZpheLaRujPPZqpQY3A0uoyUKkQvgEAqADSMrI1Y+3Pmrxivw6l5D8Ez9vTUq+2kRrSpYEa1gy0/8r19eNd9c6SPfo84ZDmbkrUvC1J6ndNlEZ0a6ywIL/SPA2gwrsi4TssLEzjxo27EocGAADn+el4uiav2K+Z635WelauJKlagLfu71xPAzrXU1hwfpg+k5Vj71O7mr9evfcqPdi1oV6et11LdhzVf1b9pP9tOKgHuzTQQ9c3VJCfd6mcD1DRXZHwXbNmTcI3gGJhzDlw+YwxWrPvhD5Yvk/ztyWr4N7JJmFVNKRLA93Rvrb8vD0veZyWkcGaMvgaxe85rhe/3a6NP6fozUW79cnqAxpxc2PdFx0lX69LHwdA8fFTDgCAciIrJ09zNh3Whyv2acuhNHv9DU1ramiXBurapMZvesZGTKPqmv3Ytfp2S5ImztuhvcfS9dxXP+qD5fv05x7NdFu7SHl48OwOoCRc8fD91VdfacaMGTp27JgaNGigBx98UB06dLjSzQIAUGGcSM/S1NU/6eP4n3TkVKYkydfLQ3d2qKMh19VXk1pBbrdhWZZ6tolQ95a1NHPdQb2+YKcOnjyrkdMT9K9lezXmlma6oWlNHqAHuMmt8L148WLde++98vPz06ZNm1S1alWX7c8884xeeOEFl3Xvv/++PvjgAw0YMMCdpgEAqPB2JZ/Shyv2a9aGg8rMyZMkhQX5auC19dXvmiiFBvqUeJvenh66LzpKvdtHavKK/Xp3yR5tS0zToMlrFdOwup7q2Vzt6lYt8XaBysKt8P3111/r2LFjuuOOOwoF702bNumFF16wJ/GvVq2aTp48qZycHP3hD39Q165dVb9+fXeaBwCgwjHGaNmuY/pg+T4t23nUXt+6drCGdmmguDaR8vG68s/IC/Dx0rCbGuu+a6L01uLd+jj+J8XvPa7b31qhuDYR+nMs0xMCv4Vb/3uXL18uy7LUvXv3QtveeecdGWNUrVo1rV+/XsePH9eaNWsUGhqqzMxMvfvuu+40DQBAhZKRnavP1hxQj9eWaeCHa7Rs51FZltSjZS1Nf7izvhreRXe0r+NI8D5ftUAfPX1rSy368w26s0NtWZY0d3Oiur+6VH/9fLOOpGU42h+gvHPryndiYqIkqVWrVoW2zZkzR5Zlafjw4Wrfvr0k6eqrr9bw4cP1/PPPa8GCBe40DQBAhXAkLUMfx/+kT1f/pJNnsiVJgT6euqdTXQ26tr7qVS8bV5frVAvQq/dcpYe6NtTEeTu0aPsRfbr6gGZtOKQHuzbQw0xPCBSLW78+Hz2a/+ewXw852bNnjw4dOiRJuuOOO1y2de3a1a75rZYtW6ZevXopMjJSlmVp9uzZ9rbs7GyNGTNGbdq0UWBgoCIjI/XAAw/o8OHDLseoX7++LMtyWV588UWXmk2bNqlr167y8/NT3bp19fLLLxfqy8yZM9W8eXP5+fmpTZs2+vrrr122G2P07LPPKiIiQv7+/urevbt27dr1m88dAFAx/JiYptHTE3TdS4s0afFunTyTrdpV/fV0XAvF/6WbxvVqVWaC9/laRATrw0GdNO3hzrqqblWdzc7VPxft1vUvL9YHy/cpMye3tLsIlGluhe+C8dypqaku67///ntJUkhIiK666iqXbdWrV5cknTlz5je3m56ernbt2umtt94qtO3MmTPasGGDnnnmGW3YsEGzZs3Sjh07dNtttxWqff7555WYmGgvI0aMsLelpaWpR48eqlevntavX6+JEydq/Pjxeu+99+yalStXql+/fho6dKh++OEH9e7dW71799aWLVvsmpdffllvvvmm3n33Xa1evVqBgYGKjY1VRgZ/pgOAyiblTJb9+V3vxGvWD4eUnWt0db1qert/By194kY92LWhgsvBFeTODavr88eu1bv3d1TDmoE6eSZbf5vzo27+x1J9/sNB5eWZ0u4iUCa5NewkPDxcP/30k7Zt22Zf0ZakefPyH5hx3XXXFdonPT1dUv4NmL9Vz5491bNnzyK3hYSEaP78+S7rJk2apGuuuUYHDhxQVFSUvT4oKEjh4eFFHufTTz9VVlaWPvzwQ/n4+KhVq1ZKSEjQq6++qocffliS9MYbb+iWW27RE088IUn629/+pvnz52vSpEl69913ZYzR66+/rqefflq33367JOnjjz9WrVq1NHv2bPXt2/c3vwcAgLLJGKPktEztPnJau4+c0q4jp7X7yGntOXpax07/Er69PCz9vk2EhnRpoKvK6ewhlmXpltbh6t4iTDPXH9Rr83fqUMpZjZq+Ue8t26cnb2mmG5vWLO1uAmWKW+G7c+fO2r9/v9555x3df//9CggI0N69e/XFF1/Isiz97ne/K7TPzp07JemCofdKSE1NlWVZhYbHvPjii/rb3/6mqKgo3XfffRo1apS8vPLfkvj4eF1//fXy8fllGqfY2Fi99NJLOnnypKpVq6b4+HiNHj3a5ZixsbH2MJh9+/YpKSnJ5YbUkJAQRUdHKz4+/oLhOzMzU5mZmfbrtLS0IusAAKUnN8/o0Mmz2nXk1LmgfVq7jpzWniOndSoz55L7fzfqejWsWcWBnl55Xp4e6ndNlHpfVVsfrtind5fmT084ePJadW4YqpHdmzjSD2OMsnLzlJGdp8ycXGVm5ynlbJbLdqC0uRW+H3zwQU2bNk2bNm1S69at1aFDBy1btkwZGRkKCAjQfffdV2ifZcuWSZKaNm3qTtPFlpGRoTFjxqhfv34KDg621z/++OPq0KGDQkNDtXLlSo0dO1aJiYl69dVXJUlJSUlq0KCBy7Fq1aplb6tWrZqSkpLsdefXJCUl2XXn71dUTVEmTJig55577jeeMQCgJGXl5Omn4+l2uC74uPfoaXvu7V/z9LBULzRAjcKqqElYFTU+t0SE+KnT/y2UJIWH+Dl5Go7w9/G0pyd8e8lufbTyJ63ae0J931tt1yQcSJEkZebkKSM71/74y+f5wfn8jxnngnRBoM7IyXXZ9/yPF8vXwz79Qa/3vUpVA0p+fnSguNwK3zfffLP++Mc/6o033tD+/fv1008/2b9VTpw4UTVq1HCpz8jIsK+KX3/99e40XSzZ2dm65557ZIzRO++847Lt/CvWbdu2lY+Pj/7whz9owoQJ8vX1veJ9u5ixY8e69C8tLU1169YtxR4BQMV3NitXe44WhOtfrmb/dPyMci4wftnHy0MNawSqcVgVNQkLskN2/RoB8vXyLFR/JuvSV8QrgmqBPvprXEsNuq6BXpu/U//bcNAOxfe9v/riO5cQy5L8vDzl4+Wh1LP5s8gs2XlUcW8u11v9O5TboT4o/9x+vPxrr72mbt26aebMmUpKSlJERIQeeOAB3XzzzYVqv/zySwUHByskJES9evVyt+mLKgjeP/30kxYtWuRy1bso0dHRysnJ0f79+9WsWTOFh4crOTnZpabgdcGQmQvVnL+9YF1ERIRLza9vRD2fr69vqf8CAAAV3f/WH9SBE2fsq9mHUs5e8KppoI+nGtcKUuOaVc4F7fyPdUMD5OnB49YvpHZVf/3j7na6v3OUer+1UpJUp5q//L095eftKV8vD9eP3h7y9fKU33kfi6rzK6LO1yt//4I6H08PWZalM1k5avls/r1odUP99fOJs7r73ZV6Oq6lHoipJ8u6sv9+57f/4/OxCvBxO3qhnCuRr4Bbb71Vt9566yXr7rnnHt1zzz0l0eRFFQTvXbt2afHixfYMKxeTkJAgDw8PhYWFSZJiYmL017/+VdnZ2fL2zr/rfP78+WrWrJl9s2hMTIwWLlyokSNH2seZP3++YmJiJEkNGjRQeHi4Fi5caIfttLQ0rV69Wo8++mgJnjEA4NdSzmRp15HT2pl8SruS8wP2zuRT9vZnvthaaJ/QQB/76nXjmlXUpFb+5+HBflc8pFVkTWsF2Z9/N+r6Ugug/30kRuO++FHfbk3SuC+3as3+E3qpT1tV8SUQwznl8qvt9OnT2r17t/163759SkhIUGhoqCIiInTXXXdpw4YNmjNnjnJzc+3x1aGhofLx8VF8fLxWr16tm266SUFBQYqPj9eoUaN0//3328H6vvvu03PPPaehQ4dqzJgx2rJli9544w299tprdrt//OMfdcMNN+iVV15RXFycpk2bpnXr1tnTEVqWpZEjR+rvf/+7mjRpogYNGuiZZ55RZGSkevfu7dwbBgAV2In0rPyAfeS0dp/7uDP5tI6dzrzoftc2qq5m4UF20G4cVkXVq/BXx4osyM9b79zfQR+u2K8JX2/T3E2J2nY4TW/f30HNwy/+F3KgpDgSvvfs2aNjx46pfv36hW4+/C3WrVunm266yX5dMD564MCBGj9+vL788ktJKjS0Y/Hixbrxxhvl6+uradOmafz48crMzFSDBg00atQol3HWISEh+u677zRs2DB17NhRNWrU0LPPPmtPMyhJ1157raZOnaqnn35af/nLX9SkSRPNnj1brVu3tmuefPJJpaen6+GHH1ZKSoq6dOmib7/9Vn5+Fe9Gm/ImKydPSakZOpx6VodTzuqn47/MPf+/9QfVKKyKokIDFBHiz5+VgVJmjNGx01n2WOzzr2YfT8+64H61q/qrSa38YSJNagWpbjV/9ft3/pjj9wdezRCASsiyLA09N73j8KkbtPdYunq/tUJ/u7217r6a+6tw5bn1XefIkSP673//K0nq37+/QkJCXLbv3r1b9957rxISEiTlf8Hffvvtev/9992a5/vGG2+86HRBl5pKqEOHDlq1atUl22nbtq39wKALufvuu3X33XdfcLtlWXr++ef1/PPPX7I9lJy8PKNj6ZlKTMnQ4ZSzOpya/zEx9awOnVt37HTmBcd3nv/naG9PS7Wr+qtuaICizlvqnltC/Mv+wzCAAnl5RruPntaqvcftdW8u3KWqAT4K9PVSFV9PBfp4qYqfl6r4ep1bl/8xwNtTHlf4F1FjjI6eyvxluMiR09qdnH8DZMGj14tSN9RfTcKCzgXtIDUJq6JGYVUKDSeoLDc84tI61qumuY931ajpCVq686ie+O8mrdl3Qs/f3lr+PoVvlkX58dPxdD331Y/267I2xaRb4XvWrFkaPny4mjRposcee8xlW2Zmpnr27Km9e/faJ22M0ezZs3X06FF7ykHgtzidmZMfqlPO6rAdsM+eC9gZSkzJUFZu0VOAnc/Hy0O1q/orIsRPYcG+mv3DYUlSl8bVdSglQwdPnlF2rtH+42e0/3jRT2UN8fd2CeTnB/SIqn7y9nTrQbKAW9IyspVwIEUbDpzUhgMp+uHASZ3KcA2g7y7dW6xjWZYU6OOlQF9PBfp6KehcKP8loHuqiq93foA/b32VX9Wcn99X7D6mAyfOaveRU9qZfFq7kk8pLaPogGxZUlRogH0Vu8m5GUYahQVyBRu/SWigjyYP6qS3l+zWq/N3aub6g9p8KFVv9+9QYeZgLy2lcaPp6cwcTVq0Wx8u3+eSAbJzK1D4/u6772RZlu64445C26ZMmaI9e/bIsizddttt6tatmxYsWKCvvvpKK1as0PTp03Xvvfe60zwqsIMnz+hkeva5QJ1hB+3E1AwdSjlbKDwUxbKksCBfRVb1z19C/BRZ1V8RIf75gbuqn6oH+tg3UZ3JyrHD93sP5P85OjfPKCktQweOn9HPJ87owLnl55P5r4+dzlLq2WxtPpSqzYdSC/XB08NSRIjfBcN51QBvbuJCiTHGaO+xdG346WR+2P4pRTuPnCr0Fx5/b0+1rROi1ftOSJLui45SZnae0jNzdPrckn5uOXXuY56RjJG9Xbr4eOrieujj9YXWeVhS/ernpu8ruJJdq4oa1awiP2+uSKJkeXhYGn5zE3WIqqbHp/2g7UmndNukFXqpT1vFtY249AHKsMoy00pentGsHw7ppW+36+ip/O9N1zWqrhV78v/C5+NVti6CufWvsGPHDkn5T7r8talTp0rKnwu84ImPI0aMUI8ePbRgwQJNmzaN8F3GHT2VKQ8rS5k5+Q82yMrJO/d53rnPCx56kKesnNzC2+zPf7V/dp4yc/OUme16zMycXLvtHq9dfLiPJAX7ef0SrKv62aE68tyV7PAQ9686e3rkDzmpXdVfMY0Kz5qTnpmjn0+e0YHj50L5iTP6+eRZO6Rn5eTp4MmzOnjyrFbuOV5o/yBfL9UJDVDtqr/cA7D5UKra1alKyMAlpWfmaOPP+Ve11/90Uj/8nKKUIoZmRIUGqENUVXWsV03to6qpeXiQsnLz7B/KT8e1uOgPZWOMMrLz7FB+fkDP/5ir05nZOp2ZWyi059fkuuyXdd6DaRrWCFTTWueGi5y7mt2gRiBf/3DctY1raO7jXTXisx+0Zt8JDZu6QWv319dfft+izIU3/OKHAyc1/qsftfHnFElS/eoBeubWlurcMFStxn1Xup27ALfC99GjRyVJderUcVl/9uxZrVq1SpZludygKElDhgzRggULtGHDBneahgNumLik1Nr29rTOXa3+JVwXhOr8q9b+ZWJqqEBfLzUPDy7yLvm8PKOjpzPzg/hx1yvmB06cUXJapk5l5mhbYpq2JabZ+937r1XysKRGNauoZWSwWkYEq1VkiFpGBis0kKeyVVbGGB04cUbrz7uqvT0pTb9+9ouvl4fa1amq9vWqqkNUNXWIqqaaQYVn8CjOsKwClmXJ38dT/j6eRR7rcqWezVK75+ZLkuY83qXCXo1D+VMr2E9TH4zWP77bqXeX7tGUlfv1w88peuu+9qpTLaC0u4fzJKdl6KVvtmvWD4ckSVV8vTTi5sYadF19+Xp5lun7O9z6jpeSkiJJ8vBw/Y1w1apVys7OloeHh7p37+6yreCR7UeOHHGnaTjg/KeD+Xp5yNc7/6EFvi7rPPPXeZ977XXuQQdeHr/UFDqGp+vxzj0QIc8Yxb25XJL0wzO/UxW/8n0jo4eHpVrBfqoV7KdO9UMLbc/IztXBk/lBfPeR03rh6+2SpGoB3jp5Jlu7zj3C+ouEw/Y+4cF+5wXyYLWMDFbdagFX/CY4OO9sVq42HUzRhgMp+Ve1D5wsclaP2lX91T4qP2h3rFdNLSKCy/xVOu6DQFnm5emhp3o2V6f61TR6xkZt/DlFcW8u12v3ttPNzd2fsQ3uycjO1QfL9+mtxbt1Jiv/L+Z3d6yjJ25pprCg8jGTnFvhu0qVKkpNTbXn0S6wZMkSSVLLli0LzWpS8MAaLy+udJR1W8b3UKCvcwH4/N9SK0OY9PP2VOOwIDUOC1LnhtXt8L18zE06nZmrHw+n6cfENP14OE1bD6dq//EzSkrLUFJahhZt/+WX1yq+XmoREeRyhbxJrSpFPtoaZZMxRj+fOKMNB07qh3M3R/54OK3QI819PD3UqnawOkZVU4d6+Ve1w0PKxw8blL4AHy/tfzGutLtRbnRrUUtzRnTR8KkbtPFgqoZMWafHbmyk0b9rKi9+gXScMUbf/Zis/5u7TQdO5E+A0CGqqsb1aqV2dauWbucuk1sJuHnz5lq9erW+/fZb/f73v7fX/+9//5NlWbrhhhsK7VMQ1Etivm9cWdwIWDos65cr5jc1D7PXn87M0fbEXwL5j4lp2p50Sqczc7R2/0mt3X/SrvXysNQ4rIpaRuRfHS+4Wl41gGErV1JentGZ7FydycrRmcxcpWfl6ExW/nhnl49ZOUo9b2z2jf9Yat8kdL6wIF91PBeyO9SrplaRwYyFBhxUNzRAMx6J0Qtzt+mj+J/09pI9Wv/TSf2zX3uFBfOLr1N2JJ3S83O2asXu/HunagX7amzPFrr9qshymVXcCt9xcXFatWqV3nvvPbVo0UJdu3bVlClT9OOPP8qyLN15552F9ikY6127dm13mgYqnSq+Xrq6fqiuPm8IS05unvYcTdePial2IN96OE0pZ7K1PemUtiedssfDSflDFFqcC+StzgXy0MDyPbynpGw+lKrcPFN0aD4/TBex/UxW/k2HZ7NzL91QEY6eypSXh6WWkcF20O4QVVW1q/qXyx8sQEXi6+Wp525vravrh+qp/23S6n0n9Ps3l+uf/doXeSM+Sk7KmSy9Nn+nPll9QLl5Rj5eHnq4a0M9emMjBZaB+75+K7d6Pnz4cL399ttKTEzU8OHDXbbFxMS4PIWywFdffSXLstSpUyd3mgag/LGJzcKD1Cw8SHe0z19njFFiaobLsJUfE9N04MQZHUo5q0MpZ7VgW7J9jCC/X74N/G/DQbWtXVVNawVVyIdMHDudqS2HUrX1cJq2/Gp6yHv/dekHbxVXwXzYAT75810H+OQ/uCbg3ANsAnzy74P4dPUBSdLHQzqpU/3qFfI9ByqKXu0i1TIyWI99skE7kk+p//ur9KcezfToDY0qxVBJJ+Xk5umzNQf0yvyd9gxOPVuH6y+/b6G6oeX/xle3wndISIgWLFigAQMGuMxe0rVrV3322WeF6jdu3Ki1a9fKsiz97ne/c6dpABdgWZY9BWP3lr8M70rLyNb2xFPaeviXq+Q7k0+5zJn+zOyt544hNageqGbhQWoeHqxm4UFqERFUbm7uNMYoOS0/aG85nKoth/LDdlJaxgX3iQjxUxVfLwX4einQx1MB5x4mE+Bz7rXvrz6et/3X4drP2+OSV6zPZOXY4fvq+qEEb6AcaFSzimYPu07PfLFF/11/UBPn7dDa/Sf02j1XqRqzUZWIlbuP6bmvftSO5FOSpGa1gjSuV0td27hGKfes5Lh9zb5FixZat26d9u3bp6SkJEVERKh+/foXrJ88ebKk/Pm/ATgn2M9b1zQI1TUNfhm2kpWTp62HU3XH2yslSTENq2tn8ikdT8/S3mPp2nssXd9s+eWG6gAfTzWtlR/Em9UKUvOIYDUPDyrVseTGGB08eVZbD+dfyd5yKP8G1WOnC88MYllSgxqBah0Zoja1Q9Q4LFCDp6yTJC380w1MeQfgkvx9PPWPu9vpmvqheuaLLVqy46ji3vxek/p3UIeoapc+AIr084kz+r+52/Tt1vyfOVUDvPWn3zVVv2uiKtwNriX2k6ZBgwb2NIIX0q5dO7Vr166kmgTgJh+v/GErBT4YlP9kz6OnMrUj6ZS2J6WdGzuepp3Jp3UmK1cJP6co4dzDDAqEB/vlXyWPCFLzc1fLG9WsUuJT3uXlGf104ow2H0rV1vOuaqeeLfxgGQ9LahIWpNa1Q9S6drBa1w5Ri4hgl/nhy/I8sADKtns61VXr2iEaNnWD9h1L173/itfYni00+Lr63KtxGdIzc/T2kt369/f7lJWTJ08PSwM619PI7k0q7CQBXOYBUEjNIF/VDPJVlya//JkvJzdP+4+f0fakNO1IOqVtifmh/ODJs/YUiEt3HrXrvTwsNapZRc0j8sektwgPVvOIIIUH+xXrB1NuntGeo6fzh44cStOWc8Nl8h9t7srb01Kz8CC1jgxRq9ohah2Z/+AjhnIAuJJaRgbry+HXacz/NunrzUl6fs6PWvfTCb3Up62CyvmzKq40Y4xmJxzSi99sV3Ja/mxPXRrX0LO9WqppraBL7F2+lWj4Tk5O1pIlS7RlyxadOHFCkhQaGqrWrVvrxhtvZHpBoBzz8vRQ47AqahxWRbe2/WX9qYxs7UzOn1lle+Kp/GCelKZTGTnakXzKHrdXINjPyx6u0jw8WPWr/3LzzP82HNSu5PzA/WNimjKyCz+F0dfLQy0igvOvZkeGqHXtEOY1B1Bqgvy89dZ9HTRl5X698PU2fb05ST8eTtPb/TuqZWThpx9D2vhzip77aqs2HEiRJEWFBujpuBb6XctaleKvBiUSvhMTEzV69GjNmjVLOTlF/xnXy8tLffr00SuvvKKIiIiSaBZAGRDk562O9ULVsd4vY8mNMTqcmqEdSWnadi6Qb09K056j6UrLyNGafSe0Zt+JQscquOGzQICPp1pF5j88qHXt/HHajWoGVrjxfwDKN8uyNPi6BrqqblUN+3SD9h8/ozveXqHnb2+lW9uSeQocOZWhl7/dof+uPygp/3v88Jsba8h1DSrVMwzcDt8bN25U9+7ddeLECRljLliXnZ2t6dOna8GCBVq4cKHatGnjbtMAyijLslS7qr9qV/V3eRxzZk6u9hxJ/2XoStIpbUtMsx8wE90gVG3rhJwbpx2i+tUD5VkOZlcBUHY5+WTP9lHVNPfxrho9I0GLdxzVmP9tVvye4460XZZl5eTp4/g9mrRotz108M4OtTXmluaqVQkfVuRW+E5PT1dcXJyOH8//wurevbseeughRUdHKzw8XFL+Ey3XrFmj999/X999952OHTumuLg4bd++XQEB5X+uRgDF5+vlaT9xs8CZrBy1fHaeJGny4E7MOAKgXKsW6KMPBnbSO0v36JXvdmh2wmF728rdxxQW7KcQf2+F+HsryM+7wl5gOP+C7G2TVtiPhG9Xt6rG92qp9pV4Zhi3fspNmjRJhw8floeHh/71r39p6NChhWqioqIUFRWlu+66Sx9++KEeeughHTp0SG+99ZaeeOIJd5oHAAAoczw8LA27qbE6RFXT8M826Pi5qU8f/Hi9S51lSUG+XgoJ8LYDecES7O+tqv4+hdb/Ety9rthzF/LyjE5n5ehURo5OZ+ToVEa2TmXkKC0jW6cz89efysg+ty1HaRk5Op2ZfW79ufrzbo4/cOKMagb56qlbmuuO9rXLxfMiriS3wvcXX3why7I0aNCgIoP3rw0ZMkQrV67Uhx9+qM8//5zwDQAAKqyYRtU169FrdcPEJZKkJmFVdCojR6lns3U2O1fGSGnnwuvPOntZx7as/Oc3FBXaQ/y9VTXAW/7ev9wf80XCIWVm5+WH4/MC9KmMnHOB+pfwXNSsUu54sGsDjeze1GWq18rMrXdh586dkqS+ffsWe59+/frpww8/tPcFAACoqGoG+dqffzH8OntoXVZOnlLPZttL2tlspZzNUuqZbKWezSm0reDzlLNZysjOkzGy1xXH2FlbLrvvPp4eCvLzUhU/LwX5eSnI19v+PNjPW1V8z633O3+917nhNFK3V5ZJkkb/rilDCs/j1jtx+vRpSfnTCRZXtWr5Y3zS09PdaRoAAKDc8vHysJ+pcLkyc3ILhfLUs9lKPZOtlPNen0zP0uId+c9f6NK4ukICfBR0fmA+7/MgP9fPq/h6uTUDCQ8xuzC3wnfNmjV1+PBhbdu2TR06dCjWPtu3b5ck1ahR4xKVAACgInByxpHKwNfLU2FBngoLuvhMIeff0P7eA1dz9bmMcGuy3M6dO8sYo1dfffWC83ufLycnR6+++qosy1Lnzp3daRoAAAAod9wK3w888IAkKSEhQXFxcTp8+PAFaw8fPqxevXppw4YNkqRBgwa50zQAAABQ7rj194devXqpd+/emj17thYsWKCGDRuqR48eio6OVlhYmCzLUnJyslavXq358+crKyt/qp077rhDcXH8+QkAULkw/AKA24N/PvvsMz3wwAOaOXOmsrKyNHfuXM2dO7dQXcFk63fffbc+/vhjd5sFAAAAilSWf9F1a9iJJPn6+mr69On66quv1LNnT/n7+8sY47L4+/urZ8+emjNnjqZPny5f38u/sxcAAAAo70rstte4uDjFxcUpNzdXe/fu1YkTJyTlT0PYsGFDeXr+9ulqAAAAgIrArfB98803S5IGDBigwYMHS5I8PT3VpEkT93sGAAAAVDBuDTv5/vvvtXTpUtWvX7+EugMAAABUXG5d+Q4LC1NSUpKqVq1aQt0BAODKKcs3YQGoHNy68t2uXTtJ0s6dO0ukMwAAAEBF5lb4fvDBB2WM0bvvvltS/QEAAAAqLLfC95133qn7779fS5cu1ZAhQ5Senl5S/QIcV/Dn6P0vxinAp8QmAgIAALC5lTA+/vhjdevWTZs2bdJHH32kL774Qr169VLbtm1VrVq1S04vWPB4egAAAKAycCt8Dxo0SJZl2a9Pnjyp//znP8Xa17IswjcAAAAqFbf/tl7w2PgLvQYAlD2lOesHM44AFR//zy/MrfC9b9++kuoHygj+swAAAFw5boXvevXqlVQ/gEod/CvzuQMArgx+tpRNbs12AgAAAKD4CN8AAACAQy4rfH/zzTfq0KGDOnTooKlTp15WQ1OnTrX3XbBgwWXtC+DKYX5zALhy+B6LXyt2+DbGaNSoUdq4caNq1qyp++6777Ia6tevn2rUqKGEhAT96U9/uuyOAgAAAOVdscP3okWLtHPnTnl4eOi111677IYsy9Lrr78uT09PbdmyRUuXLr3sYwAAAADlWbHD9//+9z9J0u9+9zu1bNnyNzXWsmVLxcbGSpL++9///qZjAAAAAOVVscP3mjVrZFmWevXq5VaDt956q4wxWrVqlVvHAQAAAMqbYo/8/+mnnyRJzZo1c6vBpk2bSpL279/v1nEAoDxj/l0AqJyKHb5TU1MlSaGhoW41WLB/WlqaW8cBAHcRgAEATit2+A4ODtbJkyeVkpLiVoMF+wcFBbl1HAAVAwEYAFCZFHvMd82aNSVJP/74o1sNbtu2TZIUFhbm1nEAAACA8qbY4fuaa66RMUZfffWVWw1+8cUXsixLnTp1cus4AAAAQHlT7PDds2dPSdJ3332n5cuX/6bGli1bpu+++87leAAAAEBlUezw3adPH9WvX1/GGN19993atWvXZTW0c+dO3XPPPbIsS/Xr19ddd9112Z0FAAAAyrNih29vb2/94x//kCQdOXJEHTt21BtvvKH09PSL7nf69Gm9/vrruvrqq3XkyBFJ0iuvvCIvr2Lf6wkAAABUCJYxxlzODn/72980btw4WZYlSQoMDFTXrl3VsWNHhYWFKTAwUOnp6UpOTtaGDRv0/fffKz09XQXNPP/883r66adL/kwqsLS0NIWEhCg1NVXBwcGl3R0AAAD8SnHz2mWHb0maPHmyRowYoTNnzuQf5FwQL0rB4QMCAjRp0iQNGjTocpur9AjfAAAAZVtx81qxh52cb/Dgwdq5c6dGjx6tGjVqyBhzwaVGjRr605/+pJ07dxK8AQAAUKn9pivfv7Z161Zt3LhRx48f16lTpxQUFKTq1aurXbt2atWqVUn0s1LjyjcAAEDZVty8ViJ3PbZq1YqQDQAAAFzCbxp2AgAAAODyEb4BAAAAhxC+AQAAAIcQvgEAAACHEL4BAAAAhxC+AQAAAIcQvgEAAACHlMvwvWzZMvXq1UuRkZGyLEuzZ8922W6M0bPPPquIiAj5+/ure/fu2rVrl0vNiRMn1L9/fwUHB6tq1aoaOnSoTp8+7VKzadMmde3aVX5+fqpbt65efvnlQn2ZOXOmmjdvLj8/P7Vp00Zff/31ZfcFAAAAlUO5DN/p6elq166d3nrrrSK3v/zyy3rzzTf17rvvavXq1QoMDFRsbKwyMjLsmv79+2vr1q2aP3++5syZo2XLlunhhx+2t6elpalHjx6qV6+e1q9fr4kTJ2r8+PF677337JqVK1eqX79+Gjp0qH744Qf17t1bvXv31pYtWy6rLwAAAKgkTDknyXz++ef267y8PBMeHm4mTpxor0tJSTG+vr7ms88+M8YY8+OPPxpJZu3atXbNN998YyzLMocOHTLGGPP222+batWqmczMTLtmzJgxplmzZvbre+65x8TFxbn0Jzo62vzhD38odl+KIzU11Ugyqampxd4HAAAAziluXiuXV74vZt++fUpKSlL37t3tdSEhIYqOjlZ8fLwkKT4+XlWrVtXVV19t13Tv3l0eHh5avXq1XXP99dfLx8fHromNjdWOHTt08uRJu+b8dgpqCtopTl+KkpmZqbS0NJcFAAAA5V+FC99JSUmSpFq1armsr1Wrlr0tKSlJYWFhLtu9vLwUGhrqUlPUMc5v40I152+/VF+KMmHCBIWEhNhL3bp1L3HWAAAAKA8qXPiuCMaOHavU1FR7+fnnn0u7SwAAACgBFS58h4eHS5KSk5Nd1icnJ9vbwsPDdeTIEZftOTk5OnHihEtNUcc4v40L1Zy//VJ9KYqvr6+Cg4NdFgAAAJR/FS58N2jQQOHh4Vq4cKG9Li0tTatXr1ZMTIwkKSYmRikpKVq/fr1ds2jRIuXl5Sk6OtquWbZsmbKzs+2a+fPnq1mzZqpWrZpdc347BTUF7RSnLwAAAKg8ymX4Pn36tBISEpSQkCAp/8bGhIQEHThwQJZlaeTIkfr73/+uL7/8Ups3b9YDDzygyMhI9e7dW5LUokUL3XLLLXrooYe0Zs0arVixQsOHD1ffvn0VGRkpSbrvvvvk4+OjoUOHauvWrZo+fbreeOMNjR492u7HH//4R3377bd65ZVXtH37do0fP17r1q3T8OHDJalYfQEAAEAl4tDsKyVq8eLFRlKhZeDAgcaY/Cn+nnnmGVOrVi3j6+trunXrZnbs2OFyjOPHj5t+/fqZKlWqmODgYDN48GBz6tQpl5qNGzeaLl26GF9fX1O7dm3z4osvFurLjBkzTNOmTY2Pj49p1aqVmTt3rsv24vTlUphqEAAAoGwrbl6zjDGmFLM/iiEtLU0hISFKTU1l/DcAAEAZVNy8Vi6HnQAAAADlEeEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHAI4RsAAABwCOEbAAAAcAjhGwAAAHBIhQ3f9evXl2VZhZZhw4ZJkm688cZC2x555BGXYxw4cEBxcXEKCAhQWFiYnnjiCeXk5LjULFmyRB06dJCvr68aN26sKVOmFOrLW2+9pfr168vPz0/R0dFas2bNFTtvAAAAlF0VNnyvXbtWiYmJ9jJ//nxJ0t13323XPPTQQy41L7/8sr0tNzdXcXFxysrK0sqVK/XRRx9pypQpevbZZ+2affv2KS4uTjfddJMSEhI0cuRIPfjgg5o3b55dM336dI0ePVrjxo3Thg0b1K5dO8XGxurIkSMOvAsAAAAoSyxjjCntTjhh5MiRmjNnjnbt2iXLsnTjjTfqqquu0uuvv15k/TfffKNbb71Vhw8fVq1atSRJ7777rsaMGaOjR4/Kx8dHY8aM0dy5c7VlyxZ7v759+yolJUXffvutJCk6OlqdOnXSpEmTJEl5eXmqW7euRowYoaeeeqpYfU9LS1NISIhSU1MVHBzsxrsAAACAK6G4ea3CXvk+X1ZWlj755BMNGTJElmXZ6z/99FPVqFFDrVu31tixY3XmzBl7W3x8vNq0aWMHb0mKjY1VWlqatm7datd0797dpa3Y2FjFx8fb7a5fv96lxsPDQ927d7dripKZmam0tDSXBQAAAOWfV2l3wAmzZ89WSkqKBg0aZK+77777VK9ePUVGRmrTpk0aM2aMduzYoVmzZkmSkpKSXIK3JPt1UlLSRWvS0tJ09uxZnTx5Urm5uUXWbN++/YL9nTBhgp577rnffL4AAAAomypF+P7ggw/Us2dPRUZG2usefvhh+/M2bdooIiJC3bp10549e9SoUaPS6KZt7NixGj16tP06LS1NdevWLcUeAQAAoCRU+PD9008/acGCBfYV7QuJjo6WJO3evVuNGjVSeHh4oVlJkpOTJUnh4eH2x4J159cEBwfL399fnp6e8vT0LLKm4BhF8fX1la+vb/FOEAAAAOVGhR/zPXnyZIWFhSkuLu6idQkJCZKkiIgISVJMTIw2b97sMivJ/PnzFRwcrJYtW9o1CxcudDnO/PnzFRMTI0ny8fFRx44dXWry8vK0cOFCuwYAAACVR4UO33l5eZo8ebIGDhwoL69fLvLv2bNHf/vb37R+/Xrt379fX375pR544AFdf/31atu2rSSpR48eatmypQYMGKCNGzdq3rx5evrppzVs2DD7qvQjjzyivXv36sknn9T27dv19ttva8aMGRo1apTd1ujRo/Xvf/9bH330kbZt26ZHH31U6enpGjx4sLNvBgAAAEpdhR52smDBAh04cEBDhgxxWe/j46MFCxbo9ddfV3p6uurWras+ffro6aeftms8PT01Z84cPfroo4qJiVFgYKAGDhyo559/3q5p0KCB5s6dq1GjRumNN95QnTp19P777ys2Ntauuffee3X06FE9++yzSkpK0lVXXaVvv/220E2YAAAAqPgqzTzf5RnzfAMAAJRtzPMNAAAAlDGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIYRvAAAAwCGEbwAAAMAhhG8AAADAIRUyfI8fP16WZbkszZs3t7dnZGRo2LBhql69uqpUqaI+ffooOTnZ5RgHDhxQXFycAgICFBYWpieeeEI5OTkuNUuWLFGHDh3k6+urxo0ba8qUKYX68tZbb6l+/fry8/NTdHS01qxZc0XOGQAAAGVfhQzfktSqVSslJibay/Lly+1to0aN0ldffaWZM2dq6dKlOnz4sO688057e25uruLi4pSVlaWVK1fqo48+0pQpU/Tss8/aNfv27VNcXJxuuukmJSQkaOTIkXrwwQc1b948u2b69OkaPXq0xo0bpw0bNqhdu3aKjY3VkSNHnHkTAAAAUKZYxhhT2p0oaePHj9fs2bOVkJBQaFtqaqpq1qypqVOn6q677pIkbd++XS1atFB8fLw6d+6sb775RrfeeqsOHz6sWrVqSZLeffddjRkzRkePHpWPj4/GjBmjuXPnasuWLfax+/btq5SUFH377beSpOjoaHXq1EmTJk2SJOXl5alu3boaMWKEnnrqqWKfT1pamkJCQpSamqrg4ODf+rYAAADgCiluXvNysE+O2rVrlyIjI+Xn56eYmBhNmDBBUVFRWr9+vbKzs9W9e3e7tnnz5oqKirLDd3x8vNq0aWMHb0mKjY3Vo48+qq1bt6p9+/aKj493OUZBzciRIyVJWVlZWr9+vcaOHWtv9/DwUPfu3RUfH3/RvmdmZiozM9N+nZqaKin/HxUAAABlT0FOu9R17QoZvqOjozVlyhQ1a9ZMiYmJeu6559S1a1dt2bJFSUlJ8vHxUdWqVV32qVWrlpKSkiRJSUlJLsG7YHvBtovVpKWl6ezZszp58qRyc3OLrNm+fftF+z9hwgQ999xzhdbXrVv30icPAACAUnPq1CmFhIRccHuFDN89e/a0P2/btq2io6NVr149zZgxQ/7+/qXYs+IZO3asRo8ebb/Oy8vTiRMnVL16dVmWdcXbT0tLU926dfXzzz9XumEulfXcK+t5S5x7ZTz3ynreUuU998p63hLn7uS5G2N06tQpRUZGXrSuQobvX6tataqaNm2q3bt363e/+52ysrKUkpLicvU7OTlZ4eHhkqTw8PBCs5IUzIZyfs2vZ0hJTk5WcHCw/P395enpKU9PzyJrCo5xIb6+vvL19S10Dk4LDg6udP9RC1TWc6+s5y1x7pXx3CvreUuV99wr63lLnLtT536xK94FKuxsJ+c7ffq09uzZo4iICHXs2FHe3t5auHChvX3Hjh06cOCAYmJiJEkxMTHavHmzy6wk8+fPV3BwsFq2bGnXnH+MgpqCY/j4+Khjx44uNXl5eVq4cKFdAwAAgMqlQobvP//5z1q6dKn279+vlStX6o477pCnp6f69eunkJAQDR06VKNHj9bixYu1fv16DR48WDExMercubMkqUePHmrZsqUGDBigjRs3at68eXr66ac1bNgw+4r0I488or179+rJJ5/U9u3b9fbbb2vGjBkaNWqU3Y/Ro0fr3//+tz766CNt27ZNjz76qNLT0zV48OBSeV8AAABQuirksJODBw+qX79+On78uGrWrKkuXbpo1apVqlmzpiTptddek4eHh/r06aPMzEzFxsbq7bfftvf39PTUnDlz9OijjyomJkaBgYEaOHCgnn/+ebumQYMGmjt3rkaNGqU33nhDderU0fvvv6/Y2Fi75t5779XRo0f17LPPKikpSVdddZW+/fbbQjdhljW+vr4aN25coaEvlUFlPffKet4S514Zz72ynrdUec+9sp63xLmXxXOvkPN8AwAAAGVRhRx2AgAAAJRFhG8AAADAIYRvAAAAwCGEbwAAAMAhhG/Yli1bpl69eikyMlKWZWn27Nml3SVHvPPOO2rbtq09CX9MTIy++eab0u6WI8aPHy/LslyW5s2bl3a3HFG/fv1C525ZloYNG1baXbviTp06pZEjR6pevXry9/fXtddeq7Vr15Z2t0rcpb6nzZo1Sz169LCfHpyQkFAq/bwSLnXu48ePV/PmzRUYGKhq1aqpe/fuWr16del0tgRd6rwHDRpU6P/8LbfcUjqdLWGXOveivt9ZlqWJEyeWTodLyKXOOzk5WYMGDVJkZKQCAgJ0yy23aNeuXaXT2XMI37Clp6erXbt2euutt0q7K46qU6eOXnzxRa1fv17r1q3TzTffrNtvv11bt24t7a45olWrVkpMTLSX5cuXl3aXHLF27VqX854/f74k6e677y7lnl15Dz74oObPn6///Oc/2rx5s3r06KHu3bvr0KFDpd21EnWp72np6enq0qWLXnrpJYd7duVd6tybNm2qSZMmafPmzVq+fLnq16+vHj166OjRow73tGQV5+fYLbfc4vJ//7PPPnOwh1fOpc79/HNOTEzUhx9+KMuy1KdPH4d7WrIudt7GGPXu3Vt79+7VF198oR9++EH16tVT9+7dlZ6eXgq9/aVjQCGSzOeff17a3Sg11apVM++//35pd+OKGzdunGnXrl1pd6NM+OMf/2gaNWpk8vLySrsrV9SZM2eMp6enmTNnjsv6Dh06mL/+9a+l1Ksr72Lf0/bt22ckmR9++MHRPjmlON/PU1NTjSSzYMECZzrlgKLOe+DAgeb2228vlf44qTj/5rfffru5+eabnemQQ3593jt27DCSzJYtW+x1ubm5pmbNmubf//53KfQwH1e+gfPk5uZq2rRpSk9PV0xMTGl3xxG7du1SZGSkGjZsqP79++vAgQOl3SXHZWVl6ZNPPtGQIUNkWVZpd+eKysnJUW5urvz8/FzW+/v7V5q/esBVVlaW3nvvPYWEhKhdu3al3Z0rbsmSJQoLC1OzZs306KOP6vjx46XdJcclJydr7ty5Gjp0aGl35YrKzMyUJJfvdx4eHvL19S3V73eEb0DS5s2bVaVKFfn6+uqRRx7R559/rpYtW5Z2t6646OhoTZkyRd9++63eeecd7du3T127dtWpU6dKu2uOmj17tlJSUjRo0KDS7soVFxQUpJiYGP3tb3/T4cOHlZubq08++UTx8fFKTEws7e7BQXPmzFGVKlXk5+en1157TfPnz1eNGjVKu1tX1C233KKPP/5YCxcu1EsvvaSlS5eqZ8+eys3NLe2uOeqjjz5SUFCQ7rzzztLuyhXVvHlzRUVFaezYsTp58qSysrL00ksv6eDBg6X6/a5CPl4euFzNmjVTQkKCUlNT9d///lcDBw7U0qVLK3wA79mzp/1527ZtFR0drXr16mnGjBkV/orI+T744AP17NlTkZGRpd0VR/znP//RkCFDVLt2bXl6eqpDhw7q16+f1q9fX9pdg4NuuukmJSQk6NixY/r3v/+te+65R6tXr1ZYWFhpd+2K6du3r/15mzZt1LZtWzVq1EhLlixRt27dSrFnzvrwww/Vv3//Qn8Bq2i8vb01a9YsDR06VKGhofL09FT37t3Vs2dPmVJ8wDtXvgFJPj4+aty4sTp27KgJEyaoXbt2euONN0q7W46rWrWqmjZtqt27d5d2Vxzz008/acGCBXrwwQdLuyuOadSokZYuXarTp0/r559/1po1a5Sdna2GDRuWdtfgoMDAQDVu3FidO3fWBx98IC8vL33wwQel3S1HNWzYUDVq1KhU3/O+//577dixo9J8z+vYsaMSEhKUkpKixMREffvttzp+/Hipfr8jfANFyMvLs8eKVSanT5/Wnj17FBERUdpdcczkyZMVFhamuLi40u6K4wIDAxUREaGTJ09q3rx5uv3220u7SyhFlfH73sGDB3X8+PFK9T3vgw8+UMeOHSvF+P7zhYSEqGbNmtq1a5fWrVtXqt/vGHYC2+nTp11++9+3b58SEhIUGhqqqKioUuzZlTV27Fj17NlTUVFROnXqlKZOnaolS5Zo3rx5pd21K+7Pf/6zevXqpXr16unw4cMaN26cPD091a9fv9LumiPy8vI0efJkDRw4UF5elefb4bx582SMUbNmzbR792498cQTat68uQYPHlzaXStRl/qeduLECR04cECHDx+WJO3YsUOSFB4ervDw8FLpc0m52LlXr15d//d//6fbbrtNEREROnbsmN566y0dOnSo3E+1ebHzDg0N1XPPPac+ffooPDxce/bs0ZNPPqnGjRsrNja2FHtdMorzMzwtLU0zZ87UK6+8UlrdLHGXOu+ZM2eqZs2aioqK0ubNm/XHP/5RvXv3Vo8ePUqv06U2zwrKnMWLFxtJhZaBAweWdteuqCFDhph69eoZHx8fU7NmTdOtWzfz3XfflXa3HHHvvfeaiIgI4+PjY2rXrm3uvfdes3v37tLulmPmzZtnJJkdO3aUdlccNX36dNOwYUPj4+NjwsPDzbBhw0xKSkppd6vEXep72uTJk4vcPm7cuFLtd0m42LmfPXvW3HHHHSYyMtL4+PiYiIgIc9ttt5k1a9aUdrfddrHzPnPmjOnRo4epWbOm8fb2NvXq1TMPPfSQSUpKKu1ul4ji/Az/17/+Zfz9/SvU//dLnfcbb7xh6tSpY7y9vU1UVJR5+umnTWZmZqn22TKmFEecAwAAAJUIY74BAAAAhxC+AQAAAIcQvgEAAACHEL4BAAAAhxC+AQAAAIcQvgEAAACHEL4BAAAAhxC+AQAAAIcQvgFUaPv375dlWbIsS1OmTCnt7hRp/Pjxdh/LsvLwXgJAWUf4BiBJio+Pl2VZCgwMVE5Ojr0+JSVFnp6esixLBw4cKMUeAgBQ/hG+AUiSVqxYIUmKjo6Wl5eXy/q8vDzVrVtXUVFRpdU9ALCVl78WAUUhfAOQ9Ev47tKli8v677//vsj15UX9+vVljJExRoMGDSrt7hRp/Pjxdh8BABUb4RuAJGnlypWSCofs5cuXF7keAABcPsI3AO3evVtHjhyRp6enYmJi7PUZGRlau3atJMI3AAAlgfANwB5y0rZtWwUFBdnr16xZo6ysLFWtWlWtW7d2u50pU6bY4zT379+vzMxM/eMf/1CHDh0UEhKi4OBgRUdH6+2331Zubu4Fj3PjjTfKsizdeOONkqRdu3Zp+PDhatKkiQICAuzjS5eeoePXY0czMjI0ceJEdejQQUFBQQoKCtI111yjSZMmudyIeiGZmZl67733FBcXp9q1a8vX11eBgYFq1aqVHnzwQc2bN6/Q8JJLjV+tX7++LMuyh82sXbtW/fr1U926deXn56e6detq8ODB2r59+0X7lpiYqLffflt33XWXmjRposDAQPn6+qp27dq6/fbbNX36dOXl5V3yHEvK119/rfvvv18NGzZUYGCg/Pz81KBBA/Xp00dTpkzRmTNnitwvLy9Pn3zyiX7/+98rPDxcPj4+qlmzpm666Sa9/fbbysrKumCbv36v09LSNH78eLVp00ZVqlRRWFiYfv/739t/CSpw5MgRPf3002rVqpUCAwNVvXp13X777frhhx8u2FZJfb0XOHr0qJ5++mm1b99eVatWlZ+fn+rXr68BAwbYf6G6kF9/De3YsUMPPfSQ6tevL19fX9WqVUt33HGHVq1adcl+SPm/sI8aNUpt2rRRSEiI/P391bBhQw0aNEjr1q274H5Lliyx35MlS5ZIkmbMmKFu3bqpZs2a8vf3V7NmzfTkk0/qxIkThfYveE+fe+45e13B8c5fCv7/F1i/fr2GDh2qpk2b2l9rdevWVceOHTVs2DB9+eWXDPuCcwyASmXy5MlGktvLvn373Gp7w4YNpmPHjhc8/vXXX29OnTpV5HFuuOEGI8nccMMNZvbs2SYwMPCC/du3b5+9bvLkyYWONW7cOHt7UlKSueqqqy7Yp169epnc3NwLnt8PP/xgGjRocNnv3fl9KEq9evWMJDNw4EDzwQcfGC8vryKP6+vra2bMmFHkMXJycoyHh8cl+/a73/3ugu/7pd7L4jp27Jjp1q3bJftSVBvHjx8311133UX3a9Gihdm/f3+RbZ//Xh84cMA0bdq0yGN4enra7+XGjRtN7dq1L/ieL1q0qMi2Surr3Rhj5s2bZ4KDgy963sOGDbvg1+f5X0OzZs0yAQEBFzzvadOmXfTfb+LEicbb2/uC/bAsyzzzzDNF7rt48WK7buHCheb++++/4HEaN25sEhMTL/ieFvf/2Kuvvlqsr/2Lvf9ASSJ8A5VMWQnfnTp1MpLMvffea77++muzbt06M3XqVHu9JNO7d+8ij1MQvhs0aGCqVKliatasaV588UWzYsUKs2rVKvPPf/7THD161BhzeeH72muvNT4+Pubxxx838+fPN+vXrzdTp041LVq0sGvefffdIvv0448/mipVqth1d9xxh5k+fbpZu3atWbVqlfn444/N/fffbwIDA39z+G7Xrp3x9vY2kZGR5p///KdZvXq1Wbp0qRkzZozx9fU1koy3t7dZu3ZtoWNkZ2cbDw8Pc/PNN5uJEyeab7/91qxfv94sWbLEfPjhhyYmJsbuwwMPPFBkP0oifKenp5s2bdrYx+nYsaP517/+ZVasWGHWrVtnPv/8czNq1CgTGRlZqI2cnByXft5www1m5syZZt26debLL780vXv3trc1atSoyDB1/nsdHR1tAgICzNixY83SpUvN2rVrzWuvvWaH3KCgILN3715Tt25dExoaav7v//7PLF++3Kxevdo899xzxsfHx0gyUVFRJjMzs1BbJfX1/sMPP9hteXt7m1GjRpnFixebNWvWmH/9618uv/A9+eSTRR6j4GuoQ4cOxs/PzzRo0MBMmjTJrFq1ysTHx5vx48cbPz8/I8kEBwebI0eOFHmcl19+2W6rbdu25p133jELFiww69atM59++qnLv88bb7xRaP/zw/e1115rn/esWbPM+vXrzddff23i4uLsmr59+7rsf/LkSbN582bz6KOP2jWbN28utGRlZRlj8n9xKgjeDRo0MK+88opZuHCh+eGHH8yyZcvMv//9b3PfffeZwMBAwjccQ/gGKpmUlBSzbds2ezn/h+Hy5cvt9Vu3brV/GP/3v/912Wfbtm32D7fL8evg/8ILLxSqyc7ONrGxsXbN3LlzC9UUhG9JJjIy0vz0008XbPNywre3t7dZvHhxoZrjx4+bWrVq2YGjKB06dDCSjIeHh/nss88u2J9jx46ZM2fOXLAPRSkITpJMvXr1Cl0NNMaYRYsW2VfEO3XqVGh7Xl6e2bVr1wX7ZYwxzz77rH3lcufOnYW2l0T4HjVqlH2MYcOGmby8vCLrMjMzTVJSksu6SZMmufyCUNS+f/nLXy4aRM9/r319fc2qVasK1cyZM8euqVmzpqlRo4bZvXt3obq33nrLrps1a1ah7SX19V4Q0D09Pc28efMKbT9x4oRp2bKl/fW3ZcuWQjXnfw117NjRpKamFqr55JNP7JpXX3210PatW7faV7zHjRtX5Pufm5trX82uUqWKOXHihMv287/fSDJ///vfCx0jLy/P9OjRw0gyXl5eRf4icKn/MwWeeeYZI8kEBgYW+no6X0pKykX/qgWUJMI3UMnNmDHDvip0voSEBCPJ+Pv7/6ag/f/t3X9MlHUcB/A3vw4U9ABPQERFEoJ5p4eH5+YVJmOuidQKIZHpFQXyYxmQm5KVOWq1ctk6LAeOTGGUMZfLWVK6EIssE5AbRJhIKmaIYKKBHjz9cXsenzvueYD7BYvPa7vt8b7f53m+3+e+J597nu8PS/jByKJFiwQDr8uXL3N/5BMSEoal84PvAwcOiJ5zLMF3QUGB4HG2bdvGBaa9vb0macePH+eOkZeXJ1oeS8YSfFdVVQkeh3830NLd75EYDAZGJpMxAJhdu3YNS7c1+O7p6eG6O6hUKsZgMIxpf/YJxMyZM5l//vnHYp779+8zkZGRDADGz8+P6e/vN0nnX+utW7cKnot/zT/++GOLee7evcv9QM3Pzx+Wbo/2fubMGe4YWVlZguU9ffo0ly8nJ0e0Po2NjRaPMTQ0xAQHBzOA8cmNufT0dAYAExMTI1gXhjF+zuyTmJKSEpM0fvCtUqkEj/PNN99w+Y4cOTIsfbTBd0ZGBgOAiY6OFs1HiDPRgEtCJjmhebzZQZgxMTHw8PCw+3m1Wq3gAMOQkBCsWrUKgHGAltBgNIlEguTkZLuVKS0tTTBNpVIBABiGQXt7u0na0aNHue28vDy7lcecn58fnnzyScH09PR0bvu7774TPdbQ0BA6OzvR2toKvV4PvV6PlpYWhISEAAAaGxvtU2iekydPcoMoN2/eDDc3t1Hv29nZiZaWFgBASkqKycBgPnd3dzz33HMAgJ6eHpw7d07wmOvWrRNMW7RoEQDjYL5nnnnGYp4pU6YgPDwcAHDx4kXR8lvb3vmf4/PPPy94fI1Gg6ioqGH7mFMoFFzdzLm4uCA6OhqA5fp89dVXAICkpCTRxW18fX2hUCgAGFfOFbJ+/XrB47DfN6GyjNasWbMAAM3Nzfj555+tPg4h9kTBNyGTnNA83uxsDxqNxiHnXbp0qWi6Wq0GANy5c0fwj294eDi8vLzsVqbIyEjBNH9/f2779u3bJmnsjBdz587FvHnz7FYec9HR0Sarj5pTKpWQSCQAgKampmHpDMOgvLwcK1euhI+PD2bPno3IyEgoFAru1dDQAAC4ceOG3cvPnxnk0UcfHdO+er2e2162bJloXn46fz9zERERgmm+vr4AAJlMBj8/vxHzmbcJc9a2d7b8EokESqVS9Bhsvdva2gRnfBFr48CDdm5en46ODnR1dQEACgsLLc4wwn+xM5789ddfguey9vs2FqmpqfDw8MDAwAA0Gg0SExOxd+9e6PV6mt2EjBsKvgmZxG7fvo3z588DEL7zvXz5coecOyAgQDQ9MDCQ27Y05RgA0aDIGlOnThVMc3V98N+l+Z14NlBl77I5ykjXzN3dnQtazK9Zf38/EhISsGHDBnz//ff4999/RY81Uro1+AH9WK8Vvz4jXYegoCCL+5kbzectloefb6SpAq1t7+y2v7+/6A8v4EG9GYZBT0+PxTzW1ufvv/8W3U+I0HSRI5VF7Ps2FpGRkaisrISfnx8MBgOOHj2K7OxsKBQKBAQEYMOGDdzTP0KcRfybTAj5XwkNDUVHR4fFtIULF1p8/4knnjD5944dO/DGG2/YXBaxx9ajNZZuC/8Htlyzt956C19//TUAYMWKFcjNzcWSJUsQFBSEKVOmcMFObGwsamtrJ/RdQXu0HWeztczjXWd+APz666+PuruXt7e3o4o0aklJSYiPj8fnn3+O48ePo7a2Fl1dXbhx4wbKy8tRXl4OrVaLsrIyk6CfEEeh4JsQMi6uX78u+tj/+vXr3Db/EfREJJPJABgXsXEk/jWxxGAwmNwpZTEMg3379gEwdvc4efKkYJAhdqfYVux1AozXav78+aPel1+fka4Dv6vDRGk71rZ3dru7uxsGg0H07jdbbxcXF7s/FZoxYwa37eHhYZdFt5xJKpUiMzMTmZmZAICWlhYcOXIEOp0OnZ2d+PTTTxEdHY2XXnppnEtKJgP6iUfIJFJdXY2mpibuxQ5q2r59u8n7Tz31FADjAET++01NTcjJybFLWdhl60dKnzp1KsLCwuxyTkdZsmQJAODPP/8UfLJgDw0NDaKrbDY2NnJ9ffnB0c2bN7nALDk5WTDw7uvrQ2trqx1LbIq9TgBw6tSpMe3Lr8+ZM2dE8/IH1k2UINHa9s6W/969e1x/fCFsvcPDw7m+//YSFhYGqVQK4EGXtPFk65OAqKgobNu2DT/99BN3d/7QoUP2KBohI6Lgm5BJJCIiAnK5HHK5HA8//DA3e8TTTz/NvS+Xy7kAbM2aNSbvy+XyEfuujtbBgwcFuzZcvXoV1dXVAIxLyU/07iWJiYnc9u7dux12nps3b3IzTlhSVlbGbcfHx3Pb/ID9zp07gvvv27dPNLi31cqVK7lAR6fTjakvb3BwMDebx6FDh9DX12cx3+DgIPbv3w/AOCaAH/CPJ2vbO/9z5H++5urq6tDc3DxsH3txc3PD6tWrARh/xLP/d4wX/kDrgYEBq48zZ84c7omEIwYZE2IJBd+ETFK//vor7t69i2nTpmHx4sXc+93d3dwf1tjYWIedv6GhAe+9996w9w0GAzIyMrg7uNnZ2Q4rg73Ex8dzTxF0Oh0+++wzwbzd3d02DWYsKCiw2O2ipqYGJSUlAIzTtPFn15g5cyY3K0dlZaXFYOWXX37Ba6+9ZnW5RsPX1xebNm0CYGx/eXl5ggHp/fv3hw3yy83NBQB0dXVh8+bNFvfbuXMnF4RmZGTA09PTXsW3ibXtXa1WIyYmBgBQWlqKEydODDvGrVu3uOvq6urqsO9MYWEh3NzcMDQ0hLVr1+LKlSuCeQcHB1FRUSGaxxb8Abt//PGHYL4vv/wSvb29gumXL1/Gb7/9BgBj6gZFiC2ozzchkxT72H/58uUmd9pOnz4NhmGwYMECBAcHO+z8MTEx2Lp1KxoaGrBx40YEBASgra0N77//Pvf4PDExEWvWrHFYGezp4MGDUKvV6OvrQ2pqKr744gusW7cOYWFhGBwcxIULF1BdXY2qqiro9XqEhoaO+RyLFy9Gc3MzVCoVCgsLoVarMTAwgGPHjmH37t1cn+A9e/aY7Ofq6oq0tDTs2bMH58+fxyOPPIKCggKEh4fj1q1bOHbsGD766CP4+PggODgYv//+u52uynBFRUX49ttv0dTUhOLiYtTV1WHTpk1QKBSQSCS4cuUKamtrUVlZiTfffBPPPvsst29WVhYqKipQV1eHTz75BB0dHcjJycH8+fNx7do1lJWV4fDhwwCAhx56yOE/JsbClvZeWlqKZcuW4d69e1i9ejVefPFFJCYmwtvbG/X19XjnnXe46Qm3bNnisK42CoUCu3btQn5+PpqbmyGXy5GZmYm4uDgEBgaiv78fly5dQl1dHaqqqnDt2jU0NTVxc8fbE38Wpvz8fGzfvh2zZs3iuqOEhobC3d0dH3zwAdLS0pCQkIC4uDhERUVBKpWip6cHZ8+ehU6n434MZ2Vl2b2chFg0Xqv7EELGV0JCgsXlnV9++WUGAJOenm73c/JX/Dt37hwTHR1tstQ0/6XRaARXMWRXuFyxYsWI5xzLCpdi+CvzWVqCnmEY5uzZs8ycOXME68S+2tvbx1QGdnVCrVbLlJaWcsvIm78kEong0va9vb2MUqkULJO/vz9TU1Mjem3tsbw8wzBMV1cXExsbO+J1snSO7u5uRqPRiO4XFRXFXLp0yeK5R/t5a7VaBgAzb9480Xxi18te7Z1hjKuoTp8+XbTeubm5gkuk89uQrfUuKSnhVioVe0kkEqatrc1k39F8j1hsvh07dlhMT0lJGfE7xl8NV+jl6urKFBUViZaFEHuibieETEJDQ0PcoCnzxU7YOW8d2eUEMPbH/fHHH/H2229DqVRi2rRp8PHxwdKlS6HT6VBTUyO4iuFEpVKp0Nraig8//BBxcXEICAiAu7s7fHx8oFAokJmZiRMnTlh115v1wgsvoLa2FikpKQgODoZEIsHs2bOxceNG1NfXC67aKJVK8cMPP6CoqAgKhQJeXl7w8fFBVFQUtmzZgsbGRod/5iyZTIaamhocPnwYa9euRUhICDw9PeHl5YWwsDAkJyejoqICqampw/b19/fHqVOncODAATz++OMIDAyEh4cHZsyYgcceewzFxcVoaGhw6GJH1rC1va9atQoXLlzAK6+8AqVSienTp8PT0xNz585FWloaamtrUVxc7JSp8jIyMnDx4kXs3LkTGo0GMpkM7u7u8Pb2RkREBJKSkrB3715cvXoVCxYscFg5ysvL8e6770KtVkMqlVqse2VlJUpKSrB+/XoolUoEBQVx38mFCxciOzsb9fX1ePXVVx1WTkLMuTDMBJ7MlRDyv7J//35u6e/29nabgtDJhJ2fXavVcoMJycRH7Z0QYgnd+SaEEEIIIcRJKPgmhBBCCCHESSj4JoQQQgghxEko+CaEEEIIIcRJKPgmhBBCCCHESWi2E0IIIYQQQpyE7nwTQgghhBDiJBR8E0IIIYQQ4iQUfBNCCCGEEOIkFHwTQgghhBDiJBR8E0IIIYQQ4iQUfBNCCCGEEOIkFHwTQgghhBDiJBR8E0IIIYQQ4iT/AXDPh2x45e/TAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pcr_fig, ax = subplots(figsize=(8,8))\n", "n_comp = param_grid['pca__n_components']\n", @@ -1497,12 +9568,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "9df0fb7f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:43.999739Z", + "iopub.status.busy": "2023-07-25T23:59:43.999596Z", + "iopub.status.idle": "2023-07-25T23:59:44.006697Z", + "shell.execute_reply": "2023-07-25T23:59:44.006373Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "204139.30692994667" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Xn = np.zeros((X.shape[0], 1))\n", "cv_null = skm.cross_validate(linreg,\n", @@ -1526,10 +9614,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "458c21a4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:44.008508Z", + "iopub.status.busy": "2023-07-25T23:59:44.008380Z", + "iopub.status.idle": "2023-07-25T23:59:44.010723Z", + "shell.execute_reply": "2023-07-25T23:59:44.010438Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.3831424 , 0.21841076])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pipe.named_steps['pca'].explained_variance_ratio_\n" ] @@ -1563,10 +9669,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "f49d464e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:44.012454Z", + "iopub.status.busy": "2023-07-25T23:59:44.012320Z", + "iopub.status.idle": "2023-07-25T23:59:44.016175Z", + "shell.execute_reply": "2023-07-25T23:59:44.015867Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
PLSRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "PLSRegression()" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pls = PLSRegression(n_components=2, \n", " scale=True)\n", @@ -1584,10 +9711,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "8e118b5b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:44.017881Z", + "iopub.status.busy": "2023-07-25T23:59:44.017786Z", + "iopub.status.idle": "2023-07-25T23:59:44.123851Z", + "shell.execute_reply": "2023-07-25T23:59:44.123517Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "             estimator=PLSRegression(),\n",
+       "             param_grid={'n_components': range(1, 20)},\n",
+       "             scoring='neg_mean_squared_error')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", + " estimator=PLSRegression(),\n", + " param_grid={'n_components': range(1, 20)},\n", + " scoring='neg_mean_squared_error')" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "param_grid = {'n_components':range(1, 20)}\n", "grid = skm.GridSearchCV(pls,\n", @@ -1607,10 +9764,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "0f98407c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:44.125903Z", + "iopub.status.busy": "2023-07-25T23:59:44.125763Z", + "iopub.status.idle": "2023-07-25T23:59:44.234276Z", + "shell.execute_reply": "2023-07-25T23:59:44.233879Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pls_fig, ax = subplots(figsize=(8,8))\n", "n_comp = param_grid['n_components']\n", @@ -1638,6 +9813,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch7-nonlin-lab.ipynb b/docs/source/labs/Ch7-nonlin-lab.ipynb index 3d947f7..798a73a 100644 --- a/docs/source/labs/Ch7-nonlin-lab.ipynb +++ b/docs/source/labs/Ch7-nonlin-lab.ipynb @@ -20,9 +20,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "77af2dc4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:45.681484Z", + "iopub.status.busy": "2023-07-25T23:59:45.681169Z", + "iopub.status.idle": "2023-07-25T23:59:46.716705Z", + "shell.execute_reply": "2023-07-25T23:59:46.716226Z" + } + }, "outputs": [], "source": [ "import numpy as np, pandas as pd\n", @@ -47,9 +54,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "b57d1d51", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.718874Z", + "iopub.status.busy": "2023-07-25T23:59:46.718688Z", + "iopub.status.idle": "2023-07-25T23:59:46.729795Z", + "shell.execute_reply": "2023-07-25T23:59:46.729463Z" + } + }, "outputs": [], "source": [ "from pygam import (s as s_gam,\n", @@ -79,9 +93,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "65f0e562", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.731616Z", + "iopub.status.busy": "2023-07-25T23:59:46.731518Z", + "iopub.status.idle": "2023-07-25T23:59:46.739064Z", + "shell.execute_reply": "2023-07-25T23:59:46.738783Z" + } + }, "outputs": [], "source": [ "Wage = load_data('Wage')\n", @@ -102,12 +123,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a391fae6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.740875Z", + "iopub.status.busy": "2023-07-25T23:59:46.740748Z", + "iopub.status.idle": "2023-07-25T23:59:46.814762Z", + "shell.execute_reply": "2023-07-25T23:59:46.813114Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept111.70360.729153.2830.000
poly(age, degree=4)[0]447.067939.91511.2010.000
poly(age, degree=4)[1]-478.315839.915-11.9830.000
poly(age, degree=4)[2]125.521739.9153.1450.002
poly(age, degree=4)[3]-77.911239.915-1.9520.051
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 111.7036 0.729 153.283 0.000\n", + "poly(age, degree=4)[0] 447.0679 39.915 11.201 0.000\n", + "poly(age, degree=4)[1] -478.3158 39.915 -11.983 0.000\n", + "poly(age, degree=4)[2] 125.5217 39.915 3.145 0.002\n", + "poly(age, degree=4)[3] -77.9112 39.915 -1.952 0.051" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n", "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n", @@ -150,9 +258,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d672f40e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.819794Z", + "iopub.status.busy": "2023-07-25T23:59:46.819458Z", + "iopub.status.idle": "2023-07-25T23:59:46.827533Z", + "shell.execute_reply": "2023-07-25T23:59:46.826805Z" + } + }, "outputs": [], "source": [ "age_grid = np.linspace(age.min(),\n", @@ -178,9 +293,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "56174265", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.833075Z", + "iopub.status.busy": "2023-07-25T23:59:46.832741Z", + "iopub.status.idle": "2023-07-25T23:59:46.848016Z", + "shell.execute_reply": "2023-07-25T23:59:46.842611Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -229,12 +350,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "1a10422f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:46.853147Z", + "iopub.status.busy": "2023-07-25T23:59:46.852739Z", + "iopub.status.idle": "2023-07-25T23:59:47.050039Z", + "shell.execute_reply": "2023-07-25T23:59:47.048574Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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df_residssrdf_diffss_diffFPr(>F)
02998.05.022216e+060.0NaNNaNNaN
12997.04.793430e+061.0228786.010128143.5931072.363850e-32
22996.04.777674e+061.015755.6936649.8887561.679202e-03
32995.04.771604e+061.06070.1521243.8098135.104620e-02
42994.04.770322e+061.01282.5630170.8049763.696820e-01
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coefstd errtP>|t|
intercept111.70360.729153.2830.000
poly(age, degree=4)[0]447.067939.91511.2010.000
poly(age, degree=4)[1]-478.315839.915-11.9830.000
poly(age, degree=4)[2]125.521739.9153.1450.002
poly(age, degree=4)[3]-77.911239.915-1.9520.051
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 111.7036 0.729 153.283 0.000\n", + "poly(age, degree=4)[0] 447.0679 39.915 11.201 0.000\n", + "poly(age, degree=4)[1] -478.3158 39.915 -11.983 0.000\n", + "poly(age, degree=4)[2] 125.5217 39.915 3.145 0.002\n", + "poly(age, degree=4)[3] -77.9112 39.915 -1.952 0.051" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summarize(M)\n" ] @@ -343,12 +668,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "0b576607", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.136679Z", + "iopub.status.busy": "2023-07-25T23:59:47.136366Z", + "iopub.status.idle": "2023-07-25T23:59:47.143007Z", + "shell.execute_reply": "2023-07-25T23:59:47.141901Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "143.59228900000002" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(-11.983)**2\n" ] @@ -367,12 +709,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "7242df77", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.147738Z", + "iopub.status.busy": "2023-07-25T23:59:47.147429Z", + "iopub.status.idle": "2023-07-25T23:59:47.178407Z", + "shell.execute_reply": "2023-07-25T23:59:47.177311Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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df_residssrdf_diffss_diffFPr(>F)
02997.03.902335e+060.0NaNNaNNaN
12996.03.759472e+061.0142862.701185113.9918833.838075e-26
22995.03.753546e+061.05926.2070704.7285932.974318e-02
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coefstd errzP>|z|
intercept-4.30120.345-12.4570.000
poly(age, degree=4)[0]71.964226.1332.7540.006
poly(age, degree=4)[1]-85.772935.929-2.3870.017
poly(age, degree=4)[2]34.162619.6971.7340.083
poly(age, degree=4)[3]-47.400824.105-1.9660.049
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+959,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "fcf376bd", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.229023Z", + "iopub.status.busy": "2023-07-25T23:59:47.228288Z", + "iopub.status.idle": "2023-07-25T23:59:47.431859Z", + "shell.execute_reply": "2023-07-25T23:59:47.430536Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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(51.0, 80.0]116.57171.55974.7510.0
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "(17.999, 33.75] 94.1584 1.478 63.692 0.0\n", + "(33.75, 42.0] 116.6608 1.470 79.385 0.0\n", + "(42.0, 51.0] 119.1887 1.416 84.147 0.0\n", + "(51.0, 80.0] 116.5717 1.559 74.751 0.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cut_age = pd.qcut(age, 4)\n", "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n" @@ -542,12 +1153,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "d3817102", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.462938Z", + "iopub.status.busy": "2023-07-25T23:59:47.462123Z", + "iopub.status.idle": "2023-07-25T23:59:47.472495Z", + "shell.execute_reply": "2023-07-25T23:59:47.471619Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3000, 7)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n", "bs_age = bs_.transform(age)\n", @@ -568,12 +1196,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "c06c0f27", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.478643Z", + "iopub.status.busy": "2023-07-25T23:59:47.478133Z", + "iopub.status.idle": "2023-07-25T23:59:47.545629Z", + "shell.execute_reply": "2023-07-25T23:59:47.543199Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept60.49379.4606.3940.000
bs(age, internal_knots=[25, 40, 60])[0]3.980512.5380.3170.751
bs(age, internal_knots=[25, 40, 60])[1]44.63109.6264.6360.000
bs(age, internal_knots=[25, 40, 60])[2]62.838810.7555.8430.000
bs(age, internal_knots=[25, 40, 60])[3]55.990810.7065.2300.000
bs(age, internal_knots=[25, 40, 60])[4]50.688114.4023.5200.000
bs(age, internal_knots=[25, 40, 60])[5]16.606119.1260.8680.385
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 60.4937 9.460 6.394 0.000\n", + "bs(age, internal_knots=[25, 40, 60])[0] 3.9805 12.538 0.317 0.751\n", + "bs(age, internal_knots=[25, 40, 60])[1] 44.6310 9.626 4.636 0.000\n", + "bs(age, internal_knots=[25, 40, 60])[2] 62.8388 10.755 5.843 0.000\n", + "bs(age, internal_knots=[25, 40, 60])[3] 55.9908 10.706 5.230 0.000\n", + "bs(age, internal_knots=[25, 40, 60])[4] 50.6881 14.402 3.520 0.000\n", + "bs(age, internal_knots=[25, 40, 60])[5] 16.6061 19.126 0.868 0.385" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n", "Xbs = bs_age.fit_transform(Wage)\n", @@ -591,10 +1322,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "8cb32db0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.550992Z", + "iopub.status.busy": "2023-07-25T23:59:47.550477Z", + "iopub.status.idle": "2023-07-25T23:59:47.578217Z", + "shell.execute_reply": "2023-07-25T23:59:47.577450Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept60.49379.4606.3940.000
bs(age)[0]3.980512.5380.3170.751
bs(age)[1]44.63109.6264.6360.000
bs(age)[2]62.838810.7555.8430.000
bs(age)[3]55.990810.7065.2300.000
bs(age)[4]50.688114.4023.5200.000
bs(age)[5]16.606119.1260.8680.385
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 60.4937 9.460 6.394 0.000\n", + "bs(age)[0] 3.9805 12.538 0.317 0.751\n", + "bs(age)[1] 44.6310 9.626 4.636 0.000\n", + "bs(age)[2] 62.8388 10.755 5.843 0.000\n", + "bs(age)[3] 55.9908 10.706 5.230 0.000\n", + "bs(age)[4] 50.6881 14.402 3.520 0.000\n", + "bs(age)[5] 16.6061 19.126 0.868 0.385" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bs_age = MS([bs('age',\n", " internal_knots=[25,40,60],\n", @@ -623,12 +1458,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f26b715f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.583782Z", + "iopub.status.busy": "2023-07-25T23:59:47.583349Z", + "iopub.status.idle": "2023-07-25T23:59:47.596998Z", + "shell.execute_reply": "2023-07-25T23:59:47.595684Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([33.75, 42. , 51. ])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "BSpline(df=6).fit(age).internal_knots_\n" ] @@ -651,12 +1503,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "d6109816", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.608918Z", + "iopub.status.busy": "2023-07-25T23:59:47.608409Z", + "iopub.status.idle": "2023-07-25T23:59:47.641795Z", + "shell.execute_reply": "2023-07-25T23:59:47.640942Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept94.15841.47863.6870.0
bs(age, df=3, degree=0)[0]22.34902.15210.3880.0
bs(age, df=3, degree=0)[1]24.80762.04412.1370.0
bs(age, df=3, degree=0)[2]22.78142.08710.9170.0
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 94.1584 1.478 63.687 0.0\n", + "bs(age, df=3, degree=0)[0] 22.3490 2.152 10.388 0.0\n", + "bs(age, df=3, degree=0)[1] 24.8076 2.044 12.137 0.0\n", + "bs(age, df=3, degree=0)[2] 22.7814 2.087 10.917 0.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bs_age0 = MS([bs('age',\n", " df=3, \n", @@ -706,12 +1637,107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "7c73a3da", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.647140Z", + "iopub.status.busy": "2023-07-25T23:59:47.646766Z", + "iopub.status.idle": "2023-07-25T23:59:47.682939Z", + "shell.execute_reply": "2023-07-25T23:59:47.682146Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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coefstd errtP>|t|
intercept60.47524.70812.8440.000
ns(age, df=5)[0]61.52674.70913.0650.000
ns(age, df=5)[1]55.69125.7179.7410.000
ns(age, df=5)[2]46.81844.9489.4630.000
ns(age, df=5)[3]83.203611.9186.9820.000
ns(age, df=5)[4]6.87709.4840.7250.468
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" + ], + "text/plain": [ + " coef std err t P>|t|\n", + "intercept 60.4752 4.708 12.844 0.000\n", + "ns(age, df=5)[0] 61.5267 4.709 13.065 0.000\n", + "ns(age, df=5)[1] 55.6912 5.717 9.741 0.000\n", + "ns(age, df=5)[2] 46.8184 4.948 9.463 0.000\n", + "ns(age, df=5)[3] 83.2036 11.918 6.982 0.000\n", + "ns(age, df=5)[4] 6.8770 9.484 0.725 0.468" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ns_age = MS([ns('age', df=5)]).fit(Wage)\n", "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n", @@ -728,10 +1754,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "d73789b4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.692145Z", + "iopub.status.busy": "2023-07-25T23:59:47.691762Z", + "iopub.status.idle": "2023-07-25T23:59:47.897115Z", + "shell.execute_reply": "2023-07-25T23:59:47.891816Z" + } + }, + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_wage_fit(age_df,\n", " ns_age,\n", @@ -758,10 +1802,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "3e70b87d", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.906535Z", + "iopub.status.busy": "2023-07-25T23:59:47.906039Z", + "iopub.status.idle": "2023-07-25T23:59:47.942756Z", + "shell.execute_reply": "2023-07-25T23:59:47.941983Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n", + " max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n", + " verbose=False)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X_age = np.asarray(age).reshape((-1,1))\n", "gam = LinearGAM(s_gam(0, lam=0.6))\n", @@ -784,10 +1848,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "efe03b12", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:47.948147Z", + "iopub.status.busy": "2023-07-25T23:59:47.947788Z", + "iopub.status.idle": "2023-07-25T23:59:48.314582Z", + "shell.execute_reply": "2023-07-25T23:59:48.314258Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", @@ -812,10 +1894,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "acff2af2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:48.316489Z", + "iopub.status.busy": "2023-07-25T23:59:48.316348Z", + "iopub.status.idle": "2023-07-25T23:59:48.751690Z", + "shell.execute_reply": "2023-07-25T23:59:48.750894Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gam_opt = gam.gridsearch(X_age, y)\n", "ax.plot(age_grid,\n", @@ -841,12 +1949,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "a2d25550", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:48.756958Z", + "iopub.status.busy": "2023-07-25T23:59:48.756585Z", + "iopub.status.idle": "2023-07-25T23:59:48.783496Z", + "shell.execute_reply": "2023-07-25T23:59:48.782677Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4.000000100001664" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "age_term = gam.terms[0]\n", "lam_4 = approx_lam(X_age, age_term, 4)\n", @@ -866,10 +1991,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "bc6322de", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:48.795407Z", + "iopub.status.busy": "2023-07-25T23:59:48.792243Z", + "iopub.status.idle": "2023-07-25T23:59:49.191938Z", + "shell.execute_reply": "2023-07-25T23:59:49.177094Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(X_age,\n", @@ -910,9 +2053,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "703d2570", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.194776Z", + "iopub.status.busy": "2023-07-25T23:59:49.194595Z", + "iopub.status.idle": "2023-07-25T23:59:49.246216Z", + "shell.execute_reply": "2023-07-25T23:59:49.244766Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -943,12 +2092,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "766a7b1f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.261161Z", + "iopub.status.busy": "2023-07-25T23:59:49.260747Z", + "iopub.status.idle": "2023-07-25T23:59:49.446433Z", + "shell.execute_reply": "2023-07-25T23:59:49.445528Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "age_grid = np.linspace(age.min(),\n", " age.max(),\n", @@ -989,10 +2155,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "2a9ed841", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.451802Z", + "iopub.status.busy": "2023-07-25T23:59:49.451326Z", + "iopub.status.idle": "2023-07-25T23:59:49.615361Z", + "shell.execute_reply": "2023-07-25T23:59:49.614953Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "year_grid = np.linspace(2003, 2009, 100)\n", "year_grid = np.linspace(Wage['year'].min(),\n", @@ -1032,9 +2216,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "ccf068b1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.617403Z", + "iopub.status.busy": "2023-07-25T23:59:49.617260Z", + "iopub.status.idle": "2023-07-25T23:59:49.637989Z", + "shell.execute_reply": "2023-07-25T23:59:49.637017Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1063,10 +2253,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "38b719f1", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.642419Z", + "iopub.status.busy": "2023-07-25T23:59:49.642108Z", + "iopub.status.idle": "2023-07-25T23:59:49.817915Z", + "shell.execute_reply": "2023-07-25T23:59:49.816775Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_gam(gam_full, 0, ax=ax)\n", @@ -1088,9 +2296,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "02142f6e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.823446Z", + "iopub.status.busy": "2023-07-25T23:59:49.822838Z", + "iopub.status.idle": "2023-07-25T23:59:49.864597Z", + "shell.execute_reply": "2023-07-25T23:59:49.863709Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1115,12 +2329,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "94587b05", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:49.871560Z", + "iopub.status.busy": "2023-07-25T23:59:49.871033Z", + "iopub.status.idle": "2023-07-25T23:59:50.059620Z", + "shell.execute_reply": "2023-07-25T23:59:50.058456Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Partial dependence of year on wage')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_gam(gam_full,\n", @@ -1141,10 +2382,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "bba4c757", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.065747Z", + "iopub.status.busy": "2023-07-25T23:59:50.064570Z", + "iopub.status.idle": "2023-07-25T23:59:50.193175Z", + "shell.execute_reply": "2023-07-25T23:59:50.192814Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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deviancedfdeviance_diffdf_diffFpvalue
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deviancedfdeviance_diffdf_diffFpvalue
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" + ], + "text/plain": [ + " deviance df deviance_diff df_diff F \\\n", + "0 3.975443e+06 2991.000589 NaN NaN NaN \n", + "1 3.850247e+06 2990.000704 125196.137317 0.999884 101.270106 \n", + "2 3.693143e+06 2987.007254 157103.978302 2.993450 42.447812 \n", + "\n", + " pvalue \n", + "0 NaN \n", + "1 1.681120e-07 \n", + "2 5.669414e-07 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gam_0 = LinearGAM(year_term +\n", " f_gam(2, lam=0))\n", @@ -1251,10 +2694,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "08026a6b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.332475Z", + "iopub.status.busy": "2023-07-25T23:59:50.332022Z", + "iopub.status.idle": "2023-07-25T23:59:50.342074Z", + "shell.execute_reply": "2023-07-25T23:59:50.339526Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearGAM \n", + "=============================================== ==========================================================\n", + "Distribution: NormalDist Effective DoF: 12.9927\n", + "Link Function: IdentityLink Log Likelihood: -24117.907\n", + "Number of Samples: 3000 AIC: 48263.7995\n", + " AICc: 48263.94\n", + " GCV: 1246.1129\n", + " Scale: 1236.4024\n", + " Pseudo R-Squared: 0.2928\n", + "==========================================================================================================\n", + "Feature Function Lambda Rank EDoF P > x Sig. Code \n", + "================================= ==================== ============ ============ ============ ============\n", + "s(0) [465.0491] 20 5.1 1.11e-16 *** \n", + "s(1) [2.1564] 7 4.0 8.10e-03 ** \n", + "f(2) [0] 5 4.0 1.11e-16 *** \n", + "intercept 1 0.0 1.11e-16 *** \n", + "==========================================================================================================\n", + "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", + "\n", + "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", + " which can cause p-values to appear significant when they are not.\n", + "\n", + "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", + " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", + " are typically lower than they should be, meaning that the tests reject the null too readily.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_33473/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n", + " \n", + "Please do not make inferences based on these values! \n", + "\n", + "Collaborate on a solution, and stay up to date at: \n", + "github.com/dswah/pyGAM/issues/163 \n", + "\n", + " gam_full.summary()\n" + ] + } + ], "source": [ "gam_full.summary()\n" ] @@ -1271,9 +2767,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "9191d615", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.348263Z", + "iopub.status.busy": "2023-07-25T23:59:50.347814Z", + "iopub.status.idle": "2023-07-25T23:59:50.369772Z", + "shell.execute_reply": "2023-07-25T23:59:50.368479Z" + } + }, "outputs": [], "source": [ "Yhat = gam_full.predict(Xgam)\n" @@ -1290,12 +2793,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "92007a5f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.375102Z", + "iopub.status.busy": "2023-07-25T23:59:50.374536Z", + "iopub.status.idle": "2023-07-25T23:59:50.481702Z", + "shell.execute_reply": "2023-07-25T23:59:50.479739Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n", + " fit_intercept=True, max_iter=100, \n", + " terms=s(0) + l(1) + f(2) + intercept, tol=0.0001, verbose=False)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gam_logit = LogisticGAM(age_term + \n", " l_gam(1, lam=0) +\n", @@ -1305,12 +2827,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "4dd6aa2f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.491174Z", + "iopub.status.busy": "2023-07-25T23:59:50.490770Z", + "iopub.status.idle": "2023-07-25T23:59:50.634371Z", + "shell.execute_reply": "2023-07-25T23:59:50.633430Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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Advanced Degree \n", + "high_earn \n", + "False 381 \n", + "True 45 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.crosstab(Wage['high_earn'], Wage['education'])\n" ] @@ -1361,9 +2980,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "c92b60be", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.664968Z", + "iopub.status.busy": "2023-07-25T23:59:50.664462Z", + "iopub.status.idle": "2023-07-25T23:59:50.672960Z", + "shell.execute_reply": "2023-07-25T23:59:50.672194Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1389,12 +3014,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "e525e8d0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.678086Z", + "iopub.status.busy": "2023-07-25T23:59:50.677710Z", + "iopub.status.idle": "2023-07-25T23:59:50.738388Z", + "shell.execute_reply": "2023-07-25T23:59:50.737375Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n", + " fit_intercept=True, max_iter=100, \n", + " terms=s(0) + s(1) + f(2) + intercept, tol=0.0001, verbose=False)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gam_logit_ = LogisticGAM(age_term +\n", " year_term +\n", @@ -1413,10 +3057,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "c3e66cf9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.744284Z", + "iopub.status.busy": "2023-07-25T23:59:50.743537Z", + "iopub.status.idle": "2023-07-25T23:59:50.893111Z", + "shell.execute_reply": "2023-07-25T23:59:50.892009Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 2)\n", @@ -1429,10 +3091,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "fd924348", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:50.897946Z", + "iopub.status.busy": "2023-07-25T23:59:50.897534Z", + "iopub.status.idle": "2023-07-25T23:59:51.054702Z", + "shell.execute_reply": "2023-07-25T23:59:51.054277Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 1)\n", @@ -1444,12 +3124,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "2d3ec90a", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:51.056652Z", + "iopub.status.busy": "2023-07-25T23:59:51.056495Z", + "iopub.status.idle": "2023-07-25T23:59:51.177538Z", + "shell.execute_reply": "2023-07-25T23:59:51.177183Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 0)\n", @@ -1475,10 +3172,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "4f2bc0eb", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:51.179519Z", + "iopub.status.busy": "2023-07-25T23:59:51.179374Z", + "iopub.status.idle": "2023-07-25T23:59:51.330925Z", + "shell.execute_reply": "2023-07-25T23:59:51.330552Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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RaBRnzpzBmjVrYDAYFrs5pIRyzpfSeK0ug+J8mpub8eCDD+I973kPnE4nnnjiCbznPe8BAAwODmLjxo04cOAAdu7ciV/96ld4+9vfjvHxcbS2tgI4X1HlM5/5DNxuN/R6vaJtUlBMyOJzu90YHByEx+NBMpmEVquFw+FAd3c3nE7nYjePEFInKCiuLwsRFNfl8IlMqVQKP/7xjxEKhbBr1y689tprSCQSuO666/gy3d3d6OzsxIEDBwAABw4cwNatW3lADAA33HADAoEA3nzzzYLbisViCAQCWX8IIYvH7Xajv78fExMTMBqNsNvtMBqNmJiYQH9/P9xu92I3kRBCSJ2o26D4jTfegNlshiRJ+NjHPoannnoKmzZtwuTkJPR6/bza862trZicnAQATE5OZgXE7HP2WSEPPPAArFYr/7Ny5Up1d4oQopgsyxgcHEQ4HIbT6YQkSRBFEZIkwel0IhwOY3BwEEvkZRghhJAFVrdBcVdXFw4dOoT+/n7ceeeduO2223D06NEF3ea9994Lv9/P/5w7d25Bt0cIKczv98Pj8eQdPywIAiwWCzweD/x+/yK1kBBCSD2p25Rser0eF110EQDgsssuw6uvvoqHHnoIf/qnf4p4PA6fz5fVWzw1NYW2tjYAQFtbGwYGBrLWx7JTsGXykSQJkiSpvCeEkAsRi8WQTCah0+nyfq7X6zE3N4dYLFbllhFCCKlHddtTnCudTiMWi+Gyyy6DTqfDs88+yz8bGhrCyMgIdu3aBQDYtWsX3njjDUxPT/NlnnnmGVgsFmzatKnqbSeEFCbLMnw+H6ampuDz+fhwCEmSoNVqkUgk8v67eDwOrVZLD7KEEEIUqcue4nvvvRc33ngjOjs7MTc3hyeeeALPP/88fvOb38BqteLDH/4w7rnnHjQ3N8NiseDjH/84du3ahZ07dwIArr/+emzatAl/8Rd/ga985SuYnJzEfffdh7vuuot+gRJSQ4pllnA4HHA4HJiYmIDT6cwaQiHLMgKBAFwuF6xW6yLuASGEkHpRl0Hx9PQ0PvjBD2JiYgJWqxXbtm3Db37zG/zRH/0RAODrX/86RFHELbfcglgshhtuuAHf+ta3+L/XaDT4xS9+gTvvvBO7du2CyWTCbbfdhi984QuLtUuEkBwss0Q4HIbVaoVOp0MikcDExAT8fj96e3vR3d0Nv98Pt9sNi8UCvV6PeDyOQCAAo9GI7u5uyldMCCFEkSWTp3gxUJ5iQhaGLMvYv39/wV5gt9sNl8uF3bt3w+PxUJ5iQkjFKE9xfVmIPMV12VNMCFnaysks4XQ64XA4qKIdIYSQilBQTAipOeVmlhAEYV5uckIIIaQcSyb7BCFk6aDMEoQQQqqNgmJCSM2xWq18SETutAeWWcLhcFBmCUIIIaqhoJgQUnMEQUB3dzeMRiPcbjei0SjS6TSi0SjcbjdlliCEEKI6CooJITXJ6XSit7cXLpcLkUgEXq8XkUgELpcLvb29lFmCEEKqJBKJ4HOf+xw2bNgAg8GA9vZ23HHHHRgbGytrPatXr4YgCAX/DA4OLtAeKEMT7QghNYsySxBCyOKKRqO45ppr0NfXB5fLhXe+850YHh7GY489hl/84hfo6+vD2rVry1rnbbfdlvfniz0kjoJiQkhNo8wShBCyeL70pS+hr68Pu3btwm9/+1uYzWYAwNe+9jX83d/9He644w48//zzZa3z8ccfV7+hKqDhE4QQQgghZJ54PI5vfvObAIBHHnmEB8QAcM8992Dbtm144YUX8Nprry1WE1VFQTEhhBBCCJnn5Zdfht/vx7p163DJJZfM+/w973kPAODpp5+udtMWBA2fIIQQQggp5P9+HJg+ttituDAtG4E/+cYF//PXX38dAHDppZfm/Zz9/PDhw2Wt98EHH8SpU6cgSRI2b96Md7/73TUxeZqCYkIIIYSQQqaPAaOvLnYrFsXIyAgAoKOjI+/n7Odnz54ta72f/vSns/7+t3/7t/jGN76BO+644wJaqR4aPkEIIYQQQuYJBoMAAKPRmPdzk8kEAJibm1O0vj/5kz/BT3/6U5w9exbhcBhHjhzBPffcg1gshr/8y7/Ez3/+c3UafoGop5gQQgghhCy4hx9+OOvvmzdvxle/+lV0d3fjox/9KD7zmc/gne985yK1jnqKCSGEEEJIHizbRDgczvt5KBQCADQ2Nla0nQ9/+MNoaWnB0NAQhoeHK1pXJainmBBCCCGkkJaNi92CC1dh2zs7OwEAo6OjeT9nP1+1alVF2xFFEevWrcP09DQmJiawevXqitZ3oSgoJoQQQggppILsDfXu4osvBgAcPHgw7+fs59u2bat4W7OzswD+ME55MdDwCULIsiDLMnw+H6ampuDz+SDL8mI3iRBCatoVV1wBq9WKU6dO4dChQ/M+f/LJJwEA73jHOyrazptvvomhoSEYjUZ0d3dXtK5KUFBMCFny3G439u/fj3379uHFF1/Evn37sH//frjd7sVuGiGE1Cy9Xo+7774bAHDXXXfxMcTA+TLPhw8fxpVXXonLLruM//yb3/wmuru7ce+992at65e//CWee+65eds4fPgw3vve90KWZfzlX/4l9Hr9Au1NaTR8ghCypLndbvT39yMcDsNqtUKn0yGRSGBiYgJ+vx+9vb01kTSeEEJq0X333Ye9e/filVdewfr16/HWt74VZ8+eRX9/P5xOJx599NGs5T0eD4aGhjAxMZH184GBAdx///1YtWoVLr74YhiNRpw+fRoHDx5EMpnEVVddhX/913+t5q7NQz3FhJAlS5ZlDA4OIhwOw+l0QpIkiKIISZLgdDoRDocxODhIQykIIaQAg8GAffv24bOf/SyMRiN+9rOf4ezZs7j99ttx8OBBrF27VtF6brjhBtxxxx2wWCx4+eWX8eSTT+LkyZPYvXs3vve972Hv3r1oaGhY4L0pTpDpt8EFCwQCsFqt8Pv9sFgsi90cQkgOn8+Hffv2wWg0QpKkeZ9Ho1FEIhFcffXVsNls1W8gIaRmRKNRnDlzBmvWrIHBYFjs5pASyjlfSuM1Gj5BCFmyYrEYkskkdDpd3s/1ej3m5uYQi8UUr1OWZfj9fsRiMUiSBKvVCkEQ1GpylnQ6jdHRUQSDQZjNZnR0dEAUa/8Fn1rtruaxrld0jAhRDwXFhJAlS5IkaLVaJBKJvD3F8XgcWq0272f5uN1uDA4OwuPxIJlMQqvVwuFwoLu7W/VxyUNDQxgYGMDMzAxSqRQ0Gg2am5vR09ODrq4uVbelJrXaXc1jXa/oGBGiLgqKCSFLltVqhcPhwMTEBJxOZ1YPmizLCAQCcLlcsFqtJddVzQl7Q0ND2Lt3L2KxGEwmE9+W2+3G3r17AaAmA2O12k2TI0ujY0SI+mr/PRwhhFwgQRDQ3d0No9EIt9uNaDSKdDqNaDQKt9vNc2KWet1czQl76XQaAwMDiMVisNlsWduy2WyIxWIYGBhAOp2ueFtqUqvdNDmyNDpGhCwMCooJIUua0+lEb28vXC4XIpEIvF4vIpEIXC6X4t40v98Pj8eTd7ymIAiwWCzweDzw+/0Vt3d0dBQzMzMwmUzzxuGKogij0YiZmZmCZVcXi1rtruaxrld0jAhZGDR8ghCy5DmdTjgcjguekLQQE/YKCQaDSKVSRbcVDocRDAYr3lauSiZtqdXuah7relXuMaLJeIQoQ0ExIWRZEAThgtOuqT1hrxiz2QyNRlN0WxqNBmazuaz1lgqMKp20pVa7q3ms61U5x4gm45WPhp3Uh4U4TxQUE0JICWpO2Culo6MDzc3NcLvd0Ol0WUMR0uk0H0fa0dGheJ2lAiM1Jm2p1e5qHut6pfQYxeNxDAwM0GQ8hdg1m0qlFrklRAl2ntRMU0lBMSGkLNV+Fask562SNlXSbjZhz+/3Y3p6GgaDAaIo8kl7JpMpa8JeJdsSRRE9PT3Yu3cvZmdnYTAYoNFokEqlEI1GYTAY0NPTo/gXAQt4Q6EQDAYDDAYD0uk0xsfH4ff70dPTg6GhIYTDYTgcDsTjcYTDYR44ezweDA4OwuFwQBCEgvumVrszj7Xb7YbFYoFer0c8HkcgEJg3OXKhz3251NxWoXUpOUZdXV38vGYGzmwyHntQYueVADqdDhqNBpFIpOw3MaT6IpEINBpNwWFEF4KCYkKIYtV+Fask562SNqnRbqfTifXr12NgYACTk5NZ7dm+fbuq2+rq6oLf70dfXx/8fj/S6TREUYTZbMaOHTsUp2NjWQpmZ2eRTqcxMzPD12U0GhGPx/H6669jbm4OOp0O586dQzgczlomc9JWIpEoum9qtZtNjmTbmpubg1arhcvlKvu8VvOaVXNbpdZV6hjpdDrFk/GomuN5giDAaDTC7/ejubkZGo1msZtECkilUvD7/TAajao+1FFQTEiNq5WesIXIi1qs3Zk5byVJgiRJSKVSWTlvm5ubS7YJAF+GrUeW5bLb7Xa7ceLECUiShPb2dsiyzHtOT5w4gebm5qxtVXKM3G43pqen0draCrvdzgNwrVaL6elpuN1uvp5ix9Dv9/Ne9nQ6DUmSeO9tMBiEKIoYGxuDLMuIx+NIJpPzlmE94ZOTkzh58mTJYz09PY22tjZ+bNh/c9tdSqnJkUqux3LOR6X3kJr3h9J1FTtGU1NTNTthsZYn/rW0tGB4eBhnz55Fc3MzJEmqmbaR89dOLBbjD/gtLS2qrp+CYkJqWK30hOXmRVXjVWyxdtvtdgwMDPDXY+FwmAdYWq0WkUgE/f39WLVqVdE2HTt2DAAwOzuLVCoFr9fLey8bGhoQi8UUtTuzxzWVSiESieRdjyzLFR+jUtuKx+N8PWxoQ6FzH41GeS+xyWTi29VqtdBoNAiFQpibm+MTVhobG+ctwz4/e/asomOduwzbpwu5RgpNjlRyPRZqT77zUeo4lqLm/VHuugodo1qdsFjrE//0ej06Ojrg8XgwMTGx2M0hBZhMJrS1tUGv16u6XgqKCalRaveEVaKcvKhKXsWW2reVK1fC7XYjlUohnU5Dq9XyHsdEIsF7HgVBQEtLS8E2TUxMIBaLIRQKzespDYVCEEURo6OjJdtdrMeVrefMmTO8UEUlx0jJtkZHR3H69GkcO3as6LmPx+NIJBIwGAx528QeMDQaTdFgLZVKYXZ2Fk1NTUWPNYCK918JJdej0vacOXMGR48eregeUvP+UGtdtThhsV6q8BmNRnR2diKZTCKZTC52c0gOrVYLrXZhwlcKigmpQWr3hFX6+k/N3LFK9u3EiROIxWIQRRE6nY4vIwgCdDod4vE44vE4YrFY0TbFYjH4fD6Ioliwp3R2dhbRaLRom5X0uPp8PthstoqPkZJtzc7OKppEddFFF/HAQ6/XzwuMkskkNBoNTCYTkskkH2LCgnD2epv1OBbbt3g8DgBVeV2v5HpU0p5AIKDKZDQ17w+11lXuhMWFthBvmxbaQgZfpDZRRTtCapDSnrCJiYmqVLXKDIzyKedVrJJ9CwQCSKfT/PVw7jKs11gUxaJtEgSBF5Qo1FOaSCR4AFUI63Ettp5UKgVBECo+Rkq2FYvF4PV6S577RCKBpqYmPgQlmUzyYDgcDkOj0cBms8FsNqO1tRVmsxmJRALhcBiJRAJmsxktLS0wm83Q6/VF902v15dcRq3X9UquRyXtYT2mld5Dat4faq5LjWqOaqEqfKQe0CMQITVIrZ4wtXrm1HwVq2Tf2KQyNuYwN+dtMpmEXq9Ha2sr/H5/wTbZ7XbMzc0V7SllwVMxer2+ZI+rwWCA3W4v2h4lx0jJtjQaDWRZLnnu2fjIZDLJxyezHniTyQSNRoPVq1dDlmVMTk5i5cqVfMKdVquFXq+Hx+OBy+UCAExOThbdNyXLqPG6Xun1WKo9FouFZ94odhxL3UNq3h9qD3uotJqjWqhSIakHFBQTUoOUTJJhgVw1JtKo+SpWyb6xANPr9fIhEiwvcCKRgEajgdPpRHd3N44dO1Y0V6vf74fP58s7NID1lBoMhqJtNhgMaGpqKrmerq6uou1RcoyUbMtisUCSpJLn3mAw8PMWDof5mGA2g5u1CQACgQA8Hg8sFgtP1+bxeGA0GrFx40a+TKF9U7KMWq/rlVyPStqzYcMGHD58uOJ7SM37YyGGPRSajFdNtTrxj5BMFBQTUoPU6glTcyKN0tyxau2by+XCiRMnMDc3h2g0yrNPNDQ0oLGxEevWrcPatWthsVgKtsnhcGB8fBzJZJJXVWM9pWazGaIooqOjo+QxslqtvMe12HpKtUfJMVKyrcze3VLnXhCErPPGeoFz26Tk3Kq1jBqUXo/FlmHXhxq9smrdH2qvq1bU4sQ/QnJRUExIDVKrJ0ztiTRqvIrN3LdC1eEy981sNs/LeZtZQa5Um9i2QqEQbDbbBVWiy2xzMBjkwxhYz5bZbFbcHqZQpb7MbRVrMzs+breb51LN7QFm21TSpmouo1SpfLZOpxN2u71oxUOl14ca1fOUtEepWhn2oJZam/hHSD6CzBJUkrKxCRp+vx8Wi2Wxm0OWoFrJU7wQ1KpWp4Rax/G5557Dq6++ilgsxoN0SZKwY8cOXHPNNVXfdyXrqVfVvPbV2la93ovVRMeILAal8RoFxRWgoJhUQ61UtFNTZr7SfL2calYaY4qtp1D+VFZGtLe3F2fOnMG+ffsQj8eh0+n4OF82Ie7qq69GT09PyXZkVuozmUx8W6FQCJIk4brrruMBrZI2h0KhvL3ttZLz9UIoOR9A/hzdmcuUs/+VXh9qt2cpq7fvK1L/lMZrNHyCkBqnZJJMLUykUapQvlL2mdKKXeUqtB4l+VOPHj2KQ4cOIZFIwGQy8dfhOp0Oer0e4XAYfX19uOyyy6DRaAq2IZ1OY2BgALFYjA+LYNvS6XTw+XwYGBjA+vXr+VCKUm3OLV5isVhqMuerUkrz2apRPTBTJdfHQrRnKaun7yuyvFCeYkJIVdVavlIl7Tl9+jSCwSD0ev288aGiKEKv1yMYDGJwcLDotkZHRzEzM5MVWGeux2g0YmZmBqOjoxW3uV5zvirZNzY5rhr7X2vtIYQsHAqKCSFVpSRfaTKZrFq+UiXticVivNx0PlqtFul0GoFAoOi2gsEgLyZSaFupVArBYLDiNlfzGKpJaY5uNoyl0DJq7X+ttYcQsnAoKCaEVJWaFbuq1R5JkiCKIpLJZN5lkskkRFEsObfAbDZDo9EU3ZZGo4HZbK64zfWa81WtanW1Vj2vXs8HIcsJBcWEkKpi+Ur9fj9y5/myfKUOh6Nq+UqVtGft2rUwm82Ix+NIp9NZy6TTacTjcZ6WrZiOjg40NzcjFArlXU84HEZzczM6OjoqbnM1j6GalOxbe3s7XC5XVfa/1tpDCFk4NNGOLBnLeUZzoZy3C7EepcdZSc7fQnmKy81XWsm5V5I/ddOmTWhoaMC+ffsQDoeh1+t5GWr22nznzp18kl2h9oiiiJ6eHuzduxc+nw9Go5Fvi2Xi6OnpKXnuFirnazUyfZSi9PoAlOforiSDS+6xLpQTupz2VNqmhTgfy/n7kxCGUrJVgFKy1Y7lnPtSrVy1auYNrmaO3Wrmqh0YGEBfXx+CwSDS6TSvMrdz506ejq0e913Ndam1HrWuR7WWqfb9UWs5mAmpZ5SnuAooKK4NSnKILtUv9nJy3la6nubmZkXHudycrqXyFBej9rlX0luWSqUwODiIQCAAi8WC7u5u3kNcTnvU6t1Xo4dPreO4EOupJI+1WvmF2TJKckKXOh/VzHlM+ZUJOY/yFJNlQWkO0aWYH7TcnLeVrKe/vx+rVq0qeZztdvsF53QF8ucpLmQhzr2S/KkajQabN2+uuD2iKKKzs1NRuyptczFqHceFXg/7TGkeazXzC7NllOSELnY+qpnzWMm2jh07BgDL8vuTkHxooh2pa0s5X2spauW8VbIer9eLkZGRksd5dHS0ajlda+3c11p7lFKr3fW4HiXXopo5iKuZ81jJtiYmJii/MiEZKCgmdW0p52stRa2ct0rWkzmhrNgywWCwajlda+3c11p7lFKr3fW4HiXXopo5iKuZ85jyKxNSPgqKSV1byvlaS1Er562S9Wi1WkV5WM1mc9Vyutbaua+19iilVrvrcT1KrkU1cxBXM+cx5VcmpHwUFJO6tpTztZaiVs5bJeux2+3o7OwseZw7OjqqltO11s59rbVHKbXaXY/rUXItqpmDuJo5j5Vsy+VyUX5lQjLQRDtS1xYqX2s9UCvnrZL19Pb2orm5GXNzc0WPsyiKis4HUF5O13xq7dzXWnuUUqvd9boeoPS1qGQZJedVzTapsf8bN25Ubd8IWQooJVsFKCVb7VjOeTbrNU9xreXFVUuttUepWjsftZant9o5oWtt/wmpZ5SnuAooKK4ty7kiUz1VtLuQdZVSa+e+1tqjVK2dj1qr6Kbmea21+6Ner1lClKCguAooKCaEEEIIqW1K4zWaaEcIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLKnXewGEEJIPZFlGX6/H7FYDJIkwWq1QhCExW4WqTN0HRFSeygoJoQQhdxuNwYHB+HxeJBMJqHVauFwONDd3Q2n07nYzSN1gq4jQmoTBcWEEKKA2+1Gf38/wuEwrFYrdDodEokEJiYm4Pf70dvbSwENKYmuI0JqF40pJoSQEmRZxuDgIMLhMJxOJyRJgiiKkCQJTqcT4XAYg4ODkGV5sZtKahhdR4TUNgqKCSGkBL/fD4/Hk3fcpyAIsFgs8Hg88Pv9i9RCUg/oOiKktlFQTAghJcRiMSSTSeh0uryf6/V6JJNJxGKxKreM1BO6jgipbRQUE0JICZIkQavVIpFI5P08Ho9Dq9VCkqQqt4zUE7qOCKltFBQTQkgJVqsVDocDfr9/3nhPWZYRCATgcDhgtVoXqYWkHtB1REhto6CYEEJKEAQB3d3dMBqNcLvdiEajSKfTiEajcLvdMBqN6O7upjyzpCi6jgipbYJM01wvWCAQgNVqhd/vh8ViWezmEEIWGOWXJWqg64iQ6lIar1GeYkIIUcjpdPLX31SJjFwouo4IqU0UFBNCSBkEQYDNZlvsZpA6R9cRIbWHxhQTQgghhJBlry6D4gceeAA7duxAY2MjWlpa8K53vQtDQ0NZy1x11VUQBCHrz8c+9rGsZUZGRnDTTTfBaDSipaUFn/rUp5BMJqu5K4QQQgghpAbU5fCJF154AXfddRd27NiBZDKJf/zHf8T111+Po0ePwmQy8eU+8pGP4Atf+AL/u9Fo5P+fSqVw0003oa2tDa+88gomJibwwQ9+EDqdDl/+8peruj+EEEIIIWRxLYnsE263Gy0tLXjhhRewZ88eAOd7irdv345///d/z/tvfvWrX+Htb387xsfH0draCgD4zne+g8985jNwu93Q6/Ult0vZJwghhBBCapvSeK0uh0/kYnXim5ubs37+ox/9CA6HA1u2bMG9996LcDjMPztw4AC2bt3KA2IAuOGGGxAIBPDmm2/m3U4sFkMgEMj6QwghhBBC6l9dDp/IlE6n8Td/8ze44oorsGXLFv7zP//zP8eqVavQ3t6Ow4cP4zOf+QyGhobw05/+FAAwOTmZFRAD4H+fnJzMu60HHngA999//wLtCSGEEEIIWSx1HxTfddddOHLkCPbv35/1849+9KP8/7du3QqXy4Vrr70Wp06dwrp16y5oW/feey/uuece/vdAIICVK1deWMMJIYQQQkjNqOvhE3fffTd+8YtfYN++fejo6Ci6bG9vLwDg5MmTAIC2tjZMTU1lLcP+3tbWlncdkiTBYrFk/SGEEEIIIfWvLoNiWZZx991346mnnsJzzz2HNWvWlPw3hw4dAgC4XC4AwK5du/DGG29genqaL/PMM8/AYrFg06ZNC9JuQgghhBBSm+py+MRdd92FJ554Aj//+c/R2NjIxwBbrVY0NDTg1KlTeOKJJ/C2t70Ndrsdhw8fxt/+7d9iz5492LZtGwDg+uuvx6ZNm/AXf/EX+MpXvoLJyUncd999uOuuuyBJ0mLuHiGEEEIIqbK6TMlWqD78Y489httvvx3nzp3DBz7wARw5cgShUAgrV67Eu9/9btx3331ZQx7Onj2LO++8E88//zxMJhNuu+02/Ou//iu0WmXPCpSSjRBCCCGktimN1+oyKK4VFBQTQgghhNS2ZZWnmBBCCCGEkEpQUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLJHQTEhhBBCCFn2KCgmhBBCCCHLHgXFhBBCCCFk2aOgmBBCCCGELHsUFBNCCCGEkGWPgmJCCCGEELLsUVBMCCGEEEKWPQqKCSGEEELIskdBMSGEEEIIWfYoKCaEEEIIIcseBcWEEEIIIWTZo6CYEEIIIYQsexQUE0IIIYSQZY+CYkIIIYQQsuxRUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNnTLnYDCCGEkKVClmX4/X7EYjFIkgSr1QpBEBa7WYQQBSgoJoQQQlTgdrsxODgIj8eDZDIJrVYLh8OB7u5uOJ3OxW4eIaQECooJIYSQCrndbvT39yMcDsNqtUKn0yGRSGBiYgJ+vx+9vb0UGBNS42hMMSGEEFIBWZYxODiIcDgMp9MJSZIgiiIkSYLT6UQ4HMbg4CBkWV7sphJCiqCgmBBCCKmA3++Hx+PJO35YEARYLBZ4PB74/f5FaiEhRAkKigkhhJAKxGIxJJNJ6HS6vJ/r9Xokk0nEYrEqt4wQUg4KigkhhJAKSJIErVaLRCKR9/N4PA6tVgtJkqrcMkJIOSgoJoQQQipgtVrhcDjg9/vnjRuWZRmBQAAOhwNWq3WRWkgIUYKCYkIIIaQCgiCgu7sbRqMRbrcb0WgU6XQa0WgUbrcbRqMR3d3dlK+YkBpHQTEhhBBSIafTid7eXrhcLkQiEXi9XkQiEbhcLkrHRkidoDzFhBBCiAqcTicfRkEV7QipPxQUE0IIISoRBAE2m22xm0EIuQA0fIIQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLJHQTEhhBBCCFn2KCgmhBBCCCHLHgXFhBBCCCFk2aOgmBBCCCGELHsUFBNCCCGEkGWPgmJCCCGEELLsUVBMCCGEEEKWPQqKCSGEEELIskdBMSGEEEIIWfYoKCaEEEIIIcseBcWEEEIIIWTZ0y52AwghhNQPWZbh9/sRi8UgSRKsVisEQVjsZhFCSMUoKCaEEKKI2+3G4OAgPB4PkskktFotHA4Huru74XQ6F7t5hBBSEQqKCSGElOR2u9Hf349wOAyr1QqdTodEIoGJiQn4/X709vZSYEwIqWs0ppgQQkhRsixjcHAQ4XAYTqcTkiRBFEVIkgSn04lwOIzBwUHIsrzYTSWEkAtGQTEhhJCi/H4/PB5P3vHDgiDAYrHA4/HA7/cvUgsJIaRyFBQTQggpKhaLIZlMQqfT5f1cr9cjmUwiFotVuWWEEKIeCooJIYQUJUkStFotEolE3s/j8Ti0Wi0kSapyywghRD0UFBNCCCnKarXC4XDA7/fPGzcsyzICgQAcDgesVusitZAQQipHQTEhhJCiBEFAd3c3jEYj3G43otEo0uk0otEo3G43jEYjuru7KV8xIaSuUVBMCCGkJKfTid7eXrhcLkQiEXi9XkQiEbhcLkrHRghZEihPMSGEEEWcTicfRkEV7QghSw0FxYQQQhQTBAE2m22xm0EIIaqj4ROEEEIIIWTZo6CYEEIIIYQsexQUE0IIIYSQZY+CYkIIIYQQsuxRUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlT7vYDSCkmmRZht/vRywWgyRJsFqtEASh7relFiVtVrpfSpZLp9MYHR1FMBiE2WxGR0cHRLG2n9XVanMqlcLg4CACgQAsFgu6u7uh0WgWoMXn1eP1qES93tO1du0v1euDkHJQUEyWDbfbjcHBQXg8HiSTSWi1WjgcDnR3d8PpdNbtttSipM1K90vJckNDQxgYGMDMzAxSqRQ0Gg2am5vR09ODrq6uRTkGpajV5oGBAfT19SEYDCKdTkMURTz77LPYuXMnenp6VG93PV6PStTrPV1r1/5SvT4IKVdtd8kU8MADD2DHjh1obGxES0sL3vWud2FoaChrmWg0irvuugt2ux1msxm33HILpqamspYZGRnBTTfdBKPRiJaWFnzqU59CMpms5q6QKnG73ejv78fExASMRiPsdjuMRiMmJibQ398Pt9tdl9tSi5I2K90vJcsNDQ1h7969cLvdMBgMsNlsMBgMcLvd2Lt377z7uRao1eaBgQHs27cPgUAAWq0WRqMRWq0WgUAA+/btw8DAgKrtrsfrUYl6vadr7dpfqtcHIReiLoPiF154AXfddRf6+vrwzDPPIJFI4Prrr0coFOLL/O3f/i2efvpp/Pd//zdeeOEFjI+P4+abb+afp1Ip3HTTTYjH43jllVfwgx/8AI8//jg+97nPLcYukQUkyzIGBwcRDofhdDohSRJEUYQkSXA6nQiHwxgcHIQsy3W1LbUoafOxY8dw7NixkvuVTqcVrau/vx+xWAw2my1rGZvNhlgshoGBAaTT6cU+NFw6ncbAwEDFbU6lUujr60MikYDRaIRer4coitDr9TAajUgkEujr60MqlVKl3fV4PSpRr/e0WteRWpbq9UHIharLoPjXv/41br/9dmzevBkXX3wxHn/8cYyMjOC1114DAPj9fnz/+9/H1772NVxzzTW47LLL8Nhjj+GVV15BX18fAOC3v/0tjh49ih/+8IfYvn07brzxRnzxi1/EI488gng8vpi7R1Tm9/vh8XjyjpETBAEWiwUejwd+v7+utqUWJW2emJjAxMREyf0aHR0tua6zZ8/C6/XCZDLNG0MpiiKMRiNmZmYwOjoK4Pwvbp/Ph6mpKfh8vry/oJUso0Sh9YyOjmJmZkZxmwsZHBxEMBjkwXDuevR6PYLBIAYHBy+o/bnq8XpUol7vabWuI7Us1euDkAu1JMYUsxu2ubkZAPDaa68hkUjguuuu48t0d3ejs7MTBw4cwM6dO3HgwAFs3boVra2tfJkbbrgBd955J958801ccskl87YTi8UQi8X43wOBwELtElFRLBZDMpmETqfL+7ler8fc3FzWua2HbalFSZvZg2Kp/QoGg4rWVWqZcDiMYDCo6jjnUoqtJxgMIpVKKWpzMYFAAOl0Glpt/q9erVaLeDyu2ndLPV6PStTrPa3WdaSWpXp9EHKh6rKnOFM6ncbf/M3f4IorrsCWLVsAAJOTk9Dr9bDZbFnLtra2YnJyki+TGRCzz9ln+TzwwAOwWq38z8qVK1XeG7IQJEmCVqtFIpHI+3k8HodWq4UkSXW1LbUoabNer4dery+5X2azWdG6Si2j0WiQTCZVG+dcSqn1JJNJaDSakm02m81Ft2OxWCCKYsG5C8lkEqIowmKxKGp3KfV4PSpRr/e02WxW5TpSy1K9Pgi5UHUfFN911104cuQIfvzjHy/4tu699174/X7+59y5cwu+TVI5q9UKh8MBv98/77W6LMsIBAJwOBywWq11tS21KGmzy+WCy+UquV8dHR0l17Vq1SrY7XaEQqF5YyfT6TTC4TCampowOzuryjjnUkMplIyr9Pl8aGpqKtrm5uZmdHR0FN1Wd3c3zGYz4vF43vXE43GYzWZ0d3cXXY9S9Xg9KlGv93RHRweam5srvo7UslSvD0IuVF0HxXfffTd+8YtfYN++fVlfIm1tbYjH4/D5fFnLT01Noa2tjS+Tm42C/Z0tk0uSJFgslqw/pPYJgoDu7m4YjUa43W5Eo1Gk02lEo1G43W4YjUZ0d3erkpOzmtvKVMmYWiVt3rhxIzZu3Fhyv0RR5Ouanp6G3+/H3Nwc/H4/pqen+bp6e3shSRJ8Pl/Wunw+HyRJwpYtWzAzM6PKOOdS4yGVjKv0er3YsmULb3MoFEI0GkUoFOJt7unpKZlnVqPRYOfOndDpdHwdiUSCr0un02Hnzp2q5SterOtxodXrPS2KInp6eope+0quo0wLfe/X4/VByIWqyzHFsizj4x//OJ566ik8//zzWLNmTdbnl112GXQ6HZ599lnccsstAM6nwRkZGcGuXbsAALt27cK//Mu/YHp6Gi0tLQCAZ555BhaLBZs2baruDpEF53Q60dvby8eMzs3NQavVwuVyqZ6Ls5rbAtQZU6u0zUqWcTqdWL9+PQYGBjA5OZmVh3X79u1wOp18WZarNRwOQ6PRwOl0oqenBzabDadOnVJlnHOp8ZBKx1WuWLECO3bsQF9fH/x+P88vbDabsWPHDsX5ZXt6ehAMBvHqq68iFotBlmUIggBJkrBjxw7V8xRX+3qslnL2q9LCFGoeQ3adFLr2y8lTXM17n5DlQJDrMNfKX//1X+OJJ57Az3/+86wvEKvVioaGBgDAnXfeiV/+8pd4/PHHYbFY8PGPfxwA8MorrwA4nxpp+/btaG9vx1e+8hVMTk7iL/7iL/CXf/mX+PKXv6yoHYFAAFarFX6/n3qN60S9Vr8qhI2FDYfDsFqt0Ol0SCQS8Pv9MBqN6O3tLeuXmhoV7TLbJEkSBEGALMuIxWLz2lSoqpfP58O+fftgNBrzjmeMRqO8B5iltsq3TCQSwdVXXz1vfkEmJduKRCK4+OKLcfToUYRCIb5P7L8mk0nxsWbHJxQKIZlM8ocGrVZb1nrKtVQrlim5HtUqTFFLFe0W494npF4pjdfqsqf429/+NgDgqquuyvr5Y489httvvx0A8PWvfx2iKOKWW25BLBbDDTfcgG9961t8WY1Gg1/84he48847sWvXLphMJtx22234whe+UK3dIItAEISiAVI9bSt3LCz7BcbGwrJgwOFwKP7lpqTNxZYp1Cb2WW6bRFFEZ2fnvPWwsY4TExN518PGOQPnJ8YWW6bUeEgl22pra8Po6CjC4TBaWlpK7lchmcenkvVciGpe+9VUbL8KBY4TExPw+/1lB45qHsNC174Si3XvE7LU1WVQrKRz22Aw4JFHHsEjjzxScJlVq1bhl7/8pZpNI6RqyskxWq1fdmq1iY119Pv9cLvdsFgsfMhEIBDgY5OB8z0Abrc7b6+0kvGQSra1YsUKHD58uOL9qsVztlQtROBYK+g6ImRh1PVEO0KWMyVjYZPJZFk5RistgqFmm9hYR5fLhUgkAq/Xi0gkApfLxXv42PjlWCyG4eFhnDx5EsPDw4jFYli/fn3Z4yoLbctsNquyXwtxzpaySq7HpVyYgq4jQhZGXfYUE0Kyc4zmGwtbbo5RNcZeqt0mp9PJU0blG+vodrtx4sQJ6PV6rF69GqIo8tnzJ06cQHNzc1mBcaFt+Xw+VfZL7eOzlFV6PS7lwhR0HRGyMKinmJA6pWaOUbWKYCxE3lM21rG1tRU2m40HxLnjc61WKxobG2G1WtHS0qI4T7GSbam1X5QXVhk1rselXJiCriNCFgYFxYTUKbVyjCopXqE0uKxm3tNqvh5Xa78oL2xpal2PSzlwpOuIkIVBwycIqWNq5BhVe9JOtfKeVvv1ONuvY8eOYWJigpesbm9vXzJ5YdVKy1VJar9qTtas58Cxlq8jQuoVBcWE1LlS425LWYjgstI2KVEr4yovJNV7NY5PudTK56tkPcWWSafTql2PSz1wrMXriJB6RkExIUtAJTlGFyq4XOi8p0pzGav1ejwz563NZuM5bycnJxEIBBYk5221Ciqolc9XyXoAFF1m06ZNVZ2sWe8ovzAh6qGgmJBlrtrBpVqq+Xp8MXLeqlmJrRi19k3Jeo4dOwYARZcZGxuD3W6vuChLJgocCSFKUFBMyDK3UMFlNXo5q/V6vNrFEtSuxFaMWvuWuR7gfHlsFsxLkgSLxYKJiQkAyMrskW9b27Zt40VZltpYYIbKKi9NdF7rGwXFhBDVg8tq9XKyti/06/FqTuqrdq+0WvvG1sOGlITDYaTTaYiiyNOqxeNxACi5LbPZvKTHAlfz/iDVQ+e1/lFQTAgBoF5wWc1eTmahX49Xc1JftXul1do3SZKQTCZx7tw5pNNpSJIEjUaDVCqFYDCIcDgMk8kESZIUbctmsy3JscCLcX+QhUfndWmgPMWEEK5Q8Qql1Mx5XEuqmfO22iV81do3i8WCZDKJSCSChoYGaLVaCIIArVaLhoYGRCIRAEBbW5vibVV6PdaapXp/LHd0XpcOCooJIaqpZkGNaqpmsYRqV2JTa98CgUBWAJxMJiHLclagrNPp0NHRsWyLTizV+2O5o/O6dFBQTMgCkWUZPp8PU1NT8Pl8y6KXoNxezmoeIyXbKrYMG3ftcrkQiUTg9XoRiUTgcrlUfTW6GJXYytm3QscoFotBq9Wio6MDZrMZiUQC4XAYiUQCZrMZHR0d0Gq1fLzwQh/HWlTttwCkOui8Lh00ppiQBVDtCRe1MuO5nPGp1TxGlRaUYMtUY1LfYlViU7JvxY4RO/d6vR6dnZ08UGDnm/19KY8XLqVWCs4QddF5XTooKCZEZdWecFFLM56V5jyOx+MYGBioyjFSo6BEZnuqkfN2sSqxFdu3Usexp6cn69wbDAb+b/PlF16OuYPrNSc4KY7O69KhalB84sQJ/K//9b9w4MABTE5OIhKJ4De/+Q0uuugivsyRI0cwMjICk8mEK6+8Us3NE7Loqp1Oq9ZmPCvp5ezq6sLQ0FBVjpHS8yHLclULcyjhdDpht9sxOjqKYDDIhyCIYvVHvSk5jkNDQ+jq6qp6D3c9Way3AGRh0XldOlQJitPpND796U/joYceQjqd5mPMBEHgeSmZkZERvP3tb4dWq8WZM2ewYsUKNZpASE2oZjqtxaiypkSpXk6dTle1Y6TkfIyPjwMoXVBCrRRoSuV7A3D27Nl5PcXVGDqj9LreunXrks4vrIbFegtAFhad16VBlaD4r/7qr/Doo49ClmWsWLECu3btwpNPPpl32be97W1Ys2YNhoeH8eSTT+KTn/ykGk0gpCZUs8hDtfPZlqPY+NSpqamqHSMl50NpQYnMyYELHYQqfQNQraEz5VzXra2ty3K8cDmqMTadVB+d1/pXcVD87LPP4vvf/z4EQcA//uM/4v7774dGoyn6iu+9730vvvKVr+C5556joJgsKdWccFHNAPxCFBozWs1jpGRber0eAGpmcmA5Qz6qNS673HO2HMcLl4uO0dJE57W+VTw47bvf/S6A8z3AX/rSl6DRaEr+m56eHgDAm2++WenmCakp1UynVe18tmqp5jFSsq329na4XK6S7YnH4+jv78fExAQvW2w0GjExMYH+/n643e6K2wsoewPgdrtx6NChqhULWIw0cYQQUm0VB8UHDhyAIAj48Ic/rPjfdHR0AAAmJycr3TwhNaWaRR7qNVCp5jFSuq2NGzcWXSZ3cuBCBqFK3gBEIpGqFguo5jkjhJDFUvHwienpaQDA6tWrFf8b9mWfTCYr3Twhi6LYuFI24eLYsWOYmJjgr+hdLhc2btyo2ivtWp/xrOQYqTUpRY1tFTtn5U4OrGTcsZKhCoIgQJZl6HQ6yLI8LyfwQgydYcfx6NGjGBkZ4ceos7MTmzZtoolEZaqV3OKEkD+oOCg2mUzw+XxlvTocHR0FADQ3N1e6eUKqrpbyAtfqjOdqFsKoxrbKGb9d6fWhJOep0+nE3NwcAoEA/H4/wuEw0uk0RFGE0WiExWJZkKEzMzMzOHv2LLxeL983WZbR1tZGQXEZauk7hBDyBxUHxWvXrsXBgwdx9OhR/NEf/ZGif/OrX/0KALB58+ZKN09IVZVbCMJms/FlJicnEQgEVM8dXGsznjOPkSRJkCQJsiwvWmEOJUU3MteT75xt3LhR0USzUCiEo0ePVjT5TckbgIsvvhgHDx7E0NAQD341Gg1SqRSCwSB8Ph+6urpUHTozNDSEvXv3IhaLwWQy8X3zeDzYu3cvAKCrq0u17S1VtZZbnBDyBxUHxddffz1ee+01PPLII/j4xz9eMrH80aNH8fjjj0MQBLztbW+rdPOEVI2SrADHjh0DgKrnDq6VGc/sGM3OziKVSsHr9fIezIaGBsRiMb7/Ho+not6ycvM0F3pdrWQ9Y2NjsNvtmJycLNh729bWhtHRUVXOfak3AA6HI2v7ucdFbel0GgMDA4jFYrDZbPx7XpIk6HQ6+Hw+DAwMYP369YtSXKRe1GpucULIeRUHxZ/4xCfw8MMP49SpU/jYxz6Gb33rW9Bq86/2mWeewYc+9CFEo1HY7XZ85CMfqXTzhFSNkqwAExMTAGqvEES1+P1+XoEtnU5n9WCGQiGIoojR0VGcPn0ax44dQygUgsFggMFgQDqdxvj4uOLesnLyNCcSiYIBuJLxwl6vFxdffDECgUDB3tsVK1bg8OHDquWNLvYGwOfzIRKJoLOzE4FAAOFwGLFYDKIoorGxERaLBZFIRLXrbHR0FDMzMzCZTPOCXjZkY2ZmBqOjo+js7Kx4e8xSG3dby7nFCSEqBMWtra34zne+gw9+8IP4/ve/j9/85je46aab+OcPPfQQZFnGyy+/zGdni6KIxx9/HGazudLNE1I1C1EIoh4VC1Si0ShmZ2eRTqdhMpn4z7VaLTQaDUKhEGZnZzE0NMSXm5mZyRoPG4/HFfWWKR3nOzk5iZMnTxZ8XX3RRRcpWo/JZCrae5tOp1XPG13oDQDbd7vdDpvNNm+inSzL8Hq9ql1nwWAQqVSq6L6Fw2EEg0FVtgcszXG3tZ5bnJDlTpWKdrfeeit0Oh3+6q/+CufOncN//Md/8F9m//N//k8Af3ilZzab8YMf/CArcCaklhQK+tQuBFGPSgUq8XgciUQCBoMhb0+YVqtFJBLhGR5ye5ODwSDvTS7VW6bkfGg0GoyMjBR9XX3u3DloNBpF58xmsxXtvV2soiQGgyHr81gspup1ZjabSx4jjUbDOzoq7eFdquNuq1m4hhBSPlWCYgB43/veh2uvvRbf+ta38PTTT+PQoUNZKdc2b96MP/mTP8EnP/lJtLS0qLVZQlRVLOhzOBwlswK4XC4AKDr21OVy1VzuYCWUBCp6vZ7/XK/Xz9v/ZDIJURQRDAah0WgK9ib7fD5Eo9Gi7VGSpaGpqQlzc3NFX1fPzc3BbDbD5/MpOmeFem+VtEetc1/NbQHnc8s3NzfD7XZDp9NlDaFIp9P8oaOjo6PiHt6lPO622ueNEFIeVWdE2O12fPazn8XAwACi0Simp6cxMTGBWCyGN954A//yL/9CATGpWSzoK1SxzOPxlCxgsHHjxpKFIOqxyEFuoFKoeIUkSWhqaoJGo0E4HEYymeTBcDgc5r2J6XQaOp2uYG9yPB7nQ1EKUVJQYuXKlSVf+6dSKXR2dlZ8zmqxKIla15koiujp6YEkSfyBhW3P5/NBkiT09PTA6/VWXPWvnHG39YaKoBBS21TrKc4limLWDGlCapnS3qndu3crLgShJHdwOp3mE9PMZjM6OjpqcvZ+ZqACnB87nDmGlQUqwPlexWQyiWQyiUAggFQqBY1Gw3Pn2u12xGIxJBIJ6HQ6RCIRvq6GhgY+5pINRSkms1DKyMgIf13f2dnJi24cO3aM91znjr1lr6vb2tpgt9sVFaYods7KKdxS6bkvVExj1apVeYvEKNlesWVYujUW3Pr9fmi1Wt6ODRs2YP/+/QiHw7wsdjgc5j3FLNsI6+EttK3McbfpdBpzc3N83xobG+eNu63mZDw17tdqFq4hhK6P8ixYUExIPSmnd0pJXmAlywwNDWFgYAAzMzM8cGxubkZPT0/N5XtlgUo8Hsfk5OS8YhHNzc388+7ubpw9exYejweJRAKyLEMQBMTjcbhcLnR1dcHv92N8fJxPtmPY+trb2+eNky1kZmYGw8PD8Hg8/Dim02m0trZiw4YNcDgcGB4e5q/5M9stiiJWr14Nq9WK48ePlyxModY5U2s9hYpptLa2ZgVXSrantE2yLPM5Ipn/z+4hnU6Hc+fO5S0owu6hqampgttqbW2FVqvF1NQUpqenEYlE+DXU0NCAlpYWGAwGSJJU1cl4at6v1SxcQ5Yvuj7KR0ExISh/VriSvMDFlilUCMHtdtdkIQRJkpBMJjE6Opp3chwreiFJEqampjAzM4NkMgmNRsPXkUwmMTMzg0QiAQAIhUI8oGL5gtPpNEKhEAAoGleppKBES0sLDh8+zJeRJAmJRAJerxeSJKGlpQXHjx8vuR4AJc9Zc3NzycItMzMzqpx7pcU0lFxrbN+i0SgPOFOpFKanp/Puf0NDAz//Xq8Xe/fuxY4dOxAKhRAKhZBMJuddI9FoFCaTCcePH8err75asD3XXXcdAGB4eBiyLEOv10MURX5tDA8Po7u7G/F4HAMDA1WZjLcQ92ulucWX6mREog66Pi6MKhXtyiUIAgwGA6xWK9avX4+dO3fiT//0T6nsM1k01ZwVXo+FECwWC5LJJCKRSFabtVotRFGEz+eD2WyG2WzG008/jWQyidbWVqTTad5byJYbGBjgY0s1Gg1EUeQ9gWz5yclJpNNpaDSagq//lBzH/v5+rF69GhaLBalUCpFIBJFIBKIoorm5GRqNBlNTUxgeHi66nr6+PgiCUPKcdXZ2lizuUmpbSs690mto3bp1JZdj+xYOh6HT6RAOh/n5YH9ny0QiET5enC3DMoocOXKED6tpbGycN4Fybm4O6XQaR44cKXnO4vE4ZFnm14cgCBBFMSsQr9ZkvFq8X5fyZERSObo+LlzFQfHw8HDW31mPTz75Puvv78cPf/hD/N3f/R0++9nP4t577620SYSUrZqzwherEEIlAoEAH/MbiUSyegFZz6FWq8XQ0FDWvuXun9FoxNTUFGKxGB8znDl8ghX+iUQiGBwcREtLS8HXf5FIpORx9Hg8EEURLS0teccUx2IxjIyMwOPxFF2P2+2GIAgwm81FtwWc75kuNARHybaUnHul19DBgwcVHaN0Og1ZlvnDH/uuZpMdJycneW9tOp3OWoYNkZmZmZmXcSRXIpFAJBIpeayB8/dkIpHIGoLDHl4DgQBGRkbm3auZx1qtIhi1eL9SERBSDF0fF67ioPi2224DABw+fBi///3vIcsy7HY7tm/fzrvm3W43Dh06BK/XC0EQsH37dmzZsgWBQABHjhzBqVOnEI1Gcd9992FiYgIPP/xwpc0ipCxsVrjf7y9YsUytWeGLUQihUizv7cqVK+H1erMqqJnNZtjtdkQiET6xrlS2h3Q6zQMoNi5VEAT+93A4jPHxcZw5c6bg6z+Hw1FyW2zIC8t0kTtOmQXKStoMlC7KEo/Hiy6jZFtKzn3mNSTLMj+mrDeVrcfn85Xcns/nQzKZ5On02DXOeooTiQTPIqLT6fIuw7KFmM1m3uuc++DEioqw81GoPSyrRENDA4xG47x9k2UZkUik5HrUKoJRi/crFQEhxdD1ceEqftfz2GOP4eqrr8abb76JtWvX4uc//zmmpqbwzDPP4IknnsATTzyBZ555BlNTU/jZz36G1atX480338SVV16Jp556CidOnEB/fz8uvvhiyLKMRx55BH19fWrsGyFlYbPCXS4XIpEIvF4vIpEIXC6XquOvMgsh5JNbCKEWsB46nU6Hzs5OrFmzBqtWrcKaNWvQ2dkJrVYLrVYLi8WiaN9EUUQymcx6Lc5ek7Of+/3+oing3G63om2xMcSFlmHBW6n1sOE1hZbRarXQ6/WqbKvUuWfXUDgcht/vx+zsLHw+H2ZnZ/lx02g0sNlsirbH5OtVAv7Qm88eXHKXYQ8zer0era2tMJvNSCQSCIfDSCQSMJvNaGlpgclkUnQc2TJseIZer+e900rPa+ZwJ1mW4fP5MDU1BZ/PV/BtZj61eL9mDvcq1CYqArJ80fVx4SruKf7973+Pj3zkI2hra0NfX1/BNGyiKOJP/uRPsGvXLlx22WW48847sW3bNlx++eXYsWMH9u7di23btmFychLf/e53sXPnzkqbRkjZ1JoVXkw5hRBqRe7wkswe18zhJd3d3Th48GDRfWttbcXMzAyCwSAfk5y5TDwe56+qM8emMuz1XygUQmNjI3w+X8FtORwOdHZ2Fi2m0tnZiXQ6zTMnFDofgiCUXKazsxNTU1MVb6vUue/o6IDZbMbU1BQ0Gg3vvWU9sew4X3rppRgaGip6Pmw2G3w+H0+Rl6/gCutxYkNPctfDJta1trYiFAph5cqViMfjfHm9Xg+Px4POzk7Islxy/wEUXUbJeWXDnSqdgV+L9ysVASHF0PVx4SruKf7a176GZDKJe++9V1FeYqfTiXvvvReJRAJf+9rX+M/tdjvuvPNOyLKM/fv3V9osQi4YmxXe2toKm82m+kQEpYUQamWSHaC86IBGoym5b729vXjLW97CJ3Gxcs8sr61Op8O2bdtKvrJOp9PYsmVLyW2VKqayceNG9Pb2Fl3Pzp07Sy7T09ODTZs2VbwtJedeEAQ0NzdnTU5k2TvS6TT/XMn5uPTSS+F0OqHRaPhEORYMR6NRiKLIHxY1Gg1/NcuC4VgsBo1GwwPNzPHVRqMRwPkA12g0YtOmTSX3v7e3V9EySorkeDyeiouJ1OL9SkVASDF0fVy4inuKX3zxRQDA5Zdfrvjf7NixAwDmBb+7d+8GcH5SB1kelmticZa+ieU9Za+7nU5nTeYpBpQXHShn3/r6+hAMBhGPxyGKIiwWC3bu3IkNGzZg3759JbOBbNiwAVarteS2SrWbtV1Jm6u5rULYuNs1a9ZgamoqK5ev0WjklUP9fn/J87FhwwaEw2GkUinMzc0hGo3yoQsNDQ1obGzEhg0bIMsyTpw4gWAwOG97ZrMZ69atw9q1a2GxWGriWDscDl5MpNIZ+LV4v6pdBIQsLXR9XBhBLmdwVR4NDQ2Ix+N48cUXccUVVyj6Ny+//DLe+ta3QpIkRCIR/vPXX38dl1xyCQwGA8LhcCXNqopAIACr1Qq/3w+LxbLYzak7lFgcSKVSGBwcRCAQgMVi4b2ttUzpg4yS6l/JZBIHDx6Ez+eDzWbDpZdeygtQ7N+/v+DrP7fbDZfLhd27d0MQBEXHUUm7lbS5mtsqZGpqCi+++CLsdjtkWYbX6+XbstvtEAQBXq8Xe/bsQWtra8ntZeY0Zb3PLFWe0WhEb28vgPPZgoLB4LwhBGazOWvcvVr7r2SZQtvy+XzYt28fjEZjwcwjkUgEV199teIZ+GpWoFSrQ6DW1kNqC53X85TGaxX3FDscDoyPj+OZZ55RHBT/5je/4f82E+v9WC4B0XJGicXzPxTMzMzU/EOB0qIDoigWTVGVu/+hUAixWIzvv9JsIEqPo5J2l9vmhdxWMWwijd/vRyAQyKoeF4lEeEntzF72YtvL7VUq9JCqZBk191/JMoW2xYJgVjwlt8Ke3W7nwz+UquScZVKzQ6DSIiBqt4fUFjWuj+Wk4qD46quvxg9/+EN89atfxdvf/vaSwyheffVVfO1rX4MgCLj66quzPjt8+DAAwOVyVdosUsMosTg9FCjd/1Kv/9Q+jsV6VcrZ1kL3zlitVjQ0NGBoaAgajQYGg4GnP5ubm4PP50NXV1dZE2nUKl9eK1gVxnPnzpWswlhNi3Hvq3VdE7LUVRwUf+Yzn8FPfvIThMNh7NmzB5/4xCfwgQ98AJs3b+Y3nSzLePPNN/Gf//mf+MY3voFoNAq9Xo9Pf/rTWet66qmnIAgC9uzZU2mzSA1b7onFl/tDQTn7XywIU/s4Fustczgcirfl8Xiq2utWKI3aha6rkvLli6FQwKe0CmM1h74txr2v1nW9FL+LCMlVcVC8efNmfP/738eHPvQhRKNRPPjgg3jwwQchSRIv2zwzM8NfUcmyDFEU8b3vfQ9btmzh6zl16hROnz6Nzs5OvOMd76i0WaSGLffE4sv9oaDc/S8UhKl5HEv1lm3cuFHRts6cOYOjR4/y4hWsYIXavW5+vx+RSASrVq3ieYkzi6lYLBZEIpElew0xxQI+nU6nqApjIBCo2jGq9r3PrutQKASDwQCDwYB0Oo3x8fGyruulfh0RwlQcFAPABz7wAaxduxZ33XUXXn/9dQBANBrF+Pj4vGW3bduGRx55ZN7443Xr1uHMmTNqNIfUuMzE4sUyCyzVxOKL9VBQ7QkXhban1v6rtR4lvXfHjx/neXwLbSsQCGBoaAizs7NIpVLwer18DGtDQwNisVjZvW6ljqHdbofVap03iSxz8t1SVepB5qKLLoJWq0VHRwfPGJH54NDc3IxoNFrVY1TNe59d17Ozs0in05iZmckaUx2PxxVd10u5g4KQXKoExQDwlre8Bb///e8xMDCAvXv34siRI5idnQUANDU1YfPmzbj22mupKAdZ9onFF+OhoNoTaYptT639V2s9Snrv2CTgYttiWTFCodC8MayhUAiiKGJ0dFRxr1s5xzC3fDUry71UHyyVPMiMjIzwktednZ15s0+woiPVUs173+/380wZ+cZUs+wirDLgcuygICSXakEx09PTg56eHrVXS5YQllhcSWaBpWghHgpqaSJNqe319PSosv9qHUclvXeCIKCxsRF+v7/gtiwWC86cOYN0Og2TycSX0Wq10Gg0CIVCmJ2dRTQarZljWK+UPMiwtGk+n69oFcZqHqNqdghEo1HeS1zoegwGg3zM/nK8jgjJpXpQTIgSyzmxuNoPBbU0kUZJD97Q0BC6uroq3n+1jqOS3judToeuri4cPXoU09PTMBgMEEWRV4kymUxYsWIFjh8/DoPBkDdQ02q1iMViiMfjNXMM65XSYQidnZ2Ix+M1c4yq2SEQj8eRSCRKXo8dHR0YGRmpmWNEyGKioJgsmnpK76Q2tR4KSvUobtq0qaoTaZROJNq6dasq+8+O47FjxzAxMYF4PA69Xg+Xy4WNGzcqWo/S3rs1a9YgkUhgYGAAk5OTSKVS0Gg0aG5uxvbt27OOP+tdzlxPMpmEXq+HXq+vqWNYj5QOQ2hra4Pdbq+pY1StDgG9Xl/yetTpdGhpaYHL5aqpY0TIYlmQoHh4eBgej4eXAi2G0q8tb7WW3qmanE4n7Hb7BVfIUtqjyCbSyLI8b1yl2hNpyplI1Nraqmj/lUwQlGWZT6RKJpMlv3cyZfbeFeoF7u7uhsfjwYkTJyBJElavXs3TwsViMZw4cQKbNm1CU1MTfD5f1phNViFOq9XCZrPx1/hKJiKm02nMzc3xYL+xsXHeMVTzwbJeqqNlPsg4HA7E43F+XbNJj+y1vyAIFd1nC6EaHQIGg4FfjywbSmb2DY1Gw69Hm822bDsoCMmkWlA8NDSEL3/5y/i///f/IhAIKPo3giAgmUyq1QRC6kq+YQ9nz55V3DtTzgSxfJXPjEZj3spnlShnIpGS/S81QdDtduO3v/0txsfHeTAsCAI8Hg9GR0dx/fXXKzqWTqcT69evL9gL7HA4sH///nkPIMAfyk6PjY1hxYoV8Pv9CIVCSCQSvD06nQ42mw0dHR2wWq2KJtFNTU1henqady4IgoCGhga0tLTAYDDw46vWg6VakzGrMamTPchMTU1hcHAQ6XSafyaKIlpaWopWPCznPlsoC90hYLVa0dHRgWQyiVQqhUgkwrNvmEwmaDQafj1Woz2E1ANVguKf/exnuPXWWxGNRsvqoSFkuVJj8puSXllRFKHRaPhMfDUqnxWjdChCPB7HwMBA0f0HUHKy2csvv4yRkRHIssz3N51OIx6PY2RkBPv378e73vWukj1ebre7aC+wVqtVNKShubkZfr+fD5Vg7UkkEvD7/ZAkCR6Pp+R+AeffuOXuVygUwvDwMLq7u1Wd/JR5PVaSX7nakzrZ75t8bw4Woz21JPMNSDgcRlNTU9Z1TeOFCZmv4qD43Llz+MAHPoBIJIIVK1bgU5/6FIxGIz760Y9CEATs3bsXMzMz+N3vfof//M//xPj4OHbv3o1//ud/hkajUWMfCKkralW1UtIrq9Fo+H2mZuWzQpRMJOrq6sLQ0BDC4TB/9R0Oh3mPosfjwbFjxwCg6DE6dOgQTp48CVmW+ZAH4HxPoSiKiEajOHXqFGZnZ3khoXwKnQ/2mdI8xX6/HydPnuRpwFjPNeuZS6VSGBoaQiQSKXnuZ2ZmIMsyNBoNRFGEIAj8ASeVSvHP1ZwcWWl+5WpWa2PbkmUZ3d3d84ZPKL2Gar1aW6XDUHLHL7NjROOFCcmv4qD44YcfRjgcRmNjI/r7+9He3o4333yTf3711VcDAG655RZ87nOfw4c//GH85Cc/wfe//3386Ec/qnTzhNQdtapaKemVbWpqwtzcXFUrn5WaSKTT6eDxeKDT6XDu3Lm8QzomJiYAgFe0y3eMzp07x4OF3PGhoihCp9PxHuNiQbFaeYpZ4M/G/aZSKb5fGo0GsVgMHo8HANDS0lJwW2fPnkUgEIDVakUikcgahsEehILBIEZHR9HZ2anwrBRWLJ9tOfmVq1mtLXNboijOy9Os9Bqq5Wptag1DWc4TmgkpV8VB8d69eyEIAv76r/8a7e3tRZdtaGjAD3/4Qxw/fhw//vGPcfPNN+OWW26ptAmE1BW1qlop6ZVduXIl3njjDdhsNlgslnmTtgCUXfksnU6XnLRU7Bfx1NQUQqEQQqEQL56QWVQgGo3yHr9iEwTZfAQ2kY39YT3F7FVxIpEouj/l5ikuNLHLaDRCluWS55WleCu0X2zdVqsVRqNxXnAtyzKfzAco700sdN6U5LNVkl+5mtXaMrdV7DgCUNyeald8LEbtYR80XpgQZSoOioeHhwGcr2jHZH6RsC8qRhRFfOITn8Dtt9+ORx99lIJisuyoWdVKSa/ssWPHEAgEeE9xJRPthoaGMDAwgJmZmazJaD09Pejq6spattAvYr1ej2AwiHg8jsbGRqRSKSQSCd6mubk5JJNJmM3mohMEjUYj74GVZRnpdJr3qLKgWKPRoKWlpeg+lZOn+LXXXss7scvpdOKiiy7C6OgowuEwksnkvIl2Wq2WB9HF9kuv12e1J/P7EwDPHGA2mxX3JhY7bw0NDYry2ZbKr1zNam1sW8Wua5b67kInfS5kxcdiqjkMhRCSreKgOBQKAQBWrlzJf2Y0Gvn/+/1+2O32rH+zefNmAMDrr79e6eYJqTtqV7Uq1isryzIaGhowNDTEA4DMXtlyJtoNDQ1h7969iMViMJlMvPfK7XZj7969ADAvMC4mlUrB5/NlZY1gAaBOp4MkSTh79mzBCYIbNmyAz+fDzMwMgPMBHDuWqVSKH+vM76Z8lJ4Ps9lcMAgRBAFtbW0wm82YmpqCRqOBTqfLmtgUDod5GroTJ04U3S9ZlvkQk8xe+HQ6zYMlSZIU9SaWOm+XXXaZKvmVq1mtzWq1lryuN2zYAJPJhMnJyYonfVYzMK7mMBRCSLaKEzWyL7jMV2uZQfCpU6fm/Rs2Po+NryNkOWHDHoxGI9xuN6LRKM+J63a7L2hWOOuVbW1tzTuGEvjDjPxCfy8mnU5jYGAAsVgMNpuNj+OVJAk2mw2xWAwDAwNZPajsNf/U1BR8Ph/fHhu+kUgkEI1GeTAsCAKi0SgPzti6Ck0QZEMVigWqub2shZYrdT7Y5MB0Oo3u7m5cdNFFWL16NS666CJ0d3cjnU7j+PHjfIY/G8rBerDT6TQEQUBzczNvb6H9EgQBvb29kCQJPp8vqz0+nw+SJKGnpwfHjx/PCpDZ+XA6nQiHwxgcHEQqlSp53o4ePQqbzQaNRsN7uVkwHA6Hs/LZVnocFyLbQaHrupzzWuo4VjOrkpJhKMlkUrXc4oSQP6g4KGY9Q6dPn+Y/a2xsxKpVqwAAv/3tb+f9m2eeeQYA6CmXLFts2IPL5UIkEoHX60UkEoHL5VK1Z8rv9yMSiaCzsxONjY1IJBIIh8NIJBJobGxEZ2cnn2hXzOjoKGZmZmAymfJOajMajZiZmcHo6CiA82Mi9+/fj3379uHFF1/Evn37sH//frjdbj7eU6fToaGhgQdgrFdbp9Px4HjVqlUwm81Z7Tabzejs7OTHzGq1QpKkrKBSkiRYLBbEYjHepmJKnQ+W0SBzYpfZbOZZLywWC8bHx5FIJLBmzRoYjUYeuCSTSRiNRqxevZqP9y62X5FIBK2trbjuuuvgdDoRi8Xg8/kQi8XgdDpx3XXXobW1VVFvIstkUey8scmBZrM5b5vY+GMlPby1dl3r9XrF57VUr2y1ZA5DyUfNYSiEkGwVD5/YtWsXDhw4gL6+Pvz5n/85//nb3/52PPLII3jwwQdxxRVX8CwU//t//2889NBDEAQBV1xxRaWbJ6RuVWNWOAvK7HY77xnMnJAky7KiiXbBYBCpVKpo71U4HEYwGCw5SWjjxo0Azk/kMplM8ybIsSwIyWQSTqcTVqs1b7tnZmb4vrEAmI2XZctkTkgrpdTkwFK9d2zM7YoVK9DS0pJ3UuPY2BjfVqH9Yuejq6sL69evzzs5LrM9xSoVBgIBReeNDS8IhUKw2Wx5K/opvS5r7bouVvVPyXlVs+KjEtUchkIIyVZxUPy2t70NX/3qV/HTn/4UX//613lO1E996lN47LHHEAwGcd1116G5uRnRaBThcJjn3/zUpz5V8Q4QwlRz9rha21roWeGZvU75xoTm9joV2i+z2QyNRlMyJ7LJZCo5Sej48eMwmUw8iAbAg2K2LBvbWqzdmRPScoMaWZZ5m8xms+LjlU6nMTY2hkAgAIvFgsbGRh5kZx7HQCDA22CxWPj/A+cndmm1Wh5MSZLEe2Azl9HpdHwCGxuzm3s+WG8lK6iReTzZRLPZ2Vn+gKDVatHc3AybzQatVguLxcLPG0tRxx4c2PY0Gg3a29uxbt06HDt2DBMTE7xNLpcLGzduXJAxtUrLd+dbJndSX+7Qjlgspqg3tdzJgWp+xxRal5KsMpkPKbWUNUNNS3W/SG2rOCi+6qqr8PnPfx7JZBJjY2M8b2ZnZyf++7//G7feeit8Ph+8Xi//N5Ik4dvf/jZ27txZ6eYJAVCd0rKLsa1KsV6n4eFhXuo1szCDRqPB6tWrS5Ye7ujoQHNzM9xud9HJX42NjYpy/ur1ekSjUd6TyWg0GrS2tsJqtfJJa4Xa3dnZCVmWMTk5yXuWMyfsiaIIl8uFjo4ORcdqYGAAfX19vKdaFEU8++yz2LlzJ3bs2AGHw4Hjx49jbm6OV+8UBAEGgwGNjY18gtzhw4fh8/my9mt4eBg2mw0XX3wxZFnGiRMnEAwG55VwNpvN2LBhQ8nz4XA40NDQgEOHDs3rxQwGg5iamsL27dvR3d2NgwcPYmJiouQxyvyOroSS+6PSZRwOh+Le1Gqsp9z7vtS6SmWVKec41qOlul+k9pUVFP/Hf/wH9uzZw19/Aud/0X3+85/Pu/yNN96IEydO4Mknn8Sbb76JZDKJ9evX433vex9WrFhRWcsJ+X+qWcq13srGCoKAlpYWHD58mJd2bWhoQDwex8zMDCRJQktLS8nSw729vejp6cHevXvh8/lgNBp57xUrDdzT04NEIqGo9HQoFMLk5CR/a8Sk02lMTk7CbDZj8+bNOHHiRMF2t7W1IZVKYWRkhPd+ajQapNNpnrZsxYoV88bS5jMwMIB9+/bx3lytVotkMolAIIB9+/YBAC/RnLtMMBhELBbD1q1bMT09zavNseMvyzKvQuf3+/nxzl0PC7a3bt2qqBQ0K16STywWw7lz5yCKIlasWIGzZ88WPUZer5dvz2az8e1NTk4iEAioWuYZKF6+W+kySnpTlVzXaq1H6X2v9Duk1DCUevsuUmqp7hepD2UFxXfeeScEQYDD4cDu3buxZ88e7NmzB9u3by/4WsNut+Ov/uqvVGksIbkWo7RsPeUPlWUZ09PTsFgsPF0VC4aam5uh0WgwPT2Nqampkvu1e/duAOD5bllmAqfTyfMU+3y+kq+jBUGA1+vlPZaZQbEsy7z3d2JiAhaLhfdERyIRiKIIu93Ox9WOjo7y6nHsjyAI0Ov10Gg0GBsbyxqakU8qlUJfXx8SiQSMRiNflgWs4XAYBw4c4JPs2FCIZDLJh5YkEgkcPXqUB/qZ5z/zNfcbb7yB9vb2rFLQbD3llII+ePAgJicni577yclJuN1ujI2NFT1Go6OjfD8rua6V3B9KSi8rLc+8e/fuor2pDocD+/fvr9p6lNz35X6HFBpeVY/fRUos1f0i9aPs4ROyLMPtduNnP/sZfvaznwE4X1LzLW95Cw+Sd+zYoSgVEiGVWqzSsvWSP5S1ubGxET6fb97nZrMZ4+PjAJSVwy02+QtQNklIEATe88V6URnWwxwOh3H69GmsWLECer1+3kSqWCyGs2fPwuv1wmq18mUyJ9rFYjGeEYMNtcjX6zY4OIhgMMjHMWcWAWHBI5s0x3oS85Vwnpqa4g8cLNDPDJBTqRTi8TimpqZ4my+0FPSpU6ey0t/lk06nsX//fszMzBQ9Rh6PhxcgqeS6VnJ/KCm9XE55ZqfTCbvdnvd69Pl8iu/XYr2y5ayn1H2v1ndIPX4XKbFU94vUj7Ii18cffxwvvfQSXnrpJRw/fpz/3O/349e//jV+/etfAwAMBgN6e3t5kLxr1y40NDSo23JCsHilZRd6W2qJxWJZJZUzi0WEQiE+IalUSeXM/RJFkc8dyKVkkpAkSUin07yCGusdZkGoLMsIhUI8bRsbt5spsxxysWUyM2IUmkQWCAR4HmEWNDIswGVp49i2ch/6M8tOs2p67HgwoigilUopuoYyS0HnTupjOZ5zjzuTmVN3bm6OZ58odIzYua20HLKS+0NJ6eVyyjPnG3t69uxZnju6nPu1UK+smve9Wuuqx+8iJZbqfpH6UVZQ/MEPfhAf/OAHAZwf98MC5Jdeegmvv/46/2USiUTwwgsv4IUXXgBw/svt0ksv5UHy7t27YbFYVN4VshwtRmnZamxLLaykMss5y4InrVYLjUbDSyqbTKaipYfL2a9Sk4Smp6chiiKvlJbbI8RKPrPgr9Cxzi2HnG8ZjUaDZDKJF154AdPT01m9q16vF9PT01ixYgUE4XzhEJYWjmElqNkxK3Xuk8lkweEabNtarRaRSASJRKJoKeipqSlMT0/Pm4zX0tJSMGjIZbVa4fP5Sh4jSZIqLoes5P5QUnpZaXnmUCiEo0ePFhx7umnTJlXu19zMI7kPjeXc92p9h9Tjd5ESS3W/SP244DEOTqcTN998M26++WYA52c8v/LKKzxIHhgY4FXu4vE4+vv70d/fjwcffBCiKGLr1q248sor8fWvf12dPSHLUrVLy9Zr/tBiFblYueFiJZWVloJmir2Obm5uxrPPPotAIMAzIDDpdJoPU1i7di2mp6cLHutVq1aVLIfscDgwNjaGsbGxefsWjUYxNjbGs1kkk0loNJp544HT6TQf0zgzM1NwW62trZicnEQsFpsXXKfTaaRSKej1ejQ1NcHtdhctBW00GnH8+HHIsszH/rLe/eHhYZ4tgp3XfOdXEARcfvnlmJubK5o1xOFwoLOzs+JyyD09PYruDwAlt1Vqmba2NoyOjhYdezo2Nga73V5yW6Wu68wMLuyYZT40iqLIM7iUotZ3SD1/FxWzVPeL1I+KK9oxZrMZ119/Pb74xS/i+eefh9/vx/79+/HAAw/gxhtvhMVi4a9JU6kUDh06hIcfflitzZNlir2ur0Zp2WpuSy3xeBxmsxmSJOUt4StJEi+gARQuPXwh2Ovo3NLTGo0GO3fuhE6nQzgcRjwe58FwOByGTqfDzp07sXnzZn6sWS82G5ZhNBqxcePGkuWQN2/ejLNnz/KJbKycNCscIggChoeHeT7fZDLJx/myoQ4ajQZ2ux0XX3wx31YoFEI0GkUoFOLb2rVrF3p6enheYNZrnEwmkUgkoNFoeOAoCALfDisFzSbANTU1YXZ2FrIsZz00iKIIrVYLWZZ5MFuMw+GA2WxGT09P0WPU29uLjRs3VlwOeWhoCF1dXUXXs3HjxpLbUrIMy5hRauzpihUrKr5fWQaXQCAAr9fL0wKKogiv14tAIJB3/HehdanxHVKP30VKLNX9IvVDkKtU1D0ej+Pxxx/HV77yFZw5c4a/Dswcv1dvAoEArFYr/H4/DQdZZJSnOD+fz8dTihUaGsHGcEqSBL/fn3cZALj66qtVndySLy+w2WzGzp070dPTAwAYGhri2S7YBLHm5mae7aLUMhqNBk8//TTMZnPeIQeJRAJ+vx/Nzc3Q6/UYHR2dN6Sho6MDkiRhz549OHv2bMk2P/fcc3j11VcRi8X4eiRJwo4dO3DppZdi3759iEajmJ6ennesW1pakEqlMD09zYc05BtikUwmsW7dOpw9exazs7NZw0JEUURTUxMuvvhi7N69G4IgKDqOxa5rnU6Hffv28THhuaLRKCKRCK6++mokEgnV8hTnjgNvb2/n44VffPFFnokkVzqdhtfrxZ49eyCKYkX3qyzL2L9/f8lc3+xYK6HWd0g9fReVY6nuF1k8SuO1BUsREYvF0NfXhxdffBEvvfQS+vr6EAqFABR/lUvIhahGadnF2FalMl9Hrly5kk9OY+NWPR4PLBYL5ubmYLFY+P/nlidWUgq6XD09PbjsssswODjIK8h1d3fzzA1utxsnTpyAXq/H6tWrs0oPnzhxAs3NzXA6nUUzYpw4caJkO1haOKfTCZfLxfdVkiTY7XYkEglEIhGEQiFMT0+jpaUFbW1t/N+n02lMT0/D7XbD6XTimmuuwZ49e3Dw4EH4fD7YbDZceuml0Gq1vKyw2Wzmx5MFqaxgyfT0NFKpFBoaGmA0GudlqGDlq9nrZYfDgWg0yqvWGQwGmM3mrB61UllDgMrLXLMJUMXKKivZVjHsd0c5Y09tNltF9yvLiNDS0lIwE0q5GRHU+g6pp++icizV/SK1T7Wg2O/34+WXX8ZLL72EF198Ea+99hqfoMK+yDQaDbZu3Yrdu3dj9+7deOtb36rW5gkpOHu8lre10KVMM7NBsADYaDQiHo/D4/HwV+Ovv/46AoEAfD4fgsEgD9TMZjOsVuuCTW5hxSUcDgd/JQ9k5yvNfTVtsVjm5SuVZRlzc3MIBAJ8mBYAXvktGo3yohUswBRFEZFIBCaTCW1tbTw1V0tLC99WvjGsDocDMzMz/Jyxv2e2R6PRYMOGDXwZFuhLkoRkMonR0VGkUik+4VEQBF7hji3PAr7cTBcs7Rsrzay0R61Y1pDc/WWBs8Vi4T3d5UyAqvT+yCzgkK+YSOb4ZYfDMe9hL3fsaSXtycyIUCqDB6D8nlbSJiXrqub3XjUt1f0ite2Cg+LJyUk+qe7FF1/EkSNH5k36MBqN6Onp4UHwrl27eM8TIUtdqV9o1XpFWCobBCtffPToUaRSqaw3OZFIBDMzM9i0aZPqk1tKva5Xmq/0+PHjRcszr169GkePHoXX6503zIAFr5dccgkGBgYKppFbsWIFDh8+zPMn5w5paGtr4+0pNnzAbrfz6nWskEZmERONRgOTyQS73Q6v11u0pDbr6VWrR63YEIsNGzaoOgGqVOnlUgUc2PjlqakpDA4OzjuvLS0tZY89LXS/lvNAUM1S0IQQ9V1wnuJTp07xn2f2ylxxxRW8F5i9MiRkuSn1C63apUyLvY6UZRmRSISPgWWlmNnkt2QyiUgkolpbgNKlXC+66CJFr+sPHjyIV199tWh55jVr1mBoaIhnhWD7zFLCrVmzBi0tLUUfHDKHSKTTad67K8sy4vE4RkdHEYvFMDk5iZMnTxZNExaNRvnkQo1GA1EUIcsyEokEUqkUYrEYLr30UvT19RUtqc2CZTV61IaGhrB3717EYjGYTCbebrfbjb179wIAf+MwPT0Ng8GQNZzFZDJlBaHFHghLnfuNGzcqeiBasWJFVjntTOUO0SsVpCt5ICiVneNCS0FLksQL3VCpY0IWVlkR6x133MF/EQDAunXreC/w7t27+YQNQpazUr/0e3p6smby5+sJq2YpU5/Ph6mpKf5aOLP0MCu6MzU1BZ/Ph6ampoq3p6SU67lz57KGEORi5aIPHz5csjzz9u3b0dzcDIvFglAoxHtBWTaK6elpbNiwoeiDg9fr5T3NLIOFLMs8yE4mk/B4PDh79mzJ8zo7O8t7mDMLl+h0OqTTafh8PrS3t+O6664rWlI783hW0lOcTqcxMDCAWCwGm83Gj6MkSdDpdPD5fBgYGMCtt96K9evXY2BgAJOTk1m9ydu3b1c0iU5JL/Dx48f5+Oh82PCIoaEhyLKM7u7uvGPlld5DSh5QSxWkyc3OUck9ze6P2dlZpFIpft2xiX2xWIxKHROyQC6oG1er1eK9730v3vOe92D37t30xErI/6Mk4Hv99dcxNzdX1VKmxQIVn8+HSCQCs9kMrVY7b2JXMplEMBiEx+NRJShWUsp1bm4OZrM5a0IZk1kuOhwO857tTKz4RzAYxOnTp9He3q5oklShXteJiQmeKSd3iAlrWyqVwvj4ONrb2wvu18TEBA/4coNiQRB4tomZmRls3ry55OQ4NV6xj46OYmZmhqeoyyxAotFoYDQaMTMzgyNHjmBkZASSJGH16tVZ+ZXZxEcARQPMTZs2lTz3fr8fQPHiHewaYEF87jhfpfeQkvt1cHAQu3fvLvomoZzhPkpKQbNzznJkZ+apFkURo6OjVOqYkAVQVlDM8mcmk0n8+Mc/xo9//GMAwPr16/mQid27d2PdunUL0lhCap2SgM/tdkOW5YK/0NQuZVqqJ4wVS2BtzDfkiQVtalBayrWzsxPxeLxkuehCQ7S0Wi1isRgvYaxkklQhc3NzAMCD2NxxvsD5YxSNRovuFwusWVCduZ7MAJkpNjmu3CE46XQ6b4DNJlYC4GOiM3uvGxoakEwmceLECaRSqbwPKSx9GoCSY4FL9QILgoDGxkb4/f6Ck+jYg1Ol5YCV3K8smFWanUNJqfRiotEoT7PHHlSAP1ShDIVCmJ2d5cWxCCHqKSso9nq9ePPNN/nkupdeegljY2M4fvw4jh8/jsceewwA0NramjWs4pJLLqHXPGRZUBLwsYBDzVKmhV6hZ/aEsQAjHA7zHkWPxwOv1wuDwYBIJMKHBmSuNxKJwGAwwG63l3UsKp241NbWBrvdrrhcdK5kMskLTFR6rNkEYUEQ+FhahmW1AACDwVB0WwaDAZIk8fLRmUMxkskkgPNBZKkJyUrOa+Yr9mKT6MxmMwDwHlo2JIS1mQ1ViUQi8wJidkxYLziArEItucso6QXW6XTo6urCa6+9lncSndPpxIYNG/jQmUrOq9IHNBbMFnqTwK7pckqlF7o/4vE4EokEDAZD3uPIHvZYfvGlaqEz8xCST9nDJzZv3ozNmzfjYx/7GABgeHiYB8gvvfQSjh8/jsnJSTz55JP4P//n/wAAT27PepN37tw5r8eGkKVAScDX0NBQcmiAWjP52WtdnU6Hc+fO5f1lHQwG0d7ejuHhYYRCoXmlkGVZxurVq3kwoOSXlRoTl9h6KykX3djYiLVr12JqaqqiY+1yufhQksx8wSygZZPm2tvbea9ivm11dnbySXtsfZm9sqwntqOjo2h7WA9nsfPKejinpqaKTqK75ppr+BhsNo6bEUURqVSK/7tiwSML0or1lrJiJ8WOkcvlgtlsLhgAsZ5kNbJhlJtqrhCr1YqGhgYMDQ0pKpVe7P7Q6/X8HLGe88x9Yw+B+R4ElwrKvEEWS8WpIVavXo3Vq1fjgx/8IIDzF3NmkMzGTz7zzDN8FrNWq8Ull1yCt771rXjwwQcrbQIhNSOzWEaxX9ZdXV1FU4ApTSelJItDKBRCKBRCMpnMGp8YDAZ55oDLL78ciUQC09PTWa9lRVFEW1sbLrvsMgiCoLgSWaUTlzL3v1DvHCsXvW/fPj62mGWfYD2Ou3btwpo1azA3N1fRsU4mk7Db7XC73bz0syAIvDwzS422atUqnDx5suC2Nm7ciNbWVh6kGo1Gfj5isRgaGhqyMksUegCJxWKKzmskEik5ie7AgQNoaGjgQydYRox0Oo1EIpE17KRY8MiCtFK9pRs2bMCxY8dKTlpLp9MFJ9GxlGxKr6FClN6v5aQjzNe7m6nU/bFp0yY0NTXB5/Px7BOZ14hGo4HNZluyHUvVzsxDSCbV86U5nU7ccsstuOWWWwCcH4uXWdTjd7/7HWKxGAYGBvDqq69SUEyWFEEQFAV8pXIHKy0/W2qS0MjICObm5pBIJNDY2DhvfOLc3BxkWUZrayvsdnvBsrpK08gpyS6gZOKS0l96rLQyy1Mcj8chiiIsFktW6eVKtyVJElpaWiBJEiYnJ/lkL0EQoNfr0dbWBqvVWnLIh9Pp5Nvr7++H1+vlAV9uZolSvYlsfxsbG/lwDBaAsvPq9Xr5JLp8kxGNRiNmZ2eh1WphNBqRSCSyso+wXktZlvk432LBYzgcLtlbunbtWlgslnnXmsvlwsaNG7MmrRWbRLd169aKz6vS+7VUcO33+xGJRLBq1Sr4/X6eh1qj0aCxsREWiwWRSAQ+n6/k/TE2NoYVK1bwCY/hcJinEzSbzRBFER0dHarnDa8FSic+UuYNslAWPIkwe305NjaGc+fOYXh4GOPj4xWVen7xxRfx4IMP4rXXXsPExASeeuopvOtd7+Kf33777fjBD36Q9W9uuOEG/PrXv+Z/n5mZwcc//nE8/fTTEEURt9xyCx566CE+vo6QC6U04K20lKmSSUJswo6SdZbKZazkl9WWLVtUmbhUjlLlokvtmxKsRzEYDKK1tRU+n48HqjabDRqNBg6Ho+SQD6a5uRmrVq3iY0j1ej06Ozt5BgcluXyB8xkvWFtYkJ458TASiSCVSpVMb5ZOp3lAGIvFeEDHMnTEYjGsWLEC586dKxo8Hjx4kJ/rTOWc03LG+SopKV2KGg+orM0NDQ3zfrfJsgydTodIJAKPx6Po/ti2bRsCgQBCoRDv4S+UE3opKWfiI2XeIAtB9aBYlmUcOnSID5946aWX4Ha75y1TiVAohIsvvhh33HEHbr755rzL/PEf/zGf+Adg3iu/W2+9FRMTE3jmmWeQSCTwoQ99CB/96EfxxBNPVNQ2QoDKgzAllAQPbHJXMpnM+ypWkiSYTCY+HrTQUAWlv6xYr2alE5fKpdFosHnz5orXU4ggCGhpacHhw4fnjc2dnZ3lPcmlhnwA2QFvS0sLX8/U1BTm5uYU5bE+fvw4dDodAoEAD3rZhD9W0pqV5y6V71mj0fBxtXq9PqtnNnMMa2trK9rb24umJcvsLc3t4WS9pWfOnMHRo0f5+HWj0Yh0Oo2JiQkEAgFs3LixqiWlgcrvV1a++9y5c0in01m95KFQCJFIhE9AVHJ/mM3mrECd7XPmm5ulqNyJj4SoreKgOJFIoL+/nwfAr7zyCk9fBMwPgNetW4e3vvWt2LNnD/bs2XNB27zxxhtx4403Fl1GkiS0tbXl/ezYsWP49a9/jVdffRWXX345AOAb3/gG3va2t+Hf/u3f0N7efkHtIuWrxRnG1WoTS2OV7xWyklRaSiYJsQk5kiTB5/PxlIparRZNTU38FSz798XGsLJfVul0Gh6Phy/jcDj4LyvWU8nSbs3NzfF2NDY2zgtokskkDh48CJ/PB5vNlrcKZiKRwMsvv4zZ2Vk0NTXhiiuumPdLM5VKFe0pZsd6ZGSEt7uzs3PesS7UHlmWMT09DYvFgmQyiVAoxIPPpqamrCIggiAUbE9mj7vFYsHp06cRjUZhMBiwZs0aBAIBHDp0CMFgkJ+baDSaNWGNZXGIRqPQ6/VIp9OIxWJ8/K4kSbwHmvU+u91u6HQ6xONx3gus1+sRDofR1NQEg8EAv9+PUCjE3wyw/2aOYbXZbGhubs67bywtmd1uh8lk4lX+JElCe3s7NBoNHw/MClO43W7eHrPZjHg8jrGxMdjtdkxOTsJut/NhInq9Hmazed44XyXXh1r3dKF7kV0XkUgEjY2NWZMMMwPd5ubmrAeQ3MmImfeHzWaD3W4vmqdaybWv5v6rRY2S2pVuq9bVa7vrXdlBcTAYxCuvvMIn07366qtZT225Se23bNmCPXv28EA4MyfqQnr++efR0tKCpqYmXHPNNfjSl77EU0odOHAANpuNB8QAcN1110EURfT39+Pd73533nWy14hMIBBY2J1Y4mpxhrFabVJS5vmFF17A9PR0Vsopr9eL6elpXHnllXA6nUVTaW3YsKHkJKH29nbIsoyjR49idnY2KwdtNBpFNBrF5s2bYbVai7aZ/bIaGRnB5ORk1npOnz7Nx9Ta7XY4HA4cP34cwWAQkUiEL8eybmzYsAFWqxXPPfcc//5gy+zbtw87duzANddcAwD4xS9+gUOHDvE8ugCwf/9+bN++HW9/+9sBAAMDA3xMMQsMn332WT6m2O124ze/+c28dk9OTmJkZAQ33HADnE4nnnvuOT4pjXnuuefQ09ODSy+9FB6Ph4+rzcR6Qtlr3ePHjxdsz4YNG+DxeDA2Nsbz+gLnv0syg25WbntycnLehDW73c7Hm8bjcR4QM2ziXUNDA0RRRE9PD375y19ifHx8Xnozk8mEt7zlLXC73QgEAggGg/PyFNtsNj6GNd/1ePDgQfT09KC1tRVarRanTp3C1NRU1jkbGxtDa2srz9MdCAQQiUSy2sNS/2m1WvT29uLcuXM4ePDgvKEhrLdUEARF14fSyaGllil2L7J9l2UZk5OT846z0Wjk6fccDgeGh4f5eOHMcyuKIlavXl3wfjx79mxWm0pd+0r3rZrUykxT6bZqube9Xtu9FJQVFF9++eV4/fXXs274zCCYZZVgQfBb3/pWVSpgleuP//iPcfPNN2PNmjU4deoU/vEf/xE33ngjDhw4AI1Gg8nJSbS0tGT9G61Wi+bmZkxOThZc7wMPPID7779/oZu/LNTiDGO12qSkzPPBgwcxNjY2b0JSNBrF2NgYXnvtNaxZs6ZoKi0AiiYJvfHGG/B4PDxLgiiKkGUZ8XgcHo8HiUQCHo+nZJuj0SjOnTvHew9ZT2I8Hse5c+d475YkSXy9mRkh5ubmEI1GsXXrVuzbtw+vvPIKDy4yx0y+8sorAM4XgTh48CD/jmHbS6VSfOxqS0sL9u3bN29bgUAA+/btgyzLGB8f5+3W6/X8WLN279+/HxaLBa+88kpWIQ6WVuzll19GOBzOyvaQec4ysz289tpr+N3vflewPcFgECdPnkQ4HM577QQCAcTjcbS2tvJX8bmZJcLhMBoaGvjxyj0+mWNP2bAY9vNM6XSaB5uSJPHsE2woBpu85/f7IUkSjh8/XvR6vO666+D3+zE+Pj5vv1i1P1mWEY1GEQ6HedDN2p1IJBAKhfg9xCYhZvaMJpNJPnmwv7+/5PXR29vLr2tJkiBJEmRZzrqngeJV+Hp7ezEzM1N033fs2JE30GfHORKJ8HObOwyH5dH2er18GA67H9kQE4PBgHQ6jfHxcd6mM2fOFL32AWDNmjU19T2rdmaaSrdViwFmvbZ7qSgrKGZfNIzBYEBPTw8fCrFr1y6YTCZVG3gh/uzP/oz//9atW7Ft2zasW7cOzz//PK699toLXu+9996Le+65h/89EAhg5cqVFbV1OarFGcZqtUnJeg4dOoQzZ85AEIR5FatMJhPm5uZw+vRpjI+PF02lNTAwgFtvvbXoJKHm5mYcOXKEBw65v7BlWcYbb7wBjUZTtM3Hjh3D5OQkr+bG2pMZiE1OTiKZTPLsA3q9PiuTgclkQiqVwrFjxzA+Ps7HwrJ1sfUmEgn09fVlBW25OYjZ3AWWMYH1sgHgAUI4HMbLL7/Ml29oaJiXfSMSieDkyZNIJBJ8QmLmuGC2b4cPH4bNZkMqlcrKoZubxYMVlCjUnoMHDxYMiJloNIp4PM7HobL1sFzMPp+PZ59g7WQy/9/v90MURQwMDCCVSvHX+pmBcDKZRF9fHwRB4EMYWM8sm2jHXs+zh4RC12NfXx8v4FGI2+3mxUEyc/Cyv8diMQSDQRw6dAjJZBKtra1Ip9O8F5Tt/4EDB/iDTrHrw2Qy8aEaXq+Xr6ehoQGxWExRFb5jx45heHi46L4fPnyYrz8z0Gf/ZcG8RqPhbwRSqRQikQgikQhEUURzczP/fGpqik+SnZmZyepNjsfjOHr0KA4dOlT0Wjtw4AAikUjNfM+qVVJbrcw8tZjFol7bvZSUFRSbzWZcccUVPAjesWNHXSQQX7t2LRwOB06ePIlrr70WbW1tmJ6ezlommUxiZmam4DhkALyngVSmFmcYq9UmJethr8QtFkveZVjO2HA4jMbGxoKptGZmZjA6OorOzs6Ck4TefPNNHrDl+xKVZZkH4e3t7QXbfPr0ad7bxnrjGDYGOBKJ4OWXX8bMzAwaGxt5WWP2C12j0SAWi/GUZqyHOHff8hWQyF0mlUrxnlODwZB3Gb1ez3seC1UH0+l0WUFqZlCceYwSiQTPhVwI6wFlRSqKtaeUYDCIhoYGRCKReZMjWdlldg5yK+yxv6dSKRw5coSXFc8sBsH+DgCTk5PQarVFzxmbLM1SguXum9Fo5Oe1GJbjuVBpbuAP1yS79nO3ZzQaMTExwfe52PVx5MgRaLXaeT3uoVAIoihieHiYv+EodO2PjIzA4/EUTWs3MzPDH/4y/2SuK5VK4dSpU/B4PGhpack7pjgWi/GH4VAolPdNgSiKGBoaQjAYLHqtBYPBkvd1Nb9n1Sqprfa2aimLRb22eykpKyj2+XzzbsB6MDo6Cq/Xy8cz79q1Cz6fD6+99houu+wyAOfHDqbTaf46jSycxZxhrGQiWSVtUrIe1hNXDPuFX2w94XA4q7ewUNYIVj449xe1LMs8wI1Go0W3xcatZvaCsUCb/T0cDvNeObZcbvDDgi5gfjDDsKCGtTmfzDawsZy5wZxWq0UkEpnXk5i7rcx15vtFlNnrJ0lSwSwerLe5UMBXLBDMlUwm0dHRgZmZmXlZHJqbmzE2NgYAfL8z9yPz2M3OzvKCD+ycsM/Ya1l2fRQ7Z2wcdanrmsl3vDOHprA3Arllrtm/Y9daoW1lrisfts5QKASz2TzvjYxGo0EoFILP50NTU1PJa79UWrvMY86uwcz9Zttm92Op7bHftYXaHQqFkEql0NDQkHc9rBR0LBarmUwOapXUXoht1Yp6bfdSUlZQXCsBMRuXx5w5cwaHDh1Cc3Mzmpubcf/99+OWW25BW1sbTp06hU9/+tO46KKLcMMNNwAANm7ciD/+4z/GRz7yEXznO99BIpHA3XffjT/7sz+jzBNVsBAzjJVQMpGs0jYpWQ+rNBaNRnlQwMiyzCccsTGdxVJpsbzahWbF585CZ9vIDSZKpe2SJAmiKBb8wmYBTVNTE0ZGRkq2O3O4Qm6bcucs5A75YD8HwB9oWECc+Tqd9USz7bHX9pnrYBXcUqkUD7ILbauhoQEtLS0IBAJ5izP4fD4e9OTrUc4MGkthZZE7Ozv5WFS9Xg+LxcKDcLb/LBBj2Jhx1ubMfco8RpnHngWp+XqK2bUPFK9ox8pWs3YV2zd2XeYO5wDA11NsW5kV//JhP2c5gvM97LBhBoIglLz2laS1Y4Eze1Bh+8X2NZ1Ow2azIRQKIRAI8LdBuVX/WK8yyyKSr93snLH7Mfchld2P7GGtmt+zhVTze3+xfsdUql7bvZQsePGOhfC73/0OV199Nf87G+d722234dvf/jYOHz6MH/zgB/D5fGhvb8f111+PL37xi1kX0o9+9CPcfffduPbaa3nxjocffrjq+7IcLURp1VKUTH5jbXI4HPNKyyptk5J9a29vh8ViwfHjx/P2OsqyjPXr1/O8v5njbgHwWetOpxMdHR1FZ8Wz18Lsl2ZmsJCJvaostO9r167lwWBuTzcLyiwWC6644gpe4KFQu9va2jA+Po54PF4w4GWp31hwkQ/r/WRlqTODXvb6vKGhAc3NzZienuaBcWZ70uk0mpub+XjQfO1h6167di0fipDb3mAwiHXr1vFjlDkRkf2XVZ/LTFlZyNatWzE2NsbTv7HzajKZeAaG8fHxvG8d2D5otVqsWbMGR44c4cc698EhlUpBr9fzssIsIM3M9pBKpdDS0gJZPl8hr9B5tdvtmJiY4A8yucsA56+VVatW4ezZs/xhhm2LBYLd3d1wu91Fr32Xy8UnIhbaFntozBw2knnO2ITJ5ubmopX6Ojs7eRrCQu1paWnhlexyO49YGy0WCy699FLMzMxgaGiIBzeZQyN8Ph9WrlzJq1AWarfJZOLzANhDDMPOKyucNTU1VbXv2WKq+b2/GL9j1FCv7V5KaqPrt0xXXXVV1i959ufxxx9HQ0MDfvOb32B6ehrxeBzDw8P47ne/i9bW1qx1NDc344knnsDc3Bz8fj8effRRqmZXJYJwvrSq0WiE2+1GNBrlM+bdbndZM4yVyJ28wHo92eQFVpq2q6sLgiBgcHAQJ0+exPDwME6ePInBwUHe5lJtUrpvl19+OR/rx8aiRqNRCIKA9vZ27NixA729vTy/cOZ6fD4fJElCT08PTpw4gb1798LtdvM8sgaDgc+KHx8fL/naXqfT8epqhfZ906ZN2Lp1KwBkpezKHJu6ZcsW6HQ69PT0FG33zp07sXXr1qxgnZ0ntt5t27Zh9erVRdu9atWqrIm9uYE/cH786e7du/nEKjaxKRKJ8PG511xzTdG5BADQ1taG1atXIxAIYGZmho/B1Wq1mJmZQSAQQGtrK3bt2pX1ijsSifD/12g02L17d8nJuStXrsSKFSswMzODyclJBINBPgFtcnISMzMzaGpqKvl9xQInm83Gg/LcID2dTqOpqQkXXXQRH2ubSqV4kMr+3t3djZ07d0KSJMzOzs77I0kSLr/8cj4BKHM4S+aDjcPhgNPpRCKR4NthDxDszYjdbi957e/atQuXXHJJwW2x+9DhcPDgkQX7rJANyzFd6n7duHGjovbk3h+sxz7z/sh8c5NOp5FMJvlDaGYw39TUpKjdAPh5ZA8jbFz31q1bsWnTpqp9z5ZSze/9av+OUUu9tnspqcueYlL/1CitqpTSyQsrVqwoOkZRKaX7dtVVVxUt3pGZi5SNLdVoNHA6nejp6cH69evxox/9qOis+FOnTilqc75Jf5n7znpvHQ4HfD4fH4vKXnvbbDb+Grerq6touzds2IDp6Wk0NzfD5/NlBU0siLNYLJiamuLbztcm1mvd2NjIe8wYjUaDhoYG3vvOArHcXlCn04nm5masXLkSXq8371g9SZKwcuVKnjWA9Q6yrAF2ux2iKGJ6ehpdXV1obW3lmThY27VaLVpbW7F69WrEYjF4vd68WSiMRiMuuuginD59mvcOZ44bZr2AbDJWMbFYjBfmYMMiWPDIxhSzlICCIMBqtfJzG4/H+TJWqxWxWAwbNmzAiRMn5uUF1mg06Orqwvbt2xEMBvH6668XPK9btmzB8PAwtFotDAZDVqDOxiUPDQ3h1ltvLXoNdXV18euMtSdzW9u3b8dNN92E/fv384Azd2y2KIro6OjAmjVr0NjYWPR+LXUvsmu61P3h8/kQiUTQ0tKC6elp+Hw+vv9seE4qleK5qFmGCtZu9qDT0dEBWZZhs9n4sc48H2x7Doejat+zSlTze7+a21JTvbZ7qRDkUjN+SEGBQABWqxV+vx8Wi2Wxm1OXCk18U9PU1BRefPFFHrzkYq9G2avtfEMIPB4PXC4Xdu/erbh9SvZNyTKFKlaNjIzgqaeegsFgyDvGLBqN8tew+SauZQZsmzdvRjQaLbjvTU1NmJub46/vWQApSRLsdjsSiQQikQiuvvpqPjmm0Dhnn8+Hffv28YIGExMTfF0ul4vnfx0dHeWBWW7FtsxJbQ6HA8D57Bes3WwCks/nw6pVq3igkVsdzev1wmaz8Vn9s7OzPJOAVquF3W6H1WrlvW82m61g1oBwOAyz2Qyfz8dznrP9amtr4z287DjKsoyTJ0/yinYXXXQRb7PX6+XbZPsqiiIfzy2KIgKBQN7hMOwci6KId77znThy5AgPnHLTm7EhGQB4DubM4Q/s4cFms8HlcqG/vz/vMBSdToerr76a58VluZtZ3mODwQCz2YzOzk7s27cvK1DPzXQRi8Xw7ne/G52dnYqqtRWraMeGTbF8v5n5sE0mU1bOVyX3otJrutD9sXXrVvT19fFjnfkWh/3dZDLh8ssv5/ms2dhi+f+lxDMajdi0aRP6+/vh8/n4OcscU8zO2Y033sjfFNRSdbRqtqfW9l2pem13rVIar1FPMVlUlcwwVkrJ5AU2Xov1uBoMhqxlLiQVjpJ9K7VMvsmBMzMz6O7uRjAYLDkrnvUKskAyM3jKDLK8Xi/a29sL7jsbS8uOT27xG9Zzm9l7KYoiOjs757Urc4Y1663LXRd7ba/X6/MO18jMXsDOa26OdDYchY351Gg088bisfMqyzIfq+dyubICXlmWebYHNmkr9xjp9Xp4vV5Eo1EYjUaMjY3xSVSstHLmcWQPG2ydwPnUaA6HgxcKYTIzNLBX8bkTA/ONqZVlGW63mwf3ExMT8yoMtrS0IBaL8Zy4QPbwGJ1Oh1QqxYdysGOfex2x3NKXXXZZVi9X7oRWVta5WKYLllWl2LWf2Vum0+lw1VVXzbvOgD/0uikpp66E0mu60P2RTqd51cDMfNds31n6xNbWVtjt9nn7z3oLU6kUP2e562FZN2ZnZ/l4+2p8z5ajmu2ptX1Xql7bXe8oKCbzLLUnVCWTFywWC+bm5vgEL4/Hw/ff4XBcUCqcQr1KSpWaHNjZ2aloVjx7Vc1ei7OeOZbon83mZ8MfcntB9Xo9D5LUmBWd+ZCi0+nmHet4PM4DRfYaPzflWGZBEDZeN7fd7Ljp9fqiDw6Z+5YvawQLpADwNs/NzfGfNzY28nbGYjH+WWaPayAQQDQa5ROnzpw5k1WNDjjf0z03N8czj7CgNLN3l53vzJ7/3CEmLGiVZZkX6PD5fHwcNNtfALyKGpvgCSCr3ay3OhaLZVXHy8S2NTc3h8HBQWzevLlgjtlIJKLomk0mkyUr0ZXTw8vOHTtPudxuN44ePYqRkRF+Xjs7O7Fp06aswLnQtsrNGsCOIRvakS9TTLFcvSzDC3s4y10PS8lWKm80ISQbBcUky1Ksuc4mLxQrHbphwwYcPnwYIyMjvEeMBQ+nT59GW1sbrFar4lQ4xTJCsLGQxeRODmS/yDMrG7Ecq6UyVAiCwMdU574aZz3fjY2N8Pv9CAQCedNENTQ08KEBlc6KZg8pb775Jh9/mXmsbTYbVq9ejampKX4NZm6PTcrSarXo6enBCy+8wHtyM5lMJlx66aUYHh4uGqywfZuamuJjODMrn2k0GqxevRqyLOPEiRO8XDVrs8FgQGNjIzo6OnDmzBn+eW5GALYcAD5OMDdPL8tewPaRBTuZGSPYfzPPZb5sIBqNBi6XC6dPn+YPDvkyS7CHIdajnSuzDLcgCHlTy7H2snzGhXq5Ojo60NzcXDQ7CRuXW6wSHavq5fF4in5fud1uvPDCC5ienubng5Vcnp6expVXXgkA+O1vf5uVzUMQBExOTmJ0dBTXX399VjWxfNtyOByKsgZIksTvI/aGIvMYsmuRBbOFjiN70GOTRnN794E/pPUjhChXl9knyMJgPZMTExMwGo2w2+28elR/fz+valWP2GtUl8uFSCQCr9eLSCQCl8uF3t5erF27FtFoFOfOneM9p6ysbjwex7lz5xCNRhUFfUNDQ0UzQgwNDZVch5LJgV6vF1u2bCmZ6YHNnGc5dLVaLZLJJPx+PyRJwlve8hYYjUaMjIzw3nKj0ch7REdGRmA0GrF9+3ZVZkWzXlnWQ8wCNNbT6vF4kEqlYDQaebDFektZhgFBEGA0GhEKhQr2hsXjcSQSCd7blq+HMxAIwOl0YtWqVTyzBAtOBEHgmSVaWlpgMBjg8Xj4+GOWxSQYDMLj8fDeafZQwXpxWZAaDof524bcQJa1h11vLIDNHQvOAmVWRpv9LHcZAFmV71iBFhY0CcL5rCesDDdbJp/MlF+FpqCw3uJSmU5EUSyZnWTz5s0YGxtDMBhEKBTKuh5DoRCCwSBGR0dx+vTpot9X09PTeO211zA2NoZ0Og2DwQCTyQSDwYB0Oo2xsTH87ne/w/79+3nPKxv/zN7KjIyMYP/+/Zieni66LY/HoyhrgMFg4JMnCx1HVl67GIPBAKPRyDOqZN5DmZlVcof5EEKKo55iAmB51Fwv9joylUphcnKSByasB4v14qXTaUxOTvIeuELS6TQGBgaKZoQYGBjA+vXriw6lUFrZaMWKFbjuuuuKztJnis2cHx4e5vubKfPv7MHizTffxOnTp/lr5rVr12Lz5s2K3ySw8rtsPzJ7Qtnkq+PHj8Nut2eNL2bY5DCbzYbf//73fFxl7qStSCSCvr4+vO997yv6lqCrqwtDQ0NFM0tMTU1heHiYB6PsVTzLCMDanFsymmFBEOthtlgsiMfjWcNZWM8em4RVLPNG5tCI3PHFgiBkBVZsCAJwfghBIpHggT9w/lrLLKudT6H8zblWrFhRcplS2UmsViv6+/uRTqcLVnSbnZ3F0NBQ0e+rQ4cO4cyZM3yYTeZ6TCYT5ubmcOrUKT7Omg3RyCx8EYvFcOrUKTQ0NJT8bty9e3fJrAEsDRtLPZc78ZFNnCs1cdtisUCj0fDrkT3csDcX7O0UTQAnpDwUFBMAy6fmeqHXkYODg4hEIvwXY2a+U9b7FYlE+JjJQkZHRzEzMwOTyTQv6GXDEWZmZjA6OorOzk5Vxih2dXXhoosuKjpLv6urC+vXry84cz4SiWDVqlW8ylZm6iqLxYJIJAK/348zZ87g9ddfRzAY5L/M5+bmYDQaFQfFg4ODCAaD/BVw7sz5RCKBcDgMm82Gzs5O+P1++P1+/ovearXCarVidnYW4XCY9+qxwJkFx1qtFsFgEF6vt2iwwsY1t7S0FMwscfbsWXi9XjQ0NMwLIFkgwl73syEOmYEk6x1kPd2lsF5XNlwh86GBPURkZo7IHc7B/j3rLW9oaCiY7SGzF52dg8x2ZAbcxQiCAJ/PpzgwLnTNZo6XzfddxMp3e71etLS0FPy+mpycRDgczptukD0UeL1e3vMeiUTmPXxpNBpEo1GMjo5i5cqVJb8biz14A+dnwLPsKOz7hk1mjEQiaGhogFar5ZN+C2HrYQ9k7M0GC+oNBoOi9RBCslFQTABQzfVAIMBTS2Xml818LZxMJhEIBIquR0lGiGKz68sdo2i1WhXP0i81c95ut/MJh5mTyIDzE7IOHjyIV199lU9IY8dkbm4O+/btAwD09PQoPtaFXrWzYI71yHZ2duZNEydJEtLpNO/5zJ2Mx85hIBAoOvmLjV0ullmC9eqyayNzLDALPDNLHLOAKnNSW2axCr/fzydEZa4nEonwY8uKWWRei2wSIpsoyR4iMisXsn1hvaPsoYGtN/M6Yr2iwB8C99xxriw3cimZPdulJr8Vu2bZw1Kxim6srcUmh+ar9pevzZmVDDO3lflztb4bY7EYtFotz4udOaTJarXCbrfz4Q9K1tPR0cF729kDWWNjI5qbmxGNRpfs9zUhC4WCYgKAaq6z3iQ2xjW38hT7eanXkWazuezZ9fkyS/T29pacHNjd3Q2Px1NyPaV6cNm5LzbRThAEHD58GIlEAkajkQdSLNAKh8M8JVex4SXsWLM0XqxXnmH/lpVV9nq98Hg8sFgsMBqNiMfj8Hg8MBqNaG5uxunTp3mvKYCsCWksSGWBfaG3BEqu/cweWpY3lq2T5VHObH/mZEb2d7ZfBoMBwWCwYBYHg8GQVcksX1DIUtCxCZK5QRG7ThsbG3kJ53wlxVnPO2t/oV5hNgaa/ZcFwJn/ZX9KTdYtlVVl48aNJdtssVggSRICgQDf/9xr1mQyIRKJIBqNzpusKcsyfwDJzLyRD+tVZkF6bgCe+d1Yat/ZtTY3N4fZ2dmsSZ3pdBp6vb5g3vF81yzLkpHv7Qa7TgghylFQTABQzfWuri5otVpEo1HeY8iwV9gGg4GPhyzUE5Y5u54FWuy1v06ny5pdr3SMYqEcqw6HA/v37+frjMfj+P/Z+9MYObI0OxQ8Zr7vu3vsEVxi4ZZkkskgMytbVZWdvVZhXi+voW7UjFZ0DwS1Bj2SIIwwI2gEaCBA+tFQ/5A00GCmNY1SA9NPT6VXvWR1MTNrYXFPJrdkRDAYu0eE7/u+mM0P53d53dzM3CMZZG5+AAcZHhbmtrnZud893zmVSoU9iKkzv58O3OPxwGazYWVlhVmzGY1Gdt5zuRwCgQAqlQqr3PE6YJrWL5VKfeUlALCwsIC//uu/Zk4LPIkmtwWXy4XFxUVkMhlN2YMoivjRj37ENJpqoOX1wF/7agEf1GgXj8eZBlhJCmmWgZrBiOQQqNrqdDrh9/shiiKTqRAMBgMjtORpS9ci/1k0mJiYmEAymcTk5KRm2MzExAQmJiaY9phmMgwGAxvATU1NoVwuo1Ao9MgngA6ht9vt7LP5ijLfSEiDBT0rtcXFRaYF1rpmd3d3MT4+rrvNMzMzKJVKePLkCSOCRJxLpRJyuRzm5+fhdruZ1ltJrmVZxvj4OFZWVvqSYq/XyxwslARcFEXMzMyg0Wjg1q1bujZyFDSzubkJWe74VdM2VSoVbG5uYmFhgd1ntcJLlPdrfnbji3C/HtRGb4ghXjWGpHgIAIPZln2ZM9dLpRL8fj+bSjcYDIyEUAXO7/cz4329MIDFxUX85V/+Jfb393uIkcPhwKlTp7C9vT2QflsPpAM3mUzY2dlRrZbxOvB+DyJyKFBagFElj8hgvV5X1V5KktRXXkLHYWRkhOmSiVgRuRQEASMjIxBFUVejGY/H4fF4kE6nuzSx/P89Ho+qhZjymC8sLGB7ext3797tsS0bGxvD9PQ0dnd3kUgkUKlUegaNFKk7MzPDJCbUGEaOESaTCa+99hrS6TQikQj29va6zhkl35XLZTZbQWSQPod+puuxXC5rVtJp4LCwsIB4PM4s52g95XIZkUgEJ06cwN7eHjsfaucrEomg3W4jkUgwOzxerkEBIdFoVNdK7f79+ygWizCbzdje3u4hvHQ+z549i3g83mWlJkkSyuUywuEw5ufncffuXXZcePA/X7hwAaVSCYlEoicQZWxsDBMTE1hdXdVtJKTvbTQaRb1eh8PhYGE45PMcDoexsrLS10burbfeQiaTYRIcmnEht5tWq8V+f/v2bdy4caNLv//+++/j8uXLWFxc/MLer7+Mtp9DfHkwJMVDMHyVM9fJLcJqtSIajfb4fk5MTMBqtSIWi+Hhw4csmYyWSafTiMfj+AaXrKU29Q2AdYr30yjGYjEW9er1ehk5jcViKBQKOH78OMrlMsrlMiNhVOElr1yHw4F6va77IDKZTMhms0ybqdSUksYX6DR08e4cAJj2VRTFgbrd8/k8rFYrJicnEYvFWNAFVZxHRkZgtVoZmdeSPVCVzWq1skYznhCT/dUgXq2ZTIY1XfH71mq1mH0fWVxRsxxtM22DzWbD+fPn4XQ6GZmh9bndbly+fBlzc3P4q7/6Kxam4ff7uz4rlUqxQYbT6USxWOwi6SaTCS6XC/V6HSaTaeDvq7JplI6RLMsolUoAwCzulOef7NBGRkZYIhufjOdwOOB0OjE+Po50Os1IHF+Zper57u4uZFlmqX08GaX3KFabvjvK8yoIAtuGqakpJvmh5lCqtpM84rXXXsPNmzfZ+aUQk9dee61LB67l9AEA2WwWbrebNcSRO4nf72fNgclkUnffo9EoazL1eDzMCYS/9u12O0qlEn74wx/i3r17Pfr9QqHQpd8/zPv1q6je9pPODCL3GmKIl4khKR6iC/26p7+sII2ey+XC6Ogostkse4D6fD44nU5mW7e3t9czZVuv17G3t4c7d+4wwjw2NtYjn8jn8/jkk0/g9Xr76o63t7d1JRbkK0z+szxRoYcNkZ6lpSXNad1jx44hHo8zOzGeONG0LpE/2jalvIT0kIMEk5De0W63w2KxdDkiWCwW2O12tFqtgZuEqOlMSa4HTQ8kG71Wq4VIJKJqk/Xw4UO2bCQSUT2vZKW1uLiICxcuqE57kyVXtVrtsuwDwCz7nE4nk7CEw2EWqkEEn86B0+ns+32la1aWZSwsLKjKLFZWVpDJZGCxWOD1epl/MSUhVqtVZLNZnDt3Do1Go8dZhSzxJicn8fTpU10rtWKxyK4pJQml8A9JkrC9vY1qtQqHw9FVTbbb7ahWq1hZWWEe1F6vt0dTK8sy0uk0G1harVYcPXqUkd96vY7V1VUcOXKkazuVUhWaEanVarruJMlkEul0mg0S1PY9m80yFxWXywW73d7jBkLb/fDhw4H0+4d1v34V1Vu6Fr/Mtp9DfPExJMVD9ECrMvdlBq+rJXLMV3ry+Tymp6cRi8WYPEH54CsWi3j69CkajQbTPiqbzux2OwuMIAsnNf22z+dDsVjUlVhks1k0Gg1WIeMbu4hIGY1GrK2t6U7rPnnyhDV+KfXU1BBH1lZ8eARPHCixr1Qq9b12LBYLyuUy4vE4q6qRBrdWq2FjYwORSKRvkxBVE0nywethifxRBLMelDZ6SjJtt9uRSqXg9Xphs9lQq9XY4IIkJ0orLVEUMT4+jmAwyPYPULfk4gdWNpsNDocDRqMRuVyOzV4Q+KTCiYkJ3f0Cuq0WqcmPB0lsSBbA27wRKDLY4XB0VSaV5ImS1fSs1KiiyztxEIgglstl7O/vM2kJVfz5qis1PNLAUrlf1JCnNrAEOt+zZDKJ9fX1rihtfsBH3xM++U/LnYSqvjwhVu47OUbwjbhKBxaaqanX66oDOxoADqrfHwRUvS2Xy7BarWw2ZG9v71Crt18V288hvtgYkuIhhlBAS6NYq9VQqVTgcrlUb+o2m43JEPpZsoVCIaRSKU094OTkJB4+fKi7Hmr6IqsuJYhI7O3tsS53tWldmsbWqs7wjVQzMzM98hKz2YyJiQlWNesHl8uFQqHQUwkjQlqpVFAoFJhrhBYocU/LS5dkJFrHkDCIjR59Dm+BxXs58xZYelU3sqLrt57Z2Vncvn0buVwOdrudnW+q9i8uLkIUxb4VvkGsFknfqmeBZjabYTabdSuTsVisr5Ua0D8IpN1uI5PJdNnKAd1V11KpxLbjRQaWmUwGVquVDf6UUhWTycT2XW9mh5alVDzlbAMdw0Firm02G3O0UAPJmQqFwgtXeKl6m81mIUkSMplMV19Co9E4tOrtV932c4gvBoakeIgh0Kli9NMoEjHSA1Wx+kkjxsbGcOzYMd1AiaWlJd31CILAKm5qDyxBENBoNJgnrhbBILs5Ir/KBzX9DTW+jY6OIp1OM1IUCATQbDZZ5bMfdnd32cORHuR80xa9v7u7q+qrTCA/ZTWPWWraazQaKBaL8Pv9mprJQW30yP9WzwKLl6po2Y0NYqU1NzcHj8ejm1Q4iD5zULs5r9eLcrmsaYHGV6y1ZpKsVmtfKzW73c5cTJR2fHQdkO8zPxtD4KuuExMTTMv7aQeWsizD5XKhWq0y/TeRYhrAeL1eBAIB5HI5TQJOBDSdTnc1ftI2U9y73W7H4uIirly5ojngmZ2dxZ07dxiRVoLXvb+oPjefz7NAH+WgmeLMo9HooVRvv+q2n0N8MTAkxUMMge4ACy2NIj3Mq9Wqpu8p6UHz+bxmJYimvilGWC1lTpZlZrlE1lW8FrRQKMDv92Nvb49NMaullRHxIEJDEcJGoxF2u53Z0PFVKaVjBvnThsNh5PN5BAIBZr9FD201CygtEkqNXV6vlw0+lJpiqgYC2rZUVOEFepPY6PPp4a5XUeOrd3Tc+HNP9mFTU1OIxWLw+XzY399n+zUxMcH2f3d3t6/dWCAQQCwWY/Zc/Pbyx9Hr9eLYsWO4e/cuk1KcP3+eNVOSPjMQCPQMUtLpNJaXl/G1r32tr9Xi2NgYZFnG1tYWa+biY4Kpsj2ITRjZv9G1zlfB6d9cLsfsyMjVhFxOKDqbr7oqNdVEFsPhMEZHR7G0tITt7W22/1NTUzhx4kTXwFLLX9hmsyEUCjHXGYpZJjJM+z4/P49bt24hkUiwyjLJfRwOB86ePYu7d++yqHilO0uz2YTP52PnFdCOueaT/niHCvpuNhoNJgt6UX1urVZjVWLyYqYBDOnJc7kcarWa5joGxVfd9nOILwaGpHiIIdBbxVDTKNpsNkxNTWFzc5Pp76iqQu4HMzMzmJmZwfvvv/+ppr63trbY1CdZaS0vL/cQ1XA4jImJCSwvL6PVaqm6BlA1mPTOJKHg12Oz2VhFLpvNwmg0dlXWqDEsFArh8uXL+OEPf6hqW0YVbnrQ6ZFQqsyS3ICXYtC2UyPZrVu3NG2pADDCTpU9ft9IUhGPx7GxsaFbUVtcXMT3v/997O3tqbovXLp0CX6/Hw8fPsTS0lLXtbG9vY1gMIg33ngD9+/fh9ls1rTII7uxRCKheV7pOK6srDDiRKRwZWUFi4uLiEQiSKVSqFaruHPnTk+TIf2+UCgMZN0FADs7O0ilUl3no9FoYGxsjG2T3vngbcLK5TLTVvPkcWFhAbu7u8jlcj3T5LVajRFxivGOxWI9Th92ux2BQABWqxXxeBybm5tIpVLsGFEz5NzcHILBIDY3N3X9hefn5/Hnf/7niMViXdd1tVrFyMgI+z7Ozs7i1q1bbJtIR3/u3DlVpw/+ZyX0YtcB4PLly/jwww/ZQJzcJ0iqcfbsWWQymRfW51JVnjTWyu81DbZJnvUiDhVkffhFtJEb4quDISke4isFrZv6oFWM+fl5NJtNJBKJruoJee9SRzgRiH5T3+RJS5ILvrmFPpukAPSwouWp8YePNOa3ud1us4ou3+1Pf09NTUT6fvrTn3YRB/pMh8OBt99+G7lcDplMRtW2LJPJIJPJDJRYdvHiRZb+BTxPgQPAGpZCoRBisRg+/PBDTVuq+fn5HiLNExLeLo8q71oBJyRdUDuO9Xod+Xweq6urSKVSqtdVKpVi5JSq8cqpaCKGRGAB9HhC0/srKyu4cuUKarUaSzgjn+ArV67gjTfeQDweZ04n5F1M6Ys7Ozuo1+uo1+uIRCKqITBEdkOhEFZWVphlmTJMJZ1OI5PJYGNjg50P0tCSN7XSJkzrs4LBIJxOJ9LptOpxbLfbcLvdcLlc2N3d7ZoJoN9XKhVGiK9cucK+y3SsU6kUrly5AgAIh8N48OCBrr/w5uYmqxTz56HVarEBFQCsrq4yXb3SxYJsDU0mE5ux4e8N9HuepGrFrtNxBMAGIFRBJ2u/6elp/OQnP3lhfS4lNdKsjNp30eVywWw2H4pDxVfZ9nOILwaGpHiIrwz63dQHqWKEQiF8/etfx+PHj7G9vc0e+tPT0yy8A9CvBNHUdzweZ5ZqRIysVitcLhez0SIiRVIBChKo1WrY3t5mVR4iRQRq9KHqElVUAXT9X5IkFItFvPbaa3A4HLh58yarupnNZgSDQVy6dAmzs7P47ne/q2tbduvWLTb1qzetSwliyoos/54sy7h+/bquLdX6+jqb4lUL6KDjSYRIq3qbSCSwubmp6YjQbrdx9erVrvAHpfYU6BDZQCDAkuvU3ElIpkCOFeVymZ1XcrZYWlrC5uYmKpUKS0Hkm78qlQoePnzImqL4pja6DhqNBtLptGazFh1jugb6WdLduHEDuVyO+VHzx5ua1MgmrN9naQ0sCFSx1NOKZzIZ3LhxA9VqFQaDoesYGY1GVKtV3Lx5EzMzM7r+wrFYDPfu3UO73Waae96SrVqt4vr166hWq7ouFmRrJwgCAoFAzzGsVCoHliHoWfuRz/WL6nNJwsLPuBDo2iZdPiURvqi/8FfV9nOILwaGpHiIrwQGNY0ftIrB62m1wiG0KkH5fB5ra2tIpVIsypeqfNVqlVUszWYzc4ZQs6UiayvSHSqblogUk9UUbRNP5gwGA1qtFlZWVnDq1ClNIr+9vc1sy9Sma+12e1cks960LnnQejweRmj5qX+r1YpSqdTXlop8bKvVquZ5p3jiRCLBqqCkyaXqLVXMiVApLelarRaKxWLXfhBh4wkUTdHzTWQ8MQI6JDsWi6najVUqFYiiiNXVVeZiQuSGPoOmsWlgp+XFTNcDpSrSta8WAjM1NdXXko6fGaF94q8jcvq4efMmmx1R+yy/38/8sLVAx4as0JTXkMFgYJVmOu48oSOSl0gkmCRFy194fX0dpVKJXWf8QI23P1tfX8fY2JiuVIEGX2rHkJoD+9kDKmEwGFRt1w5Ln0tx4nSN8RVufvbh8ePHqNfrh+YvrNWsOcQQnzWGpHiILz0OYhrfr4rBk2u1hz5fMdGSalSrVSSTSdYAp7Qkq9VqSKfTjDBpuUbw6XJkq0UgwksEgbSBRDJoeWos6xfPzDe15fN55vpAxMFms6HdbqNQKDAHCZpe5okIkZN2uw2v19vV+U/+vQAQj8d79okHkQyeNCkr5QBYYx8FnCirrtTASNV2NdJDxIC/npTXFw+DwcAGPPy2ORwOltRHP6udVzq+pO/mJSJ0vdF5pcGPct+JaBcKBWSzWd1rf3V1ta8lHQ2ueBmDUqrSbrexvr7OrNvUPovkHv1AMzA0qOGlQ6SDpeMKgP3Mv9dqtVCr1XT9hanJUxD048tpPVrHZxBbO7J3Owwclj63XC6zQSJJsgh0XGRZRiaT6TsoGPoLD/FlwJAUD/Glx0FN47WqGAch16RVVZNqZDIZFsusVgUlH1LSEOrZUhHZbLVaPd7BRCipkkcuGjzB4OOZ1Rq7/H4/FhcX4XQ6AYCFgPDEptlsolarwWw2M7eCfD7PrO2UcgXSf1LYA203kR2Kq6agEC1bKqpsOZ1OVCqVHkJjt9uZppIs7IggEWFXykrUoPa+ViMVTUe3Wi1VBwKbzYZWq6Ub8sA3RKotQ/8qK9B8MxrBaDQiHo/rXvvJZJJdC2azucfFhK5Ffn/p+uH3X5ZlVYmB8rMGAf8ZauuiCrGyEg88lw7RsdCTGJDmXi++nAj1YdraHQYOQ5/LpyeqxU7TvYi+O2oY+gsP8WXCkBQP8bnHoB3PWssd1DReaz08uQbQFYdrsVgYuV5fX8fS0pJmQlQgEOjS0CoJFk9u9CpP1IBFKWpqWtBIJIJMJoNSqdRTCSW9oNvthiiKuo1d77zzDqvSEXkjEAG0WCyYn5/Hzs4OVlZWmK0TkYNisYhcLoe5uTnUajUkEglWjaPtqtfrjFhVKhUUi0X290qiZrVaWYWSBhh80xr9DZFE2n5lYxtVX6mSrpQG8I2H/DlQAyXRkUyDX54axIg40zniLcCoQY+kL1Rx50ki38RHUgS1bXO73ZiYmMDW1pZu5Z6u3Ww2y+QifJqbJEnw+XzMboyOpfLziDj1+yweWoML3sOafkfL0vs8gVNqYWmwQzaCWraGR48eRTweZ8SV/yw6/xaLhS2ntZ6D2todFkKhEHw+n6ptHw8tGz3ejlDrHuLz+eByuYb+wkN8JTAkxUN8rjFox7Pecgcxje+XRkaNa7wulKqgfr8frVYLT5480U2IoqotVWaU4RUAGBnnrdzUKk/z8/O4fv26pv3bpUuXkM/n8f777zPdLE/ITSYTLl26hDt37ug2dl2/fr2HwNB6CKIodskwtKqcAOD3+5FIJBjJ5rfJYDAgGAxifHwcP/zhD1lnPA+j0YjTp0/j3r17jMQpG/fowU7Eu1qt9ixjs9mYcwiFitDveLI3OTmJXC6HfD6vea06nU6mGaUKI3+8SAfu9Xqxu7vbNeVP58xkMmFiYoJFS5dKpZ71mEwmhEIhhMNhfPTRRz0JcUTsT58+zbyoC4UC8vm8auXeZDLh2LFjrLGRPx8kG5iamkIul2P6bbVUOqosFgoF5HI5Jrkhez2Px9N1HdP+KGEwGLpCQJTnjAYQhUKBeRir7b/ZbMbk5CSePHnC/Ippe0wmEyKRCPt9LBbrOh8EUex4iU9OTrJ7g5aNHjCYrd1hQs0i78aNG8wiT2sZ3kavX5jIW2+9hUQiMfQXHuIrgSEpHuJzi0Gb4/ott7i4OFBTSqPRwK1bt3TTyFqtFqLRqGr6U6VSYY1f1CCnlhAliiK8Xi8ymQxrdONJaLvdRjgcxvT0NLa3t1nnPAUhOBwOVuV5/fXX4XA4+tq/RSKRLh9WoEMsI5EIbDYb03rqNXYJggCfz8csx/htttvtkCSJNdFNT08zEsYHOLjdbhQKBdTrdZZupvRGtdvtLFJbD1RRVZIiOq9EjshHWm0Zam47ffo0097yLhiC0GkifO211/DgwYMubTUPkmtQRZ7WwVcdqfKfTCZVCRjQIdPZbBbz8/OsiqxGCkdGRlCpVDRJFs1suN1u2Gw2rKyssIEffz3mcjnMz8+zQBo6bvw1YjQa2ayHGgml/Xc4HLDb7VhfX++R2FSrVWQyGUxPT7NrQGs9brcboVAIxWKxZz10HEOhENP68rpwuh5J524ymRhJ5x0zSL9OAR4UGKP8LLvdzpZTq5IDz4l9JpPpa2t3UMsxvVmyW7du6VoWEvotw5NnrXuI3+9/5f7Cg84QDjHEYWJIiof4XGJQ/W4gEBjIAmx+fl73pj4/P88sh7TWQ9W9arXKggkAsKn7XC4Hk8nEtKpajVSFQgHnz5/HnTt3GDnkq8A2m42FRRSLRVQqFfh8vi4tLP8gGsT+zel04tKlS6rJZ0+ePGHVZ63GLgoNsFqtcDqdrCmJ4o8p4pj0tIFAAB6PRzUZMBqNIpfLsUqtsmlPEDpBIuScQAMFXlbSbrfx0UcfqRIrHqQLJag141FqVyQSYalmtG+UalYqlViVmJqveBkG8DwtkN7jdcH0M0lW9EAEjo43nXf6l0j+2toaI8lq27O2toZsNtv1nVJ+x4CODCgWi8FqtcLv9/dUVMvlMvb391kVn2RIvKSBNLyUiCbLMnN0oH1utVpotVoIBoPMpkwp1bDZbAgEAiy9jhpRlXIWOjf5fJ5dM/wxooS9+/fvs7ANcqigY0h2bMVikQ3ayI1EFEV27EulEra2tiDLMhYWFnrkE6lUCo8fP2bSCT3LwtnZWU3HECX0Zq38fj9u3Liha1n4s5/9jNnlaS1DNnr9wkRetb/wYXgiDzHEp8GQFA/xucSgzXHRaHSg5c6cOaN7UzeZTH3Xs7+/DwCw2WzMH5V/yNpsNjZdSilxyvVQg9zo6Cjeffdd3Lx5k1WX6MZ/6dIlzM/PA0DXNtMyag8iWe5YYhUKha5KJ38cqaGO1kM/U1S0lvsC/Ws2m1Gr1WC321kTHABmJWez2RAOhxGNRnWTAekY8b7AVFmjaWeqABIR4kEEjHyDlQ1f/Hs8MVWui8gW6b3HxsaYRpSap9xuN+r1Ora3t5mfNICuajoRcyJ4RCiVBLwfgeextrbWVW1XSh7S6TRz1KBjSKDzX6vVsLy8jGq1iqmpKWSz2R7C7/P5WAIdacvp2qCXzWZjseV0PpXVfTrGqVSKab35wQ4RzGw2iyNHjjAvZ3IrMRqNcLvdrEJZLBYxPT3dI8NwuVzweDyQJAnBYJDpxtXS6gKBAO7fv697Pra2tmCxWJi0hs4vXdM0yMhms2xgyoPuDdvb20in07q2dplMBtFoVDOwgwcf7qPWl+D3+5mVHF3X/KwEWcnR//WWWV5exqlTpzQtJAmvyl940BnCIYZ4GRiS4iE+lxi0Oa5UKg3cRBeJRDRv6pRopbceImnBYBB7e3ssjIEqnmNjY4wsD2LNZLPZMD09zUii2WzG1NQU/H4/+5tQKIRAIKBZwQH0NYPT09PMmUJNBx0IBNgUOW2b2jaTPnN9fZ156BIEodOdPzc3h8nJSWxvb+tKVfx+P5NyUCWLQJVFNTJM61CCiJlaQxo/5a1WKaXtIxKuPEa5XA5+v7+rqVIQulMEqbGMSL7advIODYOgXq+zuGO1ansikeiySFMbENB+tVot1Ot1puHmta7kNkHnuVwud+lh6Vqla5yWowEg3+gIdHxvzWYzKpVKT3y33W5HtVpFMBhEsVjskvPQTMPIyAgmJyfx8OFD5jrCWwnS8SaiTwmCapHSFIZCJJU/LjQgoqo2HQNlcAtVv8kZRes7RNVjvftHpVJR1ccrQbM7en0J+Xx+ICs5+i7oLUN9AIPIFQTh5foLDzpDeBBP5IN89lCuMcSQFA/xucSgzXFOp/NAyU5aN/VBPo98TVOpFKu0ETFotVpIpVJwOBwDWTOVy2UsLS2hUqkgHA6zagil3PF6aWWleGtri1WK++kKL1++jFarhZ2dHV0dNEkpqLGKliGXhFAoxLS+aoSPLNlEUezrnzoxMYHHjx93TTETqPrMkxM90N8qAxyIUPOBDFqkmCqtelpx2h6SDygJOC85IKLJkxCqzPEVdj1QI6ZWtZ0IoxrJ5gcCTqcT6+vriMfjXbMBVMnd3NxEIBCAyWTqcs3gl6lWq7Db7axRjqrXJEkgrTi5i9BshTIyuFAoMFJfq9V6kghJFkLOExsbG13XW6vVQjKZRLFYRDAYxMjICJNPpVIp9l2nprbHjx93nSMe9B59d+k64WU6dD1RQ6zed4i+e3r3D2o47Id8Ps8GwXp9CXQt8JIZOo4k4xEEfbs5mi36vMgVDmqfeVj4vOz/EJ89BhM3DTHEp4Asy8jlcojH48jlcpoPcLVlKLGJKiK1Wq0rgaxQKCAYDGJiYoItp0Z6aDnqjB7k87TWMzo6CgCMJJB1mdVqZVUwAJiZmYHT6YTdbkexWEQ6nUaxWITD4WCV3t3dXVQqFdbAsru7y6ZFK5UKlpeXkUgkcPPmTezv77OKlN1ux/7+Pm7evIlYLMZ0hTabjVVLSZvZbDZx79491Ot1Jm8gwkPL0DbPzc0hGAyyv6NKn81mQygUwtGjR1n0tNa5pqlq0h9GIhHs7+/jyZMn2N/fx8jICC5duoRwOAzgeUWTHuJEItVIjBZIpkKVZZpOp/doSl6t6gw8J9M0hU7hIVS9pmMkiiIja+RCQvpp+tloNMJqtbLGLRpcUCOizWYbiBQBwPHjx3Wv/Ugk0vcYiaKI0dHRLt9p2mcAjGiR6wBJSfjjSFPuDoeDNaOOj4/D5XKxSPLx8XE0m01EIpEuGzxeekPnoNVqMamB1WqF2+2Gy+WC2+2G1WpFOp3GysoKyuUym4kBuivtpLOnhrw333wT4+Pj8Hg8GB8fx5tvvolQKMSIFX0uf874MBJ+P3kyzJNk6iWgAQpdHxQzbjQaEQgEWIOt8hqj7/rExETfc1+r1ZgfuNr1SISWzh9d6/yLqvcmk4ntG51f/v9GoxF+v1/3PjOot/RBoHUfHmSGkGY+Dgsk13iV+z/E5xfDSvEQLwWDjLz7LbOwsIBYLKZqg0S/H6QySQ1pg3werYdM/fnGNiKzRJSUVWDqdp+YmMDTp097bJ6q1SpGRkYwPj6O+/fvo1KpYH19vWe6emRkBMlkEtVqVXca8erVqyiVSl0+uwSq+JVKJUbU+m0zRfnSA5OIoMfjYQ4EeiiVSlhaWsLp06exsbGBe/fudUk6CoUCrFYrRkZGuv6OryjSedYisUqMjIxge3ubWYnxzVYmkwlnz57F3bt3u2KalaCqq8lkQjqd7rnWyFvY7/cjHo9rWsQFg0GYzWY2BcsTORpguFwu1Go1VTcMgt1ux7Fjx/Dw4UMsLy/3NKOFQiGMj4/j3r17mi4WtO1ra2usGqtVoSYiS24gpK8lMmu32+FwONh1/fTp0y5N8fb2NkZGRjA/P8+uU63PkmUZOzs7XQ2D/LUvy3KXe4VahDcFnOTzeayurmpakh0/fpyRVi2YzWZGmvVABDOTyahKh8xmc197xMXFxYGa7PgQm0Kh0HN/oIo0eUuT2wVP5EVRhM/nAwCWnqkWme33+/Hw4cNXKlc4LPvMw8BnKdcY4vOJISke4tAxSKMEgIGW0boR8e8P0hk9aPPG7Owsbt26hf39/a5Ut7Nnz8LhcDATfrIu4u3GSHu6ubnJNMp81anVaiEej2NzcxOJRALJZFJ1SjsajcLn88Hn87GgD+W+u91ubG1tdWlbldOjwPM0u37bzGtN+QQ52i7SNepBkiTE43FUKhVVSUexWMSHH36ICxcusHXz9mVUwR6UEAOAw+HAyMgI9vf3GbGhavnIyAgjoXqo1+tsipm2iwedQ9oPNbRaLeZVnUql2N/xaDab8Pl8aDab2Nvb09wer9fLpDpKbS65mzSbzYHOh5b1GY92u83cRai6zVdNyf1jY2OjZ6BHhD8Wi2F7e5tZDKptGx1H0t3TeeKvfdK/0sAM6A34oCa4a9eu4dGjR5rSIVmW4XQ6dUmxzWbrq/Ol7xidTyUpJtu/8fFxvPvuu7rWZoOANNzkiqE8RrVaDRaLhVXYyRWH//6Oj48DeB7jrDZ4opmlVCqle585TLnCYdlnHpYn8mcl1xji84shKR7iUDHIyHtpaQkA+o7O6QGoZYPEj+D1OqMHrQbIsozV1VWYzWbMzMx0Ne6srq7i5MmT7PMnJydRLBaZ1tjlcqHRaKBer+PBgwdot9uw2+0Ank/RA52p0fv377NpVmXUMzWI5XI59tCgarUyHYyIBRFrOv78FCoA1iw1NTXVsx4iWNvb25BlGfPz8yiVSmy/nE4n81gdBEajsa9V1IMHD9h2qTU38eEOdEz4faP/A53qu8PhwOLiIjKZDDv3fr8fmUwGW1tbutVUoENoSSJBTVO8PrhUKrGXHvb29uDxeNjfKUNZaJ3pdBqAurVbu91GOp3G0tIS0uk0LBYLnE4nW0+z2UQ6ncbq6upAZJc0vP3QbDZhMpkQCAR67MRKpVJXYxwFovCf02q1sLy8zPaRv/5oX0mqQNcn34hKhJ+OPf0tr52mZcn27fHjx30tyfoNiMrlct/jyDtR6F0fVC3WszYbBBaLRZWA8z+TfaHP58P4+DhSqRS79il1L5vNshkSp9PZkwpZr9eRy+XYbJHWfeagEc5aDWuD3IcHsc88TE/kg6adDvHlx5AUD3GoGGTkTdZm1KimtgxV0qirXNlspDaC12qiG2SbeLlCOBzuWo5+v7u7i0AggK2tLU0bKJvNhkqlwqpWStJDkgZlYxi/PUQoqMu8UCio2nJR5VpZueObzUwmE8bGxpDP5xEKhbqOI1VefD4fcw3Y2dlRTSIjItcP6XSaEQQlEaAHOWl36fd8lYumRenY0MOIJ09EDMxmMyPuRqORaZX58/b06dOBtpua1/L5vKrdGAWoaFULZbkTBBKLxVStxOg4JhKJrhkEZRS0KIosFZH+jr9GLBYLisWibqWZRz/JCw/lPvP7SWEq9Ht+m2ggR9pa3mpPeT3ysc1a28Cvl/5VDoyIqJLVm5bdWL8BwSBNj7ScIAiqcgblZ/SzNhsEREoFQej5fshyJxGT7PQo3ZBA32uHw8GkFMqBDF1rNCDQSzw8iFxBTxoxiO3lIPaZh9n49qrlGkN8/jEkxUMcKgYZeVN4wYsuM+gIfpBtIueFftOI09PTLJHN4XAwlwCq6rnd7q4IW574Elngq2VK9wXeZov0mgaDAVarlVVQqct/cnISbre7S9pA66T/ezweHDlyBE+fPtWsvExOTuLOnTvsocg/5Pl0vkFAzWA8seJhNBrRaDRYNazdbncFVJA+0uv1wuv1soAOGlSQ1MJgMOD06dPMrUDrvParEhOIfLTbbSYhoFkC0mfz0/1Ab+W62WyiVqv1kEZaP1VCZVlm26Z0O6BzU6/XNa/FQab8CeRV248c0myCmmOKxWJh2l8tL2si8/RZatcLHQNRFNkMhbKaTseGvtd8gyD9rDz+WnZj/D6rVWoPItOhbaaBJpF/GihRJPthoNFosMFQq9Xq+n5QA53D4cD09LTu9zoYDOLJkyfs2lQea95RZWtrSzfxcBC5Qj9pxPHjxw/FPvMwQQ3WwwjrIQhDUjzEoWJQazMAL7zMoCP4QbaJHhZ6N+xCoYB4PM6Ib7VaZQ9Fv9/P/In5hxd/kyXiADwnCLQsgXcIoG1RIyH0Lzke0AObn16lB1wkEumyrlJWXoxGI6tsKjWMpNEk/2I9ciUInRho2k9em0ygKmk4HEatVmPHkbf3ogjrt99+GxaLBbdv32b7JwidxqaLFy/i/PnzTLusdV6tVivzYdUD7TeAruYv8ublNdpqdnFEsKiRjM4fnUOy7qJBUqPR6PpM+lsiVv0e/oNOx/PSCy0IggCHw8Eqj1oVbtpGNfBNhXqQJAk+nw+VSoV5KPPb4XQ64XA4WMMj70pCyxAxJ32tlt2Ych9fFCT3IIs2/vrg71kvCovFAofDAYfDwWaJlPZ3AHos6ZTf62q1ygZ45LzB646J2GtV7weR3fDL9pNG7OzssHvki9hnHiYEQRi4UXuIrwaGpHiIQ8WgI28AiMVimsuMjY1BlmXdZZQjeC0t2yDbFAwGWUyx1g2blg2Hw6yapdTnktcr3/SlNoVst9tRLpeZTRovp6hWq3A6nSzcI5/Ps2l9o9EIj8fDktao0krbT8s4nc6uaqJeCEgmk2EVXurG590OeD0sXw0k8AOKCxcuYHV1FYVCoUvzzJNBt9uNn/u5n8OdO3f6Rli/8847+NrXvob33nsPmUwGfr8fv/zLv8ymkem8UiAEr/EuFAo4cuQII3R6oKoWEXUCVeroQanV4Eb7b7Va2TVAFXa+WS0YDLJUQ74Jk6+wUhOVWtVZlmV2fZCWl/dipvNBpPzs2bNYWVnRrYqSty9ZT5FtGZGlUqmEmZkZlrxI1VrldvMaaS0v52aziZGREWYTpyTgBoMB09PTMBqNiMfjsNvtrPGO5AOtVguhUAixWIwdRzo2yu8ZbzGnnJEBnofF6IH/XIfDoaop5iFJ0gtpivn71cTEhGo0O937BEHQrKhKkgS/349kMgmv19ujFc/lcvD5fDAYDJiamtIcEJHThx5BHUSiViwW4XQ6kcvlBrqna93PDxuvOsJ6iM83hqR4iEPFICPvEydOAOjo2PRG54MsQzfJg9itqa3r3LlzWFlZ0SXOdGOnqqlS50xT4mSVpDadSg+x48eP49atWyiXy4w0EIkwmUw4c+YMIw7ZbBbVapU90CRJYn7CVqsVtVoN6XSaESPy8bTb7XA6nWg0GrohILlcjm2DUodJFW2q4hUKhR4SQTIRv98PWZZx+fJlXLlyRXWK32Qy4fLly8yzuF+EtTKtb39/H1tbW7h8+TIWFxexsLCAra0t3L17V9XaLhgMMsKrBar8qulv2+02yuUynE4nQqEQ9vb2NCto4XCYVcKoMYumwOv1OhtgeDwets9KULNfKBTC1taWqpxBlmUcO3YMuVwOT58+7XEwIQJ45MgRVnnN5XKa++9wOOD3+7G0tNQjlSmXy12NjKurq6rbLQidZtdUKtU1I6LcNwDsOtEaEJ04cQKRSAQ/+MEPmPwD6B4QnDlzhrle8LMv/EwJ9Qbs7u4CUJdLhEIhFAoF5lCh/N4Dz5v3JElStezjv2crKyvMfYJ3rzmI+wTdQ7e2tvDxxx93Xddkf8ff+7QqqqIoYnFxEVeuXNG0iTt16hSLutaS/FSr1b4ytUEb1iiF8EXtMw8bryrCeojPP4akeIhDx6Aj78NaZlC7tUuXLmFpaQn7+/usojg6OooTJ04wIqxHnOfm5vDgwYO+sg/SF1LTCoFvXpmZmcHW1lZX1C3QqdpFIhEcOXIEOzs7LImMT5mrVqtYX19nvrjlcpkRar5aRg9l8g/WOj4jIyOs6sj/PfA85pmm2Futlu5+0cOEKmtKgk2/p+tE70HUL60PAGsCpHQuIkak8y6VSrBYLKzzXgnS8fbT6GazWczMzPQ4YxAMBgNsNhsbnPD6Z/ocWe4ET1CTpJKAiqLI4okXFhbQbDaRTCZRq9XYfomiiLGxMbzxxhsAOlIP3o4O6Aw8RkdH8e6777KKtR5KpRLW1ta6Bl4Eamh7/Pgxjh49ivX1dVXCazAYMDIywhoy1a4jGiS43W4cP36857tISXT0vQ4EAoxs8YOdQCDAjpPD4WADOTrWdK3abDbMzc0hm82iUqn0bLPdbsfx48fx4MEDeDweViXl98npdLLPV7OIo2NkMBiwu7uL69evs34D+p4lk0lcuXIFAAYmxplMRve6zmQy7DjpVVTp87Rs4iKRCNbW1pjkh+9dIMmP1+vtK1MbtGGtn+TjIPaZh41XIdcY4vOPISke4qVgkJH3YSwziJaNrNsG2WY9Eh4MBrG3t9dXGrK5uQlBEDA6OopKpcIqHXa7nXWvR6NROJ1OXLp0SXV6NBqNIp/P91hOkYVbpVJBuVxmzVv04ODJA033R6NR3eND1RiST9D+0LI0XUykZmxsjOlBSdNM8g6n04nvf//7EAQB4+PjKJfLbDmHw4F8Po9bt25hdnaWPezVHkTtdruvtdv169fh9XrRbrcRDod7pBr5fB7r6+uMtNhsNqZNJs11o9EYKLyh0WggHo/D6XRClmUmN+GdUdLpNMrlMlu3Uj5AfsiUcjc6OtojwalUKiiVSkwHrjeIA4Bvf/vbePToEVZXV9k1NDc3h1OnTiEUCmFtbW0gS7qtrS0AgMvl6tnuarWKtbU1ZDIZluBIx40GFbIsIx6Ps3XyunReYgFAM9WP1yUvLy/DZDLh/PnzqhaBW1tbLLhmfHy853qkRLxcLoeRkRE0m02WEmcwGODz+VgVlFxj3G43i56mxEfSLQMdWQlVuZXXWbPZxKNHj1Cv15ljDn3PKEKbv+71IEkSbt26pXtd07rS6XTfiqqeTRxd+9VqtWu76T6Ty+W6dMxaOEjDmp7k4yD382EVd4iXgSEpHuKlYZCR94suM6j5+sbGBh4/fswqH0QiY7EYCoUCqz70I+H9ZBiUemcymZDNZnskDUSgYrEYvF4vDAaDqpUYpbRRrKmy+Y2kGtSkRj6zNM1LDUiNRoN9ltbxIS0puUHwDgM0FU+fbbPZWHiA3W5nqWSkjV5ZWUEmk2HTpXzjIZHuTCaDaDSKqakpzSrX8vJyX2s30hC73e4ueylaxm63M30vVe7pfJDOVK1pTgv0oKYGJd7ztdVqsfNN28tvt5Ic0t85HI6uZXgMOqVrMBjY7ITyeH3yyScD7RtJF4gI8zCZTEyi4/F4mJ6ejh/p6UulEtORK3XO/KCm2WyySqDad/HkyZPsO037xoPkSXyohtpxlCQJ2WyW9QCMjY319ABQXHQul2MVUXLIoHNO79O1Tsvw136z2WQ2aGrXq91u77ru9RCNRpHJZNi61K7rTCaDR48eYXt7e6CKqpZNXKFQ6Ip8V0u8pGQ9vfv0QRvWtO7pwzCNIT5rDEnxEF9oDKJlKxQKWFlZQaVSYcb25CUcDAZ7gkD0SHi/arKyqUcpHxBFkXWu620zaUep0UXZOW6z2ZgWkqYrKeCEKl8A2O/72d+R7ZgyLpo+jz57cnIS6XS6JxkvEAigWq2ySjg1JirTuGjfS6WSrm6QtMt61m781Ho+n++pXlqtVmaxxk+vE+i9gzRB6YHOL51jpQyBfkfnhqayeS0wOQ8MYu9F08zlchlWqxV2u53prmmgd5DQAa3jQASXSGgul+tJQLTZbCywhq+28gM5k8kEn8+H1dXVvgEONMgjrbEyUIL09CTnUbORI/s/vR6AYrGIU6dO4ebNm4jH4z36fofDgdOnT2Nra0v32qcES95Rhh/8kN3dIFZ6vIyDqtD8DJDNZkOr1WIBLi9SUSWte7/Ey0Guo8NoWBuGaQzxWWNIiof4QmMQLRtN31ksFuzs7Kga1B+k+qBXwctms6wpTCsdrFarweFw6G4zPeSBztSkWhoVTX+SVzI//UwNe2Q31++zTCYTW4+SXNLxNZvNMJlMmsl4RqMRLpeLSRH45h16oNPDjK8WqlW5AoFAX2s3IjC5XK6LyFPlnKQK9XqdkTdlmAqdn0FA/sB0PHmiIssySzCk6j1PHI1GI6vIkRNAIpFAsVhky9hsNvj9flitVlgsFt1BQzAYxPLyMrLZLCRJYl7VdE03Gg0sLy9jYmICjx8/7rtvPJFXTn2Tx60oiuzz+AFGq9ViftFerxehUAjxeBzVapXtm8PhQDgchiiKrOqoVQnM5/MA9AMlKLiFBr1KMud2u9nAop/ONRgMdumXyfrPaDQiEAggEAiw2R+ta5++JzSAVV4fRqORaZT7gZah46AcWNJ3ulqt9kgV+OM4yD2N7p96iZetVmvg8IoXbVgbhmkM8VljSIqH+EJjEC0bPSByuRy7wfPNJERSD1J96Cf74MkQ/x4ApmnM5/Oscs1HWBcKBUxNTUGSJKRSKfZQJVCantfrZRG8pFPlG3Jov/x+P0u+Ujs+/Gd5PB5VDWMwGMTU1BTi8bhmMt7o6ChGR0dZhU6t2YokFLFYTLda6HK54HA4WLVJWXVtNBpwuVzsHCqPNVmH2Ww2pinW0vkCzy3ZtECk4cmTJ2x9NLVNnsMzMzOQZZlZoJGUQZIkRpKOHj0Ku92OlZUViKLIyCGds0Qigfn5eTQaDdy6dUtz0HDixAmmEaWBEX9Ni6KIaDSKX/iFX2DNilowGo3MOUIt5EGSJIRCIRSLRVbpVM6AUJV7cnISiUQCp0+fVtUCu1wu5uCiVQWmgZ9eoMTc3BwcDgdisRgmJydVY+AHsX4cHR1lhFdLv0xJlrQetWt/enqa6c6V30WSaUQiEUxMTGieB8L4+DjTzfN6errGK5UKa+R70Yqq8v6p9b0+SHjFizSsDcM0hvisMSTFQ7w06HVFEwbx9NRbhteyJRKJnuYeh8OB2dlZbG1todlsdsXmUvWGqnVUkWy321heXmaEemFhoUd7qrVvlEZVLpfZA5avutL0+PT0NB49eoTl5eUeB4JwOMxsqa5cuYJkMtlTKXY6nTh37hyuX7+uew5EUcT8/DyWlpYQj8d7GpKcTmfXZ2WzWTZdLggCs3+7dOkS/H4/isUi9vf3ezSldJxarRb8fj9SqRTTNfPrMhqNcLvdSKfTuumBmUwG586dw40bN7pCL4hYkwfvvXv32LqVcdd0jZBEgQ84oUoxaWD7kWKXywWbzcYs15ShEzStLcsyc94gQkk+xVQp5gllsVhkx5EICd9sRI2XykbMlZUVVrW1WCxswEc+1uSZ3Wq1cPnyZVy9erVHPkLb/uabb2J8fBzvvfceyuVyV5WTqrznz5/Hj3/8Y9V18O9NTk6iVCohHo+zdZlMJpTLZbjdbubgQt7b+Xye7b/H42E6Yvq+tVqtru+Q8ntfKBSQSCR6Bk10XQNgoTsk+zCbzfB6vXA6nRgfH8f9+/fZYJCIJH1P6Vo9e/Yss4ekGRAivKSXTafTSCQSXSmFdL0KggC/3991vWvdZ8ihhMKBaABG0iyakQEGDzfSul8Ncv/ktcCD3NNfZJmDapNfNQbZt1e5niEOH0NSPMRLwSA+k4N4eg6yTCgUQjgc7vKzpWnUy5cvIxAIABgsoUnpiyuKIt5//33mi9tv3+hhmslkkM1me9bv8/kQDodZpQzotXii9+fn5/Hee++x7nfguTTAarXi6NGjuH37NiNTSusqImJ+vx8A8PTp0x7LqXPnzrEGwwcPHmBpaamnCjg9Pd1lJbW+vq65HgoEqNVqSKVSPesiMkJEVKtaWCwWsbCwgEQigaWlpR6d89GjRzExMYGPP/6YyTKU55eqdSR3UNPp0hQ8AOb5rITVakWz2cT29raqHRudl2g0CpfLBZfLhXg8zpalAY3P52PpepIk9XgHU9NXoVBgevH19XVV/+V6vY56vc6m6wn1ep25kpA2+Z133sHm5iZ2dnZ6tntiYgLvvPMOAODBgweqUouxsTEmR9AKu6CmTrJwe/r0ac+5P3HiBI4ePYrV1VU8fPiQLU8gnfnc3BzTgqtdQ8FgENVqFWazGeFwGGtra13fV5fLhaNHj7L7TC6Xw9raWtf2xuNxHDt2jNkM7u7uYm9vr+v8bm5uYmxsjM1azM7O4tatW9jf3++6F509e5ZdQ8FgEPF4vOucUKokACZn0LvPTE9Pw+FwMF9svtpLjbkulwtOp1N3Bogqqv3uxf3un7z1Zb97+mEs83kN0zgs7+RX7cE8xMEwJMVDHDoG8ZnMZDK4cuWKrqcnALaMxWJhHeBK38+VlRXcvn2bBSPwDTe3b98GAFa91WrKcTgcuHv3Lu7cuaPri3vkyBHdfVtcXEQqlVIlxEDH79Zms2F3dxeyLGNhYUF16nd5eRn/7b/9N83QhVwuh+9+97ts+r3VasFms3U1bZEN2tWrV3H//v0eJ4l2u4179+4xLezKykpXhZCkBisrK/jggw8AAPfu3dNdzze/+c2uMBHlwzqdTsPhcLDEuXw+31UtdblccLvdMBqNWF5exvr6epdul/Z1fX2dNXVpVXipmk3aajWQPpPWq9ZsRlIMaiDjK5YAWJU6lUqhUqmgUCj0kOd2u414PM6IrJpvLtCpatK5zOVyPcEcjUYD0WgUXq+X2f2podlsolwuw2w244MPPsDe3p7qcnt7e+zcrqysAIDq+aeYYDWvXqqGNptN/OxnP8P29rZqU+PS0hL+4i/+AsVikUVpKxsNa7UastksyuVyVyANvx4iyk+ePOmaSaBzUi6XcePGDXg8Hjx48KCHEBPW1taYPIgn3/z2RKNRlha5uroKs9mMmZkZNjio1WpYXV2FLMvIZrPIZDIQhI4fN1/dzWQyTN/ez3/78uXLKJfLSCaTTIpBkCQJyWQSoiji/PnzWFpa0q2oplKpge7Ft2/f7ppJIznP7du34fF44Pf7+64HwKEsM4gLEH89vIqK62F5J39WHsxDDA5BHqR8NoQqCoUCPB4P8vl8Xx/HrwpkWcbVq1c1NWHJZBIjIyPY3NxEKpXq8sYEnlfQgsEgW54sr3idbrvdRiQSwe/8zu/gT//0T3WjTL1eLzweD2vyUWvckSQJOzs7KJVKjCTypI6qeK+99hoSiYSqFjiVSiEUCuHq1au6zVskaaCQCyVqtRpyuRwjKXqYm5tjBEotorXdbuPp06eo1+tdVlN0rOnBTPICNX9ZvtmNuvnV1mO1WvEHf/AH+A//4T+wiqjy/ANg0+grKys9oRokaVhYWMDq6iqKxWKXrpI+r1KpsMa3wwDJPNT0y3xktyiKqtHLdH0qq5rKfT/o9vCkkSdrg67zd3/3d/Enf/InLDJaKWchjT0Rbl4Ly5MjXl6iNnAYNDaZfJ3Vqs70MzmL8AM0Aq8JD4VCyGazXXILoDMAIY9fSrPTA78dynMPdKqzb731Fvt+q93TPB4Pq37T4JRfhlxg/tbf+lv40z/9U0Zc6ff8fYYa7ehepHbtu91u/MN/+A+RyWR0mzH73YsjkQi2trYQj8dVvc4plpvvJ9C6pwPa+u1BlxkdHcXbb789ELF9VRXXQZ5pg2z3Ya1niE+HQfnasFI8xKFiEJ/Jra0tVjHU8vQkHS1VoXiyRjfuZDKJu3fvMl9csgPjb+o0FR8MBlGr1TSbckSxE4hBVUWlPMBoNKJYLGJ9fR1+v1/TxWJpaamvmwFVe7QCRcxms+pUtxqSySR8Pp/q78g3lizX1I41/R4Aq5YqQVppALrrqdfreO+991jlTknaiOCUy2VWNSUtN1Xu6dyQJlXPp7hfUttBQLpPvkKqJGT9oFbVfJHtAcC+AwTavkHx53/+58yVgb47BBo40vS8mhsHbzGn3Da97db7fbVaZf63ymNNQRK0j8qBBoFmHWi/lBIDANjf39fdFrVtVtt+GlhOT08DQJcun/T0ZMmmZ2tHAR8UOqJ1n6Hfm0wmTWs7knxMTU1pVlRzuVzfe/H29jYSiQQbFCmdLgCwUBaKzVZbDx1rPVeRQZYZ1AXoVVZcD8s7eejB/MXAkBQPcagYxGeSiI/eMpSQZjQaWfUKeN7U1Gg0UK/Xkc1mu3xxeUsp3hc3HA4jlUohlUqxRpl6vc4qNhaLhRECasIiEDGRZZk1D2m5WGhNiytBtk3kScw/ZOn4DAKadiVLLCI26XQaVqsVgUCgq+lMCT4cQIts8e/3Ww9pv3nJg5L0UKiC1Wplel86T3a7HbIss/VYrVZWHeUbDYlQHRZov9RkFvSZtF9qUcg8oT5MvCjRpiZSNZKvtFbj90NrmcMAvy1q5GDQz+OvW7Xv66BWe4OgXq+zgBGl3CcQCLDrlxoxlUSWrleSxOjdZ+iY+3w+TY9yauSlY6ZGoga5F9frdUaG1e6zdG+lmSat9dD35kWXGcQxQ5ZfberdYXknDz2YvxgYkuIhDhWD+Ezy3rday/DTvGoPTnrgUOIbERVlgwu9Pzo6Cp/Pp9kow/v88oluAJhfLj3gAGi6WCgfxFpT6B6PB4lEgk2H8hVnURQHDnAwmUzsQUyJbfRAczgc7DOVRI5wUOKgtx5BELqSxfjBBH8eqWIZCAR00+Fo4EK/p/XQcocJcqHQaiKj40zVQK1p/36Ryq8aLperizypaTJ56C2jVv3XW5cWaHCk/J7R94sGILSsXqWcl5gQ6Pt6UPSThWxsbLB4bwIF1tjtdta0qfQppvtdu92G1+sd6D7Tz6N8EM/jQe7F/DlVu88Czwcd/e7pgL4bxiDLDOJB/KorroflnTz0YP5i4HCinIYY4hnIZzKfz6tWucjTMxAIoFwu95AQIok+n48RXnoo8P/SiHt0dJQ9TPgmKOV7zWazq1FmdnYWMzMzMJvN7H0iRmrbTXpTfhmyi6IHGYCe1CxaVrnOU6dOoVAosClgm80GURSRTqdRKBSY00U/UFWdrLlsNhuretOUqtls7tHu0rE+SNQxkRUiqPy/5FN7+fLlrgYj4PnDVflw1wOFXdAUM5FhQRCYzEKr4vJpQOslzTC9qAJuNBpx/PhxRiJImkPVcFEUMTk5eWjbw28X7Tc/CzIofu7nfo6RPTon9FIbAGhVpmmgpSefGfR8+Hw+5jVN31Heeo236uNnf5SknAgyf09QDqAGAf9ZfAWbHwwAYINe/txLksSq8RQA4nK54PP54PV64fP54HK50Gg04Pf7cfr06YHuM36/H+Vymf1MxFqWZVQqFfj9/r6ex4PciyORCLvPqi1D/QSRSER3PeRR/qLLBIPBvh7Eg1RcW63WoVVcBzmOg2z3Ya1niJeLISke4lOBpgLJ/5N/eC4sLDBdMFVWarUakskk7HY7Tpw4gUuXLjF/VX6ZXC4Hi8WC8+fPMw1bpVJBpVJhXfuVSoU12lAVj39A0ouvmj558oRNt5F8gqbbKpUK1tbW4Pf7GZlWEnBRFOF2u+FwOGAwGJBOp5HJZJDL5ZDJZJBOp2EwGJizgh68Xi/S6TTcbjf8fj/TWUqSBL/fD7fbjWw225dkGI1GluZG1QUiorxTx6lTp1g1mRKxSFpiMBiYVrIfqOue1sP/K4oiLl68yPx8Cfz5IFitVjYFTLrHdDrNAlZKpRJsNlvXetQGF4dJink3DbVrSBAEnDt3DlNTU0zTycuApqamcOHChb7bpCU/UYIGDcrBB+0/ETM9kBSFLPm0QP7AtP88cab9d7vdrCmWnwGg5UVRhMfj0YzlJphMJpw5cwY2m43Zx9GrXq/DZrPhwoULXcSY33+gc4/xeDysUY1INX1XqUpst9vZMnrHyO12d+07fe/5fSd5BG0PP6gQBAG1Wg2nTp2CxWJhg1FJklCv15HP52GxWLC4uMhIv959JhgM4syZM+z+WC6XUavV2PeF1kXX0ovci6kpzWAwoFarsWu60Wgwn/FQKMTWE4/HkUgk2Csej7N7+okTJ/re9/stM4gHMV9xVYOaR7Pa8RkUgx7Hftt9WOsZ4uViKJ8Y4sA4DJ9JaoIgD+JKpQKDwYBQKITFxUXMzc0hkUgglUr1EBQiwiMjI8z6yOFwoFwud02bkhwBAEvT0mqQKxQK8Pl8sNls2N/f75n+HBkZYQ9y0g0rq1ZURQoGg6pVcNom8qsNh8OqmmJ6kPbrnvd6vcjlcqx5Tbk9RISPHTuGQqGAtbW1ngfCzMwMjh49io2NDd2HBQ1CaOpSCb/fjzNnzgAAi7AmnTe/DrPZDJfLBa/Xi7W1tZ5GMmoKDAaDyGQycLlcqFQqPQ1JFIJBdlZaIJLWbxlRFGGxWLqavAika5YkCW+88QZu3LjRde2HQiG88cYbsNlssFqtmlVYum6JDGmBqq56y1D1jhoSlXA4HDh27BicTmdf8myxWOD3+1nTqfKcUQyymjyIQDMVXq8X2WxWddtJrjQxMYG7d+9qVkqDwSD8fj/T6Wttbz6f12y2lOVOiEo4HGbBP0pQEBDQOV7kLc3LGyKRCIvcJltHspMTBIHZHpId5MWLF3Hjxg02eyOKHb/fixcvYn5+HrlcDuFwGBaLBbFYTPU+4/F4MDc3BwC66wJe/F4cDAaxt7fHChK86wfFdh87dgxHjx5FOp3G+vp6j5cx7wk9iL/wi3oQHyT17rAcKg7LO/nz6sE8xHMMSfEQB8KgXb+D+EzOz89jdnZWNa2Or4gR8aWbNV95JDsxengqR9lUfaIIVq0GObvdzhozLly4wNwRLBYLIpEIstkssy8iPaya/RvQIeB65CGVSmF8fFx3+k8QBGQyGd1zQQ0/amSW12eura1hd3dXVcO5u7vL0rz0IMudbn96+BNJpAprq9XCRx99hHPnzrHtV061k+7YZDKhVqt1bTtV6qhaRx3+NA1NHsGUCgeA2XHpEV6a4tZbhrZfq/JEJKhQKGB/fx82mw3Hjx9n21yv17G6uopjx471rQQbjcYuaYkaeE2tFlqtFgKBADKZDEuwo/NBMyE2mw2jo6NIJpO668pms5iamkIsFoPX6+2RuTSbTTagVCb50fkjHfj4+Di7bpXXmizL8Hg8+OCDD1AsFlW3pVgs4v3332dyFrKL4681uj601sGva2xsjGlnlSAJDn3vjxw5glgsxr73IyMjzNmG0iDJUo5A1WBBEFgqXCQSwcjICLtfybKMRCLBHGeCwSCazSbeeOMNZDIZ9nl+vx+ZTIbZPfZbF9Dx/C2Xy7BarbBarZAkCXt7ewe6F4fDYTx48ABms7ln38ja7smTJ7pe8B6PB/Pz8wPd9wf1INYCVVz7pd4N4tF8UGL8Itt92OsZ4uVgSIqHGBiyfLCuX62uaB6iKGJqaqrn/Xw+j2q1iqmpqR5vYafTyWJQSYfIT+nyoPdpqkov5vmtt97C3bt3sbq6ykgtNdKEQiFMTExgb28PNpuN+Y5SRY9spog86YEqbhReoVa5ppAHPQyimWu323jy5Amazaam5+mTJ0/6rgcAi9Ol6WaCLHficTc3N1kYCUVaKy3y6FyUy+Wu+Fp+FkCSJGQyGfYQo3AVHrVarYt4a1VmqTFJDzQ1zm8Df4xkWWYkRXnt0/4nk0lsb2+z6rhWg1ir1er78JNluW/DXqPRYNeHyWSC0+nsIipEXpeXl/uuq9lsMiJL1yCtiwYvpVKp53pUftcoeIMGicr9B4BMJtM106DWiLq3tweXy8VIMA2y6LiIooh8Pj/Qfu3s7Og2h2azWXzta19DoVBghJQIViaTYQTr6dOnaDQasFqtPddHrVaD2WxGMplEpVLpsS6j62N5eRlvv/02I3SZTKaL0NHnUSBRv3XJssyivjOZTNc9pNFoDHQvJpJNqY18FZyIdjwex+bmJur1eo+vPHmk37p1C7Ozs+yc9bvvD7KMHgapgF+9evXQHSpedLsPez1DHD6GpHiIgfEqu37pwR4IBOD1enskBlS5JEshIsRqD1mqCA1SDdW6QQqCwKyWJiYmkE6nkc/nWaOax+NBIBDA1tZW19+oESOg4yGcSCTY/vCV61wu11cLeRBUKpWehzlwcL/fcrms6VVKGmFK8qNpZWU1vVQqMU0zaQP5c0dV20ajwaar1cJCKpUKk8x4vV6USqUeAk5/TxVqtfPPV6gJWpX3/f19jI+Pa177iUSCa1gS0JRFNGFAUxbRghENWURLMqEpG1CXBDSe/b4ti5AgoA0BEkS0GyIkGZAgQH72kgDIEGCABKfQgFOoI75WxPHRGcj1PKrVAiSpOxWQ4paV+6r2cy6Xw9TUFPL5vGoITDwe7+tUQk1nXq9XlWAZjUbk8/muz1c7F0CHYHu9XqZZp/VQDDifGKm3X+VymUls1ORFVK3TI1hGo5GRRjXfYLreKOK8372xH6GjeGu9+yzFP9P1rbyHiKKIaDQ6kHduNBplOlzyEKZ9azQaWF1dRaFQ0PWVz2QyiEajqgWOlwW9iusgHs1DT+Ah1DAkxUMMjFfps6i0r1G6OlD6WqlUYg8mIl4EqvJR1C9pb5UPNYvFArvdzirEWtHL29vbMBgMKJVKPRVe2maeOBDJ40EPZqpyqekqAfSdPj8ISHurhsP0+6XjSWRUrZpusVjQarVQLpdZVUl5jOh4zszMYHNzkzUXKaO55+fn8fHHHzONMV/ttVqtrMsf6La/4gc/tM30uWq+vLyHMX/ty7KMbF3GTlHCdkHA8r4Ne5U5lCUTmjBAxkueDs08eyEAs+BD2FTDiLmJaUnAKbsJDrkKk/G5y4fe9SjLcpdPLf83Sn2znnUZOXVQfDatg9Y/qAUg6fNtNpuqZZ9yG7X2i777JLnhzz05FBSLRd0QjHg8zogTyXj4z6bmUkr+U4Py3qhH6Ejm1c9fOJfLsX4K5ewXNeWpabJ5UHVfkqSu9dCxKpfLLHpcb3sqlcqhpUseBFoV16En8BCfFkNSPMTAeJU+i4M2U9A0rdlsZgSYQOSXbo52ux2JRILJF6jKQd3ghUJBN22Jqpybm5vsM4lIlctlbG5uIhgMdlWxtEB2ciQNodQxqvL10xMrt4+Oi9p7/DFQIxhaVVQlzGYziwtWng+Sj1Bjk9PpRD6f7wk6cLvdKBaLKBaLXR39PFkhV4zTp08jFArh+vXrLPSAbzaanZ3Fo0ePkM/nmYyC1kPyApJgSJLURYxpn3kfWv595XEUBAGC0YxPEg1slEU8yUrYyLdR7prBt+GzQkMWEW3YEW0Ad0rAf9sDrKINx4NWSM1J+IUKAoYqvEINBqH3XNtsNuzs7DD7OzpWxWKRNcES+Mq78niRtAFAlytMs9lEPp9n7hWAvq8xNT26XK6uAR1da06nk2n4+d+pfXdpu9SgPNdqBIvkOw6HA4VCQfWaJk9xvUAe5b1R7/PoPqu1Ll5zDaDLq53CbWgWjfZTjYBTJZ6CdJTHjtbTz6d4EN/kV4mhJ/AQnxZDUjzEwDhI1++LYtBmilwuxx7YJKvgp+LJ6N5ut2N/f78rilUUOxG3+/v7mJmZYc1WsVisR+cbCATQarWYpQ+RMKpQ84lntJ1aVTGTyQS32w2Px6MpDRlU0qBGiOlnqvJZLBZUKhVWNeLJA1W6qKOcjguB3jMYDFhYWMD6+joqlUpP5VaWZczMzGBychLb29vY3NxU9UUulUqYmZlhjY/k9sCfMwAYGRmB1WrF1tYWO3+0jMFgwNbWFqanp5mmUS3oxGAwMLkHv39KOJ1ONJvNrqpRUxaRl63ISVZkZTuSsgulNSN8iCGAPMJCAfMowWBoQ4QMwzOxgwgJBkgwog0TWjChDaPQhgltAPIzKURHJiEDkCHCgDaM3Mv0TEjRhgEtGNCAES0Y0IIRLVmEBHoJ7CVDRPvZezIEtJ/93EiZUEfnlW6bsC9bYRYkuIQGvGIdXrGOiM8Fh6GGWCaNpiSg2pTRlgUIz9wyRFGE3+9nAwz+euOvO5PJBKvVinK5zCqzRKANBgOq1SocDoemOwd/3k6cOIG1tTXWRMZrnGVZxuzsLNbX13uaWvnt8Xg8XbZ5ajIdk8nELOskSVJt+uXvexMTE2xwbDab4XQ6kU6nMTY2BlmWsbW1xeQ9/P1DFEXMzMwMdG+kz9vc3GQzLLQum83G3FmKxSKq1WpP6h1VRylMRM+BgfyWqcqtlDu1Wi1YrVZYLBYUCgVNKRP1XHxe8CqfVUN8uTAkxUMMjEGJ6kEaF/QwiH2N0WiE0+lkDyq6aVNzFPA8fY7IGz0sKASi3W6j2Wyi3W6zxhylRq9SqcBsNqNarcLj8fSkVlFlolwuY3p6Gqurq5r7NT8/31V5UZOGEPHWk1EYjUbY7fauLnxl5c7r9SIcDuPx48fsfeUys7OzAMBsspQaXEHoePReunSJeWpSo5ssd8IVxsbGcOHCBYiiyLrZ6/U684ptNBrIZrPMzcNqteLKlSuqzVLkZfvxxx9jb28PRqMRLperi4Tv7e3hxo0bPR7IPOh3J06cwJ07d3oGDpIM1GDEaCCM2M5TiM08rKjCgQo8KCMo5BEScggKeQSEAhzCl3SqNffspUCrbUC73SHmcs3cIeiywAh6EyY0YXz2MkGAA4LkQF5uoVYyoCmY0RRMaMid5SSDBV77KASvHWtbe2jCyIh/Gwbg2X1jfn6eXWuJRKJLAiCKIiKREcydfh3plhmfJB+gJhtRkw2oy0bUYURdNkIAcNQUhEEuol3LwJKvwiG2YBVaMAgyk3OEQiHYbDasrKwwe0g+7XJxcRHz8/NYWFhALBbDyspK1/XG+/hmMhk8fPgQ9XodDoeDNZqm02lmDzfIvVEQhJ7vEDnoZDIZWCwWnDx5EvF4vCtQhFLzqtUqq0KXy2UsLS1pOjCcPHkSPp8P6XS6Z4BBThuBQAALCwu4du0a63eg+z4NkHnf5M8DXvWzaogvD4akeIgD4bB9FrWqM/zn6dnXeL1eHD9+HI8fP2aNIbyLgMlkwsTEBPb399kUIVWPqIojyzJ72PCkl6rMVqsV+XwegtCxg6NgCdIzEzEHwPSvgUCAafUI5FFMxygWiyEQCCAWi6FWq8FqtWJkZITdtCmAQ8vvl7S0ZF/G62EpmY1vVtNaJpvN4jvf+Q6A58SYJ8Tnz5/Ht7/9bQDAN77xDTx+/Jg5LZjNZkxNTeHkyZMIhUKsm52mlMmz1mAwsJTCeDyOSqXCtkm5byaTCXt7e4jFYuzBTPtITU/lchnb29uoVqtsqp/W1Zn2NaHZllBKbiNd28NIexsOqQQPCvAJBQSQR1DIw48ixM1nnz+8G3aBKtcWAJD0nVAAAI1nL4JSHSEB2Nf+85bcqZlLK1bIG068AzNKTRHFlgFl2Yy6bEJdMKEWM+D+g/8PzLIRX4MBTRhRhwkNGNGUjWg8+39tz4yabEYNZhRhRhJm1GQHGjBBggEwmOCIu3D1//cxUokYhLYMi8EPkyjDJEiQig18uHsTM8eyMJqtWN01oVDyodGW0JY7jY8QDTAVLfjz3BNUKyXUKqMQZQntUgOiLMEommG3eGGBiN0bO1hIW2A1G+CyGBF0WRB0WhByWeAwd2ve6TtEldhqtQpRFJnUi5rpDAYDi5amexp5ZRsMBkSjUVQqFQQCAZRKJRQKBZjNZgQCAaTTaezu7sLr9WJ/f78ntEQQOs3FPp8P586dg91ux82bN5FOp9l9PxgM4tKlS8w3eVBoyTkOCr31DD2Bh/g0GD4GhjgwDstnsV91hqClvaPfXbhwAel0Gnt7e132XmS+Pzk5iY2NDabHU4LIMFWZY7FYj02Yw+Fgv8/lcj2BEkRkBaGTwEdeo2oyjHQ6jbNnz2Jpaamn8rS+vo5wOIw333wT0WiUVbHViCORfpvNpurZarPZmOSDSCpZ09HD02Qysc5xWreaplZ5zCkQguyyCORQUqvVWBgCoVarIRKJYG9vD+l0WlWTSj+vbmwjWTegAisyySZKkgVl2YmKbEYDRhhELwRZgiBLMKOOKSGOaSGNCSQwI8RwtLWHGSEGS7sFpAEIAAZLsx7iMwKRcLTqQCsPO4CuLD66zA6zIFntvMqyBWXYUGpbUYEVZVhRlG0o1u0o3rejCBvmZTsKcCAvO5CDAznZhZzsQLZiRCWbfbaBpFHl9OWkhErUgaVPVDfDahIRdFow5rVhxGlEM5vBqCuIMbcJAV8TNqENk+l5uE8sFuv8ndXK7jE0y9NsNpk9ZCwWgyzLePToEZMqCYLA9P/JZJLJJJRNebQegt/vx/T0NCss0IC4X2KiEocVqDHIeoaewEMcFENSPMSngh5RHQQrKyu4cuUKm2qkab1kMokrV64AwIGqD2azGXa7vavSQVUUelhQdzTvPkH2YABYWh3fXU5/W6lU4HK5IAgC8yGmago1+BWLRRaksL+/j3a7zazQyOR/Z2cHwWAQjx8/RiKRUNXdkjm/xWJBIpHonfZ/1tjn9/shyzILD+Ar7CQXqVQqrNGMGttIg80HaPz4xz/uSrXjG9Hu3r0LoJNERWb4Xq+XnbNYLIZCoYBLly6x8AAivTza7Tb29/dZ13e53upMfcP0TL9rQ1ayIlexoST3NsBYUce8sIM5MYpj2MNxcRfHhD1MCQnV5rEhhhgUDqEOB+oIf0qu1JANyMCNtPzsxf7vQRIeJGQv4rIPCdmLLFyAwpmk1pQQzVYRzVJF3ohOab0j2bEaBITsMsL2OkI2Aea6ALcowCEZYUDvd5/kWZVKBYVCgblw0L2oUqlgZ2cHXq+XuXCoge5Z6+vrTIYRDofZd58kHIMGYQwa/nSY63nRZ9UQXy0MSfEQrxySJOHWrVs9ZvBk36U0g9eDLMvMyH5hYUG1CYaM7alphHcaMBqNqNVqLMSDd0TgQdpALesnfrlMJtPzWdS4QppcnhArO/klScLt27fhcDi6SCr/mUTESZ5AvsZ8Ex2FZFC1WWk3Rh3soigyRw0luaa/uXfvHhwOR18zfNJWdrYRqMKEjGRDRrIjI9tRlMyoVU2oyZ3mMS0EkMcZcQMnhS2cELdwUtjCjBD70pNfCQIkwQhBEAG5DYOsncQ3xOcHZqGNEWQxIvR3nmnIBiTgQ0z2Iyb7sc/9uy8HEJWDSMEDmSuJ19rATlHCDpsQcjx7+WEQZPhMLfiMLQTMbQQtElzVAkLtGlq1su69iO5VvCUffy9qtVrsu/2iQRh0r/68rGeIIdQwJMVDvHJEo1FkMplDMYOn6XqTycT0c8p0uGw2yxrrtMz3jUYjKpUKALDmMQKRV2ou09LCkn0RX43lZRi0f9TUR3/Huy7Q5/OOFEqrOdLQkkMFH3/L2zLxvrtqDXv0vpo1GX8+2u022u02lpeXcfToUda9TYMPsqXai6fwIH4fd+ojSEoOZCQ7alD3CeVhQw2nhU2cFddwTlzDWWENk6J+NPHLRksWUYATJdhREeyowYoy7CjDjqroQBkOVAVbpwkNAlptmQVtPHd/MHTCOJ65RZBvsc1qgc/rhtlkgtRuoSUBMJggGkyoP7OSCwaDCIfDMJtMaNSraDfrMIuAxWxEo15DJpXC3l4UUrsNSO2umA8BgFEEjKKAibEw9rc3njlctJhEoSkLKMsmVGQjqrIRbVmACS2YhSbMaMOM5rMXvdeChd4TWrChDisasKEBq1CHHXXY0ID4JR+0HAbMQhsTSGFCSGkuU5eN2JMD2JOD2JWD2JFD2JHD7N8kR5rbsoBUw4RUw4TVCq3BDQBwCXUEDHWE2k1ETHWMmGpwGtps8NtqtZh0jS8W0L90v9rb22P3Yl7fb7FYBg7COKzwp1cZIjXEVw9DUjzEKwclZh2GGTylOpXLZRbEwbtGkK+u0+lkxFdpvk8NLfR5ajdaqs7y1VQlkSW3AyKtajHNyrAMJSGmdQEduQENHJRWSZIksSYzWZZ7LMeoiY7Wowe+Yq0G2v9arYZ8Po/9/f1OiIEE7LedSMKDODyIVkxoy3UAY5qfJULCcWEX58SnOCc8xeviGuaEnVdWAZZkAUl4kJS9yMKNAlyowIaaYENTsKAGO0pwoCpYmRsCD2Ulna1X7A390EIn6c6EYrHWY6VltVpZw6iePWC+bUZRtkOCBFnl2AkQYBSNaFTdSAnjvRuh2LWabEBCciIuuZCQnEhJdkgHFu/KsKAJB2qwC3XYUYMDNdgEjkCjAavQgBV1WNCERWg9J99oPnuv+Zx0PyPcdtRhFpowPbOs4wm+STi8oJvPCyxCC0eEOI4grvr7umxCVA5iWw6z144cxrYcwbYcRgUdR5uibEGxZcEmN+HgFJsIGyqImGoIogivnIcBvT7P/CwVXZNq16Pf79eVYLBtPqRAjWEwxxAvE0NSPMQrh9PphMFgOBQzeLPZzCQTpPkFnic7FYtFFhdNjhCksTMYDHC73fB6vYjHOw8fIrtKAsxXU2X5eUIc/yAhss0nqak1kRkMBk0iqkaQeRLO65h5v1U1bTJ1ow9K1njXCeX7hI2NDSSbZqy2xrFSd6Mma99CDGjjmLCH08IGTolbOCVu4rSwAaegn7L1oqjLJuwgjKgcxr4cQkbwoQo76oIVLcEMCL2yDWXDnxa0lhn0GBPIzYQfXDUaDeZ4AoDZAxqNRkbEyR6QrjG97aEB2iCwG2QcM1cw3Sp0ZlBkEUnJgZxsQ0WwoiyZUJRMz6rLZo20PgF1mFGHGRl+szQOjUmQYBeasIstOMQWnEYJLqMMU7sCY7MCm9CETWjCghZEvVlwmfyh2wrP5xZMHXfnThVcbMMkd4zkTHIDpmdk3GpowSjVYZYbMAutzvtyAxbUYUUdVtRg0NqJzwgWoYljwj6Oadh5JGU3tuUItp6R5E1pBFtyBJtyBFnJhZLkwXrTAyACERKCYgUjxjLGjBVEDBVYxXbX7BE5WWjZVXq93r5BGIcVqDEM5hjiZWJIiod45ZiYmIDf70cymTw0M3g97RjZgSUSiS5pA1V0S6USwuEwUqlUVzVWqfMlEkNNa1qfRZVu6gCndVL1mFwh+N/x+0/7Q24Zau4TsiyzyGTaNiUGjdQd5G9aohn3Kx48LLuQluw9vzehhTkhitPiBs4I6zgtbmBB2IFVUD9Oh4EqrEjCjzR8SMGPFPxIIIC84IYsdDce6ZFWkr5onVMAXTIWrXNG51jvuIuiyMJUtJw3SFufzWZhMBhQqVQ4q7mOF63H4+mq5KlpzmVZxtGjR9mATw8ej4e5D7RaLRghYcxYwpSxM9MSCoWwu9sJk5BkoAozSpIZlWf/lmUTKrBABp5VfdswC89eaMEstGFBC1axjWOTI6jk0gj6PD0zILlcDi6XC3t7ha7jwu9bzzE1GAAY0JKNaAsCGugOnaFQnc7KwP6lYz06OsqcUpSNugaDAZFwGMVsAq1iChbUYZGqsKEGO6qwC3XYUIPH1ILH2IShnoVTqMHaLsIiv9zBnx5CQgEhoYAL6PVLL8h2bD4jzJvyCDalEWzII9hsjOBBIwxAgF+sYsxYxqhYwHFPh4im0+muHhAarOVyOTidTrjdbt1tOqxAjWEwxxAvE0NSPEQPDstDUguiKGJxcRFXrlx5YTP4RqPBvHjL5XJPowhFtHq9Xjx9+pS5XdjtdtYUR2b429vbLMJVTeLgcDhgMBiQz+d7XBro/y6Xixntq6XSmc1meDwemM1m1pBGD23+M4PBIJxOJ7a2tno0zrS83+9HKtXRJepNfQ6CYDDI1vV8HUBSdmC5FcKmFEBbfqaLhoRjwt4zCcTaMwK8DYvwcprC2hCRQAAJIYwYQojJQSQQhD04CYPRqEv6IpEI2u22qhsGwefzod1us3hiNbhcLkxMTODRo0eq50wQBJw4cQLZbBbxeLxrIMQPqgKBAACwBkj6W947mkIfqEGSzj1JWMgSSynrUYLXnfdDJBJhCWq8S0Gr1YLBYMCpU6cQCoVw9+5diJDhQANOYxOyXEInd0PA7OwsdnZ2VGVDBJvNhnPzR3H/flHzuz8zM8NsEWm/9cDPmvDLC4LAgn2ooZQ/H+T7e+bMGZRKJRZnTOujquiZ117DRx99hFy5gXy7DYPR0OU6YzAYEAlF8MYbb+DatWvsvmkSZViaeZjqGXgNNZyfG4etlcfO49twCWW4pDwc7TwsUkVv9w4dbqGC14QNvIaNnt/lZXuHIMsj2GiPYqM1iquJEdTyVvjMo5jMVHHE1YbDLLIgHZvNBqPRiEKhoKvhFYTDCdQ4rPV8GrzsZ+MQnz2GpHiILhyWh2Q/kN0a+RTTlHAoFOrxKda7ERHpJScGPrzDbDZjZGQEdrsd+XwebrcbzWYTxWIR5XIZBoOBWYtREh2FgPAgycPY2Biy2SwLk+DJiCAILByEpBtqD3ODwQCLxYLx8XGsra0hl8t1kXDaJnpQ7+/vM+s0HhaLBTabjaVZaTX+KfdFC6FQCM1mE/l8Hk1ZxHrbj+VWCBnZAT8KeEf8CK8bnuKssIbXxHW4hAHCHD4FJAjImceRskxjWwoh2g5hr+lCkyu+GoydYyjJMnxcSIgSPp8P4XAYTqcTsixrhqkcPXoUjx49YkRQCYoDf/PNN2E2m/Hxxx/3hKC8/vrr+Na3voWrV692XYsEs9kMn8/H0g6JfCmvIVEUWWyvKIo9ceHkL00EjhoslaAq56BoNpvweDyq2002g9/61rcAQHP/jx49iv39fdjtdta0ysNutzMv8nfffVfzu0/3HTVZEH0e7bvyugfQ9f33+XzPKs97PcdxbGwMbrcb4+PjuHjxIq5fv860+aIowul04uLFixgbG8ONGzfYYJfXgVM6ZrPZxOjoKC5evIhr1651+XC73ZM4/tZb5KlOIwAA6T5JREFUGFlcRCwWw0/2/ldGuKvVKtCswCtWEDDWYK0n4Gxl4ZJycMsFuOU8HK0sDDj4rM+ngUeo4JywjnNY7/H1TtY9WK+NYiM3gqQYQsUUhNEZwsTkUYjyc02x3v36sAI1Potgjlf1bBzis8WQFA/BcFgekoNifn4es7Ozuol2/W5ENEW2u7vLqnd0A6auaUqfo6AKvlEEAJNOiKLY5WvM63nNZnNXHLPa1DfQedhms1lVUgAAlUoF6XQab775ZlczIJ/W5nQ6MT4+jps3b+o2v1HEK9BbTVPKNvTIsclkQrVWh2FkAZsGGfv72zgjPMWvGX6AC+ITHBN1YsheEDnBg6gcwR5GEMUoMpZJBEcnYTAYOufELEKQKxA4ImIwGNi1WS6XWYIgRXaThMVkMqFSqeDkyZNIJpMol8vMmo6CT3w+H3w+H6uIqpEw0r9nMhmEw2G4XC4UCgW2PS6Xi0X4LiwsYHV1VVXjbTabMTY2hvv377M0QqV8gAJWiIgrQfIii8Wie16p4uvz+foOjgwGA5tpUUpI6BhHo1Hk83nd/Xc6nWi32z0hEARKbXQ6nZiamtL87m9vb+vKWcg+jJxltMJtyJKrWCx2DULoO10sFmG321EqlbC1tQWDwcASLuk629raYpV7fpDBfy/p+kgkErh9+3bXjAMRxNu3b+PIkSOwWq3w+XyIRqNIp9NsuSwM2IADdvsp+EN+dnwrlQokNOFCCT7k4RdLCBkrcLXTcLWz8CEPN/o3JB8GQkIeISGPS+Jy5402gDzQzgmICUEUt4+gGjyKhtGLRNuJvCGEhi2EYCj8UgI1XmUwx6t+Ng7x2WFIiocA8Nl5P4qiqGm7xt+ILBYLLBYLZFnuuhEFAgHE43H20FfarZHPptVqZZ6cRqORaUTL5TIzsTebzSzBSWlvZrVakcvlGGlRklBJklCr1WAymfq6ZhSLRfh8vq5Kh5Lwt9tt7O7uak5FUzqVlt0a7QNVFtVIkSQDOckCu1xBfvXHOIWn+I64Cr/55TxkS7AjLoQRN4yj6J7HJzkLSrBD4Lqo5KaMnZ0dpicncsAPlCjS1m63o16vQ5IkuFwuWK3WrnNWLBbZOU6n06yqzvZfkpBOpxEOh1nErRqIED19+hTLy8uswYcqy6VSCR9++CFbnvSpSslLPB7vSg6kaj7bd645c5AOfK3ue359b775Jj7++GN2LJSgGY5CoaA6kCNnk729PXz00Ue4c+cOms0mrFYrI9u0/1//+tfZoEMNpMUfG+u4k2h998fHx9lx0Rp8Go1GTE1NdcmdeBmF0WjE+Pg4tra2WNojf6wpbMdqtWJlZQV7e3swGo1wuVxMh1yv17G3t9d1npXrabVaKBaLcDqd+OlPf9ojQSKkUil873vfw9//+3+fheqogZrW2u02k74IogElwYui7MGWLAPNzkC2KTVhMplgFtpwywV45Sx8cg5eOQuvlIVXzsIvFGGQX56uHwAMgoxxJIFiEijeAgDMPftdSzChZA6jencMlSMXYJ86CwSOQwgch9d7sCQ8NQjCyw/m+KyejUN8NhiS4iEAfP68H+lGROEURGpEUYTNZkO9Xsfy8jImJyeRy+UYGeandU0mE2u84HWFfAWKfs5ms6wxjrdNI2szSZJQKBQ0yQVtc6lU6quBlCQJm5ubuHDhgmalY2NjQ/PBSdCqyCk/i28QhNyGS8piUt7BSWEdC8J2p7P+kCOQc3AhLowgLo4gJoQRF0dQlB1otdswCAZYGhaU5TKAXj2sLMvIZDKsAqhF/Imctdtt5HI5NpWuJJv37t1Dq9VCJBJRbexaX1/va1vXbrfx9OlTRpD5QQZVCq9du8aWJecUXutdqVSwvLzMrqdms9kziNOScGhtE0FNTy5JEvb39zEyMsIInbJBUBAEeDwe7O3t6X5WqVTCxx9/jGazCbvdztZjNpuZ1eHPfvazvnKddruNaDSKmZkZzWUKhUKXy4qSzNL1TD7ZFEzBa4FpkMoPqvhjRMvRdUP2YjzppoEVSZj01kOfxb9PoPOxt7eHeDzOIpqB3vMBdIoBSh9zfp3896HdbqNpNCIjBpBBgP2ezsOv/9r/hNNTASC7AaTXgMz6s9cGkFkDmi9Xz2yUm/DWd+Gt7wLZ28Bd7pc2PxA4/ux1DAjOdv7vPwqYbJrrfNX4vD0bh3i5GJLiIQB0ez9SpYo3aH/V3o/5fJ5NrSptgMrlMkRRRDQaZQ8I+r2yYtRut1GtVnua5/gHKD1g+AowPXgBMI0l/zBSqwiQZGEQrK+v48KFC12knTq4BUHA7u5uX3I9EGQZ5so+Rtv7OCat4azwFFbxcCtHBdGDvP0oCs6jcBz/Gr53axOFlqkrEKCzKc+lHnrNWAC6glTUQA9+QRBYUxq5iVBDGum38/k883tWNm/a7fYuMtNvm+iaUSMzVJEkaYNytsFsNqNWqzFtLW07r089yDVE3wvS3vLT/uRSsb29DavViqmpKezv76tq7gcZXNH+88loBJIXlctlRhyVzX/0fZQkCVtbW7qkOJVKQZZleDwe5ivObzM1yebzeUxPTyOfz3dJouh7lE6n0Wg0YLFYWHCPUoJDDbF+v1+V8NhsNuRyOSbZUGq4aYCj1oeghr/6q79Co9FQdSqh+xcN2vkGS36b6D1yp6Frn0D3OpfLhRMnTwEGA+AZB2be7t4YWQaKsQ45Tq89/ze9BjmzDqH9ku/11QwQvdV5dUEAPJMdohw4/owsHwMCs4BnAhAPeQTfB0Nf5K8WhqR4CADPvR/z+TybSlUmw71K78darcYaoxwOR08Fp1wuI5vNsqlYZQc6QflwUqtk8Z3kyoQ34LnVVjfBezHCKssyVlZWWLMRPdj8fj8WFxc//Q1WluFDFkEpgUns4qSwjkD7WTbsQbMYVNAy2FFwzSJlmULCPI2SZx5mf0fqkE6nMeGYQMuchcXQVm2UIhI3aKAIQa3y1m63GcGQJKmL8JFdFDWq6T3Q9KzYlNtERJ/fHkr94/Xd5XK5R5duMpkgSRK8Xi8KhQKTttD1R9IDh8Mx8Pkn4q8k4DxRJp9uqqjTteb1ehEMBvH06dOBPktZteWhnF1Ranj5Cm4/0LG12WxwuVyo1+tsm+n6ITLodrtZIyA/iJdlmbmS8ISY9uMg318i+kRa+WNA10S/65lAM0lazjpKrTIdP+W/7XYbR44cwfr6etfAivbNZDLha1/7mn6zpSAA7tHOS0GYBUkCCtFnJPkppPQaynvLkFNP4ajuvuTGPxnIb3de6x92/8pg6VSSg1Rhnn1OnO0vLsdQw9AX+auFISkeAkDH+9Fms2FlZYVpaOlmXyx27JPm5+cP3ftRq1OZuryJNPAg0lOv1+H3+1nFRFmZ5Keo1brU+W0gIqz2cOuX+PZpIAgCrly5gnq9DqPRCLPZDEmSkEwmceXKlb7x1mzbZAFl2YApOYrXsIzTwlMEhcKhEGAAqNpGIUxdhjSxiGtRCW3fMVisz6c2qfWQkgPdbndXMhtV30m7ajQaUSqVBnbF4KtlPMEEnp9fmv7myQpVYemaaDabMJvNPQSLSPVBwW+PElR55QdpNOAyGAxYWFjA7u4uEolEjwxjdHQUPp8PV69e7bsNROh5os4TbLPZjOnpaezt7bEgELfbzb7X1WoVOzs7B7quqWKmRsKJCCsbXvlBp8FgwPT0NDsmy8vLKBQKcLvdWFhYgMFgQCAQgM1mQ7VaZVpx/riT1zdVjM1mc892NhoNOBwOpFIp1UEPHSNqeKxWqxBFsafptVqtsmuZGu6U1X26v/CuHVrw+/1MEqYcxPPSL/46V7rTEE6fPo0jR46ouma8+eabWFxc7NrfAzWkiSLgneq8jn0TIgAX/a7VgJzdQGzjE+yvPUB57wmshQ1MYQ8RIdf3GLwQ2nUgudR5KWHzcSSZI8z+o4DJ2rv8gOB9kYPBIBqNBrtGzGbz0Bf5S4YhKR6iB2ok9GVAz1mC3APooaesFLZaLZjNZgSDQRw7dgzLy8uo1Wowm81dlUNBEDA5OYn19XXdbRmkgvVpwjC0kMvlmFctH85ABCMej/foS+uyAWnJjpTkQEEyYUHYwDfFu3hX/Ahu8cUt0pqCGZXAaZgmz6MRPgth6jLcY7OM1FiuXu0Y5lusPeeDHgwLCwu4e/cu4vE4I2v0sCZZA1X/BgFPPNWqxVSBVHMgIFsyu93OqvFq4QwejweJRKLvttC+8BVHfjqbJAv879Xwxhtv4Pjx41haWsL29jYjKlNTUzhx4gR8Ph9u3rzZN0xkbm4OKysrzJ+bPzYGgwHHjh3D5OQkfvrTn6JarWoGL6iRSjVYLBbU63XU6/WeKjAANiDSIqKCICAYDGJqagq3bt3CjRs3usjc+++/j8uXL+PixYuYmZlh+8YP0GmQdezYMdjtdmxtbTFHDn5mSxRFTExMYGtrS3efaF2ffPIJk0nw22u1WnHmzBkkk0mkUil4vV5VXfrIyAi2t7d1P0sQBPz6r/86/vAP/1BVhsUTX7K14xsNaWAhy3LXIOL8+fO4e/cucrkcvF4vzp8/3zVAPHQrMaMZQmgeo6F5jC7+BgCg0Wrj//sXP8HdtX00KznYKrs4IuzjiLiPo8I+jgixl2bjyFDNassxvFOcFIP+nQXcY6px7l1/LXRcZeLxOJaXl3tmEcPh8EvzRR7i1WNIiocA0NHwVqvVLo0eeaGSRq9arR5aM0E/i5uTJ0/C5/Mhl8sx9wm+K5ymf202G95++21UKhXs7e11VXFMJhPGxsZw5MgRrK2t9d0mrcof//tB0G861WAwIJPJsMoSr4Ulx4xYOoeCJYStooyU5OgQYdmC14Wn+JuGv8Svmm7BLbxYk0xeduCRMIeSexaLv/Tb8M2/DY+xMwWozKyjB0M/w3yDwYD5+Xns7e2xQYrZbEar1UK5XIbJZGIWeP1AFmnKxD76mSqWPNElEIGo1+uYnJxELBZjlUpaL80ujIyMMGcSvW1xOp0oFApdD0X+mnA4HCiXy7rrEUUR+/v7zOWENPy8vMNoNOK1Z4ERWjh79ixmZ2exvb2t2txps9lw8uRJ5t9qs9lQqVR6GvtsNtvAlfLR0VEWJgM8rwDTsT927BjC4TB+8IMfqH5XBEHA+fPncefOHXz44YdoNjvuCSQrKRQKzMXjwoULKJVKSCQSXZpnURQxMjKCCxcuIJPJ4OHDhyyQhyq56XS6S2utB1mWWXCHcpupt8JiseDSpUu6YUNnz55FsVhU9csmeL1epNNpFrrDV9X5Y+r3+zE9PY2PP/6YWQUqw1ROnz4Ng8HQQ3jL5TLq9TojvK/KSsxsNOB/eus0RgxlVCpGSJYjeJwz4P14E8tZGS0ZCCHfIcjiPo4I+zgqxHBU2MOUkIBJGEx+8ukgA7mtzmvt/e5fmRysyU8OHEfFNo6acwqGyDw8wVHNgXjX2g+j92OIzw2GpHgIAM+bCQKBgKZGL51OH0ozgSz3t7jZ3d3F+Pg4e7ApSTpVgmga8Bd/8Rfx+PFjbG9vs6706elpnDhxAhsbvclNeuAfUvzPg9z8eKcHLWJgMBiY/tVkMqEFEYmWHcm2rfNqWVEoW0CZtD4U8FuGD/A3DR9iTtw90L7waMsCHslH8RjHETdOAs5xuDwezM3NwXvy7b4Vk0EM84lIBINBFItF1Go11Ot1CEInXczlcg1cUSG5g5J8EYmjyiUA1gTFaytbrRaazSZSqRSTp5CeWRAEZquWz+cHIk8+nw+VSkW1CkqSEb1UPADMai+dTjP7LSIqsVgMhUIBi4uL8Hg88Hg8zD2BQN3uHo8H8XgcwWCQaZSJ9JP+P5FIwOPxwGg0IhAIYH9/nxFoaiILh8PM7UHv+hZFEZVKhUk2lOEd5AGu3F7lMVxdXWWVZC0Xjxs3buDChQv4+te/jqWlJdYgSD7PCwsLCAaDWF5ehtvtZlIQkkD4/X4YDAbE4/G+31lZlrG5ucmkEEodeLvdxqNHj/D1r38dgHbYkNfrxejoKERR7PIfJgQCAUQiEZRKJYRCIVitVuzt7fVII8bGxuByuSCKIoLBIHK5HJMI0TVL10wikcCtW7c0Ce/i4iJWVlZemZWY8v5wxt7C67NGOL0BZMwR3IxW8cFKCDcrJ7r+zoA2JoQkjgr77HVE2MdRcR8jgvYg41DQLAOxB0DsAQQAjmcvAKhZQhBDczCNnkQ+b0CoZsPYkZMowoVWu83kE6lUamjJ9iXCF5IU/+QnP8G/+3f/Dh999BH29/fx3//7f8ev/dqvsd/Lsox/+S//Jf7zf/7PyOVy+NrXvob/+B//I2ZnZ9kymUwG/+gf/SN8//vfhyiK+M3f/E38+3//75mV0lcNymYCXscHgGlfqZngwBo1DrzFDYAuHZ/FYmEWN6+99hoKhQLT4FFliZLI+CmrUCiEy5cvI5fLIZPJwO1249KlS7BYLMwbth/UpumVPysrB2oNfnw1Uqm9FEUR5ZaAaNOOpOxCvO5Esm2HDOWxk3FZfIz/veEKflG8DfOnrKQsS1NYs53GhjSGgjEMO9cMQtPRFDwBdKqwemEqoVAIgUBAcxk6txMTEzAajdjf32fXyOjoKHto97MeE0URDoeDkWz+GMuyzKy4iAjxEg3aDyI1uVyOkUOy4DIajfB6vWg2m0gmkwNXFEm7qySq1NRHP2sNiCRJwvr6OgvW2NraYhrZ6elpZLNZ3Lt3D6lUihEgktkYDAY4HA6YTCZsbGzAYrEgHA7DYDBgZ2eHHefJyUm0Wi2kUilMT0+j1Wohl8vBYDDA5XJ1DRzS6XRXY5xWghzQaRLz+XwQRRGFQoEdRyKmfFWXGu/470e73cbGxgY7N2ozAEDHxWN5eRmnTp2Cz+dTlQbkcjmkUim2/5ubm6hWq7BYLJiYmECr1eoaDGs1/gGdextJNKhiTCSZAnZoe7QCR+i6ohksZZAMDQCcTifzUJ6amkIsFmPnbWRkBK1WizU7HzlyBEajkRUjKKSGrtlqtYpKpYJAIIBSqYRCoQCz2YxAIIB0Oo179+6hVCr1vc/S7N8g9/R+y+gFavxNAK22hNubWfz14xjee7iL/UITbRiwJY9gSx7Bh3i96/PsqOHIs4ryESHG5BhHhf2XLsew1pNANAlEf4bjAI4DwDbQEi0oWUc7VWXXFHymCDI7MeTTs/AGRw7t81/kGft5/qzPO76QpLhcLuPs2bP4e3/v7+E3fuM3en7/b//tv8Uf/dEf4b/8l/+CI0eO4F/8i3+BX/qlX8Ljx48Z2fvOd76D/f19/PCHP0Sz2cTf/bt/F7/3e7+H//pf/+ur3p3PBfhmAr6qAHRrRj0ezwtr1KgK3Wg0EIvFevSAfr+fPUDIR5ZHOp3GsWPHuj7rz/7sz7C0tMTIyPb2Nu7fv48TJ07gjTfe6FsJA8AsmtSIAVVpaPv5dfEPW4pfrlQqnJUbkGhbsVPzICr5kJa0PTgtaODXDD/D3zG8hxPiTp8j2Ys92Y+r0mtYwzSK5lGMR3w4fvQIjBsbEHM55rNM8gOHw4FEIoG5uTk8efJE0w2DYrfVzv3W1hY793Ru8/k89vf3WciIIAhIp9MYHR3tsifTgtlshsvl6mrW42Gz2VilFwCrAvNVPiJeZOOnlBmUy2U4nc6Bmv5kWWbSCDVS3Gp1x9xqrQPoBK9kMhksLXU3C+3u7sLv97P0PJoKJ5DbBskEvF4votEo9vf3u+Q6u7u7GB0dZRpfmlYnYkbHyGg0olarwePxdFXdlaBlyY+XAjMI1BBH1ng0GFEeIyLGdCyUsy/8wCyfz6vqjm/cuIHLly8zsr+5udlTEd7b20MkEukJauHPg/IcEYnljzWfYke2fVqBI263mwWZ0OfyTaJEoilQhO6zFFJDy9GAvlgssgJAOBzuOR/pdBq1Wg2CIODRo0dd3zOaASCJhslk0r3P1uv1ge7pg973BUE7UMNoEOFvpXA0dxd/055BwmjERsOF7bYHu9VeOlKBFZ/IM/hEnnl2YtjRQgh5VlE+Kuxj3hjHgjmBUHMPojxYI++ngVGqw1vZhLeyCaR/xt6XV/8l4J0GgnMdzXJo/tn/5w7sjPEqI6WH8dXdEOQvuCBGEISuSrEsyxgbG8M/+Sf/BP/0n/5TAJ3qVSQSwR//8R/jt3/7t7G0tISTJ0/i9u3beOONNwAA7733Hn71V38V0WiU2Xz1Q6FQgMfjQT6fh9vtfin79yrB68/UNKOXLl0CAFWNWj6fZ8vQF0lr9JnL5fBXf/VXyOVyPR7EJJGghhY9u6iTJ0/it37rt/Bnf/ZnePz4seZyU1NT2NnZ6UuKnU6nbhodVVz0pshpmWSuhH3JhZ22B9G2B1XoNzONIo3/g/GH+B3DB/AJB0uUS8sefISTWMJxZIXuKTyXywWn08mq7Xylk+ykIpEIzp49i2vXrrFzxZ8Pi8WCd999F36/v++5N5lM+N73vteV6kYPa/q8QCCAfD6ve6ydTicMBoPusSYixuvICTzxo0Y8LQxqqUUkh5dwAOiach9kPVarVXdAYLPZ2JS5FkwmE/NYVmp8CaFQCN/+9rfxwQcfIB6PM2s62t9mswlBEBAKhVCpVJDNZjUr3G63G7Varcv2jqCUGwwK5QwLT1ZJ9kSNtrzDg8lkwqVLl/DgwQNdDa/L5UK1Wu2rFe+3zYIg4Dd/8zdx6tQpzWVyuRy77uk406Cs2WxCFEVEIhH82q/9GprNpu599uTJk7h//z7sdruqzVetVkMmk0GpVGIWe8rPo4q12WxGtVrVvc8uLi5iaWlJ93sNDH7f18PKygpz3aFZD4oSbxrtMM+cx8OMgKtPU2i0Pl1jsxEtTItJvBsu4m/4czhhjsNX3YKQegqU+zfUvhTYg89I8iwQnAdCc51/PRM9sjUtHfhBj/UgeJWf9VljUL72hawU62FjYwOxWAzvvvsue8/j8eDSpUu4fv06fvu3fxvXr1+H1+tlhBgA3n33XYiiiJs3b+LXf/3XP4tN/8zRTzMaDAZx9erVgTRqpLNSG30GAgG0Wi3djnibzdbVOa7W8LC0tIRSqdRVcVNbrl9XOKFfiEG1WtWcUqrLBsQlJ5IZL3ZbDmQkm4okohenhA38nvEv8C3xBozC4A+BKix4iAU8xAlEhVF2Y1V+orKJSJmCRtPed+7cQbVahcFg6HLDMBqNqFaruHnzJmZmZvqe+zfffBOFQkG1cQkAq6ZRhK0WlIRZ7bwWi0W43W5WkefJKRHEQXW+g0BZhVbu16DuJINcZ/3QbDa7qv5qJD2TyTAtMJErGkRQRRHozHwo3Tt4UEMYX2VWm1b/NO4sWo1MOzs7uul5d+/e7bpGtK6PflHYgxB5We44VOihVquxcBOataBZBQo8qVarqNVqGBkZ6Xuf3d3d1Z21CwQC2NvbQ6vV6gpUoWuzVqshn8/D7/fr3medTiei0aju95rury+qTZYkCbdu3UK9Xu/aHovFApPJhFwuh2B+Bf+vv/Ud1FoSfrqawvtLcXywnESqNHgvSwtGrEmjWIuN4v/5LDww5LLg63MhvHvUire9OThLm0B6FdXoQ1S27sHTTsGIl1ddRiUFbKWArZ91v29yPKsqLwChOcjBOWxtl1EtywiFR16qDnyQ3p6volb6S0eKKUIzEol0vR+JRNjvYrFYz5SU0WiE3+/viuBUguyICIMmYX2RoKcJIx1fv7jLjY0NPH78WNdZgjriSQfIVzAoRYpft/KzqKr0J3/yJz1Shk8L0gTSOviKmCAIjEyQW0ReMmO96cVmy4uUZEMvJdWCjG+I9/G7hr/A1wyfHGgbNzCJuziNZRxHS9B/4NM+kBevMnSCiEy73UYmkwHwXItLyxFZSiQSEEWRVQ209InU2KNHsPhmL3qPoPae2s8EImJ8nDXtF68xPizw6+e1ucqmsVcBLRcMAjWJNZtN2Gw2mEwm5hFtNBrhdDpZohtVF6m6z38Gr7Wlz1L7vEGPNX2OkkjT39NAjKwYlYMds9ncI4XRuz76abwHwY0bN/CNb3xDU3PP+6qr+WHTs4MGcHr3WQB9nV5GRkbw8ccf93ynab9EUWTXo959VpZlxGIxeL1ezXv6/v4+AOguM0jMcTQaRSaTYemSPEjSkclkEI1GMTU1hV86NYJfOjUCSZLxYDeP95fiuLKUwNL+wZ+7yWId/8tHUfwvHwEGUcDrk5P4xvx5zJ/6LXySeQ82qxkBQwXuVgKeZhyeZgLuZgKeRhwO6SU+55tlYP9e54XOE+QNAOcFIyrb46jYJ1B2TKFin0DFPomG039okdLD+Gp1fOlI8cvEv/k3/wb/6l/9q896M146tDRhg8RdFgqFvh3PFBAyOTnJuvB5Z4lAIIDl5eWBtpWidQ8L5AxBoIcpdabn2yZExTCe1t1ItbW1wWpwoIpvGW7g7xnew8IB9MJl2PAxTuMuTiMr+A70mQSePBL4ZjciRUT+CTRIIG0v6cBJV0rNW6RPzOVyfUMM+MqcGvk9SFWCD+XgyRqRp0HT6uhvtMgT/UtESumaoNaA9ypAmlfleQXAXBlMJhOKxSKbRifk83nYbLYuMqwkqjSY4m3v9I5Rv/0XBAF2u501eSo/i8J6yKOXyCW/v/zAldap9XO73WaWgMpGO2osHgTZbFY3gZIG/+SCwUt6aGaA7An57dQiG/1m7ZLJJGtmpIAi2m8a3NOx1bvPZrNZJklRA5FxAC8cc1wqlZjUQ2s9lUqlZ5ZIFAWcm/Ti3KQX/+QX57GXq+LDlQQ+XE7gZ0/TqDYP1oTclmTc2crizlZHemMXj2DGWsGCx4NjjiActo47BvURiK0KfO0UfO0UvO0kfK0k/FIKPjkDI16OlZwot+Asb8FZ3gKSz6vLEkSUzGGYC2eAibPPKszPtMsHDCgZxler40tHikdGOt2f8Xgco6Oj7P14PI5z586xZZRm/a1WC5lMhv29Gv75P//n+Mf/+B+znwuFAiYnJw9x6z/fGCTukqb36GavVlGk6WyTyYSpqake+zfqBu83xQ481w0eFtQInSwDG1Ur7rWOIiEdzJ1EgIRL4jJ+y/Bj/Ip4C3Zh8BvMHsK4ifP4BHNoCy/+VVVWxZRVOrWULb5hitwDlNII8q8OhUJsSn8Q9GtIGwQ8WeOrT0QWDpJWp5aexx8Li8XCiBwfjkDSCfr9qwZPFGl76Vz6fD7s7+93Ve95TXm5XIbb7Wb7zMcZ88spBxxKaOmalSBXEUmSWFIcVfWNRiOb2q/VaqjVaj1SFdJC89CSc9DvKKFT6QRDUpJB0G63VbWwlED51ltvwW63o1gsMk0xhbmQ7Mrr9fak831aJwca7ND9WC1ljwaMevdZIul69/RBlhkk5pj6BPTWQ37gehjz2vCdS9P4zqVp1Jpt3FhP48PlBD5cSWI7c3Dv9opkwOOKC48rACBj3NrCUVsNk6YSxq0GWFwBxAomFJzHsP/MkUSSJBgEwCPnYa9E4azv4/ykE87aHpBc7gSJvASIkOBuxICNGLDxw+e/EETANwOETnRIcvhEhzAHZwGTevFmGF+tji8dKT5y5AhGRkbw/vvvMxJcKBRw8+ZN/IN/8A8AAG+++SZyuRw++ugjXLhwAQDwwQcfQJIk1lSgBovF8pW4QLRu1oM4VFDntJ6zBFUqiEgpHxSFQgEnT57E1atXuwgKvwzQeeB95zvfwR/+4R/23Sd6iGmBqorKB3pCcuCj5jhi0sEaKcPI4ncMH+A3DT/BlJgc+O8kCHiMOdzE64ibptDUmZKn5qMXBV8B1QJVE0ulEgwGQ5cvcLPZRKlUgsPhGLhJVfn5BK2Kn9YyRNiITPHvH+TYUIAEXyklkigIHVcRh8PBpCj8NhBB9nq9A3njHhaI2CilMSSJsVqtuHz5Mm7evMn2hd8vWpaqsu1n3qvK46gMmADQswzw/Dqi80GfRdtFUdDj4+OIx+NoNBpdFmiiKMJsNuPo0aPIZrMspIc/93Sd0vmiQZHa9tD6qBrGD2Sookq2fvz6+WUI5ASipYV9+PAhI+D0mSS3IkJOHtLAi3f8T0xMwO/3I5lMaqbsUXpgPB7XvM+OjY0xCYWe6xCAvsv0iznmt5kaA/lzRrOLvCNHP1hNBnxjPoxvzIfxf5dlrKfK+NFKEj9aSeDmegaN9kF17gJ2aybs1kwAXLAZJJz0iwi3jZiuVRAxo+s6KspBxIw2iOZ5zL3zG3BSUa2cBlIrQOoJkHzy/P+5wfpbDgxZAjLrndfKX3C7IwK+I89JMkeWD+I49VXCF5IUl0qlLleCjY0N3Lt3D36/H1NTU/iDP/gD/Ot//a8xOzvLLNnGxsaYQ8WJEyfwy7/8y/jd3/1d/Kf/9J/QbDbx+7//+/jt3/7tT/VQ/zKh3826n9Ztbm4Ot27dQjQa7el4LpVKLLBgbm4OS0tLmus5c+YM0uk0c5VQIxonTpxg/qv9EuSOHDmC1dVVzanfqamprtS7jGTD3eYYdqSDyRUiyOD/ZP4+/mfhfViEwUlZHWZ8hDO4ifMoCC54PB7MT07ik08+0dzm+fl5rK2t6VYnKfVNj/DyZIZ+JtB7pCvV0jBS9apSGaxSw0s31PaPwjBIHqO2jN1u7wn34LebyGs/T2Sj0QiXy8XcUJQOHTSIczgcbNpfOaVvtVqZs0M/twNAv7nPZDL1vV7Pnz8Pu92Oa9euMekLb0FnMBhw8eJFrK2tsel0+lv+GNE0O08yaTl+9oB+TzIE5SwDyRFEUWThHrRNtB4K+fB6vdja2kKr1YLL5eqKcCaLuEAggHg8zrad3x4KzfD7/ez7oZYMR4l+8XgctVqtx3mDgj6KxSLrD1E73k6ns68WNp1Ow+12syq4zWbr2maqhBcKhS73Cb2UOWpy48NLRkdHceLECRYaopeyd+nSJbZ/yWQSFouFbVO9XmcplECncKR1Lz5x4kTfZXjPeK2iiiiKfbd5cXHxQLM7yu/FsZATx0JO/P23j6Bcb+HaWho/WkngRytJ7OYOPqNYbYv4KAkAQQBAKNXArKOBWVcTY+YaWo3n6apd3v6OAOB4C5h+q3uFjQqQXn1OlJPPyHL6KSC9hJ4EWQIya53X8p8/f18wQAgcw6LnCLYqDqQKIbQDc2i5plBvSarn9auCLyQpvnPnDr75zW+yn0nS8Lf/9t/GH//xH+Of/bN/hnK5jN/7vd9DLpfD22+/jffee6/rov3ud7+L3//938fP//zPs/COP/qjP3rl+/J5wqCRoJcuXdJMjwsEArh27VrfjucjR47A7XbrpqOR3RrvPwx0bn4nTpzAb/3Wb+GTTz5hD3YtAkGVTb/fj2w226Nj9Pl87GFfhxG3GhN42g5g0MY5ATLOm3fxz/w/wcXiDyG2B5dIFODEDZzHXZxBXejMQgSDQYTDYVZZJ9slAt2EQ6EQ8vk8dnd3Nffd7XYz2yo97SU/ba48PnzTHbk9kJ8zVeNI18tXUfRA03LKhilB6CTftdtteDweOBwO1ebXkZERGI1GxONxRsp5KYXavmgFU9D1Qde7cv9NJhPMZjMsFguOHDmCZDLJLO4MBgN8Ph9CoRBisRj7bDXSS+TOZrOhVqtpJuN5vV5885vfhMvlwr1793rO/blz5/Dtb3+bvXf79m2mvaXK5MWLF/HOO+/g+vXrkGWZeXDz66JzR+eN9l8ZlU5kkhr2KEyE3yaHw8EqsnyjGU+YKRSIZpTsdjurwJI+l86px+OBzWZDNBrtkQZMTEzAYrHg7bffhsViYceIH8CdO3cO3/rWt3D16lW0222Wrsg7b7hcLoyNjaFYLMJmsyGRSPRci+FwmKX1USS3UoZB2kugW79LZJj0u+Q+sbq62rfjX5Zl/PjHP0YymeyaLUun04jH4/jGN77BvMO1Uvbo97Ozs7h16xbzs6ZjffbsWVaV7pdUScssLS1he3ubEd6pqSlG0oH+RZVBt/kw4LAY8QsnI/iFkxHIsoy1ZOlZFTmJWxufpooMJJtmJHNmXMsBZkHCEUcTc64WvumPDFZRNduB0bOdF492E8hsMKJci95Hc/8T2Eo7MMr6PRqfCnIbSD2BJfUEcwDmACAKtAUjSpZR1D1HYXe/AWfGABhPAJ4p4FMOVr5o+ML7FH+W+DL5FMuyjKtXr2pOpSSTSYyOjuLtt9/GkydPcPPmTaTTaXbjCwQCuHTpEiKRyEAexL/yK78ycIpSo9HABx98gEwmA7/fj3feeYfp3D755BP8j//xPxgxVj7QiSyNjo4yUpzJZNh2+/1+eL1e7O/HcHO/hZvNSdTQ39VBgIxxQxHnDJv4juGHuCjfP5ClT9Y2jej4t5EI/xw2tndZqtnMzAwLvwA6U/IGg0Ez+aperzPPUiVRcTqdsFqtzMQf0J4epmOlfF/ZZOZ0OlGtVrumvYnsNBoNnDlzBteuXetbmbZarfB4PMwCjs6H3W5Hq9VCpVJBOBxGq9XC3t5eF4E0mUwYGxtjUb4U3azcbqpU8tVOIr1Edmm99D1WSmjo74LBIFwuFyRJQjweZ01roijCZrMhEomgVqthb2+vi0Tyn0UVylAohFqtxhqy+GVsNhtCoRB+5Vd+Bc1mEw8fPsTy8jI79wsLCzhz5kzXFHur1VJNfQM634/vfe97upVpCoio1+uoVqs9gwubzQaPx8Oa9UibSiDCa7PZ4HQ6GSGkQRf9S+SZZGj5fL6nYZO32PN6vZqJbtVqFd/85jfRbDbx6NEjLC8vo1arwWq1YmFhAadPn2Ykkwb6JL2g6rXSE1gURUSjUfZZExMTaLfbSCaTSKVSLAhFeY8hXSYNZmlQoNTvVqtVXLhwAR999JGuBzERxc3NTfb3/D201Wphfn4ev/RLv8T2SSthkva/XC4z6zYKYXE4HAP5yhNWVlZw8+ZNpFIpds6CwSAuXbqE+fn5A3ne9kvOfNmoNFq4vpbGj590SPKn0SIrcSzkwNfnwvj6fAiXjvhhNRleaH2yLCOfy6KZWoettAVHeQdC6klHs5xcARoH87N/IZidz+UX4ZPP/3WGe3yWP6/4yvoUD/HpMKg9y927d3Hjxg32ALLZbOzBceXKFVy8eBFGoxGTk5NIpVI9cbDBYJA9WAcFTXMSoeZvnn6/v2tqn28SoooOxbUSgSIy1m63UalUkKq08YNUCE+b/d0kXCbgjHEfZ4RN/LxwA6+3P4ZpwPSkMmx4KJyE8Pp30PDNYX9/Hz5FA51Sm03T1krQtCNFXxMR4/WZymStdrvdUymkaXUaZNBDV1nhI0KgrLQDHf/SWq0Gt9uNiYkJFk6hVZm1Wq3wer0oFousKkwkirSFwWAQoiiy0BV+Cr3VamFnZwfHjh1jjWRqnyXLMoLBYBfZo8o3XR+075Sap9W0ValUYLPZsL29DVnuxEwTwahUKtjc3MTU1BTTpisf8LTdJpMJgUAAW1tbbL/4anyr1YLP50Oj0cCtW7eYFzORuXg8jkql0kUwBEFg0g6Hw9G1/fPz88yeix/08P8XRRETExNYXl7uaaqjgWY4HIbRaEQmk+mJZ6ZmufHxcZbYRk4k9N33+XyMRKVSKeRyuR6LQ6rmOhwOFvASCATYtD9do6R1pGNE1wvtF5EzfmZLq3rJewIHg0GMjo6yZQwGA7LZLKamptBoNBCPx7sa/2RZZtd5JBJh+t1AIMDs16jRjbaZ1ziTjIEnz2azGel0mnlM2+12dqxpm4rFIjY3N5HL5VjstlrKniw/96ENBoMolUqo1Wowm80IBoNIp9NdPrTUDKgGrdCNVCqFK1eusMLJoJ639EyhAdKrnqK3m434+RMR/PyJjnXrZqqMH60k8OMnSVxfT6PWPHgVeS1ZxlpyA//vn23AYhRx+WgA35gP4etzIRwJOg68j4IgwOvzAz4/OkZtHGQZyEc75Di51CHKCSLLh+vIBKBDwHfvdF487IFnJPkkEDn5nDBbXJqrGqQQ9lliSIqHADC43dq9e/dQqVRgMpm6Qh7o50ePHrGqUi6X66qoUeXYarXCYrEM1GzywQcfsOlh+qwPP/yQTQ/TupTWTQR6mNLfk+av80AW8UndjytJJxqyfpXChBbO2zJ42xnH+dL7uCjdhXmAyrAM4AmO4mOcxiqOQIIB1qUU/vbf/hbu3r2rGvVLwTL379/H2toaS4cjbG5uIhKJwOVyMY2ixWLpkTSQ1ZLNZlN1hSACRKl3VPmn5YjICYIAv9+PcrncFRjB9vGZTMFoNCIUCiEUCiEajaoeD0HoJKidOXMGP/zhD5kHKqFQKMBiseCNN97AD37wA0bQ1Pxs4/E4Jicnsbe3pyofkWWZNSGRA4NSGmG32+F2u5FMJhm55KerBaFjC0bVOyWRpYEFaeaJZCmdU0giEAqF2PI0aOMHMkS4lpeXEY1GmcUdLx/wer2sSeb27ds9Ucjvv/8+Ll++jMXFRRSLRXZtKDXXBKryU2KcslJOlVWKElb+Pf1cqVSYzp2CJQiVSgVjY2OYnZ3F1tYW80tWDsDIZuzy5cu4ceMG7t6927PM6Ogo5ufnsbKygng8zsg0LWO1WuFyuRgJG8QTOBaLsUEBf32EQiGcOHECmUwGsVhM9fiQHOHEiROIRqOq2zw2NoaFhQVWWS4UCsjn8z2NyBQX3Wg04Ha7VQsUNpsNpVIJqVQKPp92zwMVOiRJ0o2C7udDO0joxrVr19h1qVdUodmYz1us8EzQgb8TPIK/87UjqDXbuL2ZwY9XkvjRkySeJg5eka23JPz4SRI/ftJpsp702/CNuTC+PhfCW8cDsJtfkHoJAuCd7LxmnweVQZaBwu4zgrz0/N+XVVmupIHNn3ZePLxTQPjUc6IcOQUEjiOZyX3uzr0SQ1I8BIDB7FkajQaLgiW7Fqre0ZRnLpeDyWRCNBrVrKgtLCx0VXm09MsPHz7EtWvXWIWPHs61Wg3Xrl0DALz++us9WlkePLlpNpuMROZaRnxYHsdey6F7XAyQcMIYx6JxG79kuY9zxWuwoL/GSwbwEAv4KS4hJQS6fler1fC9732vK6CEBzmjlMtl7O3tsff5xp29vT3WOU5JdDypIv0oaR75v+fXB4Bp7alaq7Q2oyYoquwpm9poXXzDn9Z0Pb2/t7enOVtQr9dx584d1rSn9pAFOo4A29vbmlINSZKQyWRY5U7Nkq5er3dJRwD07D8tS9eqUndL3x0iZnr73m63mUac18ET8W+320xHS4SG35Z6vd6lO71+/TrzqaXtLhQK+PDDDwEAPp9P1T2CP5bNZpPpRNXIbr1ex9raWo/+W7lcKpXCvXv3EIvFekJA2u02YrEYotEok45QlZqOPc18EGkkmRNtJ/DcOnN3dxdra2vsGPFNdKTdNRgMOHPmDAud0CN+WpUqQRBQKpWQz+dVjyFdA/l8HtFolA0s+X1vtVqs+js3NwebzYaVlZUeaQTFNkciEbY9kiRpfpf7Vdfq9Tqy2SyTO/BR0JVKBTs7O2wmRQ+DhG5Qw7PWMSbddSwWw9OnT1Eqldi6ms0mdnd3u/pWPktYTQb83GwIPzcbwv8NwG6uij//aB1/eXcDy1mgLh28qrmTqeJPbmzhT25swWwQcfGID5enXLg44cD8qEc1GOVTQRA60dGeiV6ynN/hyPKzV3IFaB2epSlDbrvzevJXzzdBNMFsGcWkeQxhz3FU3UeQM45jf2/vc3PugSEpHuIZBrFnsdlsjAzzRILXa9ZqNeRyOd2KWiaTwdLSku5U2+PHj3Hr1q2uBzqvi22327h9+zbm5ub6+hSTXrfzcJGw1A7jejmMFvSrw1NiFn/DvIafN3yEy+2bsA3gQatHhnnE43Hd9Tx+/LiraY0eICQxADp6QbvdDqBDKJTnjI43OQFoNX+RNRaRapJUAGB/Xy6XWeWZCBxf4SRSHI/HdVMhgQ4h3tnRDy/Z2NhgD34tey9qoCIoNawAmJ5aj6iWSqUuza8yCIG3L7PZbCx8gm+2kmWZabv57eHPB9A57yRFURtctFotpFKproqk0rqKKt9kfQigq5osCAIajQZu3LiBX/iFX2Ckh84dvxyFsvSLeVaTzShRq9Xw4MEDtNtt5jXLnzOKZ+YHKMrzQoPeu3fvotFowGq19uiuG40GPvroI6TTabTbbeb0ADyXGFSrVSSTSdbwqwWSGEiSxAbrVMEym80srp587R0OBzsP/AA0mUzio48+QqvVQiQSUbVIu3XrFo4fP9712cptAToJdDabDfl8XrOaTs17eqAGQb0oaGog1MMgoRs0U9HPg3h7exvxeBylUqmncl0ulz+XscJjHitOmjPwTVfgOx/Eel7Gw1QbD5JtbBcPLrNotCX87GkaP3uaBgD4zDJeHzHjV1+fwa+cPwqH5SXQMkHoVG+9U8DcLz5/X5KA3OZzkkyv1BNAGjz4aKBNkJrwVLfhqW4D+Rvs/abRhRXfN7H8jIN81ud+SIqHANB5gPezWwsEAlhZWWHLK/8eAGvi0KuoFQoFbG9vIxwOa061PXnyhD3M1abrgc5D+M6dO6yCqVYJJWJTrVZRFh24UhrBXh/tsMPQxq/4EvjfyR/gjeqPYW/3n3aSATzCPH6Cy7pk+CDgCRm/X3yFhYIB1I4jkSN6mCpJGE391mo1Vkmr1+s954ykM+12m1W2lA99IpRLS0t9K0+DBiZQJVENajdOtal9WZY1K/KEUqkEm83GZiKItJH+GQCzWqNABCWomqck0QR6T9kUqOYJzFeQ1c4rnUe+cqv0KQY6FePNzc2u74/av/ygQXkt0WdpEWYlqtUqS6VTXmuUWKbX9Ad0jmU6nWb7wg9S6NpMp9Nd54IfpNH13mw2kclkukKclOB7KfjzTaCYY9Luq7mr0IxDLpeD2+3umrEhUITx8vIyqtUqpqamUCgUulLmqNGQtOE0oOOt7aiaPj4+3jd6t1gssuuRBtP8MSKtebFYhN/v19R6DhK6QdPg5D2vVlTx+XyIRqNd1X2+cq2s7n9ewF8jJoOIeT8w7zfgf54DcnUJ9/ZruJ9sYa1kRqF+8IS7bEPAB9tNfLC9iv/L959i8Ygf35wP4xvzIRwPO18uSRRFwH+081r41vP3200gvQYkPulUlxOPO6/MBjpPu8ODqVWE2Wr/3ERKD0nxEAz9okVJc0wPCOWNj/dD1auopdPpvtGiSu9drYdyPp9n5EltGVEU0Wy1cafkxY1yEO0+1eHXbDn8nz0/xmLxB3C0c7rLEj7BLH6MN5EUggMtfxDQMVaTPQBgTU92u72nCkiEmf6Wt66jhyL9TA980p/y07V8g1qz2VTVwtI5PuxEN55A8uSO9nsQska6aOU1wkt/xsfHsbe3xyQQhGazyRwNkskkEokEI8h89Y7ihCmFTdmMBqDreNPPStmG0lpQj5D2Ww9JCXhCrwR//uhn5XYPSoj5ddLAgr9G+OhhPdA5oSY15YwUVXOB54NwNRvBQcjEIL0UROL57xe/rfQeET2t9VQqFdZ4HAgE2CCUb7QjGQq5ZSi//0S2+X4OLZTLZXZtqvmHU1W9XC7r9ncMGrqxuLiI27dvaxZVxsfHmX2eVuU6lUr1re6/auhdI16LiL8xZcVJewoOpxGP41VEW248TLWxkZcOTB9bkoxra2lcW0vj//GXSxj32vCN+RC+MR/GW8cCL6eKrAaDCQgvdF48GpVnTX1Lz4ly/DFQ0p8d7Ieq+yhardbnIlJ6SIqH6IJeU0oul0MoFNI1w/f5fKhUKqyqoKyskCcqdenrVR4GAXUua7kP1CUBP2rMIirpe0j6jA38X30f4hcq34cnlxnos1dwFB/iLcSF8EDLfxqoRS8riZUoil26UqqoUVc/TT9TZZhfN1WiLBYLaw5TWk7Jcse+qtFoMI9aPmmMyJbdbh/4vA0CvnkQ6JaO0JTroGEhgPaMAx1PXlPLV2R5O7T9/X3WwU+JguVymUXpUkWd1sF/lppUQO/nQaE1awM8j4DWqs7T+VcGqShlKAcBBarwZJ2q6AeB3gwIDYRpcMJf1zQIslgscLm0u+CBwXopqJlXS14DoMuGT08+4Ha7uz5PWZkm1woKOaKmO76B1mg0olQqIRqNqrpOEJxOJztGgHpaJP2+nz/9IKEbkUhEt6iSSCR69O8EUeyEmzQajb7V/VeNQa4RWZZRKhbx2pgXFy1m/PosUGrIeJRqP5NatFD8FGqE3VwV3725je/e3IbZIGLxiJ+R5GOhgztavDDMdmD8fOfFo5IB4p88I8ufdIhyYmlgJ4yseQxG6fMRKT0kxUP0gKbSlfB4PDh27BjTcpK1Gnm1ulwuzM7OYnNzE6lUit1IiESRQ0UoFGL2RcFgsEfHVygUMDExwXR8ejh16hQ2NjZY/C7w/IFekMz469ox5GVtuYQACf9H64/wu4b/DYFi/88DgKeYxod4C3uC+o3bbrd3dep3fd6nIHP0dwSeEDudTiZl4KuXVNEhMsZbnvHkkAhFMBhkdmP8Q58qVlNTU8jlcmy71SrXLpcLIyMjPY4anxZ+vx+ZTIaRen6/TSYTG4ANAuU+Kytw+/v77Lgpq46SJOHhw4c4d+4cgsEgisUiKpUKu+acTidcLhfC4TCePHmiSYqV76lJDJTLDwJlVZr/rOnpaSwvL/esX3lcqKLIV6b59ZBEph+oksvLdXhiPGg0OVW3teK7LRYLRFHscgWhz6KBusViwfj4ONuXTxtdPzU1BUmSkEgk2ACU/55JkoRwuDMwpvueLD9PQxQEgd33FhYWkMlk+vZu0EBUEATV2bZcLtelX1fD+Pg4jEYjKpUK6z3gzwfZDFKIhp6V2ttvvw0AzKe4WCz2+BQD+kWVRCLRNdjUqrh/3jDINcJbaBKcZgGXx4y4PGZEq23EnbU4VksmbDWcWMsdvIrcaEu4+jSFq09T+Nd/sYRJvw3fnA/jm/Ph/z97fx4fV1be+eOf2vdFVSWV9sW2LHnvttuy3SuYhg4hfBmWBAhhSDJ5zUymmQBhkjBLQnjNLwNZZoEsZBkGQgIhMIFAM6EB06bTmy233e32IsnyIstaa1Ptu+r+/hDP8albdauuWmWpJJ3368WLtlSqe+7+nOd8ns+D4zu8sBjX5ou8JqweYOCRlf8RkrRSbBe4CmnxCkKXfwxj7DocuUVocfcZW9RZEcga0dHpa4qW0iIoFqhGo1nRHVOmmH/wGwwGuFwu7NmzB36/H//0T//ErLL4JX273Y6RkRF4PB4EAgFFGyQS3NcKDuhlY7Va2YuKMkbzRRt+mBlATvESl/DTupfxn4x/j25pDlAhBYu37Mc/xvfjVkm5FbhOp8Pu3bsxPj5e5tZA/28wGLB79268+uqrdbfncDiYbrRaQEIWSKlUihWB8RpRyixRsFwtW0iFO+R1nM1my+zvNJoViytqrOFyuZjNHq8pJrlMNT2p0nGq1+pYr9fD5XKVFaXxmlG1mUf+RSwP+Oj39ALX6/UV+1YsFpFMJnHz5k1Yrday4j7CarWyTmyRyMpKQ7XVC5fLVVbYxmc4+WwggJoFpBaLhWlZlSQWlGnnNa7yAJOOJU1GlbBarTXdJ4C7fsmFQqHquaHfU2OaWjgcDqa35bWntAJCVmSkseUnOXSfUaBCWneldsn1ainomfb000+XOaLQ8afGFADw9NNPs+CPl8NYrVaMjIxAp9PV3V5vby9zIFFabaPmPLUgj2ty45BnuA0GA2w2GxYXF6u6H2g05VZqHo8H/f390Gq1ZR3tPB5Pxd9VS6o4HA6YTCZWa1It464mu7/e0Huv1jnbvXs3XnvtNcVscrFQwIBLhwFXCUZjFotLCVyNSLiZNuFWzorU8uoD2juRDL780m18+aXbMOm1OLHTy4LkXq+1Ebu+NjQaoKUPaOmDZuitwJ4P4bmzZ5FNxtBhSKAlPwdz/CYKuQysNlvTtJQWQbFg1VBwwhccyQMhym7wfyMP7pRuAPo5ZYuUoMCKqrBpaX+i4MWL+R6UFPTDx7VX8DvGv8Ne3FRVMzCn6cCo/S1oH3k/Zn/8YyCvbMmm1+sxMjICh8PBvD0Jk8mEo0ePYs+ePbh27VrNLKfZbGaNC0KhUMXvqXuW3W5n2SR5oxSdTgev18sym9WgDHI6nWYvKXlWVq/XY2lpCQaDAX6/n3n+0jk1mUzMRqqnp4f5Jyths9lYdb1Sq2OHwwGDwYDW1laWyeL9XD0eD+bm5lg2XCkrT/tQr6MbBYikp+azjiQvoJci6Yfp7zKZDO7cucNaF1NXMjk0biokU8ru0zmrFRQbjUaYzWYEg0HFJik+nw82m40FkeQNzG/Pbrcjl8uxhitKba6pNbX8vuY/R1lRpfuWVovqnTOtdqUZBWUk5d0DqT1zKBRiQRY/JpJO6PV6LCws4PLlywgEAmWfCYfDCAQCeOyxx+rWUpBNFHXT4+VI1IyFAkP+MyTjkH+GticP1MnL2Ov14uLFi3U1vN3d3YrXBwDWaKOzsxOzs7NlXs60PQB16zt4KzXaNkksFhYWEI/HVdlpdXd3M/mdXJdP7i9q9msjUDpnNLny+XyYm5urmU3u6OhAOp3GxMQEdDodDrrNuN9bQrGYxJ1ECTcyZixqvbiV0GC5tLo8cq5YYi2sP4kr2NlqWwmQh9twtN8Do37jWzTz91kgFMKc3gt92/0rqw3Cp1iwGZEkifmjKtkXjY2N4dq1a0yLJ3845PN5PPfcc9i9e3dNGyQKPmpRKpXgcDhgs9lgs9kQjcXx3Tt6nM+7q37+kOY6fsvw93hQe0XV/gY0rXje8Bhum/dhuVRC+OrVuhlQSZKQSqUQiURgt9tZppb2LRKJIJPJsJeDUhaUXq5kFyYnmUzCZrPh8OHDbDmWf8lks1l0dXVhcHAQV65cYcEGBSR88JvP55k0gnSG/P5QgOd2uxGPx1mQxWd5wuEw3G43zGYz2tvbmUWTPFOu1d5tKcwv+/NLp+Q4YDQaYTQa0dvbW7VtLi0J16KaLrUaVOgj19TSpAEAK+hSKhKKx+OsjbHX62VBKFloJZNJpFIpHD16lHWFpCVyyuSbzWYcPHgQTz/9dM3xJhIJtLW1sQ5zcqihBDkH0ISGmtyQjpzvvFYrK8/76SpBLbs1Gg0cDkeFlVo2m2XthsnxpJrsw263Y/fu3cxdhQrQaCwulwu7d+/GjRs3kM/nKzqi0TW9tLSE8fFxzM7OQqfTwWw2s33IZrOYnZ3F+fPn8cQTT9Rc9qfnntFoxOHDh5kVntFohMPhYM89ADU/U89ujJ+IqtHwKjmzECaTiXUWpHMgt4lzuVxsYlHPSk1ttzol+P2i+5dvX22xWFTtVzOiJps8PDyMCxcusM/f/VvAbyrAq8tgaKgNJx49iRduhHF6PIAfXwsimFh98Rl11/vfz9+CzajDw4O+nzhatKHdZa7/BfeIeo10mgERFAtUo6YVNOmJ+WpzuR4uGAzCZDLVtGQLBoOqAlCbzQafz4cbd+bxf4NtuJKqDBCGNNP4uP4beIvuvKr9jGg8eF7/CK5q90Cj1ULzk0CBjPlrUSgUcP78eSwsLLDAg142kiRhdnYWdrsdO3bsYNpseRbU4XBg586duHjxIgqFAtMY8hrFTCaDeDyOXC6HcDhckVUsFAoIh8OsGx4VxvFZYDofpVKprCNaNQ0n6Q8zmQycTifS6TQL+KxWK+LxOOx2OyRJgtlsRk9PD+sARphMJrS3tzN7KgBMywrctYijhgXt7e2sbS7tKwWc8Xgcfr8fMzMzZdvgIX10PQs40rrSOOT2ZrQcz08Y5Npsklnw2m0e/t87duyA3W7HSy+9hGAwyI5jW1sbjh8/jmg0WteloVQqYWlpicka5E4PlLF2OBzMOYACELqGyZXA5XLBbrejtbUVoVAIkUiEXTMej4e1ZlcKwGk8fMadd5+gZwB9jloO08oOodPpYLPZ4Pf7YTQaK3TY/ASKpAs0SZFLXqhByMzMDDQaDdP589dsMpksa5dcKpUwOzvLNKIOhwM6nU7Vc486M5JsIJfLMWkLWa3xHd3Onj3LJghWqxWl0or3NGVdSaM7OjrKVkqoI+LIyAj7fS3oPk0kEiwoJsiKzWq1oq+vD4uLi4oZTrfbjWQyqapbHXWQnJmZQTKZhN1uR3d3N7tv5PvFd3qU75eSDlx+3Slta7XU2h61Dq91zuqtABgMBmQyGfT19bFuhlSTY7fbmdRFyqfx0wc68NMHOiBJEq7MxfHstSBOjwdwYXoJq0wiI5VfxvevLOL7V1a88fd2OPHG4VacHG7DfT0t0GmbJyBtBkRQLFANZeto2UwezHm9XvbioayRXLtJy2TkUKH0PZRxqqdhDIfD0Ht78bkfRhHJl7+w+zXz+Jj+H/B27UvQauo/SWJw4jndg7iwPAypqAW4znWkNVUTqM/OzrJl9WrG+1NTU3j88cdx584d5kVKf5vL5dDa2so6kdFxlC9pk6b2+eefx9LSUtWxLC0t4fLlyyxQ4INHkrJQJpt3H+DlAwRplJeXlzE7O1uxHXKeoGYBlN2i7yatLAUjpAPlA1YKdihAbWlpwY0bN3D9+vWK49je3o4DBw7g0qVLikHxapBnx+XXHhVPAdW1vmRvRcvNFPTLjxHJJ2KxGHsB0zGPRqOIxWJ1fZUJ6gom10pT1nlpaQn5fB4jIyN46qmnKtphU7B43333YXp6GgsLCxUtxekaJv/dWvAFdfJ7hYJ1WmWgiZb8M2azGd3d3Sw4peYi/IQwHo/j8uXLZcdB/j10vjKZDPR6Pebm5qoG4NlsFuFwGJOTk4rtsvv6+lAsFhGLxTA/P1/RdKKjo4Ndg4FAALOzs2XFeDdv3kRXVxfLkE9OTrJmKDTRoGdfPp9nWdehoSEMDg6+7qAvFouxa1Xu/kFZ+kwmg66uLiQSiZoa50uXLtWVWORyOUxMTLCAl59Y8QGvmv2qZRFHy+xqtqWWWtvz+XwYHx9Xdc6qQdcmvT+9Xi9cLldVO75wOFwmudNoNNjf5cL+LheefOMuRNN5PDcZwumJAJ6dCCKcWv2z7+p8HFfn4/jT0zfgthrw2O6VAPnRwVa02Co92BuJmvO60YigWKAaWo67c+cOSqVSRYtSCmTJI1UOLWvSf9f6HqBSp1yNs3MF/Mm5q8gW7z5QOxHCr+m/iffo/hl6TX1f1CRsuGA/iefTO1EoaYEqE+fV2EmRBhIAy9BSoEJLynfu3GG6Ur6QplQqIRwOs4cGBYM8fGFKtaIvnsXFRZbVkB9POle8pIL/vfy/efcJOel0GpFIhMlHSM5B2V/6+fXr19kyvdIxJRuqqakpJjGhoIr2fXFxEdPT08xGTgm1jUKAlcwaZS/5bLrNZmMOHtWODR03avVc6xjF43GMj4/jzJkzrPkETXASiQROnz7NHBPqQc4uNAYaB+lO6X7jg28emoQtLy8jFovVbClODXBqQVZsvG0eDwUuNpsN169fr7j3KfBcXl5mK07k/cvrt0nywU/m5FDGnvfo5Y8RafDNZjMmJiZw+fLlivNB7bKPHTuGVCqFxcVFNh4aczqdxs2bN+Hz+djqTKlUKrvv6Vpub29HPp9nwWC1Z59Wq8XMzAzLupK++vVAARZNZuXngwrmANTUVBsMBoyNjdW10JyZmcGZM2eYlpl0x8FgEKdOnQJwN1Nca78oK1vLIi4SiTAZRr1t1aPe9vbs2aPqnN28eZN1anW73RW6671799a146MAWQm31Yi3H+rE2w91olSScGk2htMTAZyeCOK1mSjqvC4riKYL+Parc/j2q3PQaoD7e1twcnilWG9Ph6OhsgY157UZAmMRFAtU43Q62cuRHtgAmG0SdXSSBwwE/3OdTodEIlHze3jk31OSgPP5Trz20t0saSui+Hf6b+PndT+CSVM/iM1oLDhnfAgvFvfDqHWhIJW3DK42bjVQoEKZFT7DWSgUWGahVkvYyclJ5n8p/x4A7HvUQN/Du1PQ/5Nmkw+M5dui7GM9+7NIJAKn04mlpSXmWy33Rabgj4LValkvWhqfnJxk3b2qjfvVV19d9blRQqNZcUZxu90V0hByn6DMmpKchdrdEnIZCh2jCxcuoFAowGq1ss9QMJZOp+u2ySaqdaHjzxkFPC+88ALL9MvHtLy8jOeff76s6Qp9hg9wKUCtNx6+SFZ+zmiycefOHWi1WthsNlbYqNGsOFMUi0VMTk4iGo3WbPJALZApAKV95rPV/ISSJlX8flHAevXqVSZT4ic41C76lVdeYVZs/Dmj5xVJFGiCIS+OoxWRSCQCrVbLMo50XdN3USONaDTakCY4NBGnFQG5VpzvXllPU13Pkqy9vR1XrlxBLpcre6abTCYYDAbW5npwcLBmppv027X0y2NjY5iamlrzttRub2JiQtU5m5iYqPk9s7Oz8Hq9WFhYqFmMp9aWTKvV4FCPG4d63Pjo47sRSubwz9eCOD0RxD9fCyKWWZ0xckkCzt9ewvnbS/jD70+g3WnGG4db8cahNjy0y7emxiFqjnOztPgWQbFANfF4HHq9nmlL+RkzFUrUa1BAkIWY0vfQS4xf1ieykg7/nN+B2Z805HAiiX+j/y5+Sfd9WDX1ixJyMOKC6UGcNx5DelkPjVaqCDDXEmyR9lSe5eV1qolEgrWWlT+4yfZLXvT1esdGATlVmfMNV/R6PSuKoswtH2TxxT9qdK7nz59nRVRKY6Egnb6/WvAIgOn3+MCaMBqNdTOXq4GCLbvdDpvNxn5OATgV/RUKBbYiwn+GLOSqeRTTvykwTCQSZcEeP4bXs1+8fIKnVCrhwoULLKAndxF+ezRJoX/TOOma43XnaqB94rPttP90T8diMWajyAfOJEcg2Qc5fMi/n65l2nelFQf5Nat0jOj88n7YNFa9Xs+as/BFszTuYrHI5AZkFyc/H5IkMbeF69evs4LKateIXq9nmfC1Qu5A/D1c7VjQ5zSa6lZqGk39IjK3242lpSXYbLaq54zqCOo1HFGj356enkYoFFrzttRuLxgMssJApXOWyWQQDodr1smEQiEcPHgQ8Xi8ZjHe6w0KfXYT3nW4G+863I3icgmv3Ini9PhKFnlsXtkNSImFeBZ/N3oHfzd6B0adFsd2eFgWud9nq/8FHGqOs2jzLNh00PJOd3c3K/7gCwU8Hg9mZ2frWi7RC4W+J5FIsKVVKgyidrryl2uoZMXp/E4kJROsyOKXdE/j3+i/C6emfhOHAgy4aBrB83gAGVigKWhgNq9YW5EuVyn4UxMU8vCBBUESBspU1dLoUQaMt+3ij2E9mzE5AwMDTA/JZ486OjqQy+WQyWTY8mC1fTcYDKpacJJFGHUAk+s4dTody4LRdSLfJn+s+SYQfPDUyM55wMoqSC6XQzKZrOggJkkSOjo6kEql2MRPrvHmJ4QUxMs/A9wNFpXGTy9Y+o5q57jW73g0Gg1isViZRIcP+uQTT37yUe3aVYvSvULfT6tN8sJOkkSQXIY/7/yY5WOrdwz475KPk35OqzW8hKhYLLL7jPSqdP/QGIxGI2uAAaDsuucnFjqdjhXf0bKx0WismFyRfzAf0L7eSn2TyQSr1crqN6r5AlutVlVdxOrZ1pFbUL021/Uajqhpu81n5NeyLbXbo0lNrXNGk6F6umu73a7K/k8tSteHXqfF0X4PjvZ78Js/NYz5WAY/ngjimfEAXrgeQjqv/t0BrDQOeW4yhOcmQ/jUU1exw2fDG4fbcFKl5Zua40yrLRuNCIoFqiHvz1o2WbQUTy+TakFRqVRiWZdqUPaFsoj0HdeKPpwp9EKPIn5J9z08qf82fJr6M+Bl6HDJcBjnrG9AOKf/yUutUPZwIx0hvcTkS7+rgV62QPXsFGWg6tkg0ZKtUqCqNoNHdlutra2Ix+MsC0eBYCwWg9lsVtQnUxCn5oHFSz34v6+WeebdSfjsHL/MTQGI/DpqNIODg7h8+XJFgRyw4ihw8OBBnDp1ChqNhhVWUbBEsgl+VaNa4MIv5dM1LodesHQcSRcrD7Do5/WgBg98oEZU0/zKM8Wrvf7p3PGdFnnZA00I8/k8O7/83/Jtk0kbLYeOD12P/P1G+8VfU3RvVytWpeOipD2nY0yTCpfLVdFhjpIFfIEhf//T9avVatHS0oKWlhamz5evkul0OmZtuNaiJLPZzBxFstlsRaEdOffIta1K1JJY0ESu3jOtXsMRNS2V6ZitdVur2Z7b7UYqlVI8Z06ns661Hb0n3W53Q2zJVnN9dLgseP9IL94/0otccRmjtyI4PR7E6YkAboVq12VU42YohZvP38IXnr8Fu0mPh3f5cHK4DW8YbkWbo/J6UnOc6+mp1wsRFAtUI293yT9MSRO1c+dOLC0tsSVbfmZILx9qBzw5OVnhH5pIJBCNRtHX18de/EVJg7OFXtxY9uDdumfxEf030aUJ1x1vCRqMGe/HK87HEdO4EQ6HAeTLMib5fB75fB5tbW0wGAxs5i7XIhaLReY1W49aWVXKJpDnr5I5P//QtNlsVTWsNputbqEdsFJwEovF0NraWqZX4/WACwsLLHiRB0WryRLu3r0bs7OzZRIA+i76Hmo8kUwm2TVE+waAyRgkSSpb+icoULbZbHUL7dSg0WgQiUQUXR+i0SjrQEjHRH7tAytBCOk0gfLlagquaN9TqVTVFsb5fJ5JOKjNOS9noe/yer0so62EVqvFoUOHMDY2plj4Sp+ja5y2xUt9aFIkD6rlaDQrumzKTJJUh7StWq0WZrOZZVurac5plcHlciEYDFbdTrFYREtLCyvsqzaJpXGaTCYWjFd7FtE1VGu/tFotPB4PYrEYk8kQdL+2tbUhHA5XvV5ppcfhcODw4cMsmbC8vMwKB0ljrdPp0N3djXw+j9HR0TUVJblcLnR3d7PsOxWRUrEjrfqtprWuksSiu7ubWf+tpeHIatpuh0KhNW1L7fY6OjoAALdv31Y8Z/39/ZAkSbVeWOk4qmUtRWsmvQ6PDLbikcFW/M7b9+JWKIVnxgP48UQAZ29GkF9W/7wHgGSuiKevLODpKyu1EAe6XHjjcBveONSKQ91uaLXq2qmvRk99LxFBsaACJe9HNdqy4eFhzM7O4vr16xWZTHpptbe3s5uimr4IuPsiSZaMOJ0fwHHNZfy58RvYoVVXhHTdfBBnLSeRNHeuFEbF42XZSj7oJU6cOIEf//jHbNZKv6es3n333Ydz587VzJgajUaW5eWLgIC7tmZGoxEHDhzAyy+/jGg0WpF5MJlMzG6M2i7TOPnld74NtBJarRa7du3CjRs3FM8Z2b9R5rKaxpe8fGu5cFD22WKxVGT4+DHa7XYcPXoUP/7xj5HJZFjwRHZ/RqMRDz74IG7evMmuI7nzg0ajYV3NlCzpgBVZBDWBqDXu8fHxsmMm3//x8XF0d3dDo9Ewz1heJ0xL1Uajkbk4VHP86OjowN69e3H69GmmVeU13kajESdOnMDc3BwCgUDF8SYpBGnwakFBaD2dstFoRFtbG6anp8vuC/4+6erqQj6fZ3681fD5fMzblwJjfizUBpyKRPnJF78tanhBbibyZX/KclJzFAr0gLuWgpRNdrvdWFxcrLotjUaDlpYWNjFWQq/XY9++fTh//rxiM43jx4/jwoULZc89+bba29vL2jyn0+myJjikWx0aGqpbtKWmKIl/XqfTaXg8nopt8RrWtUg1tNrGNBxR846htttr3dZqtges1NQonbPh4WH2mXuhF+aRpMYWrQ34bPhXDw/gXz08gFSuiOevh36iRQ5gMb56OcOl2RguzcbwuR9Nwmsz4rGhFcu3vf276sYOjTg+a0UExYIy6nk/1tOWGQwG1tVM7nmq0+ng9/uh1WoRj8drmpgvLS3hTs6CYjGGLxv+G/Zqb6sa/3XtLjyrewRBtMMCC9p+4phBWSFyFuDHZLFYkMvl0NHRgX379uHVV18te0nqdDrs27ePLbFrNJqq1eFms5kF09QsQL7/VETY0dHBOpuR7pP2/+jRo+ju7sbNmzfLXuqERqNhDQ7sdjtSqZSixMLpdMJms9XVAwIrWRMlzSR1a6MgXQ4tC5IsRmnZnSYZu3fvRiqVYpMM2h61wh4aGsLc3Fzd64jkBEraW8pUkrZSDrlLVNPV0r9piZ1cVwKBACuE1GhWisM8Hg+b8ABgY6ZjQGOmbmzJZLLmvo+NjSm+IDQaDauGr8Xy8jImJydVvWgMBgM6OzsVx22xWJhuWt5YR6PRoLW1lbUx7urqQiwWq6gVcDqdSCaT0Ol07FqTt/o1m80ssO3v70cgECjzBbbZbGhra2MTVco8K32PxWJBb28v5ufnK67r9vZ2JvEwGAxVtfu0auRwOPD4448rNtPw+/1lTWuqbctsNrMVG/5+pKVv/hnaqKKketuibGIj/GMb0XCk2pir6W5pTGvdltrtAVB1HBupF1biXhat2Ux6PLGvHU/sa4ckrTQO+fFEAM+MB/DKndVbvoVTeXzzwiy+eWEWOq0GhzqdGLSb0VdIwmdIwGBo/PFZKyIo3mbU6gA0MTGhyvuxlrZscXGRBWlkq8YvjZdKK93TjEYjW86Xa5NLpRImJi7hl0pP4ajhmqr9uqPvx0uWNyNoHlh5if1EehEIBNDa2soCYWqgIT8mhUIB165dw+3bt6su6d++fRutra0ss+fz+RCPx9lLn7oRkc1VLpdjumhe9kDZwFQqhUAggLa2Nng8HvY9er0egUAAXq8X0WiUBazyynH6DAUBcu0xFcdRoEYNQS5cuIBoNAq3243Dhw9XFHYpaSYNBgO6urpw584dAHcz1rR8bTQa0d/fj3w+j2w2y5aq+QkIHfdsNosbN24gnU6jv7+/LNvH25LVu45isRhSqRQrTOQDEdp3GovBYIDD4ahoFMNr1msF8hSokaSBvJ9JbhIIBNDT04NisYidO3eiv78fMzMz7P7o7u6GTqdDOBxmDWv6+vpYYR9NcNLpNObm5th5dzgcZQ1QyAs5FovV1frSsiSv0ZefN5qkZLNZ7Ny5Ezt27MCdO3eY9VxPTw80Gg1r2EKNF65fv84+s2vXLmi1WoTDYdjtdiQSCXR1dbGOZSaTCR6PB5FIBF6vF4lEAqVSCW1tbRXabDo/kiTB7/fD7/cjFAqx76EGCaFQCE6nE7FYDD6fr+IZEo/H4XK52LXf3d3NfHupK6JGs9JYg46FxWKpaE3NS1SGhoawa9cujI+Ps653w8PD0Ol0zE+7t7cXPT09VbfFN2ao9wxdTVFSvQxvvda6jfSPXWvDEbVjbuS21G6vUZ9ZK+tVtKbR3G0c8uGTg4ik8nj2WgDPjAfx7EQA8ax6734AWC5JuDCTwEqjawPabBY8OODE24cH4HB71jTWRiKC4m1ErSzw4OAgRkdHVXs/KmmiqOgon8/D4Sg3/5YkCYlEAsViETabraqJuSF6A5ZLf41PLF8EVDzbcr59eM33L/DcrA7LpRIkTmNKLzu+CEbu2kAFG1qtFjdu3GCBmLyDWiqVwpUrV+B2uxGLxVgLXcokZrNZlhGjNr18QEjbpGM3PT3Nsn3yQK1QKLDmHtV8TykAjMfjLHsnD5AoAKIM5OjoaEXHrjNnzuD48eN44IEHyvSA1TSTra2tOHDgAObn51lxCb88rNfrMTAwgHQ6zaQWVCjJ65NpYnDjxg1oNJoKCyNJWmkDPj09zfyMla4jmoDwxU10HPiGH3a7nbXXpd+T8T5lFfnmFvJsIa9PjUajZU0z6BqiyRKwsoRK10ipVEI2m8Xc3BycTid0Oh2mp6exuLiIRCKBbDbLjk8ymYTD4UA2m2X2XhQ00pjp3KltSkKZcr1ez1pV8+eDgkCdbqWdcTweZ5M7apNMS53ASgacz97mcjlcuXIFbW1tMJvN2L17N1566SW88sorZffQ7du3WUaIuvaRHSM5NtDkjIqWaDz8/UFtxg0GA+6//348//zzCAaDbH9oImCz2XD48GFMTU2x50xbW1vZsclmszCZTDAajWUWhfQ9dIxNJhMcDkfVbGokEsHw8HBFIVG1bckLiZSeoaspSlKb4VXaVqOX4oHajTlWgxrdbaO2pXZ7jfrMWtioojWPzYh33t+Nd96/Yvl2YTqKZ8YDOD0ewMRi/doWOYFUEf94OYJ/vBzBgQ4bnvrIGxo63teLCIq3CfWywMFgEJFIpCHej0ClVpiHWr3SUqJGo4ElPYuOya+gd+kFVfsTgBfPG9+Iw2/7L7h+5gyMpjB7UfNjoH7zwF2LJX6JnLJwRqMRS0tL7EXOf4YszEKhEPbs2VO2nC4vuPD7/az4TekYlEqlsvbW1TokTUxMlFn98PtF46POb0rL6BSYv/baa1U7qFHHLgCq9ICBQAAtLS1sGZwmVna7HTqdDoFAAH6/HxqNhgXO8mIzCpiz2WyZtpw/ZySfIV2mEhqNpmzyQRMO2hYAdp3zATF/HAuFAtxuN5NDVPsMfTdNqOTHm4LzpaUluN1u3L59m72U+PNKBaRzc3Nl3droM5lMBtlslgXKFJTJr9fV0NHRwbqRyVdISKNrMBiY/6tS4evQ0BDS6TQmJiZYkE26+VQqhampKQwPD7MmFXSv8eOORCIoFAqs+IsmXLx0SqvVoq+vj22LMrY0yYrH42w83d3d8Hq9LHNG14ter4fX60VXVxeSyWTdoi3K9NMEmc8ULy8vo7W1FSaTqWY2dWRkpGGFRGqLkhpRjLeZ/GMFKzRD0Zpep8XIgAcjAx584q3DmFlK4/REEKfHA3jxRgjZwuqK9Tr1SQSDwaaQUIigeBtQKpXKssAUSJD9TzQaxeXLl+suyfDej/l8Hs888wwikQg8Hg9OnjzJAinSuaZSqYqlcZPJBJvNhr6+Ply/fh2J2QkcXPoeesP/DC3q30gRuPBjnMBV7V7YrS7sTqVYoRVV5MuXY6PRKMtsUlaR4LNmfDW7fGJAkghaCk2n07DZbGVL6FarFT09PRgfH2dZSnlmjjK4sViM+dtSFtNgMDCtJVXEm81mdgz5ZW+SPfCNMOj7+cKZbDaL8+fPs45dxWKRBSHUPOXMmTN48sknAQBnzpxhL0HKOh0/fhx+vx9Xr15FW1sb9Hp9xfJwoVBAKBRCT09PVQssug6Bu3ZYlLmUL33TdUT7ThIA/jqi5gfx+F1LPv5Y8+eNtMTVrNxKpRLi8Th8Pp+i2wEAtvxfKpXYceQlDblcDsFgkNlA0T1G540vWAwEAqxbG2X9tVotc0qg4knKtPPQNUzHsZ77hN1uR2trK+bn5xXtzVpbWysCb/nqgyRJbP8BlGmh6ToPh8M4e/YsisUi2tra2P2v1+ths9kQi8UwOjqKt7zlLUxzTLZ8NFl1OBwYHh7GhQsX2D3HO4zQ8QZWih8NBgMOHz5cIUMJh8OYmJhgziu0wkHXkMlkgtPpxN69e9He3o7vf//77NlG54scZ0ZGRnDt2jXmCkOTRbo/QqFQ2bYCgQBrzkIrBTabraKQqFgsVpUzqSn+4ovxlMbEZ3iVJBYb5R+7lqK+Zt7WeqDm+ljvorXuFis+eLwPHzzeh2xhGS/dDOP0+IoWeWapfjOiXZas6GgnWD9mZmYQiUSY1k6uvSSNKwBV3o/f+MY3MDY2VvbSHB0dxZ49e/DmN78ZNpsN8XicVY8TGo0GPp8PNpsNHS4T2pLfg/PG30Mv1V8KjkgOvKgZwSvYj5JGB5PRxIJTWmqutm8UoJG2lrJwPLS/FIRUy5RTQY5er2dSE7kM5dChQ6wFMAWt8v23WCzsuwqFwk9s4lbIZrNIJBIsw0h6T7nfM/991Zb8+SCZsqS0PC9Hp9MhmUxifHy8ouMZfQdwV8dGAQZf/DQ/P4+Ojg62PavVyiYn8qCNtJt6vb7q8rjVamUPeaPRiMXFxapWaS0tLbDb7RVZkmrHggIzpUIquuZbWlqqOlm0tLTAarUiGo1Co9FUWHjR9+TzeSQSCbS1tSEQCCAajZYV47W1tbFrVKvVMqkJf2zIGo0PFvlgn65NuQyoGnQ+2tvbWVOVal69pGmncVNQzo87FAqx/eelGxRAGgwGRCIRdt3Oz8+XjY9e1qQzbmtrw82bN8vkPHa7HTt37mSrNiQTqaZfDwQCrLiTJhQ0YaIC01AohAMHDgAAbt68WXGs77vvPpaZIrtFuQbebrdDr9cz+687d+5UvV5pW/RsWFhYKHs28NsCgGeeeaai0PL06dM4evQoTp48qaqgORQKwWg01hwTTbjHxsYwPz/PJg4dHR3Ys2fPhizFN6Korxm3tZ6oLQ7cCMwGHd44tNL57lP/n4TrgST+36u38dTLt3ArqUVJVgrhMAD7OmxNsyIhguJtQDKZLLNBkvv00kvO4XAwmygl78czZ85gbGysYhuSJOHq1auQJAnpdLpq1k2SJEQDcziefx6OsQ9Dm6/v+RuVbPiRdALj2j0oau5mM3jZgkajYVkn+b5lMhlmA1XNMQK4qy3kM8nyYIuWZqminwrL+GzQ5OQkdu7ciWKxyLJy8u9Jp9OwWCxlWlE5VNhmMBjKur8RFCRTwxQlqskB5FDgev36dVYYZrfb2VJsKBTCqVOn8OCDDyKVSmFxcZEFQbTv6XQat27dgt/vBwCWAVYaE+nU6y3Xk+66GktLSxXnqdZxoGucz9jTzzUaDbsGPB4PC8RIK0xdtORBpfz7tVota9Gr1+vhdrvLVgfi8XjZdqtltek6I21xtbbCBoMBFotF0VeZHxd9Pzk18Bpv2p/l5WXWmIBWj/jtxeNxdo3zRYny/SfLQXm7cNp/mvBdu3aNyYNcLleZHeG5c+dQKpUQDAbZxEteZEotdWlliJciUVDo8XhQLBbx3HPP4eLFi2xs/HheffVVNoFLJBJwOBwVxyeRSODixYvs+BSLxQpZDGWCFxYWcP36dZhMJvT397Nt5XI5TE5OwuPxoLW1Fc888wxefPFFdo3xz5AXX3wRAFhgXK+gmbLxtcZ06dIl5hhC+xYOh7G4uIjHHntsXZfiG1nU10zb2gjWo6hvrWg0Ggz6HXj/fa3oio/B4vTg6pKEi4FlvBYqIpEH9rfqYDYZkUomRUc7wfpgs9nYi4qCP+Cu5pYuxP379+PixYuKutLDhw/jH/7hH2pua2xsrGwplrallZZxv/QaHsMZ2KP1WzKnJBP+bvlxTOkGYdBVr7jL5XLsRV8LWiasBeltC4UCy9TxL1CNRsO6TGWzWXi93rLqevr31NQUC2SrZV0pWK+X5aOMK31XtSBTbevlekiSxAJipSLLS5cusSIzcm0A7mpt0+k04vE40+fScrg8MylJKw05KIBWykzzTg9KUOay3r7x303j5idAFPwtLy/D4XBUuG+Q3rieBVqpVGISHLvdztoWU3acMrC8hEcehFDQ4na7YTAYFC3J8vl81e57PLQfmUymzP6Qz8xSV0NyUiHbPQrGLRYLa/rAF4vy8AE2HSMKiPl9pGt/cnKyTMpFKzAU6FMQSis81YpM6btmZmaYTp10x4lEAul0Gg6HAzdu3GC/lwe8xWIRo6OjcDqdbIJN46HtJpNJzM3NsQkxX/Sp1+vLro/bt28zOQNJDoxGY5mcwe1249y5cxUFtLS9QqGAc+fO4dFHH2WToVoFzWQ1yctw+GttbGwMc3NzFRr3XC6Hubk5XLhwAYcPH16Xpfh7UdTXDNvaSO51UV+joBUJA4o46jfioLuI/ICE2YwONrNBdLQTrC8Oh4Mt7yplQY1GI/bu3YvW1lZF78ebN2/WzTwCd9vFAoBUKmEfJnASL8CDWN2/zUs6fGX5cTxjegv6NXMwaJS3VyqVWFETOVjI/UqpXayagIYy5RQA0PfQy8/n87FGEC+//HKFVKO9vZ0VoPHHl5AXgdUjlUoxG7FqfseNnFXHYjF4vV7FIstgMMhe5ErZy2KxiJs3b7JA0GQyVWiqqeNbMpms6VNN3aPqoeZ6rPd5/rxQ4M8XW60GyrxS4w3+GiIdNR03Ota8NIIyxWRVuH///qp6WbWteaPRKIrFIrxeb1X7Q0mSMDc3xzKa5JlNaLValsnn97EeSqsUlH0kiVU1KVcsFmPHji+epPFQsE/XERXT8t9DHuH5fJ4Fw/z5oONNEwKn06k4nnQ6XbF6JocKLSVJwqVLl8rGw0tQXnjhhapFqLRvZIF44cIFjIyM1D3OlMmngluSidFkfm5ujgXK1QLnqakpHDt2bNP7627ktgT1oeLAqakpVlRM12vJYkHwJ10BRUc7wbpQKBTg8XiY3ye/9E1V6bRsXMv78ezZs6q3qdFoMFC6hTfhOXSgdsYPAEqSBt8qPYzPFt+DR4Y6cTQ+gaWl+gFPLBZj2lLK9tLLSJKkMglCPUhTmcvlKoI+k8mEffv24ZVXXmH6TD6bTFkri8XCvq/aBGQ1lEoluN1uuN1u1qCAMmoAWOe0RkDHqhoUqABgY6nW4COVSrECPTom8v2n7HE+n4fT6VQM1KamphqyX6RDpSBBvs/U4EOn0zErNJ58Pg+DwaA6g0HL4Lw3NX0P7SPfKEKeBSXNLPnzhsNhOJ1OOBwO5PN5hMNhWK1W1eOhDAwVcpK8gzTbFDTSi4rPYNOEmfyIeQ37WigWi2xb1aRcfEaa96amY0T/5jPh5OJB/9ZqtWVFqHwwTN9DkOQAQNUW8ABYrQCtmsk7UOr1ekSjUTap4F1F0uk0pqenWXApP+88dC7qSWPoHNIkjr/P6BlPnQytVqtiwJ/JZBAKhTA4OLhl/HVf77YkaWsV5DUTGs2K/eZrr73GugCSjJDqneT2nBuFCIq3AVREZLFYKpZjrVYr8xilF62S9yNV1tejQ1rE48vPYQemVX3+B8tH8EfFn8OisRcfHjGhx1LAdF7dS59m+Xq9HouLixUZGq/Xq/pF3tnZiYMHD7JMOWU8KVPu8/lw6tQpttzLv9g0Gk3Zy57P/BH00lY7HlpOJccOnmw2q6rYSi38tuRQYEXjVmrwQXpUsrKian+CL7IjqYrcpxoAW3ZuFLSSQJpQPjCgYIeCsWoPZfKwVQMFUXJ9On0v7Rs5f1TrxKbRrLSw3rlzp2L2bnFxUZX7RFtbG4xGI65du4ZkMllxf9jtdnR1dbFJCK8DpgwrAFUTS/q7WsEzL0lSknKR1rZaAEv3HE3oeQcPkluQpIrOWbWMM/+9dO9S4M+PhwJup9PJOtJVW9nI5XK4c+cOlpeXy76HVpkymQyWlpYwMDDAro1qgTGtFNTLXvKNTpTkLFTEVyvg54/zvV6KX8+iPn5bVBPAT7zl29qqBXnNgiSt2B46nU5Wh0KTV1qhDAQC2L1794YHxiIo3gbwvoZKy7E+n6/u0sXDDz+MS5cuKf6+RYriJF7AfkyoGtfZ0jB+v/A+XJB2Y9CtxafuM8Fl0iAYjMPv92N+fr7ud/T29iKXy7GirZaWFva7QqHAvHMDgYBihoZeRJ2dnRgYGFDMlF++fJkFTtWW5PiXHW2LspR85o2cEOrh9/sRj8cVCx89Hg8WFxfrfo8aWlpa6hZZStKK7ZY8+0IZtNbWVhw+fBgXLlxghZZKjUB6e3uxuLioWNwzMDCAUChUd9x8AFYtK09SBNqu3NqNOp+RPpeCUv6cyYP7WvCaVLnkgKQVLS0tSCaTaG1trSjqI+9uuuaUsnfVgrxqx6azs5NlA+U+1dQ8xOfzseuazhe/ysF7d9e6bmniRI1T5N/DZ+ZrFbTS5IDkW/z1RseQJjN0f8n3m8ZK51Bp1YZqBShI5OUtJDUwm83weDyIRqPo6ekpy/objUaEQiFYrVZWzFcNCtJ7e3uZ9V41vTRZ9R0+fLjmuZUkiU1UaOWI30fShQOoGfCTO8Z6sJ7+urWW6y0WC3Tccv1WL8hrBkjOQpN0+SQll8s1jZxl9f0QBRuCJEmIRqPMokpJr1ftMxrNiq+h1WpFOBxm+jN+OVZNMYXX6626HGWV0nir9AyexJdUBcRXS334xfxv4r3538YFaTceaS/hV4cL0OQSCAaDsFqt8Pv9dfWccmkEvZDpf7T/RqMRVqsVAMqKq/jiKZvNxl4upDnzer1wOp3suCQSibLgV/499DsqROO9eqmwh2bGfPBejba2Njz88MMwmUxYWlpCPB5HIpFAPB7H0tISTCYTBgcHq3rv8lSzmJOj1+tx3333sW2lUilks1mkfuIBbTKZcOzYMRw/fhx6vR4LCwsIBoMIh8MIBoNYWFiAXq/HyMhImU6Wz5jzwQkAdj1S4SLJDuj8DwwM1M0W813kan2G9o08jek8x+NxmEwm7Ny5k9mTyYvJ+O58alCjtx0YGGAd2yggJu9qapRS75yR7KEWpJmdmJiATqdjTi0ULFLDmenplRUduq7pv2l/6Lqud6z5FQ3+XPP/bbfb4Xa7mbczSSby+TyTLfl8PvT09LAsnryIj9wd9Ho9WwKn80f7TMWe1CKcstMUePKrHi6XizlP0FgoIHY4HGhpaUFvby+sViubqNHzhALitrY2NtEiTTo9F0iiRisqR48eZY1CaFJE/63T6XD06FE2OSmVSpiensbVq1cxPT3NjgFN4kiKQ+cbQJlsgI4JPfNpTHT98Nn6ew3/HlK67/n3kJp3Xq1tkQViJBJhKyMajQaRSATxeJx1HeQL8kwmEzsura2tSKfTGB8fb4hsaKNYy3FsFLychSaidrudJSGMRiPz0N9oRKZ4E6BmaafeZ1pbW9HW1lbR6tdut+P48eOqZsKzs7OsoYIkSTBIBZzAeTyIczCh/vLydKkV/734s/hO6UFI0MKo0+Dt7Qn0lhYwdbPS79dsNlf1ugXuFgABQCaTgdvtxsLCQlkxIWkzC4UC2trasLCwUOEdDKwESZ2dncxdQsnTk6rUq2lUKWCgTCBlROTa2/b2drhcLhw6dAjf/e53FX1x3/Oe96C1tRVzc3M4d+4c88bVaFbaNu/Zswe7du3C5cuXkUqlqi7vkxaWpAxKx5H2X6tdaf1Muki6Po4ePYqhoSEEg0E4HA5W9U7o9Xo4HA54PB7mYkFNSHh5ANmC5XI55HK5msU96XS6bClfDr88TcEM/znKehqNRnR2dqKlpUWxgFSn0+GVV16B1WplhYD8mEl/SOdeye+Y76JXLBbLjhEV7UmShM7OTrhcrprHGqh9T8uDIvl46Hq8du0aIpEIHA4Ha2csl73QZI/GV+26JikRFaPKJQ0UJFJAUe3lRkvabrcbZrMZCwsLZT69BoMBbW1t7P4olUpYXFysKFj0+/04ePAgKxCkSbD8+tDpdGhra4PNZsPi4mJZMEzdJylo9nq9rKEITVQcDgf7fXt7O7xer+L1mslkWBae76BI1xBdH3a7HSdPnkQ6ncarr75a9nwj7+STJ08CWOlCWs0PnSZNVMxKQbz8ms1kMrDZbDCZTMzPXX7OXC4Xk/ysB2r9ddcqZ1C7XE8FkFu1IK9ZZCEb1Zr69SCC4iZHzdIOgLqfiUQiOHfuHAqFQpk3aDabxblz5+ByudjLWAnSpnlbXNgRewEPF5+HA6mafwMAIcmJPy6+E19dfhMKP7nk/HY9fnFXAR4NYDZX+v0+8MADNV0j6HcejwdLS0us+I1m+pQRWlxchM/nQ0tLS5lNGJ/F1Gq1aGlpQT6fx7PPPqvo6fnII4/AbrczqyKgfMkynU7D6XRix44dCAQCeOCBB8ps26grms/nw8DAAJ544gmcOXMGc3Nz7KXX2dnJJikTExMYGxtj2le+8G9sbAydnZ2seyAFNHINq81mY8v/FFzwn6HCr1QqhUAgwFov8/KBQCCAQCCACxcusEp9+Wfi8TjOnz+Pnp4eLC8vw+12w+l0Mh2vXq+H1WplWYtkMone3l5FecDk5CQAsIyXvEiIXwWgiQIF/hSYUSW+0WhEb2+voixmaWmJFX1U00snEomyLKvdbq/oaJfNZmE0GlkWTv6CJR0nnUd6Ibe3t5dd04FAgElPat3T1FmRAiP5mMlBhc49ZWjkrZ6NRiObYGazWcXrmvaf/Jzln6GXHbUep3uLPkPHpVgswmw2Y3FxEVartUxeRNny9vZ2DAwMwOFwVExQOzs7MTw8XLYfSpMmuq+1Wi0GBgZYEG4ymdDe3o5IJIKOjg5IkoSFhQVFaQQt6Ws0GsXrlZ5FwWAQLpeLZWRplYSXxdD5HRwcZMWdJBkBVp75kUgEp06dQi6Xg81mY+c/GAzi1KlTOHHiBCwWCwqFAgv85FZyZrMZLpeLaaGVAv71DkTq+es2Qs6gdrk+HA5vSEe/9aCZZCHN0JpaLSIobmIkqb7XIjXSqPeZqakp5g3Ka1vpgTk6OorBwcGaS7J2mw07C+N4OHEKXims+DkiJZnwV8tvw18V34YU7roy9JvS+LX7HCim0/D5WtmLyGQyweFwIBQK4eLFi2XZH7n2Drhbxb60tFRR4ELHL5PJMN9lWhKlQIoCTLJZO3/+fE1Pz1dffRXHjx/H6dOnkU6ny/SZpEc8fvw4BgYGkEwmEYlEynw/I5EIWyIMhUKYnJxky/f8vk1OTsLtdle05uZfetFoFOfOnYPb7WZ+vhTg0JItsFKISEu+FCjy26KsIHXF8nq9VTXnr776KqampqDV3m2Ywh/nRCKBqakpdHd3Q6fTsWCYt4ril5GpaLNWcQ9lMkk7StDyv8FggMPhQCaTYS8tPkizWq1oaWlhwQZlfkwmU9mysdvtRn9/PyYmJpBKpWA2m1mxIGXod+3ahXQ6jYmJCVbdr9frWUGTRqNBX18fZmdny1wP+GNE+zQzM4N0Os38huVBGC3X1rqnaQJIGmm5Wwp9vqWlhS3V18rQDA8P49VXX2Wtgunap4Dh/vvvx+XLl9m+yLW3/PfRfsoLUWkSTuOjgJ6/z/h7vlbwtLCwUDYpkV/XNPHt7e3FjRs3WLbc6XRCkqSyexFYkdIEg0FWpEf2dNXaM1dDq9ViZGQEp06dYjIYOu409pGREWg0GvZMl1fbS5JU9XlN+8b7hl+5cgW9vb2YnJxEJpNhv+NdRHbs2MEaeNQL+NcbpftezTtPjb9wteV6Hgp2JUnakAymJN1bp4tGHcdGQdKZZmpNrYQIipsY3msRQJmpvslkgtPpZMVo1IWKh4KA6elphEIhpquUZ9SMRiMikQhmZmbQ29tb/YadeRk9p/4LejNn6o67IOnwd8sn8bniuxBC+QP3iDmIN/hSyCdXCkqmp6eZty8FSy6Xi5ny80u+lFGiYp3l5WXcvHmTBbu8xpeOBenpAoEA05LyWUfKXtJSLC0r8svzlC0kT08ATIZC2jyn04njx48zb9FaS4Q+nw/PP/88lpaW2NIenQ8K3mnZtFZr7lAoxLLOcucN0jnSdVBt38m+jrKlpVIJly9fruqxOj8/j1QqVaax5q8z8j2lQHp+fr6iuImCx46ODnR3d9e8higTCtzVg/LfR9eBx+PBjRs3WOaWXwHIZrNoaWlhxTS1lhGPHDmCZDLJOoXxy/VdXV04cuQIgJXJJ8liCDq3R44cwfT0dE3JR6FQYOdVqT0vWe3VuqdTqRTcbjcrfJSf10KhwAofJyYmEAwGaxZRvvnNb4bBYKhoPWwymXD06FEcOnQI4+PjbDLBT3bIRg8Ac5WoJsMg6Uk8Hq/pUZ3JZOouV9PEw+Fw1JQr2Gw21nqZWk/zMi3KlA0ODuLs2bOYm5tj14fX6y1rz1zvGhoaGkIsFsNLL72EaDRaVRYTjUbrLtfT89pms1UkKeg6iUQiOHjwIFKpFNPn0nWn1WrR2dmJBx54AMBKwB8KheB0OlkHP9JCyzW8G21J1ih/YbXL9T6fb90zmOshaWhGn+Zmbk3NI4LiJoZmu7VamVJmptbyD+kkqcKaf2FTRlCn0yGZTFbcsK7lMO6PfAetgReg5vH43eVj+KPiz2FK6ij7uQHLeMR4CwO6OLQaD1KpFMLhMNLpdNl4MpkMUqkVSYYkSSz7KV8eJqkF6UMpKyzXVvL2YC0tLexlyX9XsVhkvqBKgQgFfaFQCCMjIzhy5AjGx8eZnGB4eLgsm1kryxWNRtkyPmX8+BatWq2WZT9rteamJemuri60tbUhkUiw8+lwOADc1YFT8xaSHtCSLwUQVMzHd9qioOnOnTuw2Wx1CzRIkkETDSUNMz/pqPVdVOBGQS5BwS+v36xWIEYrAMFgEKOjo3WXEQ8cOMBalNPLqqWlBQcOHGAP7CeeeIIVPdGx7u3txd69e1mjDABlVld0LVNmnjyfldrzUiaPzpV86ZeyXPv378e5c+eY7IGXRFmtVoyMjLACyFOnTil2qhwZGWH3Ym9vb8X9kU6nsbi4CLvdzrLnfAFpPp9nAVwmk2H3He03HQeauJRKpZoe1eFwGLlcrmbwQMeHvku+kkINN1KpFG7cuKHYlp2cFy5dusR045RdTCQSuHTpEvuMGhkbNROie4Cu49u3b2NgYIAV3NZ7XtN9qPQZ2r83vOENijUQdM2uh4a3UTTKy1jtcr3b7V7XDOZ6SRrW0xN6NWyG1tQiKG5iaHm0VitTKqSo5cdIlZ30wpY/IGg5uFAosBvWZ9VgMPgtdM0/Da1U347qTGkPPl14Py5Kuyp+59Zk8CbzTfiMy1he1rCiDz67wetcyRWAlh/lLgS0ZEvFMtevX2fBozxTXCqVKgIU3paLD2TVBn302VQqhUQiUdUWij5fbRaezWZZZtZqtbIAjoreKHtGbhWU+aOXvsFgYPtLv6v28OObNJBuki+ytFgsTFc4MzPDgjSSlNC2c7kcUqkUrFYrMpkMC9D545bJZJjsIBwOs+BDjlarRTgcRjQarenAQdlDmgDxmWIKfCRJKrPtk0+IgJVJwSuvvMKkIRRwUWvucDjM5AoUGNHqgDwwooLVBx98EJIkYWlpCS0tLXjwwQdhMBgwOzsLSZJgMpnKpCkAmLVYoVBgEzm73V7RlCWZTKJYLLKOb6S/lq+k6PV67N69Gy6XC2fPnkUoFGKfoYwM1QjQ/585c4Zlh/R6Pfvc7t278fzzzyOdTjNNOX9Mg8EgszykfZJf7yRfokCerl36LF0LRqORPa+Umono9Xokk0mMjY0xOQv5Ec/NzSEWi2HPnj1oaWlBNBpFKpWq0C9TQR9l4+VSBafTyaQK6XQas7OzzKGDn1zMzs7i/PnzsFqtrIUzTSgocCTJC7WEpsJTufzq/PnzOHbsWJl/LnWjMxqNcDqdyOfzFc8++SSF/IXtdruqIKO1tRVer7eqnh4oD9RIWkT31usJ1NaScW5UQdZqluvXK4O5npKG1fo0rye1JHPNgAiKmxin04lisVizlanNZkNHRwdri1vNj7G1tRW3bt0CcLcAhaCsW7FYxPz8PLLJGI7kzqBv4v9Cv5yuO8ZrpS58uvjzOF26D6iSS+7TRvCIcQoGlJDPg2VmyYZIKXAyGAxoaWlBKBSqyG7TPnR0dODw4cM4e/Yskslk2Wfov0ullfbNpJ3m7dooi0OZHQB1gz6v14tnnnmmYpn59OnTOHr0KKscrwVlenU6XVVpBGlVSVMrn83TZMJqtaK1tZW97Ko1ZhgcHEQqlcLY2BgLVCi4z+fzmJ+fR1dXFyvMomJK+fnQaDRMRkHXI68plSQJ/f39yOfzLNtNL3Re9iJJEsu41wqKeRcRedts3saMt5qSB2qlUgmJRAILCwuQJIlJbfhj3d7ezgqb6gVGTzzxBE6fPl1x7s+ePYujR4+ip6eHBfF0jPnJHl94lc1mq7ZUJlmUyWTCzZs3yxwT6PqMRCLYu3cvXC4XCoUCy4BSENLb21vVe1bpRatmqTWRSCCTySCbzZYVbdLns9ks3G43c3mhFQ3+PtNoNPD7/ejt7cX169cVr9ndu3djdnaWTRwjkUjZClk+n8fs7Cy6uroQi8WY+wp/Xt1uN3w+HxKJRM39mpubQzgchkajKdPK6/V62Gw2JBIJ3LhxA16vt67kJRQKseeufKJLLZWpCdC1a9eYTzSN22w2w+FwYHBwkBVd8m4ndByXl5fh9/uZBKlekFEtC3z79m0m5RofH2d1GeFwuOz9kcvlVhWorTXj3MiCrNUEu+uRwVxPSQPv01xNoqfVapumrXKzIYLiJoaWs0sl5VamFPSRVrha+0R6uFDgwL+I6YWrAWC+9h08lvw+LPn6TRPCkhOfKb4P/7D8KEpV7K41kHBEP4v9+gXw9z8fJNVyligUCvD5fMwJAkDZPkiShIGBgTKpRTV4nXAoFGJZJDqOJC0h265r166x7BQfGFHQ98orr+DFF19kgRm/HPviiy8CQN3AmAr9KMssl0ZQAK6kTeX3i7TFSo0ZDhw4wLKjvByFlr1pIqVkfUfno1Qqobe3l0l5aBLCB5dHjhzB7OxsWfMGeQMLYCVTT+et1jHKZDKKdlEUDCs1k6DgmYIqWvKnDDAd65mZGbS0tLBAVikwmpqawve+9z1cuHCBNWmg7CSd+8OHD8NsNpcV8NBnyJGBfHzpmgLKWyqT9IHkM9XOGRWY8rKQ1tZWloleWFhAPB5nGb6JiQnmZGC329nnQqEQTp06haNHj7KlViW5RjgcRjabZcedP47ktZvL5TA0NISFhQWWCaZ7iK5PylrXumYHBwcxOztbU17EB8XyZeJisYhYLMb+pp4MIZPJKAYqFosFsVisruRFo9GoaqkcDodhMpmq7n8ymUQul8OBAwcwPDy8kqT4iasJfYacZoaGhmoWRRP1luv37NmjKOWiDPzMzIyqQK0R0oDVZHjVsJpg915nMNdT0kBJDGqrzK8o0zXYLG2Vmw0RFDcxpJmt1cqUCn9q+TEuLS2xh7PczxUAdurmcbL4DDoj9bujZWHEl4s/hf9RfCeyqL70YkIBbzDeRKcuUfX3SsEXD3WjI7spypZQtblOp8Ps7CyGhoaqZjd5UqkUC1AoSKHAjaydMpkM3vSmNyGVSikWWx06dAhf/vKXy3S3wN0GGYVCAefOncOjjz5aYX1VdnxkLhDVoGxiLRKJBG7dugWdTlcmkaHga3l5GZcvX0Y+n4fZbK7I3lHgTYFFLYrFIlv6phUIyjzQz4G7TQ2qyVY0Gg27fulzSlCWvN6Y6kGZaT5IlY9naWkJRqMRLS0tNQOjixcvsqy1/BpeXl7Ga6+9Bq/XywJ+/jM0ObDZbAiFQuxapjHSf5dKJRaAUcU8f16pYHJhYYG5RdRaivV4PGUuJkpOBk6nE/F4vKZ1l9zPmA/6SqUSk3pQhpYKgzUaDRwOBxwOB7LZLKamppichGom6JoolUq4du0amxDIJyk6nQ6pVIpNdGgFgs+m0nV3+/ZtOJ3Omkvx8mu0GhTwarUrzTzk46FJEF1r9Dt+8kXbkiQJExMTrACPnukajYb9e3x8HH19ffD5fCybTkWqDocDdru9bKVCCTXL9deuXWMZeaVjvbS0VPdZ1EhpQKPlDM2yXL+eXr2SdNenmRIfdA17PB7odLqmaavcbIiguImhZXa6kXjo4Z/L5RAOh2v6MQaDQWi12rK2pwDglSJ4M57DUPFG3bGUoMM54zH8VuLnMCW1KX7Oo0nhpPEGHNq1GcKTLs3j8TANLb2oeYuzS5cuscYC9NLhl6wpS5VIJJiGk58YkC0dn6VJp9PMv1Kv18PpdOLAgQO4c+cOcrlchd0UAKb3y+VyuHDhAkZGRmpq62i7AMpe6AaDgRUH8pnEascHAMLhMFwul2JjhkgkwnSupOXmX6aklVTD2NgYe3nyMgabzYZsNovx8XEmIaCiKn7s9G+y4qoF2X81Al62wMPLGupN1KhQtRa06kAOCnJNMQW0lGnms638fUkZQY/Ho1gYGo/HMT8/z3yOqznTkNY1EonUdDKIx+Ow2WxMgsUfC8putrW1sclgNU9sOj6Li4vo7u6uqpfN5XK4ffs2wuEw89iVj8doNLJJgd1ur3rO9Ho9UqkUm7hSgE2QnGZpaYk15VBaim9vb2eyEJro8QV7tGpTTZ7DQ8+eYrF2S2V6blEGtNo9S3KO7u5uGAyGigJaco+ol71Vs1wfDAbZCqPSsSbddy0aLQ3YDAVZq2U9vXo3U1vlZkMExU0MLbPH4/Gay+ySdNc3tJofI99pCQCsyOAx6SUcwWvQoX7W9oZxH/4Wb8ffJvajIClnOHfqQnjQcBt6TWPaSPL7xWd6NRpN2dInD//i519iFBzz2Tb6OQWlpAs0m83YsWMHO9a5XA6Tk5Nl2epqkEtGNBqt2RmvVCqxFz7/EqVAU6/Xl+2XPCPE7xfvxVmtMQMVfFGmR77sHYvFVL9oQqEQ+05eXsIvs7a3t8Nms7HsLC+X0Wg0LENWLygmN5BGQedGrqcnWQI14FDSk/MykFqQ3nNubq5Mx0fdFakTHV8YRtuja4sPwKqdV378tZxpKHhWIyFIpVJlcg1es02Fl3zgLr/P6Of8tmgpnsZPk1n6Tsre8880qjOgAlJ6BvLbotUGmqDS9c9/htpGt7a2snbk1Zbi77//fkiShKtXr7IMPkHHvru7G6lUih0nuSc0Zd1pIkguL/x9RnrxdDpdt5kKBcG0GiUPktQus6tZrqdJRa1jTe8QtdtSkuCsVhrQLBneRtFoaUgt1Po0b8amJPcaERQ3Mfwyu5IWmDKntapM6eWjxzJGpAt4BKMwo/7NsGjoxQv2t+FryQMYTbgVP6dBCSOGGezRBdCA+5lBwaHch5SK0XQ6HWtOQMdHHvQA5a1q+aYN9DsKBgKBALLZbNVZfDAYLFs6rxYYUyZUr9fj2WefxeLiYpmfaygUQiAQwOHDh2Gz2WC325lXKwVs5NUaDAbLAlb5y5p+pmY5jsbAv6gpY0svbDVQcaDSMisFsq2trSiVSmXWXfTytVqt8Pl87CGtlE2vVYS3WigYp4kRQRl/jUaD9vZ2ZksmbyghSRLcbnfFBEwJajdO2wbAgleaTFAxpTybzC871yv6pEYg1XS36XSadRXkgx6y+6JsPWmdE4kEk9NQ0EoBtiStuN3QCgZJAQjaB5PJxApa4/F4VRkGTapo9YLuW7qv6XiTE0O1IJSeeXwrcPn5ps87nU7s2rWr5lL8wMAAa8pC1wpljE0mEwYGBjA1NQWDwYDFxcUyPb3FYoHX6y17NtGqE59xdjgcTGerppkKPdPXssyuZrneZDLB7XazoF9+7et0OtaWW822SIJTrRiRH3OtVbStzGqkIWs5Rusp1dhqiKC4yaEHpLy/Pb0YSAsZCAQUq0ztNhsG85fxiHQKbsTqbjOqbcFp3WN4VdqLHwX7MFN0KH7WggLeYLyBdt2Klk7JiksOn3WqhsFggN1uRzQaZQ4RfEYpnU7D7/fjoYcewmuvvYZ4PF6xbQqE7HY7e9FXgwLGdDqtqCulhgm09EQvO/47qHEB6SapmIrPCGYyGdjtdni93pqdpgYHB1kxjhw6blTgFgqFFBszkP6QNOnyAIuCeHKf4ANuefCt1+srsnJ0fGiZla5HsieTywOKxaKqhhoPPfQQnn/++bKgUQ5tt9oxIqjISe6HDYAtXTscDjz++ON47rnnFBsh9Pb2ltm/1YKyszwkL7DZbKx4j7+m6ZiS/GJwcBCTk5OKRZ8DAwMIhULIZDJlWmFaUYlGo7Db7RgaGsKFCxdY4wq5G4hOp4PL5UIymWQFunLomqYW51QQyF8/Wq0WbW1t8Hq9zOmEhyRJAwMDLPjNZrMV9ysFot3d3ZiZmUEikSgLQs1mMywWCzo6OhCLxZi8TH7tU4aTrPSUluJpMuzxeNhx4O3vyL+dGg3RRJw/r4FAAP39/azOI5FIVDQTofPe19fH2kEr3bM+nw+9vb1YXFxc0zK7muX6zs5OSJKE27dvs+3zzVS0Wi26u7tVbctisWBiYoIFW/wkLRqNYmhoSFUjna2OGmlIM7l4bDfUpYgEGwItu9OLjIIm8iqmjm9utxvxeJz5w1osFuYHaw5fwcmp38fbs/9QNyDOaix40fEz+GrLR3HJ8Si+nR2uGRC3apJ4u/kqC4gBMDupWphMJlaFr4TVamWNNKgYhaQOFNyR5vL48eMsi0WTBb7y/ODBg/D5fNDpdEx7SQEaaQmpM1atpcZSqYR9+/axTA8tl9KYdDodDhw4gMnJSZb9pawYBc3UKrizs5M5YtD+AmCdpvbt21f34dfa2oqRkRGYTCZEo1EWZGSzWUSjUZhMKy2kedkJBen0wqbf0YtevjxO/+bdDeTBJR1LvsqeXzK2WCxs1YNvqHH27FnMz8/DarXC6/XCarVifn4eZ8+eZS/RWgwMDLAiMCVcLhdr7UvIXxAtLS3w+/14wxvegP3796O1tRVOpxOtra3Yv38/3vCGN8Dj8agqjuS1xDQRou2RtGZwcJBlZHnJBGVtDxw4gKNHj6Krqwta7UpnvlQqhWw2C61Wi66uLgwNDcFgMMBisTCvYzoPmUwGFouF+f12dXUx2QJwd+WEfub1egGAdWikoIZWGSgo7enpKTvX5EtN2x4eHkY2my3z2KaAtVgsIpfLsUkhyQr480HXlsPhQEtLC7ue6d8Oh4Nd3x0dHWhtbWUrPfy9SBlOn8/HpFK0FO/3+8sa9PDay76+PuzatQs7d+7Erl270NfXx4rE+GVm/hlD0DNvbm6OyYusVisrkqYJVUtLS9179tixY9i7dy+sViubpNFngsGg6mV2Wq6v9z3k+Ww2m9kEsLOzE2azGS0tLate0q/2fCDq3ffBYFD1djYzStcj0JhjpPbcb4fs/GoRmeImhjLEFLjI/Sq12pUWxtFolHka08vThQQeLz6DnenzdbezDC3Oae7HK47HUdDbMVXy4TsLThQl5RtmWB/CiP42dJpyGQctBQUCgaoFXNR+OBAIKGaVab8oS0fWRHzmhQqMYrEYa61MrZfphUytl3fv3o1YLAaLxYJAIMCCNo3mbjtkCnjqmZ0/8sgjsNlszKuWsqtmsxlHjx5Fd3c3Lly4UFV7SQEyZdtqLaNR9tZsNrMlZYKOAelXH3/8cdYWmhpDUMBsNptx7ty5siIt/hqiANnhcMBoNGJxcbGsyI8apFChVL1lViq2ouVWkm7wWu7FxUXWUEOpUn1sbAx+vx+zs7Os8IrH5XKhra2NNRWplnWkQCWfz7MsqHzfjEYjC0hqZXCog1gtb20KYOnf/Dmjaz2dTiOfz8Pn8yEajZa5D9BStsFggM/nw2OPPYaxsTFMT0+XeRCTLl2v16Onp4fJPvgMn9frZRXns7OzTHIk3x7VLFBASdlOus5Ia03/Xc1Zwm63w+FwIBqNMokIHXe5a0YkEmH3C7lq0PXBT/iXlpbKHHVIXkSOOslkEjt27GCZSHmLc7vdjp07d9bNhKnRXlLhW29vL+LxeNmxJllIKpVimmR5/QclMKjolSZ7Svcs/b4RDgxql+v5z9CzrrOzU/W2YrEYMplMzWOUTqdVOaY0onnFZkWSmtfFY7sgguImh+9Exz+w6ee5XA5LS0twOByIxWIwSDmMZH6Mw9kXoUd9y6oxDOKHeBgxrRdWWPBi3IvzSeUXiQ4STrpDON66jFTKzQJHm82GVCoFnU6H9vZ2pv2TyzlcLhdaWlowPz9f5rTAV2DTi5sKTqhQidfokdaQMji1Wi9LksSWkg4cOFBRzR0KhViQzS8jKpmdnzx5Eo8++iguXLiAaDQKt9uNw4cPQ6/X44UXXmBBk9zxgNcqBoNB7N69WzEIu379OjKZDLPPoWBfr9eXtdsNh8MYGhrC4OBg1Y5VkUiEVb9TUA7c7QpI2TyNRoOdO3dix44duHPnDss29vT0QKPRIBwOs4c0WfzQS4+aXXR3d7MCUF6vystHjEYjUqkUFhYWKrqnAXelKpRZo/PCd+Kz2+3w+/2s4QB1p5MfI/p3oVCA3W5nWUz6HtLAZrNZhMNhJp2pVtxjMplgtVpZowg5pK3mO7nRcaZ9pPMfCASYjCAUCrFzT93SqCqcoOIlfhWDMrkGgwG9vb1Vq8v1ej0ikQhzn+C7PtK5ILcHmuDQd/ByF/psNBqt6SxBBX80Aal27kknTdIGuasMyReWlpbqVs4fPHiQOWfIJ592u11VJkyN9pL2w+Vywe12V21NPT09jVQqxZITvHyCtpFMJjEzM4Pe3t6a9yzRKAcGtV3v1rItOiZer1fxGFFyw+v1Kt73290RQbh4bDwiKG5i6AUO3M1E8VpHCnJSqRRi0SXsSp3D8dQPYSvF6373LNrxQ81jmNZ0Q6PRICvp8MNYD6ZzFsW/ceiW8Z6OKHyaFCKRNBtboVBgtj52ux0tLS24fv06K3ShMVNrZ5vNViYBqVZIQ8U/lIWkymj6TDweZ5XyhE6nw759+yrGTUtJ9MBxOp1l1kZWqxV79uxhFm/08KBAoZrZuV6vZxlqHjXeufznlIIweWaYPFz5QJuv+tdqtejt7a34HnrZk38qX4REx580q1QgRQF0JpPBzMwMK5LZvXs3k4BQAEmV5rQcF41GWcBOkh9eC04BZT1HBCqyIn0uf/4zmQzu3LkDp9NZFtTSsaDJotVqZRMn2n/KjtJ1lEwm2cSpFmazmWW75FlgOqZUHEeZT146wZ8rCnBJhyvfd+rCd/36dVYwJ2/MQZ3RSDPIT5h5zSBdAxQQy/efJqQul4u5I/DXML2IzWYzc0Sg4yh3lqDvq3XuJUmC3W4HANYqmZ4FtAJBQWq9Jgd2u70sE3avtJfkF0z3kjybTCs5pVKJWZvJJ/qSJDEfZ0LpnuVRej6sFjXfs5ZtyScX1Y4RXQvr0bxis3IvGnw06hraLoiguImhFxnpXeXFG/TCd0Wv4NH099C2PFf3O+Nw4Bnto7is2QNoNDDodAguW/CDdB+SkrLtzoCjhA/0ZWEsAbHYSjAsn23SSyAajcJsNqNQKLACJ1qaNJvNbCmaHqJK+0U/5wtpaBmX9MD1ghmi3lIStTulcdOSMmm0zWazKrNzfhKgpK2jQKMWpIekl6hc9gAAFosFPp+v5vdQURPpHylTTYVWwMp1ZrVacfv2beh0urLCrkQiwfS9O3bsgNPprAhC+OU4+s5CoaC4FE9a2HqV0VSwRLpygv4dj8fh9/sVi/GoyJKakyiNh5bla0EuDnR8eFcR3imAn6zKAyzars1mq9tQYnp6uuYS6sTEBIaGhuraO/FBLu+8otGUuz3YbDYmgZAXm7lcLqZHpolTNXcBCgjp3NP1Jj/3LpeLFbhVc3Kgc1RrW5SBpHbOrzcTxk+YlY7jfffdh4mJibqBM3WyowCRh+5BmhDQ326VDN7rmVzIEY4IwjWiGRBBcRNjNBpZtyjS1/ESAl1iBk9kn8Zg4Urd78rDiDP64zinPwZJbwbN46+mHXg23YnlGjWXJ3t0eNybQleHH7dv30ahUIDH42HZEcpexmIxZLPZMl0aFRFQZiqfzyORSLAXsNvtrtivaDTKKvRJSy1fHiUNYCQSgcfjAbASBFWTTxC1lpKi0ShmZmaQSqWYZIMyfZS1VNPutK+vD2azmWkceejfZrMZfX19Nc8XFWGMj4+zlQGNRlPmItHf3183A0CBJS1Zy4816T4p81BtyY6n3nIcTX5IAlNtKd5iscDj8dRsqmC1WlnmUd4shYoWNRoN07Z6PB7Wbpi8a2OxGJaXl2GxWJBKpRTHQ1ZktYjH42XSFb4NN1n9WSwWttxP45QfR7PZjLa2NsRiMSaX4J1H4vE4WlpakEgkmB5WqTHHgQMH6moGI5EIK5iTF1nSGI1GI1pbWxGPx1krb7kbSkdHBysSVZo49ff3s8lONfs6Ovdms5lNwKo5OQwNDUGSJFy7dg1arZZNoOna4J0M6HvXkglTo72k55tS4Hz06FHmT67kKtHa2oru7m4Aa3cXaDYaNbnY7o4IwjVi4xFBcRND1b/RaJTpNA0GA7SFFPaHf4D7Ms9DB2XLKgCQoMF1x3FMdv8cxmejKC0vo7S8jGUJeCnTjks55UyjXgv8/KAG+20rD7Xu7m7Mzc3BYrEw1waNRsOCF2pCQYUptORfKpXKvEWj0SiOHDmCl19+mRXesPFKEiwWC4aHh/HKK68wXTLf1tRgMMBqtZYVTY2OjrJCOwr6fvSjH+H48eNlMgd6sJCOjzK7VJVLumh+6RsAaylbr91pS0sLBgcHMTY2VjY+4G7Xu8HBwTK3B6UAk9opyzPiFMjxTUiUvoffH3r58t23yC83nU6jr6+PeYzyRVvUoY0mBLWCkHw+z35Hrgj8uB0OB9xuN/r6+nD9+nXFF6jX68WVK7Une/zqQiQSqSi0o8wwWUspSQP4TKgSpNHt7u5mxVFU/OVwOODxeBCNRuFyubC0tMQCbgqcAbDAp6+vD5cvX8b4+HjFmNva2tDT04NLly4xuYQ8U+r1etlExu/315yk0ASWru1qBZsejwf9/f24fv06kxdZrdYyedHw8DAuXLjA/o6H/r28vAyr1cqygfL7mlYI+H2m7LvcQ5q+jzTq/LbUNlLhqZeVrTfZUxM4j4yM4NSpU4hGo7BareyaJlnIyMgItFotcxdIp9PMu7hQKGB+fh6xWAzHjh3blIFxIyYX290RQc3kYrsfo3uNCIqbGJfLhe7ubhZ0ZtIpDGTO43jqB7CVEnX/ft48iLHeX0DM0oeDBw8ipb2AQCCAeB74f0t+zOaV9cNuo4R/uSOHAZcWPt/KQ40q3n0+H+bm5pBIJMqWPjs7O1khkslkYsuJfDBLS0MdHR1oa2vD2NhYxYu6r68Phw4dwuXLl5mmlA9SC4UCkskknE4nfD4fRkdHcfr0aeYcQfKKeDyO06dPA1gpxJuYmGAV35RR9Hg8zKWBgg+SKvBFQlTcV0/LpdFo8PDDDyOVSmF+fr5i/zs6OvDwww9Do9HUzBYZDAYsLCwwaYDcUm15eRkLCwvMq1Xpe+x2e1lHw2pSFSomogCxWpFMOBxWpWMjuz2bzabYwAFYKaDzer2KL9BAIMC+j/aX4AMp3ltZ/qIg7a/JZGKTy2rSANpOvf2izKlSYRsVrrlcLiwuLjJ9MbV49vv9zMaMl1PwkE6+WCzizp07NRtz0JhrTVIog81niwmSNVgslrrng4o1a02cyHKNNKVyiYkkSchms8jn8zVdCigwpcI8+ZgNBgOWlpZUFxupzcrWyzjXC5zVuEo00l2gGWnE5GK7I47RxiKC4iaGnzVagq/i/vjfw5Odrvt3cb0Pr3W8F7m+kwhHIujw+Zge9KkXL+EvzqUQLyrLJY7v8OC//cwu2HSlColBsVhENBplFlz8EjK1SCYDez6Io6V/KrS7ePEiJiYmygJP+uzExAS8Xi+zJSP5AEHL57ScfebMGRQKBeYSAYAFfOl0GmfOnIHD4cAzzzzDdI0kJQgEAjh16hT27t3LAmKg3DmC1+GqCQxbW1vxxBNP4OrVq5ienmZV+r29vdi7dy97+Z09e5Y1ZyA/07m5OcRiMezYsQPBYJAVJsllD5TZnpubw82bNxW/5+jRoyxT6HK5qkoM3G43063r9StdqeiF5vV62c/VdKPil/96enqqOn3Q8p9Go1F8gVKxCa/R5KU6lLnlPat5mYHZbGaTmM7OTsRiMfT09DAvXqPRCLvdjnA4XLYcqbRv/H61tLSUuUb4/f6ywraFhQUcOHCATSToOIbDYfh8PtbYZGhoqOpnZmZmmAShVmMOmmCUSiVFFwMqDqQgkz5Pv8/lckin03A6nUxbXe27FhcXmbuA0sRpbm6O1QCQ24XcWYKeFW1tbXC5XBXuGwAwOzuLaDTKxiO/9mm1iVZtau3/emdlh4aGWAe9ajKuRrsLAM2nTV7r5EIgjtFGIoLiJqfVkMGjgS/COX2q7mezMOEF3YN4WXMYpYABhqVX0d7ezpZbvnMpiE+fzaBYUg6If/mhAfynnx6GXlf5GbIbkr+sgZUXUzQaZab2lN2WQ0v3V65cYQ4E8u8pFAoYHR1lATZZRRGUuS0Wi7hw4QKSySSMRmNFcwXKUCUSCZw+fRqpVIrZiVEwThm4iYmJMkcIeXZKnq2sR2trKx599FHFLlrj4+NYWlpCqVRiS/+0PJ7P53H16lXWEISCC/4YUiOMq1evsoxkte+5du0aRkZG8P3vfx+BQKBiAmKz2fDQQw8hEAjgypUrzDuXz2673W7s27dPVTcqmsgtLi4qygP45T+lF6jFYkFraysWFhaq6lP1ej2z9MpkMkwzzBcjUmZ3586deOWVVzAxMVExntbWVjaeevs2PDyMq1evVnzPrVu30NbWhje+8Y0AVnyY5Z8JhUJobW1FV1cXLl68iOXlZVy+fLnsWpyfn0dbWxuzo6PGHHJPaMr8xuNxLC4uKq5+UCEeHT9aEQDAJps6nY5JY4rFYsX+3759G8PDw6rcBciej7rXyY+11WplE9fFxUXmGS7ff5oUkdWa/L6mQtx8Pl9z9Wf37t0NzcqqyThX+0wkEmGfabS7wGbVJq9VB74dEMdoYxBBcZOT/8a/hnP2pZqfkTRaXDIcwTM4gZzOvhI0/sQCLRwOYzEYxmdOTeEfLgYUv8Ok1+IP3nMQ77ivS/EzVGxU62VNFe21HAGomE1eQAXc1d2SHR1VcfMV/eStm8vlsLi4WPY5OfQ5sgoDwDS2lLUqFotYWloqs8xSop72lEfpoRaLxVhmq9ryOGXDJOlu1y9eR8kH7xR80DGg40nfMzMzg87OTng8HvZC5jWuHo8HHo8Hs7OzCIVCLLDQ6XQolUrME7ZQKCAUCqnOutWSB6jB5XKhvb0dwWCQrQwQND6v11sWWPHjJgswh8PB9L1K5whQl1G8dOkSa6fOQysOly5dwoEDB2puK5/PY2lpie2XwWBg5z6dTmN6ehput5tp+GkZnpcqUFb82rVrrImMzWZjYw4Ggzh1amUSrdVqy+ywqhX/5XI53Lx5E/Pz84r7L7eAq1YA5PP5EA6Hy+z/5MeJHDpu3brFpCK0/6lUClNTU+jr64PJZGJyKPm2KKicn5/Hyy+/rLj/6XS6YVlZNdcHgLqfaaS7QKOz4M2WcRYINgIRFDcxkiThSsd7cH+NoHip5RCes/00rsdXtIx2mSPEbCSJX/3GOO5kqmcmAMBtWMZ/OGbD/3eos+Z4qhUbyV/WkUikrBhNngUG7gaW8oCY/xteL8r/DwCTYhQKBdbSulgslnkWExQQUSBJrhX0PRqNhmlt6efVAjcKxhvxkshmsyxLbLPZ2HdSERwt+9N+yo8T/VyjWWkrTP9OpVLs85SBj0ajmJiYgNFoxOHDh6vKB65cuYJLly6x48NLXsj95NKlS6zosVbWjXSpkrTS9reak8FqsnNGo5FNGmhMvKSGgn++MQrtR6lUQiKRYA1Zao2HfHKV9u3y5csYHR0tcz6h8dDkZXR0lI1DaVvT09Msq0mFqfy5z2QybNm9ln45n8/j8uXLyOVyZas2VIwbjUYxOjqKkZERNknlJ4P86kcul8P09DQKhcKaLOAOHTqEH/zgBzXdaXhdOz+Jo4kuNe1paWlh2uVq3RNdLheuXLlSc/8vXLhQphuvdm2pycqq1QHXu4bGx8fx0EMPNcRdoNHa5M2acRYIGo0IipuYWCyGqYIXbb5H0RX657LfZcztGOv9BVxd7kcgGGRdxXim03r8fbQVqWXlau0+UwbvaA1Dl0ixjIlSxoAvNuru7q7QQ1KTAKrKp2CNoMCGfkaBHVDe+Yv/G7Jj4/eNtL8GgwE7d+7E2NgYy2LLg3AKADOZDPsOuS0VBeF8Uwb5y4o+x/uM1kPpOFIWs5oVGC39UyadMqB8ppz2g4IIkqrwwTxJL2i1oK2tjQUTPE6nEzdv3mQSFKUOg8lkEjdv3kRn58rESckmbGZmhmXnyAFCvj012Tm+bWwsFmP2aqRldzqdzG6ONKt0zOmY0nEOBAKslXe18czNrfh7k0Zefj6cTicmJydZQMZ7HdP/0+rG5OQkent7Fbe1uLjIOglWg65Fq9XKLOuqNeYwm81IJBIV3dzoO6xWKyKRCJt88assBP2cbNX8fn/NjGo9CzgqpCV3GgpQl5eXkc1mYbFYWNBL+nZ54Se17O7o6GAT42rdEx0OB4I/ee4p7X88Hmce0rVat9fLyqrRAau5hkKhEOLxeEPcBRqpTd6qbhgCwetBBMVNDD3Eb+38l2iPnIWulENRZ8Xtvp/DTPfPYBk65G7dqugOJknAy1EzvrdoRwnKD9eHPGmc9CWRSRdY4UqtjIHP54PP58O1a9eQTCYr9IB2u51lvyhQ4DN8AMokDBR8ySH7JvLRVfL7pcYax48fx+nTp5FOp8vcJ6gD1+7du3Hp0qWKNsfygJwK+/gCP9oPykQ7HA5V567WcaTgs9bysF6vZ4ERjYmykPR7l8uFaDTKsspyKDtO2c1q0AuZLySsJlWgIsl8Pl/VJszj8bAOcavRTCpNHHipB2UM+aw5tRKmwJ2OCU1s6N9k61Vv/wHU/AyNt14wS4WctbbFTzbkvsnkx+v3+xEKhRQDJ5/Ph+vXr9fcVjqdRiqVYseEn4QCdyelpE9Xc85qWcAtLi5Cr9ejp6cH4XC4YiXJ6/Wygj2Xy8VsFat1fqNJjFL3RJ/Ph8nJybr7b7VameSlVuv2WqjRAau5hvhjuFZ3gUZpk7e6G4ZAsFpEUNzEUGY2pV0JhM3ZRdwa+AAKRjcAIP+TbAy9YE0mEwol4J8W7Xglpmy3ZtBIeEdHAvudOQAaprsNBAKsk1YtTRxpTPkANJFIIJvNskpr+dIpcDcDrNfr0dvby3SFQHlxm0az0phiYWEBACqCZ8oq0fjIh5h8iinwcDqdOH78ONrb2zExMcF0ynwwzMsEXC4XsxCjoJzPSre0tDCNby39Xb3My969e5lFmNLysNPpZN9LulmSk9hsNrS1taFYLCIcDrPxVJOqUAa8loaRAvNsNsuCJDouxWKRSUtIo0xBOgVwiUSC2YTZ7XbVmslaEweTyYRUKsU043Lt7c2bN9HS0gKDwcD0p0p2c/T7WvsPoOZn6OfV5Cz88abGJbW+p96Yl5eX0dnZiZ07dyoGTrTyUa8znsVigclkKltRIGgiSO2a1epclbTy9LwyGAw1bev4bSl1fpPvv7x7otr99/v9uHjxItMd03Gv1rpdCTU6YDXXEH8M1+ousFptstLz6l64YQgEmxkRFDcxvA3U7d53V9Wf9fb2olQqIRQKIQMTvj7vxlxWWT/cYijifV1x+M3L7HvohTMzM1MzYzA2NoapqSm2XE0vWgrUlpeXEYvFYLPZkEwmmXxCbrtmt9vR09ODWCzGCuAoIKblcbfbzazfSD8s18uSLyyw4kN85MiRqlZIpN+k6nt5VTywEsy0t7ejpaVFMQg1m811gzlqF13rOM7OzqKrq+uu/3SV5WHqeEf2XtWszUwmEwvy5VlvCmzJYaJW97iBgQEEAgE2Bj5LDtwtkDKbzQgGg0z7ymfYs9ks7HY7urq6cPv27bqayXw+j9HRUcWJwwMPPMB8rnmrPcoGp9NpZLNZpmOn1sFyDavP50Nvby8WFhYUx9PZ2QlJkmp+ZnBwEJFIhB2jahaBJpMJg4ODCAQCit/D369KY6bOZ1qtVjFwKpVKzGqvVge1oaEhvPbaa6wwtpp3MPkU17pG1Ohc5d24qsk+ent7IUkSQqFQ3c5va91/n8+H5eVlOJ1Odp9lMhlotVp4PB7odDpVrdvVdBnjryGlToXyY6g0uVDDajqf1Xpe8ZP+aqzWDUMg2OyIoLiJ0Wjqd7fZs2cP/H4/vvjd5/DtqRZkJOVTusOcwU97ArBDi1zu7pKtTqdjbZVraeJu376NcDgMh8MBo9FYsfSZy+WwtLTEutHJO1QBK8uL9913HyKRCAYHB6HT6bCwsMBeeu3t7SgWi4jFYrDb7cxGjV7gFFhT8My/eHU6Hfbt21exzUQiwbqyyVv9UnMFynIWi0Xs379f0WOWgjklX+A9e/aoyrwcPHiQNS+w2WzsOEqSxM4rsOL4EQwGy7ZFWkraBq9x5Y8R4fP52PdUu4Z6e3sxNjaGUCjEJgz85IEmBuRxTDpe2k42m2XBdCKRqHvNDg0NYWJioubEYXR0lElWqrVnputv165duHjxImKxWNUuYseOHYPH46m5/8PDw2XHutpn9u/fD0mS8OKLL7L9p5UE0jqPjIxg3759SCaTde/XU6dOKY6ZOp/Rsa8WOGm12rIOavLVBvoe6lY3MTHBVk/44sBSqYT+/n7cf//9GB0dVaVzVco6qnle7d27F+3t7Th16hSWlpbK2kVns1mYzebXtf/VjuP+/ftx+/ZttLW1VdUUk7NKvSyomv2ia0jJipC3/msEasdUzzFm7969DXPDEAi2AiIobnLqdbfx+Xz47rUkvhHqwHINt6t37DJiPxaQSuYRjWZZ8ETtgltbW5FIJOrq5iirQBlC+WdoGd3j8VTYV1GGxu/3IxAIsAxPd3d32fdQ1qy1tZUVefGZSYvFAovFgu7ublU94GnptLOzE7OzsxU+vF1dXTCbzayoS8ljloK5Wv7C165dU6XPtNvtGBwcrOqxeujQIaYrpM9Qdzv6zH333ccq9mkM8qJG+nlHRwf27NmDsbExzM/Ps4wz/ZyW1K1WK/M8luu2yd3CYDAgn88zrTddB9TxLJvNor29vW5BVr2Jw/z8PEqlElwuFzKZTIXMwGKxIJVKwePx4PHHH6/ZRQyAKg1nvc+cPHkSAJgNGq0kmM1mHD16lP2+3vfQ9uqNuR7kBnHmzBnEYjF2vu12O44ePcq+58iRI0gmkwgEAmV+3zqdDh0dHThy5IjqLlr1XArUfE9ra6uqcavZ/1rH0e1248aNG+x5JS98VKtvB9R1GQsGg4pB773Q46p5Nzz//PN1V628Xm/NVRI1qwQCwVZBBMWbACX9Wa5Ywse/cRHfvDCr+Lc2ow7/4733oV8fw6lT11AqleBwOMqyStlsFm1tbchms3V1c/WyCjqdDtPT08hms2Vdwuhhm81mMTExUVcPaDAY0N7ejps3b7IldMpw5fN5NmY1LxvSVCaTSVYox48pmUyyZc5aL7VEIlHmL1zNF1iSpDKPVaWK92QyicnJSRiNRvT397OgNpvNYnJyEh6PBwAwOTkJk8mE/v7+smKjyclJ7NmzB3a7nXUP5C3pKGCz2+3w+XxlwVC1402evh6PpyyjTrITytjzkyl+W7QqQAVHtTSTVGxVa+LAZ8BdLlfVVQlq1dzb24vBwUHFrmb1xrOaz5w8eRKPPvooLly4gGg0CrfbjcOHD5dNENV8z9DQUN0x1yMYDDJnjfb2dvZz8k0OBoMsCH3ssccqJkWdnZ1lAW+9cat1KVDzPYFAAO3t7RWrG/y41VDrOEaj0Ybo29UcHypYq2f91+iCtVpjikajq1q1WosbhkCwVRBB8SZlZimDX/3KeVyejSt+ZkerDX/5wSPY2WrH889PMm0dtfGl5g3kjVsvY9DX11emB5S3caUscSgUYgGZvEVrMpnE/Pw8BgYGmOazmv6uvb0diUSCFS5RMwsKyMxmsyo9IKCuE5/NZsOdO3dqvtQoS0zLzvyYeOcDn8+HQCDAmnHQMbLb7dDpdOjv78fs7CzS6XRFYO90Opl+G0BFlofOB7V4Jku6YrFYUYxoMpmwa9cu5qFL54eCmYWFBcTjcezcuZP9TJ7955slkAaa99cFwDTGpPfmx1CrIKtWsOJwONi5Iasv/pzx2lNgJaPd29tb8zpQo+FU8xm9Xs+KO9eCmjErwbsGyK3U6PrggzC1hV1K+89vj+7ZdDrNgkd50Kfme5Su69UGj0rHUa3utp6+nbckU9qve1WwVit7zX9/te9U61Bht9vX7IZxL1Cz7wJBoxFB8SZAnsW4mTLgb24YkSwo6yUe3+PH/3zvITjMBpYx0Ov1CIfDbImcspN+vx/hcBiHDh2qmTEgPeQ//dM/YW5urkIaYbPZsGPHDrzyyiswmUysUIqXKhiNRmSzWfh8PiQSCcVWwF1dXRgdHWVZY4vlrpsG2a3NzMyoesmo6cQHrBS1UdBczWM2GAwinU6zLCyvc6X9pMKe69evI5VKlRUYkq/s/v37MTU1VVc+ANT3PT148CBzaeDbHBsMBrS1teHw4cN19bt37txBS0sL5ubmmDSEPx9WqxUtLS0sC1zNSoys26rpyOWoDVb2799fUzPKa0+bifVohPB6grC1FHbR9oxGI+7cuVNhbybfXjO4HajR3arRt6sJ0ikArWdZuJqCtbVeR6txqHC73Wtyw2g0opmIYKMQQXGTwy9ZOp1OPDOnwTeuFSGhekCs0QAfe3w3PvzGXdBq77ZxVWotm8lkMD09jdbWVthstroZg0gkUtH8ArhrbwbcbeUsSVJZ8ETL9HzBzsqYK1sBUztcedc3YCXDkUqlmLdyPdR04otGo3X9bCnolWdLKQil7nIUoPINR+izxWIRt27dapjvqd1ur7k0rka/SzZ29CLnoays1+tlzReoYp0PwOVuILVQWyTUKO3terJejRAa5VO7mu2lUimkUikUi8WK1uTZbBY2mw25XK6p3A7q6W7V3B9qgnSSaM3MzFRt3U6rNGoL1hpxHa3GoYL2txls10QzEcFGIoLiJoZfanS2+PB/ruQxuqCsDXWY9Phf77sPb9rjL/u5wWDA0tJSzdayS0tLMBgM8Hg8NW2QRkdHAQAdHR1sOZ18TuPxOK5fvw4ArGMbX0VO1l1GoxHBYBCSpNwKeHJysm7XN2p3Ww++E18t/1Qat1JWRaO5201OKVuq1WoxOzsLvV4Pt9utKB+hDoCN8D2tleVRo9+l4JS8jvlgnnTE4XAYfr8fiUQCpVKJXUc0UdBqtRVuIGstXAIao71dL9azEcJqfWpXsw/VzpnRaEQymUShUIDdbq94hiQSCUiShGQyibGxsaZyO1irvl1NkK4k0SJZVTQahd1uh9PprDveRl1HaiefzSRJEM1EBBuNCIqbGFpqzBmc+P+dzWImqSyXGGyz4y8+eAQ7WivbEFM73FrduAqFAhKJBDwej2LGYGZmBpFIBDabjTVv4KHWqmTXVc1KizKL0WgULS0tilKFSCTCxqXU9Y3kGPVQ45+qxqvW6/WyY6mULaXW0tTmWH7MLRYLkskkTCZTXV9YAGuuClcTPKVSKRYsUJBLULDA/56yxxQM2+125iJC4yFddDW3C7WFXcRatLfryXpKA1abBVRDrQwv3eu81Z8cSZJw48aNpnQ7WIu+XU2QrkaiRYmDeue+kdeR2slnsyCaiQg2GhEUNzG5XA6XwyX83dQy0soJYpwcbMHnfmEEdlP105lKpVigWqu1bCqVqjkeKhqrl1VxOBxMTsC7Hmg0GjgcDlgslrq2ZZIkweFwIJPJIJVKVc3Kqs1Mqs2YAHe9ak0mE9setZal74hGo4rZUoPBgFgsVvM4AkBvby/m5+dr6rfVjEej0ahqzT0/P69Y1EjBLllX8fIY+jct/+ZyOSSTyTKNd6lUgt1uLxvPs88+W2HJFw6HEQgE8Nhjj9UtXNoo1lLcs56ShkZnAestWe/atYv5hlfrwmgymdgqj9frrauDbxa3A35yobbpRjVIolWrxTU16anHaq+jetfsWrvnrSfrLQsSCOSIoLiJMRiNODVvVAyINQDe1lPEf33XHsWAGADsdnuZnZpSa1m7vTLLLP8eNa1V7XY7bDYb4vE4a5us0+ngcDjgdDrLCraUvoe8k+/cuYNEIoFkMlnhrSzPTK7VPxW46ws8Pz9f4R08MDCA2dlZpqutli31+/24efMmstksC+QJSZKQyWRgsViwY8cO9Pb2rmk8lH2rp78bHh7GwsKCYlOBHTt24Pbt26wphvxlSasMnZ2d0Ov1Vb2VBwcH0draCkmScP78eczOzkKn01U0Z5idncX58+fxxBNPNN1LeT0LmxpBo7KAapas79y5A6vVyu5redBHzXFoclWNZnQ7oMlFrftDTZCupsW12nO/mutI7TXbbJNPJdb7HhII5IiguIlpcbvxsWNO/Odn40gtly/Dm7UlvLcnicf3d9d92HV3d7OWqPVaywLKmQf+e2q1VqW2uj09PVX1wmqkCh0dHWhra8Pk5GRNb2XKTDbKP7WedzBl51KpFNMO0mdsNhvuu+8+lEolVtEuz6hJkoT+/n7mKrGW8bS0tKiqnB8aGlJ8qWs0GgwMDODcuXNsCVh+XvP5PJxOJ7xeL15++WVF32SPxwO9Xo+pqSloNJqyAkm9Xs+6Jk5NTTH5TLOwEYVNjaARWUA1S9YUzEajUcX72ufzMd3xZnM7qHV/qEGNREvtuVebvV6NlZwamsECbSPuIYGARwTFTYxGo8Gevna83TOJvw+2Q8LKA6LNkMdbnTNoK2nR1na47oOLb4lar7VsvcxDvdaqfFvdUCgEp9PJur2FQiHVbXXJKsnpdFZkZb1eL7RaLQKBAAYHB1dVmKGUMeGzZUrewePj43j44YfLslz0oucbIRw5cgSJRALBYBDZbJYFj5RtPXLkSJlzxesdz8WLF5FIJNgLIpvNlmWn6HNkwVerqPH48eM4ffo00uk0Wz0giymDwYDjx49jcnKyrr9se3s7MplMWTEWfz1TJ7pwONw0QfFmL2xaaxZQ7ZJ1b28vu4+r3df33XcfJiYmNpXbQaOabjTy3NN3KbWMbmtra5iVHNEsFmibsThQsLUQQXETQx2e9rWZ8IQ2hacX7dhjTeKt3iW47E4WGFIDi1KppFilX68l6tDQEMuWpVIp1iCjVCphbm6OZR7oe86ePcuWPnU6HXw+X9nvjx07hrGxMUxPT7PMQ29vb1mhlRqrpLa2Nuj1eszPz7Pv6ejoQKFQQCgUwszMzKoKM9T4p9ILnArESPZB31MvO9fa2oo3vOENdQvNasGPB1AOeGm5WskblVov00SiWlFjKBTCG9/4RgDAmTNnmEWbVquF0+nE8ePHsXv3bpw+fbrmeEKhUJkEh7TWfCc6+nmtgq31ZrXFPY1w1VCLmuzdWjN8apes29vb4fV6a97XdC81KqBpVPZSzX1f6/5YTVHbWu57+ZiB6paViUSiYQVpzWaBttmKAwVbCxEUNzH0wG5ra8PPdRmwcz6HYacNBoOL6dTowbe4uFhV68n7udayt6KMCXkDRyKRsgArn8+zzIPH40FfXx/TiZrNZvT19bHWxAAQiUQwNTWFUCjExkONQtS4D5BVUiAQwOzsbJkOemZmBl1dXTCbzUgmk6oLM9T4p8ZiMczPzzN9JGU3Ozo6oNFoWIFHvSzXWpe11TQDoKYhtbxRLRYLDAaDquMzMjKCI0eOYHx8HPF4HE6nE8PDw9DpdOx81GtOYLfbmcMGgDKHDnIwsFgs8Pl8qo7DerCa4p61tgNeDWq21YgM32qWrK9du1bzvm5kQNOo7GUz+SbzKAXq9CyuZVl57dq1usXKasbdrBZom6k4ULC1EEFxE8O/rLVaLY52Wcp+Tw++a9eu4dy5c8jlcrDZbGymHwwGcerUKQB3M8VK9laxWIwFy8vLy8xyjbISOp0OMzMzuHXrFi5cuFDmLJBKpXDlyhUEg0E89thjiEQiOHXqVMV4QqFQxXhqWSVFo1EsLi6iVCox9wxq7DE1NYX29nbY7XZVWS41/qnUGY4anJCGN51O49atW/D7/asq8FjL8rCaZgAulwulUqmmN6rJZGJuH2oKV3Q6Hfbt2/e6xkM6Ub/fz17qRqORBU7U3IQ01c2C2kxpvWtITTtgtajJ3gFoSIZP7ZL1tWvXVN3XjQhoGpW9rPc9e/bsaVhhF7+tau3U+THXCtT5hiJK2WtyuFnruJvZAq0Z5DXNTjPowLcaIihuYtS8rLVaLS5fvoxcLlcWGJlMJuYHPDo6isHBwZoND7LZLJaWlpgzBJ8pNRgMKBaLWFpawiuvvFLTWeDll19GMBhc83jsdjvLesuL+shaLhKJoKOjQ1WWa3Z2tmY2hFpGFwoFWK1Wtj3yGk6n04jH43A4HGpP35oeWGqaAZjNZhb0Knmjms1mViC1lsKV1TQnsFgsMJlMTDpBkyfqpMjbuTUDajOl9a6hRmXU1GbvyCqvEeOpl+H1er14+umnVd/XawloGpW9VPM9jfJNXs2YQ6FQXfu7etlrjWbF3rKe17kaKzlhgbY5aRYd+FZDBMVNjJqXtcViQSKRgM1mqwgyaWk7EolgZmamZgOEfD7PdKIAKtozAytZidnZ2ZrOApOTkygWi6rHoxQ4TkxMMD9iuQZVkiTodDoUi0Vcu3atbparq6sLFy9erJkNmZ6eZsuR1ZqO0M9nZ2dVNZJY6wNLTTMA2pd63qhUILUWnafa5gSzs7PIZDLYsWMHotEoW3kgqz6Xy4VMJtNU5vtqMqVqrqFGZdTUZO/m5uYAgLmYNGI8tTK809PTrHHPWp4zamhU9lLN94TDYRw6dGjNvslqxxyNRlXZ39WzvjQYDBgaGsLVq1fXNG5hgbY5aTYd+FZCBMVNjJqXtc/nw+TkJAwGQ9XCJqPRiHQ6zTSeShgMBiwvL6NYLCq2Zyb3BqUHv8ViwdLSEvu+avDjqRU4xuNxSJLEMo58lzWdTgedTodcLod4PI59+/bVzHKp0Q2SXZrb7UYmk6nwcibXhHrHEWjMA0tNM4BYLMYy+bW8UalAai06T7XNCUjj7fV64XK5KsZELaObLfNUL1O6ntpTNdk7mqiuV4ZPTeMeNc8ZHqUJcaOyl2q/x2azrVkHrXZboVBoVfZ3tbLAAwMDcDgcaxq3sEDbfDSrDnyrIILiJqfeyzqTyUCn07FAjg/mDAYD9Ho9y9LVolAoMO1ntUwpOQfUgwLneg0+CoUCCxxNJhMLmChw9Hg8rACQfsd3WaOGEk6nkx0npSxXNBqtmw2hzCew8qKQTy5yuZyq49ioB5aaZgAWi6Xs5VnLG1Wjqe2JXA+1zQnkGm+5HnI1TQzWm7VeQ43aLzXZO2pvTm3Q5efj9Yyn1iR1NY171rqtRmUvV/M9a/VNVrstjUazKvu7elngteq3hQXa5qOZdeBbAREUbwJqPfioxe7i4iIrjuNlD+l0Gn6/nzXmUMJoNJZliOWZUuCuvrZWtzZq8RyLxRQbfLS2tiIajWJpaQnLy8sIh8MsALVYLMjlcqwrHk0ClBpKkOcxoFyYoSYb0tvbi1KphFAoxCYT1cZd7zg26oGlthnA0NAQRkdHVb3Q1qLzVDue7u5u3L59e9NmntZyDTVqv9Rsixrg3L59m12fvBuIVqtFf3+/6vHUW904evRo3cY9au4PNdsaGRlpyLFe7Tlr5P2htC2v17sq+zs1WeC1FqQJC7TNhdCB31tEULxJUHrwaTQaeDweBAIBFItFltmkjC/9vt5M32w2o6WlBdFoFKVSCWazmQXX1DTD5XLBarXi9u3bNbu1DQwM1GzwsX//foyPjyOZTFY4GaRSKWi1WszPz+P+++/HSy+9VLOhhJoMtppsyJ49e+D3+2uOmxqc1KJRDyy1GZz1eqGpHY9Wq92QzNO9rsJez4ya2m1FIhFcunSJuUGYTCYUCgWEw2GYTKaKpi9KqFnduHbtGo4ePYof/ehHWFpaqiiyNZvNqu4PNduamJjA0NDQmo91M54zykirCdTXurqzGoQF2uZxchA68HvLlgyKf/d3fxef+tSnyn42NDSE8fFxACtOCx//+Mfxta99DblcDk888QT+7M/+DH6/fyOGq4pa5vMA0N/fj0AgwGQUVAzX1tYGAKoyk93d3SgWiygWi0ilUqxAymazMT3p0NAQs3tT6tZGgdjZs2cRDodZoEYNPlwuF1566SWUSqWKgj2dTodUKoWlpSW88Y1vhNlsxksvvYRkMsk0rA6HAydOnMDIyIjq46cmeKRx12pwUo9GPrDUNgNYrxea2gB8vTNPjazCVtOYQ34++G6GjaLeuff5fBgfH4fT6cTy8jIymQwymQy0Wi08Hg90Ol1ZY59a+6Z2dePAgQM4evQozpw5g1gsxjLTdrsdR48eVXV/rGZbjbiG1vNaVLut1QTqa80CrwY129osgeNq2UxODkIHfm/ZkkExAOzbt495ZwIoWw7/2Mc+hv/3//4fvvGNb8DlcuHDH/4w3vWud+GFF17YiKHWRY35vN/vR3t7e1knNqfTqbqwiTIdCwsLCAaDKJVKkCSJ+cu2trayB8QbVHRrowYfGo2Gfaa3t5d1WSsUCiwbLR+HXq9HLpdDPp/HwMAAMpkMbt68yTJSO3bswMDAwKqPo5rgsVaDEzVs1ANrvV6eagPw9QrUG1mF/XpfjBvRnY9v7FNNU8w39nG73Q1pYLGwsIBAIID29nY2Gab/DwQCCAaDqgpI1a6k+P3+hlxD65kFVbOtzSpX2EyB42rYbE4OQgd+b9myQTHpsuTEYjF84QtfwFe/+lWcPHkSAPDFL34Re/bswZkzZ3D8+PH1HmpNVms+Lw+2VlvYRDcS6YflPwfqP/j5Mbe1tbExLy4uIpFIYOfOnexn5LlJUGGf0WhEKpXCa6+9hnQ6jc7OzorveT0PKzXBo1KDE7Xf36gH1mqaAawnagPwex2oN7IKe7XNMu71+ah37nkvW41GU1HQKO/Ct9YGFjqdDtPT0xXHGlg5D6stIFW7ktKoa6jZMq6bTa6w2QJHtWxWJ4fNOrHaDGzZoHhychKdnZ0wm804ceIEPv3pT6O3txfnz59HoVDA448/zj47PDyM3t5evPTSS00VFG+E+XypVFJsLco/HJQe/GrGPDMzA7fbjVgsVlWbrNPp4HK5cOfOnU33sAIa88Dij6PP52PaZsrOyM/HZmUty7GNKmrciGYZtVAzHjVetnTvXr58ueZ1xD9D6DP8vR+Px9HS0oJEItHwAtLtvPS7noH6WtisgaMaNrOTw2abWG0WtmRQfOzYMXzpS1/C0NAQ5ufn8alPfQqPPPIILl++jIWFBRiNxooL3O/3Y2Fhoeb35nK5MhlCPB6/F8NnbJT5vFJr0UYZ5icSCfh8PmZ7Jve81Wq18Pl8SCaTm/JhBaz9gUXH0WAwsMkB7y7Q7PuvhrUuxzaqqHGjmmWsZTxqvWwBIBQKwWg0Kl5H9AwJBAJsYkxotVq0tbWhp6cHly5damgBaSAQYI43pVIJ2WwWNptNLP02GZs5cKzHZndy2CwTq83ElgyK3/rWt7L/PnjwII4dO4a+vj58/etfX1OL2U9/+tMVBXz3kmY0n2+UYT75cKZSKdY2ln8x9vb2NuQlvJGs5YGVy+WQSqWQSqVQLBbLsunJZJIdp2be/1qsZjlWKZvcqKLGZmuWsdp7qNZkmO6xetdRPp9n2mh54CNJErMpbFQB6eDgIEZHR7GwsMAKej0eD+677z6x9NtkbPbAsRbCyUEgZ0sGxXLcbjd2796N69ev481vfjPy+Tyi0WhZwLK4uFhVg8zzH//jf8Sv//qvs3/H43H09PTcq2HfE/P5ex1gqP0euQ8n/Zwq+Q0GA8bGxrbtw8poNCKZTCKfz8PhcFQ4dCQSCUiSxDykNxOrWY4lmUi1bLLP52vIUvxqm2U0Q/MOtV62S0tLSCaTKBQKsNvtitfR7du3IUmSonRKjcRCrewhGAxicnISJpOJ+S1Twd7k5CQ8Hk/DA+Ot6pqwHmzlwFHIeQRytkVQnEwmcePGDXzwgx/EkSNHYDAY8KMf/Qjvfve7AQATExOYnp7GiRMnan4PdV5bLxptPl9rubpRAcZqxlzLh1OSJPGwQmXWbiugdjn25s2bGBsbq5lNbkRR42qaZaxVu6+GRt1DPLUcMpaXl7G0tISWlhZF6RRJLBYXF6tKLMidpt6xpgkRNe7JZDIVjXsarU/dqq4J68VWDhyFk4NAzpYMiv/Df/gPePvb346+vj7Mzc3hk5/8JHQ6Hd7//vfD5XLhX/2rf4Vf//Vfh8fjgdPpxL//9/8eJ06caKoiO+DeORncywBjtWNWCuS3+8Mqn8/DbrcjlUpVLUY0mUxs2XuzoXY59tq1a3WzyQ8//PCapUNqrzUATLtvMpnY5C2Xy21II4h69xCg7jqiLGC985HP5xX3bzVaebI7VGrcMzMz0zB96lZ1TVhPtvqzWDg5CHi2ZFA8MzOD97///QiHw2htbcXDDz+MM2fOsIv7f/7P/wmtVot3v/vdZc07mpFGOxk0KsBQ0+RgMxnvNxsU9NrtdubSwRcjOp1O9rnNhprl2FKphHg8rqqwrRFV2GqvNdLCzs/Pl2lhDx06tCGNIOpB15HNZkM8Hq96HdHESo0lm1p3GiWy2SyWlpbqNu7JZrOv46iVs5VdE9abrf4sFk4OAmJLBsVf+9rXav7ebDbjT//0T/Gnf/qn6zSitdEoJwM11cNqtqVmObJRD5nt+rDilyx7enqqBiGbdclSzXIsOSyoLe5pRBW2Gv/tyclJGI1G9Pf3lxWH3gstbCOufTXXkRppiNvtZm4wa3Gnyefzqhv3rJWt7JqwEWz1Z7FwchAAWzQo3oqouWGVsrf8cjUt9/Ldr1YTYPDLkaSxliSp6nLkWsa82n3frCjtP79kGQqF4HQ6YbVakc/nEQqFNvWSpZrl2KGhIVy8eFF1cU+jCqmUrjU+69jW1lb23U6nsyLr2CyFXWqvIwA1bR0b5QZjNBpVNe5pRAHpVnZN2Ci28rNYIABEULxlqJW9peXqWCzGllDlXqVqqoflRTLhcHhNRTLbvQCm3v5v5SXLevvm8/kwOzurqrhnPa6j1WQdC4VCQ8bTqP1Sex3V+kyj3GDMZjNaWloQjUYVG/e43e6KTPTrYSu7JggEgnuDCIq3APWKSUZGRmCxWDAxMQGdTgez2cxeRIlEAtFoFENDQ3WX4htZJLPdC2DU7v9WXrKst29qintCodC6XEdqs44LCwu4fv36msfT6PtDzXVU6zONcoNxuVzo7u5GsVhUbNzT3d297i4eAoFAAIigeNOzmha1QGWV+GqCq0YVyWz3ApjV7v9WXrKstW9qssnPP//8ulxHarKOVIy21vHcq/tDzXV0r91g+O9RatyzUS4eAoFAIILiTYKSRlHNsu78/DwAoK+vT9HJIJPJrFuRzHYvgNnu+78aamUvo9Houh1HNVlHvhhtLeNp1uvjXrnKyBv3NKOLh0Ag2B6IoHgTUEtbWCqV6hbRUZDa2toKl8tV8RlJkhAOh9etSGa7F8Bs9/1fLUrZy/U8jmqyjqstRlNTGHuv92u1bEZXma0sQRIIBI1FBMVNTj1t4Z49e6DX6xGPx1kWWF5EJ29RKy9iyeVy61oks90LYLb7/jeK9T6O9bKOqylGU1MY26zXR6PkPOspC9rKEiSBQNA4RFDcxKjRFs7OzsJsNuPatWvsRUmBajKZRDQaxe7du2Gz2dbcona1RTJKmbDtXgDD77/P56vwjt3q+98oNuI6akQxWj6fx+joaM3C2O18f2wUzWKjJ2gszXhem3FMghVEUNzEqNUW6nQ6AGDFdARfXDc8PFzTh7TRRTL17KS2cwEMHceFhQWMj4+jVCqx32m1WnZ8tur+N4qNKqRaSzHa0NAQJiYmak50JyYmMDQ0tG3vj41gu9tDblWa8bw245gEdxFBcROjRlsYDoeh0WjQ29tb0cbV4XCwIjqj0bhuRTJq7aS2ewGMUlAjgh31NNt1pEZioWaie+DAgabar63MdreH3Ko043ltxjEJyhFBcROjRltIy7Yulwtut7tmEZ3f77/nRTKrsZPargUwdIxKpRKGh4ertt7dypZ0jUbtdbReS5a1xrO4uKi6iK5R96tAme1uD7lVacbz2oxjElQiguImRo1m0ufzIZlMqi6iu9dFMqu1k2pkAcxm0Wnxx0ir1VacM2HJtnrqXUfrvWSpNJ7VFtGJArF7S7Pa3wnWRjOe12Yck6ASERQ3MWo0ivfddx8mJiaapihno+ykNpNOq5ktt7YizbRkud2LTJsNcS9uTZrxvDbjmASVaDd6AILakEaxo6MDmUwG4XAYmUwGHR0dOHbsGNra2jA8PAyr1YpgMIhsNssK34LB4LoX5fCZsGrcCzspCnrm5+dhtVrh9XphtVoxPz+Ps2fPIhgMNmxbjWAjjtF2Rb5kaTKZoNVq2ZJlOp0u6/h4r6GJbrPcr9sdcS9uTZrxvDbjmASViEzxJqCeZrKZio3W225sM+q0RLZw/bgXS5ZrlenQ/To2Nob5+Xnk83kYjUZ0dHRgz549TbeysZUR9+LWpBnPazOOSVCJCIo3CfW0hc1StEaZsMXFxap2Y5TZbtS4NqNOa6OsxLYjjV6y3EwyHUF9xL24NWnG89qMYxJUIoLiLUQzFeXwHsnVft4oNqtOq5my+1uZRnaHa5Q2mf8et9vNvmdhYQHxeFzYMq0z4l7cmjTjeW3GMQnKEUGx4HVRKpUwMzODZDIJu92O7u5uaLVaJmeQJAlDQ0NIJpNsedhutyMcDjdUznAvWuI2g3XXVqAZ3EAatWTZKJnOZpT7bAea9V5shntoM9OM57UZxyS4iwiKBatmYmICo6OjiEQiWF5ehk6ng8fjwcjICPx+P0KhEAwGA2ZmZpBOp1EqlaDVamG1WhsuZ2i0TqtZrLs2O80iM2jUkmWjZDqbUe6zXWi2e7FZ7qHNTrOdV6A5xyRYQQTFglUxMTGBU6dOIZfLwWazsaXfYDCIU6dO4ejRo0ilUkilUigWizCZTNDpdFheXkYymWTtoBslZ2ikTquZrLs2M812HBuxZNkomc5mlfsI1pdmu4cEgu2CCIoFqimVShgdHUUul4Pb7YZWu+LoZzKZYDAYEI1GcfnyZeRyORQKBTgcDhaM6vV66HQ6JBIJSJIEo9HYsHE1IugRy9qNoVmP41qXLBsl07kXch/B1qJZ7yGBYDsggmKBamZmZhCJRGCz2VhATJA8IhqNQq/Xr/vDeq1Bj1jWbgzNfBzXsmTZKJmOsGUS1KOZ7yGBYKsjgmKBapLJJJaXl2EwGCBJEpaXl5leWKfTsaVfq9UKjUaDdDpdJp+gYNVmsyGfzzd8fGsJesSydmPY7MdRqbCpUTIdYcu09VEqQlbLZr+HBILNjAiKtxD3ulLZbrdDp9Mhk8mgUCigUChAkiRoNBoYDAbo9Xro9XrYbDY4HA7EYjGk02nkcjlotVrY7XY4nU4AaLrlYbGs3Rg283GsV9jUKDslYcu0dalVhDw0NKTqOzbzPSQQbHZEULxFCAaD97xDVnd3N+x2OxYXF6HT6WAwGKDRaCBJEnK5HNLpNPx+P3p7e7G4uIienp6KjnahUKgpl4fFsnZj2KzHUW1hU6PslIQt09ajXhEyAFWB8Wa9hwSCrYAIircAwWAQzz77LAKBQFkHuXA4jEAggMcee6whgbFGo4HH40EgEGBZEK1Wi1KphOXlZfb7PXv2IJFIIBQKwel0wmq1Ip/PIxQKNe3ysFjWbgyb8TiutrCpUXZKwpZp66CmCHl0dBSDg4N1pRSb8R4SCLYK6oVOgqZEkiScP38es7OzKJVKMJvNsNlsMJvNKJVKmJ2dxfnz5xvSSS4WiwEA+vv7YbPZUCwWmf7NZrOhv78fwIrm7dixY+jo6EAmk0E4HEYmk0FHR0dTWwnRsvZmG3ezsdmO42oKmwSCaqgpQo5EIpiZmVH1fZvtHhIItgoiU7zJiUajmJqagkajgc1mK7NAs9lsSCQSmJqaQjQaRUtLi6rvVNImUwDs9/vh9/uRSCSYVMPhcABYyU7ncjn4/f5NuTwslrUbw2Y6jqKwSbBW+CLkahiNRqTTaSSTSdXfuZnuIYFgqyCC4k0OZRDsdnvVLJfFYkEqlUI4HFYVFNcqNpIXgMg1bdlstqwAZLMuD2/WcTcbm+U4isImwVqhIuRa15BOp4Pdbl/V926We0gg2CoI+cQmR40sQpIkVZ+jYqP5+XlYrVZ4vV5YrVbMz8/j7NmzyOfzLHMh/z4qAPH5fKIARLCpoMImcV0LXi/d3d3weDxIpVJldR3Ait44nU7D4/Ggu7t7g0YoEAjUIILiTY7P54PFYkE2m636Qs9kMrBYLPD5fDW/R15sZDKZoNVqWbFROp3GxMQEhoaGYLVaEQwGkc1mUSqVkM1mEQwGRQGIYFNChU3iuha8XrRaLUZGRmAymRCNRsuuoWg0CpPJhJGRkVX5FQsEgvVHyCc2OW63G/39/ZiYmEAqlWLd5CRJQrFYhCRJ6O/vr7sEp7bY6MCBA6o9Vu+1b7JAQKz1WtvM3sHiPmsOyG6NfIrT6TR0Oh1aW1tX5VMsEAg2DhEUb3I0Gg2OHDmCcDiMubk5FghrNBro9Xp0dnbiyJEjdV+Sqyk2UlNEV68RgkDQKBp1rW3GwiZxnzUXQ0NDGBwcXFNHO4FAsHGIoHiLYDQaYbVasby8zIJiar2shtUWG9UqAFHbCEEgWCuNvtY2U2GTuM+aE61Wi97e3o0ehkAgeB2I6esmh7TAkiRhz5492L17N3bu3Indu3djz549Zb+vRaOKjdRok9WMRyCox3a+1rbzvgsEAsG9QgTFmxxeC6zVamE2m2G322E2m6HValU3HmhUsZFohCBYL7bztbad912weiRJQjQaxeLiIqLRqJgsCQQKCPnEJqeRjQcaUWwkGiEI1ovtfK1t530XrA6hOxcI1COC4k1OoxsPrLXYSDRCEKyGtTgnbOdr7fXsu3Cp2H4I3blAsDpEULzJIS3w/Pw8Wltby15ypAXu6OhYVeOBtRQb3YvxCLYma81gbedrbbX7LrKF2w+57pyuEdKd0zXh8/nE5Egg+AkiKN7kkBY4FoshGAzC6XTCaDQin88jHo+ve+OBZhuPHJEtaw4akcFq9mvtXrKafW/WbKG4F+8tq9GdbxbHFYHgXiOC4i1AszUeaLbxECJb1hw0MoPVrNfaeqBm35s1WyjuxXuP0J0LBKtHBMVbhGZrPNBs42nWbNl2pNEZrGa71taTevvejNlCcS+uD9tZcy8QvF5EULyFaLbGA80ynmbNlm1X7kUGq1mutY2g1r43W7ZQ3Ivrx3bW3AsErxfhUyzY8mwHT9fN5EPKZ7CqITJYjaPZjvV2uBebhUZ5zwsE2wmRKRZseZotW9ZoNps+U2Sw1o9mO9Zb/V5sNraz5l4geD2IoFiw5dnK2rrNqM/c7K4Rm8k1odmO9Va+F5uV7ay5FwhWiwiKBVueZsuWNYrNrM/crBmszZaVB5rrWG/Ve7HZ2c6ae4FgNYigWLDlabZsWaNoRmeB1bDZMlibMStPNMux3qr3okAg2BqIoFiwLWimbFmj2Ar6zM2SwdrMWXmiWY71VrwXBQLB1kAExYJtQ7NkyxqF0GeuH5s9K99sbLV7USAQbA1EUCzYVjRLtqwRCH3m+rEVsvLNxla6FwUCwdZA+BQLBJsU4UO6fjSb369AIBAIGo8IigWCTQzpMzs6OpDJZBAOh5HJZNDR0dHUhV+bDcrKx2KxisYolJX3+XwiKy8QCASbGCGfEAg2OUKfee8RrgkCgUCw9RFBsUCwBRD6zHuPcE0QCASCrY0IigUCgUAlIisvEAgEWxcRFAsEAsEqEFl5gUAg2JqIQjuBQCAQCAQCwbZHBMUCgUAgEAgEgm2PCIoFAoFAIBAIBNseERQLBAKBQCAQCLY9IigWCAQCgUAgEGx7RFAsEAgEAoFAINj2iKBYIBAIBAKBQLDtEUGxQCAQCAQCgWDbI4JigUAgEAgEAsG2RwTFAoFAIBAIBIJtjwiKBQKBQCAQCATbHhEUCwQCgUAgEAi2PSIoFggEAoFAIBBse0RQLBAIBAKBQCDY9oigWCAQCAQCgUCw7RFBsUAgEAgEAoFg2yOCYoFAIBAIBALBtkcExQKBQCAQCASCbY8IigUCgUAgEAgE2x4RFAsEAoFAIBAItj36jR7AZkaSJABAPB7f4JEIBAKBQCAQCKpBcRrFbUqIoHgNJBIJAEBPT88Gj0QgEAgEAoFAUItEIgGXy6X4e41UL2wWKFIqlTA3NweHwwGNRrPRw1FNPB5HT08P7ty5A6fTudHD2dKIY71+iGO9fohjvX6IY71+iGO9PmzEcZYkCYlEAp2dndBqlZXDIlO8BrRaLbq7uzd6GK8bp9Mpbvx1Qhzr9UMc6/VDHOv1Qxzr9UMc6/VhvY9zrQwxIQrtBAKBQCAQCATbHhEUCwQCgUAgEAi2PSIo3oaYTCZ88pOfhMlk2uihbHnEsV4/xLFeP8SxXj/EsV4/xLFeH5r5OItCO4FAIBAIBALBtkdkigUCgUAgEAgE2x4RFAsEAoFAIBAItj0iKBYIBAKBQCAQbHtEUCwQCAQCgUAg2PaIoHiL8ulPfxpHjx6Fw+FAW1sb/sW/+BeYmJgo+0w2m8WTTz4Jr9cLu92Od7/73VhcXNygEW9ePv/5z+PgwYPMiPzEiRP43ve+x34vjvO94zOf+Qw0Gg0++tGPsp+J490Yfvd3fxcajabsf8PDw+z34jg3ltnZWfzCL/wCvF4vLBYLDhw4gJdffpn9XpIk/M7v/A46OjpgsVjw+OOPY3JycgNHvDnp7++vuK41Gg2efPJJAOK6biTLy8v47d/+bQwMDMBisWDnzp34r//1v4L3d2i261oExVuUZ599Fk8++STOnDmDH/7whygUCnjLW96CVCrFPvOxj30MTz31FL7xjW/g2WefxdzcHN71rndt4Kg3J93d3fjMZz6D8+fP4+WXX8bJkyfxjne8A1euXAEgjvO94ty5c/iLv/gLHDx4sOzn4ng3jn379mF+fp797/nnn2e/E8e5cSwtLeGhhx6CwWDA9773PVy9ehX//b//d7S0tLDP/MEf/AE+97nP4c///M9x9uxZ2Gw2PPHEE8hmsxs48s3HuXPnyq7pH/7whwCAn/3ZnwUgrutG8vu///v4/Oc/jz/5kz/B2NgYfv/3fx9/8Ad/gD/+4z9mn2m661oSbAsCgYAEQHr22WclSZKkaDQqGQwG6Rvf+Ab7zNjYmARAeumllzZqmFuGlpYW6X//7/8tjvM9IpFISIODg9IPf/hD6bHHHpM+8pGPSJIkrutG8slPflI6dOhQ1d+J49xYfuu3fkt6+OGHFX9fKpWk9vZ26Q//8A/Zz6LRqGQymaS/+7u/W48hblk+8pGPSDt37pRKpZK4rhvM2972NumXf/mXy372rne9S/rABz4gSVJzXtciU7xNiMViAACPxwMAOH/+PAqFAh5//HH2meHhYfT29uKll17akDFuBZaXl/G1r30NqVQKJ06cEMf5HvHkk0/ibW97W9lxBcR13WgmJyfR2dmJHTt24AMf+ACmp6cBiOPcaL7zne/ggQcewM/+7M+ira0N999/P/7qr/6K/f7WrVtYWFgoO94ulwvHjh0Tx3sN5PN5/O3f/i1++Zd/GRqNRlzXDebBBx/Ej370I1y7dg0AcPHiRTz//PN461vfCqA5r2v9hmxVsK6USiV89KMfxUMPPYT9+/cDABYWFmA0GuF2u8s+6/f7sbCwsAGj3NxcunQJJ06cQDabhd1ux7e+9S3s3bsXr776qjjODeZrX/saLly4gHPnzlX8TlzXjePYsWP40pe+hKGhIczPz+NTn/oUHnnkEVy+fFkc5wZz8+ZNfP7zn8ev//qv4z/9p/+Ec+fO4dd+7ddgNBrxoQ99iB1Tv99f9nfieK+Nf/zHf0Q0GsUv/uIvAhDPj0bziU98AvF4HMPDw9DpdFheXsbv/d7v4QMf+AAANOV1LYLibcCTTz6Jy5cvl+kBBY1laGgIr776KmKxGP7v//2/+NCHPoRnn312o4e15bhz5w4+8pGP4Ic//CHMZvNGD2dLQ9kcADh48CCOHTuGvr4+fP3rX4fFYtnAkW09SqUSHnjgAfy3//bfAAD3338/Ll++jD//8z/Hhz70oQ0e3dblC1/4At761reis7Nzo4eyJfn617+Or3zlK/jqV7+Kffv24dVXX8VHP/pRdHZ2Nu11LeQTW5wPf/jD+O53v4vTp0+ju7ub/by9vR35fB7RaLTs84uLi2hvb1/nUW5+jEYjdu3ahSNHjuDTn/40Dh06hM9+9rPiODeY8+fPIxAI4PDhw9Dr9dDr9Xj22Wfxuc99Dnq9Hn6/Xxzve4Tb7cbu3btx/fp1cV03mI6ODuzdu7fsZ3v27GFyFTqmchcEcbxfP7dv38apU6fwK7/yK+xn4rpuLL/xG7+BT3ziE3jf+96HAwcO4IMf/CA+9rGP4dOf/jSA5ryuRVC8RZEkCR/+8IfxrW99C8888wwGBgbKfn/kyBEYDAb86Ec/Yj+bmJjA9PQ0Tpw4sd7D3XKUSiXkcjlxnBvMm970Jly6dAmvvvoq+98DDzyAD3zgA+y/xfG+NySTSdy4cQMdHR3ium4wDz30UIVl5rVr19DX1wcAGBgYQHt7e9nxjsfjOHv2rDjer5MvfvGLaGtrw9ve9jb2M3FdN5Z0Og2ttjzM1Ol0KJVKAJr0ut6Q8j7BPedXf/VXJZfLJf34xz+W5ufn2f/S6TT7zL/9t/9W6u3tlZ555hnp5Zdflk6cOCGdOHFiA0e9OfnEJz4hPfvss9KtW7ek1157TfrEJz4haTQa6Qc/+IEkSeI432t49wlJEse7UXz84x+XfvzjH0u3bt2SXnjhBenxxx+XfD6fFAgEJEkSx7mRjI6OSnq9Xvq93/s9aXJyUvrKV74iWa1W6W//9m/ZZz7zmc9Ibrdb+va3vy299tpr0jve8Q5pYGBAymQyGzjyzcny8rLU29sr/dZv/VbF78R13Tg+9KEPSV1dXdJ3v/td6datW9I3v/lNyefzSb/5m7/JPtNs17UIircoAKr+74tf/CL7TCaTkf7dv/t3UktLi2S1WqV3vvOd0vz8/MYNepPyy7/8y1JfX59kNBql1tZW6U1vehMLiCVJHOd7jTwoFse7Mbz3ve+VOjo6JKPRKHV1dUnvfe97pevXr7Pfi+PcWJ566ilp//79kslkkoaHh6W//Mu/LPt9qVSSfvu3f1vy+/2SyWSS3vSmN0kTExMbNNrNzfe//30JQNXjJ67rxhGPx6WPfOQjUm9vr2Q2m6UdO3ZI//k//2cpl8uxzzTbda2RJK61iEAgEAgEAoFAsA0RmmKBQCAQCAQCwbZHBMUCgUAgEAgEgm2PCIoFAoFAIBAIBNseERQLBAKBQCAQCLY9IigWCAQCgUAgEGx7RFAsEAgEAoFAINj2iKBYIBAIBAKBQLDtEUGxQCAQCAQCgWDbI4JigUAg2OQ8++yz0Gg07H8vvvjiRg9JIBAINh0iKBYIBIJNzl//9V+X/fvLX/7yBo1EIBAINi+izbNAIBBsYjKZDPx+PxKJBOx2O5LJJFpaWjA/Pw+TybTRwxMIBIJNg8gUCwQCwSbmW9/6FhKJBADgc5/7HABgaWkJTz311EYOSyAQCDYdIigWCASCTQxJJQ4ePIhf+qVfwtDQUNnPBQKBQKAOERQLBALBJmV+fh6nTp0CAPzCL/xC2f8//fTTCAaDdb8jHA7jN3/zNzE0NASLxQK/3483v/nN+Na3vgUA+NKXvsQK+KamphS/J5vN4k/+5E/wpje9Ce3t7TAajWhra8Pjjz+OL3zhCygWi2vcW4FAILi3CE2xQCAQbFL+6I/+CL/xG78BrVaL6elpdHV14datW9i5cyckScJnP/tZ/Nqv/Zri31+6dAlvfvObsbi4WPX3//pf/2ucOHECv/RLvwQAuHXrFvr7+ys+d/HiRbzjHe/A7du3Fbd19OhRPPXUU/D7/avbSYFAIFgnRFAsEAgEm5RDhw7htddew8mTJ/GjH/2I/fzhhx/GCy+8gCNHjuDll1+u+rfRaBT79u3D3NwcAOCDH/wgfv7nfx6tra24fv06PvvZz+Kll17CsWPHcPbsWQDVg+Lr16/jgQceQCwWg9PpxJNPPomRkRH09PQgHA7jO9/5Dv7iL/4CxWIRx44dw3PPPQeDwXBvDohAIBCsBUkgEAgEm45XXnlFAiABkP7P//k/Zb/7/Oc/z3535cqVqn//0Y9+lH3mf/2v/1Xx+2KxKL3jHe9gnwEg3bp1q+JzDz74oARAuv/++6VgMFh1W9/73vckrVYrAZD+8i//cvU7KxAIBOuA0BQLBALBJoQK6SwWC9797neX/e7nfu7nYDQayz7Hk8vl8KUvfQnAiqzhIx/5SMVndDod/uIv/gJms1lxDM899xxrFPLXf/3X8Pl8VT/3Uz/1U3jPe94DAGy7AoFA0GyIoFggEAg2GcViEV/96lcBAG9/+9vhdDrLfu/xePDTP/3TAICvfOUrKJVKZb9/+eWXEY1GAdwtzKuG3+/HE088ofj773znOwCAoaEhHDhwoOaYH330UQDAuXPnRNGdQCBoSkRQLBAIBJuM73//+6w4TimopZ/PzMzg9OnTZb+7fPky++8jR47U3NYDDzyg+DvSK09MTJS1ma72vw9/+MMAgEKhgEgkUmcPBQKBYP0RQbFAIBBsMkgS4fV68VM/9VNVP/MzP/MzcLvdZZ8nlpaW2H+3trbW3Fat3wcCATXDrSCdTr+uvxMIBIJ7iX6jByAQCAQC9cRiMSZbCIfDTDtci29+85v4sz/7M9hstoaOZXl5GcCKC8bf/u3fqv67rq6uho5DIBAIGoEIigUCgWAT8fWvfx3ZbHZVf5NMJvHNb34TH/zgBwEALS0t7HfBYBC7d+9W/NtaDUC8Xi/7/v37969qTAKBQNBsiKBYIBAINhEkhejo6MD/+B//o+7nf+M3fgMzMzP48pe/zILiffv2sd+fP38eDz30kOLfK/kcA8D999+PF198ETdv3sTCwgLa29vV7oZAIBA0HaJ5h0AgEGwS+G51H/7wh/HHf/zHdf/mox/9KD772c+Wdb3LZrNob29HLBbD0aNHMTo6WvVvFxcX0d/fzzLT8uYdP/jBD5g7xSc+8Ql8+tOfXvtOCgQCwQYhCu0EAoFgk/DlL38ZlMcg39960OdKpRLT/ZrNZvzLf/kvAaxYpH32s5+t+LtSqYR/82/+TU2pxlve8haMjIwAAP7wD/8QX//612uO5dKlS3jqqadUjVsgEAjWG5EpFggEgk3Crl27cOPGDbS1tWF+fh5abf28RqlUQnd3N+bn57Fv3z5mxxaJRLBv3z4sLCwAWGnz/IEPfKCszfOLL76IkZERlkmemppCX19f2fffuHEDIyMjzGbt7W9/O9773vdicHAQOp0OgUAAr7zyCp566imcOXMGH//4x/FHf/RHjTwsAoFA0BCEplggEAg2AS+88AJu3LgBAHjnO9+pKiAGAK1Wi3e+8534sz/7M1y5cgXnz5/HkSNH4PF48PTTT+PNb34zgsEg/uZv/gZ/8zd/U/a3v/iLv4hHHnmEBcXVutvt3LkTL730Et797nfj8uXLeOqpp2pmg+WNRgQCgaBZEPIJgUAg2ATwXsPyts714D/Pf8+hQ4dw9epVfPzjH8fg4CBMJhN8Ph/e+MY34qtf/Sq++MUvIh6Ps8+7XK6q37979268+uqr+OpXv4p3v/vd6O3thcVigdFoREdHB97whjfgv/yX/4Lz58/jd37nd1Y1doFAIFgvhHxCIBAIBIr8yq/8Cr7whS+gu7sbd+7c2ejhCAQCwT1DZIoFAoFAUJVMJoNvf/vbAIDjx49v8GgEAoHg3iKCYoFAINim3LhxA0qLhcvLy/jVX/1VhEIhAMCHPvSh9RyaQCAQrDtCPiEQCATblF/8xV/E6Ogo3ve+9+HYsWNoa2tDJpPBa6+9hr/6q7/ChQsXAACPP/44fvCDH0Cj0WzwiAUCgeDeIdwnBAKBYBszNjaGT37yk4q/f+ihh/C1r31NBMQCgWDLIzLFAoFAsE2ZmJjAP/zDP+DUqVOYmppCMBhEoVCA1+vFAw88gPe+97143/vep9r+TSAQCDYzIigWCAQCgUAgEGx7xPRfIBAIBAKBQPD/b7cOBAAAAAAE+VsPclG0J8UAAOxJMQAAe1IMAMCeFAMAsCfFAADsSTEAAHtSDADAnhQDALAXbmxtNHKGXO4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lowess = sm.nonparametric.lowess\n", "fig, ax = subplots(figsize=(8,8))\n", @@ -1503,6 +3218,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch8-baggboost-lab.ipynb b/docs/source/labs/Ch8-baggboost-lab.ipynb index 6054a2c..2fb8cfa 100644 --- a/docs/source/labs/Ch8-baggboost-lab.ipynb +++ b/docs/source/labs/Ch8-baggboost-lab.ipynb @@ -21,9 +21,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4cc7120f", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:52.562040Z", + "iopub.status.busy": "2023-07-25T23:59:52.561717Z", + "iopub.status.idle": "2023-07-25T23:59:53.673192Z", + "shell.execute_reply": "2023-07-25T23:59:53.672771Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -48,9 +54,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "864fa15e", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.675446Z", + "iopub.status.busy": "2023-07-25T23:59:53.675242Z", + "iopub.status.idle": "2023-07-25T23:59:53.720701Z", + "shell.execute_reply": "2023-07-25T23:59:53.720361Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -90,9 +102,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "bfb4c83b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.722793Z", + "iopub.status.busy": "2023-07-25T23:59:53.722661Z", + "iopub.status.idle": "2023-07-25T23:59:53.727612Z", + "shell.execute_reply": "2023-07-25T23:59:53.727266Z" + } + }, "outputs": [], "source": [ "Carseats = load_data('Carseats')\n", @@ -114,9 +133,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "5d9e7a14", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.729444Z", + "iopub.status.busy": "2023-07-25T23:59:53.729340Z", + "iopub.status.idle": "2023-07-25T23:59:53.738990Z", + "shell.execute_reply": "2023-07-25T23:59:53.738679Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -143,12 +168,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "970c4515", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.740700Z", + "iopub.status.busy": "2023-07-25T23:59:53.740605Z", + "iopub.status.idle": "2023-07-25T23:59:53.745991Z", + "shell.execute_reply": "2023-07-25T23:59:53.745690Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clf = DTC(criterion='entropy',\n", " max_depth=3,\n", @@ -175,12 +220,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "2d9a51dc", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.747622Z", + "iopub.status.busy": "2023-07-25T23:59:53.747497Z", + "iopub.status.idle": "2023-07-25T23:59:53.750600Z", + "shell.execute_reply": "2023-07-25T23:59:53.750316Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.79" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "accuracy_score(High, clf.predict(X))\n" ] @@ -205,10 +267,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "cbc04b67", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.752228Z", + "iopub.status.busy": "2023-07-25T23:59:53.752135Z", + "iopub.status.idle": "2023-07-25T23:59:53.755654Z", + "shell.execute_reply": "2023-07-25T23:59:53.755371Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4710647062649358" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n", "resid_dev\n" @@ -230,12 +310,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "809830c7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:53.757328Z", + "iopub.status.busy": "2023-07-25T23:59:53.757207Z", + "iopub.status.idle": "2023-07-25T23:59:54.066251Z", + "shell.execute_reply": "2023-07-25T23:59:54.065834Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = subplots(figsize=(12,12))[1]\n", "plot_tree(clf,\n", @@ -261,10 +358,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "abe8c7fc", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.068192Z", + "iopub.status.busy": "2023-07-25T23:59:54.068071Z", + "iopub.status.idle": "2023-07-25T23:59:54.071170Z", + "shell.execute_reply": "2023-07-25T23:59:54.070769Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|--- ShelveLoc[Good] <= 0.50\n", + "| |--- Price <= 92.50\n", + "| | |--- Income <= 57.00\n", + "| | | |--- weights: [7.00, 3.00] class: No\n", + "| | |--- Income > 57.00\n", + "| | | |--- weights: [7.00, 29.00] class: Yes\n", + "| |--- Price > 92.50\n", + "| | |--- Advertising <= 13.50\n", + "| | | |--- weights: [183.00, 41.00] class: No\n", + "| | |--- Advertising > 13.50\n", + "| | | |--- weights: [20.00, 25.00] class: Yes\n", + "|--- ShelveLoc[Good] > 0.50\n", + "| |--- Price <= 135.00\n", + "| | |--- US[Yes] <= 0.50\n", + "| | | |--- weights: [6.00, 11.00] class: Yes\n", + "| | |--- US[Yes] > 0.50\n", + "| | | |--- weights: [2.00, 49.00] class: Yes\n", + "| |--- Price > 135.00\n", + "| | |--- Income <= 46.00\n", + "| | | |--- weights: [6.00, 0.00] class: No\n", + "| | |--- Income > 46.00\n", + "| | | |--- weights: [5.00, 6.00] class: Yes\n", + "\n" + ] + } + ], "source": [ "print(export_text(clf,\n", " feature_names=feature_names,\n", @@ -289,12 +423,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "f7a1f736", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.072938Z", + "iopub.status.busy": "2023-07-25T23:59:54.072835Z", + "iopub.status.idle": "2023-07-25T23:59:54.079362Z", + "shell.execute_reply": "2023-07-25T23:59:54.079048Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.685])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", " test_size=200,\n", @@ -328,9 +479,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "c663a623", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.081283Z", + "iopub.status.busy": "2023-07-25T23:59:54.081145Z", + "iopub.status.idle": "2023-07-25T23:59:54.083681Z", + "shell.execute_reply": "2023-07-25T23:59:54.083365Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -355,12 +512,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c2f2a403", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.085505Z", + "iopub.status.busy": "2023-07-25T23:59:54.085354Z", + "iopub.status.idle": "2023-07-25T23:59:54.089710Z", + "shell.execute_reply": "2023-07-25T23:59:54.089437Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.735" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clf = DTC(criterion='entropy', random_state=0)\n", "clf.fit(X_train, High_train)\n", @@ -378,9 +552,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b2505658", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.091391Z", + "iopub.status.busy": "2023-07-25T23:59:54.091299Z", + "iopub.status.idle": "2023-07-25T23:59:54.094646Z", + "shell.execute_reply": "2023-07-25T23:59:54.094353Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -402,12 +582,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "09a7bd33", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.096582Z", + "iopub.status.busy": "2023-07-25T23:59:54.096441Z", + "iopub.status.idle": "2023-07-25T23:59:54.355162Z", + "shell.execute_reply": "2023-07-25T23:59:54.354839Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.685" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "grid = skm.GridSearchCV(clf,\n", " {'ccp_alpha': ccp_path.ccp_alphas},\n", @@ -428,12 +625,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "a75dea32", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:54.357064Z", + "iopub.status.busy": "2023-07-25T23:59:54.356931Z", + "iopub.status.idle": "2023-07-25T23:59:55.201547Z", + "shell.execute_reply": "2023-07-25T23:59:55.201208Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "Truth No Yes\n", + "Predicted \n", + "No 94 32\n", + "Yes 24 50" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(accuracy_score(High_test,\n", " best_.predict(X_test)))\n", @@ -512,9 +813,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "1ca6bc7a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.216363Z", + "iopub.status.busy": "2023-07-25T23:59:55.216227Z", + "iopub.status.idle": "2023-07-25T23:59:55.224376Z", + "shell.execute_reply": "2023-07-25T23:59:55.224038Z" + } + }, "outputs": [], "source": [ "Boston = load_data(\"Boston\")\n", @@ -535,9 +843,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "15cfb553", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.226102Z", + "iopub.status.busy": "2023-07-25T23:59:55.226001Z", + "iopub.status.idle": "2023-07-25T23:59:55.228520Z", + "shell.execute_reply": "2023-07-25T23:59:55.228200Z" + } + }, "outputs": [], "source": [ "(X_train,\n", @@ -559,10 +874,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "d18820a3", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.230261Z", + "iopub.status.busy": "2023-07-25T23:59:55.230165Z", + "iopub.status.idle": "2023-07-25T23:59:55.529885Z", + "shell.execute_reply": "2023-07-25T23:59:55.529499Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "reg = DTR(max_depth=3)\n", "reg.fit(X_train, y_train)\n", @@ -595,9 +928,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "619f8d94", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.531947Z", + "iopub.status.busy": "2023-07-25T23:59:55.531809Z", + "iopub.status.idle": "2023-07-25T23:59:55.575222Z", + "shell.execute_reply": "2023-07-25T23:59:55.574850Z" + } + }, "outputs": [], "source": [ "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n", @@ -623,12 +963,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "779a14b6", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.577111Z", + "iopub.status.busy": "2023-07-25T23:59:55.576968Z", + "iopub.status.idle": "2023-07-25T23:59:55.580045Z", + "shell.execute_reply": "2023-07-25T23:59:55.579754Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "28.06985754975404" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "best_ = grid.best_estimator_\n", "np.mean((y_test - best_.predict(X_test))**2)\n" @@ -652,12 +1009,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "2b04030b", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.581772Z", + "iopub.status.busy": "2023-07-25T23:59:55.581667Z", + "iopub.status.idle": "2023-07-25T23:59:55.879963Z", + "shell.execute_reply": "2023-07-25T23:59:55.879563Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = subplots(figsize=(12,12))[1]\n", "plot_tree(G.best_estimator_,\n", @@ -696,12 +1070,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "7dedce31", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:55.882069Z", + "iopub.status.busy": "2023-07-25T23:59:55.881917Z", + "iopub.status.idle": "2023-07-25T23:59:56.036284Z", + "shell.execute_reply": "2023-07-25T23:59:56.035918Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor(max_features=12, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestRegressor(max_features=12, random_state=0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n", "bag_boston.fit(X_train, y_train)\n" @@ -720,10 +1114,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "1f2c7336", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:56.038138Z", + "iopub.status.busy": "2023-07-25T23:59:56.037994Z", + "iopub.status.idle": "2023-07-25T23:59:56.137043Z", + "shell.execute_reply": "2023-07-25T23:59:56.136608Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14.634700151315787" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " importance\n", + "lstat 0.356203\n", + "rm 0.332163\n", + "ptratio 0.067270\n", + "crim 0.055404\n", + "indus 0.053851\n", + "dis 0.041582\n", + "nox 0.035225\n", + "tax 0.025355\n", + "age 0.021506\n", + "rad 0.004784\n", + "chas 0.004203\n", + "zn 0.002454" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "feature_imp = pd.DataFrame(\n", " {'importance':RF_boston.feature_importances_},\n", @@ -857,10 +1417,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "887b4fa9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-25T23:59:57.024067Z", + "iopub.status.busy": "2023-07-25T23:59:57.023921Z", + "iopub.status.idle": "2023-07-26T00:00:00.413857Z", + "shell.execute_reply": "2023-07-26T00:00:00.413519Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
+       "                          random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n", + " random_state=0)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "boost_boston = GBR(n_estimators=5000,\n", " learning_rate=0.001,\n", @@ -881,10 +1465,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "6c56696c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:00.415790Z", + "iopub.status.busy": "2023-07-26T00:00:00.415651Z", + "iopub.status.idle": "2023-07-26T00:00:00.945897Z", + "shell.execute_reply": "2023-07-26T00:00:00.945457Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "test_error = np.zeros_like(boost_boston.train_score_)\n", "for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):\n", @@ -913,10 +1515,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "8a636f63", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:00.947856Z", + "iopub.status.busy": "2023-07-26T00:00:00.947708Z", + "iopub.status.idle": "2023-07-26T00:00:00.959031Z", + "shell.execute_reply": "2023-07-26T00:00:00.958695Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14.481405918831591" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y_hat_boost = boost_boston.predict(X_test);\n", "np.mean((y_test - y_hat_boost)**2)\n" @@ -936,12 +1556,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "4e2ab3c7", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:00.960939Z", + "iopub.status.busy": "2023-07-26T00:00:00.960794Z", + "iopub.status.idle": "2023-07-26T00:00:03.124886Z", + "shell.execute_reply": "2023-07-26T00:00:03.124529Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "14.501514553719565" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "boost_boston = GBR(n_estimators=5000,\n", " learning_rate=0.2,\n", @@ -986,12 +1623,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "b0b41c04", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:03.126938Z", + "iopub.status.busy": "2023-07-26T00:00:03.126803Z", + "iopub.status.idle": "2023-07-26T00:00:04.882575Z", + "shell.execute_reply": "2023-07-26T00:00:04.882239Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
BART(burnin=5, ndraw=15, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "BART(burnin=5, ndraw=15, random_state=0)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n", "bart_boston.fit(X_train, y_train)\n" @@ -1007,12 +1664,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "fda8fffc", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:04.884481Z", + "iopub.status.busy": "2023-07-26T00:00:04.884344Z", + "iopub.status.idle": "2023-07-26T00:00:05.801195Z", + "shell.execute_reply": "2023-07-26T00:00:05.800892Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "20.739185417498764" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n", "np.mean((y_test - yhat_test)**2)\n" @@ -1029,10 +1703,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "be77f0e4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:05.803030Z", + "iopub.status.busy": "2023-07-26T00:00:05.802900Z", + "iopub.status.idle": "2023-07-26T00:00:05.806091Z", + "shell.execute_reply": "2023-07-26T00:00:05.805780Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "crim 25.466667\n", + "zn 30.600000\n", + "indus 24.933333\n", + "chas 21.133333\n", + "nox 27.333333\n", + "rm 28.800000\n", + "age 23.466667\n", + "dis 26.000000\n", + "rad 25.000000\n", + "tax 21.733333\n", + "ptratio 26.800000\n", + "lstat 31.866667\n", + "dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n", " index=D.columns)\n", @@ -1045,6 +1749,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch9-svm-lab.ipynb b/docs/source/labs/Ch9-svm-lab.ipynb index 9c07d86..94aa676 100644 --- a/docs/source/labs/Ch9-svm-lab.ipynb +++ b/docs/source/labs/Ch9-svm-lab.ipynb @@ -23,9 +23,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "eeaa5be0", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.016487Z", + "iopub.status.busy": "2023-07-26T00:00:07.016196Z", + "iopub.status.idle": "2023-07-26T00:00:07.822651Z", + "shell.execute_reply": "2023-07-26T00:00:07.822165Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -47,9 +53,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "41a59634", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.824899Z", + "iopub.status.busy": "2023-07-26T00:00:07.824653Z", + "iopub.status.idle": "2023-07-26T00:00:07.850806Z", + "shell.execute_reply": "2023-07-26T00:00:07.850496Z" + } + }, "outputs": [], "source": [ "from sklearn.svm import SVC\n", @@ -68,9 +81,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c9a175d7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.852684Z", + "iopub.status.busy": "2023-07-26T00:00:07.852555Z", + "iopub.status.idle": "2023-07-26T00:00:07.854324Z", + "shell.execute_reply": "2023-07-26T00:00:07.854058Z" + } + }, "outputs": [], "source": [ "roc_curve = RocCurveDisplay.from_estimator # shorthand\n" @@ -101,12 +121,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a7216b47", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.855974Z", + "iopub.status.busy": "2023-07-26T00:00:07.855853Z", + "iopub.status.idle": "2023-07-26T00:00:07.959154Z", + "shell.execute_reply": "2023-07-26T00:00:07.958758Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "rng = np.random.default_rng(1)\n", "X = rng.standard_normal((50, 2))\n", @@ -129,12 +166,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ed329198", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.961084Z", + "iopub.status.busy": "2023-07-26T00:00:07.960943Z", + "iopub.status.idle": "2023-07-26T00:00:07.965329Z", + "shell.execute_reply": "2023-07-26T00:00:07.964984Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
SVC(C=10, kernel='linear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "SVC(C=10, kernel='linear')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "svm_linear = SVC(C=10, kernel='linear')\n", "svm_linear.fit(X, y)\n" @@ -153,10 +210,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "95494b8b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:07.967158Z", + "iopub.status.busy": "2023-07-26T00:00:07.967019Z", + "iopub.status.idle": "2023-07-26T00:00:08.140288Z", + "shell.execute_reply": "2023-07-26T00:00:08.139902Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ], + "text/plain": [ + "Truth -1 1\n", + "Predicted \n", + "-1 25 0\n", + " 1 0 25" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n", "y_hat = svm_.predict(X)\n", @@ -400,12 +760,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "2e4ed2f5", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.448970Z", + "iopub.status.busy": "2023-07-26T00:00:08.448846Z", + "iopub.status.idle": "2023-07-26T00:00:08.571150Z", + "shell.execute_reply": "2023-07-26T00:00:08.570802Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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1025
\n", + "
" + ], + "text/plain": [ + "Truth -1 1\n", + "Predicted \n", + "-1 25 0\n", + " 1 0 25" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n", "y_hat = svm_.predict(X)\n", @@ -451,12 +892,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "c67591a1", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.579932Z", + "iopub.status.busy": "2023-07-26T00:00:08.579791Z", + "iopub.status.idle": "2023-07-26T00:00:08.710958Z", + "shell.execute_reply": "2023-07-26T00:00:08.710610Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_svm(X,\n", @@ -487,9 +945,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "322be574", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.712863Z", + "iopub.status.busy": "2023-07-26T00:00:08.712722Z", + "iopub.status.idle": "2023-07-26T00:00:08.714989Z", + "shell.execute_reply": "2023-07-26T00:00:08.714695Z" + } + }, "outputs": [], "source": [ "X = rng.standard_normal((200, 2))\n", @@ -508,12 +973,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "04fda182", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.716729Z", + "iopub.status.busy": "2023-07-26T00:00:08.716589Z", + "iopub.status.idle": "2023-07-26T00:00:08.810747Z", + "shell.execute_reply": "2023-07-26T00:00:08.810365Z" + }, "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(X[:,0],\n", @@ -534,10 +1026,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "0c2690d1", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.812539Z", + "iopub.status.busy": "2023-07-26T00:00:08.812440Z", + "iopub.status.idle": "2023-07-26T00:00:08.816647Z", + "shell.execute_reply": "2023-07-26T00:00:08.816383Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
SVC(C=1, gamma=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "SVC(C=1, gamma=1)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(X_train, \n", " X_test,\n", @@ -561,10 +1074,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "3eb171e8", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:08.818364Z", + "iopub.status.busy": "2023-07-26T00:00:08.818238Z", + "iopub.status.idle": "2023-07-26T00:00:09.069609Z", + "shell.execute_reply": "2023-07-26T00:00:09.069226Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "best_svm = grid.best_estimator_\n", "fig, ax = subplots(figsize=(8,8))\n", @@ -703,12 +1344,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "0607fc41", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:09.558386Z", + "iopub.status.busy": "2023-07-26T00:00:09.558248Z", + "iopub.status.idle": "2023-07-26T00:00:09.656670Z", + "shell.execute_reply": "2023-07-26T00:00:09.656343Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "roc_curve(best_svm,\n", @@ -731,10 +1389,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "5211a882", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:09.658742Z", + "iopub.status.busy": "2023-07-26T00:00:09.658594Z", + "iopub.status.idle": "2023-07-26T00:00:09.805114Z", + "shell.execute_reply": "2023-07-26T00:00:09.804797Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "svm_flex = SVC(kernel=\"rbf\", \n", " gamma=50,\n", @@ -762,9 +1438,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "12acc4ff", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:09.807019Z", + "iopub.status.busy": "2023-07-26T00:00:09.806879Z", + "iopub.status.idle": "2023-07-26T00:00:09.811100Z", + "shell.execute_reply": "2023-07-26T00:00:09.810783Z" + } + }, "outputs": [], "source": [ "roc_curve(svm_flex,\n", @@ -786,10 +1469,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "21c81913", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:09.812971Z", + "iopub.status.busy": "2023-07-26T00:00:09.812862Z", + "iopub.status.idle": "2023-07-26T00:00:09.916772Z", + "shell.execute_reply": "2023-07-26T00:00:09.916366Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "rng = np.random.default_rng(123)\n", "X = np.vstack([X, rng.standard_normal((50, 2))])\n", @@ -846,12 +1565,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "5396f2df", "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T00:00:10.013499Z", + "iopub.status.busy": "2023-07-26T00:00:10.013374Z", + "iopub.status.idle": "2023-07-26T00:00:10.566891Z", + "shell.execute_reply": "2023-07-26T00:00:10.566514Z" + }, "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "
" + ], + "text/plain": [ + "Truth 1 2 3 4\n", + "Predicted \n", + "1 3 0 0 0\n", + "2 0 6 2 0\n", + "3 0 0 4 0\n", + "4 0 0 0 5" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion_table(khan_linear.predict(Khan['xtest']),\n", " Khan['ytest'])\n" @@ -970,6 +1900,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,Rmd", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, From b399ab273b17485272e474abcc3da97e58ce978a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 21:42:22 -0700 Subject: [PATCH 047/195] fix_labs script to modify lab 2,6 --- docs/fix_labs.py | 44 +- docs/source/labs.rst | 1 + docs/source/labs/Ch10-deeplearning-lab.ipynb | 7210 +------------- docs/source/labs/Ch11-surv-lab.ipynb | 2082 +--- docs/source/labs/Ch12-unsup-lab.ipynb | 2329 +---- docs/source/labs/Ch13-multiple-lab.ipynb | 932 +- docs/source/labs/Ch2-statlearn-lab.ipynb | 6559 +------------ docs/source/labs/Ch3-linreg-lab.ipynb | 2205 +---- docs/source/labs/Ch4-classification-lab.ipynb | 3642 +------ docs/source/labs/Ch5-resample-lab.ipynb | 621 +- docs/source/labs/Ch6-varselect-lab.ipynb | 8730 +---------------- docs/source/labs/Ch7-nonlin-lab.ipynb | 2211 +---- docs/source/labs/Ch8-baggboost-lab.ipynb | 1078 +- docs/source/labs/Ch9-svm-lab.ipynb | 1253 +-- 14 files changed, 2562 insertions(+), 36335 deletions(-) diff --git a/docs/fix_labs.py b/docs/fix_labs.py index cb616c1..7e69d15 100644 --- a/docs/fix_labs.py +++ b/docs/fix_labs.py @@ -8,17 +8,17 @@ for nbfile in glob('source/labs/*nb'): base = os.path.splitext(nbfile)[0] - fname = os.path.split(base)[1] + labname = os.path.split(base)[1] colab_code = f''' - + Open In Colab ''' # allow errors for lab2 - if fname[:3] == 'Ch6': + if labname[:3] == 'Ch6': colab_code = (''' ```{code-cell} :tags: [hide-cell] @@ -27,47 +27,39 @@ warnings.simplefilter('ignore') ``` +```{attention} +Using `skl.ElasticNet` to fit ridge regression +throws up many warnings. We have suppressed them below. +``` + ''' + colab_code) - if fname[:3] == 'Ch2': + if labname[:3] == 'Ch2': nb = nbformat.read(open(nbfile), 4) nb.metadata.setdefault('execution', {})['allow_errors'] = True nbformat.write(nb, open(nbfile, 'w')) - if fname[:4] != 'Ch10': + if labname[:4] != 'Ch10': + + os.system(f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') - os.system(f'jupytext --set-formats ipynb,md:myst {base}.ipynb; jupytext --sync {base}.ipynb') + os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') myst = open(f'{base}.md').read().strip() new_myst = [] for l in myst.split('\n'): if l.strip()[:9] != '# Chapter': + if 'Lab:' in l: + l = '# ' + l[6:] + '\n' + colab_code new_myst.append(l) - new_myst.append(colab_code) - + myst = '\n'.join(new_myst) # remove the "Chapter %d - myst = myst.replace('# Lab:', colab_code + '\n# ') open(f'{base}.md', 'w').write(myst) - os.system(f'jupytext --sync {base}.ipynb') + os.system(f'jupytext --sync {base}.ipynb; rm {base}.md') + # add a warning for ridge # at ## Ridge Regression and the Lasso -code_to_insert = ''' - - -```{attention} -Using `skl.ElasticNet` to fit ridge regression -throws up many warnings. We have inserted the code below to filter them. -``` - - -```{code-cell} -import warnings -warnings.simplefilter('ignore') -``` - -''' - diff --git a/docs/source/labs.rst b/docs/source/labs.rst index 38dba3d..a70370e 100644 --- a/docs/source/labs.rst +++ b/docs/source/labs.rst @@ -28,6 +28,7 @@ To ensure you have the same package versions as those built here, run: labs/Ch7-nonlin-lab labs/Ch8-baggboost-lab labs/Ch9-svm-lab + labs/Ch10-deeplearning-lab labs/Ch11-surv-lab labs/Ch12-unsup-lab labs/Ch13-multiple-lab diff --git a/docs/source/labs/Ch10-deeplearning-lab.ipynb b/docs/source/labs/Ch10-deeplearning-lab.ipynb index 3f0f930..61de0ed 100644 --- a/docs/source/labs/Ch10-deeplearning-lab.ipynb +++ b/docs/source/labs/Ch10-deeplearning-lab.ipynb @@ -2,16 +2,13 @@ "cells": [ { "cell_type": "markdown", - "id": "8140eb0d", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "1334dfc7", + "id": "fd22c2e6", "metadata": {}, "source": [ - "# Deep Learning\n", + "\n", + "# Chapter 10\n", + "\n", + "# Lab: Deep Learning\n", "In this section we demonstrate how to fit the examples discussed\n", "in the text. We use the `Python` `torch` package, along with the\n", "`pytorch_lightning` package which provides utilities to simplify\n", @@ -26,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "aeb913e2", "metadata": { "lines_to_next_cell": 2 @@ -63,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "3a3e91e7", "metadata": {}, "outputs": [], @@ -89,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3f4fdb8c", "metadata": {}, "outputs": [], @@ -114,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "71cc6f03", "metadata": {}, "outputs": [], @@ -134,18 +131,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "fd1290f1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 0\n" - ] - } - ], + "outputs": [], "source": [ "from pytorch_lightning.utilities.seed import seed_everything\n", "seed_everything(0, workers=True)\n", @@ -164,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c85308d1", "metadata": { "lines_to_next_cell": 0 @@ -199,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "4de9f03c", "metadata": {}, "outputs": [], @@ -228,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "4ef95fe3", "metadata": {}, "outputs": [], @@ -257,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "493821a9", "metadata": { "lines_to_next_cell": 2 @@ -279,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "a427e224", "metadata": { "lines_to_next_cell": 0 @@ -307,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "feac4b15", "metadata": { "lines_to_next_cell": 0 @@ -343,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "6a75a640", "metadata": {}, "outputs": [], @@ -368,21 +357,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "32d8de64", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "259.71528833146243" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hit_lm = LinearRegression().fit(X_train, Y_train)\n", "Yhat_test = hit_lm.predict(X_test)\n", @@ -404,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "46786db6", "metadata": {}, "outputs": [], @@ -427,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "2b55b38b", "metadata": { "lines_to_next_cell": 0 @@ -452,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "cc44c559", "metadata": {}, "outputs": [], @@ -479,23 +457,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "1bd1fd13", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "257.2382010799497" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "trained_lasso = grid.best_estimator_\n", "Yhat_test = trained_lasso.predict(X_test)\n", @@ -521,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "4b2ccc5a", "metadata": {}, "outputs": [], @@ -579,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "108ad1f1", "metadata": {}, "outputs": [], @@ -614,53 +581,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "2f20059e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "HittersModel [175, 19] [175] --\n", - "├─Flatten: 1-1 [175, 19] [175, 19] --\n", - "├─Sequential: 1-2 [175, 19] [175, 1] --\n", - "│ └─Linear: 2-1 [175, 19] [175, 50] 1,000\n", - "│ └─ReLU: 2-2 [175, 50] [175, 50] --\n", - "│ └─Dropout: 2-3 [175, 50] [175, 50] --\n", - "│ └─Linear: 2-4 [175, 50] [175, 1] 51\n", - "===================================================================================================================\n", - "Total params: 1,051\n", - "Trainable params: 1,051\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 0.18\n", - "===================================================================================================================\n", - "Input size (MB): 0.01\n", - "Forward/backward pass size (MB): 0.07\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.09\n", - "===================================================================================================================" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary(hit_model, \n", " input_size=X_train.shape,\n", @@ -692,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "6ca3030d", "metadata": { "lines_to_next_cell": 0 @@ -714,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "86723b3e", "metadata": {}, "outputs": [], @@ -749,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "999279fd", "metadata": {}, "outputs": [], @@ -781,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "7bd9cc5c", "metadata": {}, "outputs": [], @@ -809,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "5be2f822", "metadata": {}, "outputs": [], @@ -836,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "87334d33", "metadata": {}, "outputs": [], @@ -867,709 +793,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "1a474999", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "---------------------------------------\n", - "0 | model | HittersModel | 1.1 K \n", - "1 | loss | MSELoss | 0 \n", - "---------------------------------------\n", - "1.1 K Trainable params\n", - "0 Non-trainable params\n", - "1.1 K Total params\n", - "0.004 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e5fe2eab339740c99a234f68dff24548", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": 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"application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "hit_trainer = Trainer(deterministic=True,\n", " max_epochs=50,\n", @@ -1596,49 +825,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "e3b24643", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ee861e12d14b4d349e535c091f331ca4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 103304.8515625\n", - " test_mae 224.26962280273438\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 103304.8515625, 'test_mae': 224.26962280273438}]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hit_trainer.test(hit_module, datamodule=hit_dm)\n" ] @@ -1660,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "f9b266e7", "metadata": {}, "outputs": [], @@ -1679,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "c5752a0b", "metadata": { "lines_to_next_cell": 0 @@ -1722,23 +914,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "ff0c9fa0", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = subplots(1, 1, figsize=(6, 6))\n", "ax = summary_plot(hit_results,\n", @@ -1769,23 +950,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "1ee2b61b", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(224.2696, grad_fn=)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "hit_model.eval() \n", "preds = hit_module(X_test_t)\n", @@ -1794,7 +964,7 @@ }, { "cell_type": "markdown", - "id": "fd356e46", + "id": "8140eb0d", "metadata": {}, "source": [ " " @@ -1814,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "0ca069a3", "metadata": { "lines_to_next_cell": 2 @@ -1847,26 +1017,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "af6a0a33", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset MNIST\n", - " Number of datapoints: 60000\n", - " Root location: data\n", - " Split: Train\n", - " StandardTransform\n", - "Transform: ToTensor()" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(mnist_train, \n", " mnist_test) = [MNIST(root='data',\n", @@ -1902,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "0df8a0c8", "metadata": {}, "outputs": [], @@ -1925,23 +1079,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "0c7dd6ee", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n", - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n" - ] - } - ], + "outputs": [], "source": [ "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -1964,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "63e48c70", "metadata": {}, "outputs": [], @@ -2004,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "92405826", "metadata": {}, "outputs": [], @@ -2023,21 +1166,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "2c8f7a48", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([256, 10])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mnist_model(X_).size()" ] @@ -2054,56 +1186,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "9f502df8", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "MNISTModel [256, 1, 28, 28] [256, 10] --\n", - "├─Sequential: 1-1 [256, 1, 28, 28] [256, 10] --\n", - "│ └─Sequential: 2-1 [256, 1, 28, 28] [256, 256] --\n", - "│ │ └─Flatten: 3-1 [256, 1, 28, 28] [256, 784] --\n", - "│ │ └─Linear: 3-2 [256, 784] [256, 256] 200,960\n", - "│ │ └─ReLU: 3-3 [256, 256] [256, 256] --\n", - "│ │ └─Dropout: 3-4 [256, 256] [256, 256] --\n", - "│ └─Sequential: 2-2 [256, 256] [256, 128] --\n", - "│ │ └─Linear: 3-5 [256, 256] [256, 128] 32,896\n", - "│ │ └─ReLU: 3-6 [256, 128] [256, 128] --\n", - "│ │ └─Dropout: 3-7 [256, 128] [256, 128] --\n", - "│ └─Linear: 2-3 [256, 128] [256, 10] 1,290\n", - "===================================================================================================================\n", - "Total params: 235,146\n", - "Trainable params: 235,146\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 60.20\n", - "===================================================================================================================\n", - "Input size (MB): 0.80\n", - "Forward/backward pass size (MB): 0.81\n", - "Params size (MB): 0.94\n", - "Estimated Total Size (MB): 2.55\n", - "===================================================================================================================" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary(mnist_model,\n", " input_data=X_,\n", @@ -2125,7 +1211,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "d1a7590f", "metadata": {}, "outputs": [], @@ -2144,490 +1230,12 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "e2fe6de3", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNISTModel | 235 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "235 K Trainable params\n", - "0 Non-trainable params\n", - "235 K Total params\n", - "0.941 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "19132f797f854efbaa5bc297129f7288", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - 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"application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "outputs": [], "source": [ "mnist_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -2669,23 +1277,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "73755987", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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4dOItu73i7JL8/IoxGQMG1Ns9Ge4O6JSknJwcDRo0qM51Z2dna+DAgTW+xmEMAGgYPOlDCRqNhLcXz/L2QlYAgMDHGA3UyJuLZ/nqQlYAgMaJoNEIeHPxLIkLWQEAPMcFuxoBbwZ0VuJCVgAATxA0GgFvLp51scpTRgEAcBWHThoBb+8BAgCApwgajQADOgEAViFoBCh3L7zFgE4AgBW4jkYA8uTCW5W4eBYAwFNcsMsFgR40PL3wFgAA3uKCXQ2cNxfeAgDACgSNAOLthbcAAPA3gkYA8cWFtwAA8CeCRgDx1YW3AADwF4JGAOHCWwCAQEPQCCBceAsAEGgIGgGGC28BAAIJ19EIUFx4CwDgb570ody9NUBxJ1UAQCDg0AkAADCN5UHjhRdeUEJCgpo1a6akpCRt27btkvOeP39eCxcuVJcuXdSsWTMlJiZqw4YNfqwWAAC4w9KgsWbNGs2YMUPz58/Xjh07lJiYqJSUFB0/frzG+efMmaM//elPev755/Xll1/qwQcf1MiRI7Vz504/V+4b7t6BFQCAQGPpYNCkpCT169dPy5YtkySVl5crPj5eU6dO1aOPPlpt/g4dOmj27NmaPHmyY9odd9yhsLAw/e1vf3OpzfoyGNSbO7ACAGCFgLqp2rlz57R9+3YNHjz4v8UEBWnw4MHaunVrjcuUlZWpWbNmTtPCwsL08ccfX7KdsrIyFRcXOz2sVnkH1qr3LTl27JjS0tKUmZlpUWUAAPiWZUHjxIkTstvtioqKcpoeFRWlgoKCGpdJSUnRM888o6+++krl5eXauHGjMjMza723x+LFixUZGel4xMfH+/R9uIs7sAIAGhPLB4O6Y+nSperatauuuuoqhYSEaMqUKRo/fryCgi79NmbNmqWioiLH48iRI36suDruwAoAaEwsCxpt27ZVcHCwCgsLnaYXFhYqOjq6xmXatWun9evXq7S0VN9++60OHDigFi1aqHPnzpdsJzQ0VBEREU4PK3EHVgBAY2JZ0AgJCVGfPn2UlZXlmFZeXq6srCwlJyfXumyzZs0UGxurCxcu6M0339Tw4cPNLtdnuAMrAKAxsfTQyYwZM/Tyyy/r1Vdf1f79+zVp0iSVlpZq/PjxkqSxY8dq1qxZjvk/++wzZWZm6uuvv9bmzZs1ZMgQlZeX6ze/+Y1Vb8Ft3IEVANCYWHoJ8lGjRun777/XvHnzVFBQoN69e2vDhg2OAaJ5eXlO4y/Onj2rOXPm6Ouvv1aLFi00dOhQvfbaa2rVqpVF78B9lXdgTUtLk81mcxoUyh1YAQANDTdVs0hN19GIj4/XkiVLuI4GAKBe8qQPJWhYiDuwAgACCXdvDTDcgRUA0NAF1HU0AABAYCFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMY3nQeOGFF5SQkKBmzZopKSlJ27Ztq3X+JUuWqFu3bgoLC1N8fLwefvhhnT171k/VAgAAd1gaNNasWaMZM2Zo/vz52rFjhxITE5WSkqLjx4/XOH96eroeffRRzZ8/X/v379fy5cu1Zs0aPfbYY36uHAAAuMLSoPHMM89owoQJGj9+vHr06KGXXnpJ4eHheuWVV2qc/5NPPtENN9ygMWPGKCEhQbfddptGjx5d514QAABgDcuCxrlz57R9+3YNHjz4v8UEBWnw4MHaunVrjctcf/312r59uyNYfP3113r33Xc1dOjQS7ZTVlam4uJipwcAAPCPJlY1fOLECdntdkVFRTlNj4qK0oEDB2pcZsyYMTpx4oR+/OMfyzAMXbhwQQ8++GCth04WL16sBQsW+LR2AADgGssHg7ojJydHixYt0h//+Eft2LFDmZmZeuedd/TEE09ccplZs2apqKjI8Thy5IhPa7Lb7crJydGqVauUk5Mju93u0/UDABDILNuj0bZtWwUHB6uwsNBpemFhoaKjo2tcZu7cubrnnnv0wAMPSJJ69uyp0tJSTZw4UbNnz1ZQUPXcFBoaqtDQUN+/AUmZmZmaNm2ajh496pgWFxenpUuXKjU11ZQ2AQAIJJbt0QgJCVGfPn2UlZXlmFZeXq6srCwlJyfXuMyZM2eqhYng4GBJkmEY5hVbg8zMTKWlpTmFDEk6duyY0tLSlJmZ6dd6AACojyw9dDJjxgy9/PLLevXVV7V//35NmjRJpaWlGj9+vCRp7NixmjVrlmP+YcOG6cUXX9Tq1auVm5urjRs3au7cuRo2bJgjcPiD3W7XtGnTagw3ldOmT5/OYRQAQKNn2aETSRo1apS+//57zZs3TwUFBerdu7c2bNjgGCCal5fntAdjzpw5stlsmjNnjo4dO6Z27dpp2LBh+u1vf+vXujdv3lxtT8bFDMPQkSNHtHnzZg0cONB/hQEAUM/YDH8fc7BYcXGxIiMjVVRUpIiICI/WsWrVKo0ZM6bO+dLT0zV69GiP2gAAoL7xpA8NqLNO6ouYmBifzgcAQENF0PDAgAEDFBcXJ5vNVuPrNptN8fHxGjBggJ8rAwCgfiFoeCA4OFhLly6VpGpho/L5kiVL/DpAFQCA+oig4aHU1FRlZGQoNjbWaXpcXJwyMjK4jgYAAGIwqNfrs9vt2rx5s/Lz8xUTE6MBAwawJwMA0CB50odaenprQxAcHMwprAAAXAKHTgAAgGkIGgAAwDQEDQAAYBqCBgAAMI3bQSMhIUELFy5UXl6eGfUAAIAGxO2gMX36dGVmZqpz58669dZbtXr1apWVlZlRGwAACHAeBY1du3Zp27Zt6t69u6ZOnaqYmBhNmTJFO3bsMKNGAAAQoLy+YNf58+f1xz/+UTNnztT58+fVs2dPPfTQQxo/fvwl7wViJV9fsAsAgMbCrxfsOn/+vNatW6cVK1Zo48aN+tGPfqT7779fR48e1WOPPaYPPvhA6enpnq4eAAA0AG4HjR07dmjFihVatWqVgoKCNHbsWD377LO66qqrHPOMHDlS/fr182mhAAAg8LgdNPr166dbb71VL774okaMGKGmTZtWm6dTp0666667fFIgAAAIXG4Hja+//lodO3asdZ7mzZtrxYoVHhcFAAAaBrfPOjl+/Lg+++yzatM/++wzffHFFz4pCgAANAxuB43JkyfryJEj1aYfO3ZMkydP9klRAACgYXA7aHz55Ze67rrrqk2/9tpr9eWXX/qkKAAA0DC4HTRCQ0NVWFhYbXp+fr6aNPH4bFkAANAAuR00brvtNs2aNUtFRUWOaadOndJjjz2mW2+91afFAQCAwOb2Loinn35aN954ozp27Khrr71WkrRr1y5FRUXptdde83mBAAAgcLkdNGJjY7Vnzx69/vrr2r17t8LCwjR+/HiNHj26xmtqAACAxsujQRXNmzfXxIkTfV0LAABoYDwevfnll18qLy9P586dc5r+s5/9zOuiAABAw+DRlUFHjhypvXv3ymazqfLmr5V3arXb7b6tEAAABCy3zzqZNm2aOnXqpOPHjys8PFz//Oc/tWnTJvXt21c5OTkmlAgAAAKV23s0tm7dqg8//FBt27ZVUFCQgoKC9OMf/1iLFy/WQw89pJ07d5pRJwAACEBu79Gw2+1q2bKlJKlt27b67rvvJEkdO3bUwYMHfVsdAAAIaG7v0bjmmmu0e/duderUSUlJSXryyScVEhKiP//5z+rcubMZNQIAgADldtCYM2eOSktLJUkLFy7UT3/6Uw0YMEBt2rTRmjVrfF4gAAAIXDaj8rQRL5w8eVKtW7d2nHlSnxUXFysyMlJFRUWKiIiwuhwAAAKGJ32oW2M0zp8/ryZNmmjfvn1O0y+77LKACBkAAMC/3AoaTZs21eWXX861MgAAgEvcPutk9uzZeuyxx3Ty5Ekz6gEAAA2I24NBly1bpkOHDqlDhw7q2LGjmjdv7vT6jh07fFYcAAAIbG4HjREjRphQBgAAaIh8ctZJIOGsEwAAPGP6WScAAADucPvQSVBQUK2nsnJGCgAAqOR20Fi3bp3T8/Pnz2vnzp169dVXtWDBAp8VBgAAAp/Pxmikp6drzZo1euutt3yxOtMwRgMAAM9YOkbjRz/6kbKysny1OgAA0AD4JGj88MMPeu655xQbG+uL1QEAgAbC7TEaVW+eZhiGTp8+rfDwcP3tb3/zaXEAACCwuR00nn32WaegERQUpHbt2ikpKUmtW7f2aXEAACCwuR007r33XhPKAAAADZHbYzRWrFihtWvXVpu+du1avfrqqz4pCgAANAxuB43Fixerbdu21aa3b99eixYt8klRAACgYXA7aOTl5alTp07Vpnfs2FF5eXk+KQoAADQMbgeN9u3ba8+ePdWm7969W23atPFJUQAAoGFwO2iMHj1aDz30kLKzs2W322W32/Xhhx9q2rRpuuuuu8yoEQAABCi3zzp54okn9M033+iWW25RkyYVi5eXl2vs2LGM0QAAAE48vtfJV199pV27diksLEw9e/ZUx44dfV2bKbjXCQAAnvGkD3V7j0alrl27qmvXrp4uDgAAGgG3x2jccccd+v3vf19t+pNPPqmf//znPikKAAA0DG4HjU2bNmno0KHVpt9+++3atGmTT4oCAAANg9tBo6SkRCEhIdWmN23aVMXFxT4pCgAANAxuB42ePXtqzZo11aavXr1aPXr08ElRAACgYXB7MOjcuXOVmpqqw4cP6+abb5YkZWVlKT09XRkZGT4vEAAABC63g8awYcO0fv16LVq0SBkZGQoLC1NiYqI+/PBDXXbZZWbUCAAAApTH19GoVFxcrFWrVmn58uXavn277Ha7r2ozBdfRAADAM570oW6P0ai0adMmjRs3Th06dNAf/vAH3Xzzzfr00089XR0AAGiA3Dp0UlBQoJUrV2r58uUqLi7WnXfeqbKyMq1fv56BoAAAoBqX92gMGzZM3bp10549e7RkyRJ99913ev75582sDQAABDiX92i89957euihhzRp0iQuPQ4AAFzi8h6Njz/+WKdPn1afPn2UlJSkZcuW6cSJE2bWBgAAApzLQeNHP/qRXn75ZeXn5+uXv/ylVq9erQ4dOqi8vFwbN27U6dOnzawTAAAEIK9Obz148KCWL1+u1157TadOndKtt96qt99+25f1+RyntwIA4Bm/nt4qSd26ddOTTz6po0ePatWqVd6sCgAANEBeX7Ar0LBHAwAAz/h9jwYAAEBtCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANPUi6DxwgsvKCEhQc2aNVNSUpK2bdt2yXkHDhwom81W7fGTn/zEjxUDAABXWB401qxZoxkzZmj+/PnasWOHEhMTlZKSouPHj9c4f2ZmpvLz8x2Pffv2KTg4WD//+c/9XDkAAKiL5UHjmWee0YQJEzR+/Hj16NFDL730ksLDw/XKK6/UOP9ll12m6Ohox2Pjxo0KDw8naAAAUA9ZGjTOnTun7du3a/DgwY5pQUFBGjx4sLZu3erSOpYvX6677rpLzZs3r/H1srIyFRcXOz0AAIB/WBo0Tpw4IbvdrqioKKfpUVFRKigoqHP5bdu2ad++fXrggQcuOc/ixYsVGRnpeMTHx3tdNwAAcI3lh068sXz5cvXs2VP9+/e/5DyzZs1SUVGR43HkyBE/VggAQOPWxMrG27Ztq+DgYBUWFjpNLywsVHR0dK3LlpaWavXq1Vq4cGGt84WGhio0NNTrWgEAgPss3aMREhKiPn36KCsryzGtvLxcWVlZSk5OrnXZtWvXqqysTL/4xS/MLhMAAHjI0j0akjRjxgyNGzdOffv2Vf/+/bVkyRKVlpZq/PjxkqSxY8cqNjZWixcvdlpu+fLlGjFihNq0aWNF2QAAwAWWB41Ro0bp+++/17x581RQUKDevXtrw4YNjgGieXl5Cgpy3vFy8OBBffzxx/rHP/5hRckAAMBFNsMwDKuL8Kfi4mJFRkaqqKhIERERVpcDAEDA8KQPDeizTgAAQP1G0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAAprE8aLzwwgtKSEhQs2bNlJSUpG3bttU6/6lTpzR58mTFxMQoNDRUV155pd59910/VQsAANzRxMrG16xZoxkzZuill15SUlKSlixZopSUFB08eFDt27evNv+5c+d06623qn379srIyFBsbKy+/fZbtWrVyv/FAwCAOtkMwzCsajwpKUn9+vXTsmXLJEnl5eWKj4/X1KlT9eijj1ab/6WXXtJTTz2lAwcOqGnTpi61UVZWprKyMsfz4uJixcfHq6ioSBEREb55IwAANALFxcWKjIx0qw+17NDJuXPntH37dg0ePPi/xQQFafDgwdq6dWuNy7z99ttKTk7W5MmTFRUVpWuuuUaLFi2S3W6/ZDuLFy9WZGSk4xEfH+/z9wIAAGpmWdA4ceKE7Ha7oqKinKZHRUWpoKCgxmW+/vprZWRkyG63691339XcuXP1hz/8Qf/zP/9zyXZmzZqloqIix+PIkSM+fR8AAODSLB2j4a7y8nK1b99ef/7znxUcHKw+ffro2LFjeuqppzR//vwalwkNDVVoaKifKwUAAJKFQaNt27YKDg5WYWGh0/TCwkJFR0fXuExMTIyaNm2q4OBgx7Tu3buroKBA586dU0hIiKk1AwAA91h26CQkJER9+vRRVlaWY1p5ebmysrKUnJxc4zI33HCDDh06pPLycse0f/3rX4qJiSFkAABQD1l6HY0ZM2bo5Zdf1quvvqr9+/dr0qRJKi0t1fjx4yVJY8eO1axZsxzzT5o0SSdPntS0adP0r3/9S++8844WLVqkyZMnW/UWAABALSwdozFq1Ch9//33mjdvngoKCtS7d29t2LDBMUA0Ly9PQUH/zULx8fF6//339fDDD6tXr16KjY3VtGnTNHPmTKveAgAAqIWl19GwgifnAAMAgAC7jgYAAGj4CBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKZpYnUB9ZXdbtf58+etLgM+0LRpUwUHB1tdBgA0SgSNKgzDUEFBgU6dOmV1KfChVq1aKTo6WjabzepSAKBRIWhUURky2rdvr/DwcDqmAGcYhs6cOaPjx49LkmJiYiyuCAAaF4LGRex2uyNktGnTxupy4CNhYWGSpOPHj6t9+/YcRgEAP2Iw6EUqx2SEh4dbXAl8rfIzZdwNAPgXQaMGHC5pePhMAcAaBA0AAGAagoZJ7Ha7cnJytGrVKuXk5Mhut1tdktsSEhK0ZMkSl+fPycmRzWbjjB0AgANBwwSZmZlKSEjQoEGDNGbMGA0aNEgJCQnKzMw0pT2bzVbr4/HHH/dovZ9//rkmTpzo8vzXX3+98vPzFRkZ6VF7AICGh7NOfCwzM1NpaWkyDMNp+rFjx5SWlqaMjAylpqb6tM38/HzHv9esWaN58+bp4MGDjmktWrRw/NswDNntdjVpUvdH365dO7fqCAkJUXR0tFvLAAAaNvZo1MEwDJWWlrr0KC4u1kMPPVQtZFSuR5KmTZum4uJil9ZX03pqEh0d7XhERkbKZrM5nh84cEAtW7bUe++9pz59+ig0NFQff/yxDh8+rOHDhysqKkotWrRQv3799MEHHzitt+qhE5vNpr/85S8aOXKkwsPD1bVrV7399tuO16seOlm5cqVatWql999/X927d1eLFi00ZMgQp2B04cIFPfTQQ2rVqpXatGmjmTNnaty4cRoxYoSLnxAAoD4jaNThzJkzatGihUuPyMhIHTt27JLrMgxDR48eVWRkpEvrO3PmjM/ex6OPPqrf/e532r9/v3r16qWSkhINHTpUWVlZ2rlzp4YMGaJhw4YpLy+v1vUsWLBAd955p/bs2aOhQ4fq7rvv1smTJy85/5kzZ/T000/rtdde06ZNm5SXl6dHHnnE8frvf/97vf7661qxYoW2bNmi4uJirV+/3ldvGwBgMYJGI7Fw4ULdeuut6tKliy677DIlJibql7/8pa655hp17dpVTzzxhLp06eK0h6Im9957r0aPHq0rrrhCixYtUklJibZt23bJ+c+fP6+XXnpJffv21XXXXacpU6YoKyvL8frzzz+vWbNmaeTIkbrqqqu0bNkytWrVyldvGwBgMcZo1CE8PFwlJSUuzbtp0yYNHTq0zvneffdd3XjjjS617St9+/Z1el5SUqLHH39c77zzjvLz83XhwgX98MMPde7R6NWrl+PfzZs3V0REhOPy3jUJDw9Xly5dHM9jYmIc8xcVFamwsFD9+/d3vB4cHKw+ffqovLzcrfcHAKifCBp1sNlsat68uUvz3nbbbYqLi9OxY8dqHF9hs9kUFxen2267ze+Xwa76Hh555BFt3LhRTz/9tK644gqFhYUpLS1N586dq3U9TZs2dXpus9lqDQU1ze/q2BMAQODj0IkPBQcHa+nSpZKqX4my8vmSJUvqxb02tmzZonvvvVcjR45Uz549FR0drW+++cavNURGRioqKkqff/65Y5rdbteOHTv8WgcAwDwEDR9LTU1VRkaGYmNjnabHxcWZcmqrp7p27arMzEzt2rVLu3fv1pgxYyw5XDF16lQtXrxYb731lg4ePKhp06bpP//5D5cMB4AGgkMnJkhNTdXw4cO1efNm5efnKyYmRgMGDKgXezIqPfPMM7rvvvt0/fXXq23btpo5c6aKi4v9XsfMmTNVUFCgsWPHKjg4WBMnTlRKSkq92lYAAM/ZjEZ2wLy4uFiRkZEqKipSRESE02tnz55Vbm6uOnXqpGbNmllUYeNWXl6u7t27684779QTTzzhs/Xy2QKA92rrQy+FPRqw1Lfffqt//OMfuummm1RWVqZly5YpNzdXY8aMsbo0AIAPMEYDlgoKCtLKlSvVr18/3XDDDdq7d68++OADde/e3erSAAA+wB4NWCo+Pl5btmyxugwAgEnYowEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqChlnsdiknR1q1quKn3W51RbUaOHCgpk+f7niekJCgJUuW1LqMzWbT+vXrvW7bV+sBANQ/BA0zZGZKCQnSoEHSmDEVPxMSKqabYNiwYRoyZEiNr23evFk2m0179uxxa52ff/65Jk6c6IvyHB5//HH17t272vT8/HzdfvvtPm0LAFA/EDR8LTNTSkuTjh51nn7sWMV0E8LG/fffr40bN+po1TYlrVixQn379lWvXr3cWme7du0UHh7uqxJrFR0drdDQUL+0BQDwL4JGXQxDKi117VFcLD30UMUyNa1HkqZNq5jPlfW5eL+7n/70p2rXrp1WrlzpNL2kpERr167ViBEjNHr0aMXGxio8PFw9e/bUqlWral1n1UMnX331lW688UY1a9ZMPXr00MaNG6stM3PmTF155ZUKDw9X586dNXfuXJ0/f16StHLlSi1YsEC7d++WzWaTzWZz1Fv10MnevXt18803KywsTG3atNHEiRNVUlLieP3ee+/ViBEj9PTTTysmJkZt2rTR5MmTHW0BAOoPLkFelzNnpBYtfLMuw6jY0xEZ6dr8JSVS8+Z1ztakSRONHTtWK1eu1OzZs2Wz2SRJa9euld1u1y9+8QutXbtWM2fOVEREhN555x3dc8896tKli/r371/n+svLy5WamqqoqCh99tlnKioqchrPUally5ZauXKlOnTooL1792rChAlq2bKlfvOb32jUqFHat2+fNmzYoA8++ECSFFnDdigtLVVKSoqSk5P1+eef6/jx43rggQc0ZcoUpyCVnZ2tmJgYZWdn69ChQxo1apR69+6tCRMm1Pl+AAD+wx6NBuK+++7T4cOH9dFHHzmmrVixQnfccYc6duyoRx55RL1791bnzp01depUDRkyRG+88YZL6/7ggw904MAB/fWvf1ViYqJuvPFGLVq0qNp8c+bM0fXXX6+EhAQNGzZMjzzyiKONsLAwtWjRQk2aNFF0dLSio6MVFhZWbR3p6ek6e/as/vrXv+qaa67RzTffrGXLlum1115TYWGhY77WrVtr2bJluuqqq/TTn/5UP/nJT5SVleXuZgMAmIw9GnUJD6/Ys+CKTZukoUPrnu/dd6Ubb3StbRddddVVuv766/XKK69o4MCBOnTokDZv3qyFCxfKbrdr0aJFeuONN3Ts2DGdO3dOZWVlLo/B2L9/v+Lj49WhQwfHtOTk5GrzrVmzRs8995wOHz6skpISXbhwQRERES6/h8q2EhMT1fyiPTk33HCDysvLdfDgQUVFRUmSrr76agUHBzvmiYmJ0d69e91qCwBgPvZo1MVmqzh84crjttukuLiKZS61rvj4ivlcWd+l1nMJ999/v958802dPn1aK1asUJcuXXTTTTfpqaee0tKlSzVz5kxlZ2dr165dSklJ0blz53ywgSps3bpVd999t4YOHaq///3v2rlzp2bPnu3TNi7WtGlTp+c2m03l5eWmtAUA8BxBw5eCg6WlSyv+XTUkVD5fsqRiPhPceeedCgoKUnp6uv7617/qvvvuk81m05YtWzR8+HD94he/UGJiojp37qx//etfLq+3e/fuOnLkiPLz8x3TPv30U6d5PvnkE3Xs2FGzZ89W37591bVrV3377bdO84SEhMhex/VEunfvrt27d6u0tNQxbcuWLQoKClK3bt1crhkAUD8QNHwtNVXKyJBiY52nx8VVTE9NNa3pFi1aaNSoUZo1a5by8/N17733SpK6du2qjRs36pNPPtH+/fv1y1/+0mm8Q10GDx6sK6+8UuPGjdPu3bu1efNmzZ4922merl27Ki8vT6tXr9bhw4f13HPPad26dU7zJCQkKDc3V7t27dKJEydUVlZWra27775bzZo107hx47Rv3z5lZ2dr6tSpuueeexyHTQAAgYOgYYbUVOmbb6TsbCk9veJnbq6pIaPS/fffr//85z9KSUlxjKmYM2eOrrvuOqWkpGjgwIGKjo7WiBEjXF5nUFCQ1q1bpx9++EH9+/fXAw88oN/+9rdO8/zsZz/Tww8/rClTpqh379765JNPNHfuXKd57rjjDg0ZMkSDBg1Su3btajzFNjw8XO+//75Onjypfv36KS0tTbfccouWLVvm/sYAAFjOZhguXqyhgSguLlZkZKSKioqqDVQ8e/ascnNz1alTJzVr1syiCmEGPlsA8F5tfeilsEcDAACYhqABAABMQ9AAAACmIWgAAADTEDRq0MjGxzYKfKYAYA2CxkUqrzZ55swZiyuBr1V+plWvKAoAMBf3OrlIcHCwWrVqpePHj0uquKaDzc3LgKN+MQxDZ86c0fHjx9WqVSun+6MAAMxH0KgiOjpakhxhAw1Dq1atHJ8tAMB/CBpV2Gw2xcTEqH379jp//rzV5cAHmjZtyp4MALAIQeMSgoOD6ZwAAPBSvRgM+sILLyghIUHNmjVTUlKStm3bdsl5V65cKZvN5vTgktIAANRPlgeNNWvWaMaMGZo/f7527NihxMREpaSk1DpGIiIiQvn5+Y5H1duRAwCA+sHyoPHMM89owoQJGj9+vHr06KGXXnpJ4eHheuWVVy65jM1mU3R0tOPB7cMBAKifLB2jce7cOW3fvl2zZs1yTAsKCtLgwYO1devWSy5XUlKijh07qry8XNddd50WLVqkq6++usZ5y8rKVFZW5nheVFQkqeIOdAAAwHWVfac7F0G0NGicOHFCdru92h6JqKgoHThwoMZlunXrpldeeUW9evVSUVGRnn76aV1//fX65z//qbi4uGrzL168WAsWLKg2PT4+3jdvAgCARub06dOKjIx0ad6AO+skOTlZycnJjufXX3+9unfvrj/96U964oknqs0/a9YszZgxw/G8vLxcJ0+eVJs2bXx2Ma7i4mLFx8fryJEjioiI8Mk6Gxq2Ud3YRnVjG9WNbVQ3tlHdLrWNDMPQ6dOn1aFDB5fXZWnQaNu2rYKDg1VYWOg0vbCw0OWLKzVt2lTXXnutDh06VOProaGhCg0NdZrWqlUrj+qtS0REBF/aOrCN6sY2qhvbqG5so7qxjepW0zZydU9GJUsHg4aEhKhPnz7KyspyTCsvL1dWVpbTXova2O127d27VzExMWaVCQAAPGT5oZMZM2Zo3Lhx6tu3r/r3768lS5aotLRU48ePlySNHTtWsbGxWrx4sSRp4cKF+tGPfqQrrrhCp06d0lNPPaVvv/1WDzzwgJVvAwAA1MDyoDFq1Ch9//33mjdvngoKCtS7d29t2LDBMUA0Ly9PQUH/3fHyn//8RxMmTFBBQYFat26tPn366JNPPlGPHj2segsKDQ3V/Pnzqx2iwX+xjerGNqob26hubKO6sY3q5sttZDPcOUcFAADADZZfsAsAADRcBA0AAGAaggYAADANQQMAAJiGoOED7tzmvrF5/PHHZbPZnB5XXXWV1WVZatOmTRo2bJg6dOggm82m9evXO71uGIbmzZunmJgYhYWFafDgwfrqq6+sKdYidW2je++9t9r3asiQIdYUa4HFixerX79+atmypdq3b68RI0bo4MGDTvOcPXtWkydPVps2bdSiRQvdcccd1S6O2JC5so0GDhxY7Xv04IMPWlSx/7344ovq1auX46JcycnJeu+99xyv++o7RNDwkie3uW9srr76auXn5zseH3/8sdUlWaq0tFSJiYl64YUXanz9ySef1HPPPaeXXnpJn332mZo3b66UlBSdPXvWz5Vap65tJElDhgxx+l6tWrXKjxVa66OPPtLkyZP16aefauPGjTp//rxuu+02lZaWOuZ5+OGH9b//+79au3atPvroI3333XdKTU21sGr/cmUbSdKECROcvkdPPvmkRRX7X1xcnH73u99p+/bt+uKLL3TzzTdr+PDh+uc//ynJh98hA17p37+/MXnyZMdzu91udOjQwVi8eLGFVdUf8+fPNxITE60uo96SZKxbt87xvLy83IiOjjaeeuopx7RTp04ZoaGhxqpVqyyo0HpVt5FhGMa4ceOM4cOHW1JPfXT8+HFDkvHRRx8ZhlHxnWnatKmxdu1axzz79+83JBlbt261qkxLVd1GhmEYN910kzFt2jTriqqHWrdubfzlL3/x6XeIPRpeqLzN/eDBgx3TXLnNfWPz1VdfqUOHDurcubPuvvtu5eXlWV1SvZWbm6uCggKn71RkZKSSkpL4TlWRk5Oj9u3bq1u3bpo0aZL+/e9/W12SZYqKiiRJl112mSRp+/btOn/+vNP36KqrrtLll1/eaL9HVbdRpddff11t27bVNddco1mzZunMmTNWlGc5u92u1atXq7S0VMnJyT79Dll+ZdBA5slt7hubpKQkrVy5Ut26dVN+fr4WLFigAQMGaN++fWrZsqXV5dU7BQUFklTjd6ryNVQcNklNTVWnTp10+PBhPfbYY7r99tu1detWBQcHW12eX5WXl2v69Om64YYbdM0110iq+B6FhIRUu4FkY/0e1bSNJGnMmDHq2LGjOnTooD179mjmzJk6ePCgMjMzLazWv/bu3avk5GSdPXtWLVq00Lp169SjRw/t2rXLZ98hggZMdfvttzv+3atXLyUlJaljx4564403dP/991tYGQLZXXfd5fh3z5491atXL3Xp0kU5OTm65ZZbLKzM/yZPnqx9+/Y1+rFPtbnUNpo4caLj3z179lRMTIxuueUWHT58WF26dPF3mZbo1q2bdu3apaKiImVkZGjcuHH66KOPfNoGh0684Ivb3Dc2rVq10pVXXqlDhw5ZXUq9VPm94Tvlns6dO6tt27aN7ns1ZcoU/f3vf1d2drbi4uIc06Ojo3Xu3DmdOnXKaf7G+D261DaqSVJSkiQ1qu9RSEiIrrjiCvXp00eLFy9WYmKili5d6tPvEEHDC764zX1jU1JSosOHDysmJsbqUuqlTp06KTo62uk7VVxcrM8++4zvVC2OHj2qf//7343me2UYhqZMmaJ169bpww8/VKdOnZxe79Onj5o2ber0PTp48KDy8vIazfeorm1Uk127dklSo/ke1aS8vFxlZWW+/Q75drxq47N69WojNDTUWLlypfHll18aEydONFq1amUUFBRYXVq98P/+3/8zcnJyjNzcXGPLli3G4MGDjbZt2xrHjx+3ujTLnD592ti5c6exc+dOQ5LxzDPPGDt37jS+/fZbwzAM43e/+53RqlUr46233jL27NljDB8+3OjUqZPxww8/WFy5/9S2jU6fPm088sgjxtatW43c3Fzjgw8+MK677jqja9euxtmzZ60u3S8mTZpkREZGGjk5OUZ+fr7jcebMGcc8Dz74oHH55ZcbH374ofHFF18YycnJRnJysoVV+1dd2+jQoUPGwoULjS+++MLIzc013nrrLaNz587GjTfeaHHl/vPoo48aH330kZGbm2vs2bPHePTRRw2bzWb84x//MAzDd98hgoYPPP/888bll19uhISEGP379zc+/fRTq0uqN0aNGmXExMQYISEhRmxsrDFq1Cjj0KFDVpdlqezsbENStce4ceMMw6g4xXXu3LlGVFSUERoaatxyyy3GwYMHrS3az2rbRmfOnDFuu+02o127dkbTpk2Njh07GhMmTGhU4b6mbSPJWLFihWOeH374wfjVr35ltG7d2ggPDzdGjhxp5OfnW1e0n9W1jfLy8owbb7zRuOyyy4zQ0FDjiiuuMH79618bRUVF1hbuR/fdd5/RsWNHIyQkxGjXrp1xyy23OEKGYfjuO8Rt4gEAgGkYowEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAaBBsNlsWr9+vdVlAKiCoAHAa/fee69sNlu1x5AhQ6wuDYDFmlhdAICGYciQIVqxYoXTtNDQUIuqAVBfsEcDgE+EhoYqOjra6dG6dWtJFYc1XnzxRd1+++0KCwtT586dlZGR4bT83r17dfPNNyssLExt2rTRxIkTVVJS4jTPK6+8oquvvlqhoaGKiYnRlClTnF4/ceKERo4cqfDwcHXt2lVvv/22uW8aQJ0IGgD8Yu7cubrjjju0e/du3X333brrrru0f/9+SVJpaalSUlLUunVrff7551q7dq0++OADpyDx4osvavLkyZo4caL27t2rt99+W1dccYVTGwsWLNCdd96pPXv2aOjQobr77rt18uRJv75PAFX47oazABqrcePGGcHBwUbz5s2dHr/97W8Nw6i4ZfeDDz7otExSUpIxadIkwzAM489//rPRunVro6SkxPH6O++8YwQFBTlu/96hQwdj9uzZl6xBkjFnzhzH85KSEkOS8d577/nsfQJwH2M0APjEoEGD9OKLLzpNu+yyyxz/Tk5OdnotOTlZu3btkiTt379fiYmJat68ueP1G264QeXl5Tp48KBsNpu+++473XLLLbXW0KtXL8e/mzdvroiICB0/ftzTtwTABwgaAHyiefPm1Q5l+EpYWJhL8zVt2tTpuc1mU3l5uRklAXARYzQA+MWnn35a7Xn37t0lSd27d9fu3btVWlrqeH3Lli0KCgpSt27d1LJlSyUkJCgrK8uvNQPwHns0APhEWVmZCgoKnKY1adJEbdu2lSStXbtWffv21Y9//GO9/vrr2rZtm5YvXy5JuvvuuzV//nyNGzdOjz/+uL7//ntNnTpV99xzj6KioiRJjz/+uB588EG1b99et99+u06fPq0tW7Zo6tSp/n2jANxC0ADgExs2bFBMTIzTtG7duunAgQOSKs4IWb16tX71q18pJiZGq1atUo8ePSRJ4eHhev/99zVt2jT169dP4eHhuuOOO/TMM8841jVu3DidPXtWzz77rB555BG1bdtWaWlp/nuDADxiMwzDsLoIAA2bzWbTunXrNGLECKtLAeBnjNEAAACmIWgAAADTMEYDgOk4Qgs0XuzRAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABM8/8BLDxhn6oHWGsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -2709,47 +1306,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "f5269c40", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1a7f965ce4ad4bfa92fbaa1305bbbbe5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9681000113487244\n", - " test_loss 0.14706705510616302\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.14706705510616302, 'test_accuracy': 0.9681000113487244}]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mnist_trainer.test(mnist_module,\n", " datamodule=mnist_dm)" @@ -2770,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "97a0b304", "metadata": {}, "outputs": [], @@ -2790,430 +1350,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "ea685183", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNIST_MLR | 7.9 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "7.9 K Trainable params\n", - "0 Non-trainable params\n", - "7.9 K Total params\n", - "0.031 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "417a4d9bf4ce47c38cf544d9f63db3a0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "42056226aefe470d919ec951608b4d79", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a3a42f9e5c7a4dfcb73dd9f4b7d24317", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "outputs": [], "source": [ "mlr_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -3231,49 +1373,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "c0bd63e3", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "92b7e09cc3df40619350090e37d39bc0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9240999817848206\n", - " test_loss 0.3186827003955841\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.3186827003955841, 'test_accuracy': 0.9240999817848206}]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mlr_trainer.test(mlr_module,\n", " datamodule=mnist_dm)" @@ -3292,7 +1397,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "6b0d3daa", "metadata": { "lines_to_next_cell": 2 @@ -3323,19 +1428,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "67517b11", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Files already downloaded and verified\n", - "Files already downloaded and verified\n" - ] - } - ], + "outputs": [], "source": [ "(cifar_train, \n", " cifar_test) = [CIFAR100(root=\"data\",\n", @@ -3346,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "ee7b040e", "metadata": {}, "outputs": [], @@ -3377,7 +1473,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "bd9e74ad", "metadata": { "lines_to_next_cell": 0 @@ -3401,23 +1497,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "a553b926", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n", - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n" - ] - } - ], + "outputs": [], "source": [ "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", @@ -3440,23 +1525,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "94885e68", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = subplots(5, 5, figsize=(10,10))\n", "rng = np.random.default_rng(4)\n", @@ -3490,7 +1564,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "cdc2528a", "metadata": {}, "outputs": [], @@ -3535,7 +1609,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "845be1ae", "metadata": {}, "outputs": [], @@ -3571,69 +1645,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "be768cbb", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "CIFARModel [128, 3, 32, 32] [128, 100] --\n", - "├─Sequential: 1-1 [128, 3, 32, 32] [128, 256, 2, 2] --\n", - "│ └─BuildingBlock: 2-1 [128, 3, 32, 32] [128, 32, 16, 16] --\n", - "│ │ └─Conv2d: 3-1 [128, 3, 32, 32] [128, 32, 32, 32] 896\n", - "│ │ └─ReLU: 3-2 [128, 32, 32, 32] [128, 32, 32, 32] --\n", - "│ │ └─MaxPool2d: 3-3 [128, 32, 32, 32] [128, 32, 16, 16] --\n", - "│ └─BuildingBlock: 2-2 [128, 32, 16, 16] [128, 64, 8, 8] --\n", - "│ │ └─Conv2d: 3-4 [128, 32, 16, 16] [128, 64, 16, 16] 18,496\n", - "│ │ └─ReLU: 3-5 [128, 64, 16, 16] [128, 64, 16, 16] --\n", - "│ │ └─MaxPool2d: 3-6 [128, 64, 16, 16] [128, 64, 8, 8] --\n", - "│ └─BuildingBlock: 2-3 [128, 64, 8, 8] [128, 128, 4, 4] --\n", - "│ │ └─Conv2d: 3-7 [128, 64, 8, 8] [128, 128, 8, 8] 73,856\n", - "│ │ └─ReLU: 3-8 [128, 128, 8, 8] [128, 128, 8, 8] --\n", - "│ │ └─MaxPool2d: 3-9 [128, 128, 8, 8] [128, 128, 4, 4] --\n", - "│ └─BuildingBlock: 2-4 [128, 128, 4, 4] [128, 256, 2, 2] --\n", - "│ │ └─Conv2d: 3-10 [128, 128, 4, 4] [128, 256, 4, 4] 295,168\n", - "│ │ └─ReLU: 3-11 [128, 256, 4, 4] [128, 256, 4, 4] --\n", - "│ │ └─MaxPool2d: 3-12 [128, 256, 4, 4] [128, 256, 2, 2] --\n", - "├─Sequential: 1-2 [128, 1024] [128, 100] --\n", - "│ └─Dropout: 2-5 [128, 1024] [128, 1024] --\n", - "│ └─Linear: 2-6 [128, 1024] [128, 512] 524,800\n", - "│ └─ReLU: 2-7 [128, 512] [128, 512] --\n", - "│ └─Linear: 2-8 [128, 512] [128, 100] 51,300\n", - "===================================================================================================================\n", - "Total params: 964,516\n", - "Trainable params: 964,516\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 2.01\n", - "===================================================================================================================\n", - "Input size (MB): 1.57\n", - "Forward/backward pass size (MB): 63.54\n", - "Params size (MB): 3.86\n", - "Estimated Total Size (MB): 68.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cifar_model = CIFARModel()\n", "summary(cifar_model,\n", @@ -3679,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "67e3e2d9", "metadata": {}, "outputs": [], @@ -3692,486 +1709,10 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "1ea339ff", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - 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"application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], + "outputs": [], "source": [ "cifar_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -4198,23 +1739,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "0a9068d9", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "log_path = cifar_logger.experiment.metrics_file_path\n", "cifar_results = pd.read_csv(log_path)\n", @@ -4238,49 +1768,12 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "e71eff9b", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "44687f1d6df745d2ae89b0d3b8371ddd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.40880000591278076\n", - " test_loss 2.4764862060546875\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 2.4764862060546875, 'test_accuracy': 0.40880000591278076}]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cifar_trainer.test(cifar_module,\n", " datamodule=cifar_dm)\n" @@ -4308,510 +1801,12 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "58eae430", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bf9f95a0186f49cda30773788fcf96cb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchmetrics/utilities/checks.py:49: UserWarning: MPS: no support for int64 min/max ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:1271.)\n", - " if ignore_index is None and target.min() < 0:\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - 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}, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b0b092424de344d5805fc7b57eae4a88", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "try:\n", " for name, metric in cifar_module.metrics.items():\n", @@ -4860,31 +1855,12 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "638a1c8c", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "torch.Size([6, 3, 224, 224])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resize = Resize((232,232))\n", "crop = CenterCrop(224)\n", @@ -4907,221 +1883,12 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "7776ed1e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "ResNet [6, 3, 224, 224] [6, 1000] --\n", - "├─Conv2d: 1-1 [6, 3, 224, 224] [6, 64, 112, 112] 9,408\n", - "├─BatchNorm2d: 1-2 [6, 64, 112, 112] [6, 64, 112, 112] 128\n", - "├─ReLU: 1-3 [6, 64, 112, 112] [6, 64, 112, 112] --\n", - "├─MaxPool2d: 1-4 [6, 64, 112, 112] [6, 64, 56, 56] --\n", - "├─Sequential: 1-5 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-1 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-1 [6, 64, 56, 56] [6, 64, 56, 56] 4,096\n", - "│ │ └─BatchNorm2d: 3-2 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-3 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-4 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-5 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-6 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-7 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-8 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─Sequential: 3-9 [6, 64, 56, 56] [6, 256, 56, 56] 16,896\n", - "│ │ └─ReLU: 3-10 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-2 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-11 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-12 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-13 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-14 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-15 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-16 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-17 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-18 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-19 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-3 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-20 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-21 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-22 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-23 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-24 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-25 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-26 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-27 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-28 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "├─Sequential: 1-6 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-4 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-29 [6, 256, 56, 56] [6, 128, 56, 56] 32,768\n", - "│ │ └─BatchNorm2d: 3-30 [6, 128, 56, 56] [6, 128, 56, 56] 256\n", - "│ │ └─ReLU: 3-31 [6, 128, 56, 56] [6, 128, 56, 56] --\n", - "│ │ └─Conv2d: 3-32 [6, 128, 56, 56] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-33 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-34 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-35 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-36 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─Sequential: 3-37 [6, 256, 56, 56] [6, 512, 28, 28] 132,096\n", - "│ │ └─ReLU: 3-38 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-5 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-39 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-40 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-41 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-42 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-43 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-44 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-45 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-46 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-47 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-6 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-48 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-49 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-50 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-51 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-52 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-53 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-54 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-55 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-56 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-7 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-57 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-58 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-59 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-60 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-61 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-62 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-63 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-64 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-65 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "├─Sequential: 1-7 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-8 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-66 [6, 512, 28, 28] [6, 256, 28, 28] 131,072\n", - "│ │ └─BatchNorm2d: 3-67 [6, 256, 28, 28] [6, 256, 28, 28] 512\n", - "│ │ └─ReLU: 3-68 [6, 256, 28, 28] [6, 256, 28, 28] --\n", - "│ │ └─Conv2d: 3-69 [6, 256, 28, 28] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-70 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-71 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-72 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-73 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─Sequential: 3-74 [6, 512, 28, 28] [6, 1024, 14, 14] 526,336\n", - "│ │ └─ReLU: 3-75 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-9 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-76 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-77 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-78 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-79 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-80 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-81 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-82 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-83 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-84 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-10 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-85 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-86 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-87 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-88 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-89 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-90 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-91 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-92 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-93 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-11 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-94 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-95 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-96 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-97 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-98 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-99 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-100 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-101 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-102 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-12 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-103 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-104 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-105 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-106 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-107 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-108 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-109 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-110 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-111 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-13 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-112 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-113 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-114 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-115 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-116 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-117 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-118 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-119 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-120 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "├─Sequential: 1-8 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-14 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-121 [6, 1024, 14, 14] [6, 512, 14, 14] 524,288\n", - "│ │ └─BatchNorm2d: 3-122 [6, 512, 14, 14] [6, 512, 14, 14] 1,024\n", - "│ │ └─ReLU: 3-123 [6, 512, 14, 14] [6, 512, 14, 14] --\n", - "│ │ └─Conv2d: 3-124 [6, 512, 14, 14] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-125 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-126 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-127 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-128 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─Sequential: 3-129 [6, 1024, 14, 14] [6, 2048, 7, 7] 2,101,248\n", - "│ │ └─ReLU: 3-130 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-15 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-131 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-132 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-133 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-134 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-135 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-136 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-137 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-138 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-139 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-16 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-140 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-141 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-142 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-143 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-144 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-145 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-146 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-147 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-148 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "├─AdaptiveAvgPool2d: 1-9 [6, 2048, 7, 7] [6, 2048, 1, 1] --\n", - "├─Linear: 1-10 [6, 2048] [6, 1000] 2,049,000\n", - "===================================================================================================================\n", - "Total params: 25,557,032\n", - "Trainable params: 25,557,032\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 24.54\n", - "===================================================================================================================\n", - "Input size (MB): 3.61\n", - "Forward/backward pass size (MB): 1066.99\n", - "Params size (MB): 102.23\n", - "Estimated Total Size (MB): 1172.83\n", - "===================================================================================================================" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", "summary(resnet_model,\n", @@ -5141,198 +1908,12 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "id": "5c8e359d", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "ResNet(\n", - " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", - " (layer1): Sequential(\n", - " (0): Bottleneck(\n", - 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" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " )\n", - " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", - " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", - ")" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resnet_model.eval()" ] @@ -5351,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "80f19869", "metadata": {}, "outputs": [], @@ -5372,7 +1953,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "6892597d", "metadata": {}, "outputs": [], @@ -5391,7 +1972,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "3eb1e1b0", "metadata": {}, "outputs": [], @@ -5416,49 +1997,12 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "0e89d251", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image: book_images/flamingo.jpg\n", - " label prob\n", - "0 flamingo 0.591760\n", - "1 spoonbill 0.012386\n", - "2 American_egret 0.002105\n", - "Image: book_images/hawk.jpg\n", - " label prob\n", - "0 great_grey_owl 0.287958\n", - "1 kite 0.039478\n", - "2 fountain 0.029384\n", - "Image: book_images/hawk_cropped.jpeg\n", - " label prob\n", - "0 kite 0.301841\n", - "1 jay 0.121659\n", - "2 magpie 0.015511\n", - "Image: book_images/huey.jpg\n", - " label prob\n", - "0 Lhasa 0.151153\n", - "1 Shih-Tzu 0.129804\n", - "2 Tibetan_terrier 0.102400\n", - "Image: book_images/kitty.jpg\n", - " label prob\n", - "0 tabby 0.173628\n", - "1 tiger_cat 0.110415\n", - "2 doormat 0.093447\n", - "Image: book_images/weaver.jpg\n", - " label prob\n", - "0 jacamar 0.287284\n", - "1 bee_eater 0.046768\n", - "2 bulbul 0.037507\n" - ] - } - ], + "outputs": [], "source": [ "for i, imgfile in enumerate(imgfiles):\n", " img_df = class_labels.copy()\n", @@ -5482,7 +2026,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "69456669", "metadata": { "lines_to_next_cell": 2 @@ -5526,24 +2070,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "70b3ece2", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n", - " 4468], dtype=int32)" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(imdb_seq_train,\n", " imdb_seq_test) = load_sequential(root='data/IMDB')\n", @@ -5571,21 +2103,10 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "349b2af7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\" this film was just brilliant casting location scenery story direction everyone's\"" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lookup = load_lookup(root='data/IMDB')\n", "' '.join(lookup[i] for i in sample_review)" @@ -5605,7 +2126,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "ffa8da37", "metadata": { "lines_to_next_cell": 0 @@ -5632,7 +2153,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "11eeeabb", "metadata": { "lines_to_next_cell": 0 @@ -5669,50 +2190,10 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "5fe55a29", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "IMDBModel [25000, 10003] [25000] --\n", - "├─Linear: 1-1 [25000, 10003] [25000, 16] 160,064\n", - "├─ReLU: 1-2 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-3 [25000, 16] [25000, 16] 272\n", - "├─ReLU: 1-4 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-5 [25000, 16] [25000, 1] 17\n", - "===================================================================================================================\n", - "Total params: 160,353\n", - "Trainable params: 160,353\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 4.01\n", - "===================================================================================================================\n", - "Input size (MB): 1000.30\n", - "Forward/backward pass size (MB): 6.60\n", - "Params size (MB): 0.64\n", - "Estimated Total Size (MB): 1007.54\n", - "===================================================================================================================" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", "summary(imdb_model,\n", @@ -5741,7 +2222,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "e1ebbc70", "metadata": {}, "outputs": [], @@ -5764,496 +2245,10 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "9feddeb2", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | IMDBModel | 160 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "160 K Trainable params\n", - "0 Non-trainable params\n", - "160 K Total params\n", - "0.641 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1892: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3c8345a0fa7d41768ff584eca2ae7e32", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - 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] - } - ], + "outputs": [], "source": [ "imdb_logger = CSVLogger('logs', name='IMDB')\n", "imdb_trainer = Trainer(deterministic=True,\n", @@ -6274,49 +2269,12 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "887d7dad", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "15800f2fa0774b2d8832acab92ea61df", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8543599843978882\n", - " test_loss 1.1992340087890625\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 1.1992340087890625, 'test_accuracy': 0.8543599843978882}]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", "test_results" @@ -6336,7 +2294,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "055a9aeb", "metadata": {}, "outputs": [], @@ -6360,7 +2318,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "e3e1e181", "metadata": { "lines_to_next_cell": 0 @@ -6385,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "dd64350c", "metadata": { "lines_to_next_cell": 0 @@ -6409,7 +2367,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "0bf8b868", "metadata": {}, "outputs": [], @@ -6435,7 +2393,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "28256828", "metadata": { "lines_to_next_cell": 0 @@ -6457,7 +2415,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "a5945618", "metadata": { "lines_to_next_cell": 0 @@ -6500,24 +2458,12 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "74704884", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", "summary_plot(imdb_results,\n", @@ -6547,7 +2493,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "b1ecfca8", "metadata": { "lines_to_next_cell": 2 @@ -6593,7 +2539,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "5503cb3f", "metadata": {}, "outputs": [], @@ -6636,7 +2582,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "8b683641", "metadata": { "lines_to_next_cell": 0 @@ -6667,48 +2613,10 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "702aa8de", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "LSTMModel [10, 500] [10] --\n", - "├─Embedding: 1-1 [10, 500] [10, 500, 32] 320,096\n", - "├─LSTM: 1-2 [10, 500, 32] [10, 500, 32] 8,448\n", - "├─Linear: 1-3 [10, 32] [10, 1] 33\n", - "===================================================================================================================\n", - "Total params: 328,577\n", - "Trainable params: 328,577\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 45.44\n", - "===================================================================================================================\n", - "Input size (MB): 50.00\n", - "Forward/backward pass size (MB): 2.56\n", - "Params size (MB): 1.31\n", - "Estimated Total Size (MB): 53.87\n", - "===================================================================================================================" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lstm_model = LSTMModel(X_test.shape[-1])\n", "summary(lstm_model,\n", @@ -6729,7 +2637,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "id": "e48aa272", "metadata": {}, "outputs": [], @@ -6740,350 +2648,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "143be7e8", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | LSTMModel | 328 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "328 K Trainable params\n", - "0 Non-trainable params\n", - "328 K Total params\n", - "1.314 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d750346c2da241e5aa9290715ef3ab2f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - 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"output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=20` reached.\n" - ] - } - ], + "outputs": [], "source": [ "lstm_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -7104,47 +2674,10 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "9272e2ba", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1db51a56913246c59f746a80a10ad431", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8452399969100952\n", - " test_loss 0.7559056878089905\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.7559056878089905, 'test_accuracy': 0.8452399969100952}]" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" ] @@ -7159,33 +2692,12 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "id": "5d9d387e", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.5, 1.0)" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -7200,7 +2712,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "7c6d1072", "metadata": { "lines_to_next_cell": 2 @@ -7228,7 +2740,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "id": "8e2d1d5c", "metadata": {}, "outputs": [], @@ -7253,7 +2765,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "6f6d0e12", "metadata": {}, "outputs": [], @@ -7278,27 +2790,12 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "d3c961ec", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n", - " 'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n", - " 'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n", - " dtype='object')" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Y, train = X['log_volume'], X['train']\n", "X = X.drop(columns=['train'] + cols)\n", @@ -7316,21 +2813,10 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "cb89da88", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4128912938562521" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "M = LinearRegression()\n", "M.fit(X[train], Y[train])\n", @@ -7349,7 +2835,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "55d921d7", "metadata": { "lines_to_next_cell": 0 @@ -7373,23 +2859,12 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "id": "4e87eeb9", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45633025715446285" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "M.fit(X_day[train], Y[train])\n", "M.score(X_day[~train], Y[~train])" @@ -7425,27 +2900,12 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "id": "3689b318", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n", - " 'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n", - " 'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n", - " dtype='object')" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ordered_cols = []\n", "for lag in range(5,0,-1):\n", @@ -7465,23 +2925,12 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "5633e521", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "(6046, 5, 3)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "X_rnn = X.to_numpy().reshape((-1,5,3))\n", "X_rnn.shape" @@ -7503,7 +2952,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "979a6066", "metadata": {}, "outputs": [], @@ -7540,7 +2989,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "3528f4e7", "metadata": {}, "outputs": [], @@ -7563,50 +3012,12 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "id": "795c283e", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "NYSEModel [1770, 5, 3] [1770] --\n", - "├─RNN: 1-1 [1770, 5, 3] [1770, 5, 12] 204\n", - "├─Dropout: 1-2 [1770, 12] [1770, 12] --\n", - "├─Linear: 1-3 [1770, 12] [1770, 1] 13\n", - "===================================================================================================================\n", - "Total params: 217\n", - "Trainable params: 217\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 1.83\n", - "===================================================================================================================\n", - "Input size (MB): 0.11\n", - "Forward/backward pass size (MB): 0.86\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary(nyse_model,\n", " input_data=X_rnn_t,\n", @@ -7627,7 +3038,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "31640121", "metadata": { "lines_to_next_cell": 0 @@ -7651,20 +3062,10 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "id": "af6795f5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n" - ] - } - ], + "outputs": [], "source": [ "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", " out = nyse_model(x)\n", @@ -7685,7 +3086,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "id": "0d04344c", "metadata": {}, "outputs": [], @@ -7708,1981 +3109,12 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "id": "485d7bb1", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------\n", - "0 | model | NYSEModel | 217 \n", - "1 | loss | MSELoss | 0 \n", - "------------------------------------\n", - "217 Trainable params\n", - "0 Non-trainable params\n", - "217 Total params\n", - "0.001 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b52ec6ddbbe14c79b0c9b9c1a26b0ab0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - 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"model_id": "a1865848fe7e4983945449f3865ddcc4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 0.6207807064056396\n", - " test_r2 0.41084879636764526\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.6207807064056396, 'test_r2': 0.41084879636764526}]" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nyse_trainer = Trainer(deterministic=True,\n", " max_epochs=200,\n", @@ -9711,7 +3143,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "91d6633a", "metadata": {}, "outputs": [], @@ -9735,7 +3167,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "87137503", "metadata": {}, "outputs": [], @@ -9757,7 +3189,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "id": "3cf09a55", "metadata": {}, "outputs": [], @@ -9776,7 +3208,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "930576f3", "metadata": {}, "outputs": [], @@ -9800,281 +3232,12 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "id": "b9ae8d0e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | NonLinearARModel | 705 \n", - "1 | loss | MSELoss | 0 \n", - "-------------------------------------------\n", - "705 Trainable params\n", - "0 Non-trainable params\n", - "705 Total params\n", - "0.003 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bda98978787f41bc81b9ed227089d304", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 0.5648325681686401\n", - " test_r2 0.4639463424682617\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.5648325681686401, 'test_r2': 0.4639463424682617}]" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], "source": [ "nl_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -10084,36 +3247,21 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "1a42ae9c-f34e-41a1-8513-fb0752375547", + "cell_type": "markdown", + "id": "fd356e46", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + " \n", + "\n", + " \n" + ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", - "main_language": "python" - }, - "kernelspec": { - "display_name": "Python (islp_freeze_39)", - "language": "python", - "name": "islp_freeze_39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" + "main_language": "python", + "notebook_metadata_filter": "-all" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch11-surv-lab.ipynb b/docs/source/labs/Ch11-surv-lab.ipynb index b0131e2..f85f01f 100644 --- a/docs/source/labs/Ch11-surv-lab.ipynb +++ b/docs/source/labs/Ch11-surv-lab.ipynb @@ -1,20 +1,17 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "709be2a4", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, { "cell_type": "markdown", "id": "c7f4eb5a", "metadata": {}, "source": [ "# Survival Analysis\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", " In this lab, we perform survival analyses on three separate data\n", "sets. In Section 11.8.1 we analyze the `BrainCancer` \n", "data that was first described in Section 11.3. In Section 11.8.2, we examine the `Publication` \n", @@ -29,23 +26,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "91ac40fd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:11.792778Z", - "iopub.status.busy": "2023-07-26T00:00:11.792414Z", - "iopub.status.idle": "2023-07-26T00:00:12.793105Z", - "shell.execute_reply": "2023-07-26T00:00:12.792749Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from matplotlib.pyplot import subplots\n", "import numpy as np\n", "import pandas as pd\n", "from ISLP.models import ModelSpec as MS\n", - "from ISLP import load_data\n" + "from ISLP import load_data" ] }, { @@ -59,16 +49,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "99782418", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.795280Z", - "iopub.status.busy": "2023-07-26T00:00:12.795071Z", - "iopub.status.idle": "2023-07-26T00:00:12.854734Z", - "shell.execute_reply": "2023-07-26T00:00:12.854428Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from lifelines import \\\n", @@ -77,7 +60,7 @@ "from lifelines.statistics import \\\n", " (logrank_test,\n", " multivariate_logrank_test)\n", - "from ISLP.survival import sim_time\n" + "from ISLP.survival import sim_time" ] }, { @@ -92,31 +75,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3137149a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.856788Z", - "iopub.status.busy": "2023-07-26T00:00:12.856526Z", - "iopub.status.idle": "2023-07-26T00:00:12.862649Z", - "shell.execute_reply": "2023-07-26T00:00:12.862372Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['sex', 'diagnosis', 'loc', 'ki', 'gtv', 'stereo', 'status', 'time'], dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "BrainCancer = load_data('BrainCancer')\n", - "BrainCancer.columns\n" + "BrainCancer.columns" ] }, { @@ -130,97 +95,32 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "45963c92", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.864323Z", - "iopub.status.busy": "2023-07-26T00:00:12.864209Z", - "iopub.status.idle": "2023-07-26T00:00:12.867297Z", - "shell.execute_reply": "2023-07-26T00:00:12.867008Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Female 45\n", - "Male 43\n", - "Name: sex, dtype: int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BrainCancer['sex'].value_counts()\n" + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer['sex'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "73be61f6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.868922Z", - "iopub.status.busy": "2023-07-26T00:00:12.868804Z", - "iopub.status.idle": "2023-07-26T00:00:12.871822Z", - "shell.execute_reply": "2023-07-26T00:00:12.871554Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Meningioma 42\n", - "HG glioma 22\n", - "Other 14\n", - "LG glioma 9\n", - "Name: diagnosis, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BrainCancer['diagnosis'].value_counts()\n" + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer['diagnosis'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "572f0b9e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.873332Z", - "iopub.status.busy": "2023-07-26T00:00:12.873229Z", - "iopub.status.idle": "2023-07-26T00:00:12.875929Z", - "shell.execute_reply": "2023-07-26T00:00:12.875665Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 53\n", - "1 35\n", - "Name: status, dtype: int64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BrainCancer['status'].value_counts()\n" + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer['status'].value_counts()" ] }, { @@ -250,43 +150,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "92c39707", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.877558Z", - "iopub.status.busy": "2023-07-26T00:00:12.877447Z", - "iopub.status.idle": "2023-07-26T00:00:12.996625Z", - "shell.execute_reply": "2023-07-26T00:00:12.996279Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "km = KaplanMeierFitter()\n", "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n", - "km_brain.plot(label='Kaplan Meier estimate', ax=ax)\n" + "km_brain.plot(label='Kaplan Meier estimate', ax=ax)" ] }, { @@ -316,35 +188,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3fc7848c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:12.998859Z", - "iopub.status.busy": "2023-07-26T00:00:12.998718Z", - "iopub.status.idle": "2023-07-26T00:00:13.123101Z", - "shell.execute_reply": "2023-07-26T00:00:13.122758Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "by_sex = {}\n", "for sex, df in BrainCancer.groupby('sex'):\n", " by_sex[sex] = df\n", " km_sex = km.fit(df['time'], df['status'])\n", - " km_sex.plot(label='Sex=%s' % sex, ax=ax)\n" + " km_sex.plot(label='Sex=%s' % sex, ax=ax)" ] }, { @@ -361,102 +215,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "bf30d26f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.125066Z", - "iopub.status.busy": "2023-07-26T00:00:13.124928Z", - "iopub.status.idle": "2023-07-26T00:00:13.177803Z", - "shell.execute_reply": "2023-07-26T00:00:13.177493Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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t_0-1
null_distributionchi squared
degrees_of_freedom1
test_namelogrank_test
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test_statisticp-log2(p)
01.440.232.12
" - ], - "text/latex": [ - "\\begin{tabular}{lrrr}\n", - " & test_statistic & p & -log2(p) \\\\\n", - "0 & 1.44 & 0.23 & 2.12 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " t_0 = -1\n", - " null_distribution = chi squared\n", - "degrees_of_freedom = 1\n", - " test_name = logrank_test\n", - "\n", - "---\n", - " test_statistic p -log2(p)\n", - " 1.44 0.23 2.12" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "logrank_test(by_sex['Male']['time'],\n", " by_sex['Female']['time'],\n", " by_sex['Male']['status'],\n", - " by_sex['Female']['status'])\n" + " by_sex['Female']['status'])" ] }, { @@ -474,71 +241,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2ab78e07", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.179791Z", - "iopub.status.busy": "2023-07-26T00:00:13.179581Z", - "iopub.status.idle": "2023-07-26T00:00:13.204157Z", - "shell.execute_reply": "2023-07-26T00:00:13.203825Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefse(coef)p
covariate
sex[Male]0.4076680.3420040.233262
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" - ], - "text/plain": [ - " coef se(coef) p\n", - "covariate \n", - "sex[Male] 0.407668 0.342004 0.233262" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "coxph = CoxPHFitter # shorthand\n", "sex_df = BrainCancer[['time', 'status', 'sex']]\n", @@ -547,7 +253,7 @@ "cox_fit = coxph().fit(model_df,\n", " 'time',\n", " 'status')\n", - "cox_fit.summary[['coef', 'se(coef)', 'p']]\n" + "cox_fit.summary[['coef', 'se(coef)', 'p']]" ] }, { @@ -567,93 +273,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "4716b7b0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.205975Z", - "iopub.status.busy": "2023-07-26T00:00:13.205843Z", - "iopub.status.idle": "2023-07-26T00:00:13.211669Z", - "shell.execute_reply": "2023-07-26T00:00:13.211344Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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null_distributionchi squared
degrees_freedom1
test_namelog-likelihood ratio test
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test_statisticp-log2(p)
01.440.232.12
" - ], - "text/latex": [ - "\\begin{tabular}{lrrr}\n", - " & test_statistic & p & -log2(p) \\\\\n", - "0 & 1.44 & 0.23 & 2.12 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - "null_distribution = chi squared\n", - " degrees_freedom = 1\n", - " test_name = log-likelihood ratio test\n", - "\n", - "---\n", - " test_statistic p -log2(p)\n", - " 1.44 0.23 2.12" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cox_fit.log_likelihood_ratio_test()\n" + "metadata": {}, + "outputs": [], + "source": [ + "cox_fit.log_likelihood_ratio_test()" ] }, { @@ -673,120 +298,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "c2767d88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.213603Z", - "iopub.status.busy": "2023-07-26T00:00:13.213471Z", - "iopub.status.idle": "2023-07-26T00:00:13.246417Z", - "shell.execute_reply": "2023-07-26T00:00:13.246077Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefse(coef)p
covariate
sex[Male]0.1837480.3603580.610119
diagnosis[LG glioma]-1.2395300.5795550.032455
diagnosis[Meningioma]-2.1545660.4505240.000002
diagnosis[Other]-1.2688700.6176720.039949
loc[Supratentorial]0.4411950.7036690.530665
ki-0.0549550.0183140.002693
gtv0.0342930.0223330.124661
stereo[SRT]0.1777780.6015780.767597
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" - ], - "text/plain": [ - " coef se(coef) p\n", - "covariate \n", - "sex[Male] 0.183748 0.360358 0.610119\n", - "diagnosis[LG glioma] -1.239530 0.579555 0.032455\n", - "diagnosis[Meningioma] -2.154566 0.450524 0.000002\n", - "diagnosis[Other] -1.268870 0.617672 0.039949\n", - "loc[Supratentorial] 0.441195 0.703669 0.530665\n", - "ki -0.054955 0.018314 0.002693\n", - "gtv 0.034293 0.022333 0.124661\n", - "stereo[SRT] 0.177778 0.601578 0.767597" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cleaned = BrainCancer.dropna()\n", "all_MS = MS(cleaned.columns, intercept=False)\n", @@ -794,7 +309,7 @@ "fit_all = coxph().fit(all_df,\n", " 'time',\n", " 'status')\n", - "fit_all.summary[['coef', 'se(coef)', 'p']]\n" + "fit_all.summary[['coef', 'se(coef)', 'p']]" ] }, { @@ -821,16 +336,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "ede1d219", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.248407Z", - "iopub.status.busy": "2023-07-26T00:00:13.248257Z", - "iopub.status.idle": "2023-07-26T00:00:13.252004Z", - "shell.execute_reply": "2023-07-26T00:00:13.251659Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "levels = cleaned['diagnosis'].unique()\n", @@ -839,7 +347,7 @@ " return pd.Series.mode(series)\n", " else:\n", " return series.mean()\n", - "modal_data = cleaned.apply(representative, axis=0)\n" + "modal_data = cleaned.apply(representative, axis=0)" ] }, { @@ -854,121 +362,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "dc032a71", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.253777Z", - "iopub.status.busy": "2023-07-26T00:00:13.253667Z", - "iopub.status.idle": "2023-07-26T00:00:13.260023Z", - "shell.execute_reply": "2023-07-26T00:00:13.259719Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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sexdiagnosislockigtvstereostatustime
0FemaleMeningiomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleHG gliomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleLG gliomaSupratentorial80.919548.687011SRT0.40229927.188621
0FemaleOtherSupratentorial80.919548.687011SRT0.40229927.188621
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" - ], - "text/plain": [ - " sex diagnosis loc ki gtv stereo status \\\n", - "0 Female Meningioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", - "0 Female HG glioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", - "0 Female LG glioma Supratentorial 80.91954 8.687011 SRT 0.402299 \n", - "0 Female Other Supratentorial 80.91954 8.687011 SRT 0.402299 \n", - "\n", - " time \n", - "0 27.188621 \n", - "0 27.188621 \n", - "0 27.188621 \n", - "0 27.188621 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "modal_df = pd.DataFrame(\n", " [modal_data.iloc[0] for _ in range(len(levels))])\n", "modal_df['diagnosis'] = levels\n", - "modal_df\n" + "modal_df" ] }, { @@ -982,136 +384,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "e7c1fe43", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.261832Z", - "iopub.status.busy": "2023-07-26T00:00:13.261701Z", - "iopub.status.idle": "2023-07-26T00:00:13.270198Z", - "shell.execute_reply": "2023-07-26T00:00:13.269836Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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sex[Male]diagnosis[LG glioma]diagnosis[Meningioma]diagnosis[Other]loc[Supratentorial]kigtvstereo[SRT]statustime
Meningioma0.00.01.00.01.080.919548.6870111.00.40229927.188621
HG glioma0.00.00.00.01.080.919548.6870111.00.40229927.188621
LG glioma0.01.00.00.01.080.919548.6870111.00.40229927.188621
Other0.00.00.01.01.080.919548.6870111.00.40229927.188621
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" - ], - "text/plain": [ - " sex[Male] diagnosis[LG glioma] diagnosis[Meningioma] \\\n", - "Meningioma 0.0 0.0 1.0 \n", - "HG glioma 0.0 0.0 0.0 \n", - "LG glioma 0.0 1.0 0.0 \n", - "Other 0.0 0.0 0.0 \n", - "\n", - " diagnosis[Other] loc[Supratentorial] ki gtv \\\n", - "Meningioma 0.0 1.0 80.91954 8.687011 \n", - "HG glioma 0.0 1.0 80.91954 8.687011 \n", - "LG glioma 0.0 1.0 80.91954 8.687011 \n", - "Other 1.0 1.0 80.91954 8.687011 \n", - "\n", - " stereo[SRT] status time \n", - "Meningioma 1.0 0.402299 27.188621 \n", - "HG glioma 1.0 0.402299 27.188621 \n", - "LG glioma 1.0 0.402299 27.188621 \n", - "Other 1.0 0.402299 27.188621 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "modal_X = all_MS.transform(modal_df)\n", "modal_X.index = levels\n", - "modal_X\n" + "modal_X" ] }, { @@ -1124,153 +404,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "f89fbed7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.272136Z", - "iopub.status.busy": "2023-07-26T00:00:13.271995Z", - "iopub.status.idle": "2023-07-26T00:00:13.279105Z", - "shell.execute_reply": "2023-07-26T00:00:13.278699Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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MeningiomaHG gliomaLG gliomaOther
0.070.9979470.9824300.9948810.995029
1.180.9979470.9824300.9948810.995029
1.410.9956790.9633420.9892450.989555
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2.030.9956790.9633420.9892450.989555
...............
65.020.6887720.0401360.3941810.404936
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82.560.6887720.0401360.3941810.404936
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" - ], - "text/plain": [ - " Meningioma HG glioma LG glioma Other\n", - "0.07 0.997947 0.982430 0.994881 0.995029\n", - "1.18 0.997947 0.982430 0.994881 0.995029\n", - "1.41 0.995679 0.963342 0.989245 0.989555\n", - "1.54 0.995679 0.963342 0.989245 0.989555\n", - "2.03 0.995679 0.963342 0.989245 0.989555\n", - "... ... ... ... ...\n", - "65.02 0.688772 0.040136 0.394181 0.404936\n", - "67.38 0.688772 0.040136 0.394181 0.404936\n", - "73.74 0.688772 0.040136 0.394181 0.404936\n", - "78.75 0.688772 0.040136 0.394181 0.404936\n", - "82.56 0.688772 0.040136 0.394181 0.404936\n", - "\n", - "[85 rows x 4 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "predicted_survival = fit_all.predict_survival_function(modal_X)\n", - "predicted_survival\n" + "predicted_survival" ] }, { @@ -1285,32 +425,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "8f0329b4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.281050Z", - "iopub.status.busy": "2023-07-26T00:00:13.280921Z", - "iopub.status.idle": "2023-07-26T00:00:13.391548Z", - "shell.execute_reply": "2023-07-26T00:00:13.391175Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "predicted_survival.plot(ax=ax);\n" + "predicted_survival.plot(ax=ax);" ] }, { @@ -1328,28 +449,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "3045bfc0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.393498Z", - "iopub.status.busy": "2023-07-26T00:00:13.393362Z", - "iopub.status.idle": "2023-07-26T00:00:13.556483Z", - "shell.execute_reply": "2023-07-26T00:00:13.556104Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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coefse(coef)p
covariate
posres0.1480760.1616250.359579
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" - ], - "text/plain": [ - " coef se(coef) p\n", - "covariate \n", - "posres 0.148076 0.161625 0.359579" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "posres_df = MS(['posres',\n", " 'time',\n", @@ -1447,7 +488,7 @@ "posres_fit = coxph().fit(posres_df,\n", " 'time',\n", " 'status')\n", - "posres_fit.summary[['coef', 'se(coef)', 'p']]\n" + "posres_fit.summary[['coef', 'se(coef)', 'p']]" ] }, { @@ -1462,112 +503,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "2bbcdd0c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.590620Z", - "iopub.status.busy": "2023-07-26T00:00:13.590481Z", - "iopub.status.idle": "2023-07-26T00:00:13.629005Z", - "shell.execute_reply": "2023-07-26T00:00:13.628695Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefse(coef)p
covariate
posres0.5707730.1759601.179610e-03
multi-0.0408600.2511948.707842e-01
clinend0.5461830.2620003.709944e-02
sampsize0.0000050.0000157.507005e-01
budget0.0043860.0024657.515984e-02
impact0.0583180.0066762.426306e-18
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" - ], - "text/plain": [ - " coef se(coef) p\n", - "covariate \n", - "posres 0.570773 0.175960 1.179610e-03\n", - "multi -0.040860 0.251194 8.707842e-01\n", - "clinend 0.546183 0.262000 3.709944e-02\n", - "sampsize 0.000005 0.000015 7.507005e-01\n", - "budget 0.004386 0.002465 7.515984e-02\n", - "impact 0.058318 0.006676 2.426306e-18" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "model = MS(Publication.columns.drop('mech'),\n", " intercept=False)\n", "coxph().fit(model.fit_transform(Publication),\n", " 'time',\n", - " 'status').summary[['coef', 'se(coef)', 'p']]\n" + " 'status').summary[['coef', 'se(coef)', 'p']]" ] }, { @@ -1601,21 +546,14 @@ "`Time` of day (Morning, Afternoon, or Evening). We generate data\n", "for these covariates so that all possibilities are equally likely: for\n", "instance, morning, afternoon and evening calls are equally likely, and\n", - "any number of operators from $5$ to $15$ is equally likely. " + "any number of operators from $5$ to $15$ is equally likely." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "b8ece43a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.630972Z", - "iopub.status.busy": "2023-07-26T00:00:13.630836Z", - "iopub.status.idle": "2023-07-26T00:00:13.634830Z", - "shell.execute_reply": "2023-07-26T00:00:13.634534Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(10)\n", @@ -1644,16 +582,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "3e4f766f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.636547Z", - "iopub.status.busy": "2023-07-26T00:00:13.636436Z", - "iopub.status.idle": "2023-07-26T00:00:13.644633Z", - "shell.execute_reply": "2023-07-26T00:00:13.644339Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "model = MS(['Operators',\n", @@ -1676,106 +607,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "72f42d14", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.646387Z", - "iopub.status.busy": "2023-07-26T00:00:13.646291Z", - "iopub.status.idle": "2023-07-26T00:00:13.650688Z", - "shell.execute_reply": "2023-07-26T00:00:13.650359Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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OperatorsCenterTimeWait timeFailed
013CAfter.525.0649791
115AEven.254.6778351
27BMorn.487.7392241
37CMorn.308.5802921
413CEven.154.1746081
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"D['Failed'].mean()\n" + "metadata": {}, + "outputs": [], + "source": [ + "D['Failed'].mean()" ] }, { @@ -2039,38 +741,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "2b27af56", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:13.849508Z", - "iopub.status.busy": "2023-07-26T00:00:13.849066Z", - "iopub.status.idle": "2023-07-26T00:00:14.045745Z", - "shell.execute_reply": "2023-07-26T00:00:14.045295Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Probability of Still Being on Hold')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "by_center = {}\n", @@ -2078,7 +752,7 @@ " by_center[center] = df\n", " km_center = km.fit(df['Wait time'], df['Failed'])\n", " km_center.plot(label='Center=%s' % center, ax=ax)\n", - "ax.set_title(\"Probability of Still Being on Hold\")\n" + "ax.set_title(\"Probability of Still Being on Hold\")" ] }, { @@ -2091,38 +765,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "9625598d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:14.047564Z", - "iopub.status.busy": "2023-07-26T00:00:14.047461Z", - "iopub.status.idle": "2023-07-26T00:00:14.210828Z", - "shell.execute_reply": "2023-07-26T00:00:14.210459Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Probability of Still Being on Hold')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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t_0-1
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test_statisticp-log2(p)
020.30<0.00514.65
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t_0-1
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test_statisticp-log2(p)
049.90<0.00535.99
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null_distributionchi squared
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test_statisticp-log2(p)
020.58<0.00514.85
" - ], - "text/latex": [ - "\\begin{tabular}{lrrr}\n", - " & test_statistic & p & -log2(p) \\\\\n", - "0 & 20.58 & 0.00 & 14.85 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - "null_distribution = chi squared\n", - " degrees_freedom = 2\n", - " test_name = log-likelihood ratio test\n", - "\n", - "---\n", - " test_statistic p -log2(p)\n", - " 20.58 <0.005 14.85" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS(['Wait time',\n", " 'Failed',\n", " 'Center'],\n", " intercept=False).fit_transform(D)\n", "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()\n" + "F.log_likelihood_ratio_test()" ] }, { @@ -2469,99 +859,17 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "7cab3789", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:14.375196Z", - "iopub.status.busy": "2023-07-26T00:00:14.375061Z", - "iopub.status.idle": "2023-07-26T00:00:14.495928Z", - "shell.execute_reply": "2023-07-26T00:00:14.495606Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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null_distributionchi squared
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test_namelog-likelihood ratio test
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test_statisticp-log2(p)
048.12<0.00534.71
" - ], - "text/latex": [ - "\\begin{tabular}{lrrr}\n", - " & test_statistic & p & -log2(p) \\\\\n", - "0 & 48.12 & 0.00 & 34.71 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - "null_distribution = chi squared\n", - " degrees_freedom = 2\n", - " test_name = log-likelihood ratio test\n", - "\n", - "---\n", - " test_statistic p -log2(p)\n", - " 48.12 <0.005 34.71" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS(['Wait time',\n", " 'Failed',\n", " 'Time'],\n", " intercept=False).fit_transform(D)\n", "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()\n" + "F.log_likelihood_ratio_test()" ] }, { @@ -2577,100 +885,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "5cc4b898", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:14.497695Z", - "iopub.status.busy": "2023-07-26T00:00:14.497564Z", - "iopub.status.idle": "2023-07-26T00:00:14.651662Z", - "shell.execute_reply": "2023-07-26T00:00:14.650786Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Alaska10.02634844.5
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Idaho2.61205414.2
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Kansas6.01156618.0
Kentucky9.71095216.3
Louisiana15.42496622.2
Maine2.183517.8
Maryland11.33006727.8
Massachusetts4.41498516.3
Michigan12.12557435.1
Minnesota2.7726614.9
Mississippi16.12594417.1
Missouri9.01787028.2
Montana6.01095316.4
Nebraska4.31026216.5
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North Dakota0.845447.3
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Oregon4.91596729.3
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Rhode Island3.4174878.3
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South Dakota3.8864512.8
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Utah3.21208022.9
Vermont2.2483211.2
Virginia8.51566320.7
Washington4.01457326.2
West Virginia5.781399.3
Wisconsin2.6536610.8
Wyoming6.81616015.6
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We first `fit` the\n", "scaler, which computes the necessary means and standard\n", "deviations and then apply it to our data using the\n", - "`transform` method. As before, we combine these steps using the `fit_transform()` method.\n" + "`transform` method. As before, we combine these steps using the `fit_transform()` method." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "daf119e8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.148889Z", - "iopub.status.busy": "2023-07-26T00:00:18.148784Z", - "iopub.status.idle": "2023-07-26T00:00:18.152181Z", - "shell.execute_reply": "2023-07-26T00:00:18.151873Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler(with_std=True,\n", " with_mean=True)\n", - "USArrests_scaled = scaler.fit_transform(USArrests)\n" + "USArrests_scaled = scaler.fit_transform(USArrests)" ] }, { @@ -721,20 +187,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "a0eda7c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.153991Z", - "iopub.status.busy": "2023-07-26T00:00:18.153858Z", - "iopub.status.idle": "2023-07-26T00:00:18.155563Z", - "shell.execute_reply": "2023-07-26T00:00:18.155300Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "pcaUS = PCA()\n" + "pcaUS = PCA()" ] }, { @@ -750,33 +208,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "1430fb3c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.157118Z", - "iopub.status.busy": "2023-07-26T00:00:18.157011Z", - "iopub.status.idle": "2023-07-26T00:00:18.160394Z", - "shell.execute_reply": "2023-07-26T00:00:18.160108Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
PCA()
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" - ], - "text/plain": [ - "PCA()" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.fit(USArrests_scaled)\n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.fit(USArrests_scaled)" ] }, { @@ -791,30 +228,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "6131d8d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.162053Z", - "iopub.status.busy": "2023-07-26T00:00:18.161958Z", - "iopub.status.idle": "2023-07-26T00:00:18.164351Z", - "shell.execute_reply": "2023-07-26T00:00:18.164070Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-7.10542736e-17, 1.38777878e-16, -4.39648318e-16, 8.59312621e-16])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.mean_\n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.mean_" ] }, { @@ -828,20 +247,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "08246aad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.165967Z", - "iopub.status.busy": "2023-07-26T00:00:18.165875Z", - "iopub.status.idle": "2023-07-26T00:00:18.167798Z", - "shell.execute_reply": "2023-07-26T00:00:18.167542Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "scores = pcaUS.transform(USArrests_scaled)\n" + "scores = pcaUS.transform(USArrests_scaled)" ] }, { @@ -852,38 +263,17 @@ "We will plot these scores a bit further down.\n", "The `components_` attribute provides the principal component loadings:\n", "each row of `pcaUS.components_` contains the corresponding\n", - "principal component loading vector.\n" + "principal component loading vector." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "b682b632", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.169396Z", - "iopub.status.busy": "2023-07-26T00:00:18.169312Z", - "iopub.status.idle": "2023-07-26T00:00:18.171738Z", - "shell.execute_reply": "2023-07-26T00:00:18.171462Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.53589947, 0.58318363, 0.27819087, 0.54343209],\n", - " [ 0.41818087, 0.1879856 , -0.87280619, -0.16731864],\n", - " [-0.34123273, -0.26814843, -0.37801579, 0.81777791],\n", - " [ 0.6492278 , -0.74340748, 0.13387773, 0.08902432]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.components_ \n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.components_ " ] }, { @@ -900,29 +290,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c165e990", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.173597Z", - "iopub.status.busy": "2023-07-26T00:00:18.173475Z", - "iopub.status.idle": "2023-07-26T00:00:18.290430Z", - "shell.execute_reply": "2023-07-26T00:00:18.290051Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "i, j = 0, 1 # which components\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", @@ -933,7 +304,7 @@ " ax.arrow(0, 0, pcaUS.components_[i,k], pcaUS.components_[j,k])\n", " ax.text(pcaUS.components_[i,k],\n", " pcaUS.components_[j,k],\n", - " USArrests.columns[k])\n" + " USArrests.columns[k])" ] }, { @@ -950,28 +321,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "848c9f35", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.292401Z", - "iopub.status.busy": "2023-07-26T00:00:18.292266Z", - "iopub.status.idle": "2023-07-26T00:00:18.393479Z", - "shell.execute_reply": "2023-07-26T00:00:18.393134Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "scale_arrow = s_ = 2\n", "scores[:,1] *= -1\n", @@ -984,7 +337,7 @@ " ax.arrow(0, 0, s_*pcaUS.components_[i,k], s_*pcaUS.components_[j,k])\n", " ax.text(s_*pcaUS.components_[i,k],\n", " s_*pcaUS.components_[j,k],\n", - " USArrests.columns[k])\n" + " USArrests.columns[k])" ] }, { @@ -997,29 +350,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "34fdfe21", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.395487Z", - "iopub.status.busy": "2023-07-26T00:00:18.395355Z", - "iopub.status.idle": "2023-07-26T00:00:18.398200Z", - "shell.execute_reply": "2023-07-26T00:00:18.397868Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.5908673 , 1.00496987, 0.6031915 , 0.4206774 ])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "scores.std(0, ddof=1)" ] @@ -1035,31 +369,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "31b43c57", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.399986Z", - "iopub.status.busy": "2023-07-26T00:00:18.399859Z", - "iopub.status.idle": "2023-07-26T00:00:18.402377Z", - "shell.execute_reply": "2023-07-26T00:00:18.402074Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.53085875, 1.00996444, 0.36383998, 0.17696948])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.explained_variance_\n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.explained_variance_" ] }, { @@ -1073,31 +388,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "68e47d3a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.404041Z", - "iopub.status.busy": "2023-07-26T00:00:18.403900Z", - "iopub.status.idle": "2023-07-26T00:00:18.406482Z", - "shell.execute_reply": "2023-07-26T00:00:18.406162Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.62006039, 0.24744129, 0.0891408 , 0.04335752])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.explained_variance_ratio_\n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.explained_variance_ratio_" ] }, { @@ -1114,17 +410,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "e87fe198", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.408230Z", - "iopub.status.busy": "2023-07-26T00:00:18.408090Z", - "iopub.status.idle": "2023-07-26T00:00:18.552316Z", - "shell.execute_reply": "2023-07-26T00:00:18.551935Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "%%capture\n", @@ -1137,7 +425,7 @@ "ax.set_xlabel('Principal Component');\n", "ax.set_ylabel('Proportion of Variance Explained')\n", "ax.set_ylim([0,1])\n", - "ax.set_xticks(ticks)\n" + "ax.set_xticks(ticks)" ] }, { @@ -1150,30 +438,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "409fb0c6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.554504Z", - "iopub.status.busy": "2023-07-26T00:00:18.554354Z", - "iopub.status.idle": "2023-07-26T00:00:18.664965Z", - "shell.execute_reply": "2023-07-26T00:00:18.664571Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = axes[1]\n", "ax.plot(ticks,\n", @@ -1183,7 +451,7 @@ "ax.set_ylabel('Cumulative Proportion of Variance Explained')\n", "ax.set_ylim([0, 1])\n", "ax.set_xticks(ticks)\n", - "fig\n" + "fig" ] }, { @@ -1198,32 +466,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "e563e41b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.666854Z", - "iopub.status.busy": "2023-07-26T00:00:18.666716Z", - "iopub.status.idle": "2023-07-26T00:00:18.669407Z", - "shell.execute_reply": "2023-07-26T00:00:18.669069Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 3, 11, 8])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "a = np.array([1,2,8,-3])\n", - "np.cumsum(a)\n" + "np.cumsum(a)" ] }, { @@ -1242,38 +491,19 @@ "components of the data. We use our scaled\n", "and centered `USArrests` data as $\\bf X$ below. The *singular value decomposition* \n", "(SVD) is a general algorithm for solving\n", - "(12.6). " + "(12.6)." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "f83ad0bc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.671214Z", - "iopub.status.busy": "2023-07-26T00:00:18.671075Z", - "iopub.status.idle": "2023-07-26T00:00:18.673946Z", - "shell.execute_reply": "2023-07-26T00:00:18.673625Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((50, 4), (4,), (4, 4))" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = USArrests_scaled\n", "U, D, V = np.linalg.svd(X, full_matrices=False)\n", - "U.shape, D.shape, V.shape\n" + "U.shape, D.shape, V.shape" ] }, { @@ -1288,65 +518,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "cb9bdc46", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.675721Z", - "iopub.status.busy": "2023-07-26T00:00:18.675577Z", - "iopub.status.idle": "2023-07-26T00:00:18.678172Z", - "shell.execute_reply": "2023-07-26T00:00:18.677846Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.53589947, -0.58318363, -0.27819087, -0.54343209],\n", - " [ 0.41818087, 0.1879856 , -0.87280619, -0.16731864],\n", - " [-0.34123273, -0.26814843, -0.37801579, 0.81777791],\n", - " [ 0.6492278 , -0.74340748, 0.13387773, 0.08902432]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "V\n" + "metadata": {}, + "outputs": [], + "source": [ + "V" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "f23c101e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.679869Z", - "iopub.status.busy": "2023-07-26T00:00:18.679734Z", - "iopub.status.idle": "2023-07-26T00:00:18.682331Z", - "shell.execute_reply": "2023-07-26T00:00:18.681967Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.53589947, 0.58318363, 0.27819087, 0.54343209],\n", - " [-0.41818087, -0.1879856 , 0.87280619, 0.16731864],\n", - " [-0.34123273, -0.26814843, -0.37801579, 0.81777791],\n", - " [ 0.6492278 , -0.74340748, 0.13387773, 0.08902432]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcaUS.components_\n" + "metadata": {}, + "outputs": [], + "source": [ + "pcaUS.components_" ] }, { @@ -1359,63 +546,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "4cc49622", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.684217Z", - "iopub.status.busy": "2023-07-26T00:00:18.684084Z", - "iopub.status.idle": "2023-07-26T00:00:18.686744Z", - "shell.execute_reply": "2023-07-26T00:00:18.686436Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.98556588, 1.13339238, -0.44426879, 0.15626714],\n", - " [-1.95013775, 1.07321326, 2.04000333, -0.43858344],\n", - " [-1.76316354, -0.74595678, 0.05478082, -0.83465292]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(U * D[None,:])[:3]\n" + "metadata": {}, + "outputs": [], + "source": [ + "(U * D[None,:])[:3]" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "c96c9fe1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.688393Z", - "iopub.status.busy": "2023-07-26T00:00:18.688266Z", - "iopub.status.idle": "2023-07-26T00:00:18.690823Z", - "shell.execute_reply": "2023-07-26T00:00:18.690511Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.98556588, -1.13339238, -0.44426879, 0.15626714],\n", - " [ 1.95013775, -1.07321326, 2.04000333, -0.43858344],\n", - " [ 1.76316354, 0.74595678, 0.05478082, -0.83465292]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scores[:3]\n" + "metadata": {}, + "outputs": [], + "source": [ + "scores[:3]" ] }, { @@ -1434,16 +580,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "574409d6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.692558Z", - "iopub.status.busy": "2023-07-26T00:00:18.692426Z", - "iopub.status.idle": "2023-07-26T00:00:18.695066Z", - "shell.execute_reply": "2023-07-26T00:00:18.694787Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "n_omit = 20\n", @@ -1455,7 +594,7 @@ " n_omit,\n", " replace=True)\n", "Xna = X.copy()\n", - "Xna[r_idx, c_idx] = np.nan\n" + "Xna[r_idx, c_idx] = np.nan" ] }, { @@ -1474,23 +613,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "89f190ae", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.696744Z", - "iopub.status.busy": "2023-07-26T00:00:18.696608Z", - "iopub.status.idle": "2023-07-26T00:00:18.698816Z", - "shell.execute_reply": "2023-07-26T00:00:18.698495Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def low_rank(X, M=1):\n", " U, D, V = np.linalg.svd(X)\n", " L = U[:,:M] * D[None,:M]\n", - " return L.dot(V[:M])\n" + " return L.dot(V[:M])" ] }, { @@ -1507,21 +638,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "322f339c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.700416Z", - "iopub.status.busy": "2023-07-26T00:00:18.700304Z", - "iopub.status.idle": "2023-07-26T00:00:18.702308Z", - "shell.execute_reply": "2023-07-26T00:00:18.702041Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "Xhat = Xna.copy()\n", "Xbar = np.nanmean(Xhat, axis=0)\n", - "Xhat[r_idx, c_idx] = Xbar[c_idx]\n" + "Xhat[r_idx, c_idx] = Xbar[c_idx]" ] }, { @@ -1535,17 +659,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "7e106d1a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.703954Z", - "iopub.status.busy": "2023-07-26T00:00:18.703834Z", - "iopub.status.idle": "2023-07-26T00:00:18.705823Z", - "shell.execute_reply": "2023-07-26T00:00:18.705529Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "thresh = 1e-7\n", @@ -1553,7 +669,7 @@ "count = 0\n", "ismiss = np.isnan(Xna)\n", "mssold = np.mean(Xhat[~ismiss]**2)\n", - "mss0 = np.mean(Xna[~ismiss]**2)\n" + "mss0 = np.mean(Xna[~ismiss]**2)" ] }, { @@ -1575,33 +691,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "7cb05ce4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.707449Z", - "iopub.status.busy": "2023-07-26T00:00:18.707325Z", - "iopub.status.idle": "2023-07-26T00:00:18.710562Z", - "shell.execute_reply": "2023-07-26T00:00:18.710239Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: 1, MSS:0.395, Rel.Err 5.99e-01\n", - "Iteration: 2, MSS:0.382, Rel.Err 1.33e-02\n", - "Iteration: 3, MSS:0.381, Rel.Err 1.44e-03\n", - "Iteration: 4, MSS:0.381, Rel.Err 1.79e-04\n", - "Iteration: 5, MSS:0.381, Rel.Err 2.58e-05\n", - "Iteration: 6, MSS:0.381, Rel.Err 4.22e-06\n", - "Iteration: 7, MSS:0.381, Rel.Err 7.65e-07\n", - "Iteration: 8, MSS:0.381, Rel.Err 1.48e-07\n", - "Iteration: 9, MSS:0.381, Rel.Err 2.95e-08\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "while rel_err > thresh:\n", " count += 1\n", @@ -1614,7 +707,7 @@ " rel_err = (mssold - mss) / mss0\n", " mssold = mss\n", " print(\"Iteration: {0}, MSS:{1:.3f}, Rel.Err {2:.2e}\"\n", - " .format(count, mss, rel_err))\n" + " .format(count, mss, rel_err))" ] }, { @@ -1630,31 +723,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "6f245188", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.712332Z", - "iopub.status.busy": "2023-07-26T00:00:18.712229Z", - "iopub.status.idle": "2023-07-26T00:00:18.714918Z", - "shell.execute_reply": "2023-07-26T00:00:18.714660Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.711356743429736" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]" ] }, { @@ -1688,23 +762,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "345fb41e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.716565Z", - "iopub.status.busy": "2023-07-26T00:00:18.716476Z", - "iopub.status.idle": "2023-07-26T00:00:18.718601Z", - "shell.execute_reply": "2023-07-26T00:00:18.718325Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(0);\n", "X = np.random.standard_normal((50,2));\n", "X[:25,0] += 3;\n", - "X[:25,1] -= 4;\n" + "X[:25,1] -= 4;" ] }, { @@ -1717,22 +783,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "3a8c21a2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.720161Z", - "iopub.status.busy": "2023-07-26T00:00:18.720052Z", - "iopub.status.idle": "2023-07-26T00:00:18.750146Z", - "shell.execute_reply": "2023-07-26T00:00:18.749757Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "kmeans = KMeans(n_clusters=2,\n", " random_state=2,\n", - " n_init=20).fit(X)\n" + " n_init=20).fit(X)" ] }, { @@ -1745,33 +803,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "e3e35b5d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.752342Z", - "iopub.status.busy": "2023-07-26T00:00:18.752172Z", - "iopub.status.idle": "2023-07-26T00:00:18.754770Z", - "shell.execute_reply": "2023-07-26T00:00:18.754396Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n", - " 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0], dtype=int32)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kmeans.labels_\n" + "metadata": {}, + "outputs": [], + "source": [ + "kmeans.labels_" ] }, { @@ -1787,32 +824,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "d928650a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.756939Z", - "iopub.status.busy": "2023-07-26T00:00:18.756801Z", - "iopub.status.idle": "2023-07-26T00:00:18.874421Z", - "shell.execute_reply": "2023-07-26T00:00:18.874069Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n", "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", - "ax.set_title(\"K-Means Clustering Results with K=2\");\n" + "ax.set_title(\"K-Means Clustering Results with K=2\");" ] }, { @@ -1834,36 +853,17 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "92e5175c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:18.876530Z", - "iopub.status.busy": "2023-07-26T00:00:18.876396Z", - "iopub.status.idle": "2023-07-26T00:00:18.998430Z", - "shell.execute_reply": "2023-07-26T00:00:18.998045Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "kmeans = KMeans(n_clusters=3,\n", " random_state=3,\n", " n_init=20).fit(X)\n", "fig, ax = plt.subplots(figsize=(8,8))\n", "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", - "ax.set_title(\"K-Means Clustering Results with K=3\");\n" + "ax.set_title(\"K-Means Clustering Results with K=3\");" ] }, { @@ -1883,29 +883,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "4911ecc7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.000480Z", - "iopub.status.busy": "2023-07-26T00:00:19.000345Z", - "iopub.status.idle": "2023-07-26T00:00:19.014678Z", - "shell.execute_reply": "2023-07-26T00:00:19.014345Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(78.06117325882116, 75.0350825910044)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "kmeans1 = KMeans(n_clusters=3,\n", " random_state=3,\n", @@ -1913,7 +894,7 @@ "kmeans20 = KMeans(n_clusters=3,\n", " random_state=3,\n", " n_init=20).fit(X);\n", - "kmeans1.inertia_, kmeans20.inertia_\n" + "kmeans1.inertia_, kmeans20.inertia_" ] }, { @@ -1956,40 +937,16 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "1b42a700", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.016810Z", - "iopub.status.busy": "2023-07-26T00:00:19.016679Z", - "iopub.status.idle": "2023-07-26T00:00:19.021325Z", - "shell.execute_reply": "2023-07-26T00:00:19.021002Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
AgglomerativeClustering(distance_threshold=0, linkage='complete',\n",
-       "                        n_clusters=None)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "AgglomerativeClustering(distance_threshold=0, linkage='complete',\n", - " n_clusters=None)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "HClust = AgglomerativeClustering\n", "hc_comp = HClust(distance_threshold=0,\n", " n_clusters=None,\n", " linkage='complete')\n", - "hc_comp.fit(X)\n" + "hc_comp.fit(X)" ] }, { @@ -2003,16 +960,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "50ef7eea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.023446Z", - "iopub.status.busy": "2023-07-26T00:00:19.023277Z", - "iopub.status.idle": "2023-07-26T00:00:19.026803Z", - "shell.execute_reply": "2023-07-26T00:00:19.026438Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "hc_avg = HClust(distance_threshold=0,\n", @@ -2022,7 +972,7 @@ "hc_sing = HClust(distance_threshold=0,\n", " n_clusters=None,\n", " linkage='single');\n", - "hc_sing.fit(X);\n" + "hc_sing.fit(X);" ] }, { @@ -2036,34 +986,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "bf7a2408", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.028981Z", - "iopub.status.busy": "2023-07-26T00:00:19.028840Z", - "iopub.status.idle": "2023-07-26T00:00:19.034246Z", - "shell.execute_reply": "2023-07-26T00:00:19.033877Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
AgglomerativeClustering(distance_threshold=0, linkage='single',\n",
-       "                        metric='precomputed', n_clusters=None)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "AgglomerativeClustering(distance_threshold=0, linkage='single',\n", - " metric='precomputed', n_clusters=None)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "D = np.zeros((X.shape[0], X.shape[0]));\n", "for i in range(X.shape[0]):\n", @@ -2073,7 +999,7 @@ " n_clusters=None,\n", " metric='precomputed',\n", " linkage='single')\n", - "hc_sing_pre.fit(D)\n" + "hc_sing_pre.fit(D)" ] }, { @@ -2096,28 +1022,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "a118c0ab", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.036313Z", - "iopub.status.busy": "2023-07-26T00:00:19.036187Z", - "iopub.status.idle": "2023-07-26T00:00:19.293174Z", - "shell.execute_reply": "2023-07-26T00:00:19.292867Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cargs = {'color_threshold':-np.inf,\n", " 'above_threshold_color':'black'}\n", @@ -2125,7 +1033,7 @@ "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "dendrogram(linkage_comp,\n", " ax=ax,\n", - " **cargs);\n" + " **cargs);" ] }, { @@ -2141,34 +1049,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "b1ff41c0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.295154Z", - "iopub.status.busy": "2023-07-26T00:00:19.295014Z", - "iopub.status.idle": "2023-07-26T00:00:19.548973Z", - "shell.execute_reply": "2023-07-26T00:00:19.548621Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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"execute_result" - } - ], - "source": [ - "cut_tree(linkage_comp, n_clusters=4).T\n" + "metadata": {}, + "outputs": [], + "source": [ + "cut_tree(linkage_comp, n_clusters=4).T" ] }, { @@ -2225,79 +1095,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "1407f7a4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.556459Z", - "iopub.status.busy": "2023-07-26T00:00:19.556356Z", - "iopub.status.idle": "2023-07-26T00:00:19.560257Z", - "shell.execute_reply": "2023-07-26T00:00:19.559863Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [1],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [0],\n", - " [1],\n", - " [0],\n", - " [1],\n", - " [1],\n", - " [2],\n", - " [1],\n", - " [2],\n", - " [2],\n", - " 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Ye++9N7uezjgGWtPavupM22+/fUycODEWL14cEyZMiJRSHHjggTFy5Mj45Cc/GU8++WQsWbIkDjrooA4NCdVZ2zJ8+PA46qij4qijjopDDjkk7rjjjnjyyScbr6VsS+5x2nAd6z/90z+1ev1pe85idIaGslx11VUlGztbbPF/X6dz5syJZcuWxWc/+9nYa6+9YujQobFx48Z497vf3eq1uU0V+Rw12PQY37hxY1RUVMTNN99c8jhoOlj9xRdfHHPnzo1f/vKX8T//8z9x5plnxoUXXhh33nlnjB07ts3y0r8JkvQ6kyZNildffTWOOOKI7GVcd911seOOO8YNN9zQrNI+55xz2jX/jjvuGBERDz74YHYZ2jJp0qQ2l9/R7djUkUceGT//+c/jpz/9acyfP7/w/A09rZsOsPzkk0+WnH7SpEnx6U9/Oj796U/HI488EnvttVdcfPHF8dOf/jQiWv9C7YxjoCPGjx8ft912W9TX1zfrlXz00UfbvYyZM2fG4sWLY+LEibHXXnvFsGHDYs8994yampr4zW9+E/fee2/jGJGtae396Ur77rtv3HHHHfHcc8/F+PHju/Q4HTlyZAwbNiw2bNjQ5r4eP358PPjgg5FSaraOhssHOmrSpEkRETFq1KjNluXll1+ORYsWxYIFC+LLX/5y4/MNPZpNtbb/in6OWitvSikmTpzYomFVyu677x677757fPGLX4xly5bF9OnT44orrojzzjuv3eukf3KNJL3OnDlz4g9/+EP89re/bfHa6tWrY/369W0uo6GF3rRH8I9//GP84Q9/aFcZRo4cGbNmzYof/vCHsWLFimavdbSXscGxxx4b999/fyxcuLDFaw3r6Oh2bOpDH/pQ7L777nH++eeXXMYrr7xScnijBuPHj4+BAwe2uEbxsssua/Z/fX19vP76682emzRpUgwbNqzZcC1VVVUlf/WjM46Bjpg9e3a8+eab8b3vfa/xuY0bN8a3v/3tdi9j5syZsXz58rjmmmsaT3UPGDAgDjrooPjGN74Rb775ZpvXRzaE2M7+ZZTnn3++cYirpt54441YtGhRDBgwoPE0flcepwMHDoxjjz02rr/++pJhtendze9973vj2Wefjeuuu67xufr6+lZPiRc1e/bsqK6ujgsuuKDk6BINZSm1rRHRYiSBiGgc63HT/VddXR0jRoxo83O0OR/84Adj4MCBsWDBghZlSSk1DkW0du3aFp+X3XffPQYMGNDpQyfRN+mRpNf57Gc/GzfeeGO8//3vj7lz58bUqVOjrq4u/vKXv8R1110Xy5cvbxxKozXvf//744Ybbohjjjkm3ve+98UTTzwRV1xxRey6667x6quvtqsc//Ef/xEzZsyIffbZJ0499dSYOHFiLF++PH7961/Hn//8507Zzuuuuy6OO+64OPnkk2Pq1KmxatWquPHGG+OKK66IPffcs1O2o6lBgwbFDTfcEEcccUTMmjUr5syZE9OnT49BgwbFQw89FD/72c9im222aXUsyZqamjjuuOPiW9/6VlRUVMSkSZPipptuanGd2N///vc4/PDDY86cObHrrrvGFltsEQsXLoza2to4/vjjG6ebOnVqXH755XHeeefFTjvtFKNGjYrDDjusU46Bjjj66KNj2rRp8elPfzoeffTRmDx5ctx4442xatWqiGhfT2FDSHz44YfjggsuaHx+1qxZcfPNN0dlZWXst99+m13GVlttFbvuumtcc801sfPOO8e2224bU6ZM6fAvsTz99NMxbdq0OOyww+Lwww+PMWPGxMqVK+PnP/953H///XHWWWc1vr9dfZx+9atfjdtuuy3233//OOWUU2LXXXeNVatWxb333hu33HJL43t+yimnxKWXXhonnnhi3HPPPbHddtvFVVddtdmhi4qorq6Oyy+/PE444YTYZ5994vjjj4+RI0fGihUr4te//nVMnz49Lr300qiuro5Zs2bFv/3bv8Wbb74Zb3vb2+J//ud/4oknnmixzKlTp0ZExBe+8IU4/vjjY9CgQXHkkUdGVVVVfOxjH4uvfvWr8bGPfSz23XffWLx4caFflJo0aVKcd955MX/+/Fi+fHkcffTRMWzYsHjiiSdi4cKFceqpp8ZnPvOZuPXWW+P000+P4447LnbeeedYv359XHXVVY0hHtrU7feJ0681DLFx1113lXz94IMPbnP4n5TeGopj/vz5aaeddkpbbrllGjFiRDrooIPSRRddlN54442U0v8NoVFqiJmNGzemCy64II0fPz5VVlamvffeO910000thlfZ3DJSSunBBx9MxxxzTNp6663T4MGD0y677JK+9KUvNb7eMDTHCy+8UPJ9aDrsR6ntfOmll9Lpp5+e3va2t6Utt9wyjR07Np100kmNQ6G0dztSat/wPw1efvnl9OUvfzntvvvuaciQIWnw4MFpypQpaf78+em5555rnK7Uel544YV07LHHpiFDhqRtttkmffzjH08PPvhgs+FMXnzxxXTaaaelyZMnp6qqqlRTU5P233//ZkO3pPTW0Crve9/70rBhw1JENBsKqKPHQGvD/1RVVbWYtmE/brqdH/nIR9KwYcNSTU1Nmjt3blq6dGmKiHT11Ve3521Oo0aNShGRamtrG5/7/e9/nyIizZw5s8X0pd7vZcuWpalTp6Ytt9yy2T4usi2bWrt2bfrmN7+ZZs+encaOHZsGDRqUhg0blg488MD0ve99r9kQVyl1/XFaW1ubTjvttDRu3Lg0aNCgNGbMmHT44Yen7373u82me/LJJ9NRRx2VhgwZkkaMGJE++clPpt/85jeFhv9prW5qcNttt6XZs2enmpqaNHjw4DRp0qQ0d+7cdPfddzdO8/TTTzfWCzU1Nem4445Lzz77bMlt+8pXvpLe9ra3pQEDBjSrE+rr69NHP/rRVFNTk4YNG5bmzJmTVq5c2erwP5vWMQ2uv/76NGPGjFRVVZWqqqrS5MmT02mnnZYefvjhlFJKjz/+eDr55JPTpEmT0uDBg9O2226bDj300HTLLbds9n2ABhUpddJ5OIB+7he/+EUcc8wx8fvf/77kr8IA9DWCJECG1157rdmdshs2bIh3vetdcffdd8fzzz/f4Tv9AXoD10gCZDjjjDPitddeiwMPPDDWrVsXN9xwQyxbtiwuuOACIRLoN/RIAmT42c9+FhdffHE8+uij8frrr8dOO+0Un/jEJ+L000/v6aIBdBtBEgCALMaRBAAgiyAJAECWbr/ZZuPGjfHss8/GsGHDeuTnvQAA2LyUUrzyyiux/fbbx4ABrfc7dnuQfPbZZ2PcuHHdvVoAAAp66qmnYuzYsa2+3u1BctiwYRHxVsGqq6u7e/UAALRh7dq1MW7cuMbc1ppuD5INp7Orq6sFSQCAMtbWZYhutgEAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBli54uQH+TUor6+vqeLgZA2RgyZEhUVFT0dDGADIJkN0opxYwZM2LZsmU9XRSAsjF9+vRYsmSJMAm9kFPb3ai+vl6IBNjE0qVLnamBXkqPZA+pra2Nqqqqni4GQI+pq6uL0aNH93QxgA4QJHtIVVWVIAkA9GpObQMAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgS4eC5Fe/+tWoqKiIs846q5OKAwBAb5EdJO+66674zne+E3vssUdnlgcAgF4iK0i++uqr8Y//+I/xve99L7bZZpvOLhMAAL1AVpA87bTT4n3ve18cccQRbU67bt26WLt2bbMHAAC93xZFZ7j66qvj3nvvjbvuuqtd01944YWxYMGCwgUDAKC8FeqRfOqpp+KTn/xk/Od//mcMHjy4XfPMnz8/1qxZ0/h46qmnsgoKAEB5KdQjec8998TKlStjn332aXxuw4YNsXjx4rj00ktj3bp1MXDgwGbzVFZWRmVlZeeUFgCAslEoSB5++OHxl7/8pdlz8+bNi8mTJ8fnPve5FiESAIC+q1CQHDZsWEyZMqXZc1VVVTF8+PAWzwMA0Lf5ZRsAALIUvmt7U7fffnsnFAMAgN5GjyQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAshQKkpdffnnsscceUV1dHdXV1XHggQfGzTff3FVlAwCgjBUKkmPHjo2vfvWrcc8998Tdd98dhx12WHzgAx+Ihx56qKvKBwBAmdqiyMRHHnlks//PP//8uPzyy+POO++M3XbbrVMLBgBAeSsUJJvasGFDXHvttVFXVxcHHnhgq9OtW7cu1q1b1/j/2rVrc1cJAEAZKXyzzV/+8pcYOnRoVFZWxj//8z/HwoULY9ddd211+gsvvDBqamoaH+PGjetQgQEAKA8VKaVUZIY33ngjVqxYEWvWrInrrrsuvv/978cdd9zRapgs1SM5bty4WLNmTVRXV3es9L1MXV1dDB06NCIiXn311aiqqurhEgH0HHUilK+1a9dGTU1Nm3mt8KntLbfcMnbaaaeIiJg6dWrcdddd8c1vfjO+853vlJy+srIyKisri64GAIAy1+FxJDdu3NisxxEAgP6hUI/k/Pnz4z3veU/ssMMO8corr8TPfvazuP322+O3v/1tV5UPAIAyVShIrly5Mk488cR47rnnoqamJvbYY4/47W9/G+985zu7qnwAAJSpQkHyBz/4QVeVAwCAXsZvbQMAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAli16ugAA5SqlFPX19T1djD6rrq6u5N90jSFDhkRFRUVPF4M+RpAEKCGlFDNmzIhly5b1dFH6hdGjR/d0Efq86dOnx5IlS4RJOpVT2wAl1NfXC5H0KUuXLtXDTqfTIwnQhtra2qiqqurpYkCWuro6Pb50GUESoA1VVVWCJEAJTm0DAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAECWLXq6ADlSSlFfX9/TxSisrq6u5N+9zZAhQ6KioqKniwEA9LBeFyRTSjFjxoxYtmxZTxelQ0aPHt3TRcg2ffr0WLJkiTAJAP1crzu1XV9f3+tDZG+3dOnSXtkjDAB0rl7XI9lUbW1tVFVV9XQx+o26urpe3ZMKAHSuXh0kq6qqBEkAgB7S605tAwBQHgRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZCgXJCy+8MPbbb78YNmxYjBo1Ko4++uh4+OGHu6psAACUsUJB8o477ojTTjst7rzzzvjd734Xb775ZrzrXe+Kurq6riofAABlqtBvbf/mN79p9v+PfvSjGDVqVNxzzz0xa9asTi0YAADlrVCQ3NSaNWsiImLbbbdtdZp169bFunXrGv9fu3ZtR1YJAECZyL7ZZuPGjXHWWWfF9OnTY8qUKa1Od+GFF0ZNTU3jY9y4cbmrBACgjGQHydNOOy0efPDBuPrqqzc73fz582PNmjWNj6eeeip3lQAAlJGsU9unn3563HTTTbF48eIYO3bsZqetrKyMysrKrMIBAFC+CgXJlFKcccYZsXDhwrj99ttj4sSJXVUuAADKXKEgedppp8XPfvaz+OUvfxnDhg2L559/PiIiampqYquttuqSAgIAUJ4KXSN5+eWXx5o1a+KQQw6J7bbbrvFxzTXXdFX5AAAoU4VPbQMAQITf2gYAIJMgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsW/R0ASgtpRT19fU9XYxm6urqSv5dLoYMGRIVFRU9XQygC5RjndhblHvd3Zv4nmlJkCxDKaWYMWNGLFu2rKeL0qrRo0f3dBFamD59eixZssSHHPqY3lAn9hblWHf3Jr5nWnJquwzV19erMDMsXbpUjwX0QepEyoXvmZb0SJa52traqKqq6ulilLW6ujqtbOgn1In0BN8zrRMky1xVVZVKE+D/p06E8uLUNgAAWQRJAACyOLUN9EpdPRxMdw2ZYjgRoDcTJIFep7uHg+nKi+wNJwL0Zk5tA71OXxoOxnAiQG+mRxLo1XrrcDCGEwH6AkES6NUMBwPQc5zaBgAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsgiQAAFkESQAAsgiSAABkESQBAMiyRU8XAACgQUop6uvre7oYzdTV1ZX8u1wMGTIkKioqemTdgiQAUBZSSjFjxoxYtmxZTxelVaNHj+7pIrQwffr0WLJkSY+ESUGSdivHVmJE+bcUI3q2tQjQW9TX15d1iCxXS5cujfr6+qiqqur2dQuStEtvaCVGlGdLMaJnW4sAvVFtbW2PBKPepK6urse/9wRJ2kUrsWN6srUI0BtVVVWpM3sBQZLCtBLbrxxaiwDQVQRJCtNKBAAijCMJAEAmQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZBEkAALIIkgAAZBEkAQDIIkgCAJBFkAQAIIsgCQBAFkESAIAsW/R0AQB6u5RS1NfXF5qnrq6u5N/tMWTIkKioqCg0D0BXECQBOiClFDNmzIhly5ZlL2P06NGFpp8+fXosWbJEmAR6nFPbAB1QX1/foRCZY+nSpYV7QAG6gh5JgE5SW1sbVVVVXbb8urq6wr2XAF1JkAToJFVVVV0aJAHKjVPbAABkESQBAMgiSAIAkEWQBAAgiyAJAECWwkFy8eLFceSRR8b2228fFRUV8Ytf/KILigUA0L+llKKurm6zjwZtTZdS6pIyFh7+p66uLvbcc884+eST44Mf/GBXlAkAoF8r+qtZbY0x21W/iFU4SL7nPe+J97znPZ1aCAAA/k9n/2pWwy9idfZYtwYkBwAoYx351ayu/kWsLg+S69ati3Xr1jX+v3bt2q5eJQBAn1HOv5rV5XdtX3jhhVFTU9P4GDduXFevEgCAbtDlQXL+/PmxZs2axsdTTz3V1asEAKAbdPmp7crKyqisrOzq1QAA0M0KB8lXX301Hn300cb/n3jiifjzn/8c2267beywww6dWjgAAMpX4SB59913x6GHHtr4/9lnnx0RESeddFL86Ec/6rSCAQBQ3goHyUMOOaTLRkcHAKD38FvbAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQp/Ms25EkpRX19fbumraurK/l3W4YMGRIVFRWFywYAkEOQ7AYppZgxY0YsW7as8LyjR49u97TTp0+PJUuWCJMAQLdwarsb1NfXZ4XIopYuXdruXk8AgI7SI9nNamtro6qqqlOXWVdXV6jnEgCgMwiS3ayqqqrTgyQAQE9wahsAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBYDkgNAP5ZSKpuf162rqyv5d08bMmRIVFRU9HQxypIgSZ9VDpVjOVaKKkSgQUopZsyYEcuWLevporRQTj/9O3369FiyZIm6swRBkj6pHCvHcqkUVYhAg/r6+rKqJ8vV0qVLo76+3k8clyBI0iepHFunQgRKqa2tVS9soq6urmw6AcqVIEmfp3J8iwqRrtSVl5J01yUi/f2yj6qqKnUlhQmS9HkqR+ha3XkpSVc2hlz2AcUZ/geADukrl5I0XPYBtJ8eSQA6TW+8lMRlH5CvbIJke6+vKXqtTH+/5gWgO7mUBPqXsgiSudfXtKcF6ZoXAICuURbXSHbl9TWueQEA6Bpl0SPZVGddX+OaFwCArlV2QdL1NQAAvUPZBUmAtm6+c9MdQHkQJIGyUvTmOzfdAfScsrjZBqBBV9x856Y7gK6hRxIoWx29+c5NdwBdS5AEypab7wDKm1PbAABkESQBAMgiSAIAkEWQBAAgiyAJAEAWQRIAgCyCJAAAWQRJAACyCJIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQJYteroAQHEppaivry80T11dXcm/22vIkCFRUVFReD4A+i5BEnqZlFLMmDEjli1blr2M0aNHF55n+vTpsWTJEmESgEaCJPQy9fX1HQqRuZYuXRr19fVRVVXV7esG6AxFz+bknsnpT2dwBEnoxWpra7s82NXV1WX1YAKUk46ezSlSD/anMziCJPRiVVVVeggB2qE7z+b0pzM4giRlJecmklI6emPJpvrTaQqAvq6rzub0xzM4giRlozNuIimlMz7U/ek0BdD75DbC++toDs7mdJ5eGyTb+tAU/XD01g9DX9JTN5G0R386TdFblaoT2qoHyu1zr14jR2c1wo3mQI5eGSSLfmja8+HwYSgv3XETSXv0x9MUvVF76oRS+7GcPvfqNXL1ZCNcI5teGSS74kPjw1BenHagiNw6oZw+9+o1OkN3NcI1smnQK4NkUx390PgwQN/Snjqh3D/36jVyaYTT3Xp9kPShAZrqC3VCX9gGoH8Y0NMFAACgdxIkAQDI0utPbQNAg5zxFPvrWIrQGQRJAPqEzhhP0ViKUIxT2wD0CT01nmLDMEvQH+mRBKDP6Y7xFA2zBIIkAH2QIZSgewiSALSqPTevFLlZxY0p0LcIkgCUlHPzSlunet2YAn2Lm20AKKkrf/8b6Bv0SAL0YqVOPbd1qjnn9LLf/wZKESQBeqn2nHouFd5yTi+7eQUoxaltgF4q99Sz08tAZ9EjCdAHtOfUs9PLQGcTJKGfa2t4l6K/Q2x4l57h1DPQEwRJ6MeKDu/Snt4sw7sA9IzuuvmuKddIQj9meBeAvqGhY2Do0KHNHk07AEaPHt3i9ZkzZ0ZKKXu9eiSBiDC8C0Bv1tGb73Lrf0ESiAjX2AH0Fd15850g2cvkXP8Q4QYIAOgvurNjQJDsRK3d/dpZF7rmDj4c4QYIAKDzCZKdpL13v3bkVyY6cmNER6+BoGfogS4fXd1QBOiNBMlO0t0hr703RrgBoqW2xk1squgYik11NEDogS4f3dFQBOiNBMku0B0hz40ReYqOm9hU0X3V0QChB7p82Bf0F+1taOc0svta73xPjNlYjgTJLtDbQ15f/nB0xbiJrenMAKEHunzYF/RVuQ3t9h7nfal3PveMUV96DxoIkjTTnz4cHR03cXPXzO24446Nf28qJ3T39sZJX2Jf0Fd1dUO7L/XO99SYjeVIkKSZ/vTh6EggcM0c0Jd1tKHdVF/vne/OMRvLkSBJq/r7h2NzXDMH9GV63tuvv79XgiSt6u8fjvZyzRwA/ZUgCR0kcAPQXw3o6QIAANA7CZIAAGQRJAEAyNKvrpHsywNtAwB0t34TJPvTQNsAAN2h35za7uhA2wAANNdveiSbMtA2APRdLmXrPv0ySBr3DwD6Jpeyda9+GSSBfKVa+hFa+/QtrR3nTbV1zDfl+O8+Hb2UTUdTMYIk0G7taelHaO3T9bry1GV7j/Om2roUyvHfM1zK1vUESaDdclv6EVr7dJ6uPnXZkeO8NZ11/Lv2rxiXsnU9QZI+S4XbtdrT0o/Q2qfzdeepy/Ye563pzOPftX+UI0GSPkmF2/W09CkHXX3qspyOc9f+UY4ESfokFS70D+UU9LqTa/8oF4IkfZ4KF+hr+muApvwIkvR5KlwA6Br95icSAQDoXHok6RHuqAaA3k+QpNu5oxr6Ng3F/qWtXwHyC0B9myBJt3NHNfRdGor9S9FfAfILQH2PIEmPckc19C0aiv1LZ/8KkOOg9xEk6VHuqIa+S0Oxf+nIrwA5DnovQRKALqGh2L+0d38XuaayKddPlidBEgDoFrnX0Ea4frJcGUcSAOgWHbmmsuH6ScqLHkkAoNu195pK10+WN0ESADqB8TOLcQ1t35B1avvb3/52TJgwIQYPHhz7779//OlPf+rscgFAr9Fw7d/QoUObPZr2pI0ePbrF6zNnzoyUUg+WHDqmcJC85ppr4uyzz45zzjkn7r333thzzz1j9uzZsXLlyq4oHwCUvY6Onwm9VeEg+Y1vfCNOOeWUmDdvXuy6665xxRVXxJAhQ+KHP/xhV5QPAHqV2traePXVVzf7qK2t7eliQqcodI3kG2+8Effcc0/Mnz+/8bkBAwbEEUccEX/4wx9KzrNu3bpYt25d4/9r1qyJiIi1a9c2Ptf0upG1a9fGhg0bNluOotN3xzrKsUzdsY5yLFN3rKMcy9Qd6yjHMnXHOsqxTN2xjnIsU3eso6Nl2rBhQ5vzNH29r2x3b11HOZapO9bR1vQNOa3NSy9SAc8880yKiLRs2bJmz3/2s59N06ZNKznPOeeckyLCw8PDw8PDw8Ojlz2eeuqpzWbDLr9re/78+XH22Wc3/r9x48ZYtWpVDB8+vN/eqQYAUM5SSvHKK6/E9ttvv9npCgXJESNGxMCBA1tc21FbWxtjxowpOU9lZWVUVlY2e27rrbcusloAALpZTU1Nm9MUutlmyy23jKlTp8aiRYsan9u4cWMsWrQoDjzwwOIlBACg1yp8avvss8+Ok046Kfbdd9+YNm1aXHLJJVFXVxfz5s3rivIBAFCmCgfJf/iHf4gXXnghvvzlL8fzzz8fe+21V/zmN7/x80UAAP1MRTKkPgAAGbJ+IhEAAARJAACyCJIAAGQRJAEAyCJI9hKPP/543H777XH77bfH448/vtlp6+rqYv369RERsWrVqli0aFE8/fTT3VHMsvKd73ynp4uQ7c9//nP84he/iJtuuqnN/V3U6tWrO7yMl19+udD0L7zwQtx6663x3HPPdXjdAJSRIr+13Zn+67/+q/HvF154Ib33ve9N1dXV6eCDD05PPvlkTxWrkEcffTQdcsghaeLEielTn/pUeu211xpfO+CAAzplHX/961/Tfvvtl8aMGZOmTZuWpk2blsaMGZP222+/9OCDD7aY/sc//nEaPHhwGjt2bFq0aFHabrvt0n777ZeGDx+err766k4pU0oprV+/Pi1atChdeeWV6corr0yLFi1K69ev77TlN9iwYUOL51atWtXiuV/+8pctHqNHj278u7e4//7705QpU1J1dXUaMGBAmjJlStpmm23Shz70obRmzZpOWcegQYPSUUcdlW688caS7++m7rvvvrTnnnumvffeOz344IPpve99b9pqq63SuHHj0v33319ynhNOOCHV1tamlFJatGhRGjFiRNpvv/3SyJEj08KFCztlO5p6880307333ptWr15daL4vfvGL7Z72gQceSD/4wQ/SXXfdVbR4WV588cU2p1m5cmVatGhRevbZZ7tsHSmV/sw11Rn1QZF90Z4ylXLFFVds9vVXX301vfnmmymllF566aV0yy23tPk7w6+//npauHBh+vd///f0rW99K916661tluOxxx5Lt912W7rtttvSY4891u7yt+cY/Mtf/tLu5TV13333pYULF6Zf/epXbZYpZx1/+9vfGuuEv/3tb+n73/9+uvPOO1udPud9LeLll1/u1OW1R3vrqfZ+77WmrWmL7ou29FiQ3HvvvRv//tjHPpY+97nPpeeeey5dfPHF6eijjy60rLe//e3tnrYzK5J3vetd6dJLL0133313OuGEE9JBBx2U1q5dm1JKaa+99mp3mTZn2rRp6brrrmvx/LXXXpv222+/Fs/vvvvuafny5en+++9PNTU1jRXOI488kvbYY492rbOtL6fFixensWPHpv333z/NmTMnzZkzJ02bNi2NHTs23XHHHZtddnsrq7vuuitNmDAhbbnllunoo49OK1eubHyt6bHToKKiIh100EHpkEMOaXwMHjw4HXLIIenQQw9t13bn2tyHtmhldeCBB6YlS5aklN4Kx2eccUZat25d+sIXvpBOPPHEjhSz0c4775wuuuii9I53vCNtt9126XOf+1x6+OGHW51+1qxZaeHChenKK69MO+ywQ/rJT36SUkpp4cKF6Z3vfGfJeZoea7NmzUr33XdfSimlxx9/fLOfjfZ+wS5atChtu+22afjw4en2229P++23X5o8eXLj/6V885vfbPEYPnx449+bOuywwxor22uuuSZtv/326bjjjkvjx49vsx4ppUg9lVJK48aNa/FcZwf0Uuso2nDIqQ+K7otLLrmk8e/HH3887brrrmnw4MFpwoQJ6YEHHii5jqKNy5xG+K233pp22GGHtMcee6TKysp0xBFHpHe84x1pv/32S08//XSL6Yt2DOQcgxUVFWmPPfZI3/zmN9NLL71UcpqmchqvRdfxb//2b2nUqFFp3Lhx6aqrrkrjxo1Lxx13XNphhx2a7dsGRd/XlIp37BRtUKdUvAOsaD1V9HsvpeKfjaL7oj16LEg2/TLZY489mrVeSwWe+++/v9XHmDFjSq6jqyuSTb8Qzz///LTffvul1atXt7rTix7sO++8c8nltPZa0zKNHz9+s+VtUPTLaffddy/ZIv7Tn/6UpkyZUnIdRSurGTNmpJtuuim9+OKL6Ytf/GKaPHlyYwVSajt++MMfpoMOOijde++9jc9NmDChZFka5PSKF/2SLVpZ7bnnns3+33fffRv/LhpEWpun6bG5dOnS9NGPfjQNGzYszZw5M/34xz9uMX3T93vT4LFpeUutt+k2pPTW8bOpol+w06ZNS/fdd1+67bbb0vDhw9OiRYtSSin98Y9/TDNmzChZpoEDB6ajjjoqzZ07t/ExdOjQNHfu3DRv3rwW0zct5wEHHJCWL1+eUnqrgVlqG1IqXk+VqqMaHiNHjmwxfU5AL7qOog2HnPqg6L5oeswef/zx6dJLL00ppXTdddelI444ouQ6ijYucxrhe++9d/r73//euL0nnHBCSiml7373u+kDH/hAi+lzOgYatPcYnDJlSrruuuvSe97znlRVVZX+4R/+If3ud78rOW1KeY3XouvYdddd06pVq9KKFSvSkCFD0uOPP55Seqve3W233VpMX/R9Tal4x07RBnVDuRq0pwOsaD1V9Htv0zK157NRdF+0R48FycmTJ6cHHngg3X///S2+jEp9OVVUVKSJEyemCRMmtHgMGjSo5Dq6uiLZZZddWjz39a9/PU2dOjXttNNOJctU9GA/6KCD0k9+8pNmIWTDhg3pRz/6UTrwwANbTL/PPvukBx98MC1ZsiSNGDEi/f73v08pvdV93VrFU/TLaXOBprXXilZWm673qquuSjvvvHNasWJFqyF9+fLl6YgjjkgLFixI69evTxMnTmy1nCnl9YoX/ZItWlntu+++6W9/+1tKKaU//OEP6eCDD258bfLkySXnKRpeSr1/r776avr+97+fpk+f3uK1pp/HTfdVa1+wp59+ejrzzDPTK6+8kj7/+c+nn/70p2njxo3pv//7v0t+9op+wTY9PiZNmtTqa00tWrQoTZs2Lf3qV79qfG5zjY2dd965sYG7//77N3uttYBUtJ4aMGBAOvTQQ5vVUU3rqk0VDeg56yjacMipD4rui6bH7KbHXGuNmaKNy5xG+Kbr3meffRr/LtXQL9oxkHMMNn2vVqxYkb7yla+kHXfcMY0fPz4tWLCgzW1oT+O16DqaTr/DDjs0e63Ue1v0fS21nLY6doo2qDddR3s6wIrWUznfe0U/G0X3RXv0WJAcP358swq34fRxazt9woQJ6Zlnnim5rLFjx5Z8vqsrkqOPPjrdfPPNLZ6/+OKLU0VFRZvrSKntg/2RRx5Jhx12WKqpqUmTJ09OkydPTjU1NenQQw8tGUh+/etfp2233TaNGDEi3XLLLemQQw5Ju+yyS6qurm719EzRL6d3v/vdacGCBY29mCmlVFtbm84999z0rne9q+Q6ilZWO++8c4sevKuvvjq9/e1vb3HwN7Vx48Z00UUXpQMPPDBtv/32rU6XUvFKYdN52vMlW7Syuvnmm9Pw4cPTLrvskkaMGNF4+uO5555Lp5xySskyFQ0vRSuLd73rXSV7jZ999tk0bdq0kvOsW7cunXXWWam6ujpNmDAhVVRUpC222CLNnj27sQXcVNEv2Kb75/Of/3yz11oLVCmltGbNmnTCCSekuXPnpjVr1my2sXHOOeekY489Nj3yyCPp61//ejrvvPPSE088kS677LJ01FFHlZynaD21yy67pCeeeKLd0xcN6DnrKNpwyKkPUiq2LyZOnJhuvPHG9Mtf/jK94x3vaLNMDYo0LnMa4dOnT2+8du/aa69N7373uxtfK3XcFu0YyDkGWwsct9xyS/rIRz7S4vmcxmvRdRxwwAHppptuSldddVUaP35843fRrbfemqZOndpi+qLva0rFO3aKNqhTKt4BVrSeyvneK/rZKLov2qPHgmRr6urqSn7RnHnmmY09Wpv6+Mc/3uryurIief3119Prr79eclmtXceR04uZ0lvXLd5zzz3pnnvuaXbdRFvWr1+f7r777maV/KaKfjmtXLkyzZs3Lw0dOjQNHjw4DR48OA0dOjTNmzev1fUUrazmzZvXrLeiwTXXXNNqD3RTDz74YLr88ss3O03RSmHT59vzJZtTWb388svp7rvvbvfNNUXDS3uuaWqP1atXt3ljXF1dXXrggQfSvffeu9kbO4p+wc6bN6/k+/Poo4+2+r42de2116a99tqr1ctiGlxyySVp7NixadCgQamioiJVV1enj3/8462+h0XrqQsuuKDVGyfOO++8Fs8VDeg56yjacFi5cmU6+eSTC9UHTbVnXxx88MHNelIb6tfa2toWjd9NtbdxWaoRvvPOO2+2Ef6nP/0pjR07NlVWVqaJEyc2XpP23HPPleyZa+gYqK6uTpMnT25s5LfWMZBS8WNw057LtuQ0Xouu449//GPaa6+90j777JP+/Oc/p+OPPz5VVlam4cOHp1tuuaXF9A3v6+DBg9v1vqZUvGMnp/dt/PjxzRrqbXWAFa2ncr73in42Su2LwYMHNzv1XlTZBcmu0JGKpOGD/vOf/7xTypLTi9nVcr6cGrz00kvtCiY5lVVXK9ornlLxL9nOuulqc3IbWeWkaM97a958881m1x1vzvPPP59+/etft2vatWvXZt0lnFLe3cVtaRrQizQsGxS9WWjVqlXtajg01AdtNeI29fzzz6fLLrus0DwpvdVQrqura9e0Dz74YLrgggsKLfuPf/xju97f9t793iCnY6DhGCz63qbU9v7etPHaFcfspl588cU2rxtv+r62tQ2vv/56WrduXbPnGrajVMdOqe+tnO1etWpVqq+vb/O7ssEVV1yR1q9fn+rr69s9fVFXXHFF2rBhQ7s/G1/5ylfafcNRKf0iSDZoTy9VU+3pzSsqpxezu7S39yh32KMiPW2PPfZYlw+t1Jq6urpWTwW2ZvXq1WnFihUtnu+s3r/+or1fsDnHR9HjNuc4//Of/9zqzVil7qAsuh0NN3s1nD1573vfm4YMGbLZoZiK3nRYdBtaW37D6bbOKNNjjz2WDj300EL7ouiNcaXe281td065ipapO97bzZVpc9td5Lgt+j7lDOdW9Ljd3PStfZYa5mnvMVJ0O3K2u7PWsbljqi39Kkg2VfQO2Jw7ZovqjnUUVapMnT3sUXesozPK1Nnz9NdjqqjOOj6KzpOzjqI3YxVdR85QTEVvOiy6jpyht4rO0x37Iue97er91x3vbXdsd9Hpc7a7O/Z3V++/7tjfXTFUXp8OkkXvZs0ZYqiry9QdipYpZ9ij7lhHUTn7wjHVNbrj+Cg6T0fX0Z6bsTpSpvYOxdSRmw7bs46cobc6UqaUun5ftPe97er9193vbXdsd3umz9nu7t7fXbH/umN/56yjLVv09C/rdKW99torJkyYECmlFq+99NJLHZ6+O8rUHYqW6bXXXmv2///7f/8vttxyyzj88MPjlVde6bF1FJWzLxxTXaM7jo+i8+Sso2n5Dz300FZfy11H0eVHRMybNy8OO+yw+NjHPhYzZ86ML3zhC1FRUVFy2px1FF1+zjzdsS9y3tuu3n/d8d52x3YXnT5nu7tjf3f1/uuO/Z2zjjZ1KIaWuaJ3s+YMMdTVZeoORcuUc8NQd6yjqJx94ZjqGt1xfBSdJ2cdRW/GKrqOnKGYGrT3psPcdRQZeqvoPN2xL3K2u7v2X1e+t92x3bn1eZHt7o793V37ryv3d0fW0Zo+HSSL3s3aHXe/luMdtkXLlHPDUHeso6icfeGY6hrdcXwUnaczj8HW7njurHW0547qBkVvOiy6jpzltzVPd+yLnOm7e/91xXubU6bu/izlbkNKnbu/OzpP0e3ojv3dkfe2QUVKrfThAgDAZgzo6QIAANA7CZIAAGQRJAEAyCJIAgCQRZAEACCLIAkAQBZBEgCALIIkAABZ/j/gLnifjqhdagAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "scaler = StandardScaler()\n", "X_scale = scaler.fit_transform(X)\n", @@ -2342,7 +1127,7 @@ "linkage_comp_scale = compute_linkage(hc_comp_scale)\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "dendrogram(linkage_comp_scale, ax=ax, **cargs)\n", - "ax.set_title(\"Hierarchical Clustering with Scaled Features\");\n" + "ax.set_title(\"Hierarchical Clustering with Scaled Features\");" ] }, { @@ -2366,29 +1151,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "b7f7da12", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:19.831097Z", - "iopub.status.busy": "2023-07-26T00:00:19.830959Z", - "iopub.status.idle": "2023-07-26T00:00:20.036103Z", - "shell.execute_reply": "2023-07-26T00:00:20.035746Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = np.random.standard_normal((30, 3))\n", "corD = 1 - np.corrcoef(X)\n", @@ -2400,7 +1166,7 @@ "linkage_cor = compute_linkage(hc_cor)\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", "dendrogram(linkage_cor, ax=ax, **cargs)\n", - "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");\n" + "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");" ] }, { @@ -2418,21 +1184,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "b94424fc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.038049Z", - "iopub.status.busy": "2023-07-26T00:00:20.037914Z", - "iopub.status.idle": "2023-07-26T00:00:20.043042Z", - "shell.execute_reply": "2023-07-26T00:00:20.042732Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "NCI60 = load_data('NCI60')\n", "nci_labs = NCI60['labels']\n", - "nci_data = NCI60['data']\n" + "nci_data = NCI60['data']" ] }, { @@ -2451,31 +1210,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "cea54566", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.044884Z", - "iopub.status.busy": "2023-07-26T00:00:20.044785Z", - "iopub.status.idle": "2023-07-26T00:00:20.047376Z", - "shell.execute_reply": "2023-07-26T00:00:20.046971Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(64, 6830)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nci_data.shape\n" + "metadata": {}, + "outputs": [], + "source": [ + "nci_data.shape" ] }, { @@ -2483,51 +1223,17 @@ "id": "6ebf27f1", "metadata": {}, "source": [ - "We begin by examining the cancer types for the cell lines.\n" + "We begin by examining the cancer types for the cell lines." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "4dac41bb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.049125Z", - "iopub.status.busy": "2023-07-26T00:00:20.048998Z", - "iopub.status.idle": "2023-07-26T00:00:20.052730Z", - "shell.execute_reply": "2023-07-26T00:00:20.052393Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "label \n", - "NSCLC 9\n", - "RENAL 9\n", - "MELANOMA 8\n", - "BREAST 7\n", - "COLON 7\n", - "LEUKEMIA 6\n", - "OVARIAN 6\n", - "CNS 5\n", - "PROSTATE 2\n", - "K562A-repro 1\n", - "K562B-repro 1\n", - "MCF7A-repro 1\n", - "MCF7D-repro 1\n", - "UNKNOWN 1\n", - "dtype: int64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nci_labs.value_counts()\n" + "metadata": {}, + "outputs": [], + "source": [ + "nci_labs.value_counts()" ] }, { @@ -2544,22 +1250,15 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "d8ebadd6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.054502Z", - "iopub.status.busy": "2023-07-26T00:00:20.054401Z", - "iopub.status.idle": "2023-07-26T00:00:20.185686Z", - "shell.execute_reply": "2023-07-26T00:00:20.184416Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "nci_scaled = scaler.fit_transform(nci_data)\n", "nci_pca = PCA()\n", - "nci_scores = nci_pca.fit_transform(nci_scaled)\n" + "nci_scores = nci_pca.fit_transform(nci_scaled)" ] }, { @@ -2571,34 +1270,15 @@ "to visualize the data. The observations (cell lines) corresponding to\n", "a given cancer type will be plotted in the same color, so that we can\n", "see to what extent the observations within a cancer type are similar\n", - "to each other. " + "to each other." ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "63b5efe3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.190805Z", - "iopub.status.busy": "2023-07-26T00:00:20.190351Z", - "iopub.status.idle": "2023-07-26T00:00:20.452175Z", - "shell.execute_reply": "2023-07-26T00:00:20.451824Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cancer_types = list(np.unique(nci_labs))\n", "nci_groups = np.array([cancer_types.index(lab)\n", @@ -2617,7 +1297,7 @@ " c=nci_groups,\n", " marker='o',\n", " s=50)\n", - "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');\n" + "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');" ] }, { @@ -2628,10 +1308,7 @@ "On the whole, cell lines corresponding to a single cancer type do tend to\n", "have similar values on the first few principal component score\n", "vectors. This indicates that cell lines from the same cancer type tend\n", - "to have pretty similar gene expression levels.\n", - "\n", - "\n", - " " + "to have pretty similar gene expression levels." ] }, { @@ -2646,29 +1323,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "e20c3cc1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.454082Z", - "iopub.status.busy": "2023-07-26T00:00:20.453955Z", - "iopub.status.idle": "2023-07-26T00:00:20.657428Z", - "shell.execute_reply": "2023-07-26T00:00:20.657082Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", "ax = axes[0]\n", @@ -2683,7 +1341,7 @@ " nci_pca.explained_variance_ratio_.cumsum(),\n", " marker='o');\n", "ax.set_xlabel('Principal Component')\n", - "ax.set_ylabel('Cumulative PVE');\n" + "ax.set_ylabel('Cumulative PVE');" ] }, { @@ -2719,16 +1377,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "622de805", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.659393Z", - "iopub.status.busy": "2023-07-26T00:00:20.659262Z", - "iopub.status.idle": "2023-07-26T00:00:20.662015Z", - "shell.execute_reply": "2023-07-26T00:00:20.661717Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def plot_nci(linkage, ax, cut=-np.inf):\n", @@ -2744,7 +1395,7 @@ " leaf_font_size=10,\n", " **cargs)\n", " ax.set_title('%s Linkage' % linkage)\n", - " return hc\n" + " return hc" ] }, { @@ -2757,34 +1408,15 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "54d40449", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:20.663751Z", - "iopub.status.busy": "2023-07-26T00:00:20.663617Z", - "iopub.status.idle": "2023-07-26T00:00:22.071969Z", - "shell.execute_reply": "2023-07-26T00:00:22.071658Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, axes = plt.subplots(3, 1, figsize=(15,30)) \n", "ax = axes[0]; hc_comp = plot_nci('Complete', ax)\n", "ax = axes[1]; hc_avg = plot_nci('Average', ax)\n", - "ax = axes[2]; hc_sing = plot_nci('Single', ax)\n" + "ax = axes[2]; hc_sing = plot_nci('Single', ax)" ] }, { @@ -2810,184 +1442,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "dc80afc8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:22.073921Z", - "iopub.status.busy": "2023-07-26T00:00:22.073783Z", - "iopub.status.idle": "2023-07-26T00:00:22.084241Z", - "shell.execute_reply": "2023-07-26T00:00:22.083956Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Complete0123
label
BREAST2302
CNS3200
COLON2005
K562A-repro0010
K562B-repro0010
LEUKEMIA0060
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MCF7D-repro0001
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" - ], - "text/plain": [ - "Complete 0 1 2 3\n", - "label \n", - "BREAST 2 3 0 2\n", - "CNS 3 2 0 0\n", - "COLON 2 0 0 5\n", - "K562A-repro 0 0 1 0\n", - "K562B-repro 0 0 1 0\n", - "LEUKEMIA 0 0 6 0\n", - "MCF7A-repro 0 0 0 1\n", - "MCF7D-repro 0 0 0 1\n", - "MELANOMA 8 0 0 0\n", - "NSCLC 8 1 0 0\n", - "OVARIAN 6 0 0 0\n", - "PROSTATE 2 0 0 0\n", - "RENAL 8 1 0 0\n", - "UNKNOWN 1 0 0 0" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "linkage_comp = compute_linkage(hc_comp)\n", "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n", "pd.crosstab(nci_labs['label'],\n", - " pd.Series(comp_cut.reshape(-1), name='Complete'))\n" + " pd.Series(comp_cut.reshape(-1), name='Complete'))" ] }, { @@ -3004,32 +1467,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "40ff59f9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:22.086072Z", - "iopub.status.busy": "2023-07-26T00:00:22.085951Z", - "iopub.status.idle": "2023-07-26T00:00:22.546297Z", - "shell.execute_reply": "2023-07-26T00:00:22.545986Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hc_pca = HClust(n_clusters=None,\n", " distance_threshold=0,\n", @@ -3376,27 +1555,15 @@ "pca_labels = pd.Series(cut_tree(linkage_pca,\n", " n_clusters=4).reshape(-1),\n", " name='Complete-PCA')\n", - "pd.crosstab(nci_labs['label'], pca_labels)\n" + "pd.crosstab(nci_labs['label'], pca_labels)" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch13-multiple-lab.ipynb b/docs/source/labs/Ch13-multiple-lab.ipynb index d759391..5685600 100644 --- a/docs/source/labs/Ch13-multiple-lab.ipynb +++ b/docs/source/labs/Ch13-multiple-lab.ipynb @@ -2,17 +2,15 @@ "cells": [ { "cell_type": "markdown", - "id": "bb70c597", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "e21562bb", + "id": "75b2d75c", "metadata": {}, "source": [ "# Multiple Testing\n", - " " + "\n", + "\n", + " \"Open\n", + "\n", + "" ] }, { @@ -25,23 +23,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1f928b2d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:24.664836Z", - "iopub.status.busy": "2023-07-26T00:00:24.664535Z", - "iopub.status.idle": "2023-07-26T00:00:25.648869Z", - "shell.execute_reply": "2023-07-26T00:00:25.648538Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", - "from ISLP import load_data\n" + "from ISLP import load_data" ] }, { @@ -55,17 +46,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "eb4b32aa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.651065Z", - "iopub.status.busy": "2023-07-26T00:00:25.650806Z", - "iopub.status.idle": "2023-07-26T00:00:25.653087Z", - "shell.execute_reply": "2023-07-26T00:00:25.652803Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "from scipy.stats import \\\n", @@ -76,7 +59,7 @@ "from statsmodels.stats.multicomp import \\\n", " pairwise_tukeyhsd\n", "from statsmodels.stats.multitest import \\\n", - " multipletests as mult_test\n" + " multipletests as mult_test" ] }, { @@ -94,22 +77,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e12ac0cd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.654827Z", - "iopub.status.busy": "2023-07-26T00:00:25.654707Z", - "iopub.status.idle": "2023-07-26T00:00:25.657085Z", - "shell.execute_reply": "2023-07-26T00:00:25.656796Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(12)\n", "X = rng.standard_normal((10, 100))\n", "true_mean = np.array([0.5]*50 + [0]*50)\n", - "X += true_mean[None,:]\n" + "X += true_mean[None,:]" ] }, { @@ -124,31 +100,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "04d0f49e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.658750Z", - "iopub.status.busy": "2023-07-26T00:00:25.658609Z", - "iopub.status.idle": "2023-07-26T00:00:25.662657Z", - "shell.execute_reply": "2023-07-26T00:00:25.662324Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9307442156164141" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "result = ttest_1samp(X[:,0], 0)\n", - "result.pvalue\n" + "result.pvalue" ] }, { @@ -171,17 +129,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d1f0c695", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.664332Z", - "iopub.status.busy": "2023-07-26T00:00:25.664234Z", - "iopub.status.idle": "2023-07-26T00:00:25.684874Z", - "shell.execute_reply": "2023-07-26T00:00:25.684597Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "p_values = np.empty(100)\n", @@ -193,7 +143,7 @@ " 'Do not reject H0'])\n", "truth = pd.Categorical(true_mean == 0,\n", " categories=[True, False],\n", - " ordered=True)\n" + " ordered=True)" ] }, { @@ -207,80 +157,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "7a9594a0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.686565Z", - "iopub.status.busy": "2023-07-26T00:00:25.686471Z", - "iopub.status.idle": "2023-07-26T00:00:25.694731Z", - "shell.execute_reply": "2023-07-26T00:00:25.694405Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "H0 True False\n", - "Decision \n", - "Reject H0 2 40\n", - "Do not reject H0 48 10" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "true_mean = np.array([1]*50 + [0]*50)\n", "X = rng.standard_normal((10, 100))\n", @@ -392,15 +212,7 @@ "pd.crosstab(decision,\n", " truth,\n", " rownames=['Decision'],\n", - " colnames=['H0'])\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4b5d621", - "metadata": {}, - "source": [ - " " + " colnames=['H0'])" ] }, { @@ -419,28 +231,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "369b5bd3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.725141Z", - "iopub.status.busy": "2023-07-26T00:00:25.725014Z", - "iopub.status.idle": "2023-07-26T00:00:25.950143Z", - "shell.execute_reply": "2023-07-26T00:00:25.949764Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "m = np.linspace(1, 501)\n", "fig, ax = plt.subplots()\n", @@ -452,7 +246,7 @@ "ax.set_xlabel('Number of Hypotheses')\n", "ax.set_ylabel('Family-Wise Error Rate')\n", "ax.legend()\n", - "ax.axhline(0.05, c='k', ls='--');\n" + "ax.axhline(0.05, c='k', ls='--');" ] }, { @@ -475,35 +269,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "9ce7a19f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.952085Z", - "iopub.status.busy": "2023-07-26T00:00:25.951946Z", - "iopub.status.idle": "2023-07-26T00:00:25.992310Z", - "shell.execute_reply": "2023-07-26T00:00:25.991986Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00620236, 0.91827115, 0.01160098, 0.6005396 , 0.75578151])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Fund = load_data('Fund')\n", "fund_mini = Fund.iloc[:,:5]\n", "fund_mini_pvals = np.empty(5)\n", "for i in range(5):\n", " fund_mini_pvals[i] = ttest_1samp(fund_mini.iloc[:,i], 0).pvalue\n", - "fund_mini_pvals\n" + "fund_mini_pvals" ] }, { @@ -543,32 +319,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "de6cffed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.994255Z", - "iopub.status.busy": "2023-07-26T00:00:25.994126Z", - "iopub.status.idle": "2023-07-26T00:00:25.996766Z", - "shell.execute_reply": "2023-07-26T00:00:25.996489Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ True, False, False, False, False])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n", - "reject\n" + "reject" ] }, { @@ -582,31 +339,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "0de71500", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:25.998481Z", - "iopub.status.busy": "2023-07-26T00:00:25.998341Z", - "iopub.status.idle": "2023-07-26T00:00:26.000891Z", - "shell.execute_reply": "2023-07-26T00:00:26.000558Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.03101178, 1. , 0.05800491, 1. , 1. ]),\n", - " array([0.03101178, 1. , 0.05800491, 1. , 1. ]))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bonf, np.minimum(fund_mini_pvals * 5, 1)\n" + "metadata": {}, + "outputs": [], + "source": [ + "bonf, np.minimum(fund_mini_pvals * 5, 1)" ] }, { @@ -624,32 +362,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "f7e87bdb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.002751Z", - "iopub.status.busy": "2023-07-26T00:00:26.002618Z", - "iopub.status.idle": "2023-07-26T00:00:26.041360Z", - "shell.execute_reply": "2023-07-26T00:00:26.041041Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ True, False, True, False, False]),\n", - " array([0.03101178, 1. , 0.04640393, 1. , 1. ]))" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]\n" + "metadata": {}, + "outputs": [], + "source": [ + "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]" ] }, { @@ -663,36 +381,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e88be376", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.043177Z", - "iopub.status.busy": "2023-07-26T00:00:26.043041Z", - "iopub.status.idle": "2023-07-26T00:00:26.046129Z", - "shell.execute_reply": "2023-07-26T00:00:26.045861Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Manager1 3.0\n", - "Manager2 -0.1\n", - "Manager3 2.8\n", - "Manager4 0.5\n", - "Manager5 0.3\n", - "dtype: float64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fund_mini.mean()\n" + "metadata": {}, + "outputs": [], + "source": [ + "fund_mini.mean()" ] }, { @@ -707,31 +401,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "41149af6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.047795Z", - "iopub.status.busy": "2023-07-26T00:00:26.047695Z", - "iopub.status.idle": "2023-07-26T00:00:26.050538Z", - "shell.execute_reply": "2023-07-26T00:00:26.050256Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.038391072368079586" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ttest_rel(fund_mini['Manager1'],\n", - " fund_mini['Manager2']).pvalue\n" + " fund_mini['Manager2']).pvalue" ] }, { @@ -759,45 +435,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "61aabda7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.052147Z", - "iopub.status.busy": "2023-07-26T00:00:26.052048Z", - "iopub.status.idle": "2023-07-26T00:00:26.543846Z", - "shell.execute_reply": "2023-07-26T00:00:26.543509Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n", - "===================================================\n", - "group1 group2 meandiff p-adj lower upper reject\n", - "---------------------------------------------------\n", - " 1 2 -3.1 0.1862 -6.9865 0.7865 False\n", - " 1 3 -0.2 0.9999 -4.0865 3.6865 False\n", - " 1 4 -2.5 0.3948 -6.3865 1.3865 False\n", - " 1 5 -2.7 0.3152 -6.5865 1.1865 False\n", - " 2 3 2.9 0.2453 -0.9865 6.7865 False\n", - " 2 4 0.6 0.9932 -3.2865 4.4865 False\n", - " 2 5 0.4 0.9986 -3.4865 4.2865 False\n", - " 3 4 -2.3 0.482 -6.1865 1.5865 False\n", - " 3 5 -2.5 0.3948 -6.3865 1.3865 False\n", - " 4 5 -0.2 0.9999 -4.0865 3.6865 False\n", - "---------------------------------------------------\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n", "managers = np.hstack([[i+1]*50 for i in range(5)])\n", "tukey = pairwise_tukeyhsd(returns, managers)\n", - "print(tukey.summary())\n" + "print(tukey.summary())" ] }, { @@ -819,31 +465,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "cbcad4de", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.545878Z", - "iopub.status.busy": "2023-07-26T00:00:26.545735Z", - "iopub.status.idle": "2023-07-26T00:00:26.635976Z", - "shell.execute_reply": "2023-07-26T00:00:26.635663Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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LbqdSpUq6/fbblZycLEn68ccfdezYMT3zzDMyxuif//ynpLNXX1q0aOHer0WLFsnpdKpbt24e223Tpo0CAwO1bt06SWevOJw8eVJDhw71aOfj46O2bdu6253r/DEhUVFRhZ5P5/v999+1cOFCDR482P3adO7cuUym+V29erVCQkIUEhKiVq1aadGiRRoxYoR7zNi55/Fvv/2mo0eP6rbbbpOki74m0tnxS+d3g8o/9suWLbvg+8D5jh8/LmOMqlevXux1Sis1NVUdO3bUmTNnlJycrAYNGrgfW7RokZo2baomTZp4nAv5Y1HyzwWn06l+/frpo48+cl+VcrlcWrhwofr376+AgACPbebvV3mNYwSuZIQWwEtCQkLUtWtXzZ8/X0lJSXK5XIqLi/N2WZKkRo0aFfitl+uvv16SipxCdPfu3ZKkUaNGuT8Q5d8++OAD5ebmKjMzs8htBgcH69SpU8WqL//DdePGjQs81qRJE/fj+fz8/ArMIuR0OhUWFlZgP51OZ6HjGv7whz943A8MDFRoaKjH8dixY4cGDBggp9Op4OBghYSEuLuDnL/v9evXL9ag+6efflqBgYG69dZb9Yc//EHjxo3z6FKTkZGhX375pdBj0bRpU+Xl5RWYavjaa6/1uJ//wSl/vzt27Ki77rpLU6dOVa1atdSvXz/NmTPHo+/+hQQEBKhr167q2rWr7rjjDj366KP6xz/+oV27dmn69Onuuk+ePKnZs2cXOF/yw9yRI0cuuq2oqCht2bJFv/76q1JSUhQaGqqbbrpJrVq1cncR++qrrxQVFeVeZ/fu3crMzFTt2rULbDs7O9u93fxzunPnzgXarV69ukB9hZ1n1atXL/R8Ot/q1auVkZGhW2+9VXv27NGePXu0d+9excTE6KOPPirRB/2Ladu2rdasWaO1a9fqm2++0dGjR/X3v//dHVaOHz+uRx99VHXq1JG/v79CQkLUsGFDSQXP48Lktz3X4MGD1b59e913332qU6eOhgwZoo8//rjY+2XKYczHiBEjdOTIEW3YsEH169f3eGz37t3asWNHgfMg/33x3HNh5MiR2r9/v/v8W7t2rQ4fPlxo19f8/Srub2sBVzNmDwO8aNiwYbr//vt16NAh9ezZ0/1t5PmK+g+tJAN8y1r+h49XXnmlyGlpAwMDi1y/SZMm+u6773TgwAGFh4df1tqKmuGoqOWl+YB08uRJdezYUcHBwXruuecUGRkpPz8/bd26VU8//XSBD2fFnU2padOm2rVrl5YvX67PP/9cn3zyid59911NmjRJU6dOLXGd0sX32+FwaPHixdq4caM+/fRTrVq1Svfee69ee+01bdy48YKvY1HatGkjp9PpviqSfzzuvvtujRo1qtB1WrZsedHn7dChg86cOaN//vOfSklJcYeTqKgopaSkaOfOncrIyPAILXl5eRe8gpEfPPJrnDt3rnsigXOdP6nApczEl1/LuVf+zrVhw4Yixz2VVK1atdS1a9ciHx80aJC++eYbPfnkk2rdurUCAwOVl5enO+64o1gho7Bz29/fX8nJyVq3bp0+++wzff7551q4cKE6d+6s1atXF3nsatSoIYfDUazgV1xFvW/Gxsbq73//uxISEtxTdufLy8vTDTfcoNdff73Qdc99z+rRo4fq1KmjDz/8UNHR0frwww9Vt27dQo95/n7VqlWrtLsDXDUILYAXDRgwQGPHjtXGjRu1cOHCItvlfxN+/gDz868oFKWk3+Lt2bNHxhiP9fJ/H6Wo6VfzZ/YJDg6+4AeiovTt21cfffSRPvzwQ02cOPGCbfO7bezatavAVKG7du3y6NZxuezevdvjQ2N2drbS09PVq1cvSWdnuTp27JiSkpI8BnwXNqi/pAICAjR48GANHjxYp0+fVmxsrKZNm6aJEycqJCREVatWdc9wdK6dO3eqUqVKpQ6Bt912m2677TZNmzZN8+fP1/Dhw7VgwQKPrnYl4XK5lJ2dLelsMAgKCpLL5bro+XKh8/fWW29VlSpVlJKSopSUFD355JOSpOjoaL3//vv64osv3PfzRUZGau3atWrfvv0Fw2P+OV27du1SndPFlZOTo2XLlmnw4MGFXm195JFHNG/evMsWWi7kxIkT+uKLLzR16lRNmjTJvTz/qtOlqFSpkrp06aIuXbro9ddf14svvqg///nPWrduXZHHt3LlyoqMjCzV31H16tULvGeePn1a6enphbZ/+OGH1ahRI02aNElOp9NjAo3IyEh9//336tKly0XfT318fDRs2DAlJibqpZde0tKlS3X//fcXGszy96tp06Yl3Dvg6kP3MMCLAgMDNXPmTE2ZMkV9+/Ytsl2DBg3k4+Pj/pY637vvvlus7QQEBBT4z/tCDh486PEL2VlZWfr73/+u1q1bF/qNs3T2m/TIyEi9+uqr7g+m5zp/St3zxcXF6YYbbtC0adPcYxHOderUKf35z3+WJN18882qXbu23nvvPY8uSytXrtSPP/6o3r17F2s/S2L27Nke4ztmzpyp33//XT179pT0v2/Zz71Kc/r06WK/RkU5f5rZKlWqqFmzZjLG6MyZM/Lx8VH37t21bNkyj65qhw8fdv+AaXBwcIm2eeLEiQJXm/KvnhW3i9j51q1bp+zsbPd0sD4+Prrrrrv0ySefFJhpSfI8X/LHARR2Dvv5+emWW27RRx99pP3793tcafn111/15ptvKjIyUqGhoe51Bg0aJJfLpeeff77A8/3+++/u7fTo0UPBwcF68cUXCx3bc7FzuriWLFminJwcjRs3TnFxcQVuffr00SeffFLqY18ShZ3HktyzuZXW8ePHCywr7jnVrl07bd68ucTbjIyMLPCeOXv27AteoX722Wf1xBNPaOLEiZo5c6Z7+aBBg5SWlqb333+/wDq//vqrcnJyPJaNGDFCJ06c0NixY5WdnV3k78ds2bJFTqdTzZs3L8muAVclrrQAXlZU15hzOZ1ODRw4UG+99ZYcDociIyO1fPnyYvX5l84GirVr1+r1119XvXr11LBhQ7Vt27bI9tdff73GjBmjTZs2qU6dOvrrX/+qw4cPa86cOUWuU6lSJX3wwQfq2bOnmjdvrtGjR6t+/fpKS0vTunXrFBwcrE8//bTI9a+55holJSWpa9euio6O1qBBg9S+fXtdc8012rFjh+bPn6/q1atr2rRpuuaaa/TSSy9p9OjR6tixo4YOHarDhw8rISFBERERevzxx4t1XEri9OnT6tKliwYNGqRdu3bp3XffVYcOHXTnnXdKkm6//XZVr15do0aN0iOPPCKHw6G5c+decl/87t27q27dumrfvr3q1KmjH3/8UW+//bZ69+7tnrjghRdecP8Gxv/93/+pcuXKmjVrlnJzc/Xyyy+XeJt/+9vf9O6772rAgAGKjIzUqVOn9P777ys4ONh9ZelCMjMz9eGHH0o6GwJ27dqlmTNnyt/f3+Pb6+nTp2vdunVq27at7r//fjVr1kzHjx/X1q1btXbtWvcH3cjISFWrVk3vvfeegoKCFBAQoLZt27rHTkRFRWn69OlyOp264YYbJJ29OtK4cWPt2rWrwG9ydOzYUWPHjlV8fLy2bdum7t2765prrtHu3bu1aNEiJSQkKC4uTsHBwZo5c6ZGjBihm266SUOGDFFISIj279+vzz77TO3bt9fbb79d4uN7vnnz5qlmzZq6/fbbC338zjvv1Pvvv6/PPvtMsbGxl7y9CwkODlZ0dLRefvllnTlzRvXr19fq1asv+Yrhc889p+TkZPXu3VsNGjTQkSNH9O677yosLKzQ32U6V79+/TR37lz99NNP7jEkxXHffffpwQcf1F133aVu3brp+++/16pVqy7aFeuVV15RZmamxo0bp6CgIN19990aMWKEPv74Yz344INat26d2rdvL5fLpZ07d+rjjz92/y5NvhtvvFEtWrRwD+AvasrmNWvWqG/fvoxpAYrDCzOWAVetc6fWvJDzpzw2xpiMjAxz1113mapVq5rq1aubsWPHmh9++KFYUx7v3LnTREdHG39/fyPJPd1nUVOW9u7d26xatcq0bNnS+Pr6miZNmphFixZ5PGdR0zB/9913JjY21tSsWdP4+vqaBg0amEGDBpkvvviiWMfoxIkTZtKkSeaGG24wVatWNX5+fqZFixZm4sSJJj093aPtwoULzY033mh8fX1NjRo1zPDhw01qaqpHm1GjRpmAgIAC2+nYsWOhUwmff+zzj9GGDRvMAw88YKpXr24CAwPN8OHDzbFjxzzW/frrr81tt91m/P39Tb169cxTTz1lVq1aVeA4FbXt/MfOnbp21qxZJjo62n08IyMjzZNPPmkyMzM91tu6davp0aOHCQwMNFWrVjUxMTHmm2++8WhT1Pl3/mu5detWM3ToUHPttdcaX19fU7t2bdOnTx+zefPmQms+v36dM9Wxw+EwNWrUMHfeeafZsmVLgfaHDx8248aNM+Hh4eaaa64xdevWNV26dDGzZ8/2aLds2TLTrFkzU7ly5QLn/GeffWYkmZ49e3qsc9999xlJ5i9/+Uuhtc6ePdu0adPG+Pv7m6CgIHPDDTeYp556yhw8eLDA8enRo4dxOp3Gz8/PREZGmnvuucfjeBR1nhX293j+/leuXNmMGDGiyDa//PKLqVq1qhkwYIAx5tKnPD7/veV8qampZsCAAaZatWrG6XSagQMHmoMHDxY5HXhh7x/n++KLL0y/fv1MvXr1TJUqVUy9evXM0KFDzU8//XTRmnNzc02tWrXM888/X2SbwqY8drlc5umnnza1atUyVatWNT169DB79uy54JTH5647dOhQU7lyZbN06VJjzNmp1l966SXTvHlz4+vra6pXr27atGljpk6dWuDv0Zj/TYt+7nTr5/rxxx+NJLN27dqLHgMAxjiM4WdYAfxPRESEWrRooeXLl3u7FCskJiZq9OjR2rRpk8c3qQDKz/PPP685c+Zo9+7dlzThQXlKSEjQ448/rn379hWYsU+SHnvsMSUnJ2vLli1caQGKgTEtAADAao8//riys7O1YMECb5dSLMYY/eUvf1HHjh0LDSzHjh3TBx98oBdeeIHAAhQTY1oAAIDVAgMDiz2Gz5tycnL0j3/8Q+vWrdP27du1bNmyQtvVrFmz0AlLABSN0AIAAHAZZGRkaNiwYapWrZr+9Kc/uSfqAHDpGNMCAAAAwGqMaQEAAABgNUILAAAAAKuV+ZiW3Nxcj1+7zcvL0/Hjx1WzZk1mzAAAAACuYsYYnTp1SvXq1VOlSkVfTynz0BIfH6+pU6eW9WYAAAAAXKEOHDigsLCwIh8v84H4519pyczM1LXXXqsDBw4oODi4LDcNAAAAwGJZWVkKDw/XyZMn5XQ6i2xX5ldafH195evrW2B5cHAwoQUAAADARYeNMBAfAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFitsrcLAIDLxeVyKSUlRenp6QoNDVVUVJR8fHy8XRYAALhEJbrSMmXKFDkcDo9bkyZNyqo2ACi2pKQkRUREKCYmRsOGDVNMTIwiIiKUlJTk7dIAAMAlKvGVlubNm2vt2rX/e4LKXKwB4F1JSUmKi4uTMcZjeVpamuLi4rR48WLFxsZ6qToAAHCpSpw4KleurLp165ZFLfCinJwcb5cAlIrL5dIjjzxSILBIkjFGDodDjz76qLp27UpXMVxxAgICvF0CAFihxKFl9+7dqlevnvz8/NSuXTvFx8fr2muvLbJ9bm6ucnNz3fezsrJKVynKVGBgoLdLAMqEMUapqalyOp3eLgUoscLCOABcjUo0pqVt27ZKTEzU559/rpkzZ2rv3r2KiorSqVOnilwnPj5eTqfTfQsPD7/kogEAAABcPRzmEr7GOXnypBo0aKDXX39dY8aMKbRNYVdawsPDlZmZqeDg4NJuGpcZ3cNwpUpOTlavXr0u2m7FihWKjo4uh4qAy4fuYQAquqysLDmdzotmg0saRV+tWjVdf/312rNnT5FtfH195evreymbQTngP0Zcqbp3766wsDClpaUV2pXG4XAoLCxM3bt3Z0wLAABXqEv6ccns7Gz997//VWho6OWqBwBKxMfHRwkJCZLOBpRz5d+fMWMGgQUAgCtYiULLE088oQ0bNmjfvn365ptvNGDAAPn4+Gjo0KFlVR8AXFRsbKwWL16s+vXreywPCwtjumMAACqAEnUPS01N1dChQ3Xs2DGFhISoQ4cO2rhxo0JCQsqqPgAoltjYWPXr108pKSlKT09XaGiooqKiuMICAEAFcEkD8UujuINtAAAAAFRsxc0GlzSmBQAAAADKGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsFplbxcAALZxuVxKSUlRenq6QkNDFRUVJR8fH2+XBQDAVeuSrrRMnz5dDodDjz322GUqBwC8KykpSREREYqJidGwYcMUExOjiIgIJSUlebs0AACuWqUOLZs2bdKsWbPUsmXLy1kPAHhNUlKS4uLilJqa6rE8LS1NcXFxBBcAALykVN3DsrOzNXz4cL3//vt64YUXLndNuMLl5OR4uwSgxFwulx555BEZYwo8ZoyRw+HQo48+qq5du9JVDFekgIAAb5cAAKVWqtAybtw49e7dW127dr1oaMnNzVVubq77flZWVmk2iStIYGCgt0sALjtjjFJTU+V0Or1dClAqhQVyALhSlDi0LFiwQFu3btWmTZuK1T4+Pl5Tp04tcWEAAAAAIJUwtBw4cECPPvqo1qxZIz8/v2KtM3HiRE2YMMF9PysrS+Hh4SWrEleU7Oxsb5cAlFhycrJ69ep10XYrVqxQdHR0OVQEAADyOUwJrhcvXbpUAwYM8OjP7XK55HA4VKlSJeXm5l60r3dWVpacTqcyMzMVHBxc+soB4DJyuVyKiIhQWlpaod1oHA6HwsLCtHfvXsa0AABwmRQ3G5Ro9rAuXbpo+/bt2rZtm/t28803a/jw4dq2bRv/kQO4Yvn4+CghIUHS2YByrvz7M2bM4H0OAAAvKFFoCQoKUosWLTxuAQEBqlmzplq0aFFWNQJAuYiNjdXixYtVv359j+VhYWFavHixYmNjvVQZAABXt1LNHgYAFVVsbKz69eunlJQUpaenKzQ0VFFRUVxhAQDAi0o0puVyYEwLAAAAAKmMxrQAAAAAQHkjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWq+ztAgAAQMXmcrmUkpKi9PR0hYaGKioqSj4+Pt4uC8AVpERXWmbOnKmWLVsqODhYwcHBateunVauXFlWtQEAgCtcUlKSIiIiFBMTo2HDhikmJkYRERFKSkrydmkAriAlCi1hYWGaPn26tmzZos2bN6tz587q16+fduzYUVb1AQCAK1RSUpLi4uKUmprqsTwtLU1xcXEEFwDF5jDGmEt5gho1auiVV17RmDFjitU+KytLTqdTmZmZCg4OvpRNA0CFl5OT4+0SgFJxuVxq1qyZ0tLSCn3c4XCofv362rFjB13FcEUKCAjwdgkVQnGzQanHtLhcLi1atEg5OTlq165dke1yc3OVm5vrURgAoHgCAwO9XQJQJowxSk1NldPp9HYpQKlc4vf+KKESzx62fft2BQYGytfXVw8++KCWLFmiZs2aFdk+Pj5eTqfTfQsPD7+kggEAAABcXUrcPez06dPav3+/MjMztXjxYn3wwQfasGFDkcGlsCst4eHhdA8DgGKgexiuVMnJyerVq9dF261YsULR0dHlUBFwedE97PIobvewSx7T0rVrV0VGRmrWrFmXtTAAAHDlcrlcioiIUFpaWqHdaBwOh8LCwrR3717GtABXseJmg0v+ccm8vDyPKykAAAA+Pj5KSEiQdDagnCv//owZMwgsAIqlRKFl4sSJSk5O1r59+7R9+3ZNnDhR69ev1/Dhw8uqPgAAcIWKjY3V4sWLVb9+fY/lYWFhWrx4sWJjY71UGYArTYlmDzty5IhGjhyp9PR0OZ1OtWzZUqtWrVK3bt3Kqj4AAHAFi42NVb9+/ZSSkqL09HSFhoYqKiqKKywASuSSx7SUFGNaAAAAAEjlOKYFAAAAAMoSoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwWmVvFwDgyuRyuZSSkqL09HSFhoYqKipKPj4+3i4LAABUQCW60hIfH69bbrlFQUFBql27tvr3769du3aVVW0ALJWUlKSIiAjFxMRo2LBhiomJUUREhJKSkrxdGgAAqIBKFFo2bNigcePGaePGjVqzZo3OnDmj7t27Kycnp6zqA2CZpKQkxcXFKTU11WN5Wlqa4uLiCC4AAOCycxhjTGlXzsjIUO3atbVhwwZFR0cXa52srCw5nU5lZmYqODi4tJu+ohHycKVyuVxq1qyZ0tLSCn3c4XCofv362rFjB13FcMUJCAjwdgkAcNUpbja4pDEtmZmZkqQaNWoU2SY3N1e5ubkehV3tAgMDvV0CUCaMMUpNTZXT6fR2KUCJXcJ3eACAMlbq2cPy8vL02GOPqX379mrRokWR7eLj4+V0Ot238PDw0m4SAAAAwFWo1N3DHnroIa1cuVJfffWVwsLCimxX2JWW8PBwuocBV6Dk5GT16tXrou1WrFhR7C6jgC3oHgYA5a9Mu4eNHz9ey5cvV3Jy8gUDiyT5+vrK19e3NJupsPiPEVeq7t27KywsTGlpaYV2pXE4HAoLC1P37t0Z0wIAAC6bEnUPM8Zo/PjxWrJkib788ks1bNiwrOoCYCEfHx8lJCRIOhtQzpV/f8aMGQQWAABwWZUotIwbN04ffvih5s+fr6CgIB06dEiHDh3Sr7/+Wlb1AbBMbGysFi9erPr163ssDwsL0+LFixUbG+ulygAAQEVVojEt53+zmm/OnDm65557ivUcTHkMVAwul0spKSlKT09XaGiooqKiuMICAABKpEzGtDAdJIB8Pj4+6tSpk7fLAAAAV4FST3kMAAAAAOWB0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYrbK3CwAAACgOl8ullJQUpaenKzQ0VFFRUfLx8fF2WQDKQYmvtCQnJ6tv376qV6+eHA6Hli5dWgZlAQAA/E9SUpIiIiIUExOjYcOGKSYmRhEREUpKSvJ2aQDKQYlDS05Ojlq1aqV33nmnLOoBAADwkJSUpLi4OKWmpnosT0tLU1xcHMEFuAo4jDGm1Cs7HFqyZIn69+9f7HWysrLkdDqVmZmp4ODg0m4aAFBCOTk53i4BKDGXy6VmzZopLS2t0McdDofq16+vHTt20FUMV6SAgABvl+BVxc0GZT6mJTc3V7m5uR6FAQDKX2BgoLdLAC47Y4xSU1PldDq9XQpQKpdw/eCqUuazh8XHx8vpdLpv4eHhZb1JAAAAABVImV9pmThxoiZMmOC+n5WVRXABAC/Izs72dglAiSUnJ6tXr14XbbdixQpFR0eXQ0UAvKHMQ4uvr698fX3LejMAgIu42vtN48rUvXt3hYWFKS0trdBuNA6HQ2FhYerevTtjWoAKjB+XBAAA1vLx8VFCQoKkswHlXPn3Z8yYQWABKrgSh5bs7Gxt27ZN27ZtkyTt3btX27Zt0/79+y93bQAAAIqNjdXixYtVv359j+VhYWFavHixYmNjvVQZgPJS4imP169fr5iYmALLR40apcTExIuuz5THAACgNFwul1JSUpSenq7Q0FBFRUVxhQW4whU3G1zS77SUBqEFAAAAgFT8bMCYFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFYjtAAAAACwGqEFAAAAgNUILQAAAACsRmgBAAAAYDVCCwAAAACrEVoAAAAAWI3QAgAAAMBqhBYAAAAAViO0AAAAALAaoQUAAACA1QgtAAAAAKxGaAEAAABgtcplvYHc3Fzl5ua672dmZkqSsrKyynrTAAAAACyWnwmMMRdsV+ahJT4+XlOnTi2wPDw8vKw3DQAAAOAKcOrUKTmdziIfd5iLxZpLdP6Vlry8PB0/flw1a9aUw+Eoy01fEbKyshQeHq4DBw4oODjY2+VcFTjm5YvjXb443uWPY16+ON7lj2Nevq62422M0alTp1SvXj1VqlT0yJUyv9Li6+srX19fj2XVqlUr681ecYKDg6+KE9MmHPPyxfEuXxzv8scxL18c7/LHMS9fV9PxvtAVlnwMxAcAAABgNUILAAAAAKsRWrzM19dXkydPLtCFDmWHY16+ON7li+Nd/jjm5YvjXf445uWL4124Mh+IDwAAAACXgistAAAAAKxGaAEAAABgNUILAAAAAKsRWgAAAABYjdBiiX379mnMmDFq2LCh/P39FRkZqcmTJ+v06dPeLq1CmzZtmm6//XZVrVqVHz0tI++8844iIiLk5+entm3b6l//+pe3S6qwkpOT1bdvX9WrV08Oh0NLly71dkkVWnx8vG655RYFBQWpdu3a6t+/v3bt2uXtsiqsmTNnqmXLlu4f3GvXrp1Wrlzp7bKuGtOnT5fD4dBjjz3m7VIqrClTpsjhcHjcmjRp4u2yrEFoscTOnTuVl5enWbNmaceOHXrjjTf03nvv6U9/+pO3S6vQTp8+rYEDB+qhhx7ydikV0sKFCzVhwgRNnjxZW7duVatWrdSjRw8dOXLE26VVSDk5OWrVqpXeeecdb5dyVdiwYYPGjRunjRs3as2aNTpz5oy6d++unJwcb5dWIYWFhWn69OnasmWLNm/erM6dO6tfv37asWOHt0ur8DZt2qRZs2apZcuW3i6lwmvevLnS09Pdt6+++srbJVmDKY8t9sorr2jmzJn6+eefvV1KhZeYmKjHHntMJ0+e9HYpFUrbtm11yy236O2335Yk5eXlKTw8XA8//LCeeeYZL1dXsTkcDi1ZskT9+/f3dilXjYyMDNWuXVsbNmxQdHS0t8u5KtSoUUOvvPKKxowZ4+1SKqzs7GzddNNNevfdd/XCCy+odevWmjFjhrfLqpCmTJmipUuXatu2bd4uxUpcabFYZmamatSo4e0ygFI5ffq0tmzZoq5du7qXVapUSV27dtU///lPL1YGlI3MzExJ4n27HLhcLi1YsEA5OTlq166dt8up0MaNG6fevXt7vJej7OzevVv16tXTddddp+HDh2v//v3eLskalb1dAAq3Z88evfXWW3r11Ve9XQpQKkePHpXL5VKdOnU8ltepU0c7d+70UlVA2cjLy9Njjz2m9u3bq0WLFt4up8Lavn272rVrp99++02BgYFasmSJmjVr5u2yKqwFCxZo69at2rRpk7dLuSq0bdtWiYmJaty4sdLT0zV16lRFRUXphx9+UFBQkLfL8zqutJSxZ555psCgqvNv53+AS0tL0x133KGBAwfq/vvv91LlV67SHHMAuBTjxo3TDz/8oAULFni7lAqtcePG2rZtm7799ls99NBDGjVqlP7zn/94u6wK6cCBA3r00Uc1b948+fn5ebucq0LPnj01cOBAtWzZUj169NCKFSt08uRJffzxx94uzQpcaSljf/zjH3XPPfdcsM11113n/vfBgwcVExOj22+/XbNnzy7j6iqmkh5zlI1atWrJx8dHhw8f9lh++PBh1a1b10tVAZff+PHjtXz5ciUnJyssLMzb5VRoVapUUaNGjSRJbdq00aZNm5SQkKBZs2Z5ubKKZ8uWLTpy5Ihuuukm9zKXy6Xk5GS9/fbbys3NlY+PjxcrrPiqVaum66+/Xnv27PF2KVYgtJSxkJAQhYSEFKttWlqaYmJi1KZNG82ZM0eVKnEhrDRKcsxRdqpUqaI2bdroiy++cA8Gz8vL0xdffKHx48d7tzjgMjDG6OGHH9aSJUu0fv16NWzY0NslXXXy8vKUm5vr7TIqpC5dumj79u0ey0aPHq0mTZro6aefJrCUg+zsbP33v//ViBEjvF2KFQgtlkhLS1OnTp3UoEEDvfrqq8rIyHA/xrfSZWf//v06fvy49u/fL5fL5Z6xo1GjRgoMDPRucRXAhAkTNGrUKN1888269dZbNWPGDOXk5Gj06NHeLq1Cys7O9vhGbu/evdq2bZtq1Kiha6+91ouVVUzjxo3T/PnztWzZMgUFBenQoUOSJKfTKX9/fy9XV/FMnDhRPXv21LXXXqtTp05p/vz5Wr9+vVatWuXt0iqkoKCgAuOzAgICVLNmTcZtlZEnnnhCffv2VYMGDXTw4EFNnjxZPj4+Gjp0qLdLswKhxRJr1qzRnj17tGfPngLdC5iVuuxMmjRJf/vb39z3b7zxRknSunXr1KlTJy9VVXEMHjxYGRkZmjRpkg4dOqTWrVvr888/LzA4H5fH5s2bFRMT474/YcIESdKoUaOUmJjopaoqrpkzZ0pSgfeKOXPmXLSLKkruyJEjGjlypNLT0+V0OtWyZUutWrVK3bp183ZpwGWRmpqqoUOH6tixYwoJCVGHDh20ceNGeo/8//idFgAAAABWY9AEAAAAAKsRWgAAAABYjdACAAAAwGqEFgAAAABWI7QAAAAAsBqhBQAAAIDVCC0AAAAArEZoAQAAAGA1QgsAAAAAqxFaAAAAAFiN0AIAAADAaoQWAAAAAFb7/wAKpcWWsWpjKwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", - "tukey.plot_simultaneous(ax=ax);\n" + "tukey.plot_simultaneous(ax=ax);" ] }, { @@ -860,21 +488,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "b5842190", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:26.637968Z", - "iopub.status.busy": "2023-07-26T00:00:26.637827Z", - "iopub.status.idle": "2023-07-26T00:00:27.041757Z", - "shell.execute_reply": "2023-07-26T00:00:27.041385Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "fund_pvalues = np.empty(2000)\n", "for i, manager in enumerate(Fund.columns):\n", - " fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue\n" + " fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue" ] }, { @@ -889,32 +510,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "7c9d8bed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.043624Z", - "iopub.status.busy": "2023-07-26T00:00:27.043492Z", - "iopub.status.idle": "2023-07-26T00:00:27.046326Z", - "shell.execute_reply": "2023-07-26T00:00:27.046047Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.08988921, 0.991491 , 0.12211561, 0.92342997, 0.95603587,\n", - " 0.07513802, 0.0767015 , 0.07513802, 0.07513802, 0.07513802])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n", - "fund_qvalues[:10]\n" + "fund_qvalues[:10]" ] }, { @@ -934,31 +536,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "bfa39f7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.048023Z", - "iopub.status.busy": "2023-07-26T00:00:27.047902Z", - "iopub.status.idle": "2023-07-26T00:00:27.050271Z", - "shell.execute_reply": "2023-07-26T00:00:27.049978Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "146" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(fund_qvalues <= 0.1).sum()\n" + "metadata": {}, + "outputs": [], + "source": [ + "(fund_qvalues <= 0.1).sum()" ] }, { @@ -978,31 +561,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "70b69b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.051940Z", - "iopub.status.busy": "2023-07-26T00:00:27.051831Z", - "iopub.status.idle": "2023-07-26T00:00:27.054171Z", - "shell.execute_reply": "2023-07-26T00:00:27.053894Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(fund_pvalues <= 0.1 / 2000).sum()\n" + "metadata": {}, + "outputs": [], + "source": [ + "(fund_pvalues <= 0.1 / 2000).sum()" ] }, { @@ -1028,16 +592,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "4c0ddea1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.055856Z", - "iopub.status.busy": "2023-07-26T00:00:27.055741Z", - "iopub.status.idle": "2023-07-26T00:00:27.058467Z", - "shell.execute_reply": "2023-07-26T00:00:27.058182Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "sorted_ = np.sort(fund_pvalues)\n", @@ -1049,7 +606,7 @@ " sorted_set_ = np.arange(sorted_set_.max())\n", "else:\n", " selected_ = []\n", - " sorted_set_ = []\n" + " sorted_set_ = []" ] }, { @@ -1062,29 +619,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "0314eac9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.060132Z", - "iopub.status.busy": "2023-07-26T00:00:27.060006Z", - "iopub.status.idle": "2023-07-26T00:00:27.320810Z", - "shell.execute_reply": "2023-07-26T00:00:27.320465Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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VqxdiYmLQvXt3A1elmUSpLrZZuIfnCKWlpSE/P58ryIiITEj7hXtxr0xRbR+eKq879+/fh6+vLzIzM1XXHnvsMaxatUrtpon6VFBQACcnJ42/v416jpCYOEeIiMh0DY/7RWMI4rwg3bK3t0dkZCQAoEGDBli+fDkuXbqEF198UfR5QNVhECIiIrOSnJWH5Oz8avtwqXztXL9+HV9//bXa9hEjRmDFihVIS0vD3LlzUa9ePQNWVzt8NKaBtkNrRERkHFrP3V3tERoAH4nV1N27dxEVFYUVK1agpKQE586dg6+vr9hlVYuPxoiIyOK0DKs+BEnAEFQTSqUSW7duRbt27bBo0SLcvXsXcrkcoaGhYpemMwxCasTFxcHX19coZ7gTEVFlyVl5aBm2u9o+EgCZDEFaO3nyJHr16oWxY8dWOXczPj4ee/bsEaky3WIQUoOTpYmITMOKvZcw/JNfNfb7gZOjtZKdnY1XX30VPXr0EDybUyqVYvLkyejatasI1eme6PsIERER1VZyVh7WHhE+wuFhPq4NODlag+LiYkRGRmLVqlW4d++eYJ/+/fsjOjoafn5+Bq5OfxiEiIjIZI3+VPNIkI0U2D+zjwGqMU0KhQJbtmxBWFgYrl27JtinTZs2iIqKwpAhQ4x6KXxtMAgREZFJitx3GeUaVoe5NrTFyfkDDFOQCfr1118xY8YMtdNAnJycsGjRIrzzzjuwtbU1cHWGwTlCanCyNBGRcYs7dLXadp9m9RmCNFi/fr1gCJJKpXj77beRnp4OmUxmtiEI4D5CGnEfISIi4+M9bzeq2zh65ciOGNPdy3AFmagbN27Ax8cHRUVFqmsDBgxAdHQ0OnToIGJldcd9hIiIyCwNj/ul2hD07jNtGIK01Lx5c8ybNw8A4OPjg127dmHfvn0mH4JqgnOEiIjIZGhzfEbfts0MVI1pOHbsGG7evInRo0cLts+cORPOzs6YOHEibGxsDFyd+DgiREREJmPZ7kvVto/o7M5l8v/KzMzE6NGj0bt3b7z55pvIzc0V7Gdvb48333zTIkMQwCBEREQm5PLNArVtXTydED2mswGrMU4FBQUICwtDu3bt8P333wMA8vLysGTJEpErM04MQmpw1RgRkXFJzspDUYlcsK2hnRV+mPqUgSsyLnK5HOvWrYO3tzdWrlyJ0tLSSu2ffPIJ0tLSRKrOeDEIqcEjNoiIjEvcoQy1beeXDDJgJcbn0KFD6Nq1K9544w38/fffVdqdnZ0RGxuLxx9/XITqjBsnSxMRkUk4mlb1FzwAPO7iYOBKjEdGRgbee+897Ny5U7Dd2toa06ZNw8KFC9G4MedOCWEQIiIio7ctKQulwk/F8LhLfcMWYwTy8/OxdOlSrF69GmVlZYJ9hgwZgg8//BA+Pj4Grs60MAgREZHR++HMX2rbpvb3NmAl4lIoFPjf//6HhQsX4vbt24J9OnTogOjoaAwYwF21tcE5QkREZPRO/ZEneL1JfRuLWi4vkUiwZcsWwRDk4uKCtWvXIjk5mSGoBhiEiIjIqEXuuwy5msOggtq7GrYYkUkkEsTExFQ6Ad7GxgazZs1Ceno63nzzTVhb82FPTTAIERGRUTtyRXiSNAC8FGB5R2l07doVISEhAIBhw4YhNTUVkZGRaNSokbiFmSgGITW4jxARkXG4cL1Q8Lqbo51ZPhYrLy9HXFwcEhIS1PZZtmwZEhISsGPHDrRp08aA1Zkfnj6vAU+fJyIST+fwfci7Wy7Y9u4zbTBzQFsDV6Rf+/btg0wmQ2pqKtq3b49z587xUVct8fR5IiIyaduSstSGIMC8Dle9dOkSnn32WQwaNAipqamqa5999pnIlZk/BiEiIjJKy3anqm3zcW1gFo/F/vnnH0yfPh0dO3bE3r17q7QvXrwYxcXFIlRmORiEiIjI6GxLykLBfTU7KALYP7OPAavRvbKyMnz00Ufw9vbGxx9/DLm86s/auXNnbN++HfXrW96GkYbEB49ERGR0Yg+qPxx0oK/pPhJTKpXYs2cPQkNDceXKFcE+bm5uWL58OcaNGwcrKysDV2h5GISIiMioJGfl4UZ+idr2Z0x076CLFy9CJpNh//79gu12dnYIDQ1FWFgYGjZsaODqLBeDEBERGZVvErPUtnk2tseY7qa1d1BBQQHmzp2LtWvXQqFQCPYZM2YMVqxYgZYtWxq2OGIQIiIi45J3t1Twup21BMfmPGPgaurOxsYGu3btEgxB3bp1Q2xsLHr16iVCZQRwsrRa3FCRiEgcJeXCoybhL3QwcCW6Ua9ePaxatarSNXd3d3zxxRc4efIkQ5DIGITUmDp1KlJTU5GUlCR2KUREFiM5Kw9H0oRPVTdlL774Ip588knY29tj0aJFSEtLw7hx4yCV8tew2Pj/ASIiMhozvklW23Y2+47hCqmhnJwcvPnmm2pXgkkkEqxfvx5XrlzBkiVLuCTeiHCOEBERGYXkrDz8mXtPbbufZyPDFaOlkpISrF69GkuXLkVhYSGuX7+O//u//xPs265dOwNXR9rgiBARERmFw9WcMt+soZ1RrRZTKpXYvn07fH19MWfOHBQWVhwMu2vXLhw4cEDk6qgmGISIiMgo3My/L3jdWgokzg8ycDXqJScno2/fvhg1ahR+//33Ku0LFy4UoSqqLQYhIiIyCm5O9oLXp/ZrY+BKhN24cQOTJk1C165dcfToUcE+ISEh2L59u4Ero7rgHCEiIjIKP5z5S/C6e6N6Bq6ksvv37yMmJgbLly9HUVGRYJ9evXohJiaGW66YIAYhIiIS3bakLGTnCT8as7ES5+GFUqnEd999h9mzZ+PPP/8U7PPYY49h1apVGD16NCQSiYErJF1gECIiItElXMpR29bKxfBLzdPS0jBx4kQcP35csL1+/fqYN28eZs6ciXr1xB2xorphECIiItFl5d4VvN7OrQE6ezU2cDVAgwYNkJxcdU8jiUSCCRMmYOnSpWjevLnB6yLd42RpIiISVXJWHi7fFJ57M6FXKwNXU8Hd3R1hYWGVrvXu3RunTp3C+vXrGYLMCIMQERGJavgnv6ptE2t+EACEhobC09MTrVq1wvfff4/Dhw+jS5cuotVD+sEgREREomkZtrvadn3ODzp58iSef/553LlzR7DdwcEB8fHxSE1NxciRIzkZ2kwxCKnB0+eJiPRnW1KWxhDU2auRXuYH/fXXX3jttdfQo0cP7N69Gx988IHavr6+vrC3F97fiMyDRKlUKsUuwpgVFBTAyckJ+fn5cHR0FLscIiKT9/TKBLVL5R+wsQLSlz2n0/sWFxcjMjISq1atwr17/51pZmNjg4sXL8Lb21un9yNxafv7myNCRERkMNqEIEC3IUihUGDz5s1o27YtlixZUikEAUBZWRmio6N1dj8yLVw+T0REBlHdpokP+2OF7kLQr7/+ihkzZiApKUmw3dHREYsWLcI777yjs3uSaWEQIiIig1i2O1VjH12FoD///BNhYWH45ptvBNulUikmT56M8PBwNG3aVCf3JNPEIERERHoXsuEkCu7Lq+2jixBUVFSEFStWICoqCvfvC48+BQUFITo6Gh07dqzz/cj0MQgREZFeJWfl4UjabbXtDjZSpH4wuM732bNnD15//XXcuHFDsN3HxwdRUVF47rnnuBSeVDhZmoiI9CrzdnG17V+/0UMn92nSpIlgCGrUqBFiY2Nx/vx5PP/88wxBVAmDEBER6VV1myL29XHR2V5BgYGBePXVV1Wvrays8M477yAjIwPvvvsubG1tdXIfMi8MQkREpFdfnvhD8LpnY3tsmhio03tFRESgXr16GDRoEM6dO4ePP/4YTZo00ek9yLwwCBERkd4kZ+VhR/J1wbYnW7vU6LPkcjnWr1+PgQMHQi4XnnjdokULXLhwAXv37oWvr2+N6yXLwyBERER6M/pT9Qeq5t0t1fpzDh8+jG7duuH111/HgQMHsH79erV9H3/88RrVSJaNQYiIiPSic/g+lFdziNMz7V01fsbVq1cxYsQI9OvXDykpKarrCxYsQH5+vg6qJEvHIERERDoXsuEk8u6Wq233bGyPMd291Lbn5+dj9uzZ8PX1xY4dO6q037p1C99//71OaiXLVusgdPXqVSxYsAAvv/wy/v77bwDA3r17cfHiRZ0VR0REpmdbUla1+wbZWUtwbM4zgm1yuRyfffYZvL29ERkZidLSqo/PnnjiCezbtw+TJk3SWc1kuWoVhI4cOYKOHTvi5MmT+OGHH1BUVAQAOHv2LBYvXqzTAomIyHSs2HsJc7afr7bPN5N7Cl5PSEhA586d8dZbb+HWrVtV2ps0aYJPPvkEKSkpGDhwoE7qJapVEAoLC8PSpUtx4MCBSvsy9O/fH7/99pvOiiMiItORnJWHtUd+r7bPiM7uVfYNSk9PxwsvvICgoCCcP181RNnY2CA0NBQZGRmYMmUKrK15KALpTq2+TefPn8eWLVuqXG/WrBlu31Y/HEpEROYpOSsPIz9Rv0IMALp4OiF6TOdK11asWIFFixahrKxM8D0vvPACIiMj4e3trbNaiR5WqxGhRo0aCW5jnpycDA8PjzoXpUvZ2dno27cvfH190alTJ3z33Xdil0REZFZmbkvG8E9+haKaPg3trPDD1KeqXPf09BQMQR07dsTBgwexc+dOhiDSq1oFoZdeeglz5szBzZs3IZFIoFAocPz4ccyaNQvjxo3TdY11Ym1tjdjYWKSmpmL//v2YMWMGiourP/eGiIi0E7LhpNoNEx+QAji/ZJBg28svv4zAwP92l27atCk+++wzJCcn45lnhCdUE+lSrYLQ8uXL0a5dO3h6eqKoqAi+vr7o3bs3nnzySSxYsEDXNdZJ8+bN4e/vDwBwc3ODi4sLcnNzxS2KiMgMDI/7pdrVYQ9sf/tJKBTC40VSqRSxsbGws7PD7NmzkZ6ejsmTJ8PKykrX5RIJqlUQsrW1xeeff46rV69i165d2Lx5My5fvoyvvvqqxl/eo0ePYsiQIXB3d4dEIsHOnTur9ImLi0PLli1hb2+PwMBAJCYm1qZsnD59GnK5HJ6enrV6PxERVSyP95m/G8nZmjc0DOnijE1R72PMmDFq+/To0QPZ2dlYuXIlnJycdFkqkUZ1mnrv5eUFLy/1G2Jpo7i4GH5+fpg4cSJGjBhRpX3btm2QyWRYu3YtAgMDERsbi+DgYFy5cgXNmjUDAPj7+6O8vOrGXfv374e7uzsAIDc3F+PGjcPnn39ep3qJiCzZ0ysTkJ13X2M/qaIco+pfwsdvrUJeXh4A4NChQ+jXr59g/6ZNm+q0TiJtSZRKZTUboAubOHFite0bNmyoXTESCXbs2IFhw4aprgUGBqJ79+5Ys2YNAEChUMDT0xPTpk1DWFiYVp9bUlKCAQMG4I033sBrr72msW9JSYnqdUFBATw9PZGfnw9HR8ea/1BERGai4+J4FJYIH3b6gFKphCTrDOyTt+DKlSuV2vz8/HD69Gk+9iKDKCgogJOTk8bf37UaEXqQ7h8oKyvDhQsXcOfOHfTv3782HymotLQUp0+fxty5c1XXpFIpgoKCcOLECa0+Q6lUYvz48ejfv7/GEAQAERERWLJkSa1rJiIyRx0Wx6NIQwgqvfUnio6sR+HVM4Ltly9fRnJyMrp166aPEolqpVZBSOjcF4VCgSlTpqB169Z1LuqB27dvQy6Xw9W18sF8rq6uuHz5slafcfz4cWzbtg2dOnVSzT/66quv0LFjR8H+c+fOhUwmU71+MCJERGSpIvddrjYEye/m484vW1B0di+gZlL0mDFjsGLFCrRs2VJPVRLVjs6255RKpZDJZOjbty9mz56tq4+ts6eeekrtagUhdnZ2sLOz02NFRESm5YvjfwheV8rLUHhmN+4c3wplifC2JN26dUNsbCx69eqlxwqJak+n+5RfvXpVcNJybbm4uMDKygo5OTmVrufk5MDNzU1n9xESFxeHuLg4yOXVDwUTEZmzbUlZKCqt/PegUqnEvYxE5B1aj/I84T2E3N3dERERgVdffRVSaa3P9ybSu1oFoYcfHQEV/1LcuHEDu3fvRkhIiE4KAyqW6Xft2hUJCQmqCdQKhQIJCQl45513dHYfIVOnTsXUqVNVk62IiCzRp4evVnqtVCpx64cPcC9DeBsTe3t7zJ49G7Nnz0b9+vUNUSJRndQqCCUnJ1d6LZVK0bRpU0RFRWlcUfaooqIiZGRkqF5nZmYiJSUFzs7O8PLygkwmQ0hICLp164aAgADExsaiuLgYEyZMqE3pRESkpdBvU/DHP3crXZNIJLBt1lowCI0dOxYRERF13laFyJBqtXxelw4fPiy4r0RISAg2bdoEAFizZg0iIyNx8+ZN+Pv746OPPqq0Jbs+abv8jojIXCRn5eGl/51ASbnwrwdF6X3c3zINt3Iqzpx8sMdbjx49DFkmUbW0/f0tehAyVg/PEUpLS2MQIiKLsGLvJezbcQxeudeR2cgVWS5VR3fauTXAK02yMG/ePKxcuRIvvfQS5wGR0dF5EOrcuTMkEolWNz9zRngPCVPEESEishTnzv0ODAyGIicDMwEEAhjUqgumD52NAvsGqn7vPtMG7z7jjfv378PBwUG0eomqo/MNFR/e7ZmIiMzM1atw8ffBIqUCGwAoAfwKYFJmMj76aRXGvxiu6tq3bTNIpVKGIDILWgehxYsX67MOIiISyf3UVMQ+8QSWASh66HopgLlQ4vvMM2iZew1/OHtgSp/H0dmrsUiVEumeTvcRIiIi06FUKrH9iy/w3oQJ+ENNnyQAeQDGNS1D5zefZAgis1Or2W1yuRwffvghAgIC4ObmBmdn50p/zEFcXBx8fX3RvXt3sUshItK506dPo0+fPhitJgTVB7AUwGUAjQFMHD+QIYjMUq2C0JIlSxAdHY0xY8YgPz8fMpkMI0aMgFQqxfvvv6/jEsUxdepUpKamIikpSexSiIh05vr165gwYQK6d++OY8eOVWmXAJgAIA3AfAD1AKBbN8Db26B1EhlKrZbPt27dGh999BGee+45NGzYECkpKaprv/32G7Zs2aKPWkXBVWNEZA7u3buHqKgorFixAsXFwueCPQ0gBkDXhy9aWQG3bgGNORpEpkXnq8YedvPmTdXp7Q0aNEB+fj4A4Pnnn8fChQtr85FERKQn+fn58PPzw59//inY3hLAhwBGoGJEqJL0dIYgMmu1ejTWokUL3LhRsaNo69atsX//fgBAUlIST24nIjIyTk5O6NmzZ5XrDQGsAHAJwEgIhKDly4FWrfReH5GYahWEhg8fjoSEBADAtGnTsHDhQnh7e2PcuHE1PmvMWHGyNBGZkxUrVsDe3h5AReB5HRXzgOYAsFf3plGjDFIbkZhqNEdozZo1ePXVV9GoUaNK10+cOIETJ07A29sbQ4YM0XWNouIcISIyFXfv3oWdnR2srKwE2xcsWIDju3Yh5uxZ+Gv6sE6dgLNndV0ikcHo5awxJycnlJWVYfjw4Zg0aRL69++vk2KNGYMQERk7hUKBLVu2YO7cuViyZInakfmysjJYN2oEyd27gu2VJCVVrBYjMlHa/v6u0aOxmzdvYu3atbh+/ToGDBiAVq1a4YMPPkB2dnadCyYiopo7ceIEevbsiddeew1//fUX5s2bh8LCQsG+Nl9+qV0IevpphiCyGDUKQvXq1cO4ceNw6NAhpKen47XXXsP69evRqlUrDBo0CN999x3Kysr0VSsREf0rKysLY8eOxZNPPonExETV9ZycHERERFTunJtbEWxef13zB9evD/z4o46rJTJetZosDQCPP/44wsPDkZmZib1796JJkyYYP348PDw8dFkfERE9pKioCIsWLULbtm2xdetWwT4XLlyAUqkE9u0DXnwRcHUFTp/W7gaHD3O5PFmUOp81JpFIYG1tDYlEAqVSaTYjQnFxcYiLi4NcLhe7FCIiKBQKfPXVV5g7d65q+5JH+fj4ICoqCs+1aweJoyNQVCTYT4gSgKR/fz4SI4tTq52lASA7OxsbN27Epk2bkJWVhd69e2PSpEkYOXKkaommOeBkaSIS2y+//IKZM2fi1KlTgu2NGjXC+++/jymtW8N23braPdp67DEgOZmjQWQ29LKzdGlpKX744Qds2LABP//8M5o3b46QkBBMnDgRjz/+eJ2LJiKi//zxxx+YM2cOvv32W8F2KysrTJkyBe/7+KDJrFlAaWntb/b55wxBZJFqFITc3Nxw9+5dPP/88/i///s/BAcHQyqt9TQjIiJSIzk5GT179kRJSYlg+6BBgxDVqxd8P/igTgFICUDSpAkwYECtP4PIlNUoxSxYsADZ2dn4/vvvMXjwYEilUmzdulXtAX5ERFQ7fn5+6NChQ5Xr7dq1w56lS7H3yBH4LlxYt1EgAJLGjSv2DCKyULWeI/SAo6MjUlJSzPbRGOcIEZFYfvnlFzz99NMAAGdnZyyZPRtv7t0LmyNH6v7hNjbAN98AI0bU/bOIjJBeT59/WB1zFBGRRcvJyYGrq6tg21NPPYVXXnkFLi4uWLRoEZxfeAHKX36p+027d69YWs85QUS130eIiIhqLz8/H7Nnz4aXlxeOHTumtt9XX32F2EWL4Pzss8Avv1Q9IV5bAwYAS5cCaWlAYiJDENG/ajQipFAoEBkZiZ9++gmlpaV45plnsGfPHrPcRJH7CBGRPsjlcqxbtw4LFy7ErVu3AAAzZsxAUlKS4OITiUQCjB0L5cmTtQtB9esD588DrVrVrXAiM1WjEaFly5Zh3rx5aNCgATw8PLB69Wps3LgRdnZ2+qpPNFOnTkVqaiqSOImQiHQkISEBnTt3xltvvaUKQQBw5swZfPnll8Jv+ugjYN++moUgJydgzBhg//6KTRUZgojUqtGI0JdffolPPvkEb775JgDg4MGDeO6557Bu3TouoyciUiM9PR2zZs3CTz/9JNhuY2ODG+fP//foqkkTIC8P2Lq1ZqvC7OyAPXuA/v11VDmR+avRqjE7OztkZGTA09NTdc3e3h4ZGRlo0aKFXgoUG1eNEVFt3blzBx988AE+/vhjtccPvdChAyJzcuD90AhRrXz8MfDOO3X7DCIzopdVY+Xl5VWOz7CxsTGb88WIiHShvLwc//vf/7Bo0SL8888/gn06Wlsjprwcz1y4UKd7KQFIgoMZgohqqUZBSKlUYvz48ZXmBN2/fx9vvfUW6tevr7r2ww8/6K5CIiITsm/fPshkMqSmpgq2NwWwFMCk8nJY6eB+kh49Kh6hEVGt1CgIhYSEVLn26quv6qwYIiJTtnHjRkycOFGwzRbADADzADjp6obdugEnTujq04gsUp13ljZ3nCNERNoqLCyE9+OPI+f27UrXRwBYBaC1Lm/m7AxkZHA/ICI1DLazNBGRxUpLA44cAXJygLt30fCbb7D89m1M+rfZH0AsgD66vm+PHhWrwxiCiOqMQUgNbqhIRFX8G3wyTp9Gm/37gczMKl1CAGwHMPLff9bFPCAAgIcH8N57wLPPAt7euvpUIovHR2Ma8NEYkYVLS6sYffnoI6RmZkIG4DCAKwAe08f9bGyAV1+t2BQxNxfw8QFefJHhh6iG+GiMiKimHn7U1bAhsHEjcPYsbgN4H8BaAA/GiMMA6Gytlrs78PzzwKhRFWeCEZHBMAgREeXmVjxyOnmy0uVSAJ8AWALgziNv+QbANABP1uZ+LVoAvXoBHTtytIdIZAxCRGSZHoz+ZGZWnOdVXKxqUgLYBSAUQLqatzcHkFfTe7ZtC2zeXLHsnYiMAoMQEVmOB/N91q4FrlwR7HIegAzAQTUfYQ9gFoA5ABpoe18GICKjxSBEROYvN7di/s2hQ2q73AKwCMD/ACjU9HkZwAoAXtrcs1Ur4N13ucqLyMgxCBGReUtLA15+GcozZyARaC4B8DGADwAUqPmIAAAxqGY+UKtWwNixQL16gKsr0KcPww+RiWAQIiLzlJsLvPAC8MsvACAYgoCKx1xr1LR5AFiJipEg6cMND4JPy5YMPUQmjkGIiMxPYmLFMvQCdWM8/5Gh4nFY6UPX6qFiDtAsAKrjpFu3BqZN46MuIjPDIERE5uORUSBttAIwExUjPwDwKoCIxx5Di5kzAV9foLwcaNOG4YfITDEIEZF5qGYU6D4qlsF3VPPWeQAuAZjv7Y2ALVu4uovIgjAIEZHperAcft064OLFKs1KVJz79R4qHn1dwSNL3v+d6+PYsiV+5FwfIovEIEREpkeL5fCnUfHI69hD11YBCAeggATSp3oBx44JvpeILIdUcxfLFBcXB19fX3Tv3l3sUojogbQ0YO9eYOBAKNWEoOsAJgDojsohCAAiAWQBkAYPBH76Sa+lEpFp4OnzGvD0eSIjkJgITJkCnDmjtss9AFGo2PCwWE2fp9u1w+exsWgbHKyHIonImPD0eSIyXWlpwNWrFed/RURUG4CUALahYrl7lpo+Ld3dEbl6NUaOHAmJRN2OQkRkiRiEiMh45OZWbFS4b59W3RNRMQ/oVzXtDaysMH/+fMyYOxf29va6qpKIzAiDEBEZj7FjoThwUOPkxb8AzAWwWU27BMCkF17AB2vXws3NTaclEpF5YRAiIuOQlgbs26fVCo6XABxX09a3a1fErFsHf39/3dVGRGaLq8aISHyJiRVHV2hpqcC11l5e2LFjB35OSmIIIiKtMQgRkXhyc4FBg4DAwIrJ0VrqC2D4v//saG2NyCVLcDEtDcOGDeNkaCKqET4aIyLDeLASzMoKkMsrzu+aOBGKX44L/hdZFoBCAE882tClCzB/PiLv3IFbQgLej4lBs2bN9F4+EZknBiEi0i8NK8EeDUFFqNgBOhJAJwAnHvTp0gX47DPVOWCtAXwycaKeiiYiS8FHY0SkP2lpwIABUBw4oLGrAsAXANoC+AAVB6UmAtgyaVLF55w+zcNQiUjnGISISDceHH+Rnl4x+blrV6BtW+DMGUgVimrf+guAQADjUXFExsPC4uNR7O6un5qJyOLx0RgR1U0NN0F82B+o2BH6WzXtVlZWeOGFF1BeXl6HAomI1GMQIqK60XITxIcVouJMsCgAJWr6DBw4ENHR0XjiiSrTpYmIdIZBiIhqb98+rTdBBAA5KuYBzQdwU02fdu3aISoqCoMHD+ZSeCLSOwYhItLswdL3Nm0ApRJISQHWrAGOHdP6I46g4lywZDXtja2tsWTpUrwlk8HGxkYHRRMRacYgREQVHg07V68CLi7AwoW1mv/zsHsAXgTwt0CbtZUV3n7lFSyOiYGzs3Od7kNEVFMMQkSW4uGg4+3932sNYUeJikNM66IegA98ffFmamql68899xw+/PBDtGvXro53ICKqHbMPQnfu3EFQUBDKy8tRXl6Od999F2+88YbYZREZjtCqriZNgH/+Ub2sLuzUeZbOvxshTurcGXFduuDcuXPw9fVFdHQ0goOD6/rpRER1IlEqlUqxi9AnuVyOkpISODg4oLi4GB06dMCpU6fQpEkTrd5fUFAAJycn5Ofnw9HRUc/VEunBoEEVq7oUctUlXYzyCPkZgPfmzfAMCAAyMv4bffrXkSNHcPHiRUyePBnW1mb/32FEJCJtf3+b/d9EVlZWcHBwAACUlJRAqVTCzLMf0X/S0gRXdek6BKUDmAXgJwCv7N2Lza+8UikAPdCnTx/06dNHx3cnIqo90XeWPnr0KIYMGQJ3d3dIJBLs3LmzSp+4uDi0bNkS9vb2CAwMRGJiYo3ucefOHfj5+aFFixZ477334OLioqPqiYzEw7s6P/z66FG93vYOKgLQE6gIQQDw9ddf4+TJk3q9LxGRrog+IlRcXAw/Pz9MnDgRI0aMqNK+bds2yGQyrF27FoGBgYiNjUVwcDCuXLmiOnHa399fcOfZ/fv3w93dHY0aNcLZs2eRk5ODESNGYNSoUXB1ddX7z0akd1rM/6krocdo5QDWAVhobY3bAv/uzZw5E8ePH+c+QERk9EQPQoMHD8bgwYPVtkdHR+ONN97AhAkTAABr167F7t27sWHDBoSFhQEAUlJStLqXq6sr/Pz8cOzYMYwaNUqwT0lJCUpK/tvrtqCgQMufhMhAHl79NW1alV2dlf/8Uym4PHgQ/Oi16l4/TBIcDCxdCty6BbRpgwPHj2Pm++/j4p9/AgIhqGnTpggJCYFSqWQQIiKjJ3oQqk5paSlOnz6NuXPnqq5JpVIEBQXhxIkTWn1GTk4OHBwc0LBhQ+Tn5+Po0aOYMmWK2v4RERFYsmRJnWsn0jk1Z3ppmv8jFEUkj4waPRp2AFSZ7HzlyhXMksmwa9cuwfJsbGwwY8YMzJ8/H05OTjX4wYiIxGPUQej27duQy+VVHmO5urri8uXLWn3Gn3/+icmTJ6smSU+bNg0dO3ZU23/u3LmQyWSq1wUFBfD09KzdD0CkS7U406uSzz8HPDz+Czfp6YIru1T+vZabm4vw8HDExcWpPfx0+PDhWLVqFdo8CFFERCbCqIOQLgQEBGj96AwA7OzsYGdnp7+CiLT16E7PNTjTS1CfPpUDj7e3cAD6V1lZGT777DMsXrwYubm5gn38/PwQGxuLvn371qUyIiLRGHUQcnFxgZWVFXJycipdz8nJgZubm17vHRcXh7i4OMjlcs2diXRJ6BFYly5av/3R+T4KqRWkA4KqDT1CsrKyIJPJUFZWVqXN1dUVy5Ytw/jx42FlZVWjzyUiMiaiL5+vjq2tLbp27YqEhATVNYVCgYSEBPTs2VOv9546dSpSU1ORlJSk1/sQVVn6/u8jsIcpklO0/jjJI5uFSgcEAVu31ris1q1bY/r06ZWu2draIiwsDGlpaZg0aRJDEBGZPNFHhIqKipCRkaF6nZmZiZSUFDg7O8PLywsymQwhISHo1q0bAgICEBsbi+LiYtUqMiKTJTTy89RTwC+/VPkvFKlSAQBQSKWQKhSq66rRno8/rjzfR9P8Hy0tWLAAX3zxBW7fvo3Ro0dj5cqVaNWqVa0/j4jI2Ih+xMbhw4fRr1+/KtdDQkKwadMmAMCaNWsQGRmJmzdvwt/fHx999BECAwMNUh+P2CC9ETj6QiGRqkKPoC5dgDNn/nsdHFwx2tO4ca1KKC0txfr16xESEqLagf1RO3bsgIuLC55++ula3YOISAza/v4WPQgZq4fnCKWlpTEIkW6lpQFt29bufUCdR3uUSiV27dqF0NBQpKenY8mSJVi0aFGtPouIyBgxCOkIR4RIJx5eAebtXTEn6Nln1XZ/dGRI9QgsPr7OpZw/fx4ymQwHD/43D8nBwQFpaWnw8PCo8+cTERkDbX9/G/VkaSKTl5sLDBpUMfrz7LOAj0/Faw3n3Umf6lX5dS0nPD/s1q1bmDJlCvz9/SuFIAC4e/dupY1LiYgsheiTpYnMmsAmiKrXwcFV5wg9PPKjownPJSUl+Pjjj/HBBx+oPTImICAAb731Vq3vQURkqhiEiDR59LGWtn3S0gQ3QZQq5BUrxZKSKtoeWjVWaeRHw4aHmiiVSvz444+YNWsWrl69KtjHw8MDK1aswNixYyGVcoCYiCwPg5Aa3FCRBJe3P7pKq7o+asKHyq1bOh35eVhKSgpmzpyJw4cPC7bXq1cPc+bMwaxZs1C/fn2d3JOIyBRxsrQGnCxtwYSWtz86abm6Ph99VP3KsLQ0nQWfB3JycrBgwQKsX78e6v7VfvXVVxEREYEWLVro9N5ERMaEk6WJ6uLBYy1F5RFB1WOt9HTNfSSSinlA0sq7LyukVhWjRjoOQQBw7tw5rFu3TjAE9ezZEydPnsRXX33FEERE9C8GIbJMjx5r8ShNj7UyMrTrs3VrxejQQ3SxAkydAQMGYMiQIZWueXp6YuvWrTh+/DgCAgL0cl8iIlPFOUJk+rSZzPyANvN+AKB16+o/58GJ8Jr6NG6st3lA6nz44YfYu3cv7OzsEBYWhtDQUNSrV0+v9yQiMlUcEVIjLi4Ovr6+6N69u9ilkDrq9ujJy1P/HqEDTQ8cBF5+uXI/Hx/Nj7W06fOAtzcweLBOQtCNGzewbNkytXOAfHx88NVXXyEtLQ0LFixgCCIiqgYnS2vAydJGTJvJzA/TdKzFo5OX8/IqAlJ1o0fa9NGRe/fuITo6GhERESguLsY333yDMWPG6PQeRETmgkds6AiDkJGqaagBNB5rgT17KkZtHqXNYy09PvpSKpX49ttvMXv2bGRlZamue3l54fLlyxzxISISwFVjZN60maj8KG3m/QjR5rGWDh99PSwpKQlPP/00XnrppUohCACysrIQGxur0/sREVkaBiEyTbUJNTWZ0yOya9euISQkBAEBATh+/HiVdolEgkmTJmHChAkiVEdEZD4YhMg01TbUGHg5e03dvXsX4eHh8PHxwZdffinYp0+fPjh9+jTWrVsHNzc3A1dIRGReuHxeDR6xYQK2boX0kYnKGkONCMvZtaFUKrF161bMmTMHf/31l2Cfxx9/HJGRkRg+fDgkEomBKyQiMk+cLK0BJ0ubACMLNTWVmJiId999F7/99ptge8OGDbFw4UJMnz4ddnZ2Bq6OiMg0afv7myNCZPrqeEq72E6dOiUYgqRSKV5//XWEh4fD1dVVhMqIiMwf5wgRiWzy5Mnw9fWtdK1///5ITk7GZ599xhBERKRHDEJEIrO2tkZMTAwAoE2bNvjxxx9x8OBBdOrUSeTKiIjMH4MQkQEcP34cy5cvV9s+cOBAfPfdd7h48SKGDh3KydBERAbCOUIknpoclmqi/vjjD8yZMwfffvstgIrA061bN8G+o0aNMmRpREQEjgiRGGpzWKqJKSwsxPz589GuXTtVCAKAGTNmqD0slYiIDI9BSA2ePq9H2p4Ab4IUCgU2btwIHx8fLF++HCUlJZXajx8/jp9++kmk6oiI6FHcR0gD7iOkY7U5LNVEHD16FDNmzEBycrJge+PGjfH+++9jypQpsLGxMXB1RESWhYeuknGqzWGpRu7333/HqFGj0KdPH8EQZGVlhenTpyMjIwPTp09nCCIiMiKcLE2GVdsT4I1QQUEBli9fjpiYGJSWlgr2efbZZ/Hhhx+iffv2Bq6OiIi0wREhMiwTOgG+Ohs3boS3tzdWrlwpGIJ8fX0RHx+P3bt3MwQRERkxBiEyPCM/AV4bZ8+exd9//13lepMmTRAXF4ezZ88iODhYhMqIiKgmOFlaA06W1iMTPiw1NzcX3t7eyM3NBVCxO/T06dOxYMECNG7cWOTqiIiIk6XJ+Hl7A4MHm1wIAgBnZ2csWbIEADB06FBcvHgRUVFRDEFERCaGk6WJBJSXl2PdunWQy+WYOnWqYJ8333wTHTt2RJ8+fQxcHRER6QqDENEjDhw4gJkzZ+LixYto0KABRowYgebNm1fpZ2NjwxBERGTi+GhMDe4sbXmuXLmCIUOGYODAgbh48SIAoKioCAsWLBC5MiIi0hdOltaAk6XNX15eHsLDw7FmzRqUl5dXaZdIJEhPT0drTXsgERGR0eBkaSINysrKsGbNGrRp0waxsbGCIcjPzw8JCQkMQUREZopzhMgixcfHQyaT4dKlS4LtzZo1w/LlyzF+/HhYWVkJ9iEiItPHIEQW5dKlSwgNDcXevXsF221tbSGTyTB37lw+CiUisgAMQmQRysrKIJPJ8Omnn0Iulwv2GTVqFFatWoVWrVoZuDoiIhILgxBZBBsbG1y9elUwBHXp0gUxMTHo3bu3CJUREZGYOFmaLEZUVFSl+T5ubm7YuHEjkpKSGIKIiCwUgxBZjPbt2+Ptt9+Gvb095s+fj/T0dIwfPx5SKf81ICKyVNxHSAPuI2Q6bt26hcWLFyMoKAgjRowQ7JObm4vCwkI89thjBq6OiIgMifsIkcUoLS1FdHQ0vL298emnn2LWrFm4f/++YF9nZ2eGICIiUmEQIvXS0oC9e4H0dLErEaRUKvHjjz/iiSeeQGhoKPLz8wEAmZmZWL16tcjVERGRKWAQoqpyc4FBg4C2bYFnnwV8fCpe5+WJXZnKuXPnEBQUhGHDhiEjI6NKe0xMDEpKSkSojIiITAmDkBoWfejq2LFQHDhY6ZLiwEHg5ZdFKug/OTk5mDx5Mjp37oyff/5ZsM8rr7yCpKQk2NnZGbg6IiIyNZwsrYHFTZZOS6sYCaqu3dvbcPX8q6SkBKtXr8bSpUtRWFgo2KdHjx6IjY1FYGCggasjIiJjw8nSVDtXr1bfLvAYSp+USiW2b98OX19fzJkzRzAEeXp6YsuWLfj1118ZgoiIqEa4szRVpumU9TZtDFMHgJs3b2LMmDE4evSoYLuDgwPCwsIQGhoKBwcHg9VFRETmgyNCVJmPDxAcDIW08onrCqkVEBxs0MdiTZo0QU5OjmBbSEgI0tLSsHDhQoYgIiKqNQYhqmrrVkgHBFW6JB0QBGzdatAybGxsEBUVVelar169kJiYiE2bNsHDw8Og9RARkfnhZGkNLG6y9MPS0yvmBLVpI8oEaaBijtCgQYNw5coVrFq1CqNHj4ZEIhGlFiIiMh2cLE115+0NDB6s1xD04MDT5ORkwXaJRIJNmzbh0qVLePHFFxmCiIhIpxiESBTXrl1DSEgIAgICcOzYMcycORPqBiebN2+OevXqGbhCIiKyBAxCZFB3795FeHg4fHx88OWXX6quHzlyBDt37hSvMCIiskgMQmIx8nO8dE2pVGLLli1o27YtFi9ejLt371bp88knn4hQGRERWTIGIUMzgXO8dO23337Dk08+iVdeeQV//fVXlfaGDRti1apV2LVrlwjVERGRJWMQMjQjPsdL17Kzs/HKK6+gZ8+e+O2336q0S6VSTJ48Genp6Xjvvfd4NhgRERkcd5Y2pLQ0YN++KulTqpAD+/ZVPCYTaZm6LhUXF2PVqlWIjIzEvXv3BPv0798fMTEx6NSpk4GrIyIi+g+DkCFpc46XiQeh3377DSNHjsT169cF29u0aYOoqCgMGTKES+GJiEh0fDRmSEZ0jpe+tGnTBsXFxVWuOzk5ISoqChcvXsTQoUMZgoiIyCgwCBmSEZ3jpS8uLi5YvHix6rVUKsXbb7+N9PR0yGQy2NrailgdERFRZTxiQwOdH7GRl1cxMXrfvv+uBQdXnOPVuHHdP99AFAoFpFLhHF1aWooOHTqgVatWiIqKQocOHQxcHRERWToesfGIu3fv4rHHHsOsWbPELaRxYyA+vmLi9J49Ff83Pt5kQpBCocDGjRvh6+uLv//+W7CPra0tfv31V8THxzMEERGRUbOYILRs2TL06NFD7DL+Y4BzvHTt6NGj6N69OyZOnIgrV65g0aJFavu6uLhwHhARERk9iwhC6enpuHz5MgYPHix2KSbp999/x6hRo9CnTx+cOXNGdf3zzz/HuXPnRKyMiIiobkQPQkePHsWQIUPg7u4OiUQieN5UXFwcWrZsCXt7ewQGBiIxMbFG95g1axYiIiJ0VLHlKCgoQFhYGNq3b4/t27dXaVcoFPj+++9FqIyIiEg3RN9HqLi4GH5+fpg4cSJGjBhRpX3btm2QyWRYu3YtAgMDERsbi+DgYFy5cgXNmjUDAPj7+6O8vLzKe/fv34+kpCT4+PjAx8cHv/76q95/HnMgl8uxceNGzJ8/X+08oPbt2yM6OhqDBg0ycHVERES6Y1SrxiQSCXbs2IFhw4aprgUGBqJ79+5Ys2YNgIpRCE9PT0ybNg1hYWEaP3Pu3LnYvHkzrKysUFRUhLKyMoSGhqqd31JSUoKSkhLV64KCAnh6eupu1ZiRO3ToEGbOnImzZ88Ktjs7OyM8PByTJ0+GjY2NgasjIiLSjlmsGistLcXp06cRFBSkuiaVShEUFIQTJ05o9RkRERHIzs7GH3/8gQ8//BBvvPFGtZN8IyIi4OTkpPrj6elZ55/DFGRkZGD48OHo37+/YAiytrbGjBkzkJGRgalTpzIEERGRWTDqIHT79m3I5XK4urpWuu7q6oqbN2/q5Z5z585Ffn6+6k92drZe7mNMNmzYAF9fX8H5WQAwZMgQXLhwATExMWhsIsv8iYiItCH6HCFDGj9+vMY+dnZ2FncKekBAAORyeZXrHTp0QHR0NAYMGCBCVURERPpn1CNCLi4usLKyQk5OTqXrOTk5cHNz0+u94+Li4Ovri+7du+v1PsagQ4cOmDx5suq1i4sLPv30UyQnJzMEERGRWTPqIGRra4uuXbsiISFBdU2hUCAhIQE9e/bU672nTp2K1NRUJCUl6fU+hlRaWqq2LTw8HE2bNsWsWbOQnp6Ot956C9bWFjVgSEREFkj033RFRUXIyMhQvc7MzERKSgqcnZ3h5eUFmUyGkJAQdOvWDQEBAYiNjUVxcTEmTJggYtWmJS8vDx988AHi4+ORnJws+OivadOmyMzMRP369UWokIiISByiB6FTp06hX79+qtcymQwAEBISgk2bNmHMmDG4desWFi1ahJs3b8Lf3x/x8fFVJlBTVeXl5fjss8+wePFi/PPPPwCANWvWIDQ0VLA/QxAREVkao9pHyJjExcUhLi4OcrkcaWlpJreP0L59+yCTyZCamlrpuqOjIzIyMtC0aVORKiMiItI/s9hHSEymOkfo0qVLePbZZzFo0KAqIQgA7t+/j+PHj4tQGRERkfFhEDIT//zzD6ZPn46OHTti7969gn1GjRqFS5cuVdq5m4iIyJKJPkeI6qasrAyffvop3n//feTl5Qn26dKlC2JiYtC7d28DV0dERGTcGIRMlFKpxJ49exAaGoorV64I9nFzc0NERATGjRsHqZSDf0RERI9iEFLj4cnSxmjKlCn47LPPBNvs7Owwa9YszJkzBw0bNjRwZURERKaDq8Y00HbWuaH9+OOPgnN9xowZg5UrV+Kxxx4zfFFERERGgqvGzNzQoUPRv39/1etu3brhl19+wTfffMMQREREpCUGISOmVCpRWFgo2CaRSBAdHQ1PT0988cUXOHnyJHr16mXgComIiEwb5wgZqXPnzmHmzJmQSCQ4cOAAJBJJlT5+fn64evUqbGxsRKiQiIjI9HFESA2xTp/PycnB5MmT0blzZ/z8889ISEjArl271PZnCCIiIqo9TpbWwFCTpUtKSrB69WosXbq0yuMwHx8fnD9/Hra2tnq7PxERkTnhZGkToVQq8cMPP8DX1xdz5swRnBN07949/P777yJUR0REZN4YhESUnJyMfv36YeTIkYJBx8HBAeHh4bh8+TLatWsnQoVERETmjZOlRXDz5k3Mnz8fGzduhLonk+PGjcPy5cvh4eFh4OqIiIgsB4OQAd2/fx8xMTFYvnw5ioqKBPv06tULMTExBp+kTUREZIkYhNTQ9REbCoUCPXr0wNmzZwXbH3vsMaxatQqjR48WXCpPREREusc5QmpMnToVqampSEpK0snnSaVSvPrqq1Wu169fH8uWLcOlS5fw4osvMgQREREZEIOQAU2bNg2tW7cGULEz9MSJE5Geno558+ahXr16IldHRERkeRiEdOzevXvIz88XbLOzs0NkZCR69+6NU6dOYf369WjevLmBKyQiIqIHGIR0RKlUYuvWrWjbti3mzZuntt+wYcNw+PBhdOnSxYDVERERkRAGIR14cODp2LFjkZ2djbVr1+LChQuCfSUSCecBERERGQkGoTr466+/8Nprr6FHjx44ceKE6rpCoUBoaKjaPYKIiIjIOHD5fC0UFxcjMjISq1atwr179wT7lJWVoaioCA0bNjRwdURERKQtjgipIXT6vEKhwObNm9G2bVssWbJEMAS1bt0aO3fuREJCAkMQERGRkePp8xo8OL32wIEDmD9/PhITEwX7OTo6YtGiRXjnnXdgZ2dn4CqJiIjoYdqePs9HY1oaMGCA4HWpVIrJkycjPDwcTZs2NXBVREREVBcMQnUQFBSE6OhodOzYUexSiIiIqBYYhGrBx8cHUVFReO6557gUnoiIyIRxsnQNNGrUCLGxsTh//jyef/55hiAiIiITxxEhLU2ePBnLly9HkyZNxC6FiIiIdISrxjTQdtY5ERERGQ+uGtORBzmxoKBA5EqIiIhIWw9+b2sa72EQ0qCwsBAA4OnpKXIlREREVFOFhYVwcnJS285HYxooFApcv34d/fv3x6lTp9T26969O5KSkrRuKygogKenJ7Kzs436kVt1P5cxfX5tPqcm79Gmr6Y+/I6I9/nm+P0A+B3R5eeb43fE0r8fSqUShYWFcHd3h1Sqfm0YR4Q0kEqlaNGiBaytrav9IllZWaltr67N0dHRqL+g1dVuTJ9fm8+pyXu06aupD78j4n2+OX8/AH5H+B3h3yHqVDcS9ACXz2tp6tSptW7X9F5jpu/adfX5tfmcmrxHm778jhjv5/P7IS5+R/gdqY7YtfPRmEi4Go004XeENOF3hKrD74d2OCIkEjs7OyxevJgHtJJa/I6QJvyOUHX4/dAOR4SIiIjIYnFEiIiIiCwWgxARERFZLAYhIiIislgMQkRERGSxGISIiIjIYjEIGaldu3ahbdu28Pb2xrp168Quh4zM8OHD0bhxY4waNUrsUsgIZWdno2/fvvD19UWnTp3w3XffiV0SGZk7d+6gW7du8Pf3R4cOHfD555+LXZJouHzeCJWXl8PX1xeHDh2Ck5MTunbtil9//RVNmjQRuzQyEocPH0ZhYSG++OILfP/992KXQ0bmxo0byMnJgb+/P27evImuXbsiLS0N9evXF7s0MhJyuRwlJSVwcHBAcXExOnTogFOnTlnk7xmOCBmhxMREPPHEE/Dw8ECDBg0wePBg7N+/X+yyyIj07dsXDRs2FLsMMlLNmzeHv78/AMDNzQ0uLi7Izc0VtygyKlZWVnBwcAAAlJSUQKlUwlLHRRiE9ODo0aMYMmQI3N3dIZFIsHPnzip94uLi0LJlS9jb2yMwMBCJiYmqtuvXr8PDw0P12sPDA9euXTNE6WQAdf1+kPnT5Xfk9OnTkMvl8PT01HPVZEi6+I7cuXMHfn5+aNGiBd577z24uLgYqHrjwiCkB8XFxfDz80NcXJxg+7Zt2yCTybB48WKcOXMGfn5+CA4Oxt9//23gSkkM/H6QJrr6juTm5mLcuHH43//+Z4iyyYB08R1p1KgRzp49i8zMTGzZsgU5OTmGKt+4KEmvACh37NhR6VpAQIBy6tSpqtdyuVzp7u6ujIiIUCqVSuXx48eVw4YNU7W/++67yq+//tog9ZJh1eb78cChQ4eUI0eONESZJKLafkfu37+vfPrpp5VffvmloUolkdTl75EHpkyZovzuu+/0WabR4oiQgZWWluL06dMICgpSXZNKpQgKCsKJEycAAAEBAbhw4QKuXbuGoqIi7N27F8HBwWKVTAakzfeDLJs23xGlUonx48ejf//+eO2118QqlUSizXckJycHhYWFAID8/HwcPXoUbdu2FaVesVmLXYCluX37NuRyOVxdXStdd3V1xeXLlwEA1tbWiIqKQr9+/aBQKDB79myLnMlvibT5fgBAUFAQzp49i+LiYrRo0QLfffcdevbsaehySQTafEeOHz+Obdu2oVOnTqq5I1999RU6duxo6HJJBNp8R/78809MnjxZNUl62rRpFvv9YBAyUkOHDsXQoUPFLoOM1MGDB8UugYzYU089BYVCIXYZZMQCAgKQkpIidhlGgY/GDMzFxQVWVlZVJqXl5OTAzc1NpKrIWPD7QZrwO0Ka8DtSMwxCBmZra4uuXbsiISFBdU2hUCAhIYGPNojfD9KI3xHShN+RmuGjMT0oKipCRkaG6nVmZiZSUlLg7OwMLy8vyGQyhISEoFu3bggICEBsbCyKi4sxYcIEEasmQ+H3gzThd4Q04XdEh0RetWaWDh06pARQ5U9ISIiqz8cff6z08vJS2traKgMCApS//fabeAWTQfH7QZrwO0Ka8DuiOzxrjIiIiCwW5wgRERGRxWIQIiIiIovFIEREREQWi0GIiIiILBaDEBEREVksBiEiIiKyWAxCREREZLEYhIiIiMhiMQgRkcWSSCTYuXOn2GUQkYgYhIjIJI0fPx7Dhg0TuwwiMnEMQkRERGSxGISIyOT17dsX06dPx+zZs+Hs7Aw3Nze8//77lfqkp6ejd+/esLe3h6+vLw4cOFDlc7Kzs/Hiiy+iUaNGcHZ2xgsvvIA//vgDAHD58mU4ODhgy5Ytqv7ffvst6tWrh9TUVH3+eESkRwxCRGQWvvjiC9SvXx8nT57EqlWrEB4ergo7CoUCI0aMgK2tLU6ePIm1a9dizpw5ld5fVlaG4OBgNGzYEMeOHcPx48fRoEEDDBo0CKWlpWjXrh0+/PBDvP3228jKysJff/2Ft956CytXroSvr68YPzIR6QBPnycikzR+/HjcuXMHO3fuRN++fSGXy3Hs2DFVe0BAAPr3748VK1Zg//79eO655/Dnn3/C3d0dABAfH4/Bgwdjx44dGDZsGDZv3oylS5fi0qVLkEgkAIDS0lI0atQIO3fuxMCBAwEAzz//PAoKCmBrawsrKyvEx8er+hOR6bEWuwAiIl3o1KlTpdfNmzfH33//DQC4dOkSPD09VSEIAHr27Fmp/9mzZ5GRkYGGDRtWun7//n1cvXpV9XrDhg3w8fGBVCrFxYsXGYKITByDEBGZBRsbm0qvJRIJFAqF1u8vKipC165d8fXXX1dpa9q0qeqfz549i+LiYkilUty4cQPNmzevfdFEJDoGISIye+3bt0d2dnal4PLbb79V6tOlSxds27YNzZo1g6Ojo+Dn5ObmYvz48Zg/fz5u3LiBV155BWfOnEG9evX0/jMQkX5wsjQRmb2goCD4+PggJCQEZ8+exbFjxzB//vxKfV555RW4uLjghRdewLFjx5CZmYnDhw9j+vTp+OuvvwAAb731Fjw9PbFgwQJER0dDLpdj1qxZYvxIRKQjDEJEZPakUil27NiBe/fuISAgAK+//jqWLVtWqY+DgwOOHj0KLy8vjBgxAu3bt8ekSZNw//59ODo64ssvv8SePXvw1VdfwdraGvXr18fmzZvx+eefY+/evSL9ZERUV1w1RkRERBaLI0JERERksRiEiIiIyGIxCBEREZHFYhAiIiIii8UgRERERBaLQYiIiIgsFoMQERERWSwGISIiIrJYDEJERERksRiEiIiIyGIxCBEREZHFYhAiIiIii/X/XSL/1Wv4qiwAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(np.arange(0, sorted_.shape[0]) + 1,\n", @@ -1094,7 +632,7 @@ "ax.set_ylabel('P-Value')\n", "ax.set_xlabel('Index')\n", "ax.scatter(sorted_set_+1, sorted_[sorted_set_], c='r', s=20)\n", - "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);\n" + "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);" ] }, { @@ -1112,38 +650,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "b59b8137", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.322735Z", - "iopub.status.busy": "2023-07-26T00:00:27.322608Z", - "iopub.status.idle": "2023-07-26T00:00:27.397859Z", - "shell.execute_reply": "2023-07-26T00:00:27.397542Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2 29\n", - "4 25\n", - "3 18\n", - "1 11\n", - "Name: Y, dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Khan = load_data('Khan') \n", "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n", "D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n", - "D['Y'].value_counts()\n" + "D['Y'].value_counts()" ] }, { @@ -1163,29 +678,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "96fb2f61", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.399700Z", - "iopub.status.busy": "2023-07-26T00:00:27.399569Z", - "iopub.status.idle": "2023-07-26T00:00:27.403423Z", - "shell.execute_reply": "2023-07-26T00:00:27.403115Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-2.0936330736768185, 0.04118643782678394)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "D2 = D[lambda df:df['Y'] == 2]\n", "D4 = D[lambda df:df['Y'] == 4]\n", @@ -1193,7 +689,7 @@ "observedT, pvalue = ttest_ind(D2[gene_11],\n", " D4[gene_11],\n", " equal_var=True)\n", - "observedT, pvalue\n" + "observedT, pvalue" ] }, { @@ -1216,29 +712,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "fdc229fa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:27.405191Z", - "iopub.status.busy": "2023-07-26T00:00:27.405090Z", - "iopub.status.idle": "2023-07-26T00:00:28.262471Z", - "shell.execute_reply": "2023-07-26T00:00:28.262168Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0398" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "B = 10000\n", "Tnull = np.empty(B)\n", @@ -1251,7 +728,7 @@ " D_null[n_:],\n", " equal_var=True)\n", " Tnull[b] = ttest_.statistic\n", - "(np.abs(Tnull) > np.abs(observedT)).mean()\n" + "(np.abs(Tnull) > np.abs(observedT)).mean()" ] }, { @@ -1267,29 +744,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "e3894695", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:28.264140Z", - "iopub.status.busy": "2023-07-26T00:00:28.264026Z", - "iopub.status.idle": "2023-07-26T00:00:28.488348Z", - "shell.execute_reply": "2023-07-26T00:00:28.487989Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", "ax.hist(Tnull,\n", @@ -1305,7 +763,7 @@ " c='b',\n", " label='Observed')\n", "ax.legend()\n", - "ax.set_xlabel(\"Null Distribution of Test Statistic\");\n" + "ax.set_xlabel(\"Null Distribution of Test Statistic\");" ] }, { @@ -1327,16 +785,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "3b7392cb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:28.490300Z", - "iopub.status.busy": "2023-07-26T00:00:28.490163Z", - "iopub.status.idle": "2023-07-26T00:01:51.994441Z", - "shell.execute_reply": "2023-07-26T00:01:51.994033Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "m, B = 100, 10000\n", @@ -1356,7 +807,7 @@ " ttest_ = ttest_ind(D_null[:n_],\n", " D_null[n_:],\n", " equal_var=True)\n", - " Tnull_vals[j,b] = ttest_.statistic\n" + " Tnull_vals[j,b] = ttest_.statistic" ] }, { @@ -1373,16 +824,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "cac15616", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:01:51.996501Z", - "iopub.status.busy": "2023-07-26T00:01:51.996360Z", - "iopub.status.idle": "2023-07-26T00:01:52.220394Z", - "shell.execute_reply": "2023-07-26T00:01:52.220065Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "cutoffs = np.sort(np.abs(T_vals))\n", @@ -1392,7 +836,7 @@ " V = np.sum(np.abs(Tnull_vals) >= cutoffs[j]) / B\n", " Rs[j] = R\n", " Vs[j] = V\n", - " FDRs[j] = V / R\n" + " FDRs[j] = V / R" ] }, { @@ -1414,48 +858,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "9661eb10", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:01:52.222331Z", - "iopub.status.busy": "2023-07-26T00:01:52.222198Z", - "iopub.status.idle": "2023-07-26T00:01:52.225010Z", - "shell.execute_reply": "2023-07-26T00:01:52.224751Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['G0097',\n", - " 'G0129',\n", - " 'G0182',\n", - " 'G0714',\n", - " 'G0812',\n", - " 'G0941',\n", - " 'G0982',\n", - " 'G1020',\n", - " 'G1022',\n", - " 'G1090',\n", - " 'G1320',\n", - " 'G1634',\n", - " 'G1697',\n", - " 'G1853',\n", - " 'G1854',\n", - " 'G1994',\n", - " 'G2017',\n", - " 'G2115',\n", - " 'G2193']" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])\n" + "metadata": {}, + "outputs": [], + "source": [ + "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])" ] }, { @@ -1469,60 +877,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "18ad4900", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:01:52.226732Z", - "iopub.status.busy": "2023-07-26T00:01:52.226617Z", - "iopub.status.idle": "2023-07-26T00:01:52.229181Z", - "shell.execute_reply": "2023-07-26T00:01:52.228899Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['G0097',\n", - " 'G0129',\n", - " 'G0158',\n", - " 'G0182',\n", - " 'G0242',\n", - " 'G0552',\n", - " 'G0679',\n", - " 'G0714',\n", - " 'G0751',\n", - " 'G0812',\n", - " 'G0908',\n", - " 'G0941',\n", - " 'G0982',\n", - " 'G1020',\n", - " 'G1022',\n", - " 'G1090',\n", - " 'G1240',\n", - " 'G1244',\n", - " 'G1320',\n", - " 'G1381',\n", - " 'G1514',\n", - " 'G1634',\n", - " 'G1697',\n", - " 'G1768',\n", - " 'G1853',\n", - " 'G1854',\n", - " 'G1907',\n", - " 'G1994',\n", - " 'G2017',\n", - " 'G2115',\n", - " 'G2193']" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])\n" + "metadata": {}, + "outputs": [], + "source": [ + "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])" ] }, { @@ -1537,53 +897,23 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "28c276b6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:01:52.230871Z", - "iopub.status.busy": "2023-07-26T00:01:52.230743Z", - "iopub.status.idle": "2023-07-26T00:01:52.309521Z", - "shell.execute_reply": "2023-07-26T00:01:52.309196Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(Rs, FDRs, 'b', linewidth=3)\n", "ax.set_xlabel(\"Number of Rejections\")\n", - "ax.set_ylabel(\"False Discovery Rate\");\n" + "ax.set_ylabel(\"False Discovery Rate\");" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch2-statlearn-lab.ipynb b/docs/source/labs/Ch2-statlearn-lab.ipynb index 674581e..d49a478 100644 --- a/docs/source/labs/Ch2-statlearn-lab.ipynb +++ b/docs/source/labs/Ch2-statlearn-lab.ipynb @@ -2,17 +2,15 @@ "cells": [ { "cell_type": "markdown", - "id": "06a14452", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "0025f21f", + "id": "46456e09", "metadata": {}, "source": [ "# Introduction to Python\n", - "\n" + "\n", + "\n", + " \"Open\n", + "\n", + "" ] }, { @@ -31,7 +29,7 @@ "To run the labs in this book, you will need two things:\n", "\n", "* An installation of `Python3`, which is the specific version of `Python` used in the labs. \n", - "* Access to `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*. " + "* Access to `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*." ] }, { @@ -39,7 +37,7 @@ "id": "f922cc9e", "metadata": {}, "source": [ - "You can download and install `Python3` by following the instructions available at [anaconda.com](http://anaconda.com). " + "You can download and install `Python3` by following the instructions available at [anaconda.com](http://anaconda.com)." ] }, { @@ -61,7 +59,7 @@ "To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. \n", "Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.\n", "\n", - "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell.\n" + "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell." ] }, { @@ -69,7 +67,7 @@ "id": "7a44fbd9", "metadata": {}, "source": [ - "## Basic Commands\n" + "## Basic Commands" ] }, { @@ -78,10 +76,7 @@ "metadata": {}, "source": [ "In this lab, we will introduce some simple `Python` commands. \n", - " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/). \n", - "\n", - "\n", - " \n" + " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/)." ] }, { @@ -101,27 +96,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "88d0bfff", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.336320Z", - "iopub.status.busy": "2023-07-25T23:59:03.335906Z", - "iopub.status.idle": "2023-07-25T23:59:03.345178Z", - "shell.execute_reply": "2023-07-25T23:59:03.344494Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fit a model with 11 variables\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ - "print('fit a model with', 11, 'variables')\n" + "print('fit a model with', 11, 'variables')" ] }, { @@ -134,19 +114,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ebe7aca8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.348214Z", - "iopub.status.busy": "2023-07-25T23:59:03.348018Z", - "iopub.status.idle": "2023-07-25T23:59:03.350992Z", - "shell.execute_reply": "2023-07-25T23:59:03.350615Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print?\n" + "print?" ] }, { @@ -159,30 +132,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "df2f8168", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.353557Z", - "iopub.status.busy": "2023-07-25T23:59:03.353389Z", - "iopub.status.idle": "2023-07-25T23:59:03.357986Z", - "shell.execute_reply": "2023-07-25T23:59:03.357641Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "3 + 5\n" + "metadata": {}, + "outputs": [], + "source": [ + "3 + 5" ] }, { @@ -199,30 +154,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "4cc5c2c1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.360072Z", - "iopub.status.busy": "2023-07-25T23:59:03.359922Z", - "iopub.status.idle": "2023-07-25T23:59:03.362322Z", - "shell.execute_reply": "2023-07-25T23:59:03.362042Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'hello world'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"hello\" + \" \" + \"world\"\n" + "metadata": {}, + "outputs": [], + "source": [ + "\"hello\" + \" \" + \"world\"" ] }, { @@ -232,7 +169,7 @@ "source": [ " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n", " The three most important types of sequences are lists, tuples, and strings. \n", - "We introduce lists now. " + "We introduce lists now." ] }, { @@ -248,31 +185,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e5d81180", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.364153Z", - "iopub.status.busy": "2023-07-25T23:59:03.364018Z", - "iopub.status.idle": "2023-07-25T23:59:03.366529Z", - "shell.execute_reply": "2023-07-25T23:59:03.366235Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[3, 4, 5]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = [3, 4, 5]\n", - "x\n" + "x" ] }, { @@ -289,31 +208,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "425a714c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.368332Z", - "iopub.status.busy": "2023-07-25T23:59:03.368198Z", - "iopub.status.idle": "2023-07-25T23:59:03.370618Z", - "shell.execute_reply": "2023-07-25T23:59:03.370345Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[3, 4, 5, 4, 9, 7]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "y = [4, 9, 7]\n", - "x + y\n" + "x + y" ] }, { @@ -324,8 +225,7 @@ "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n", "element-by-element? \n", " In `Python`, lists hold *arbitrary* objects, and are added using *concatenation*. \n", - " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`. \n", - " " + " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`." ] }, { @@ -338,7 +238,7 @@ "functionality comes from other packages, notably `numpy`\n", "and `pandas`. \n", "In the next section, we will introduce the `numpy` package. More information about `numpy` can be found at \n", - "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html).\n" + "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html)." ] }, { @@ -350,7 +250,7 @@ "\n", "As mentioned earlier, this book makes use of functionality that is contained in the `numpy` \n", " *library*, or *package*. A package is a collection of modules that are not necessarily included in \n", - " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*. " + " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*." ] }, { @@ -363,17 +263,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "2f88669a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.372629Z", - "iopub.status.busy": "2023-07-25T23:59:03.372498Z", - "iopub.status.idle": "2023-07-25T23:59:03.424071Z", - "shell.execute_reply": "2023-07-25T23:59:03.423098Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np " @@ -399,17 +291,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "e8ec382a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.429959Z", - "iopub.status.busy": "2023-07-25T23:59:03.429583Z", - "iopub.status.idle": "2023-07-25T23:59:03.436080Z", - "shell.execute_reply": "2023-07-25T23:59:03.435328Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "x = np.array([3, 4, 5])\n", @@ -424,7 +308,7 @@ "Note that if you forgot to run the `import numpy as np` command earlier, then\n", "you will encounter an error in calling the `np.array()` function in the previous line. \n", " The syntax `np.array()` indicates that the function being called\n", - "is part of the `numpy` package, which we have abbreviated as `np`. " + "is part of the `numpy` package, which we have abbreviated as `np`." ] }, { @@ -433,45 +317,17 @@ "metadata": {}, "source": [ "Since `x` and `y` have been defined using `np.array()`, we get a sensible result when we add them together. Compare this to our results in the previous section,\n", - " when we tried to add two lists without using `numpy`. " + " when we tried to add two lists without using `numpy`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "04de39ad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.441429Z", - "iopub.status.busy": "2023-07-25T23:59:03.440989Z", - "iopub.status.idle": "2023-07-25T23:59:03.449302Z", - "shell.execute_reply": "2023-07-25T23:59:03.448567Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 7, 13, 12])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x + y" - ] - }, - { - "cell_type": "markdown", - "id": "c04d19a8", "metadata": {}, + "outputs": [], "source": [ - " \n", - " \n" + "x + y" ] }, { @@ -480,49 +336,20 @@ "metadata": {}, "source": [ "In `numpy`, matrices are typically represented as two-dimensional arrays, and vectors as one-dimensional arrays. {While it is also possible to create matrices using `np.matrix()`, we will use `np.array()` throughout the labs in this book.}\n", - "We can create a two-dimensional array as follows. " + "We can create a two-dimensional array as follows." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "fee5d798", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.454224Z", - "iopub.status.busy": "2023-07-25T23:59:03.453864Z", - "iopub.status.idle": "2023-07-25T23:59:03.463532Z", - "shell.execute_reply": "2023-07-25T23:59:03.462539Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2],\n", - " [3, 4]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = np.array([[1, 2], [3, 4]])\n", "x" ] }, - { - "cell_type": "markdown", - "id": "477c992d", - "metadata": {}, - "source": [ - " \n", - "\n" - ] - }, { "cell_type": "markdown", "id": "86de84a5", @@ -531,33 +358,15 @@ "The object `x` has several \n", "*attributes*, or associated objects. To access an attribute of `x`, we type `x.attribute`, where we replace `attribute`\n", "with the name of the attribute. \n", - "For instance, we can access the `ndim` attribute of `x` as follows. " + "For instance, we can access the `ndim` attribute of `x` as follows." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "5871ca00", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.468600Z", - "iopub.status.busy": "2023-07-25T23:59:03.468292Z", - "iopub.status.idle": "2023-07-25T23:59:03.475945Z", - "shell.execute_reply": "2023-07-25T23:59:03.475149Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x.ndim" ] @@ -574,29 +383,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "5a32759c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.481118Z", - "iopub.status.busy": "2023-07-25T23:59:03.480680Z", - "iopub.status.idle": "2023-07-25T23:59:03.488333Z", - "shell.execute_reply": "2023-07-25T23:59:03.486555Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype('int64')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x.dtype" ] @@ -609,36 +399,17 @@ "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n", " If\n", "we had passed in any decimals, then we would have obtained an array of\n", - "*floating point numbers* (i.e. real-valued numbers). " + "*floating point numbers* (i.e. real-valued numbers)." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "55f01b16", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.495516Z", - "iopub.status.busy": "2023-07-25T23:59:03.494194Z", - "iopub.status.idle": "2023-07-25T23:59:03.507143Z", - "shell.execute_reply": "2023-07-25T23:59:03.506347Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype('float64')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array([[1, 2], [3.0, 4]]).dtype\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.array([[1, 2], [3.0, 4]]).dtype" ] }, { @@ -648,25 +419,17 @@ "source": [ "Typing `fun?` will cause `Python` to display \n", "documentation associated with the function `fun`, if it exists.\n", - "We can try this for `np.array()`. " + "We can try this for `np.array()`." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "2a9de618", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.516160Z", - "iopub.status.busy": "2023-07-25T23:59:03.515522Z", - "iopub.status.idle": "2023-07-25T23:59:03.522135Z", - "shell.execute_reply": "2023-07-25T23:59:03.521022Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "np.array?\n" + "np.array?" ] }, { @@ -679,31 +442,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "33e3e6ea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.528006Z", - "iopub.status.busy": "2023-07-25T23:59:03.527073Z", - "iopub.status.idle": "2023-07-25T23:59:03.535281Z", - "shell.execute_reply": "2023-07-25T23:59:03.533210Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype('float64')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array([[1, 2], [3, 4]], float).dtype\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.array([[1, 2], [3, 4]], float).dtype" ] }, { @@ -717,31 +461,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "623f8434", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.540712Z", - "iopub.status.busy": "2023-07-25T23:59:03.540315Z", - "iopub.status.idle": "2023-07-25T23:59:03.545866Z", - "shell.execute_reply": "2023-07-25T23:59:03.545195Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 2)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.shape\n" + "metadata": {}, + "outputs": [], + "source": [ + "x.shape" ] }, { @@ -760,29 +485,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "235738a7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.550091Z", - "iopub.status.busy": "2023-07-25T23:59:03.549770Z", - "iopub.status.idle": "2023-07-25T23:59:03.555222Z", - "shell.execute_reply": "2023-07-25T23:59:03.554501Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = np.array([1, 2, 3, 4])\n", "x.sum()" @@ -793,34 +499,15 @@ "id": "72373ccf", "metadata": {}, "source": [ - "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function. " + "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "7f3494e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.559801Z", - "iopub.status.busy": "2023-07-25T23:59:03.559468Z", - "iopub.status.idle": "2023-07-25T23:59:03.565409Z", - "shell.execute_reply": "2023-07-25T23:59:03.564548Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = np.array([1, 2, 3, 4])\n", "np.sum(x)" @@ -844,34 +531,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "0ca8f936", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.570100Z", - "iopub.status.busy": "2023-07-25T23:59:03.569563Z", - "iopub.status.idle": "2023-07-25T23:59:03.575143Z", - "shell.execute_reply": "2023-07-25T23:59:03.574126Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "beginning x:\n", - " [1 2 3 4 5 6]\n", - "reshaped x:\n", - " [[1 2 3]\n", - " [4 5 6]]\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = np.array([1, 2, 3, 4, 5, 6])\n", "print('beginning x:\\n', x)\n", "x_reshape = x.reshape((2, 3))\n", - "print('reshaped x:\\n', x_reshape)\n" + "print('reshaped x:\\n', x_reshape)" ] }, { @@ -880,7 +548,7 @@ "metadata": {}, "source": [ "The previous output reveals that `numpy` arrays are specified as a sequence\n", - "of *rows*. This is called *row-major ordering*, as opposed to *column-major ordering*. " + "of *rows*. This is called *row-major ordering*, as opposed to *column-major ordering*." ] }, { @@ -895,29 +563,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "23fba624", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.579248Z", - "iopub.status.busy": "2023-07-25T23:59:03.578903Z", - "iopub.status.idle": "2023-07-25T23:59:03.584347Z", - "shell.execute_reply": "2023-07-25T23:59:03.583515Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x_reshape[0, 0] " ] @@ -928,34 +577,15 @@ "metadata": {}, "source": [ "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n", - "of `x_reshape`. " + "of `x_reshape`." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "1a1df926", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.588305Z", - "iopub.status.busy": "2023-07-25T23:59:03.587875Z", - "iopub.status.idle": "2023-07-25T23:59:03.596795Z", - "shell.execute_reply": "2023-07-25T23:59:03.596057Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x_reshape[1, 2] " ] @@ -968,46 +598,21 @@ "Similarly, `x[2]` yields the\n", "third entry of `x`. \n", "\n", - "Now, let's modify the top left element of `x_reshape`. To our surprise, we discover that the first element of `x` has been modified as well!\n", - "\n" + "Now, let's modify the top left element of `x_reshape`. To our surprise, we discover that the first element of `x` has been modified as well!" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "4bfcc1e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.601576Z", - "iopub.status.busy": "2023-07-25T23:59:03.600688Z", - "iopub.status.idle": "2023-07-25T23:59:03.607941Z", - "shell.execute_reply": "2023-07-25T23:59:03.606891Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x before we modify x_reshape:\n", - " [1 2 3 4 5 6]\n", - "x_reshape before we modify x_reshape:\n", - " [[1 2 3]\n", - " [4 5 6]]\n", - "x_reshape after we modify its top left element:\n", - " [[5 2 3]\n", - " [4 5 6]]\n", - "x after we modify top left element of x_reshape:\n", - " [5 2 3 4 5 6]\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print('x before we modify x_reshape:\\n', x)\n", "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n", "x_reshape[0, 0] = 5\n", "print('x_reshape after we modify its top left element:\\n', x_reshape)\n", - "print('x after we modify top left element of x_reshape:\\n', x)\n" + "print('x after we modify top left element of x_reshape:\\n', x)" ] }, { @@ -1015,10 +620,7 @@ "id": "10ff8c21", "metadata": {}, "source": [ - "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory.\n", - " \n", - "\n", - " " + "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory." ] }, { @@ -1032,33 +634,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "973c562a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.616078Z", - "iopub.status.busy": "2023-07-25T23:59:03.615705Z", - "iopub.status.idle": "2023-07-25T23:59:03.778706Z", - "shell.execute_reply": "2023-07-25T23:59:03.778393Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'tuple' object does not support item assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m my_tuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n", - "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "my_tuple = (3, 4, 5)\n", - "my_tuple[0] = 2\n" + "my_tuple[0] = 2" ] }, { @@ -1067,39 +649,17 @@ "metadata": {}, "source": [ "We now briefly mention some attributes of arrays that will come in handy. An array's `shape` attribute contains its dimension; this is always a tuple.\n", - "The `ndim` attribute yields the number of dimensions, and `T` provides its transpose. " + "The `ndim` attribute yields the number of dimensions, and `T` provides its transpose." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "56b5c382", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.780591Z", - "iopub.status.busy": "2023-07-25T23:59:03.780456Z", - "iopub.status.idle": "2023-07-25T23:59:03.782964Z", - "shell.execute_reply": "2023-07-25T23:59:03.782700Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((2, 3),\n", - " 2,\n", - " array([[5, 4],\n", - " [2, 5],\n", - " [3, 6]]))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_reshape.shape, x_reshape.ndim, x_reshape.T\n" + "metadata": {}, + "outputs": [], + "source": [ + "x_reshape.shape, x_reshape.ndim, x_reshape.T" ] }, { @@ -1111,36 +671,17 @@ " \n", "We will often want to apply functions to arrays. \n", "For instance, we can compute the\n", - "square root of the entries using the `np.sqrt()` function: " + "square root of the entries using the `np.sqrt()` function:" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "2e624622", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.784591Z", - "iopub.status.busy": "2023-07-25T23:59:03.784467Z", - "iopub.status.idle": "2023-07-25T23:59:03.786863Z", - "shell.execute_reply": "2023-07-25T23:59:03.786571Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.23606798, 1.41421356, 1.73205081, 2. , 2.23606798,\n", - " 2.44948974])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sqrt(x)\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(x)" ] }, { @@ -1153,30 +694,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "6f45bd25", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.788435Z", - "iopub.status.busy": "2023-07-25T23:59:03.788312Z", - "iopub.status.idle": "2023-07-25T23:59:03.790500Z", - "shell.execute_reply": "2023-07-25T23:59:03.790237Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([25, 4, 9, 16, 25, 36])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x**2\n" + "metadata": {}, + "outputs": [], + "source": [ + "x**2" ] }, { @@ -1189,32 +712,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "6980f628", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.792166Z", - "iopub.status.busy": "2023-07-25T23:59:03.792045Z", - "iopub.status.idle": "2023-07-25T23:59:03.794224Z", - "shell.execute_reply": "2023-07-25T23:59:03.793976Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.23606798, 1.41421356, 1.73205081, 2. , 2.23606798,\n", - " 2.44948974])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x**0.5\n" + "metadata": {}, + "outputs": [], + "source": [ + "x**0.5" ] }, { @@ -1231,45 +734,18 @@ " By default, this function will generate random normal variable(s) with mean (`loc`) $0$ and standard deviation (`scale`) $1$; furthermore, \n", " a single random variable will be generated unless the argument to `size` is changed. \n", "\n", - "We now generate 50 independent random variables from a $N(0,1)$ distribution. " + "We now generate 50 independent random variables from a $N(0,1)$ distribution." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "89e32100", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.795835Z", - "iopub.status.busy": "2023-07-25T23:59:03.795720Z", - "iopub.status.idle": "2023-07-25T23:59:03.798190Z", - "shell.execute_reply": "2023-07-25T23:59:03.797947Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.16941099, -1.65360995, 0.385241 , 1.37725872, -0.33090576,\n", - " 0.95225998, 1.38477584, 0.03399787, -1.01406121, -0.30788104,\n", - " 0.93226375, -0.70561647, -0.59502338, 0.0957298 , -0.54067538,\n", - " 1.23057972, -1.09400881, 0.08400338, 0.42994564, 0.30192903,\n", - " -1.35097931, -0.5317783 , -0.00317528, 0.95776295, -0.38856995,\n", - " 0.89187793, -0.83973487, 0.36758703, -1.94125031, -0.4910692 ,\n", - " 1.43410404, -1.68234372, 1.23075143, -0.26745071, 0.71955302,\n", - " -0.76446116, -0.69642788, 1.18213409, -1.93529368, -0.32528807,\n", - " -0.86482767, 1.11610801, -0.23379597, -0.67712932, -0.55311178,\n", - " -1.22714226, 0.17073539, -0.73072959, 0.50918715, 1.73268696])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "x = np.random.normal(size=50)\n", - "x\n" + "x" ] }, { @@ -1282,17 +758,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "e3428155", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.799778Z", - "iopub.status.busy": "2023-07-25T23:59:03.799649Z", - "iopub.status.idle": "2023-07-25T23:59:03.801472Z", - "shell.execute_reply": "2023-07-25T23:59:03.801218Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "y = x + np.random.normal(loc=50, scale=1, size=50)" @@ -1304,34 +772,15 @@ "metadata": {}, "source": [ "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n", - "correlation between `x` and `y`. " + "correlation between `x` and `y`." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "df70172e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.803109Z", - "iopub.status.busy": "2023-07-25T23:59:03.802986Z", - "iopub.status.idle": "2023-07-25T23:59:03.805319Z", - "shell.execute_reply": "2023-07-25T23:59:03.805046Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1. , 0.64138518],\n", - " [0.64138518, 1. ]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "np.corrcoef(x, y)" ] @@ -1349,38 +798,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "1d996956", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.806958Z", - "iopub.status.busy": "2023-07-25T23:59:03.806838Z", - "iopub.status.idle": "2023-07-25T23:59:03.809025Z", - "shell.execute_reply": "2023-07-25T23:59:03.808727Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.0841301 3.82641665]\n", - "[ 1.97096548 -10.3124576 ]\n" - ] - } - ], - "source": [ - "print(np.random.normal(scale=5, size=2))\n", - "print(np.random.normal(scale=5, size=2)) \n" - ] - }, - { - "cell_type": "markdown", - "id": "d3d0e0fb", "metadata": {}, + "outputs": [], "source": [ - " " + "print(np.random.normal(scale=5, size=2))\n", + "print(np.random.normal(scale=5, size=2)) " ] }, { @@ -1400,26 +824,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "37b3a30b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.810769Z", - "iopub.status.busy": "2023-07-25T23:59:03.810650Z", - "iopub.status.idle": "2023-07-25T23:59:03.813103Z", - "shell.execute_reply": "2023-07-25T23:59:03.812814Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 4.09482632 -1.07485605]\n", - "[ 4.09482632 -1.07485605]\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(1303)\n", "print(rng.normal(scale=5, size=2))\n", @@ -1446,68 +854,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "b4f99e0e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.814729Z", - "iopub.status.busy": "2023-07-25T23:59:03.814608Z", - "iopub.status.idle": "2023-07-25T23:59:03.817050Z", - "shell.execute_reply": "2023-07-25T23:59:03.816803Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.1126795190952861, -0.1126795190952861)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(3)\n", "y = rng.standard_normal(10)\n", "np.mean(y), y.mean()" ] }, - { - "cell_type": "markdown", - "id": "3b42d9c6", - "metadata": {}, - "source": [ - " \n" - ] - }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "dbd70319", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.818703Z", - "iopub.status.busy": "2023-07-25T23:59:03.818597Z", - "iopub.status.idle": "2023-07-25T23:59:03.820913Z", - "shell.execute_reply": "2023-07-25T23:59:03.820642Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2.7243406406465125, 2.7243406406465125, 2.7243406406465125)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "np.var(y), y.var(), np.mean((y - y.mean())**2)" ] @@ -1518,33 +880,15 @@ "metadata": {}, "source": [ "Notice that by default `np.var()` divides by the sample size $n$ rather\n", - "than $n-1$; see the `ddof` argument in `np.var?`.\n" + "than $n-1$; see the `ddof` argument in `np.var?`." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "bf281844", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.822530Z", - "iopub.status.busy": "2023-07-25T23:59:03.822409Z", - "iopub.status.idle": "2023-07-25T23:59:03.824738Z", - "shell.execute_reply": "2023-07-25T23:59:03.824473Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.6505576756498128, 1.6505576756498128)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "np.sqrt(np.var(y)), np.std(y)" ] @@ -1555,42 +899,15 @@ "metadata": {}, "source": [ "The `np.mean()`, `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n", - "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums. " + "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "f751b1dd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.826313Z", - "iopub.status.busy": "2023-07-25T23:59:03.826201Z", - "iopub.status.idle": "2023-07-25T23:59:03.828777Z", - "shell.execute_reply": "2023-07-25T23:59:03.828496Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.22578661, -0.35263079, -0.28128742],\n", - " [-0.66804635, -1.05515055, -0.39080098],\n", - " [ 0.48194539, -0.23855361, 0.9577587 ],\n", - " [-0.19980213, 0.02425957, 1.54582085],\n", - " [ 0.54510552, -0.50522874, -0.18283897],\n", - " [ 0.54052513, 1.93508803, -0.26962033],\n", - " [-0.24355868, 1.0023136 , -0.88645994],\n", - " [-0.29172023, 0.88253897, 0.58035002],\n", - " [ 0.0915167 , 0.67010435, -2.82816231],\n", - " [ 1.02130682, -0.95964476, -1.66861984]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = rng.standard_normal((10, 3))\n", "X" @@ -1601,33 +918,15 @@ "id": "f9028e1a", "metadata": {}, "source": [ - "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`. " + "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`." ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "cbf92de5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.830471Z", - "iopub.status.busy": "2023-07-25T23:59:03.830350Z", - "iopub.status.idle": "2023-07-25T23:59:03.832659Z", - "shell.execute_reply": "2023-07-25T23:59:03.832413Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.15030588, 0.14030961, -0.34238602])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X.mean(axis=0)" ] @@ -1642,39 +941,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "efacc213", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.834289Z", - "iopub.status.busy": "2023-07-25T23:59:03.834173Z", - "iopub.status.idle": "2023-07-25T23:59:03.836374Z", - "shell.execute_reply": "2023-07-25T23:59:03.836131Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.15030588, 0.14030961, -0.34238602])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.mean(0)" - ] - }, - { - "cell_type": "markdown", - "id": "4b70cd2e", "metadata": {}, + "outputs": [], "source": [ - " " + "X.mean(0)" ] }, { @@ -1712,34 +984,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "637fb67d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:03.838008Z", - "iopub.status.busy": "2023-07-25T23:59:03.837889Z", - "iopub.status.idle": "2023-07-25T23:59:04.142408Z", - "shell.execute_reply": "2023-07-25T23:59:04.141987Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "from matplotlib.pyplot import subplots\n", "fig, ax = subplots(figsize=(8, 8))\n", "x = rng.standard_normal(100)\n", "y = rng.standard_normal(100)\n", - "ax.plot(x, y);\n" + "ax.plot(x, y);" ] }, { @@ -1754,28 +1008,10 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "109d681d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.144429Z", - "iopub.status.busy": "2023-07-25T23:59:04.144254Z", - "iopub.status.idle": "2023-07-25T23:59:04.222416Z", - "shell.execute_reply": "2023-07-25T23:59:04.222078Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.plot(x, y, 'o');" @@ -1827,7 +1044,7 @@ "source": [ "Different values\n", "of this additional argument can be used to produce different colored lines\n", - "as well as different linestyles. \n" + "as well as different linestyles." ] }, { @@ -1840,28 +1057,10 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "27133a4e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.308443Z", - "iopub.status.busy": "2023-07-25T23:59:04.308303Z", - "iopub.status.idle": "2023-07-25T23:59:04.393126Z", - "shell.execute_reply": "2023-07-25T23:59:04.392823Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(x, y, marker='o');" @@ -1875,47 +1074,18 @@ "Notice that in the code blocks above, we have ended\n", "the last line with a semicolon. This prevents `ax.plot(x, y)` from printing\n", "text to the notebook. However, it does not prevent a plot from being produced. \n", - " If we omit the trailing semi-colon, then we obtain the following output: " + " If we omit the trailing semi-colon, then we obtain the following output:" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "ef41f90d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.395040Z", - "iopub.status.busy": "2023-07-25T23:59:04.394901Z", - "iopub.status.idle": "2023-07-25T23:59:04.506567Z", - "shell.execute_reply": "2023-07-25T23:59:04.506227Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(x, y, marker='o')\n" + "ax.scatter(x, y, marker='o')" ] }, { @@ -1925,10 +1095,7 @@ "source": [ "In what follows, we will use\n", " trailing semicolons whenever the text that would be output is not\n", - "germane to the discussion at hand.\n", - "\n", - "\n", - "\n" + "germane to the discussion at hand." ] }, { @@ -1937,34 +1104,15 @@ "metadata": {}, "source": [ "To label our plot, we make use of the `set_xlabel()`, `set_ylabel()`, and `set_title()` methods\n", - "of `ax`.\n", - " " + "of `ax`." ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "16a9cb99", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.508470Z", - "iopub.status.busy": "2023-07-25T23:59:04.508345Z", - "iopub.status.idle": "2023-07-25T23:59:04.612557Z", - "shell.execute_reply": "2023-07-25T23:59:04.612259Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(x, y, marker='o')\n", @@ -1979,48 +1127,20 @@ "metadata": {}, "source": [ " Having access to the figure object `fig` itself means that we can go in and change some aspects and then redisplay it. Here, we change\n", - " the size from `(8, 8)` to `(12, 3)`.\n" + " the size from `(8, 8)` to `(12, 3)`." ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "6707420e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.614466Z", - "iopub.status.busy": "2023-07-25T23:59:04.614335Z", - "iopub.status.idle": "2023-07-25T23:59:04.681782Z", - "shell.execute_reply": "2023-07-25T23:59:04.681462Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig.set_size_inches(12,3)\n", "fig" ] }, - { - "cell_type": "markdown", - "id": "00c8816e", - "metadata": {}, - "source": [ - " " - ] - }, { "cell_type": "markdown", "id": "3cf4ed3f", @@ -2033,34 +1153,15 @@ "situations, there is often a relationship between the axes in the plots. For example,\n", "all plots may have a common $x$-axis. The `subplots()` function can automatically handle\n", "this situation when passed the keyword argument `sharex=True`.\n", - "The `axes` object below is an array pointing to different plots in the figure. " + "The `axes` object below is an array pointing to different plots in the figure." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "6c78ef8b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:04.683675Z", - "iopub.status.busy": "2023-07-25T23:59:04.683534Z", - "iopub.status.idle": "2023-07-25T23:59:04.980489Z", - "shell.execute_reply": "2023-07-25T23:59:04.980118Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "axes[0,1].plot(x, y, 'o')\n", "axes[1,2].scatter(x, y, marker='+')\n", @@ -2114,9 +1195,7 @@ "metadata": {}, "source": [ "Type `subplots?` to learn more about \n", - "`subplots()`. \n", - "\n", - "\n" + "`subplots()`." ] }, { @@ -2131,21 +1210,13 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "80e18950", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:05.188103Z", - "iopub.status.busy": "2023-07-25T23:59:05.187975Z", - "iopub.status.idle": "2023-07-25T23:59:06.284103Z", - "shell.execute_reply": "2023-07-25T23:59:06.283751Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "fig.savefig(\"Figure.png\", dpi=400)\n", - "fig.savefig(\"Figure.pdf\", dpi=200);\n" + "fig.savefig(\"Figure.pdf\", dpi=200);" ] }, { @@ -2153,34 +1224,15 @@ "id": "b8e17a71", "metadata": {}, "source": [ - "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it. " + "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "fe8c404d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.286372Z", - "iopub.status.busy": "2023-07-25T23:59:06.286067Z", - "iopub.status.idle": "2023-07-25T23:59:06.544075Z", - "shell.execute_reply": "2023-07-25T23:59:06.543737Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "x = np.linspace(-np.pi, np.pi, 50)\n", "y = x\n", "f = np.multiply.outer(np.cos(y), 1 / (1 + x**2))\n", - "ax.contour(x, y, f);\n" + "ax.contour(x, y, f);" ] }, { @@ -2249,29 +1282,10 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "c613a5df", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.655043Z", - "iopub.status.busy": "2023-07-25T23:59:06.654897Z", - "iopub.status.idle": "2023-07-25T23:59:06.789816Z", - "shell.execute_reply": "2023-07-25T23:59:06.789401Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.contour(x, y, f, levels=45);" @@ -2295,32 +1309,13 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "bef21527", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.793238Z", - "iopub.status.busy": "2023-07-25T23:59:06.793090Z", - "iopub.status.idle": "2023-07-25T23:59:06.903016Z", - "shell.execute_reply": "2023-07-25T23:59:06.902666Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "ax.imshow(f);\n" + "ax.imshow(f);" ] }, { @@ -2343,32 +1338,13 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "4435ed50", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.904985Z", - "iopub.status.busy": "2023-07-25T23:59:06.904852Z", - "iopub.status.idle": "2023-07-25T23:59:06.907631Z", - "shell.execute_reply": "2023-07-25T23:59:06.907307Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "seq1 = np.linspace(0, 10, 11)\n", - "seq1\n" + "seq1" ] }, { @@ -2383,31 +1359,13 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "7a2c664a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.909501Z", - "iopub.status.busy": "2023-07-25T23:59:06.909370Z", - "iopub.status.idle": "2023-07-25T23:59:06.911861Z", - "shell.execute_reply": "2023-07-25T23:59:06.911478Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "seq2 = np.arange(0, 10)\n", - "seq2\n" + "seq2" ] }, { @@ -2424,29 +1382,10 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "810eafdf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.914013Z", - "iopub.status.busy": "2023-07-25T23:59:06.913879Z", - "iopub.status.idle": "2023-07-25T23:59:06.916405Z", - "shell.execute_reply": "2023-07-25T23:59:06.916096Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'lo '" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "\"hello world\"[3:6]" ] @@ -2457,35 +1396,17 @@ "metadata": {}, "source": [ "In the code block above, the notation `3:6` is shorthand for `slice(3,6)` when used inside\n", - "`[]`. " + "`[]`." ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "f7a9f652", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.918188Z", - "iopub.status.busy": "2023-07-25T23:59:06.918057Z", - "iopub.status.idle": "2023-07-25T23:59:06.920501Z", - "shell.execute_reply": "2023-07-25T23:59:06.920172Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'lo '" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"hello world\"[slice(3,6)]\n" + "metadata": {}, + "outputs": [], + "source": [ + "\"hello world\"[slice(3,6)]" ] }, { @@ -2495,27 +1416,7 @@ "source": [ "You might have expected `slice(3,6)` to output the fourth through seventh characters in the text string (recalling that `Python` begins its indexing at zero), but instead it output the fourth through sixth. \n", " This also explains why the earlier `np.arange(0, 10)` command output only the integers from $0$ to $9$. \n", - "See the documentation `slice?` for useful options in creating slices. \n", - "\n", - " \n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - "\n", - " \n" + "See the documentation `slice?` for useful options in creating slices." ] }, { @@ -2529,34 +1430,13 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "24427538", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.922296Z", - "iopub.status.busy": "2023-07-25T23:59:06.922171Z", - "iopub.status.idle": "2023-07-25T23:59:06.924760Z", - "shell.execute_reply": "2023-07-25T23:59:06.924501Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2, 3],\n", - " [ 4, 5, 6, 7],\n", - " [ 8, 9, 10, 11],\n", - " [12, 13, 14, 15]])" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "A = np.array(np.arange(16)).reshape((4, 4))\n", - "A\n" + "A" ] }, { @@ -2570,30 +1450,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "18684ed5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.926485Z", - "iopub.status.busy": "2023-07-25T23:59:06.926366Z", - "iopub.status.idle": "2023-07-25T23:59:06.928716Z", - "shell.execute_reply": "2023-07-25T23:59:06.928382Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[1,2]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[1,2]" ] }, { @@ -2611,31 +1473,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "34d27b73", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.930420Z", - "iopub.status.busy": "2023-07-25T23:59:06.930302Z", - "iopub.status.idle": "2023-07-25T23:59:06.932693Z", - "shell.execute_reply": "2023-07-25T23:59:06.932407Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 5, 6, 7],\n", - " [12, 13, 14, 15]])" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[[1,3]]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3]]" ] }, { @@ -2650,33 +1493,12 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "1bb799c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.934417Z", - "iopub.status.busy": "2023-07-25T23:59:06.934293Z", - "iopub.status.idle": "2023-07-25T23:59:06.936701Z", - "shell.execute_reply": "2023-07-25T23:59:06.936383Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 2],\n", - " [ 4, 6],\n", - " [ 8, 10],\n", - " [12, 14]])" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[:,[0,2]]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[:,[0,2]]" ] }, { @@ -2691,30 +1513,12 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "de853e7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.938410Z", - "iopub.status.busy": "2023-07-25T23:59:06.938279Z", - "iopub.status.idle": "2023-07-25T23:59:06.940843Z", - "shell.execute_reply": "2023-07-25T23:59:06.940553Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 4, 14])" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[[1,3],[0,2]]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3],[0,2]]" ] }, { @@ -2727,30 +1531,12 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "e71bf672", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.942617Z", - "iopub.status.busy": "2023-07-25T23:59:06.942490Z", - "iopub.status.idle": "2023-07-25T23:59:06.945126Z", - "shell.execute_reply": "2023-07-25T23:59:06.944774Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 4, 14])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array([A[1,0],A[3,2]])\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.array([A[1,0],A[3,2]])" ] }, { @@ -2763,31 +1549,12 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "458fe5c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.946927Z", - "iopub.status.busy": "2023-07-25T23:59:06.946793Z", - "iopub.status.idle": "2023-07-25T23:59:06.973966Z", - "shell.execute_reply": "2023-07-25T23:59:06.973611Z" - } - }, - "outputs": [ - { - "ename": "IndexError", - "evalue": "shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[63], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mA\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n", - "\u001b[0;31mIndexError\u001b[0m: shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) " - ] - } - ], - "source": [ - "A[[1,3],[0,2,3]]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[[1,3],[0,2,3]]" ] }, { @@ -2797,45 +1564,17 @@ "source": [ "We can see what has gone wrong here. When supplied with two indexing lists, the `numpy` interpretation is that these provide pairs of $i,j$ indices for a series of entries. That is why the pair of lists must have the same length. However, that was not our intent, since we are looking for a submatrix.\n", "\n", - "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns.\n" + "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns." ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "id": "703832ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.976099Z", - "iopub.status.busy": "2023-07-25T23:59:06.975973Z", - "iopub.status.idle": "2023-07-25T23:59:06.978782Z", - "shell.execute_reply": "2023-07-25T23:59:06.978390Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 6],\n", - " [12, 14]])" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[[1,3]][:,[0,2]]\n" - ] - }, - { - "cell_type": "markdown", - "id": "63980dff", "metadata": {}, + "outputs": [], "source": [ - " " + "A[[1,3]][:,[0,2]]" ] }, { @@ -2851,33 +1590,13 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "dbf328b7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.980758Z", - "iopub.status.busy": "2023-07-25T23:59:06.980608Z", - "iopub.status.idle": "2023-07-25T23:59:06.983480Z", - "shell.execute_reply": "2023-07-25T23:59:06.983138Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 6, 7],\n", - " [12, 14, 15]])" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "idx = np.ix_([1,3],[0,2,3])\n", - "A[idx]\n" + "A[idx]" ] }, { @@ -2894,40 +1613,12 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "65784eb0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.985438Z", - "iopub.status.busy": "2023-07-25T23:59:06.985317Z", - "iopub.status.idle": "2023-07-25T23:59:06.987978Z", - "shell.execute_reply": "2023-07-25T23:59:06.987611Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 6],\n", - " [12, 14]])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[1:4:2,0:3:2]\n" - ] - }, - { - "cell_type": "markdown", - "id": "0c679e7b", "metadata": {}, + "outputs": [], "source": [ - " " + "A[1:4:2,0:3:2]" ] }, { @@ -2938,18 +1629,7 @@ "Why are we able to retrieve a submatrix directly using slices but not using lists?\n", "Its because they are different `Python` types, and\n", "are treated differently by `numpy`.\n", - "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited.\n", - "\n", - "\n", - "\n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " " + "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited." ] }, { @@ -2959,34 +1639,15 @@ "source": [ "### Boolean Indexing\n", "In `numpy`, a *Boolean* is a type that equals either `True` or `False` (also represented as $1$ and $0$, respectively).\n", - "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`. " + "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`." ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "3ddb0d83", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.990333Z", - "iopub.status.busy": "2023-07-25T23:59:06.990160Z", - "iopub.status.idle": "2023-07-25T23:59:06.993012Z", - "shell.execute_reply": "2023-07-25T23:59:06.992640Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, False, False, False])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "keep_rows = np.zeros(A.shape[0], bool)\n", "keep_rows" @@ -2997,36 +1658,18 @@ "id": "f3007c8d", "metadata": {}, "source": [ - "We now set two of the elements to `True`. " + "We now set two of the elements to `True`." ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "90e72009", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.994861Z", - "iopub.status.busy": "2023-07-25T23:59:06.994740Z", - "iopub.status.idle": "2023-07-25T23:59:06.997372Z", - "shell.execute_reply": "2023-07-25T23:59:06.997059Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, True, False, True])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "keep_rows[[1,3]] = True\n", - "keep_rows\n" + "keep_rows" ] }, { @@ -3041,30 +1684,12 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "fb3b6534", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:06.999118Z", - "iopub.status.busy": "2023-07-25T23:59:06.998976Z", - "iopub.status.idle": "2023-07-25T23:59:07.001465Z", - "shell.execute_reply": "2023-07-25T23:59:07.001171Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.all(keep_rows == np.array([0,1,0,1]))\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.all(keep_rows == np.array([0,1,0,1]))" ] }, { @@ -3082,38 +1707,17 @@ "metadata": {}, "source": [ " However, even though `np.array([0,1,0,1])` and `keep_rows` are equal according to `==`, they index different sets of rows!\n", - "The former retrieves the first, second, first, and second rows of `A`. " + "The former retrieves the first, second, first, and second rows of `A`." ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "b146db22", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.003376Z", - "iopub.status.busy": "2023-07-25T23:59:07.003257Z", - "iopub.status.idle": "2023-07-25T23:59:07.005689Z", - "shell.execute_reply": "2023-07-25T23:59:07.005311Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1, 2, 3],\n", - " [4, 5, 6, 7],\n", - " [0, 1, 2, 3],\n", - " [4, 5, 6, 7]])" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[np.array([0,1,0,1])]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[np.array([0,1,0,1])]" ] }, { @@ -3121,36 +1725,17 @@ "id": "c0b18644", "metadata": {}, "source": [ - " By contrast, `keep_rows` retrieves only the second and fourth rows of `A` --- i.e. the rows for which the Boolean equals `TRUE`. " + " By contrast, `keep_rows` retrieves only the second and fourth rows of `A` --- i.e. the rows for which the Boolean equals `TRUE`." ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "6bf7b4a3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.007666Z", - "iopub.status.busy": "2023-07-25T23:59:07.007533Z", - "iopub.status.idle": "2023-07-25T23:59:07.009740Z", - "shell.execute_reply": "2023-07-25T23:59:07.009471Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 5, 6, 7],\n", - " [12, 13, 14, 15]])" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A[keep_rows]\n" + "metadata": {}, + "outputs": [], + "source": [ + "A[keep_rows]" ] }, { @@ -3173,34 +1758,15 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "288011d9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.011387Z", - "iopub.status.busy": "2023-07-25T23:59:07.011272Z", - "iopub.status.idle": "2023-07-25T23:59:07.014033Z", - "shell.execute_reply": "2023-07-25T23:59:07.013669Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 6, 7],\n", - " [12, 14, 15]])" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "keep_cols = np.zeros(A.shape[1], bool)\n", "keep_cols[[0, 2, 3]] = True\n", "idx_bool = np.ix_(keep_rows, keep_cols)\n", - "A[idx_bool]\n" + "A[idx_bool]" ] }, { @@ -3213,41 +1779,13 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "3e3b1954", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.015743Z", - "iopub.status.busy": "2023-07-25T23:59:07.015607Z", - "iopub.status.idle": "2023-07-25T23:59:07.018145Z", - "shell.execute_reply": "2023-07-25T23:59:07.017835Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4, 6, 7],\n", - " [12, 14, 15]])" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx_mixed = np.ix_([1,3], keep_cols)\n", - "A[idx_mixed]\n" - ] - }, - { - "cell_type": "markdown", - "id": "76ff57a7", "metadata": {}, + "outputs": [], "source": [ - " " + "idx_mixed = np.ix_([1,3], keep_cols)\n", + "A[idx_mixed]" ] }, { @@ -3256,7 +1794,7 @@ "metadata": {}, "source": [ "For more details on indexing in `numpy`, readers are referred\n", - "to the `numpy` tutorial mentioned earlier.\n" + "to the `numpy` tutorial mentioned earlier." ] }, { @@ -3292,7 +1830,7 @@ "as this notebook file, then we are all set. \n", "Otherwise, \n", "the command\n", - "`os.chdir()` can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.) " + "`os.chdir()` can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.)" ] }, { @@ -3300,231 +1838,19 @@ "id": "42760180", "metadata": {}, "source": [ - "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`: " + "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`:" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "d02bfb28", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.020113Z", - "iopub.status.busy": "2023-07-25T23:59:07.019977Z", - "iopub.status.idle": "2023-07-25T23:59:07.197194Z", - "shell.execute_reply": "2023-07-25T23:59:07.196835Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130350412.0701chevrolet chevelle malibu
115.08350.0165369311.5701buick skylark 320
218.08318.0150343611.0701plymouth satellite
316.08304.0150343312.0701amc rebel sst
417.08302.0140344910.5701ford torino
..............................
38727.04140.086279015.6821ford mustang gl
38844.0497.052213024.6822vw pickup
38932.04135.084229511.6821dodge rampage
39028.04120.079262518.6821ford ranger
39131.04119.082272019.4821chevy s-10
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392 rows × 9 columns

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" - ], - "text/plain": [ - " mpg cylinders displacement horsepower weight acceleration year \\\n", - "0 18.0 8 307.0 130 3504 12.0 70 \n", - "1 15.0 8 350.0 165 3693 11.5 70 \n", - "2 18.0 8 318.0 150 3436 11.0 70 \n", - "3 16.0 8 304.0 150 3433 12.0 70 \n", - "4 17.0 8 302.0 140 3449 10.5 70 \n", - ".. ... ... ... ... ... ... ... \n", - "387 27.0 4 140.0 86 2790 15.6 82 \n", - "388 44.0 4 97.0 52 2130 24.6 82 \n", - "389 32.0 4 135.0 84 2295 11.6 82 \n", - "390 28.0 4 120.0 79 2625 18.6 82 \n", - "391 31.0 4 119.0 82 2720 19.4 82 \n", - "\n", - " origin name \n", - "0 1 chevrolet chevelle malibu \n", - "1 1 buick skylark 320 \n", - "2 1 plymouth satellite \n", - "3 1 amc rebel sst \n", - "4 1 ford torino \n", - ".. ... ... \n", - "387 1 ford mustang gl \n", - "388 2 vw pickup \n", - "389 1 dodge rampage \n", - "390 1 ford ranger \n", - "391 1 chevy s-10 \n", - "\n", - "[392 rows x 9 columns]" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "import pandas as pd\n", "Auto = pd.read_csv('Auto.csv')\n", - "Auto\n" + "Auto" ] }, { @@ -3537,20 +1863,12 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "ca6090af", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.199106Z", - "iopub.status.busy": "2023-07-25T23:59:07.198977Z", - "iopub.status.idle": "2023-07-25T23:59:07.202273Z", - "shell.execute_reply": "2023-07-25T23:59:07.201978Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n" + "Auto = pd.read_csv('Auto.data', delim_whitespace=True)" ] }, { @@ -3560,8 +1878,7 @@ "source": [ " Both `Auto.csv` and `Auto.data` are simply text\n", "files. Before loading data into `Python`, it is a good idea to view it using\n", - "a text editor or other software, such as Microsoft Excel.\n", - "\n" + "a text editor or other software, such as Microsoft Excel." ] }, { @@ -3569,47 +1886,17 @@ "id": "1cae3ad2", "metadata": {}, "source": [ - "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: " + "We now take a look at the column of `Auto` corresponding to the variable `horsepower`:" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "7220aa87", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.204037Z", - "iopub.status.busy": "2023-07-25T23:59:07.203937Z", - "iopub.status.idle": "2023-07-25T23:59:07.206918Z", - "shell.execute_reply": "2023-07-25T23:59:07.206626Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 130.0\n", - "1 165.0\n", - "2 150.0\n", - "3 150.0\n", - "4 140.0\n", - " ... \n", - "392 86.00\n", - "393 52.00\n", - "394 84.00\n", - "395 79.00\n", - "396 82.00\n", - "Name: horsepower, Length: 397, dtype: object" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Auto['horsepower']\n" + "metadata": {}, + "outputs": [], + "source": [ + "Auto['horsepower']" ] }, { @@ -3625,44 +1912,12 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "ca1939f0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.208643Z", - "iopub.status.busy": "2023-07-25T23:59:07.208518Z", - "iopub.status.idle": "2023-07-25T23:59:07.211113Z", - "shell.execute_reply": "2023-07-25T23:59:07.210855Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n", - " '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n", - " '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n", - " '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n", - " '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n", - " '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n", - " '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n", - " '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n", - " '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n", - " '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n", - " '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n", - " '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n", - " '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n", - " '97.00', '98.00', '?'], dtype=object)" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.unique(Auto['horsepower'])\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.unique(Auto['horsepower'])" ] }, { @@ -3670,8 +1925,7 @@ "id": "c35e48dc", "metadata": {}, "source": [ - "We see the culprit is the value `?`, which is being used to encode missing values.\n", - "\n" + "We see the culprit is the value `?`, which is being used to encode missing values." ] }, { @@ -3686,34 +1940,15 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "cfb084e7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.212804Z", - "iopub.status.busy": "2023-07-25T23:59:07.212685Z", - "iopub.status.idle": "2023-07-25T23:59:07.216615Z", - "shell.execute_reply": "2023-07-25T23:59:07.216305Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "40952.0" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto = pd.read_csv('Auto.data',\n", " na_values=['?'],\n", " delim_whitespace=True)\n", - "Auto['horsepower'].sum()\n" + "Auto['horsepower'].sum()" ] }, { @@ -3727,30 +1962,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "2e02b6a5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.218286Z", - "iopub.status.busy": "2023-07-25T23:59:07.218192Z", - "iopub.status.idle": "2023-07-25T23:59:07.220539Z", - "shell.execute_reply": "2023-07-25T23:59:07.220268Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(397, 9)" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Auto.shape\n" + "metadata": {}, + "outputs": [], + "source": [ + "Auto.shape" ] }, { @@ -3766,32 +1983,13 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "d391f8d0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.222133Z", - "iopub.status.busy": "2023-07-25T23:59:07.222045Z", - "iopub.status.idle": "2023-07-25T23:59:07.224928Z", - "shell.execute_reply": "2023-07-25T23:59:07.224644Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(392, 9)" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto_new = Auto.dropna()\n", - "Auto_new.shape\n" + "Auto_new.shape" ] }, { @@ -3806,34 +2004,13 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "a222ca60", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.226571Z", - "iopub.status.busy": "2023-07-25T23:59:07.226451Z", - "iopub.status.idle": "2023-07-25T23:59:07.228762Z", - "shell.execute_reply": "2023-07-25T23:59:07.228488Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n", - " 'acceleration', 'year', 'origin', 'name'],\n", - " dtype='object')" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto = Auto_new # overwrite the previous value\n", - "Auto.columns\n" + "Auto.columns" ] }, { @@ -3850,110 +2027,12 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "a75921ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.230365Z", - "iopub.status.busy": "2023-07-25T23:59:07.230248Z", - "iopub.status.idle": "2023-07-25T23:59:07.235053Z", - "shell.execute_reply": "2023-07-25T23:59:07.234775Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
018.08307.0130.03504.012.0701chevrolet chevelle malibu
115.08350.0165.03693.011.5701buick skylark 320
218.08318.0150.03436.011.0701plymouth satellite
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" - ], - "text/plain": [ - " mpg cylinders displacement horsepower weight acceleration year \\\n", - "0 18.0 8 307.0 130.0 3504.0 12.0 70 \n", - "1 15.0 8 350.0 165.0 3693.0 11.5 70 \n", - "2 18.0 8 318.0 150.0 3436.0 11.0 70 \n", - "\n", - " origin name \n", - "0 1 chevrolet chevelle malibu \n", - "1 1 buick skylark 320 \n", - "2 1 plymouth satellite " - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Auto[:3]\n" + "metadata": {}, + "outputs": [], + "source": [ + "Auto[:3]" ] }, { @@ -3966,881 +2045,13 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "7469ff2b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.236689Z", - "iopub.status.busy": "2023-07-25T23:59:07.236568Z", - "iopub.status.idle": "2023-07-25T23:59:07.250946Z", - "shell.execute_reply": "2023-07-25T23:59:07.250654Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpgcylindersdisplacementhorsepowerweightaccelerationyearoriginname
33827.24135.084.02490.015.7811plymouth reliant
33926.64151.084.02635.016.4811buick skylark
34025.84156.092.02620.014.4811dodge aries wagon (sw)
34123.56173.0110.02725.012.6811chevrolet citation
34230.04135.084.02385.012.9811plymouth reliant
34339.1479.058.01755.016.9813toyota starlet
34439.0486.064.01875.016.4811plymouth champ
34535.1481.060.01760.016.1813honda civic 1300
34632.3497.067.02065.017.8813subaru
34737.0485.065.01975.019.4813datsun 210 mpg
34837.7489.062.02050.017.3813toyota tercel
34934.1491.068.01985.016.0813mazda glc 4
35034.74105.063.02215.014.9811plymouth horizon 4
35134.4498.065.02045.016.2811ford escort 4w
35229.9498.065.02380.020.7811ford escort 2h
35333.04105.074.02190.014.2812volkswagen jetta
35533.74107.075.02210.014.4813honda prelude
35632.44108.075.02350.016.8813toyota corolla
35732.94119.0100.02615.014.8813datsun 200sx
35831.64120.074.02635.018.3813mazda 626
35928.14141.080.03230.020.4812peugeot 505s turbo diesel
36030.76145.076.03160.019.6812volvo diesel
36125.46168.0116.02900.012.6813toyota cressida
36224.26146.0120.02930.013.8813datsun 810 maxima
36322.46231.0110.03415.015.8811buick century
36426.68350.0105.03725.019.0811oldsmobile cutlass ls
36520.26200.088.03060.017.1811ford granada gl
36617.66225.085.03465.016.6811chrysler lebaron salon
36728.04112.088.02605.019.6821chevrolet cavalier
36827.04112.088.02640.018.6821chevrolet cavalier wagon
36934.04112.088.02395.018.0821chevrolet cavalier 2-door
37031.04112.085.02575.016.2821pontiac j2000 se hatchback
37129.04135.084.02525.016.0821dodge aries se
37227.04151.090.02735.018.0821pontiac phoenix
37324.04140.092.02865.016.4821ford fairmont futura
37436.04105.074.01980.015.3822volkswagen rabbit l
37537.0491.068.02025.018.2823mazda glc custom l
37631.0491.068.01970.017.6823mazda glc custom
37738.04105.063.02125.014.7821plymouth horizon miser
37836.0498.070.02125.017.3821mercury lynx l
37936.04120.088.02160.014.5823nissan stanza xe
38036.04107.075.02205.014.5823honda accord
38134.04108.070.02245.016.9823toyota corolla
38238.0491.067.01965.015.0823honda civic
38332.0491.067.01965.015.7823honda civic (auto)
38438.0491.067.01995.016.2823datsun 310 gx
38525.06181.0110.02945.016.4821buick century limited
38638.06262.085.03015.017.0821oldsmobile cutlass ciera (diesel)
38726.04156.092.02585.014.5821chrysler lebaron medallion
38822.06232.0112.02835.014.7821ford granada l
38932.04144.096.02665.013.9823toyota celica gt
39036.04135.084.02370.013.0821dodge charger 2.2
39127.04151.090.02950.017.3821chevrolet camaro
39227.04140.086.02790.015.6821ford mustang gl
39344.0497.052.02130.024.6822vw pickup
39432.04135.084.02295.011.6821dodge rampage
39528.04120.079.02625.018.6821ford ranger
39631.04119.082.02720.019.4821chevy s-10
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" - ], - "text/plain": [ - " mpg cylinders displacement horsepower weight acceleration year \\\n", - "338 27.2 4 135.0 84.0 2490.0 15.7 81 \n", - "339 26.6 4 151.0 84.0 2635.0 16.4 81 \n", - "340 25.8 4 156.0 92.0 2620.0 14.4 81 \n", - "341 23.5 6 173.0 110.0 2725.0 12.6 81 \n", - "342 30.0 4 135.0 84.0 2385.0 12.9 81 \n", - "343 39.1 4 79.0 58.0 1755.0 16.9 81 \n", - "344 39.0 4 86.0 64.0 1875.0 16.4 81 \n", - "345 35.1 4 81.0 60.0 1760.0 16.1 81 \n", - "346 32.3 4 97.0 67.0 2065.0 17.8 81 \n", - "347 37.0 4 85.0 65.0 1975.0 19.4 81 \n", - "348 37.7 4 89.0 62.0 2050.0 17.3 81 \n", - "349 34.1 4 91.0 68.0 1985.0 16.0 81 \n", - "350 34.7 4 105.0 63.0 2215.0 14.9 81 \n", - "351 34.4 4 98.0 65.0 2045.0 16.2 81 \n", - "352 29.9 4 98.0 65.0 2380.0 20.7 81 \n", - "353 33.0 4 105.0 74.0 2190.0 14.2 81 \n", - "355 33.7 4 107.0 75.0 2210.0 14.4 81 \n", - "356 32.4 4 108.0 75.0 2350.0 16.8 81 \n", - "357 32.9 4 119.0 100.0 2615.0 14.8 81 \n", - "358 31.6 4 120.0 74.0 2635.0 18.3 81 \n", - "359 28.1 4 141.0 80.0 3230.0 20.4 81 \n", - "360 30.7 6 145.0 76.0 3160.0 19.6 81 \n", - "361 25.4 6 168.0 116.0 2900.0 12.6 81 \n", - "362 24.2 6 146.0 120.0 2930.0 13.8 81 \n", - "363 22.4 6 231.0 110.0 3415.0 15.8 81 \n", - "364 26.6 8 350.0 105.0 3725.0 19.0 81 \n", - "365 20.2 6 200.0 88.0 3060.0 17.1 81 \n", - "366 17.6 6 225.0 85.0 3465.0 16.6 81 \n", - "367 28.0 4 112.0 88.0 2605.0 19.6 82 \n", - "368 27.0 4 112.0 88.0 2640.0 18.6 82 \n", - "369 34.0 4 112.0 88.0 2395.0 18.0 82 \n", - "370 31.0 4 112.0 85.0 2575.0 16.2 82 \n", - "371 29.0 4 135.0 84.0 2525.0 16.0 82 \n", - "372 27.0 4 151.0 90.0 2735.0 18.0 82 \n", - "373 24.0 4 140.0 92.0 2865.0 16.4 82 \n", - "374 36.0 4 105.0 74.0 1980.0 15.3 82 \n", - "375 37.0 4 91.0 68.0 2025.0 18.2 82 \n", - "376 31.0 4 91.0 68.0 1970.0 17.6 82 \n", - "377 38.0 4 105.0 63.0 2125.0 14.7 82 \n", - "378 36.0 4 98.0 70.0 2125.0 17.3 82 \n", - "379 36.0 4 120.0 88.0 2160.0 14.5 82 \n", - "380 36.0 4 107.0 75.0 2205.0 14.5 82 \n", - "381 34.0 4 108.0 70.0 2245.0 16.9 82 \n", - "382 38.0 4 91.0 67.0 1965.0 15.0 82 \n", - "383 32.0 4 91.0 67.0 1965.0 15.7 82 \n", - "384 38.0 4 91.0 67.0 1995.0 16.2 82 \n", - "385 25.0 6 181.0 110.0 2945.0 16.4 82 \n", - "386 38.0 6 262.0 85.0 3015.0 17.0 82 \n", - "387 26.0 4 156.0 92.0 2585.0 14.5 82 \n", - "388 22.0 6 232.0 112.0 2835.0 14.7 82 \n", - "389 32.0 4 144.0 96.0 2665.0 13.9 82 \n", - "390 36.0 4 135.0 84.0 2370.0 13.0 82 \n", - "391 27.0 4 151.0 90.0 2950.0 17.3 82 \n", - "392 27.0 4 140.0 86.0 2790.0 15.6 82 \n", - "393 44.0 4 97.0 52.0 2130.0 24.6 82 \n", - "394 32.0 4 135.0 84.0 2295.0 11.6 82 \n", - "395 28.0 4 120.0 79.0 2625.0 18.6 82 \n", - "396 31.0 4 119.0 82.0 2720.0 19.4 82 \n", - "\n", - " origin name \n", - "338 1 plymouth reliant \n", - "339 1 buick skylark \n", - "340 1 dodge aries wagon (sw) \n", - "341 1 chevrolet citation \n", - "342 1 plymouth reliant \n", - "343 3 toyota starlet \n", - "344 1 plymouth champ \n", - "345 3 honda civic 1300 \n", - "346 3 subaru \n", - "347 3 datsun 210 mpg \n", - "348 3 toyota tercel \n", - "349 3 mazda glc 4 \n", - "350 1 plymouth horizon 4 \n", - "351 1 ford escort 4w \n", - "352 1 ford escort 2h \n", - "353 2 volkswagen jetta \n", - "355 3 honda prelude \n", - "356 3 toyota corolla \n", - "357 3 datsun 200sx \n", - "358 3 mazda 626 \n", - "359 2 peugeot 505s turbo diesel \n", - "360 2 volvo diesel \n", - "361 3 toyota cressida \n", - "362 3 datsun 810 maxima \n", - "363 1 buick century \n", - "364 1 oldsmobile cutlass ls \n", - "365 1 ford granada gl \n", - "366 1 chrysler lebaron salon \n", - "367 1 chevrolet cavalier \n", - "368 1 chevrolet cavalier wagon \n", - "369 1 chevrolet cavalier 2-door \n", - "370 1 pontiac j2000 se hatchback \n", - "371 1 dodge aries se \n", - "372 1 pontiac phoenix \n", - "373 1 ford fairmont futura \n", - "374 2 volkswagen rabbit l \n", - "375 3 mazda glc custom l \n", - "376 3 mazda glc custom \n", - "377 1 plymouth horizon miser \n", - "378 1 mercury lynx l \n", - "379 3 nissan stanza xe \n", - "380 3 honda accord \n", - "381 3 toyota corolla \n", - "382 3 honda civic \n", - "383 3 honda civic (auto) \n", - "384 3 datsun 310 gx \n", - "385 1 buick century limited \n", - "386 1 oldsmobile cutlass ciera (diesel) \n", - "387 1 chrysler lebaron medallion \n", - "388 1 ford granada l \n", - "389 3 toyota celica gt \n", - "390 1 dodge charger 2.2 \n", - "391 1 chevrolet camaro \n", - "392 1 ford mustang gl \n", - "393 2 vw pickup \n", - "394 1 dodge rampage \n", - "395 1 ford ranger \n", - "396 1 chevy s-10 " - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "idx_80 = Auto['year'] > 80\n", - "Auto[idx_80]\n" + "Auto[idx_80]" ] }, { @@ -4848,133 +2059,17 @@ "id": "27eb1780", "metadata": {}, "source": [ - "However, if we pass in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*. " + "However, if we pass in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*." ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "ac455404", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.252607Z", - "iopub.status.busy": "2023-07-25T23:59:07.252480Z", - "iopub.status.idle": "2023-07-25T23:59:07.257221Z", - "shell.execute_reply": "2023-07-25T23:59:07.256935Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpghorsepower
018.0130.0
115.0165.0
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mpgcylindersdisplacementhorsepowerweightaccelerationyearorigin
name
chevrolet chevelle malibu18.08307.0130.03504.012.0701
buick skylark 32015.08350.0165.03693.011.5701
plymouth satellite18.08318.0150.03436.011.0701
amc rebel sst16.08304.0150.03433.012.0701
ford torino17.08302.0140.03449.010.5701
...........................
ford mustang gl27.04140.086.02790.015.6821
vw pickup44.0497.052.02130.024.6822
dodge rampage32.04135.084.02295.011.6821
ford ranger28.04120.079.02625.018.6821
chevy s-1031.04119.082.02720.019.4821
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mpgdisplacementhorsepower
name
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buick skylark 32015.0350.0165.0
plymouth satellite18.0318.0150.0
amc rebel sst16.0304.0150.0
ford torino17.0302.0140.0
............
ford mustang gl27.0140.086.0
vw pickup44.097.052.0
dodge rampage32.0135.084.0
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mpgorigin
name
ford galaxie 50015.01
ford galaxie 50014.01
ford galaxie 50014.01
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toyota corolla2350.03
datsun 200sx2615.03
mazda 6262635.03
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volvo diesel3160.02
toyota cressida2900.03
datsun 810 maxima2930.03
buick century3415.01
oldsmobile cutlass ls3725.01
ford granada gl3060.01
chrysler lebaron salon3465.01
chevrolet cavalier2605.01
chevrolet cavalier wagon2640.01
chevrolet cavalier 2-door2395.01
pontiac j2000 se hatchback2575.01
dodge aries se2525.01
pontiac phoenix2735.01
ford fairmont futura2865.01
volkswagen rabbit l1980.02
mazda glc custom l2025.03
mazda glc custom1970.03
plymouth horizon miser2125.01
mercury lynx l2125.01
nissan stanza xe2160.03
honda accord2205.03
toyota corolla2245.03
honda civic1965.03
honda civic (auto)1965.03
datsun 310 gx1995.03
buick century limited2945.01
oldsmobile cutlass ciera (diesel)3015.01
chrysler lebaron medallion2585.01
ford granada l2835.01
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dodge charger 2.22370.01
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honda prelude2210.03
toyota corolla2350.03
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mazda 6262635.03
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volvo diesel3160.02
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buick century3415.01
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ford granada gl3060.01
chrysler lebaron salon3465.01
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chevrolet cavalier 2-door2395.01
pontiac j2000 se hatchback2575.01
dodge aries se2525.01
pontiac phoenix2735.01
ford fairmont futura2865.01
volkswagen rabbit l1980.02
mazda glc custom l2025.03
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plymouth horizon miser2125.01
mercury lynx l2125.01
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honda accord2205.03
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chrysler lebaron medallion2585.01
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toyota celica gt2665.03
dodge charger 2.22370.01
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subaru2065.03
datsun 210 mpg1975.03
toyota tercel2050.03
mazda glc 41985.03
plymouth horizon 42215.01
ford escort 4w2045.01
volkswagen jetta2190.02
honda prelude2210.03
toyota corolla2350.03
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mazda 6262635.03
volvo diesel3160.02
chevrolet cavalier 2-door2395.01
pontiac j2000 se hatchback2575.01
volkswagen rabbit l1980.02
mazda glc custom l2025.03
mazda glc custom1970.03
plymouth horizon miser2125.01
mercury lynx l2125.01
nissan stanza xe2160.03
honda accord2205.03
toyota corolla2245.03
honda civic1965.03
honda civic (auto)1965.03
datsun 310 gx1995.03
oldsmobile cutlass ciera (diesel)3015.01
toyota celica gt2665.03
dodge charger 2.22370.01
vw pickup2130.02
dodge rampage2295.01
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ford maverick2587.01
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ford mustang3139.01
datsun 12001613.03
ford pinto runabout2226.01
ford pinto (sw)2395.01
datsun 510 (sw)2288.03
ford maverick3021.01
datsun 6102379.03
ford pinto2310.01
datsun b2101950.03
ford pinto2451.01
datsun 7102003.03
ford maverick3158.01
ford pinto2639.01
datsun 7102545.03
ford pinto2984.01
ford maverick3012.01
ford granada ghia3574.01
datsun b-2101990.03
ford pinto2565.01
datsun f-10 hatchback1945.03
ford granada3525.01
ford mustang ii 2+22755.01
datsun 8102815.03
ford fiesta1800.01
datsun b210 gx2070.03
ford fairmont (auto)2965.01
ford fairmont (man)2720.01
datsun 5102300.03
datsun 200-sx2405.03
ford fairmont 42890.01
datsun 2102020.03
datsun 3102019.03
ford fairmont2870.01
datsun 510 hatchback2434.03
datsun 2102110.03
datsun 280-zx2910.03
datsun 210 mpg1975.03
ford escort 4w2045.01
ford escort 2h2380.01
datsun 200sx2615.03
datsun 810 maxima2930.03
ford granada gl3060.01
ford fairmont futura2865.01
datsun 310 gx1995.03
ford granada l2835.01
ford mustang gl2790.01
ford ranger2625.01
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foodbarpicklesnackpopcorn
00.3455840.8216180.330437-1.303157NaN
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\"food\" has 16.54% missing values\n", - "Column \"bar\" has 25.98% missing values\n", - "Column \"pickle\" has 29.13% missing values\n", - "Column \"snack\" has 21.26% missing values\n", - "Column \"popcorn\" has 22.83% missing values\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "for col in D.columns:\n", " template = 'Column \"{0}\" has {1:.2%} missing values'\n", " print(template.format(col,\n", - " np.isnan(D[col]).mean()))\n" + " np.isnan(D[col]).mean()))" ] }, { @@ -7674,40 +2488,10 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "30a8a663", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.356627Z", - "iopub.status.busy": "2023-07-25T23:59:07.356508Z", - "iopub.status.idle": "2023-07-25T23:59:07.458864Z", - "shell.execute_reply": "2023-07-25T23:59:07.458518Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'horsepower' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[102], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m);\n", - "\u001b[0;31mNameError\u001b[0m: name 'horsepower' is not defined" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.plot(horsepower, mpg, 'o');" @@ -7723,32 +2507,13 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "81fdedb1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.460807Z", - "iopub.status.busy": "2023-07-25T23:59:07.460673Z", - "iopub.status.idle": "2023-07-25T23:59:07.556888Z", - "shell.execute_reply": "2023-07-25T23:59:07.556504Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');\n" + "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');" ] }, { @@ -7760,52 +2525,15 @@ "Using this method,\n", "the variables can be accessed by name.\n", "The plot methods of a data frame return a familiar object:\n", - "an axes. We can use it to update the plot as we did previously: " + "an axes. We can use it to update the plot as we did previously:" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "id": "61850515", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.558736Z", - "iopub.status.busy": "2023-07-25T23:59:07.558598Z", - "iopub.status.idle": "2023-07-25T23:59:07.668649Z", - "shell.execute_reply": "2023-07-25T23:59:07.668299Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", - " scatter = ax.scatter(\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Horsepower vs. MPG')" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = Auto.plot.scatter('horsepower', 'mpg');\n", "ax.set_title('Horsepower vs. MPG')" @@ -7823,16 +2551,9 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "f2e0c165", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.670624Z", - "iopub.status.busy": "2023-07-25T23:59:07.670488Z", - "iopub.status.idle": "2023-07-25T23:59:07.713512Z", - "shell.execute_reply": "2023-07-25T23:59:07.713197Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "fig = ax.figure\n", @@ -7854,31 +2575,13 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "id": "35c72e77", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.715540Z", - "iopub.status.busy": "2023-07-25T23:59:07.715402Z", - "iopub.status.idle": "2023-07-25T23:59:07.907697Z", - "shell.execute_reply": "2023-07-25T23:59:07.907310Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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9L/xr1671d6gAgDDH+wwAwBWvZpL17dtXM2bM0ObNm21t7e3t2rx5s+bMmePwmubm5i6/OKKjoyVJhmF4Gy8AoBc5cuSIfvSjH+mFF15Qv379/NZvb/sX/jJLo7YUV6m8uinUoQBASPE+AwBwxauZZJK0evVqXX/99TrnnHM0a9YsPfbYY2pqatKyZcskSUuXLtUZZ5yhdevWSZIuu+wyPfroo8rMzNTs2bNVWlqqe++9V5dddpntlwsAAI7s3btXVVVVmj59uq2tra1N27Zt0+OPP653331XLS0tqqurs5tN5mpGgNR7/oW/rrlFq/KKtK3EYmvLzkhWbk4mO2AC6LV4nwEAOON1kuzqq6+WxWLRfffdp8rKSk2bNk3vvPOOrfjl4cOH7f6l5Z577pHJZNI999yjzz//XMnJybrsssv0y1/+0n9PAQDokS644ALt37/frm3ZsmWaMGGC7rzzTqWkpCgmJkabN2/WVVddJUkqLi7W4cOHnc4I6E1W5RWpoLTarq2gtFor8wq1afmsEEUFAKHF+wwAwBmTEQFzhBsaGpSQkKD6+nrFx8eHOhwAiHiR/Lm6YMECTZs2TY899pgk6ZZbbtHbb7+t5557TvHx8Vq5cqUkaefOnR73GW7jUWZp1KHjzUpNGqA08wCf+zj/kXynx7fcscDnvgHAnXD7XA01xgMA/CtQn6tezyQDACCc/Pa3v1VUVJSuuuoqnTx5UosXL9YTTzwR6rB84u3ySFfJtEPHm13eq6KmiSQZAAAA0AFJMgBARNm6davd9/369dOGDRu0YcOG0ATkR54uj/QkmTZmSJzLe6UmkSADAAAAOvJqd0sAABAYZZZGbSuxqK1TFYQ2w9C2EovdzpSukmlWY5MHKjsjWdEmk9150SaTsjOSmUUGAAAAdEKSDACAMODJ8kjJu2Rabk6mstLNdudlpZuVm5Ppp6gBAACAnoPllgAAhAFPl0d6U2ssIS5Gm5bPUnl1kypqmrq1EQAAAADQ05EkAwAgDFiXRxaUVtvNEos2mZSVbrYlt3ypNZZmJjkGAAAAuMNySwAAwoQnyyOpNQYAAAAEBjPJAAAIE54uj8zNydTKvEK73S2pNQYAAAB0D0kyAADCTMflkWWWRh063myXMKPWGAAAAOB/JMkAAAhDdc0tWpVXZDdbLDsjWbk5mappOmlLnC0cP9TuuvziKhUdrdP00Ymal5EctHgdJfMAAACASEKSDACAMLQqr0gFpdV2bTtKLFrw8BbVNrfa2qyJs7qvWrRkQ4HdscS4GL2x4jylJLku9t8drpJ5CXExAbsvAAAA4G8U7gcAIMyUWRq1rcRit8ulJLVLdkkwSSoordbKvMIuCTL9+9zLN+wIaKyOknnWmAAAAIBIQpIMAIAwc+h4s8fnthmGtpVYuiTIrGqbW7W9wywvf3KWzLPGVF7dFJD7AgAAAIFAkgwAgDAzZoh/l0fuO1zr1/6s3CXzKmpIkgEAACBykCQDACDMjE0eqOyMZEWbTH7pb/roRL/005m7ZF5qEgX8AQAAEDlIkgEAEIZyczKVlW726NyZqYlKdFIkPzEuJmC7XDpL5kWbTMrOSGaXSwAAAEQUkmQAAIShhLgYbVo+S1vuWKDbv5Xh8tzr56bqjRXndUmUxffrozWXTAhobTBHybysdLNyczIDdk8AAAAgEPqEOgAAAOBcmnmALpsyUr99v8TpOfH9+qi0ulGv/jBLR2ubtbO0WjtKqrX/iwb99E/7JUnZGcnKzclUgpMZZ76yJvPKq5tUUdOk1KQBzCADAABARCJJBgBAmLMuayworbbbSTLaJMX3j9HSZ/bY2rIzktXa1q5Pvzxh10dBabVW5hVq0/JZAYkxzUxyDAAAAJGN5ZYAAEQAR8sa4/vHqL651a5tR6lFu8pq7JJpktRmGNpWYgno0ktvlFkataW4KmziAQAAAJhJBgBABOi8rDHaJLsZZFbthoOLO6ioaQrpjK+65hatyivSthKLrS1QS0EBAAAAbzCTDACACJJmHqCF44eqzU0yzJnUpNAuiVyVV6SC0mq7NutSUAAAACCUSJIBABCBfPkFPnNMYkhnkZVZGrWtxBL2S0EBAADQO5EkAwAgArX7cM0Nc1P9HYZXDh1vdnm8ooYkGQAAAEKHJBkAABFozJA4r6+ZeEZCACLxnLuYQ70UFAAAAL0bSTIAACLQ2OSBys5IVrTJ5PbcaJNJ2RnJIV1qKTmPOVziAwAAQO9GkgwAgAiVm5OprHSz2/Oy0s3KzckMQkTuOYo5nOIDAABA79Un1AEAAADnyiyNOnS8WalJA7rMtEqIi9Gm5bO07bMqLX1mj9M+1l5xthLiYgIdqkesMZdXN6mipsnhcwEAAAChQJIMAIAwU2Zp1D+/bNCmnRXaU1Fra8/OSFZuTqZdwquuuUUPvVPssr8PymqcJqSsSbhok0lthuFT0spVIs/ZsTQzyTEAAACEF5JkAACEibrmFq3KK9K2EovD4wWl1VqZV6hNy2fZ2lblFenTLxpc9rvm1f22P1sTbYYMp/dylIzzNF5X/XvaLwAAABAK1CQDACBMrMorUkFptdPjbYahbSUWlVc3STo9S2tbiUXtXtzDmmhzdS/rOb7E66p/T/sFAAAAQoGZZAAAhAFrwssTFTVNSjMP0KHjzV7fx5po8+Sc8uom25LIzssmncXrqn9H/QIAAADhgiQZAABhwJuEV2rS6QTTmCFxgQpH0ulkXGJcjMNlk1efM6pb/ZIkAwAAQLhhuSUAAGHAk4RXtMmk7IxkW4JpbPJAZWckK9pk6nLeOWMSux1TatIAp8smn91Z0a1+AQAAgHBDkgwAgDDgLOHVUVa6Wbk5mXZtuTmZyko3dzlv4/UznSbQsjOSXd7Leo7x7+WRbYZhd7zNMPTRoVrNTE30qv/OST4AAAAgnLDcEgCAMJGbk6mVeYV2SxtnjknUDXNTNfGMBIfJpYS4GG1aPkvl1U2qqGmy1Qxz1l/HRFvnY53P2Xek1mW8189NVf+Yox737yjJBwAAAIQLkmQAAIQJRwkvwzA8qleWZh7QJYnmKoEmye5YnyiTTrUbdue4WwJ69sgEbVo+0qP+Ox8DAAAAwg1JMgBAWHvyySf15JNPqqKiQpJ09tln67777tPFF18sSVqwYIHy8/Ptrvn+97+vp556Ktih+k2aeYDTgvm5OZlKiIvxqJ+OO1IuHD/U6b2cJa+sS0ALSqvtllxGm0zKSjfbrnPVh9FpqSYAAAAQrkiSAQDC2qhRo7R+/XplZGTIMAw9//zzuuKKK1RYWKizzz5bknTTTTfpgQcesF0TFxfYXR+DwVnB/JV5hdq0fJbLa+uaW7qdYLNyt2QzGDEAAAAAwUCSDAAQ1i677DK773/5y1/qySef1AcffGBLksXFxWn48OGhCC8gyiyNDmuFtf27kH55dZPLpYvdSbB15m7JZjBiAAAAAIKB3S0BABGjra1NL730kpqamjRnzhxb+wsvvCCz2axJkyZpzZo1am52XcPr5MmTamhosPsKJ+5qkH1QVqMtxVUqr27qcsyaYHO0I6U1weaLNPPpJZueJMgCFQMAAAAQSMwkAwCEvf3792vOnDn6+uuvNXDgQL322muaOHGiJOmaa67RmDFjNHLkSH388ce68847VVxcrFdffdVpf+vWrdPatWuDFb7X3BXMX/PqftufOy9hdJdgq6hxPQvNH8IhBgAAAMBbJMkAAGFv/PjxKioqUn19vf70pz/p+uuvV35+viZOnKibb77Zdt7kyZM1YsQIXXDBBTp48KDGjRvnsL81a9Zo9erVtu8bGhqUkpIS8OfwlLOC+Y50XsLoLsGWmhT45FQ4xAAAAAB4i+WWAICw17dvX6Wnp2vGjBlat26dpk6dqv/+7/92eO7s2bMlSaWlpU77i42NVXx8vN1XuMnNyVRWutntedYljC/tPqzy6iZbgi3aZHJ4/v1/+afqm1v9Ha4dZzFEm0zKzkhmFhkAAADCEkkyAEDEaW9v18mTJx0eKyoqkiSNGDEiiBH5n7Vg/pY7FujZZTO1/srJLs+/69X9WvjwVi3duFu/XDLJaYLNOvPME2WWRqe1z9xxlOTzZFdMAAAAIFRYbgkACGtr1qzRxRdfrNGjR+vEiRN68cUXtXXrVr377rs6ePCgXnzxRV1yySVKSkrSxx9/rNtvv13Z2dmaMmVKqEP3izTz6d0kyyyNHp1fUFqtu1//RD+/fKLOfyS/y3FPdsisa27Rqrwiux02O9c+c8fXXTEBAACAUGEmGQAgrFVVVWnp0qUaP368LrjgAu3Zs0fvvvuuvvWtb6lv377629/+pgsvvFATJkzQj3/8Y1111VX661//Guqw/c7dMkoraxLsrf1fujyvosb57LBVeUUqKK22a/NmBlpH3uyKCQAAAIQSM8kAAGFt48aNTo+lpKQoP7/rbKmeKjcnUyvzCu1meDnzyHufuTzurHh+maXRYf+ezEBz1t+h483MJAMAAEDYI0kGAECE6LiE8YOyGq15db/f73HoeLPL4xU1niXJ/LFkEwAAAAgmllsCABBh0swDNDttSLf66Lzc0lqkP9r1ak6nM9A68+eSTQAAACAYmEkGAEAEcjfjy50+UaezYY5mfCXGxai+uVXtHc6PNpmUlW72aBaZv5dsAgAAAMHATDIAACLQmCFx3br+VLshyfGMr4avWrssicxKNys3J9OuzTr7rLzaflaauwTeh2U1voYNAAAABAwzyQAAiEDW3S4LSqvVZhheX5+aNMDFjC+ptrlV/7t8lk61G12K7rurN+YugXfXq/v19v5K6pMBAAAgrDCTDACACJWbk6msdLPX1yXGxSjNPMDtjK9T7YYWjh/aZWmku3pj1gRetMl5gTPqkwEAACDckCQDACBCWXe73HLHAk06I15RboruW9U2t6q8usntjC9HRfqts886z17rWG9Mcp/A63w+AAAAEGokyQAAiHBp5gF6Yfm5Oi892eNrKmqanM74ijaZlJ2R7LC4vrvZZ9ZdM60JvHVXTvbofAAAACDUqEkGAEAQ5RdXqehonaaPTtS8DM+TWu5Yk1Ll1U2qqGlStEla+swep+dbZ4n9+MIMHW8+qU8+b7Adc1Sk38rb2Wez04Z4dT4AAAAQKiTJAAAIgkM1TVqyoUC1za22tsS4GL2x4jylJHVvp8qO0szfFNl3VNg/2mRSVrpZiXExWrpxt13x/Ukj4/Wrb0/WlJTBTvt3tmGAtd/Os8+8Pd+dMkujDh1v7rKZAAAAANBdLLcEACAIOifIpNO1wS7fsCNg93RUF8w6S8xR8f0DX57Qw+991q1+/XG+I3XNLVq6cbfOfyRfy57do4UPb9XSjbtV32lMAQAAAF8xkwwAgAB76+MvuiTIrGqbW7W9xOLXpZdWnZdgWmdfWYvvd9axmL6rWVrO+vXX+Y642lFz0/JZXvUVjpghBwAAEHokyQAACLCH3i12eXzf4dqAJMmsOi7BlDwrvu9JoqZzv/4+36q7Sb1wVtfcolV5RXbPl52RrNycTCXExYQwMgAAgN6H5ZYAAARQmaVRh2pcJ6Wmj04MUjSn46ms/9rlOeFWTN/THTUjkasZcgAAAAguZpIBABBA7hI8A2Oj/TqLzNmyPUczlpy5/y//DKuZTN7uqBkpevIMOQAAgEhEkgwAgAByl+D5n+vO8ct93C3bczRjyZlwq/Xl7x0yw4W/lr0CAADAP1huCQBAAFkTPNEmk117lOl0Emtup10ffeVq2V5+cZW2lVjsEkyudJzJFC78sUNmuOmpM+QAAAAiFTPJAAAIsNycTK3MK7Sb5XVeerJXCR5Xux9ak2CdWZNdniyxdMTTmUz+3pnRUX/+2CEz3PTUGXIAAACRyqeZZBs2bFBqaqr69eun2bNna/fu3S7Pr6ur04oVKzRixAjFxsbqzDPP1Ntvv+1TwAAARBprgmfLHQv07LKZ2nLHAm1aPsujml91zS1aunG3zn8kX8ue3aOFD2/V0o27Vd/cajt2/bN7AhJ3nyiTy+OuYvOFJ/2lmQdo4fihPSaB1BNnyAGRgPcZAIAjXs8ke/nll7V69Wo99dRTmj17th577DEtXrxYxcXFGjp0aJfzW1pa9K1vfUtDhw7Vn/70J51xxhk6dOiQBg8e7I/4AQCIGGlm72c/udv90NM6Y45EmaR2FyswT7k66CY2X+qZ+bu/SNATZ8gB4Y73GQCAM14nyR599FHddNNNWrZsmSTpqaee0ltvvaVnnnlGd911V5fzn3nmGR0/flw7d+5UTMzpfzFPTU3tXtQAAPQC7nY/7K6BsX3U8PUpp8dd1cTy986MvX2nR18SqAB8w/sMAMAZr5ZbtrS0aO/evVq0aNE3HURFadGiRdq1a5fDa9544w3NmTNHK1as0LBhwzRp0iT96le/Ultbm9P7nDx5Ug0NDXZfAAD0Nu52P+yuhq9PafIZ8V3+YyDaZFJ2RrLLpI0nOzN6w9/9AYAjvM8AAFzxKklWXV2ttrY2DRs2zK592LBhqqysdHhNWVmZ/vSnP6mtrU1vv/227r33Xj3yyCN68MEHnd5n3bp1SkhIsH2lpKR4EyYAAD2Cu90P/WH/5w1q79TmSU0sf+/MyE6PAIKB9xkAgCs+Fe73Rnt7u4YOHar/+Z//0YwZM3T11Vfr7rvv1lNPPeX0mjVr1qi+vt72deTIkUCHCQBA2LHufhhtsi+gH20yadLIeL/fL8okzUxN9GhTAVexuZuFFoz+AMBfeJ8BgN7DqySZ2WxWdHS0jh07Ztd+7NgxDR8+3OE1I0aM0Jlnnqno6Ghb21lnnaXKykq1tLQ4vCY2Nlbx8fF2XwAA9EbOdj/85bcn+/1e7Ya0p6JW5dWeLW30986M7PQIINB4nwEAuOJV4f6+fftqxowZ2rx5s5YsWSLp9L+sbN68WbfeeqvDa7KysvTiiy+qvb1dUVGnc3KfffaZRowYob59+3YvegAAejhXux9mZySroLRabYbrXSi9tfLFfXrhxnPdzibz986M7PQIINB4nwEAuOL1csvVq1fr6aef1vPPP68DBw7olltuUVNTk213mKVLl2rNmjW282+55RYdP35cP/rRj/TZZ5/prbfe0q9+9SutWLHCf08BAEAEKLM0aktxlccztTpKMw/QwvFD7ZJGjmZe+cOnXzRoZV6h0+OdnyPNPEBjhsSpoqbJp2frzNGzAoC/8D4DAHDGq5lkknT11VfLYrHovvvuU2VlpaZNm6Z33nnHVvzy8OHDtn9hkaSUlBS9++67uv322zVlyhSdccYZ+tGPfqQ777zTf08BAEAYq2tu0aq8Im0rsdjasjOSlZuT6Xa2liuG/DuDzKpd0rYSi8qrm+wSVY6eY+64JBmGtKusxtbmj2cDgEDhfQYA4IzJMPy8RiMAGhoalJCQoPr6etbzA4Af8LlqL9DjsXTj7i7LIqNNJmWlm7Vp+Sy/9uupKElxsdFqPNnm9Jxnl83UwvFDvb6fP54NQGTj94w9xgMA/CtQn6sB390SAIDerMzSqG0lli6JpTbDsM3W8me/njovI1kv3Hiuy3NSk76ZRebN/br7bAAAAEAoeL3cEgAAeO7Q8WaXxytqmnyqveWuX0fWXzlZwxL62Qril1kaXZ5/4uvWbt3P12cDAAAAQoEkGQAAATRmSJzL4x1na3mqzNKoyvqvvL5u9tgku6SVu8TXj//4D/3s0rOUmjTA7XM44suzAQAAAKFCkgwAgAAamzxQ2RnJTmuSeTPTylHhfE84u5e7xFdJVaOWPbtH0uli/HPHJenDsuMe1yRjFhkAAAAiCTXJAAAIsNycTGWlm+3astLNys3J9KqfVXlFKiit9vr+zu41NnmgJo30rNBpQWm1DENdnmPuuCTNGZvk0f0AAACAcMZMMgAAAiwhLkabls9SeXWTKmqabDXBvGEtnO/Muisn69yxSTpa26x9h2s1MqG/zINi3d7rl9+erCs2FLi9f5thaFdZjbbcsUCSujxHd54NAAAACAckyQAACJI0s+8JJHf1w4Yn9LP1Py8j2eN+p6YMVnZGsnaUWtTuwUaZFTVNWjh+aJfn6M6zAQAAAOGA5ZYAgLD25JNPasqUKYqPj1d8fLzmzJmj//f//p/t+Ndff60VK1YoKSlJAwcO1FVXXaVjx46FMOLA8HQDgPziKv335s/0xz1HtKW4SuXVTSqzNNr+7EhuTqbOS/cssdYnytSlzV3/AAAAQCRgJhkAIKyNGjVK69evV0ZGhgzD0PPPP68rrrhChYWFOvvss3X77bfrrbfe0iuvvKKEhATdeuutuvLKK1VQ4H4JYSRxtQFA5ujB2l1eoyse36GGr0+57Cc7I1m5OZlKiIuxtRnyYArZv123cbetD0NGl40EHPUPAAAARAKTYbjZoioMNDQ0KCEhQfX19YqP96zAMADAuUj/XB0yZIh+85vf6Dvf+Y6Sk5P14osv6jvf+Y4k6V//+pfOOuss7dq1S+eee65H/UXKeNQ3t2r583v00aFaW1tiXIxqm1s97sO68+Sm5bNsbUs37u6SfPOkD0lOd+3s2L9VmaVRh443U7cM6AUi5XM1WBgPAPCvQH2uMpMMABAx2tra9Morr6ipqUlz5szR3r171draqkWLFtnOmTBhgkaPHu0ySXby5EmdPHnS9n1DQ0PAY++uuuYWrcorskuQxffro4avPE+QSacL8G8rsai8uklp5gFuNwRw1Ycn/XeMnRlnAAAACGfUJAMAhL39+/dr4MCBio2N1Q9+8AO99tprmjhxoiorK9W3b18NHjzY7vxhw4apsrLSaX/r1q1TQkKC7SslJSXAT9B9q/KKVFBabdfW8PUptfk4H7yi5nT9MHcbAvjK2r/kOPaC0mqtzCsMyL0BAAAAX5AkAwCEvfHjx6uoqEgffvihbrnlFl1//fX69NNPfe5vzZo1qq+vt30dOXLEj9H6n3W2l6fLIT3xxJZS1Te3ut0QwFfWjQScxd5xxhkAAAAQDkiSAQDCXt++fZWenq4ZM2Zo3bp1mjp1qv77v/9bw4cPV0tLi+rq6uzOP3bsmIYPH+60v9jYWNtumdavcBaI2V77DtVpZV6hbUOAaJP9rpXRJpOyM5J9PmZdauku9o4zzgAAAIBQIkkGAIg47e3tOnnypGbMmKGYmBht3rzZdqy4uFiHDx/WnDlzQhihfwVitlfHmVy5OZm2QvxWWelm5eZk+nzM09itM84AAACAUKNwPwAgrK1Zs0YXX3yxRo8erRMnTujFF1/U1q1b9e677yohIUHLly/X6tWrNWTIEMXHx2vlypWaM2eOxztbRgLrbK/OO0lGSUrwcnfLzipqThfY37R8lsqrm1RR09Rl90lfj7mK3boLJrtcAgAAIFyQJAMAhLWqqiotXbpUX375pRISEjRlyhS9++67+ta3viVJ+u1vf6uoqChdddVVOnnypBYvXqwnnngixFGfrsV16HizXeLIUZuncnMytTKv0G6HyPP+vUPk8eYWVdQ0KdokLX1mj1f9dqwd5i7ezjFbj3deculJ7J1nnAEAAAChZjIMP1YBDpCGhgYlJCSovr4+7OvGAEAk4HPVnj/Ho665RavyiuwSQnPGJslkknYerLG1Zf87wZUQF+NV/65mbUnS0o27u8zacsQ6k+t3OdO8jtfRM3ryXO5iB9Bz8HvGHuMBAP4VqM9VapIBAOBHq/KKVFBabde2q6zGLuEkSQWl1VqZV+h1/2nmAVo4fqjTJJOjOmGOWGdy+RKvo2scnedt7OGizNKoLcVV7LwJAADQy7DcEgAAPymzNDqcXeVIx8L5/kwaJcTFaNPyWXp592Hd+ep+p+fdlJ2mmqaTXse77bMql9cE6rmCwdEMOV9n/AEAACDyMJMMAAA/OXS82etrKmoCM1up8sTXLo/vO1zrU7yFR+o8Oi9QzxVIjmbI+TrjDwAAAJGHmWQAgIBITEyUyUFBd5PJpH79+ik9PV033HCDli1bFoLoAmPMkDivr7EWzve3aaMGuzw+fXSizhjc3+t+M1Nc92sVqOcKFGezACN5ZhwAAAC8w0wyAEBA3HfffYqKitKll16qtWvXau3atbr00ksVFRWlFStW6Mwzz9Qtt9yip59+OtSh+s3Y5IHKzkh2u9ujdLpwfnZGcsASL/PHD1WikyWCiXExmpeR7FO82WcOdXmNq+cK51pf7mbVReLMOAAAAHiHmWQAgIDYsWOHHnzwQf3gBz+wa//973+v9957T3/+8581ZcoU/e53v9NNN90Uoij9LzcnUyvzCt3uFmktnB9Ib6w4T5dv2KHa5lZbW2JcjN5YcV634nV0jaPzrCKh1pe7WYCRNjMOAAAA3jMZhps94sMAWyYDgH8F43N14MCBKioqUnp6ul17aWmppk2bpsbGRh08eFBTpkxRU1NoZ+kEYjzKq5tUUdOk1KQBtllVjtqCYXuJRfsO12r66ETNy0j2KV7DMHToeLPD432iTDrVbjh9rqUbd6ugtFptHf6TI9pkUla6WZuWzwrAE/smUuIEIgH//W6P8QAA/wrU5yozyQAAATFkyBD99a9/1e23327X/te//lVDhgyRJDU1NWnQoEGhCC/g0sxdE0aO2oJhXkay0+SY1eGaJn18tE59oky2GNPMA9TwVYtWvrhPn3zRYDvXOgvM0fOUWRrtkmn5xY53w7TW+tpeYnEbW7A4miEXjBl/AAAACA8kyQAAAXHvvffqlltu0ZYtWzRr1ulZOHv27NHbb7+tp556SpL0/vvva/78+aEMs9c7VNOkJRsKuizJ/L//mq1fv1vsMMG1rcSiW17YqxdvOtfW5mhJZWJcjF2/jly3cbdfl152TtJ5IyEuRpuWzwrZjD8AAACEFsstAaAXCtbnakFBgR5//HEVFxdLksaPH6+VK1dq7ty5AbunL3rz75nMB95zmMjqE2VSu2Go3cV/JWy5Y4EtieRoqaKn/LGkMRLqngG9SW/+XHWE8QAA/2K5JQAg4mRlZSkrKyvUYcCJ/OIqpzO9TrnKjv3bh2U1SjMPUJml0eGMM09Zl16WVzf5PHNrVV6RCkqr7doKSqu1Mq+QemIAAADwCEkyAEDAtLW16bXXXtOBAwckSRMnTtQVV1yhPn349RMOio7Wdet6axrt0PHmbsciSRU1viXJnCXp/JF8AwAAQO/BWwoAICD++c9/6vLLL1dlZaXGjx8vSfr1r3+t5ORk/fWvf9WkSZNCHCGmjRrcrevPHZskSRozJM4P0ZxedrmluMrrWmDuknS+Jt8AAADQu5AkAwAExI033qizzz5bH330kRITEyVJtbW1uuGGG3TzzTdr586dIY4Q88cPdVpcv0+USYYhpzXG5oxNsiWexiYPVHZGss81yaJ0umj+0md229q8qSfmLkmXmkSCDAAAAO5FhToAAEDPVFRUpHXr1tkSZJKUmJioX/7ylyosLAxhZOjojRXnKbFTIioxLkZvrMhSVrrZ4TWJcTF66Kopdm25OZldzu/crzMJcTFq+Mo+UWetJ+YJa5Iu2mSya482mZSdkcwsMgAAAHiEmWQAgIA488wzdezYMZ199tl27VVVVUpPTw9RVL1bmaVRh4432y1nTEmKU+F9F2p7iUX7Dtdq+uhEzctIliRtWj5L331yp/YeqlV7h34avjqlu1//xFYQ39rv2itO/6wrapps9yivbrJ9/1HFcRUcrFbWOLPOSR2iipomRZtMdjPIrKz1xF7afVizO8xacyY3J1Mr8wrtapNlpZuVm5PZnSEDAABAL0KSDAAQEOvWrdOqVav085//XOeee64k6YMPPtADDzygX//612poaLCd689tm9FVXXOLVuUV2SWQOi9nnJeRbEuOWZVZGrXnUG2X/qwJrH8cqdMj733mst808zcJuTTzAH33nBTbuWnmAdpSXOUy9rte3e+w384S4mK0afksu6QcM8gAAADgDZNh+FA8JMgaGhqUkJCg+vp6XqQAwA+C8bkaFfXNin7Tv5fBWX/ldPzeZDKpra0tIDF4Kpx/zzia/eWtpRt3d6kXFm0yKSvdbJsN5siW4iote3aP0+MZQwfqoKVR7R3+SyJK0sSR8cq9ZrrSzAP0+OYSFRys1ryMZJ09Ml5FR+vsZquVWRp1/iP5bp/Bk3gBhI9w/lwNBcYDAPwrUJ+rzCQDAATEli1bQh1CRPNk9pcnyiyNdn1YWWeDlVc73/nRXUH8kqrGLm3tkj75okELH95q176r7Ljd96frnp3ncdF/T+IFAAAAuoPC/QCAgJg/f75mz56t/v3768SJE6qvr7f7mj9/vu0LXa3KK1JBabVdmzfF7K0OHW92ebyipsnpMWcF8f2htrlVl2/YIclx0X9nXMXrL2WWRm0prlJ5deDvBQAAgPDBTDIAQEC88847Wrp0qaqrq7scC4clluGsO7O/OnM3Gyw1yfuC+P5S29yq7SUWzctIttUT+6CsRmv+XYfMEXfxdmd5qr9m7wEAACAyMZMMABAQK1eu1He/+119+eWXam9vt/siQeZad2Z/deZsNliU6XQCyF0iyVoQf8sdC/Tssplad+Vkj+/tiX2Hv9kYIM08QDmzRjuMN9pkchlvXXOLlm7crfMfydeyZ/do4cNbtXTjbtU3t3oci79m7wEAACAykSQDAATEsWPHtHr1ag0bNizUoUSc7s7+6iw3J1Ozxw6xa2s3pNa2do+TSGnmAVo4fqhmpw1xf7IXpo9O7NLmaPllVrpZuTmZTvvpboLLOnuvc120jrP3AAAA0LOx3BIAEBDf+c53tHXrVo0bNy7UoUQcZ8XsrTs8eruMMCEuRn2iohSl04X1rXaXH9fKvEKvdoy0xrajxGLXly8S42Jsu1x2jte6/LKipsnt0kl/LE/1ZPYeGwYAAAD0bCTJAAAB8fjjj+u73/2utm/frsmTJysmxr6m06pVq0IUWWRwVAvM3WwqZ/xZ48wa27V/+ECffNHgdSxW1t0tXUkze1ZXzB8JLn/P3gMAAEDkIUkGAAiIvLw8vffee+rXr5+2bt0qU4caUyaTiSSZG97OpnLF37OkEuJi9LucTJ3/SL5P8fzmO1P03XNSfLrWEX8kuPw9ew8AAACRhyQZACAg7r77bq1du1Z33XWXoqJ6dgnM/OIqFR2t0/TRiQ6XD3aHp7OprDru7mgYhg4db1ZVw9cur0lNGuDwGcosjfqw/LhMkkYO7qc2Q7Zknauk0lkjB+mTz53PMmtrN2xxRptk168v/JHgKrM06nszR+mr1lPaU/HNZgK+zt4DAABA5CFJBgAIiJaWFl199dU9OkF2qKZJSzYUqLZD8XvrMsKUJNezm/ytrrlFq/KKHC6rdCbaZNL0MYN15RP2zzC4f4xSk+JUdLTe4XXZGcnKzcl0uiT0xxdm6IoNO53e965X97vsNyEuxuFxV3xdnupo3GaOSdQNc1M18YwEZpABAAD0IibD6LSNUxhqaGhQQkKC6uvrFR8fH+pwACDiBeNz9fbbb1dycrJ+9rOfBaR/f/J1PDIfeM8uuWSVGBejwvsu9GeIbi3duLvLTCp3sjOS9Y+jdar/yrMdLq2sM7SsBf8dLQn1JZ7O/frC2+WpjuL0RxxAb8d/v9tjPADAvwL1ucpMMgBAQLS1temhhx7Su+++qylTpnQp3P/oo4+GKDL/yC+ucpggk6Ta5lZtL7H4femlM84K87vyv8tnqb3d0PXP7vH6fp0L/jtaEupoZpe3/frCm+Wp/t7QAAAAAJGNJBkAICD279+vzMzTS90++eQTu2Mdi/hHqqKjdS6P7ztcG7QkmbvC/I6cajf0sZtncMdVwX/rxgN5uw9rjZPllb7060/+3tAAAAAAkY0kGQAgILZs2RLqEAJq2qjBLo9PH50YnEDkfndHR1KTBqi71eI82TVydtoQr/uNNpm0pbiqW8X8PeGPXTEBAADQc5AkAwDAB/PHD1ViXIzTmmTBmkUmOd/d0ZGOOz6mmQc4fQZP+3ClrrlFP3/jU4/7NUkaHBejpc/strV1p5i/O+52xTQMIyjJOgAAAISHnrvlGAAAAfbGivOU2Cl5Y93dMthyczKVlW52e17nHR8dPYO3fTizKq9IBaXVHvc7qF8fNXTaRKCgtFor8wq9is8bjsZtVtoQnWpv1/mP5GvZs3u08OGtWrpxt+q9TCYCAAAgsrC7JQD0Qnyu2uvueGwvsWjf4VpNH50Y1BlkjnTc3VE6XVerT5RJp9oNlzOitpdYdM/rn+jI8Wa1d/gvA5NJSk8eqPsum+i2j47KLI06/5F8j2KOMklnj4zX/s8bnJ6z5Y4FAZ3N1XHc7v/LP9nxEugmfs/YYzwAwL/Y3RIAgDA1LyM55Mkxq867O3b8c5ml0enywfZ2Q4dquhayNwyppKpR1n9TO3K82ZZMcpW02lJ8zOOYz0tP1vdmjtKtLzqfMebPIvpllkYdOt5s9wzWcWPHSwAAgN6LJBkAIKytW7dOr776qv71r3+pf//+mjt3rn79619r/PjxtnMWLFig/Hz7WUvf//739dRTTwU73LBU19yiVXlFdskfa60vQ0aXY44sfWZPlzZH9cIc3cudr1tP6a4/f+zyHH8U0Xc1DglxMf8+7nppJzteAgAA9FzUJAMAhLX8/HytWLFCH3zwgd5//321trbqwgsvVFNTk915N910k7788kvb10MPPRSiiMOPo9pg1lpf3tYNc9RH53vt8CJBJkm7K2rVeLLN4bFok0nZGcl+SUy5Ggfr8U+/cL7kU2LHSwAAgJ6MmWQAgLD2zjvv2H3/3HPPaejQodq7d6+ys7Nt7XFxcRo+fLhHfZ48eVInT560fd/Q4DoxEsncLR/sjs5LEJ3dqzsmjhzk0SYB7rgbh22fVbmMPcp0elkos8gAAAB6LmaSAQAiSn19vSRpyJAhdu0vvPCCzGazJk2apDVr1qi5uWt9Lat169YpISHB9pWSkhLQmEPp0HHn4+AvFTVNAbvXoonD7JZz+spdbIVH6lwenzgy3i/JOgAAAIQvkmQAgIjR3t6u2267TVlZWZo0aZKt/ZprrtH//d//acuWLVqzZo3+93//V//5n//ptJ81a9aovr7e9nXkyJFghO8Ta7H98uom9yc7MGZInM/3fuiqyR6dZ12C6O5eUSbvY5g+OtH7ixxwF1tmymCXx3NzpvslWQcAAIDwxXJLAEDEWLFihT755BPt2LHDrv3mm2+2/Xny5MkaMWKELrjgAh08eFDjxo3r0k9sbKxiY2MDHm93uCsy76mxyQOVnZGsgtJqtf17h0rpdK2vrHSzJDk99r2Zo/Xmx5Vdjnc+z7oE0dm9rBL6xyg9eaD2Ha5zeLyzxLgYv+0a6m4css8c6vI4yywBAAB6PmaSAQAiwq233qo333xTW7Zs0ahRo1yeO3v2bElSaWlpMEILCHdF5r2Rm5NpS4hZZaWblZuT6fKYs2sdnefqXlb1za1qPHlK00cPtmuflTpECf3t/90uMS5Gb6w4z+2zecOXZ3X0jAAAAOiZmEkGAAhrhmFo5cqVeu2117R161alpaW5vaaoqEiSNGLEiABHFxjuisxbC+V7KiEuRpuWz1J5dZMqapqUmjTA7npXxzpf2yfKpFPtRpfzOp7/88sn6vxH8rsca5d0oPKEJGlmaqKun5uqs0cm2PrZXmLRvsO1mj460W8zyDrH5s2zOntG6fTP6NDxZpfnAAAAILKQJAMAhLUVK1boxRdf1F/+8hcNGjRIlZWVkqSEhAT1799fBw8e1IsvvqhLLrlESUlJ+vjjj3X77bcrOztbU6ZMCXH0vnFXZL6ixrskmVWa2XlCx9UxT4535EkB/32H6tQ/5qg2LR9pa5uXkRyQ5Fhn3XlWfy2DBQAAQPghSQYACGtPPvmkJGnBggV27c8++6xuuOEG9e3bV3/729/02GOPqampSSkpKbrqqqt0zz33hCBa/3BXZN5aKN+f8ourVHS0zu0sLk9mUHmyWYB1Vtz2EovtftYYRiT0U/Kgfqpq+FqVDV/7fWZZd2aBuVoGu2n5LL/FCAAAgOAjSQYACGuGmwLvKSkpys/vurQvkrkrMu/P5X2Hapq0ZEOBaptbbW3WemApSd8ku9zNoOqYeHJXwL+j6zbu1qzURBUfO6H6r045Pc9RTN7q7iwwfy+DBQAAQHihcD8AAGEoWEXkOyfIJKm2uVWXb7DfQdTZDKof/N9eLd24W+c/kq9lz+7Rwoe3aunG3frlkklOC/h3trui1mWCzBrT//f4dpVZGrWluErl1U0e9e3JM3i6GYIny2ABAAAQuXxKkm3YsEGpqanq16+fZs+erd27d3t03UsvvSSTyaQlS5b4clsAAHoNaxH5LXcs0LPLZmrLHQu0afksv9a9yi+u6pIgs6ptbtX2f8+ass6g6jwrrM0wtKusRjtK7WdXFZRW6+7XP9Gm5bO06b9m+i3e+q9OdUnG1TuJvzNXz2CdBeZOKJbBAggc3mkAAJ15nSR7+eWXtXr1at1///3at2+fpk6dqsWLF6uqqsrldRUVFbrjjjs0b948n4MFAKC3STMP0MLxQwOyjK/oaJ3L4/sO10pyP4OqvdOKyo6Jp8/rvu5OiC4FexaYdRlptMlk1x5tMik7I5mllkAE4Z0GAOCI10myRx99VDfddJOWLVumiRMn6qmnnlJcXJyeeeYZp9e0tbXp2muv1dq1azV27NhuBQwAAPxj2qjBLo9PH50oybNC/I6cTjy5rknWHaGYBRasZbAAAot3GgCAI14V7m9padHevXu1Zs0aW1tUVJQWLVqkXbt2Ob3ugQce0NChQ7V8+XJt377d7X1OnjypkydP2r5vaGjwJkwAAOCB+eOHKjEuxuGSy8S4GNuOks4K8UdJanfRf2rSAJ8TbN6oqHFfMH9s8kCXz+rpLDDrMtjy6iZV1DT5tEMmgNAKxjsN7zMAEJm8mklWXV2ttrY2DRs2zK592LBhqqysdHjNjh07tHHjRj399NMe32fdunVKSEiwfaWkpHgTJgAA8NAbK85TYqc6Z9adJDtyNIPqvIxkzR2X5HL54djkgZo7LsllDLNShyihv+8bbnecBeassH+ZpdFl/TVvNwII5DJYAIEVjHca3mcAIDL5/l+kHjhx4oSuu+46Pf300zKbPdvhSpLWrFmj1atX275vaGjgFwsAAAGQkhSnwvsu1PYSi/YdrtX00Ym2GWQdOZtBVd/cqpV5hdpW8k3x/s7LD5+8dkaXc2aOSdQNc1M18YwEW6LJGsPg/jF6Ze9RffL5NzMvEuNi1PBVq9o6rN6MNpmUlW5WmnmA6ppbtCqvyO4e2RnJys3JVEJcjEc1yUh4AXDEl3ca3mcAIDJ5lSQzm82Kjo7WsWPH7NqPHTum4cOHdzn/4MGDqqio0GWXXWZra28/vTCjT58+Ki4u1rhx47pcFxsbq9jYWG9CAwAgopRZGnXoeHPYLNebl5HcJTnmKMY08zd/th5fe8XZkk4nmqJNUpshHW9uUUJcjMNznD1z+793ABibPFBvrpynbZ9VqfBInaaPTtSUMwa7TMatyitSQWm1XX/Wwv6bls8K+s6U4fbzBfCNYLzT8D4DAJHJqyRZ3759NWPGDG3evNm25XF7e7s2b96sW2+9tcv5EyZM0P79++3a7rnnHp04cUL//d//zb+mAAB6HXcznsKBuxgdHZ87LkmGIe0qq7G1da4B5uw5D9U0acmGArtz+0SZdKrDtpnWa483t3RJtJVZGu1isepY2N9ZXbWOs9H8IRJ+vkBvxzsNAMAZr3e3XL16tZ5++mk9//zzOnDggG655RY1NTVp2bJlkqSlS5faimD269dPkyZNsvsaPHiwBg0apEmTJqlv377+fRoAAMKcqxlP4cJdjI6O7zxYY5cgk9SlBpiz5+ycIJNklyDreK2jWmCeLKWUgrMzZST8fAHwTgMAcMzrmmRXX321LBaL7rvvPlVWVmratGl65513bIUvDx8+rKgor3NvAAD0eJ7MeAr10jx3MW77rMrhcU84es784iqnBfXdXWvl6VLKQO9MGQk/XwCn8U4DAHDEp8L9t956q8OpyJK0detWl9c+99xzvtwSAICIFwnF493FWHikrtv36PicRUe968/RGHm7lLJjXTV/ioSfL4Bv8E4DAOiMfx4BACBIgl083hfuYsxMGdzte/SJMtn+PG2Ud/05GyNvllKWWRq1pbhK5dVNXt3bnUj4+QIAAMA5n2aSAQAA7wWreHx3uIsx+8yhDo9747qNu23F7OePH9qlwL8j7sbIk6WUgS6qHwk/XwAAADjHTDIAAIIoGMXju8tdjI6Ozx2XpDljk+zaOs4Y66xjMfs3VpynxE5Jqs7XejpGjgr7WwWjqH4k/HwBAADgmMkwfPxn4CBqaGhQQkKC6uvrFR8fH+pwACDi8blqLxTjEaji8f7kLkZHx61tT2wp1b5DdW5nm225Y4Ht2u0lFu07XKvpoxM1LyPZr2NUZmnU+Y/kexSHP0TCzxcIJH7P2GM8AMC/AvW5ynJLAABCIFDF490pszTq0PFmh8mbzsfcxejouGEYqqz/Wnsqaj2Kx1rMvszSqKO1zRo6qJ9GJcZ16OsrVdZ/bbufq/hdPZ+7ovoflNX49ecRqp8vAAAAfEeSDACAXsBVPS5DRrdrdTnq3xND4mJ0zdMfaOfBGrv2+H591PD1Kbu2hP59VP/VN20dY3RXb8xdUf01r+7X/9tf6bf6ZAAAAIg81CQDAKAXcFWPyx+1uhz14Uq0yaTsjGQ98l5JlwSZpC4JMkl2CbLOMbp7BmtR/WiTZ3XSAAAA0PuQJAMAoIcrszRqW4mlS32wNsPQthKLy2Pl1U0+9+9KVrpZP74ww+uZZ45i3PaZZ8/gqKi+q/MBAADQu7DcEgCAHs5dPS5XrDXD/NH/zNREXT83VWePTFCaeYC2FFf5HFdHhUdc1z+zPkNCXIw2LZ+ll3Yf1l2v7nd6/gdl1RTdBwAA6IVIkgEA0MO5q8flSmqS+ySRu/7XXzlZs8cmdUk4dSeujjJTEl0e7/wMzhdcnrbm1U9sf/a2NhsAAAAiF8stAQDo4ZzV47LWBXN1zJOZVO76/49Zox32Y73OV7b4z/TsGQ7VNCnzgfd0p4tZZJ1tK7HoB/+31+cYAQAAEDlIkgEA0As4qseVlW5Wbk6my2P+6N/ddXPHJXVpj+/XdbJ7Qn/7to79e3L/JRsKVNvc6vpBHNhVVkOdMgAAgF6A5ZYAAPQC1npc5dVNDuttuTrmj/5dXffiTeeqvLpJH5bVyJB07r+XZjprc9S/u/vnF1e5TJBdNf0M/Xnf506Pf1BWQ30yAACAHo4kGQAAvUia2XnyytUxf/Tv7jrDMOw2AXDUl7v+nR0vOlrn8v71X7meYeaujhkAAAAiH0kyAAB6kTJLow4db/b7zo3WfqNNJrUZhu1/O97H2b3rmlu0Kq9I20ostjZ/Fcy33nN4fD+X51149jD97YDz3TZnj+26JBQAAAA9C0kyAAB6gUAlohz129nccUkyjNO1vRzde1VekQpKq+2uKSit1sq8Qm1aPstvcfWJMulUu9Hl3MH9Y/S9c0br9cIvtPNgTZfjc8d13ZkTAAAAPQ+F+wEA6AVcJaK645b/2+cyQSZJOw/W2CXIOt67zNKobSUWtRn2yas2w9C2EovPBfMdPa+jBJkkZQwdKEl68toZXXbbzM5I1pPXzvApBgAAAEQWZpIBANDDWRNRnXVMRPkyU6rM0tgl+eUp6713lx93eV5FjXexlVka9WF5jdvEXUd7DtXaxqC7GxgAAAAgcpEkAwCgh+tYDN8RbxNRVh+6SXB5wvHcrm+kJtnH5U1dM290HAN/bGAAAACAyEOSDACAHm7MkDiXxzsnojznLsXl3rljkzR3XJLbWmDuaqr98IV9DvvwlO9jAAAAgJ6CmmQAAPRwY5MHKjsjWdEmk117tMmk7Ixkn2dNzU7zfcfHjvc2nOTaOra7qqmWX1zVrQQZAAAAIJEkAwCgV8jNyVRWutmuLSvdrNycTJ/7HJs8UHPHuU+UnTV8kKalDHZ4b1d1zXaV1ai8usltcf/rn93j8zNYVdT4tkFAqJRZGrWluMrnjQ0AAADQFcstAQDoBRLiYgJSlH79lZN1xYYC1Ta32toGxkbrzovG6+WPjuqTzxt0oPKE0+s9qZfWXTfMHaPzJwzT0md2Oz0nUpZbult2CgAAAN8xkwwAENbWrVunmTNnatCgQRo6dKiWLFmi4uJiu3O+/vprrVixQklJSRo4cKCuuuoqHTt2LEQRh7c08wAtHD/Ub4Xp73n9n2r46pRd21ct7Xr0/RId+MJ5cmxHqUU3btrjUb00d+e4c/3cNGWfmRyQJadScGd1uVp2CgAAgO4hSQYACGv5+flasWKFPvjgA73//vtqbW3VhRdeqKambxISt99+u/7617/qlVdeUX5+vr744gtdeeWVIYy6d3C1DLK2ubVLe0fthrSnolZ3/uljzUod4vAca+H+sckDNSs10acY54z9pvj/g0vOVnx/+0n08f376JdLJvnUd11zi5Zu3K3zH8nXsmf3aOHDW7V0427Vd5hV50/ulp2y9BIAAKB7SJIBAMLaO++8oxtuuEFnn322pk6dqueee06HDx/W3r17JUn19fXauHGjHn30UZ1//vmaMWOGnn32We3cuVMffPBBiKPv2dwtlfTE3kO1Kj7W4PBYx1xQ8THns9Kcyc5I1lP/OcP2/Z1/3m+3LFSSaptb9dM/f+x131LwZ3UFY2kqAABAb0ZNMgBARKmvr5ckDRlyevbR3r171draqkWLFtnOmTBhgkaPHq1du3bp3HPP7dLHyZMndfLkSdv3DQ2OkzRwrbvLICWpXVJ9p+WaVtbC/Ydrmpye01mUSZo4Ml65OdPtllB6skGAN0surbO6Ous4q8tfS1qtPFmaCgAAAN8xkwwAEDHa29t12223KSsrS5MmnV4iV1lZqb59+2rw4MF25w4bNkyVlZUO+1m3bp0SEhJsXykpKYEOvUcamzxQiQEuFl9R06Sio3Uenz9xZLxeWH5ulwTVh+XHXV73gZMEmjOhmNU1NnlgwOqqAQAAgCQZACCCrFixQp988oleeumlbvWzZs0a1dfX276OHDnipwh7lzJLY5fli/6WmjRA00YN9vj83JzpTnZ5dF4fTZJMLo92FapZXbk5mcpKN9u1TR8zWLk5mQG5HwAAQG9CkgwAEBFuvfVWvfnmm9qyZYtGjRplax8+fLhaWlpUV1dnd/6xY8c0fPhwh33FxsYqPj7e7gveczebypP/yIg2mZQYF+NydtT88UPdzlhzN5tqdlqSy+tnj3V9vLNQzepKiIvR73KmaeaYbzYy2FNRq5V5hQHbMAAAAKC3IEkGAAhrhmHo1ltv1Wuvvaa///3vSktLszs+Y8YMxcTEaPPmzba24uJiHT58WHPmzAl2uL2Ku9lUM8a435EyK92sN1ac12V2VFa62W521BsrznOZKLOeX2Zp1Jbiqi47PY5NHqi54xwnwqy7aHrL0awud3H4w6q8Iu07XGfX5mzDgEDGAQAA0NNQuB8AENZWrFihF198UX/5y180aNAgW52xhIQE9e/fXwkJCVq+fLlWr16tIUOGKD4+XitXrtScOXMcFu2H/1hnUxWUVqutw1aU0SaTstLN2rR8lrZ9ZtHSZ3Y77WPtFWcrJSlOm5bPUnl1kypqmpSaNKBL0iolKU6F912o7SUW7Ttcq+mjEzUqMc52fmJcjFbmFdoV08/OSFZuTqZt+eWT185weo4vEuJiusTtSRzd4emGAXXNLVqVVxSwOAAAAHoikmQAgLD25JNPSpIWLFhg1/7ss8/qhhtukCT99re/VVRUlK666iqdPHlSixcv1hNPPBHkSHun3JzMLkmhjrPAio7Uury+ouabXSANw75uWJmlUYeON9slzf5xuE4fHKxRTFSU5nVY1rh0424VlFbbXW+dXfXzyyfa+umc1DIMQ/uO1DpMzHkqzTzAozg2LZ/lU/8debJhQJp5gFblFQU0DgAAgJ6IJBkAIKx1Tpw40q9fP23YsEEbNmwIQkToyHBSEP/I8SYteHi328L+qUmOZz0lxsXYXTtl5CB9/MUJ2/e7yo7roXeL9fJN5yo5Ptbl7KrzH8m3tVlnUyXGxfh9ppWns7y6w5MNA4IRBwAAQE9ETTIAAOAzZzOWljyx0+OdLx310fnajgmyjq5++gO3s6s6x7Yyr9DlTCtfeTLLq7s82TAgGHEAAAD0RCTJAACAT6wzlto6zfZrMwydanc/A1CS3vzH5w778EZBSbX7k/7NOpvKWdzWmVa+8GSWlz+42jAgmHEAAAD0NCy3BAAAPvFmBpczNU0t3e7jn1/UO9xAwFcd66R5w91GBv5a4uhow4COfQcrDgAAgJ6GmWQAAMAn7mYseeL8CUO73ce8f9cS6zy7ylfdmWnlbpaXP6WZB2jh+KEOk16exlFmadSW4iqfZ88BAAD0JMwkAwAAPnE1Y8lkktsll3PGJin7zKHdngX2w4XpktRldtX9f/mnV/36Y6aVu1leweIuDkebJXR34wIAAIBIx0wyAADgM2czlt5YkaVEF8mW7IxkPfWfM5z20SfK5OiyLl6+6Vy77zvOrvJ2dpk/Z3y5muUVTM7iCMTGBQAAAJGOmWQAAMBnrmYsFd53obaXWLTvcK2mj07UqMQ4h7OaOvcRbTJp6TO7nd5zWkqCLpw43DaDzJPYnt9Zrud2HnJ67h0Xnqlbz8/w8ukDq8zSqEPHm/0+G8264UJnHTcuCHVyDwAAIBRIkgEAAIe8SdKkmR2f095hyaXhZtmj9fgXdV+5PK9/TLSSB8U6PPby7sPaVV6jrHFmffecFFts44fHu+zTW91NYLm63pOlkN25v7sNF3zduAAAACDSkSQDAAB2/FGv6lBNk5ZsKFBtc6vD4x37c3Q/V3aVHdeusuNa8+p+vbEiSxPPSND+o3X69hM7bXXQXi/8wu747LQhLvt8+L3PtLu81u0zdndsPLne1VLI3+VM6/bPxt2GC93ZuAAAACCSUZMMAADY8Ue9KlcJss79ObqfJ061G7p8Q4Ek2SXIHB0fmzxQCf1d/9ugJ8/Y3bFxd711KWTnzQasSyFvev6jbv9sxiYPdFovLjEuhllkAACg1yJJBgAAbNwlacqrm9z2kV9c5TJB1rG/bZ9ZHN7PU6faDd3xx3843UnzVLuhVz46ojJLo+q/OuVRTM6esbtj48n17pZC7jlU262fjTUOZz+f2uZWj/vxRpmlUVuKqwLSNwAAgL+w3BIAANj4Wq+qY42soqN1Ht+v8EittyF28UG561loBQerZXZSw8wRZ8/Y3Vpenlzvbilkd+7vTRz+mk3mj6W7AAAAwUKSDAAA2Hhbr8pREmTSGZ4Xyc9MSfQuQAfOTTPrT7VHnR7PGmf2KvnkrCZXd2t5JfZ3nRSyFuHPzkhWQWm13YyxaJNJ08cM1p4K50lFR/d3VOA/mDXJXC0v3bR8lt/uAwAA4A8stwQAADZjkwcqOyNZ0SaTXXu0yaTsjOQuM4wcJUEOfHFCfaLsr+/M2l/2mcmaMzbJ53j7RJn08PemOr1fnyiTvntOisYmD3R7H2fPaOXt2HT26PslTo91vD43J1NZ6Wa741npZv1h6UxlZyQ7/I+3PlEm9ekQV11zi5Zu3K3zH8nXsmf3aOHDW7V0427VN7d2+zk85Y+luwAAAMFEkgwAANhxlqTJzcm0a3OVBDnVbii+n/MJ6x37M7nOp0mSBsX2UXSn8/pEmfTGiixJ0hsrsrokyjoe9+Q+jp6xM0/HpjPrWDlzx4Vn2v6cEBejTctnacsdC/TsspnacscCbVo+SwlxMcrNyVSUg4Tg6U0Kdti+d7dBgK/P4Q1PlnUCAACEE5ZbAgAAO9YkTXl1kypqmuyW6nXkLgny3zmZ6hNl0r7DtZo+OlGjEuO69FdmadTOgzVO+7hh7hhdcNYwzctIliS98tERFRysVtY4s757TortvIlnJKj0V5c4Pe7uPv+7fJbtHq54OjaduRurmuaWLm1p5q59Fx2pdbpJQW1zq7aXWHTG4P4OE3IdZ3ClmQf49BzeCOayTgAAAH8gSQYAABxylKTpyJMkSJp5gF3yqXN/7pJH88cPtbv+u+ek2CW/OnN23N19nCWenHE3Np35K2HkblOEfYedJ9GsOhbm9/Y5vGFd1umovlpWujlg9wUAAPAVyy0BAIBP/FHbytvkUZmlUVuKq7yuZxXsWU2d43Q2VlEmeVUHbNqowS6PTx+d6NWz+jqengrGsk4AAAB/YSYZAADwWW5OplbmFdot7/MmCeLpbCNHu2hmZyQrNydTCXGud4203mdWaqJ2O9gdclbqEL/NanIVZ25Opm55Ya/dss92Q2pta1d9c6tHzzF//FAN7h+juq9auxwb3D/GNuvO3Zh6M56Odsj0lK/LUwEAAEKBmWQAAMBnrorMe8qT2UbuCtF7ovjYCSftDR734Y6rOBPiYtQnKqrLf3ztLj/u1XNkDB3ott3dmHoynq52yPRWmnmAFo4fSoIMAACENWaSAQCAbutObSt3s42c7QzZuRC9K/nFVar/6pTDY/VfndL2EotHhftdcRfnts8s3X6OMkuj9hzqOhtOkvYcqrX14WpMPR1PV4m0TctnuYwTAAAgEjGTDAAAhAVns43cFd2vqHFfT8uTgvfecFTLy12chUdc38OT5/iw/LhXfTgaU0/G05pI67hcU7JPpIWbQNdXAwAAPR8zyQAAQFjzR9F9Twree8JVLS93cWamuL6Hq+dwdF9v+7DyZDzdJew67pAZat2tVwcAAGDFTDIAABDW/LGL5vzxQ5XoJGGSGBfj8VJLV0sQ3cWZfWayz8/h6L7e9mHlyXgGezfQ7vBHvToAAACJJBkAAIgAnhT3d+eNFed1SZQlxsXojRXneXS9J0sQ3cXp7rijJYPO7uusD0+4i8MficlgiMRloQAAIHyx3BIAAIQ9d8X9PZGSFKfC+y7U9hKL9h2u1fTRiV4V6/eklleaeYDLOJ09h3UnSUdLBt3dd/2Vk/Ufs0Z7/Byu4ugoNydTK/MK7WLyNhkXaJ7+TAAAADxBkgwAAESMwzVN+vhonfpEmRwmP/KLq1R0tM5lAuyMwf11qt3QqETXSwo782YJorvdPjsfd7Vk8OeXT3R53zMS+7s87kyZpVH/u+uQjtQ2a/HZw7vE64/EpDexHDre7PU9ImlZaKTx9WcCAEAkI0kGAADC3qGaJi3ZUKDa5lZbm3WpZEpSnNvjUvcLvFuXIBaUVtst74s2mZSVbvY5kWBdMtiZdcmg6d9LHDvf1+q6jbu9eo665hYte26PCg/X2dr+dqBKd/35Y/311vM08YwEu/PdJfy6wx8/kzljk7SrrKbLsTljk0ju+ICNEAAAvRk1yQAAQNjrnACTpNrmVl2+YYdHxyX/FHj3R220zjxZMujovh158xyr8orsEmRWbYZ0+YYCj/rwF3/8TDqVTXPbDtfYCAEA0JsxkwwAAIS1/OKqLgkwq9rmVm34e4nL49tLLDpjcH+Xs7XKqz2rXRWIJYieLBm03nfbZ1Va+syeLud4+hzOZq1ZnWo39MpHR/Tdc1I8fwAfuZtB58nPpMzSqJ0Hu84ik6SdB2s8/rniNH/8TAAAiGTMJAMAAGGt6Gidy+M7Os166Wzf4VqPZmt5I808QAvHD/VLwsCbnSTbnG9wKcn9c7gbB0kqOOh6PP3FHz8Tf/9cezvGEwDQ25EkAwAAYW3aqMEuj5/nYhmiJE0fnej3Au9llkZtKa5SebV/kgaeLuN09xzH6r+2i6lznO6ul6Ssca7H01/88TMJ959rpGEjBABAb+dTkmzDhg1KTU1Vv379NHv2bO3evdvpuU8//bTmzZunxMREJSYmatGiRS7PBwAA6Gj++KFKdFIwPDEuRivOz3B5fF5GsleztVypa27R0o27df4j+Vr27B4tfHirlm7crXonyz09ZV1OueWOBXp22UxtuWOBNi2f1aVQurPnsLrr1f1a+PBWXfP0B8r5nw+6xJk0IFbZTnb9lKQ+UaagLLWUvim674inRffD/ecaafw1npGCdxoAQGdeJ8lefvllrV69Wvfff7/27dunqVOnavHixaqqqnJ4/tatW5WTk6MtW7Zo165dSklJ0YUXXqjPP/+828EDAIDe4Y0V53VJhFl3r/TkuOSfovuBLmruyTJOd0X8pdP1uDrv+GiNMzcnU9NHD+5yTbRJemNFlk9x+8ofRfcj4ecaSQKxOUU44p0GAOCIyTAc7CXuwuzZszVz5kw9/vjjkqT29nalpKRo5cqVuuuuu9xe39bWpsTERD3++ONaunSpR/dsaGhQQkKC6uvrFR8f7024AAAH+Fy1x3hEju0lFu07XKvpoxM1z8GMKHfHJflcdL/M0qjzH8l3enzLHQuCOtOmvLpJH5TVaM2r+726zhpneXWT/m9XhQ4db9bis4cHbQaZlb/Hs6f8XMNFdzenCPfP1WC/04T7eABApAnU56pXu1u2tLRo7969WrNmja0tKipKixYt0q5duzzqo7m5Wa2trRoyZIjTc06ePKmTJ0/avm9oaPAmTAAA4ER+cZWKjta5TCKFszMG99epdkOjEh3XTpqXkez2ubz890EbT4qae5pMKLM06tDx5m7tjplmHuBTIXVrnGnmATIM6Whts4orT/gUg6ccPa8/x1OS7Zm81d04/PGzDEe+jmckCMY7De8zABCZvEqSVVdXq62tTcOGDbNrHzZsmP71r3951Medd96pkSNHatGiRU7PWbdundauXetNaACAHmrbtm36zW9+o7179+rLL7/Ua6+9piVLltiO33DDDXr++eftrlm8eLHeeeedIEca3g7VNGnJhgLVdqixZF2OmJLkvph7qNU1t2hVXpG2lVhsbdkZycrNyexStytQffijqLk/nsObmBxJTRqg9z75Ujf/3z5b278qG/WHHeXauPQcXTBxmIurvePqecOlSLyvcfj7Z4ngCcY7De8zABCZgrq75fr16/XSSy/ptddeU79+/Zyet2bNGtXX19u+jhw5EsQoAQDhpKmpSVOnTtWGDRucnnPRRRfpyy+/tH3l5eUFMcLI0DlBJkm1za26fMOOEEXkHX/UjOpuH/4oau7v2lfuCvl31ifKpDTzALsEWUfLN33kUxzOuHresckDXW64EKxZTL7GQR2z3suTdxreZwAgMnmVJDObzYqOjtaxY8fs2o8dO6bhw4e7vPbhhx/W+vXr9d5772nKlCkuz42NjVV8fLzdFwCgd7r44ov14IMP6tvf/rbTc2JjYzV8+HDbV2Jioss+T548qYaGBruvniy/uKpLgsyqtrlV2zvMhAlHZZZGbSuxqK3TMsk2w9C2EovKq90vOfRHH1L3ipr7KwZPYnLmVLuh7z/vOhH24Juf+hRHZ+6ed9tnFpd/L30dD2+VWRq9jiNQP0sERzDeaXifAYDI5FWSrG/fvpoxY4Y2b95sa2tvb9fmzZs1Z84cp9c99NBD+sUvfqF33nlH55xzju/RAgDgwNatWzV06FCNHz9et9xyi2pqalyev27dOiUkJNi+UlKCW7A82IqO1rk8vu9wbXAC8ZEnNaOC0YckJcTFaNPyWdpyxwI9u2ymttyxQJuWz/JoeZ2/YnAX0w1zx7g8/6Mjx10e31Hqn6Spu+ctPOL6752v4+EtX34ugfpZIjh4pwEAOOP1csvVq1fr6aef1vPPP68DBw7olltuUVNTk5YtWyZJWrp0qV0RzF//+te699579cwzzyg1NVWVlZWqrKxUY2Oj/54CANBrXXTRRdq0aZM2b96sX//618rPz9fFF1+strY2p9f0tmUw00YNdnl8+mjXM+9CzR+1q/xd/yrNPEALxw/1aklgoGtwWWNaOH6oy/POSXG+eZIkjRkywC8zodw9b2aK67934VyTLFzqqcF3vNMAABzxOkl29dVX6+GHH9Z9992nadOmqaioSO+8846t8OXhw4f15Zdf2s5/8skn1dLSou985zsaMWKE7evhhx/231MAAHqt//iP/9Dll1+uyZMna8mSJXrzzTe1Z88ebd261ek1vW0ZzPzxQ13WXAr3XS79UQvMH310V7BicPfz/v31rmfAvPvpMS18eKuWbtyteifLED3h7nmzz0wO+c/EkzgdxREOf5/QPbzTAAAcMRm+7oMeRA0NDUpISFB9fX2Pf5EBgGCI1M9Vk8nUZXdLR5KTk/Xggw/q+9//vkf9Rup4eONITbMu37AjYne3rG9u1cq8wm7tJOiPProrWDG4+3lv/vSY2yL90SaTstLN2rR8ls9xuHvecPiZeBKnv67pTXrD56o3GA8A8K9Afa6SJAOAXihSP1c9SZIdPXpUo0eP1uuvv67LL7/co34jdTx8sb3Eon2HazV9dGLYzyBzpLy6SRU1TUpNGuDzbB1/9NFdwYrB3c/7wTc/1d//dUxl1c5rbG25Y0G3Y3T3vOHwM/E1jnCJPdz0ps9VTzAeAOBfgfpc7eO3ngAACIDGxkaVlpbavi8vL1dRUZGGDBmiIUOGaO3atbrqqqs0fPhwHTx4UD/96U+Vnp6uxYsXhzDq8DUvIzkik2NWaebuJyL80Ud3BSsGdz/ve/6/icrKMGvZs3ucnlNR0xTwMQ+Hn4mvcYRL7AAAoPtIkgEAwtpHH32khQsX2r5fvXq1JOn666/Xk08+qY8//ljPP/+86urqNHLkSF144YX6xS9+odjY2FCFDPhdmaVRh443281WctTmbV+Pby7RjoOud7N0V4Tekzh8jdUqv7hKRUfrInYGpL+5G09PxpsxBQCgK5JkAICwtmDBArmqDPDuu+8GMRoguOqaW7Qqr8iu7tWcsUkymaSdB2tsbZ7UwnLUlzt9okzq06k4vav+OsfhyTmuHKpp0pINBRFbS8/f3I2nJ+PNmAIA4JzXu1sCAAAgOFblFamgtNqubVdZjV2CTJIKSqu1Mq/Q677cOdVu6PINOzzur3McnpzjSudkjiTVNrc6jamnczeenow3YwoAgHMkyQAAAMJQmaVR20osavNgj6U2w9C2EovKq5u63Vdntc2t2t5p9pmz/jrG4ck5ruQXV3VJ5riKqadzN57bPqtyO96MKQAArpEkAwAACEOHjjvfcdKZihrHiSdf+upo3+Far/qrqGny6BxXio7WeRVTT+duPAuP1Lk8XlHTxJgCAOAGNckAAADC0Jgh3teHclZk35e+Opo+OtGr/lKTBrisJWg9x5VpowZ7FVNP527MM1MGuzyemjTA7b+O97YxBQCgM2aSAQAAhKGxyQOVnZGsaCeF8zuKNpmUnZHsdCdDb/rqLDEupsvuh8766xiHJ+e4Mn/8UCU6Ke7vKKaezt14Zp851O14M6YAALhGkgwAACBM5eZkKivdbNc2Z2yS5o5LsmvLSjcrNyfT6746i+u0xsC666Gn/XWOw5NzXHljxXldkjquYurp3I2nJ+PNmAIA4JzJcDcXPgw0NDQoISFB9fX1io+PD3U4ABDx+Fy1x3gg3JVXN6mipkmpSQNsM7ActXnb1xNbSrWrrFpzxpr1m+9OlSRtL7Fo3+FaTR+d6NHMIk/i8DVWK29j6uncjacn4x3oMeVz1R7jAQD+FajPVWqSAQAABFGZpVFvfvyFjje16oKzhnqUoEgzd012ePLvnPnFVSo6WmeXCOnY15ghcTpaG2fX97yMZLuYyiyNOnS82S7h0rnNXeLLXayO4uzoi9qvVF7dpJEJ/d0+c3c4etZw5G48PfmZdP45AwAAkmQAAABBUdfcops2faQ9Fd/sIPjczgol9I/Rm7eep5Qkz4rr1zW3aFVekbaVWGxt2RnJys3JVMK/l9EdqmnSkg0Fqm1utZ1jXVKXkhSnnaUWXfOH3bZju8qO66F3i/XyTedq9r+Xcjq6z5yxSTKZpJ0Ha5ze25tY3cW5/2idvv3ETp1qP50Uer3wC615db/eWJGliWckeDRenvBkTMNBpMQJAEC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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, axes = subplots(ncols=3, figsize=(15, 5))\n", - "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n" + "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);" ] }, { @@ -7886,7 +2589,7 @@ "id": "95b0f3de", "metadata": {}, "source": [ - "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`. " + "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`." ] }, { @@ -7902,32 +2605,13 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "id": "2b6fb1ad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.909709Z", - "iopub.status.busy": "2023-07-25T23:59:07.909567Z", - "iopub.status.idle": "2023-07-25T23:59:07.912864Z", - "shell.execute_reply": "2023-07-25T23:59:07.912497Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CategoricalDtype(categories=[3, 4, 5, 6, 8], ordered=False)" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n", - "Auto.cylinders.dtype\n" + "Auto.cylinders.dtype" ] }, { @@ -7941,31 +2625,13 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "id": "1f19f48c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:07.914563Z", - "iopub.status.busy": "2023-07-25T23:59:07.914464Z", - "iopub.status.idle": "2023-07-25T23:59:08.031995Z", - "shell.execute_reply": "2023-07-25T23:59:08.031639Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.boxplot('mpg', by='cylinders', ax=ax);\n" + "Auto.boxplot('mpg', by='cylinders', ax=ax);" ] }, { @@ -7978,32 +2644,13 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "929b6901", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:08.033904Z", - "iopub.status.busy": "2023-07-25T23:59:08.033752Z", - "iopub.status.idle": "2023-07-25T23:59:08.146764Z", - "shell.execute_reply": "2023-07-25T23:59:08.146364Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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AAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKCYXsfmz3/+8/z+7/9+Ro4cmcGDB+c973lPnn322e73VyqV3HLLLRk9enQGDx6cKVOm5LnnnjugQwMA0Df0KjZ/+ctf5pxzzkljY2Mee+yx/O///b/zla98JUcddVT3dRYtWpS77ror9957b55++ukcccQRmT59erZv337AhwcAoL4N6M2V77jjjowdOzZLly7tPjZhwoTu/65UKlm8eHG+8IUv5NJLL02S3H///Rk1alQefvjhfPjDHz5AYwMA0Bf0Kjb/6q/+KtOnT88VV1yRlStX5rd+67fyyU9+Mtdff32S5MUXX8zGjRszZcqU7tsMGzYsZ511VlatWrXb2Ozo6EhHR0f35fb29iRJZ2dnOjs79+lJHUxvzVjvszb1r9T28ftVevzKnu1pV/V+jtVCX/nzVw/sqnp2VT27qt6htqvePI+GSqVS9Wf/QYMGJUnmzJmTK664Is8880w+/elP5957780111yTp556Kuecc042bNiQ0aNHd9/uyiuvTENDQ7797W/vcp/z58/PggULdjne1taWIUOGVP1EAAA4OLZt25arrroqW7ZsSXNz8ztet1exOXDgwJx55pl56qmnuo996lOfyjPPPJNVq1btU2zu7pXNsWPH5he/+MVeh68HnZ2dWb58eaZOnZrGxsZaj7NHk+Yvq+njN/Wr5EtndmXes/3S0dVQ01nq3Z529eP502s4VX3qK3/+6oFdVc+uqmdX1TvUdtXe3p6jjz66qtjs1V+jjx49OqecckqPYyeffHL+x//4H0mSlpaWJMmmTZt6xOamTZty+umn7/Y+m5qa0tTUtMvxxsbGPvWbUe/zduysj8Dr6Gqom1nq3dt3Vc/nV63V+5+/emJX1bOr6tlV9Q6VXfXmOfTqu9HPOeecrFu3rsexn/70pzn++OOT/PqbhVpaWrJixYru97e3t+fpp59Oa2trbx4KAIBDQK9e2Zw9e3Z+53d+J//1v/7XXHnllfn+97+fP/mTP8mf/MmfJEkaGhoya9as3HbbbTnppJMyYcKEzJs3L2PGjMlll11WYn4AAOpYr2Lzfe97Xx566KHMnTs3t956ayZMmJDFixfn6quv7r7OZz/72WzdujU33HBDNm/enHPPPTePP/549zcXAQBw+OhVbCbJ7/7u7+Z3f/d39/j+hoaG3Hrrrbn11lv3azAAAPo+PxsdAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxvf5H3YHaGH/zo7UeoaiXFl5c6xEAKMArmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxA2o9AECSjL/50V7fpql/JYsmJ5PmL0vHzoYCUx04Ly28uNYjANSEVzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoplexOX/+/DQ0NPR4mzhxYvf7t2/fnpkzZ2bkyJE58sgjM2PGjGzatOmADw0AQN/Q61c23/3ud+eVV17pfvve977X/b7Zs2fnkUceyYMPPpiVK1dmw4YNufzyyw/owAAA9B0Den2DAQPS0tKyy/EtW7bkvvvuS1tbWy644IIkydKlS3PyySdn9erVOfvss/d/WgAA+pRex+Zzzz2XMWPGZNCgQWltbc3tt9+ecePGZc2aNens7MyUKVO6rztx4sSMGzcuq1at2mNsdnR0pKOjo/tye3t7kqSzszOdnZ29He+ge2vGep+1qX+lto/fr9LjV/bMrqrXl3ZV648RfeVjVT2wq+rZVfUOtV315nk0VCqVqj9KP/bYY3nzzTfzrne9K6+88koWLFiQn//85/nxj3+cRx55JNdee22PcEySyZMn5/zzz88dd9yx2/ucP39+FixYsMvxtra2DBkypOonAgDAwbFt27ZcddVV2bJlS5qbm9/xur2KzbfbvHlzjj/++Hz1q1/N4MGD9yk2d/fK5tixY/OLX/xir8PXg87OzixfvjxTp05NY2NjrcfZo0nzl9X08Zv6VfKlM7sy79l+6ehqqOks9c6uqteXdvXj+dNr+vh95WNVPbCr6tlV9Q61XbW3t+foo4+uKjZ7/dfov2n48OH5N//m3+T555/P1KlTs2PHjmzevDnDhw/vvs6mTZt2+zWeb2lqakpTU9MuxxsbG/vUb0a9z9uxsz4+EXd0NdTNLPXOrqrXF3ZVLx8f6v1jVT2xq+rZVfUOlV315jns17+z+eabb+aFF17I6NGjc8YZZ6SxsTErVqzofv+6deuyfv36tLa27s/DAADQR/Xqlc3PfOYzueSSS3L88cdnw4YN+eIXv5j+/fvnIx/5SIYNG5brrrsuc+bMyYgRI9Lc3Jybbropra2tvhMdAOAw1avY/NnPfpaPfOQjee2113LMMcfk3HPPzerVq3PMMcckSe68887069cvM2bMSEdHR6ZPn5677767yOAAANS/XsXmAw888I7vHzRoUJYsWZIlS5bs11AAABwa/Gx0AACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADF9OofdQdg34y/+dGaPn5T/0oWTU4mzV+Wjp0NRR7jpYUXF7lfoG/zyiYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAx+xWbCxcuTENDQ2bNmtV9bPv27Zk5c2ZGjhyZI488MjNmzMimTZv2d04AAPqgfY7NZ555Jl/72tdy6qmn9jg+e/bsPPLII3nwwQezcuXKbNiwIZdffvl+DwoAQN+zT7H55ptv5uqrr87Xv/71HHXUUd3Ht2zZkvvuuy9f/epXc8EFF+SMM87I0qVL89RTT2X16tUHbGgAAPqGAftyo5kzZ+biiy/OlClTctttt3UfX7NmTTo7OzNlypTuYxMnTsy4ceOyatWqnH322bvcV0dHRzo6Orovt7e3J0k6OzvT2dm5L+MdVG/NWO+zNvWv1Pbx+1V6/Mqe2VX17Kp6B2NX9f5xsFp95eN6PbCr6h1qu+rN8+h1bD7wwAP5wQ9+kGeeeWaX923cuDEDBw7M8OHDexwfNWpUNm7cuNv7u/3227NgwYJdjn/3u9/NkCFDejtezSxfvrzWI7yjRZNrPcGvfenMrlqP0GfYVfXsqnold/U3f/M3xe67Fur943o9savqHSq72rZtW9XX7VVsvvzyy/n0pz+d5cuXZ9CgQb0ebHfmzp2bOXPmdF9ub2/P2LFjM23atDQ3Nx+Qxyips7Mzy5cvz9SpU9PY2FjrcfZo0vxlNX38pn6VfOnMrsx7tl86uhpqOku9s6vq2VX1Dsaufjx/epH7Pdj6ysf1emBX1TvUdvXW30RXo1exuWbNmrz66qt573vf231s586defLJJ/PHf/zHWbZsWXbs2JHNmzf3eHVz06ZNaWlp2e19NjU1pampaZfjjY2Nfeo3o97n7dhZH5+IO7oa6maWemdX1bOr6pXcVT1/DNwX9f5xvZ7YVfUOlV315jn0KjYvvPDC/OhHP+px7Nprr83EiRPzuc99LmPHjk1jY2NWrFiRGTNmJEnWrVuX9evXp7W1tTcPBQDAIaBXsTl06NBMmjSpx7EjjjgiI0eO7D5+3XXXZc6cORkxYkSam5tz0003pbW1dbffHAQAwKFtn74b/Z3ceeed6devX2bMmJGOjo5Mnz49d99994F+GAAA+oD9js0nnniix+VBgwZlyZIlWbJkyf7eNQAAfZyfjQ4AQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgmAG1HgCAQ8P4mx+t9QgHRFP/ShZNTibNX5aOnQ3dx19aeHENp4K+yyubAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMX0KjbvueeenHrqqWlubk5zc3NaW1vz2GOPdb9/+/btmTlzZkaOHJkjjzwyM2bMyKZNmw740AAA9A29is3jjjsuCxcuzJo1a/Lss8/mggsuyKWXXpp//Md/TJLMnj07jzzySB588MGsXLkyGzZsyOWXX15kcAAA6t+A3lz5kksu6XH5y1/+cu65556sXr06xx13XO677760tbXlggsuSJIsXbo0J598clavXp2zzz77wE0NAECfsM9fs7lz58488MAD2bp1a1pbW7NmzZp0dnZmypQp3deZOHFixo0bl1WrVh2QYQEA6Ft69cpmkvzoRz9Ka2trtm/fniOPPDIPPfRQTjnllKxduzYDBw7M8OHDe1x/1KhR2bhx4x7vr6OjIx0dHd2X29vbkySdnZ3p7Ozs7XgH3Vsz1vusTf0rtX38fpUev7JndlU9u6qeXVVvT7uq94/ztdBXPgfWg0NtV715Hg2VSqVXH3l27NiR9evXZ8uWLfnOd76TP/3TP83KlSuzdu3aXHvttT3CMUkmT56c888/P3fcccdu72/+/PlZsGDBLsfb2toyZMiQ3owGAMBBsG3btlx11VXZsmVLmpub3/G6vY7Nt5syZUpOOOGEfOhDH8qFF16YX/7ylz1e3Tz++OMza9aszJ49e7e3390rm2PHjs0vfvGLvQ5fDzo7O7N8+fJMnTo1jY2NtR5njybNX1bTx2/qV8mXzuzKvGf7paOroaaz1Du7qp5dVc+uqrenXf14/vQaTlWf+srnwHpwqO2qvb09Rx99dFWx2eu/Rn+7rq6udHR05IwzzkhjY2NWrFiRGTNmJEnWrVuX9evXp7W1dY+3b2pqSlNT0y7HGxsb+9RvRr3P27GzPj65dHQ11M0s9c6uqmdX1bOr6r19V/X8Mb7W6v1zYD05VHbVm+fQq9icO3duLrrooowbNy5vvPFG2tra8sQTT2TZsmUZNmxYrrvuusyZMycjRoxIc3NzbrrpprS2tvpOdACAw1SvYvPVV1/NRz/60bzyyisZNmxYTj311CxbtixTp05Nktx5553p169fZsyYkY6OjkyfPj133313kcEBAKh/vYrN++677x3fP2jQoCxZsiRLlizZr6EAADg0+NnoAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGIG1HoAAOgLxt/8aK1HKOqlhRfXegQOUV7ZBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKCYXsXm7bffnve9730ZOnRojj322Fx22WVZt25dj+ts3749M2fOzMiRI3PkkUdmxowZ2bRp0wEdGgCAvqFXsbly5crMnDkzq1evzvLly9PZ2Zlp06Zl69at3deZPXt2HnnkkTz44INZuXJlNmzYkMsvv/yADw4AQP3r1c9Gf/zxx3tc/sY3vpFjjz02a9asyfvf//5s2bIl9913X9ra2nLBBRckSZYuXZqTTz45q1evztlnn33gJgcAoO7t19dsbtmyJUkyYsSIJMmaNWvS2dmZKVOmdF9n4sSJGTduXFatWrU/DwUAQB/Uq1c2f1NXV1dmzZqVc845J5MmTUqSbNy4MQMHDszw4cN7XHfUqFHZuHHjbu+no6MjHR0d3Zfb29uTJJ2dnens7NzX8Q6at2as91mb+ldq+/j9Kj1+Zc/sqnp2VT27qt7huqt9+TzWVz4H1oNDbVe9eR4NlUpln/40feITn8hjjz2W733veznuuOOSJG1tbbn22mt7xGOSTJ48Oeeff37uuOOOXe5n/vz5WbBgwS7H29raMmTIkH0ZDQCAgrZt25arrroqW7ZsSXNz8zted59e2bzxxhvz13/913nyySe7QzNJWlpasmPHjmzevLnHq5ubNm1KS0vLbu9r7ty5mTNnTvfl9vb2jB07NtOmTdvr8PWgs7Mzy5cvz9SpU9PY2FjrcfZo0vxlNX38pn6VfOnMrsx7tl86uhpqOku9s6vq2VX17Kp6h+uufjx/eq9v01c+B9aDQ21Xb/1NdDV6FZuVSiU33XRTHnrooTzxxBOZMGFCj/efccYZaWxszIoVKzJjxowkybp167J+/fq0trbu9j6bmprS1NS0y/HGxsY+9ZtR7/N27KyPD5gdXQ11M0u9s6vq2VX17Kp6h9uu9udzWL1/Dqwnh8quevMcehWbM2fOTFtbW/7yL/8yQ4cO7f46zGHDhmXw4MEZNmxYrrvuusyZMycjRoxIc3NzbrrpprS2tvpOdACAw1CvYvOee+5Jkpx33nk9ji9dujQf+9jHkiR33nln+vXrlxkzZqSjoyPTp0/P3XfffUCGBQCgb+n1X6PvzaBBg7JkyZIsWbJkn4cCAODQ4GejAwBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQzoNYD1IvxNz+6T7dr6l/JosnJpPnL0rGz4QBPBQDQt3llEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGJ6HZtPPvlkLrnkkowZMyYNDQ15+OGHe7y/UqnklltuyejRozN48OBMmTIlzz333IGaFwCAPqTXsbl169acdtppWbJkyW7fv2jRotx1112599578/TTT+eII47I9OnTs3379v0eFgCAvmVAb29w0UUX5aKLLtrt+yqVShYvXpwvfOELufTSS5Mk999/f0aNGpWHH344H/7wh/dvWgAA+pRex+Y7efHFF7Nx48ZMmTKl+9iwYcNy1llnZdWqVbuNzY6OjnR0dHRfbm9vT5J0dnams7PzQI73jpr6V/btdv0qPX5l9+ypenZVPbuqnl1V73Dd1b58zn3rNgfz83VfdajtqjfPo6FSqezzn6aGhoY89NBDueyyy5IkTz31VM4555xs2LAho0eP7r7elVdemYaGhnz729/e5T7mz5+fBQsW7HK8ra0tQ4YM2dfRAAAoZNu2bbnqqquyZcuWNDc3v+N1D+grm/ti7ty5mTNnTvfl9vb2jB07NtOmTdvr8AfSpPnL9ul2Tf0q+dKZXZn3bL90dDUc4KkOHfZUPbuqnl1Vz66qd7ju6sfzp/f6Np2dnVm+fHmmTp2axsbGAlMdOg61Xb31N9HVOKCx2dLSkiTZtGlTj1c2N23alNNPP323t2lqakpTU9MuxxsbGw/qb0bHzv37gNLR1bDf93E4sKfq2VX17Kp6dlW9w21X+/M592B/zu7LDpVd9eY5HNB/Z3PChAlpaWnJihUruo+1t7fn6aefTmtr64F8KAAA+oBev7L55ptv5vnnn+++/OKLL2bt2rUZMWJExo0bl1mzZuW2227LSSedlAkTJmTevHkZM2ZM99d1AgBw+Oh1bD777LM5//zzuy+/9fWW11xzTb7xjW/ks5/9bLZu3ZobbrghmzdvzrnnnpvHH388gwYNOnBTAwDQJ/Q6Ns8777y80zewNzQ05NZbb82tt966X4MBAND3+dnoAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMUMqPUAAAAHw/ibH63ZYzf1r2TR5GTS/GXp2NlQ5DFeWnhxkfvdX17ZBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFDOg1gMAALU3/uZHe32bpv6VLJqcTJq/LB07GwpMxaHAK5sAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAoRmwCAFCM2AQAoBixCQBAMWITAIBixCYAAMWITQAAihGbAAAUIzYBAChGbAIAUIzYBACgGLEJAEAxYhMAgGLEJgAAxYhNAACKEZsAABRTLDaXLFmS8ePHZ9CgQTnrrLPy/e9/v9RDAQBQp4rE5re//e3MmTMnX/ziF/ODH/wgp512WqZPn55XX321xMMBAFCnisTmV7/61Vx//fW59tprc8opp+Tee+/NkCFD8md/9mclHg4AgDo14EDf4Y4dO7JmzZrMnTu3+1i/fv0yZcqUrFq1apfrd3R0pKOjo/vyli1bkiSvv/56Ojs7D/R4ezTgV1v37XZdlWzb1pUBnf2ys6vhAE916LCn6tlV9eyqenZVPbuqnl1V72Ds6rXXXityv7vzxhtvJEkqlcrer1w5wH7+859XklSeeuqpHsf/4A/+oDJ58uRdrv/FL36xksSbN2/evHnz5s1bH3t7+eWX99qGB/yVzd6aO3du5syZ0325q6srr7/+ekaOHJmGhvr/v6T29vaMHTs2L7/8cpqbm2s9Tt2yp+rZVfXsqnp2VT27qp5dVe9Q21WlUskbb7yRMWPG7PW6Bzw2jz766PTv3z+bNm3qcXzTpk1paWnZ5fpNTU1pamrqcWz48OEHeqzimpubD4mTpzR7qp5dVc+uqmdX1bOr6tlV9Q6lXQ0bNqyq6x3wbxAaOHBgzjjjjKxYsaL7WFdXV1asWJHW1tYD/XAAANSxIn+NPmfOnFxzzTU588wzM3ny5CxevDhbt27NtddeW+LhAACoU0Vi80Mf+lD+7//9v7nllluycePGnH766Xn88cczatSoEg9XU01NTfniF7+4y5cC0JM9Vc+uqmdX1bOr6tlV9eyqeofzrhoqlWq+Zx0AAHrPz0YHAKAYsQkAQDFiEwCAYsQmAADFiM0qPPnkk7nkkksyZsyYNDQ05OGHH+7x/kqlkltuuSWjR4/O4MGDM2XKlDz33HO1GbbG9rarj33sY2loaOjx9sEPfrA2w9bY7bffnve9730ZOnRojj322Fx22WVZt25dj+ts3749M2fOzMiRI3PkkUdmxowZu/zAhMNBNbs677zzdjm3/vN//s81mrh27rnnnpx66qnd/3B0a2trHnvsse73O6f+1d525ZzavYULF6ahoSGzZs3qPua82r3d7epwPK/EZhW2bt2a0047LUuWLNnt+xctWpS77ror9957b55++ukcccQRmT59erZv336QJ629ve0qST74wQ/mlVde6X771re+dRAnrB8rV67MzJkzs3r16ixfvjydnZ2ZNm1atm7d2n2d2bNn55FHHsmDDz6YlStXZsOGDbn88strOHVtVLOrJLn++ut7nFuLFi2q0cS1c9xxx2XhwoVZs2ZNnn322VxwwQW59NJL84//+I9JnFO/aW+7SpxTb/fMM8/ka1/7Wk499dQex51Xu9rTrpLD8Lza609Pp4cklYceeqj7cldXV6WlpaXy3/7bf+s+tnnz5kpTU1PlW9/6Vg0mrB9v31WlUqlcc801lUsvvbQm89S7V199tZKksnLlykql8uvzqLGxsfLggw92X+cnP/lJJUll1apVtRqzLrx9V5VKpfKBD3yg8ulPf7p2Q9Wxo446qvKnf/qnzqkqvLWrSsU59XZvvPFG5aSTTqosX768x26cV7va064qlcPzvPLK5n568cUXs3HjxkyZMqX72LBhw3LWWWdl1apVNZysfj3xxBM59thj8653vSuf+MQn8tprr9V6pLqwZcuWJMmIESOSJGvWrElnZ2ePc2vixIkZN27cYX9uvX1Xb/nmN7+Zo48+OpMmTcrcuXOzbdu2WoxXN3bu3JkHHnggW7duTWtrq3PqHbx9V29xTv2rmTNn5uKLL+5x/iQ+Vu3Onnb1lsPtvCryE4QOJxs3bkySXX460qhRo7rfx7/64Ac/mMsvvzwTJkzICy+8kM9//vO56KKLsmrVqvTv37/W49VMV1dXZs2alXPOOSeTJk1K8utza+DAgRk+fHiP6x7u59budpUkV111VY4//viMGTMmP/zhD/O5z30u69aty1/8xV/UcNra+NGPfpTW1tZs3749Rx55ZB566KGccsopWbt2rXPqbfa0q8Q59ZseeOCB/OAHP8gzzzyzy/t8rOrpnXaVHJ7nldjkoPrwhz/c/d/vec97cuqpp+aEE07IE088kQsvvLCGk9XWzJkz8+Mf/zjf+973aj1K3dvTrm644Ybu/37Pe96T0aNH58ILL8wLL7yQE0444WCPWVPvete7snbt2mzZsiXf+c53cs0112TlypW1Hqsu7WlXp5xyinPq/3v55Zfz6U9/OsuXL8+gQYNqPU5dq2ZXh+N55a/R91NLS0uS7PJdd5s2bep+H3v227/92zn66KPz/PPP13qUmrnxxhvz13/91/n7v//7HHfccd3HW1pasmPHjmzevLnH9Q/nc2tPu9qds846K0kOy3Nr4MCBOfHEE3PGGWfk9ttvz2mnnZY//MM/dE7txp52tTuH6zm1Zs2avPrqq3nve9+bAQMGZMCAAVm5cmXuuuuuDBgwIKNGjXJe/X9729XOnTt3uc3hcF6Jzf00YcKEtLS0ZMWKFd3H2tvb8/TTT/f4uh9272c/+1lee+21jB49utajHHSVSiU33nhjHnroofzd3/1dJkyY0OP9Z5xxRhobG3ucW+vWrcv69esPu3Nrb7vanbVr1ybJYXluvV1XV1c6OjqcU1V4a1e7c7ieUxdeeGF+9KMfZe3atd1vZ555Zq6++uru/3Ze/dredrW7Lxc7HM4rf41ehTfffLPH/3G8+OKLWbt2bUaMGJFx48Zl1qxZue2223LSSSdlwoQJmTdvXsaMGZPLLrusdkPXyDvtasSIEVmwYEFmzJiRlpaWvPDCC/nsZz+bE088MdOnT6/h1LUxc+bMtLW15S//8i8zdOjQ7q9tGjZsWAYPHpxhw4bluuuuy5w5czJixIg0NzfnpptuSmtra84+++waT39w7W1XL7zwQtra2vLv/t2/y8iRI/PDH/4ws2fPzvvf//7d/rMjh7K5c+fmoosuyrhx4/LGG2+kra0tTzzxRJYtW+acept32pVz6l8NHTq0x9dHJ8kRRxyRkSNHdh93Xv3a3nZ12J5Xtf52+L7g7//+7ytJdnm75pprKpXKr//5o3nz5lVGjRpVaWpqqlx44YWVdevW1XboGnmnXW3btq0ybdq0yjHHHFNpbGysHH/88ZXrr7++snHjxlqPXRO721OSytKlS7uv8y//8i+VT37yk5WjjjqqMmTIkMp/+A//ofLKK6/Ubuga2duu1q9fX3n/+99fGTFiRKWpqaly4oknVv7gD/6gsmXLltoOXgMf//jHK8cff3xl4MCBlWOOOaZy4YUXVr773e92v9859a/eaVfOqXf29n++x3m1Z7+5q8P1vGqoVCqVgxm3AAAcPnzNJgAAxYhNAACKEZsAABQjNgEAKEZsAgBQjNgEAKAYsQkAQDFiEwCAYsQmAADFiE0AAIoRmwAAFCM2AQAo5v8B0x6PiZTyGhgAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.hist('mpg', ax=ax);\n" + "Auto.hist('mpg', ax=ax);" ] }, { @@ -8016,32 +2663,13 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "8d48f1e9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:08.148651Z", - "iopub.status.busy": "2023-07-25T23:59:08.148520Z", - "iopub.status.idle": "2023-07-25T23:59:08.257224Z", - "shell.execute_reply": "2023-07-25T23:59:08.256869Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.hist('mpg', color='red', bins=12, ax=ax);\n" + "Auto.hist('mpg', color='red', bins=12, ax=ax);" ] }, { @@ -8058,31 +2686,12 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "id": "400690e6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:08.259243Z", - "iopub.status.busy": "2023-07-25T23:59:08.259102Z", - "iopub.status.idle": "2023-07-25T23:59:09.325925Z", - "shell.execute_reply": "2023-07-25T23:59:09.325581Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ], - "text/plain": [ - " mpg weight\n", - "count 392.000000 392.000000\n", - "mean 23.445918 2977.584184\n", - "std 7.805007 849.402560\n", - "min 9.000000 1613.000000\n", - "25% 17.000000 2225.250000\n", - "50% 22.750000 2803.500000\n", - "75% 29.000000 3614.750000\n", - "max 46.600000 5140.000000" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Auto[['mpg', 'weight']].describe()\n" + "metadata": {}, + "outputs": [], + "source": [ + "Auto[['mpg', 'weight']].describe()" ] }, { @@ -8248,40 +2743,13 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "39bf4091", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:09.593932Z", - "iopub.status.busy": "2023-07-25T23:59:09.593794Z", - "iopub.status.idle": "2023-07-25T23:59:09.598241Z", - "shell.execute_reply": "2023-07-25T23:59:09.597941Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 392.000000\n", - "mean 23.445918\n", - "std 7.805007\n", - "min 9.000000\n", - "25% 17.000000\n", - "50% 22.750000\n", - "75% 29.000000\n", - "max 46.600000\n", - "Name: mpg, dtype: float64" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto['cylinders'].describe()\n", - "Auto['mpg'].describe()\n" + "Auto['mpg'].describe()" ] }, { @@ -8294,22 +2762,13 @@ } ], "metadata": { + "execution": { + "allow_errors": true + }, "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch3-linreg-lab.ipynb b/docs/source/labs/Ch3-linreg-lab.ipynb index 8cf09d3..ab4e953 100644 --- a/docs/source/labs/Ch3-linreg-lab.ipynb +++ b/docs/source/labs/Ch3-linreg-lab.ipynb @@ -2,20 +2,17 @@ "cells": [ { "cell_type": "markdown", - "id": "3b9db985", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "4d8e6029", + "id": "82bce88a", "metadata": {}, "source": [ "# Linear Regression\n", "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", + "\n", "## Importing packages\n", "We import our standard libraries at this top\n", "level." @@ -23,22 +20,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ca5277a6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:10.810120Z", - "iopub.status.busy": "2023-07-25T23:59:10.809784Z", - "iopub.status.idle": "2023-07-25T23:59:11.236722Z", - "shell.execute_reply": "2023-07-25T23:59:11.236402Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n" + "from matplotlib.pyplot import subplots" ] }, { @@ -56,20 +45,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "675f24e6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.238770Z", - "iopub.status.busy": "2023-07-25T23:59:11.238614Z", - "iopub.status.idle": "2023-07-25T23:59:11.732369Z", - "shell.execute_reply": "2023-07-25T23:59:11.731999Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "import statsmodels.api as sm\n" + "import statsmodels.api as sm" ] }, { @@ -89,21 +70,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a0ee23c2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.734483Z", - "iopub.status.busy": "2023-07-25T23:59:11.734356Z", - "iopub.status.idle": "2023-07-25T23:59:11.737321Z", - "shell.execute_reply": "2023-07-25T23:59:11.737059Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence \\\n", " import variance_inflation_factor as VIF\n", - "from statsmodels.stats.anova import anova_lm\n" + "from statsmodels.stats.anova import anova_lm" ] }, { @@ -120,22 +94,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "b35eb887", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.738949Z", - "iopub.status.busy": "2023-07-25T23:59:11.738837Z", - "iopub.status.idle": "2023-07-25T23:59:11.834618Z", - "shell.execute_reply": "2023-07-25T23:59:11.834237Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from ISLP import load_data\n", "from ISLP.models import (ModelSpec as MS,\n", " summarize,\n", - " poly)\n" + " poly)" ] }, { @@ -152,67 +119,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "961908f7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.836672Z", - "iopub.status.busy": "2023-07-25T23:59:11.836534Z", - "iopub.status.idle": "2023-07-25T23:59:11.839996Z", - "shell.execute_reply": "2023-07-25T23:59:11.839716Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['In',\n", - " 'MS',\n", - " 'Out',\n", - " 'VIF',\n", - " '_',\n", - " '__',\n", - " '___',\n", - " '__builtin__',\n", - " '__builtins__',\n", - " '__doc__',\n", - " '__loader__',\n", - " '__name__',\n", - " '__package__',\n", - " '__spec__',\n", - " '_dh',\n", - " '_i',\n", - " '_i1',\n", - " '_i2',\n", - " '_i3',\n", - " '_i4',\n", - " '_i5',\n", - " '_ih',\n", - " '_ii',\n", - " '_iii',\n", - " '_oh',\n", - " 'anova_lm',\n", - " 'exit',\n", - " 'get_ipython',\n", - " 'load_data',\n", - " 'np',\n", - " 'open',\n", - " 'pd',\n", - " 'poly',\n", - " 'quit',\n", - " 'sm',\n", - " 'subplots',\n", - " 'summarize']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir()\n" + "metadata": {}, + "outputs": [], + "source": [ + "dir()" ] }, { @@ -233,193 +145,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "662caa15", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.841679Z", - "iopub.status.busy": "2023-07-25T23:59:11.841579Z", - 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}, - "outputs": [ - { - "data": { - "text/plain": [ - "19" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A.sum()\n" - ] - }, - { - "cell_type": "markdown", - "id": "2e42056b", "metadata": {}, + "outputs": [], "source": [ - " " + "A.sum()" ] }, { @@ -491,33 +196,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "1ea46cee", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.850247Z", - "iopub.status.busy": "2023-07-25T23:59:11.850129Z", - "iopub.status.idle": "2023-07-25T23:59:11.854617Z", - "shell.execute_reply": "2023-07-25T23:59:11.854342Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n", - " 'ptratio', 'lstat', 'medv'],\n", - " dtype='object')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Boston = load_data(\"Boston\")\n", - "Boston.columns\n" + "Boston.columns" ] }, { @@ -530,89 +215,19 @@ "We start by using the `sm.OLS()` function to fit a\n", "simple linear regression model. Our response will be\n", " `medv` and `lstat` will be the single predictor.\n", - "For this model, we can create the model matrix by hand.\n" + "For this model, we can create the model matrix by hand." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "26c0ba88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.856310Z", - "iopub.status.busy": "2023-07-25T23:59:11.856187Z", - "iopub.status.idle": "2023-07-25T23:59:11.861185Z", - "shell.execute_reply": "2023-07-25T23:59:11.860911Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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interceptlstat
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coefstd errtP>|t|
intercept34.55380.56361.4150.0
lstat-0.95000.039-24.5280.0
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OLS Regression Results
Dep. Variable: medv R-squared: 0.544
Model: OLS Adj. R-squared: 0.543
Method: Least Squares F-statistic: 601.6
Date: Tue, 25 Jul 2023 Prob (F-statistic): 5.08e-88
Time: 16:59:11 Log-Likelihood: -1641.5
No. Observations: 506 AIC: 3287.
Df Residuals: 504 BIC: 3295.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
intercept 34.5538 0.563 61.415 0.000 33.448 35.659
lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874
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Omnibus: 137.043 Durbin-Watson: 0.892
Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373
Skew: 1.453 Prob(JB): 5.36e-64
Kurtosis: 5.319 Cond. No. 29.7


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: medv R-squared: 0.544\n", - "Model: OLS Adj. R-squared: 0.543\n", - "Method: Least Squares F-statistic: 601.6\n", - "Date: Tue, 25 Jul 2023 Prob (F-statistic): 5.08e-88\n", - "Time: 16:59:11 Log-Likelihood: -1641.5\n", - "No. Observations: 506 AIC: 3287.\n", - "Df Residuals: 504 BIC: 3295.\n", - "Df Model: 1 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "intercept 34.5538 0.563 61.415 0.000 33.448 35.659\n", - "lstat -0.9500 0.039 -24.528 0.000 -1.026 -0.874\n", - "==============================================================================\n", - "Omnibus: 137.043 Durbin-Watson: 0.892\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 291.373\n", - "Skew: 1.453 Prob(JB): 5.36e-64\n", - "Kurtosis: 5.319 Cond. No. 29.7\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.summary()\n" + "metadata": {}, + "outputs": [], + "source": [ + "results.summary()" ] }, { @@ -1091,33 +389,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "a0edf555", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.951447Z", - "iopub.status.busy": "2023-07-25T23:59:11.951309Z", - "iopub.status.idle": "2023-07-25T23:59:11.954106Z", - "shell.execute_reply": "2023-07-25T23:59:11.953845Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 34.553841\n", - "lstat -0.950049\n", - "dtype: float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.params\n" + "metadata": {}, + "outputs": [], + "source": [ + "results.params" ] }, { @@ -1134,78 +411,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "fdc5a3f3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:11.955744Z", - "iopub.status.busy": "2023-07-25T23:59:11.955619Z", - "iopub.status.idle": "2023-07-25T23:59:11.959606Z", - "shell.execute_reply": "2023-07-25T23:59:11.959329Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Parameters 'cmap' will be ignored\n", - " scatter = ax.scatter(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = Boston.plot.scatter('lstat', 'medv')\n", "abline(ax,\n", " results.params[0],\n", " results.params[1],\n", " 'r--',\n", - " linewidth=3)\n" + " linewidth=3)" ] }, { @@ -1479,8 +588,7 @@ "an argument to make it of width 3.\n", "There is some evidence for non-linearity in the relationship between `lstat` and `medv`. We will explore this issue later in this lab.\n", "\n", - "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays.\n", - "\n" + "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays." ] }, { @@ -1501,35 +609,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "b35a2fd3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.082076Z", - "iopub.status.busy": "2023-07-25T23:59:12.081936Z", - "iopub.status.idle": "2023-07-25T23:59:12.186713Z", - "shell.execute_reply": "2023-07-25T23:59:12.186372Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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AFiUGA10iIiKiJPDzScUYX9xD8fcuAAsmFyM1RbTv1/oY6BIRERHZlM8voaK6Hmsq96Kiuj5mxYRl15+Lp6adhdzMTiGPF7oz8fR1I1BWUmhkcxOuU+xNiIiIiMhKfH4JT23YieXv7sbh422BxwvdmVgwuThqwPq94b0wYWghttQ04MCRZvTIbU9XcFJProxLAIfhEsBERERkZeVVtbj/5U9w+Fhbh9/JoaoTe2eDcQlgIiIiIocpr6rFbSu2RQxygfblfAFg0evbHbXwg1ZMXSCyAJ9fSoohJCIi0s7nl7Do9e2IFb5KAGq9zdhS04DSgV0NaYddvrMY6BKZrLyqFote345a78kC3SI5VkRElFy21DSEfFfEYsTCD3b7zmLqApGJ5CGo8AtXnbcZt63YhvKqWpNaRkREVqM2cNV74Qel76xabzNuXbENT67fabl0CQa6RCaJNgTFHCsiIgqnJnDVe+EHkbSJx9d/gTGPvGmpThoGukQmiTUEFZxjRURENLKoAIXuTKGlfPVe+EE0baKuscVSI5IMdIlMIjoEZUSOFRER2U9qigsLJhcDgGKwm5+dhmcMKC2m9rvIKiOSDHSJTCI6BKV3jhUREdlXWUkhnr5uBDzu0O+G/Ow0zBl3GrY+MN6QSWFqvousNCLJqgtEJpGHoOq8zRFznlwAPDrnWBERkf2VlRRifLEnoSW+Yn1nRWKFEUn26BKZJNoQlPyz3jlWRETkDKkpLpQO7Iopw3ujdGBXw78rgr+zRFlhRJKBLpGJlIagPO5Mxy/fSERE9hL4zsqLHsC6oH/VB61ckiSZnylsIaJrJxPpyU6rzBARUXLz+SU8tWEnHl+/s8Pv5G8uoztrROM15ugSWYA8BEVERGR1qSku3DHuNJzuye2wSprHYqukMdAlIiIiItXMmBSnFgNdIiIiItLE6iOSnIxGRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkTkYjw7A2LBEREZmJgS4ZoryqtkNtvUKL1dYjIiIiZ2PqAumuvKoWt63YFhLkAkCdtxm3rdiG8qpak1pGRETkDD6/hIrqeqyp3IuK6nr4/FzoNhL26JKufH4Ji17fjkinm4T2pQEXvb4d44s9TGMgIiLSgKOm4tijS7raUtPQoSc3mASg1tuMLTUNiWsUERGRQ3DUVB0GuqSrA0eUg1wt2xEREVG7WKOmQPuoKdMYTmKgS7rqkZup63ZERETUjqOm6jHQJV2NLCpAoTsTStm3LrTnEY0sKkhks4iIiGyPo6bqMdAlXaWmuLBgcjEAdAh25Z8XTC7mRDQiIkooJ1Qp4Kipeqy6YCKnLqhQVlKIp68b0WFGqIczQomIyAROqVIgj5rWeZsj5um60P5dy1HTk1ySJNnvlsZAjY2NcLvd8Hq9yMvLM+x1nHLSRePUQJ6IiOxDrlIQHuzI30ZPXzfCVt+78vsBEPKe7Pp+tBKN1xjohklEoOu0k46IiMiKfH4J5z+6QXECl9wDumnuWFt1xCRDZ1ksovEaUxcSjAsqEBERJYaaKgWlA7smrmFxKispxPhiD0dNBTDQTTCnnnRERERW4+QqBakpLsYJAlh1IcGcfNIRERFZCasUkK0C3XfeeQeTJ09Gr1694HK58Oqrr4b8fsaMGXC5XCH/ysrKzGmsAp50REREicHa7mSrQLepqQnDhg3D0qVLFbcpKytDbW1t4N+qVasS2MLYeNIRERElBmu7k61ydCdMmIAJEyZE3SYjIwMejydBLVJPPuluW7ENLkQuDcKTjoiISB+s7Z7cbBXoinjrrbfQo0cPdOnSBWPHjsVDDz2Erl2Vk7VbWlrQ0tIS+LmxsdHwNvKkIyIiShxWKUhejgp0y8rKcNVVV6GoqAjV1dX42c9+hgkTJqCiogKpqakR/2bJkiVYtGhRglvKk46IiCiRWKUgOdl2wQiXy4VXXnkFU6dOVdzmyy+/xMCBA7F+/XpceumlEbeJ1KPbp08fw1dGIyIiIiJtRBeMsNVkNLUGDBiAbt26YdeuXYrbZGRkIC8vL+QfEREREdmfo1IXwn3zzTeor69HYSFzXomIiMiZfH6JqZAKbBXoHj16NKR3tqamBpWVlSgoKEBBQQEWLVqEq6++Gh6PB9XV1bjvvvswaNAgXH755Sa2moiIiMgY5VW1HSa3F3Jye4CtcnTfeustXHLJJR0ev+GGG/D0009j6tSp+PDDD3H48GH06tULl112GRYvXoyePXsKv4ZozgcRERGRmcqranHbim0ID+TkvtynrxvRIdh1Su+vaLxmq0A3ERjoEhERkRUFB6ndcjJw94sfoa6xOeK2LrSXLd00d2wgkHVS769ovGar1AUiIiKiZBQpSI1GAlDrbcaWmgaUDuyq2Ptb523GbSu2Rez9dQJHV10gIiIisjs5SBUNcoMdONIMn1/Cote3dwhygZMrtC56fTt8fucN8jPQJSIiIrKoaEGqiB65mdhS0xA1SA7u/XUaBrpEREREFhUrSFXiQnv+7ciiAhw4Ivb3otvZCQNdIiIiIovSEnzKNRQWTC5GaooLPXIzhf5OdDs7YaBLREREZFFagk+POzNkctnIogIUujOhVEQsuPfXaVh1gYiIiMii5CC1ztscMU/XBaBnXgb+73+G4+DRloi1cVNTXFgwuRi3rdgGFxDyPOG9v07DHl0iIiIii5KDVAAdemTlnxdecSbGDOqGKcN7o3Rg14gBa1lJIZ6+bgQ87tAe4vDeX6fhghFhuGAEERERWY1eiz1wZbQkx0CXiIiIrMgpQaoeuDIaERERkYOkprhQOrCr2c2wFeboEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIiciQGukRERETkSAx0iYiIiMiRGOgSERERkSMx0CUiIiIiR+ISwEREREQO4fNL2FLTgANHmtEjNxMjiwqQmuIyu1mmYaBLRERE5ADlVbVY9Pp21HqbA48VujOxYHIxykoKTWyZeZi6QIbw+SVUVNdjTeVeVFTXw+eXzG4SERGRY5VX1eK2FdtCglwAqPM247YV21BeVWtSy8zFHl3SHe8oiYjIbMk0hO/zS1j0+nZE6lKSALgALHp9O8YXexy7D5Qw0CVdyXeU4SebfEf59HUjGOwSEZGhkq3DZUtNQ4ee3GASgFpvMzZX1yMlxZUUwb+MgS7FJfiOuVvnDCx87VPeURIRkWmSscPlwBHlIDfYrJXbcPh4W+BnJwf/Mga6pFmkO+Zo5DvKLTUNKB3Y1djGERFR0knWIfweuZlC2wUHuYCzg38ZJ6ORJkpJ7yJE7zyJiIjUEB3C31LTkLhGJcDIogIUujOhNnSXbwgWvb7dsZPGGeiSatHumEWI3nkSERGpIdqR4rQOl9QUFxZMLgYATcGuE4N/GQNdUi3WHbMSF9rzgUYWFejfKCIiSnqiHSlO7HApKynE09eNgMcd+t7ys9KE/t5pwb+MObqkmpaTQb7DXDC52FF5UUREZB3yEH6dtzniqKMLgMfBHS5lJYUYX+wJKavmlyRc+8f3Y/6tE4N/gIEuaaDlZPAkwcxOIiIylzyEf9uKbXABIcFusnS4pKa4QiZ8+/wS8rPTcPhYW8TtnR78M9Al1UTvmH/9/WE42NSSNLX6iIjIfPIQfnhVoGTtcFm3vU4xyAXabwacHPwz0CXVRO+Yx5zazYTWEVG4ZFohigiIPISfjMe9PHk8mi7ZaRhf7ElQixKPgS5pwjtmIntIthWiiGThQ/jJSGTy+KFjbY6ub89AlzTjHTORtSXjClFEyS54BGfn/qNCf+PUigsAA12KE++YiawpWVeIIkpmalcslTm14gLAOrpERI6UrCtEESUrLSuWJkN9ewa6REQOlKwrRBElIy0rliZLuTWmLhAROVAyrxBFlGy0rFiaLJPHGegSETlQsq8QRZRMREdmZl8yEKf2zE2qyeNMXSAiciC53jVwcohSlixDlkTJQnRkZsyg7pgyvDdKB3ZNmnOfgS4RkUPJ9a497tAvQY87k6XFiCzK55dQUV2PNZV7UVFdD58/duatPIKjFLomw6QzJUxdICJyMNa7JrIPrQu8iK5YmoznvUuSJDWT9ByvsbERbrcbXq8XeXl5ZjeHiIiIkoDSAi9yaCoyCpNMKyGKxmvs0SUiIiIykV4LvHAEpyMGukREREQmUrPAS6zVSLliaSgGukREBJ9fYi8QkUm4wItxGOgSESW5ZMrrI7KiRC3wkow3tAx0iYiSmNIEmDpvM25bsY1lyChuyRhcqZWIBV6S9YaWgS4RUZLSawIMkZJkDa7UMro8WDLf0HLBCCKiJKVmAgyRWnJwFX6MycFVeVWtSS2zJqMWeIl1Qwu039CKLExhR+zRJUogDuGRlXACDBmFowXaGFEeTM+KDnbEQJcoQTiER1aTqAkwlHySPbiKh97lwZL9hpapC0QJwCE8siJ5AoxSX5EL7Tdj8UyAoeSU7MGVlST7DS0DXSKDJTo/yueXUFFdjzWVe1FRXe/YvCuKnzwBBkCHYFePCTB2xvMoPskeXFlJst/QMnWByGCJHMJjegSpJU+ACT9uPEl83PA8il8iymWRGKMrOlidS5Ik3qYGaWxshNvthtfrRV5entnNIQdYU7kXd6yujLndk9OGY8rw3ppfR6l8jHzpcnL5GIofJ0q243mkH3lfApGDK+7LxHLaDZxovGar1IV33nkHkydPRq9eveByufDqq6+G/F6SJDz44IMoLCxEVlYWxo0bh507d5rTWKLvJGIIL9nLx1D85AkwU4b3RunArkkZ5PI80pdR5bJIm7KSQmyaOxarZo7Gk9OGY9XM0dg0d6zjPwdbpS40NTVh2LBh+NGPfoSrrrqqw+8fe+wx/Pa3v8Wf//xnFBUVYf78+bj88suxfft2ZGYyD4jMkYghPM5wJoofzyP9GVEui7TTu6KDHdgq0J0wYQImTJgQ8XeSJOGJJ57AAw88gClTpgAA/vKXv6Bnz5549dVXMW3atEQ2lSggEflRnOFMFD+eR8ZIxuCKrMNWqQvR1NTUoK6uDuPGjQs85na7MWrUKFRUVCj+XUtLCxobG0P+EenN6CE8znAmih/PIyLnsVWPbjR1dXUAgJ49e4Y83rNnz8DvIlmyZAkWLVpkaNuIAGOH8DjDmSh+PI/sg5MnSZRjAl2t5s2bh7vuuivwc2NjI/r06WNii8jJjBrCS/byMUR64HlkD06rHkDGckzqgsfjAQDs378/5PH9+/cHfhdJRkYG8vLyQv4R2RFnOBPFj+eRtXGVSVLLMT26RUVF8Hg8ePPNNzF8+HAA7b2z77//Pm677TZzG0eUIJzhTBQ/nkfWFKv8mwvt5d/GF3v4WcXJSakhtgp0jx49il27dgV+rqmpQWVlJQoKCtC3b1/ceeedeOihh3DqqacGyov16tULU6dONa/RRAnGGc5E8eN5ZD0s/5YYTksNsVWg+8EHH+CSSy4J/Czn1t5www14/vnncd9996GpqQm33HILDh8+jPPPPx/l5eWsoUtERGRzLP9mPKWVAeXUEDum73AJ4DBcApiIiMh6KqrrMX3Z5pjbrZo5mj26Gvj8Es5/dINir7lcdWTT3LGWSGNw5BLARERElJzk8m9KIZYL7UPsLP+mjZrUEDthoEukgc8voaK6Hmsq96Kiuh4+PwdGiIiMJJd/A9Ah2GX5t/g5NTXEVjm6RFbgtER9IiK7kMu/hV+DPbwGx82pKwMy0CVSwYmJ+kREdsLyb8Zw6sqATF0gEhSrhiPQXsORaQxERMaSy79NGd4bpQO7MsjVgVNTQxjoEglyaqI+EZETcO5E/Jy4MiBTF4gEOTVRn4jI7jh3Qj9OSw1hoEskyKmJ+kREdsa5E/pz0sqATF0gEsQajkRE1sK5ExQLA10iQU5N1CcisivOnaBYGOgSqeDERH0iIrvi3AmKhTm6RCo5LVGfiMiuOHeCYmGgS6SBkxL1iYiM4PNLhncIOHWRA9IPA10iIiLSVaLKfclzJ25bsQ0uICTY5dwJApijS0RERDqSy32FTxKTy32VV9Xq+nqcO0HRsEeXyAISMcRHRGS0WOW+XGgv9zW+2KPrNU7r3Ak7XXvt1FYrYaBLZDKu6ENETqGm3Jfe8xzUzp2w07XXTm21GqYuEJko0UN8RERGsku5Lztde+3UVitioEtkEq7oQ0ROY4dyX1a79vr8Eiqq67Gmci8qqutDXtdqbbUjpi4QmcTMIT4iIiPYodyXla69sVISrNRWu2KPLpFJ7DLER0Qkyg5LpVvl2iuSkmCVttoZA10ik9hhiE+NaMNvRJQ8rF7uywrXXtGUhG45GULPZ5fvCTMwdYHIJHYY4hPFGcFEFMzKS6Vb4dormpIAF0xvq92xR9em2Htmf3YY4hNh5RnBVj5PrNw2ShwnHwdyua8pw3ujdGBXy1zLrHDtFU01OHi0xfS22h17dG2IvWfOIQ/xhX+eHpt8nmYVhxdh5fPEym2jxOFxYB6zr71q0idKB3a19feE2VySJDnn9lEHjY2NcLvd8Hq9yMvLM7s5Hci9Z+EfmhxCWCH/idSz64o3FdX1mL5sc8ztVs0cndAZwfGeJ0Z+HjyHCeBxYBVmXXtbT/gxesmbaGhqjfh7OSVh09yxgfbY9XvCKKLxGnt0bcTKvWcUH7Ur+liFFWcEx3ueGNnLxnOYAB4HVmLGtVe+xkQLcoGOKQl2/Z4wG3N0bURNPT2iRLDC7OVw8ZwnRucb8xwmgMdBMiuvqsWtEa4xwaxSncIpGOjaiBV7zyi5ybOXlfqcXGjvDU3kjGCt50kiViDiOUwAj4Nk5fNLuP/lT6JuU5CThrfvvYRBro4Y6NqIFXvPKLlZYfZyOK3nSSJ62XgOE2D+ceDkSg9W9tSGXTh8rC3qNg1Nbdj61aEEtSg5MEfXRqxQ+4+cT+2EB7NnL4fTep4kopeN5zAB5h4HrPRgDp9fwvJ3a4S2ZU++vhjo2ojce3bbim1wASEXSNbTIz1o/RK0UnF4redJInrZeA4TYN5xoFTpQc5BZ16ocbbUNODw8ei9uTKO6OiLqQs2Y/WlFcm+4p2IFU9xeL2HUrWcJ4nKN+Y5TEDij4NE5KA7id7XJNFe2vysNI7o6Iw9ujZkpd4zcgYzyx0ZNZSq9jxJZC8bz2ECEnscqMlBT/YSVkZck0R7aW8c05/XAZ0x0LUp1tMjPZn1JWj0UKra8ySR+cY8hwlI3HHASg9ijLomxcrLBoAu2WmYPfZU1c9N0THQJSJTvgStWjSfva3kRGZXerADo65J8gTfCSUePPfu7g4jRvjuuZdcNYTXGQMw0CUiU74ErTyUyt5WchpW/IjNiGtSpDQIlwuQgj4EVr0wFgNdIjLlS5BDqUSJw4ofsel9TVJKg5Dntd00pj/GFXs4YmQwVl0gIlMWfuBQqj1wcQHnYMWP6PS8JkVLgwDar6v/rKpjkJsA7NElIgCJX/iBQ6nWx8UFnIc56Mr0vCZZOTUr2TDQJaKARH4JcijV2ri4gP0prXLIHPTIgq9J4dRek5iaZR0MdIkoRCK/BK22fDC1s2pFDBLH3njt3NlpOHwsdBWz/Ow0LLlqiPC+Y2qWdTDQJSJTcSjVejjsam/sjddGab8BwKFjYsv3ypiaZR3Cge5rr70m/KRXXHGFpsaQ8ykNpZG9xfu5cijVWjjsal/sjddGZPKYmv1mZmoWv2dDCQe6U6dOFdrO5XLB5/NpbQ85GIfSnImfq/Nw2NW+kqk3Xs+Azoj9ZkZqFq/HHQkHun6/38h2kMNxKM2Z+Lk6E4dd7StZeuP1DuiM2m+JTM0SuR4nY5oYc3TJcBxKcyZ+rs7Fihj2lQy98UbcYBu53xKRmiVyPZ738idY+NqnqGtsCfwuGXp7NQe6TU1NePvtt7Fnzx60traG/O6nP/1p3A0j50imobRkws/V2VgRw56c3htv1A223febyPU40oS6ZBh90xTofvjhh5g4cSKOHTuGpqYmFBQU4ODBg8jOzkaPHj0Y6FKIZBlKSzb8XJ2PFTHsx+m98UbdYNt9v2m9zibD6JumJYDnzJmDyZMn49ChQ8jKysLmzZvx1Vdf4eyzz8avf/1rvdtINpcMQ2nJiJ9rcpCHXacM743SgV0d+UXoNE5e6tfIG2w777d4rrPBNwdOpKlHt7KyEs8++yxSUlKQmpqKlpYWDBgwAI899hhuuOEGXHXVVXq3k2zM7kNCTqPXTGV+rkTW5dTeeKNvsO2632Jdj0U4dfRNU6CblpaGlJT2zuAePXpgz549GDx4MNxuN77++mtdG0j2Z/chISfRc6YyP1cia3NifepE3GDbcb9Fux6Lcurom6bUhbPOOgv//e9/AQAXXXQRHnzwQbzwwgu48847UVJSomsDyRnsPCTkFPJM5fD8NnkyQnlVrern5OdKRIkkB3TAyRtqmRVvsH1+CRXV9VhTuRcV1fXw+bX2t8amdD0udGciPzutw/6Sub7bxqmjby5JklTv9Q8++ABHjhzBJZdcggMHDuD666/He++9h1NPPRXPPfcchg0bZkRbE6KxsRFutxterxd5eXlmN8dxuGKLOXx+Cec/ukFxEofcC7Jp7lhNnwc/VyJKJDssjCDaRr2vn5Geb932Oty2YhuAyKNvduyYEI3XNAW6TsZAl5yooroe05dtjrndqpmjbTdkR0TJyco32Eq1fsMDy0QG7Ha4OVBDNF7jghFESYClwMgKrByYkP1YNZdWtNav3y9h1soPE7aypF0n2sVLU6BbVFQEl0t5x3z55ZeaG0RE+mMpMHtwciDotN4kIiWitX4fWFOlGAwDwP0vfYLczDSMHqBfWT+r3hwYSVOge+edd4b83NbWhg8//BDl5eW499579WgXEemIpcCsz8mBoBFLtlqRk29USJzoyFhDU8eVyoIdPt6Ga//4vmOuA2bRFOjecccdER9funQpPvjgg7gaRET6f2GyFJi1OSUQjHTcAjBkyVarcfKNih1Y6SZD75Exu10HrEbXyWhffvklhg8fjsbGRr2eMuE4GY3MZuQXZqTn7pqTjsVTSjBxKC+gZjC6IkaiKB23087tg8fX74z593aeCCk68YiMYeQ1U0sALZ/T0UbQCnLSUd/UKtwOu1wHEkk0XtNUR1fJP/7xDxQUmDf0uXDhQrhcrpB/Z5xxhmntIVLLiFq3wcpKCjF/0mAU5KQFHqtvasXitdvjfm7SRjSfz8rLc0Y7bkWCXMC+EyFjTTwC2nusjayfmsyMvGaWV9Xi/Ec3YPqyzbhjdSWmL9uM8x/dEPM5RWr9Lpp8JtTEq3a4DliVptSFs846K2QymiRJqKurw7fffovf//73ujVOizPPPBPr168P/NypEwtLkD2IztSNZ4i3vKo2obN8KTa7V8QQCfRE2HUipJobFSN6rK00ZJ9oRl4z400nkhdvCO9p9nzX0+zOSoeWex+rXgesTFMUOHXq1JCfU1JS0L17d1x88cWm96B26tQJHo/H1DYQaWH0F2YiAmlSz+4VMWIdt7HYfSKkmTcqyZ4XbNQ1U69rZbRyXmsq9wq3J5hVrwNWpinQXbBggd7t0M3OnTvRq1cvZGZmorS0FEuWLEHfvn0Vt29paUFLS0vgZzvnF5M59OpRMfoL0+yeJ4rsUFMLUlxQ7N2xeiCo5nh04kRIs25UnDKBMR5GXTP1vFYqlfNSezxY/TpgZcKBrpoA0KxJXKNGjcLzzz+P008/HbW1tVi0aBEuuOACVFVVITc3N+LfLFmyBIsWLUpwS8kp9OxRMfoL0+5D5E6klEoSzsqBoOjxOGfcaVj93z0Rh3HtHJCZUbqPozPtjLpmJuJaGeu4CeaEG0IzCQe6+fn5UReJCObz+TQ3KB4TJkwI/H/o0KEYNWoU+vXrh7///e+46aabIv7NvHnzcNdddwV+bmxsRJ8+fQxvK9mf3j0qRn9h2n2I3GmiBSuyFBfw1HRr98yJHrezxw7C7LGDHJdPakbpPo7OtDPqmpmIa2W04yacywXMvKDIkOtAMuR4Cwe6GzduDPx/9+7duP/++zFjxgyUlpYCACoqKvDnP/8ZS5Ys0b+VGuXn5+O0007Drl27FLfJyMhARkZGAltFTmBEj4rRX5hcNMJaRHJb/RLQJSc9QS3SRu1x68TAK9bEI70DFI7OtDPqminS25qfnQa/X4LPL2m+JisdN+H8EvCHd2pwVt8uuh5LyZLjramO7qWXXoqbb74Z06dPD3l85cqV+MMf/oC33npLr/bF5ejRo+jbty8WLlyIn/70p0J/wzq6JKKiuh7Tl22OuZ2W2qBG19G9bcU2AJG/FJIhr88q1lTuxR2rK2Nu9+S04ZgyvLfxDYpTsnxpRpOo3jEjrz92ZMSxp3StDKfHMe7zS9j8ZT1mvbANh49HXi1N7zq6Tqj9LBqvaZqMVlFRgWeeeabD4+eccw5uvvlmLU+pi3vuuQeTJ09Gv379sG/fPixYsACpqakdAnKieBnZoxJtpm68Et3zRMqclkpi5HFrF0oTj/TG0ZlQRhx7or2ttd5m3LpiG35/zVmYOLSXptdKTXEhxeVSDHIBfdNRki3HW1Og26dPHyxbtgyPPfZYyON//OMfTc1v/eabbzB9+nTU19eje/fuOP/887F582Z0797dtDaRMxkdpBj5hRnPl0Iy5HMlihODlUQFesmOS3p3ZMSxJ18rY/W2AsDsVR/iKbg0rzCZyHSUZMvx1hToPv7447j66qvxr3/9C6NGjQIAbNmyBTt37sRLL72kawPVWL16tWmvTcnF7kGKli8FDk3ri8EKxYOjM4kh0tsKtOfR3r5yG55J0Tbkn8gRnmTL8da0BPDEiRPxxRdfYPLkyWhoaEBDQwMmT56ML774AhMnTtS7jUSWI7LEo5OCFKOXJk5WcrDicYd+eXncmbbIkSNzlZUUYtPcsVg1czSenDYcq2aOxqa5Y3nc6ExNwKd1uWe580TpG8OF9o4FPTpPnJY2FYumyWhOxslopEYy9HL6/BLOf3SD4lCX3pMkkhFTQoisS3Tyn0zrJMBETRaWr+mxRiStfk3XfTLaxx9/jJKSEqSkpODjjz+Ouu3QoUPFW0pkY8kwASfZ8rnMwNxWIuuSe1tFl7rWOuSvJh0lnpvjZEubEg50hw8fjrq6OvTo0QPDhw+Hy+VCpM5gl8tl2oIRRGZwepCSbPlcpA/2UlM8rHT8yIHhrd/1tsYSz5C/SOeJHiOJyZTjLRzo1tTUBKoX1NTUGNYgIrKWZMvnovglQ0oPGceKx09ZSSF+f81ZmL3qQyil4Oo1CTla54meK3Imw4gkwBzdDpijSxTKKflclBhOKEQfzEo9i8nA7OMn1uf9z49rcfvKjj27iWgf50uEEo3XNFVd+POf/4y1a9cGfr7vvvuQn5+P8847D1999ZWWpyQii0q2ChOkXaxC9ID2WelmKK+qxfmPbsD0ZZtxx+pKTF+2Gec/uoFVRgxi9vEj8nlPHFqIZ64bgUITKqWomS9BJ2kKdB9++GFkZWUBaF8l7amnnsJjjz2Gbt26Yc6cObo2kIjMxzJYJMJJX8QsqZd4Zh4/aj5vs8q6cb6ENpoWjPj6668xaNAgAMCrr76K73//+7jlllswZswYXHzxxXq2j4gsIlnyuUg7LV/EVkwNSLYlUq3CrEBOy+dtxiRkzpfQRlOg27lzZ9TX16Nv377497//jbvuugsAkJmZiePHj+vaQCKyDqdXmKD4qP0ituKkI4Al9cxiViBnl8/b7itymkVT6sL48eNx88034+abbw5ZDe3TTz9F//799WwfERHZhJrVnaycGsAhYnMkcnWwYHb5vDlfQhtNge7SpUtRWlqKb7/9Fi+99BK6dm2/w9m6dSumT5+uawOJrM7nl1BRXY81lXtRUV1vm4k2RHoT/SIGYOlJaxwijp+W66JZgZyen3fw+35350G8u+ugrt8NnC+hHsuLhWF5MVLDqkOvRGaKdV6ILqmqdSnVeLGkXnzivS4m+rqq1+cdqd3B9HwPVsxtTzTReE1zoPuf//wHzz77LL788ku8+OKL6N27N/7617+iqKgI559/vuaGm42BrrPpeXEwu94jkZVFO9fWVO7FHasrYz7Hk9OGY8rw3ga3NDL5/AYiL5HK8zsyva6LiQ7k4v28ld53MB47+jK0ju5LL72Eyy+/HFlZWdi2bRtaWloAAF6vFw8//LC2FhMZTM+amGbXeySyOnni4pThvVE6sGtIkGKH1AAOEaun53Ux2vFjhHg+72jvO5jSPmD6m7E0VV146KGH8Mwzz+D666/H6tWrA4+PGTMGDz30kG6NI9KLnssmAvaZpUtkRUbNHte7F5Al9dSx+3VR6fMGgIrqesVjINb7Dha+D5j+ZjxNge7nn3+OCy+8sMPjbrcbhw8fjrdNRLoyoiamXWbpElmRPOnothXb4ELkoWK1k46MChhYUk+cE66L4Z+3yHGl5f0cONKsewcMRaYpdcHj8WDXrl0dHt+0aRMGDBgQd6OI9GTEajt2GHolMovIUKyeqQFWLlWWTJx2XRQ9rrS8n26dM5j+liCaenRnzpyJO+64A8899xxcLhf27duHiooK3H333XjwwQf1biNRXIzoZWDhbqLI1PSs6pEaYIdVzJJlhryTrotqjqtY7zuYvA8gwdZpHnaiKdC9//774ff7cemll+LYsWO48MILkZGRgXvvvRc333yz3m0kiosRvQxGDL3qJVm+VJ3GCZ+blqHYeFMDrJ4Xmkw5mFa+Lqql9rhSet/BgvfBwaYWoXZYOc3DLjSlLrhcLvz85z9HQ0MDqqqqsHnzZnz77bdwu90oKirSu41EcTFqtR0rzsrWs7IEJY4TPjezKpFYOS80GVMqrHJdjLeSgdrjSul9BwveB05L87AyVT26LS0tWLhwIdatWxfowZ06dSqWL1+OK6+8EqmpqZgzZ45RbSXSxMheBivNyubEBnuyy+cWq8fZrJ5VqwYMdkipMIrZ10U9etG1HFfh77tbTgbgAg4ebemwD5yU5mF1qgLdBx98EM8++yzGjRuH9957Dz/4wQ9w4403YvPmzfi///s//OAHP0BqaqpRbSXSTL7bDr/4eRwyKzuZv1TtzC6fm54zz/XuWbVqwGD1lAqjmXVd1OvGUetxJfq+RTpg5k8qjniz4IQ0p0RSFei++OKL+Mtf/oIrrrgCVVVVGDp0KE6cOIGPPvoILhd3Mlmb2b0MRkr2L1W7ssPnJho4mNWzanReqNagwsopFU6l541jIvKNo3XAXDGsEIvXdry5/N5QD17athcNTW0hjzsx51svqgLdb775BmeffTYAoKSkBBkZGZgzZw6DXLINK/S+GoFfqvZk9c9Nz5nnRvasGjViE88QuFVTKsySiF5IvW8cjRwJDH6N8A6YQ00tmLXyww7nUa23Gcv+s7vDc9RaLM3JalQFuj6fD+np6Sf/uFMndO7cWfdGEZE6/FI9yU7Delb/3PSaeZ6IGfd6j9jEOwRu1ZQKo0Q77xJVecKIG8dEjAQGd8D4/BLOf3RDzDJl4SRYI83JilQFupIkYcaMGcjIyAAANDc349Zbb0VOTk7Idi+//LJ+LSSimJLtS1WJ3Uo5Wf1z0zrz3MgesGj0GrERqSDxs1c+wfE2Pzx5kQMfJ5XaiiXaeQcg5g2DXoGkUTeOiRwJVLOccDiz05ysSlWge8MNN4T8fN111+naGCLSJpm+VJXYpXpBMKt/bnrMPLd6r3okIsFGQ1Mb5vytEkD0BTHMDPwTIdZ5585Oi5r6cv/Ln2Dha9tR1xj/zanVbxxFxJumlIg0JzuNmgGAS5Ikri8XpLGxEW63G16vF3l5eWY3h0gVu/Vo6kUe7lMKTuQvuE1zx1rygmzVz03er7ECB6vuV63WVO7FHasrhbeX37nSzZTdAgNRsc47rWLtz2jkwBuIfONo5g2vyHFQUV2P6cs2a36NVTNHG9qja6VrlWi8pmllNCKyJif0pmlhh+oF0Vj1c7N6j7MILUGm2qHtWDP6nToJNp5h9mhEKyRE+my19KIn4kZENEBUs5xwuIKcNEN7q+04agYw0CVyHKd+qUZj9eoFIqz6udl5+F1r75OWYMPqN1NGMPJ8irU/Y322ojeOieihVBMgRru5jOWhKSWG3XTapeZ3JJqWACYishKrVy+wu7KSQmyaOxarZo7Gk9OGY9XM0dg0d6zlg1yty+/KwQZwsudalJVvpvSWiPMp0v4U+WzlG8cpw3ujdGBXxSDX6CWatSyNLbKccLgfX1iEiUN7xdfYKNSMmlkNe3SJNHBqzp1dOWESitVZtcc5Ej16n5R6smNJppspkfMuPzsNh461qe6dlIXvT716FkUD0Hh7KLWmVUWur9vaYRGJrjnpWDylBBOHGnvTaedRMwa6RCpZKRmf2jkhl5T0o1fOdnCwUec9jsVrd+BQUytvpr4jct4tuWoIPtxzCMv+U4Pgqe8uANnpqTjW6lO1P/X6bEXyi/VIRYknQIx0c3l5iTm5/HYeNWPqApEKeg91+fwSKqrrsaZyLyqq60OGr0gdpeE+jzvTspMknMRKx7KevU9ysHHliFPw8JUlADqmMyTzzVSs8w4A/vBODcIPBwlA03dBrpr9qddnG1zOTI/tlOgdIIqkZBhB7r1XejUX2jt8rHijxx5dIkF6J+OzZ1h/cg/c5up6VHx5EED7l8LoAfYYcrcrqx3LRvU+2XlinpGUJn8BiLrKlwuAOzsNmZ1SQwLKaPtTr8+24WiL0POIbqfEymlValLw7DxqxkCXSJCeJazsWqbFDtZtrwsJRJ7auIs3EAay4rFsZHChtRSc0/P6Iw2zV1TXx7xmHj7WhhduGoGUFJfQvtHrsy3ISY/xjtRtp8SqAaKWm1O73ugx0CUSpNeQmZ3LtFidFYMuJ7PqsWx0cKF2Yp7VerwTRfSaebCpBVOG9xbaVq/P1uPOEno90e2isVqAGM910qo1v6NhoEskSK8hM7svbmBVegRdTu9104u8n97d9a1lj2WrBBfJfPNl5RQSuWc42vGrZ86pSICYiOuPHtdJO1VgARjoEgnTa8jMzmVarCzeG4hk7XVTK9J+isWsY9ns3ier9ngbTQ7Y6hqbUZCThoamtojbmZ1CMu3cPnh8/U7FtumdUhAtQEzU9ScZO1oY6BIJ0mvIzM5lWqwsnhuIZO51U0NpP8Vi5rFsZu9TMgYVojdCVkkhyc9OA9CeKyxLxA1ucMC9++AxPLH+i4Rcf5Kxo4WBLpEKeg6ZWXEWrp1pvYEws9fNTqkS0faTkmQ/lpMtqFBzI2SVFBLvdwHunHGnoX+37ISch6I3A0Zcf5Kxo4WBLpFK8Q6HWnUWrlpWC9K03kCY1etmt1QJkQL7wex0LBtFNFjolpNhcEuMF+tGyIX2CgYPTBoMjzvLcikkq/+7B5vmjjW8TWpHRbRcf6Jdm5Oxo4WBLpEG8Q6HWmWijFaJDtIiXbgBdHhMyw2EGb1uZqdKaLlJUfv+7XIsayG6/2IFFbK7X/wIC6+w974SuWGsb2qFx52V8DQNq6SQaBkVkYmef7GuzU7paFGDgS6RScyeKKNVooM0tXl1sW4gwoMU0d40vYbyzJ6gpPUmRfT9z75kEMYM6maLY1kLNfsvWlARbH+j/XPBrZymYZW2qR0VCSZy/olem+3e0aIWA10iE9mtTEuigzSlC3dwgCsLvphvmjs24g1EpCDFk5eJ/Oy0iM8J6D+UZ2bvUjw3KaJDnnPGn+bY8m1a9p8cVCx87VPUNUZeZcsJFRisnPtplbZpDaRTXMChptao26i9Ntu1o0ULBrpEJCyRQZraYb7wi3n46ysFKfsbow8rA/oO5ZnVuxTvTUq8Q552y0kOD8rP7tdF8/4rKylEbmYarv3j+4qvZ/cKDFbO/bRK27QG0n4JmLVyG55OUb4R1XJttltHi1YMdIlIWCKDNC3DfErBQqwgL5pxxT10DcSM7l1S6jXV4yZF65CnUeku0XqI4+k9jhSUF+SkoyFKr1qs/XfwaOTe3HB63uAksgfdirmfkWrmmtk20ZxtJeE3UsHvb+f+o0LP4ZQKH2ow0CUiYYkcAoznghz+t/Hkxr254wBaT/iR3ilFc3uCGdm7FK3XtOWEX+g5Yu33spJCjD2jJ/5asRtfNRxDv4Js/G9pf8X9I9qTPPaMntj61SHhoCzae8V3z6ml91gpKI8W5AZT2n9GnDvRAlkzetCtlPspmtufyLaJ5mxHEn4jpWXhFsBZZcNEMdAlImGJHAKM54Ic/rfxBM1+CfhrxW7cdMEAzc8RzKier1i9pneOO1XoeWLt90hfsL/bsAs3jumP2WNP7dBu0Z7k0UveDAkmowVl0d7rrSu2RXwdkd7jeGbFyw4eacGayr2Gl3WKFeibVdXDCrmfVqqZG07pZiA/Kw2Hj0eeJxDswJFmTQu3OLFsmCgGukQkLJHDk1qG+ZQu5vH2YnzVcCyuvw+nd8+XSK/pqi174MnLVMxJFvkiVJwceLwNj6/fieXv7cYjVw0Jab/oTUZ4j6lSUNZ6wo+fvVKlOg1FJA85np5/oH3S0OK1OwI/G1XWKVagn5+dZuqyw2bmflqpZq6SSDcDfkmKmsMt69Y5A/e8+JHqIBdwXtkwUfqMxRFR0pCDNI87NHj0uDN17SmSAwPg5IU6mmgXczlo1nqJ71eQrfEvlZWVFGLT3LFYNXM0npw2HKtmjsamuWM17T+RXtO6xhZMH9kXQMf9KfJFKNLbefhYG25bsQ3lVbWBx7TeZEjf/Vv0+nb4/O2vWl5Vi9FL1gunEUR6Tnn4N5J48xf9YTtHDtbl/aHHuSOSb65UQUTeJto+sDs1uehmkm8GpgzvjdKBXTF6QNeo1ygX2m+cIEH1zZje12a7YY8uEamWqOFJpZ7PLt/1WInm2sWTG5fiAv63tL/m9xCNXj1fogFa/27ZmnuSRXs75eBU7jGMdwKOHJR4j7eqHq5VUtfYjIrq+g7HrmhQXpCThoamk8deiqtjkAsYU9Yp3l5nmVMnJVmlZq5aoj3+B5vEJjXmpKdi2rl9MK7Y49iyYaIY6BKRJokanlQKDICOK6NFu5grBc3Z6ak41upT/LuZFxTpNhHNKGomOpUO7Kop0FITGARPmonnJiPwfIeP41f//lyXIBcAFr/xaUigKqcYjC/2COXRvn3vJYGJcwePtISkK4TTu6yTXgGaUyclWaVmrhbjiz24c9xpWP5uTUi+bvCNaEV1vdBzNbX68Ny7u3Fukge5AANdIrIBpcBAbbCgFDQ/Vr4Dy/5TE9Irl+JqD3LnTSyOt/mGUzvRSUugpTYwCA7IlG4y8jI7obH5RMzn+vDrQ7r0YsqCg1wgNB9YpFctvVNKYP+tqdwr9JpWCVCdPinJKjVz1YpYJSIrDTeO6Y/bLh6ErV8dwprKveiWkxE11z6cnRch0QsDXSKLs/tqUlYTKcibN7EYd192hnDJLKtJxCRBOYAQDTjDA7JINxn7Dh3D3f/4OOZz6dWTG+355RSDTXPHqkrv2H2wSeg19OpBFAnk3NlpgQoDoseCU64zVqznG4tilYigSZ7BaVpKkw3DRavt7JTPWwQDXaIE0XJhsdtqUnaW3ilFtxJiZjC6hqkcQCiV75JF6zELv8l4d6dYCKvHZEClPFpZcFAgmkdbXlWLx9fvjPq6ansQRRbBmFDiwXPv7lYM5B65agiAjrWElY4Fp11n4j0XEhkEaplcKN/ExEq7koWPJjjt846FgS5RAmi5sBi1mhQ5l9GTBMtKCvHMdSNw/8ufRJzZr7rHTLBZZ3jy4prQBkQPcoPJQUGs9A45QBGhpmxYxxXZ0nDl8N7Iy0rDqi17UNd4cjKSywVIQe8rPJATDdatdp3RI9DUei4kOgjUugKkC0BGpxShQDd4NMGKn7fRGOgSGUzLhUV0Nalkz72KJZmG52RGTxKUA4inNuyKOmlGhOiyuA3HWuOe0CZKNMVANEC5c9xpQvtDeUW2Nvzp3d0R/0YO3m8a0z/i7HrRYN1K1xk9A02154IZQaDW3G0JwKFjbSjIScehplahfGQrft6J4MhAd+nSpfjVr36Furo6DBs2DL/73e8wcuRIs5tFSUjrhUVNLUi9ghqnBYV6fWE6bb/oITXFhTvGnYrZYwep2jfh+7JbTobQ68nVIiINR6tRkJOGQ01tukxSUlPSLZZ4VmRzAfhnVR1+Nkl93qkZ15lozOxtFLlWL3ztU+RmpuHg0RbdrgXx5m5PHd4Ly6OksQSPJljt804UxwW6f/vb33DXXXfhmWeewahRo/DEE0/g8ssvx+eff44ePXqY3TxKMlovLImuBem0nC29vjCtvF8SFYDLr1PnPY6GplYUdG6f9S2/nugXYqR96cnLRP53E6dEgk+5N/nxdZ/jqY3Vwu9Bfp75kwZj1soPdZmkpGcZq3hq48YTnFip5qzZvY2ii64Er14mei2Idq7GW2d6/Hc9+SL5yFb6vBPJcYHub37zG8ycORM33ngjAOCZZ57B2rVr8dxzz+H+++83uXWUbLReWBJZC9JpOVt6fWFaeb8kKgCP9DpaXk9pXwaXSBINPlNTXBgzqLuqQFcCMO3cvri8pBBPX+fSZcKenmWs9AgstDyHlWrOmt3bqGX/iVwLYp2rWutMBx9fqSkuoXxkK33eieSoQLe1tRVbt27FvHnzAo+lpKRg3LhxqKioUPVcTU1NSE1N7fB4amoqMjMzQ7ZTkpKSgqysLE3bHjt2DJIU+ZB3uVzIzs7WtO3x48fh9/sV25GTk6Np2+bmZvh8yknxarbNzs6Gy9V+gra0tODECeU6m2q2zcrKQkpKe7mo1tZWtLUpL5OpZtvMzMzAsRK+bW6qD/7Wkxc4V6c0uFLat5V8JyD5TgS2Cz4+zuyRAU/nNOw/2t7TFbwt0H6R6+nOwJk9MtDU1ISMjAx06tR+Op84cQItLcq5j+np6UhLSwMAtLS24cGXtsLXGnn7lNROgaAQkh/NzcpfBmlpaUhPTwcA+Hw+4W39fj+OHz+uy7adOnXCtm+OotbbDEmSILVFfl97v23Gps9qcVFxLwCAJEk4duxY4Pc+vxSyX1wpKXB1am+DBEBqbcaDL23Fef0u7vBlYvQ1Qg4afW3NId+K+75txo+few9PThuOy0oK475GrPu0DnesruzwxZuS3v7ear3N+PHyCjz5w2EYf6Yn4nPn5OQEbjz8J1ohRbieyOWwsrOzAxOtpBOt6JGbhp9NGIwLivI67Jdz+3cJBJn+E22Q/MrXE1daBlwuFx5f/wVWVuzCvMtPw/+bPQof7G7At0db0L1zBs7p3x4U+P1+VdcIOUCBrw3+oGuaHLDMvfQMNB8/FvEa4fNLgTbUH2kJXCeUrhER31vQtgWZqVGPn0jXiDN7ZKB7poT9jaHniSu1E1yp7dt6OqcFrjPhfH4JH+09ioZmH3rkZuLsvm40NzdH3LdA9GvEngMNodfK1FS4UtuvU5Lkh9TWGthuqCc0EFN7jcjIyPjueU+e9+HXagBwpaTC1SktsG2k64kLwIMvbcUFAy5FTnbouax0Du2rbw0JkC8oysPjV52Bh/+1A3XesNdwuZCSdjLFx9/a3OH4kg3vlY2srJM3AeHn/Zk9MtAjS8J+bwskF5CSdnI/+tua4ZJCv1dONsGacUS04z2E5CB79+6VAEjvvfdeyOP33nuvNHLkyIh/09zcLHm93sC/r7/+Wl5iPeK/iRMnhvx9dna24rYXXXRRyLbdunVT3Pacc84J2bZfv36K2xYXF4dsW1xcrLhtv379QrY955xzFLft1q1byLYXXXSR4rbZ2dkh206cODHqfgv2/e9/P+q2R48eDWx7ww03RN32wIEDgW1vv/32qNvW1NQEtr3nnnuibltVVRXYdsGCBVG33bJlS2Dbxx57LOq2Pac/LPWb+4bUb+4bUsH4W6Nuu2jpX6T+c9+Q+s99Q+o68c6o2/79738PtOHvf/971G2XL18e2PZXy1ZG3bZg/K1Sv7lvSO/tOiht3Lgx6raPPfZY4Hm3bNkSddsFCxYEtq2qqoq67T333BPYtqamJuq2t99+u/Tqh99I/ea+IZ3ykxeibnvJ5B8Envfo0aNRt80+fUzgc+s3942o2xp5jTjh80ujH14v9Zv7hpSa10NxW6OuESlZeSH7IaNPifI+++4a8d6ug1K/uW9IWQOUnxeAtGnnt9J7uw5KL2/9Wjr74glRtz169Kj0r0/2Sf3nviHllFwaddtTfvJCoL25Z02Kuq2Wa8S/Ptkn9R77v1G3Neoa0f37C6T+c9+QRj+8XvrTn56Luq2aa0TXiXcG2nD9gt9H3Va+RvSb+4Y0cEb096bmGuEeMz3wvIU/Whp1W7XXCEmSpBM+v7T2/c+ibptTcmmgDX3m/CPqtpeUXRFyHkXbNmvAOYHP7YTPH/UakdGnJOScS8nKU9xWTRyR1rVvyPOmde2ruK3V4wiv1ytFY49q6AZasmQJ3G534F+fPn3MbhIRAODsfgV4+roR8LiNG0byHlfusQpmp5wt0WG3rLSOIzZWJ5rLeVyg5FCiiB47B4+2wHu8FY/9v8+xo7Yx6rbvf1mP8cUePH3dCGSliX+NScJbiisrKcSNY4qEt/+8Lvp7UyM4vSPFoAmS5Z/uF972aEv0Ve6+qm+CT7TOm4HKq2px/qMb8OMVW3V7zpYT6s45CSdTMdRIcenzOXdKdUX92Ulc3915OEJrayuys7Pxj3/8A1OnTg08fsMNN+Dw4cNYs2ZNh79paWkJGeZtbGxEnz59sG/fPuTl5XXYnqkLkbdl6kLk1AXZuk/r8PC/dmB/kz8w1NizcyfMu2yQ4rCvPNTo80t474v92HfoSIehwPBtAXWpC//5fD+ufXaT4rbyEOaqmaMxsn++bqkLKamdULn3KA4caUa3nHQM8WQp5suqHZbslJaO8x/dgNrDx+FXGGrs6c7AW/eNQ3ZW+7l8wufHO9u/CQy3+iUJP3r+g5N/E5S6AKDDEKfHnYGfTRiM8Wd6DL1G/PvzBtyxurK9DWGpC8F+/YNh+J/SQYGf1V4jXqv8Bve+GHnFMjl1ob0NLYAk4Vc/GIrvDe3VYducnBxUVNdj+rLNkBRSF2R3TxyCJ9bvbO+iCdpWHqYNXu3LlZaBXvlZuGJYIV754CvUHT45fJufnRZS41dOXQAAKSjNoUtOGhZNPjPk/As/75tbWhWH4eVty6tqseCVj1B76ORnF3w8ACevET6/hPN++f9Ctg2nJnWhV0EuFk4dgrKSQrS1taG1tVVx2+BrREtrGy5Y8v86DpPLbQhKXYDfhx45KVh/V3uajs8vYdxv3gr8bfC2kt8H6YTytdKVmopeBblYMLkY4wf36HCNkIf6AQBBqQv4LnXhyWnDI14vY10jgtNEvjnUiqfe2S13EQbSEeSrz4/O74e1n9ShztsilLog+8vNo3Fxce/AxLM1H1Rj5ftfR94PQdeTJ6cNx7hT8wG038DNWP7fsI07pi48f+O5GDWgY56ymjjCLwFV+5sDOb0lPTOhdK9k1Tji0KFD6NWrF7xeb8R4TeaoHN309HScffbZePPNNwOBrt/vx5tvvonZs2dH/JuMjIxAvk6wnJyckJ2qRGQbLdsGH1R6bht8Eui5bfAXu57bKn0+8W6bnp4euDDGu63PL6Giuj7qJICpIwdi8jkDNM2ST01x4YIzPAAiB8ThOnXqFPhCi+W8U3ugd/d8oQk1qSku4WM4NTVVcdt4JlKlpKQItUHOnUxNzwx5X/Le/sXVIwJBbuRqABkoyM9VrAYQHOwBwLfHgTkvf4ans7M7vAc9rxHBvdXB+XXh+vToEvKz2mtE3x4FHd5jJPIXcN8eBYptPzlpK3JcLh9jq7bsCfze1Sk9ZC0JF4AjJ4CU9JO98LXeZjz7Tk17O4La2hi2XchrdUqDC+2Bi7dN+TMDgA1f1Mc8ToMn2QW3Qel42FLTgP1NPqF9C4QFnN+ZUOJBWYmnwzUkLS0tcAMby7avG3HguEusHSmpOHAc+PRAC0oHdm2/1in8rSslFS6FfS+rjTKBa+rIgcjMzu643/OzsWDyOUKTBcOvEdEmVLpcLriC3ocLQPnnXmz6+URs/erQdzfiGbj7xY/aJ05Ges9oP34vOKNXh9cS2b89cjMD7T3iOxzzb1LSM3HEp3x9DRbrvC/tLH5tCmaVOEL02uqoQBcA7rrrLtxwww0455xzMHLkSDzxxBNoamoKVGEg0pOaoM3oQv5aJHpd+ERVMhBdAlS5GkDLyaALsYe8JSSm2LqeM/1FXidWmoTI64kcY9PO7YvH138RV5u1ivSZKR0XwUHa+GKP6uoeeqQAXV/aP+7riJZ2yH+jx3uQANz94kcRzxU9V/dT+hyjtavW24ytXx0K2ccLr4h9jVy3vU7Va0U6d3YfFJtc5bSqCEZzXI7uD3/4Q/z617/Ggw8+iOHDh6OyshLl5eXo2bOn2U0jh5EvouHBgBy0lVfVmtQydeSgMDwX2OPO1LWElsia7ote365bDl9ZSSE2zR2LVTNH48lpw7Fq5mhsmjs28H5EypDlZ6ehZ57Yl4rWnDs15KAR6Lh6rp43JvLriDyLyOvFOsZEFlUwQqTPLNbiDRKAeS9/gvd2HRQuhyWLJ0Bxof0mOt6bGK3tkP9GryCrqcWHO1dvi/g7uVNgyvDeKB3YVdPxHM8iHOHBfKzjN9pNTySRzlWfX8KqLXti/q1ex0AycVyPLgDMnj1bMVWBSA9mFzfXW7y9KCKLF5hRJzNaL7pIew4fa8MLN41ASooL/6qqxV8qvor5mkZP3BPtrTbqdWRq6/ZGO8Yqqut1abNWwZ+ZyIS/Q8facPNfPoi6TaTnFu0pD6f36IqaRQrCex7jXeAg2Bsf1+H//seP9E769rn5/BKef7dG8yIckYL5WMevmteKdK5uqWkIlNeLZtq5fW3xnWIljgx0iYxmdnFzI2hNrRBN37DSqjw+v4R3dx0U2vZgUwumDO8NAEKBbiKGFfUc3hV9HaWV0dRQOsb0DJ6C5WZ2wpHm6JUAgNDPTPT4azmhPMFG6blTU1yYP2kwbl/5odDfygpy0vHLK0tU38Qo3YCKLlIQKcDWusBBJBKAv1bsxk0XDIjjWUJFy8mNJVYqjtLxK3rMXF/aDxNKCiOeO3ouKU2hGOgSaWCloM1ManJurbIqj9ovQrk9icqPFZWonO9EvI6ewRNw8rPYcPfFGPPom2hoilwJINJnpufx1yU7DWf36xIyWdWdLTYJNtgDkwarDnJj3YDG6rEHlEcJlP42P7t9Mlxw1YtYvmo4FnsjQWpzcoPF02suesxc/l3FiDc+3tfh5tQq10cnYqBLpAEvSurTN6wQKKr5IgxvTyIn7omkgjiNUvDkcgFqimAGfxZZ6al4+Moh7auXQewzG1lUgPysNBwWrDEdTcsJPy58bCPqGoOCwSyxygjBPG7xmetA9Ml0t67YhjnjTsXssad2GBnolpMBuNprGsc67pRGFQBgc3U9bnx+C1p9sT+4fgX69FDGk5MLxJf6I3Jty89Ow91/rwxJTwi+8bDC9dGpGOgSacCLkvr0jURXeAin5otQqT1G5ccGB7a7DzZh1ZY9il+IThYcPK3bXofn3t2tGOTmZ6fhh+ecgtc+qo36Waj9zFJTXLhxTJEuVSCOtfpwLGzxDrUBtNrJRyLH+ePrd2LVlq/x4PeK0SUnXfMNlVJvf0qKSyjIdQH439L+wq8XjehiKvLrSgDmjDsV/bvlxH0zGevaJqE9rztc+MiXUlpLIq6PTsZAl0gDs4M2K9CSvpGoiVSRqPkijNYevfNjRVIp9C6/ZmWpKS6c3a8LZq2MPCNflpWWivvKBuO+ssExPwu1n9nssYOw/L0aVUPwRlF7HRE9zusam3F72D7W64ZK9Now9ozuuk1EU5MmZsT1Jtq17XibL+KxFDzy5fcDi9fuSFh7kwkDXSKNzAzarEBr+kaiJlKFq/Mqr6gWbPYlAzFn/OlR26NX3qpoKoXWSh52TIEor6rFz175RDGvVhY8WiDyWaj5zFJTXHjkqiG4dUX0YNtoOTEWX4gknnkBet1QiV4bbr5goObX0Pqa8ycNxowxRYacB5GubX6/hGv/9L7i38gjX+E3HaFtdv73iZEY6BLFwaygzQriSd9I9OIZ5VW1ir0l4cYM6p6Qz09tTqHaSh7xrEAX3MZEHttqJxPpPdkz/P3+/pqzsHjtjpB92CU7LeIwtBGaWn24dcU2PKMi8IxnXoBepRHP7tcFKa72ZWaVpLjat9OL6PXIqCBXFn5tW1O5N67ncwFYvHY7Li+xR6lKK2KgSxQnK654lgh2Sd8QDZ4SnVetJpUimEhwp8cKdHoEympomUwUK6hTE6grvd/5kzrmsf6/qjrMWqltdn+wy4t74v9t3x9zOzWBZ7yl2vQojbj1q0NRg1ygPQgOX4EsHla9HsU7IVn+PB5f9znGDOqeNB0penLcymhElDiJWlVNK7XBUyK/CLX2RooEd/GuQGfGqn9qJxPFmqRVXlWL8x/dgOnLNuOO1ZWYvmwzzn90Q8S2R3u/s1Zug/d4a8gqXe6sNF3q/YoEuUDsFfd8fgkV1fVYU7kXW2oaMH/S4Ljb9u6ub7Gmci8qqutVr1ZoVvlFK16P5BuPeK8qT22sjnoMkzL26BJRXKycviEaPBXkpOHhK4ck9ItQbU+PaI9zvIuZmLXqn9qgJ9pNiZoebS3v993qb4XbqUddYAAhJcqCKfVE33JhEdZU7hNabSuSpzZWhzyfmp58M8svWu16JFKRQY1kmpiqF/boElHc9Fib3giiwdP8752Z8C+NkUUFKMhRV1NVpMc53t40NYGynkSDnq456VG/5NX2aGt5v/sOi+3jkf27dOhd1GrxG5926MmL1hP9h3dq8OD3zsSccafF/dpqe/Jj9WLKPfJ+v6S51zgaq12PykoKsfSas9Al7Hz3uNvzwNX0+IqOytBJDHSJyLFEgydPXuIX9khNceGhKSVC23ryMoR7cOLtTTNr2FlkiLcgJw0V8y6Nuh/UBq6i7yO4R7V3F7EFHM4tKsCmuWOxauZozL4kvgoDDU1tIcGmSEC/eO12zB47CM9cNwKFcQTc0nf/7n/pEyx7pxqvfBg9OE1NceGKYYWKvZUSgONtPlz7p/djppU4gTwZNriSSEFOOuZPKsbEob2wYHIxAKgKdqPdbAansuh9E2FHTF0gIsey+sIeE4f2wo+/OYxn36lR3GbOuNMwe+wg4V6peN+zaKC8+2CT0HaiRCYTPXzlkJh1V9UG6qLvd/EbnyIrLQVlJYU4b2A3LA0a2ldy3sBugd5FvW4MFr2+HbkZaaj48qBwQF9WUgi/X4q4GIEah4+34Zf//Czws1JKQ3lVLf4Q5ZgGOi4T7NQheaU0mkNNrZi1chueThkhtBxzJJGOqURPIrUD9ugSkWPJwRPQsbfEKpUh5k0sxu+vGYGCnPSQxwvdmXjmuhG4Y9ypUdsX3nsDIGoPkQRg2rl9FJ9PdPLM4+t36t4Dp8dkIrU92qLvN7hHdfSArsjPjp520jmjE87tf/JmQo98VDl4vfZP74fk0EZz4EgzfH4pZnm9jFT154C8pPAvXv800HOodSleKw7Jx9szqiaNpqykMKj3f5DQ84cfU2ZMIrUDlySpWUXc+RobG+F2u+H1epGXl2d2c4hIB3bo5dBSszba+wIQtYco2vsXKckm9wxvmjtW9xuFeOr3+vwSzn90Q8we7eB2y+8XiD45KPhv122vi7mgRH5WGm4cU4TZY9sDl1jtyk5PRVPYksHxWjVzNABg+rLNuj5vJIXuTEw7t2/cyyevmjna9JKNelwzKqrrhfZ7+PvVcgzLf6N0vht5vppFNF5jjy4ROV5wb8mT04Zj1czR2DR3rGWCXED9BJpYvTcAsGnuWMXJSNF6ecpKCnFnjElMRk1KA+KbTKSlF799stAIdM6Mns0Xng7wzHUjouZ3Hz7ehsfXf4GzH1qHddvrsGBycdS81Vsu1G+lsOASbOu21+n2vNHUeZvjDnIB/fO/1dKrZ1RrvruWY9isSaR2wECXiJKC1WZix0PNkOjq/+6J+Byxhor7d8sWaovZQUkksVIgxhd7Qoak//lxLRav3Y4jzSeEnl9+z2UlhXjnvktiVs84fKwNt67Yhg/3HIq63ak9OutSczU4GAKAVyv3xfmMYvQaHjai7JgoPepQy+KZGKo2jcesSaR2wMloREQ2I9p789eK3Zpr6iaqFqrPL2Hzl/Xf5RdLKB3QDaN1uBFRqqe6bntd1CFeEcHveetXh0Jm00ez7D/KE7TkpV7nTxqMWSs/jKv+rssFzLygCGUlhaiorkdDU6vGZ0ossyeHAvHXoQ4W78RQNTWBzaxdbHUMdImIbEa0V+arhmOan+/sfl2Q4kLUpVxTXO3baVVeVYv7X/4kZAb+UxurkZ+dhkeuin8Bj/DluUWXg1YSKTBR00MWbV/KAVSXnAxNM/DDX+cP79TgrL5d0HLCr+k59KAlWDd7cqiePaN6LEssusS81SvMmImpC0TkSE6uJSnaK9OvQCz9INLzbf3qUNTADGgPqLZ+FX04Xkl5VS1uXbGtQ5kp4ORQv56zxLVWA5ApBSZ695C9u+sgWk748evvD8MLN4/Ck9OG44WbR8GTl6E6pWHR69vRrXOGru0TNWfcqaoWy0hxAUuvOSuumxs9znm9e0bVpCDE0347VJgxC3t0ichx7FBlIR6ivTf/W9off9xUo6mXR0vPlmi1BJ9fwsLXtsd87oWvfarbUsOiy0Er8SgcP/JnEc9zB3tq467A/+Vjdsygblh4xZkRewaVyD3EkBD1WDFCoTsTs8eeitljT8WWmga8u+vbmOXQ/BLQJUd7UK7XOT+yqAD52WkRb8AAbT2jIikIerRfqR6v0rGbLBjoEpGjKA1PO6kgveiQaHqnFM1Dp2p7ttR8UW+paQhZaUxJXWOLUC6kiHgm4cyfNBgzxhRF3E/Bn4XegWT4MaslpWHDZ/sVj4FYPHkZaD7hh/dYm6q/mz/p5DGlZrEMrZ+Rnuf8uu11ikEu0L7/RHtGRW/89Gy/mrzeZMHUBSJyDD1nTFud6JCo1kUYYi2kEFy+Sm05JjUBjV6zxLWkGMjvUSnIlcn7ONYiEmpFX1RArBTZK5V7Mb7YE/EYCFbozsTvrxkRUoLv3fsvxSNXDQEgvjwtAHQJW/xEdN8fPNJi6KIMos8VTZfsNIwv9nT4u/CUg/KqWpz/6AZMX7Y56jLHRlyznFRhRg/s0SUix9BzxrQdiPbeaOnlEe01BhD1i9r13e+DUxDUBJ165cDGSvcIpzavUd7HT23YieXv7sbh42KVGGIJP2blIGZkUQFWbvk6ZkWFhqa2kGWAH1hTFVIlon1Ri/6YPTbyCnxaepLDb05E9/3itTvwx001qobZ9TznRdJbDh1rC3muSCMZSqkPkXpok+2aZQb26BKRYyRjLUnR3hstvTwivcGiX9SPr/s80Ns1sqgg6kILgdfJy9Btlni0yToRX1vFssPBr3HHuNOwdf54vHDTKORn6dfDG2lRganDewn/bXlVLWat/LBDKTTv8TY8sX5n1EUl5J7k+ZMGC71e+M2Jmn0fPAogMjlLz3Ne7XMpjWQopT5E6qFNxmtWorFHl4gSJp6lXUWwlqT+YvUGi34BP7WxGk9trA7k7U4ZXohn31GuKwsAC684U3Eym5bjSKl3stCdifmTBqNLToYux2ZqigspKS7denWByMfs+GIPnnt3d8y/7ZaTgXv+8ZGqXvdwqSkuzBhTpHlyo2jPsNye+1/+BAtf2x6Syx0p51vPc17Nc2mt4hHeQ8trlvEY6BJRQiSiEgJrSRojWi1PtV/Add5m3PrdEsVKstNT8Zv/GRbxuIj3OErUZB29euCCj9nwAP/sfl2iVnyQ/xYu6DI8Hm9dWHnfP/9uDRav3RG1Pe29oqE3CpGG/vU859U8V7xVPOTjg9cs4zF1gYgMp9fa8bGwlmTixZq0Fk6kB8yd1XHCD6DfcRSexgFA95rLevXAybP85RXdgic3jV7yJr43tBAuRD/eDx5tEXotkeBc6+RGWWqKC91ytZURizT0r+c5r+a54r2RkY8PXrOMx0CXiAyV6EoI8X4Rkzpqc19FyL2LwYw6jkRnx6sl3wDEKz87DR/uORQxwG9oasWy/9RgXHGPqMe7EYsgyNUf5AoNm+aOFV78IJ6bgODe5+D2RDrnC3LSsfSaERhf7BG+kRG9fmh9D8HVStS+JmnjkiTJ/nV2dNTY2Ai32w2v14u8vDyzm0NkexXV9Zi+bHPM7VbNHK3rrGKj84EpdB/vPngMy9+riVqDVI0npw3HlOG9Az8bcRwp1S+VjxLRIEPpWJNXf4uHaO3bp6adha65kXOMfX4J5z+6Iebw+Ka5YxXPETXnU6z0kljtERF+fADAPz/e17GqxHcl34KPS7kt0VJYYr1fLe8h1nFlxDXLyddB0XiNObpEZCizZhWLrhFP2kQKZvQU3mOm93EUq4dYnqA19oye2PrVIc0rWs0ZdxoeX/+FUJsiEQ2iHny9Cv/9+fiYi1poya1VkxetdPNQG5Zfq3URC1n48SFXlQh/LqUyX7eu2NahDFjwe4p1/Yi1TyV0LDMWa4Uyva9ZTl8hUhRTF4jIUJxV7DxKubJ6iDS0C+h/HImWRRu9ZL1iWoNIzvDssYPgydO+tK0ouV6uEq3D40rvsTZCXnSsSgQSQhe/iNievAzkZ6cJLVQi+rqR2gF0DILV5npH26fPXDcCWx8YHzW9w0iJmhdhB+zRJSJDJWJWsZWG50TbYqU2q6G1rJKIaL2Leh9Hoj2/4XVn5UBh6TVnYfHaHUIluxZecSZu+y6FIdL2I/sXYMtu5SBVVLT35PNLcGel476yM9BwtAUFOenwuLOiHncigeu8lz8JlCUTqUQQXN1BqQLGuu11qnqf462AEPx+RHvyZbGqeJgxqiQ6WhGtnJyTMNAlIkPFO2wai5WG50TbomebEx0w6xVUAEBBTlpIIKk0tCu/xwkl7XVj9TiOtI4gyIFCeC5opO3koC5WDVk9glxA+T1FO96i7S/RlcKe2rATd4w7DXXe40LtDN4u0nC90v5yZ6fhxvOKOlTk0DPtKbgnP/jzjXZ+Wi1NiquthWKgS0SGU/riipWzFotSPmCkeptGE22L2jZHC2T1CJjVBsp6BBVy7+vb914S6DXrlpMBuICDR1tQUV0fMqEr/D26XEDwNGotx5HaJYGDSejY06tE3l/BSwQ/vn6nylcU8/6X9SE1drd+dQjrttdFXFRC5BwR/ayXv7sbs8eeGnM5YpnIdpGWVD58rA2Pr/8Cq/+7J+TzNiLtSakn3w5VELjaWigGukSUEHoX6rfS8JyaiU1q2hwtkAUQd5CvJVDWI6iQAMyfVIz0TikoHdgV5VW1uOcfH3VoxxXDCvGHd2o6vEe5OtRNY/pjXLFH03EkjzTEWxUhlvD9tfq/X6v6e/ldXTq4B9bvOBB12yfePBlAp7hO7qdIRM4R0c/68PH2/OCCzmK5yKLbrdtehyfW74x5jMdz0yLKTkP+nBcRipPRiChhwgv1x/NloWZ4zmiibflrxW7hNkebTHLrim24/+VP4qopq3WyitoFIpQsXrsd//y4Fk+u34lbFdrxbIQgV+YC8M+qOlNzmwty0lVNmtKS9uFxZ+LOcafhDI+6cpci5YRjnSOHmlqFP+cDR5rhyRMLnES2U1M32YhazpEYdU2JVnNYi1jnqNKET6dioEtEtmSl4TnR1/iq4ZjQdnWNzTG/5KPVq431hRzP4gvBQUU8ar3NuH3lNsXSW7G+6uMNOuR9EI9LTu8OQHxFK9HjZEJJTzw5bTjmjDsVkiTh8fVf4KmNu+JqazSR2tVerqvjiIESeYQm1iIZogGW2htZpQoIXbLTkJ8VOngt/6w1KNbzmmLEgiVcbS0UA10iUkXv3getrDQ8J/oa/QqyhbZrONqiy4QvpS/keHvD5aAiNzM17jbG691dBzUdi3pMqntp2164s9Pg/m5RAplSyS7R4+RfVfuxfZ8XT6zfibpGseV74xHeLjWVNcJ7B6ed2zfq9rF+L9NyI1tWUoi3770E8ycNxvWl/TB/0mAsvuJMZKaFBrqZaZ3w4wuLOgTFovS6phhZAoyrrZ3EHF0iEmalCgeJKFumd1v+t7Q//ripJuZ2BTnpurRL6QtZj97wspJCvLurHn/d/JWmtukluKdTzbGoV6+c91gbJABzxp2K/t1youaey8eJSIC97D/KaRt6UTpH1N4ELJhcjHXb64QWEIk0mUwWPDHy4BGxAD/4GBddxGR/YzP+8E4Nll5zFrrkZKCusRmL3/g05gRDPa8piZhjoPe8CLtijy4RCbFaAXIrDc+JtGX+pGJs/eoQJpR4Al9kStuJzl5XEisHT6/e8P5dxXqoE0XNsahXr5z8Wa7+79f43tBeUXPP1aR9GD1QEu0cEb0JyM9Kw9PXjQAAVQuIRPqcwofwF6/dgWinbvgxrmYRE3nXLl67AyOLCuDJyxSqoiFPotxS0xD3iFai5hjoOS/CrhjoElFM8eR0GslKw3PR2nLLhUVYvHY7pi/bHCj15Ar7vgnebvHaHVFfywUEVo/SEuSPLCpAfthwe/hziORSXjOqX9TfJ5qaY1GvSXXy64oGJWUlhbhpTH8dXjU+BTnpWHpN5HNE9CZg6bUjML7Yo3oBkfDPSSlIVfoIw49xLYuYBH9mooH92DO6B85jOZ/23F+uwz8/3qc6pctKcwycjqkLRBSTlQuQW2l4LlJbDjW1YNbKD2OWyFLaLpz8rh65aggAaKpNvG57XczJbNPO7YM3Pt4XdX9Wfn04Rmvj4wJwy4VFeO2jWuHeQtFj0YjyYqJBybhiD/4UobZtItU3tWLx2u1ISUGHY0U0FWf0gK6ac53lz2nzl/Uxg9TwUmnhx3g8+dZ1jeLVIjZ89m2Hxxqa2nD7yg+Rnf4xjrX6Ao/rVaYvWUqAGYmBLhHFZPXeByutTBTcFp9fwvmPbohZImvuhMG46FcbhXqkwr/k1Qb5ItUGXC6ELGqg9KWt5vMOX80sluDXvK9scOA97tx/BE9trI759yJtKyspxJxxp+q2gINoUCJS9zXlu0UxtI6RxKqjCyjXXFazmmG853xFdX3MINUvAfMnDUa33IyIx3g8bXhwzSdYMnWI0OcRbX8GB7lA7HrWVppj4HRMXSCimNj7oI1e9XVl8ycNxqa5YzsEJWpy8ER6v6Swb16l3FfRz/t7QwtVzXCfM+7UkPcZ/B7HDOou9ByibevfLUe4XUrU1iWNldPtAjDzgiLF3wPokHpS6M7EnHGn4clpw7Fq5mh8tngCVs0cjcf/Z5ji5MZoqR5yKk7PvOhpQfGf82KhfLfcjMAxDiAkTaCb4AIUkRxp9mH26kqU9G6vU6y0v9VmZakp02f2HAOnY48uEcXE3gdt9K6v2y03I+4vPi29X0qzwM/u1wVdstNwKEoaBABs/epQyHK/ckrH4rU7VFfw0PtY1OvmTG1QEm1Z7PmTitElJx1tPgmvVu4LmZwo9+gH9+QHL58s93jKqr9tijq5MXaqR+heliQJfn97oHngSDO6dc6AJy8D+xtbVPU+y59T6YBuQj308ucUqbKCJy8D+dlpgQoYWqzbfgA3nV+EVz7c22F/TyzRlmoSa98atTQ6hWKgS0QxqRnKpJP0rq+rR1Cm9TnCv7TlgCNWkIvv/m7rV4c6fNlfXlKoOrda72Mx3uVjPXkZWHjFmZqCEqWc7sVrQwOfgpw0XDm8d4fljpWWT5Z7e6PlYYcLvwGSJ4h1WH63sQW3rwzNa87JSA3cDInW3gXaP6fRA7sK37gotSk4yFabIhPsuU0dS7odaz2B/YKlzpTEKtNnlTkGTsXUBSISYqUKB3YhuhTn/5b2T9iSnfFWGzhwpFlVKafgvwuntfSRnsdivCu93XT+AIwv9mj+++B94D3eilkrP+ywXw81teG5d3fDe7w1ZB8pfQ6Hj7WpCnKB0BsgtVUMmlra81Oz0kMXECl0Z+LHFxZ1WC0t+HMSHcIHELPubH52WodUCzUiPbf3+Am88XF8pRNj3VyyBJixXJIUno2V3BobG+F2u+H1epGXp25tcaJkEFzUnb0PscnBCBC591H+whfdzsg2iXjh5lG458WPVM9yXzVztO4TBn1+CZur61Hx5UEA7cHC6AHaAoXyqlrc/9InOHxcXYAI6LNoijxxUWm/yj2bm+aODZTUira9qPDnBdrTEqYv26zp+e689FQUdQ9dOEPkmhFrMRrRNv31RyNxx98+FKqLa7RI+5b0IxqvMXWBiFSxUoUDOxDNw0tkvp7Sa0WbWS5/aUOCquDKyPzt8NW4ntq4S3PQWVZSiNyMNFz7p/dVtyPWDHsRakv46bGEsVKqRzxVDP66+Sts+fm4wPMpBbnhj48v9kQdwhdt0/s1DZYJcgGmdFkBA10iIoOJ5uElMl8vcn5oK2atVO5VXjC5GAebxPMVjfyyV8whjSPojJUvqkSPJVvVlvDTo5Sf0k1UPLng9U2tHfK4w3tprxhW2KE2crQbFJ9fEl4SWHuGrr665KThoSklTOmyAAa6REQJINoTnsge80iv9XRK9F7liup64ec3avZ4rJX6tAad0Sa6xRLvoilqS/jFE4zOvmQgxgzqrngTFe8EveA87vC/r/U249l3ajr8jdINSqRgORK1VRyM1tDU1r6McYqLwa7JGOgSEVFArF5lkSAoPysNS68doTlfNhYjV+pTSusQpbWnVW3ZNC3BqPwcc8afHvVziSfgB4BunTNwz4sfxd0rrhQsh4tUxSHetA496JHSQvFj1QUiIkFq17O3q2izwEUWO3jk6iEYM6ibYjAV7340eqW+spJCbJo7FqtmjsaT04bjhZtG4ecTBwv9rdaeVrULCETbPhK1aSRKlS1i6ZKdpjqPWxZ8g6Km8oNSFQdRE0t6qm6riOBFI1pP+JPi2mFF7NElIhIQa1Z4Moln4pwe+zERK/WFp3WMHtgVz71bY+iiKWr3q9L2keroakkjCe/dr/m2CU++uTNq8Ckh/vzhOu9x4cl28ycNxowxRXGNHLxX3YAfX1iEv33wjeqybLHIwfvoJetDJskl67XDDCwvFoblxYgonNIQqhGlv+xEbak5vfajXForVtCptqxTrPdjVAm48Nc9u1+XkFXkYu3XSO0GoOukxvKqWty6YlvsDdEefC5eu0PzaxXkpOPK4b2EViN7/H+G4coRpwR+1lp2zQXgjktPRZvPj32Hj+P4CR/Kq/bHtQBFrNcDkvfaoQfReI2BbhgGukTOoUfNX7X1TSkyvfej3kGnaE+z3j37dhgpUBs8/t8PhuHX//5c82Q2NcFlbmYnPHrVUEwc2r6v4qkBHEypOkRBTppu5ct47YgP6+gSUVLTK4AwcuJTMtF7P8rD9gtf2466xvjqDqspVTa+2IPcjDTdFqnQu0SaEdTW7P3lP3fgB2f3xh/eqdHUIypvn+ICJCn63x9pPoHbV27DzK+L8PNJxbqUXQPaP4M/vFODpdecBXdWeuDzHlVUgDv+VomGpta4X4PXjsRgoEtEjqNnAGH0xKdkYdx+DP2U1Q5SqilVFr5ABQC8tO0bTb2vRpVIC35+vVIX1H4mh5pa8Yd3anDLhUUdekSjLUoSTs18rWX/qQEgYewZ2pdjDiZ/Bj97tQqZnVIDN1NPbQSyw5Y6jhevHcZioEtEjqJ3AJGIiU/JQM1+FFlNa/fBY3hi/RcdPuf9jS2qbmZEe5qf2rATT6zvOBFLa++rkSMFeqdDqD225fPstY9q8fa9l2DrV4ewbnsdnnt3t6rgFQB+NKY/Xq3cK5QusOw/uzGsd35cNYCDSZAn9IW+9rFWn9Df52V2QmPziZjb8dphLEeVF+vfvz9cLlfIv0ceecTsZhFRAqkJIETI9UqVQmIX2oMII5a4dRLR/XioqRXnP7oB05dtxh2rKzF92Wac/+gGLPnn9pDHH48Q5AKhJZ1ESjiJ9qYtf3e3Lq+n9nXl7URLssmjGeHngByQl1fVCrdRFuuzi0Q+z7Z+dQgjiwrwr6o61a8LtKeKzP/emcLbP/j6p5g/qdj09dG65qTj/Z+N47XDAhwV6ALAL37xC9TW1gb+/eQnPzG7SUSUQHoPkautb0qRiezHK4YVYtbKjkGavJqWaJ6ompsZ0d60w8eVexTV3jyped0euZkor6qNGPyHB62xRjMA9QE5oL5mb7ADR5pV5/jKryMHgZ488R7PhqY27DxwNFBiLdHkWtK/vLIEWempUfebBGDauX0S2Lrk5LhANzc3Fx6PJ/AvJyfH7CYRUQIZkWqgVDw/uFA9xRZtP/5u2ll4ceteXXviRG5mRHqaRYMmNbmWanq4RXto9R7NCKZ1AYkeuZlYv119b257ENgXQPu+KshJF/7bx9d/oXs9XCX5WaHHRvg1IdZ+e3z9zog3LaQfR5UX69+/P5qbm9HW1oa+ffvimmuuwZw5c9Cpk3IqcktLC1paWgI/NzY2ok+fPiwvRmRTRtVYlZ9bz9qkySp8Px5qasEDa6p0K9skWzVztFB+a6xSZXeOOw2Pr/9Ct9cTfd2l15yFxWt3CJdkW1O5F3esroz5uk9OG44pw3sLtzOY/NnVNTZj8RufKn5mctvmTxqM21d+qOm1AMCTl4GFV5wJvx+4faVYHd9EeuGmUUhJcUWsYVzX2IyGoy0oyElHj7xMbKlpwJNv7uzwHKypq01Slhf76U9/ihEjRqCgoADvvfce5s2bh9raWvzmN79R/JslS5Zg0aJFCWwlERlJHma9bcW2DqWN4k01CF8ti7QJ3o/lVbWYtfJDXXty1a5SFmtFsvHFHqz+7x7dV0WL9bq5GWmqJqwlesW4rLSUqIH6/EnFWLx2u9DzZqaloLnN3+HxusYW3LpiG565bgRmXlD0XXUF88mf+eiwJbIjTQSUKV1y9KiyQcos36N7//3349FHH426zY4dO3DGGWd0ePy5557Dj3/8Yxw9ehQZGRkR/5Y9ukTOZIdC/MlO6ypW0YT3jqnphY+2rVGroim97rrtdbj/pU+i5gbL5B5aI0czlNq7++AxrNqyJ6SWsXyeubPSdVm8AWhPH9n6wHg88q/tWPaf3bo8Zzxc6PiZK5U1VEPtqEAyc0yP7t13340ZM2ZE3WbAgAERHx81ahROnDiB3bt34/TTT4+4TUZGhmIQTET2VVZSiPHFHqYaWJiWSUqxBC8YofZmJ1qPfaze13hunsJfV23AJPfQGjmaEdy2DvsgLwNzxp2K/t1yQs6zNZV7Nb9OuMPH2rC5uh4/n3QmzurTpUOqS0FOGkYVddVc3UGNFBcw84KikM882kRANVhTV3+WD3S7d++O7t27a/rbyspKpKSkoEePHjq3iojsgKkG1hbvl7oczEUKsoxYdSwRN09qAqZIKRNGBuTlVbW4dUXHPNn9jS14Yv1OPH3diJDzTe/6sBVfHsSYU7th4tBeuLykEFtqGrBuex1erdyHhqZWVUGu/IlFWtQiFr8E/OGdGpzVt0tgf+p108aauvqzfKArqqKiAu+//z4uueQS5ObmoqKiAnPmzMF1112HLl26mN08IiIKI/ql3jUnHd8/u3eHgEQpeDNy1TGjb57UBEwSIvfQxgrItUyq9Pkl3P/yJ4rtADruU7myhB6LN7Q72cbUFBe8x1sV6xvHEnzs3Fc2GFtqGvDuroN4auMu4ecIfr963LRpyfOm2BwT6GZkZGD16tVYuHAhWlpaUFRUhDlz5uCuu+4yu2lERBSBSCBUkJOGinmXIr1TSiAgiRWgGbnqmNHUBEzRyp4pBeRac9ef2rArZsmu8H0aLZVCi+D3ozZVwJOXgekj+3bo+ZfbWTqwK0YWFeClbd8IBebyMfT8uzWYMaYorp5Y1uM2lmMC3REjRmDzZn2S3omIyHgiOaUPXzkE6Z1SAtuLBKZ6LxqSSGoCJu+xNlVpGFrTOXx+Cc+9K1bt4J2dB+CXJBw82oIeuZkYX+yJmEqhVpfsNIwecPKzF+35nn3JQIwZ1F2o1zr4eBS1eO0O/HFTDeZPGizce53iQshSyHqklZAyxwS6RERkP0bklCaizJZR1Az3q0nD0JLOIac4vLvrW3gFqj8AwNNvfYmn3/oy8LPcW7xp7lhsqWnAvkPHcM9LH0NtvaerzuqNLTUNgYBV9Cbl1J65qnrt5ePxZ698IlzXuc7bjFkrP8QtFxbhD+/EviG46fwijD2jZ0idXXdWOnx+iT26BmCgS0REptJ7klesYNHK+ZBqh/tF0zDUpnNEqwerRnhvcUU1VAW5cu/nn97djT+9uzsQOBt5M1NWUoixZ/TE6CVvoqGpNeb28o3Cax/V4rfTz8Idqz9EtFWWX9r2DU74/Fjz0b6QYJrlD43huCWAiYjIfuS0hCnDe6M0rAi/ludaMLkYQPD0JYT8bOV8SC3L7cbq4VSTziGnOOhRRSB4oprPLwm346LT2qsthQeMcuC84bP9MZ+jUMPNjM8voaK6Hv+qqsUNpf3hQsdjKBL5RuFAY3PUIBcAGprasPy9rzr0GEda1pnixx5dIiJyHCPLbCWC3Mv9/Ls1WLx2R8ztY/VcivZsdsvJwD3/+EjXleqCe4tF2/HJXq/ic7kA/GlT7BSB+ZMGq7qZidSLLU/4izURT/bOzoPCrxeOK6QZg4EuERHZmlK5LLsvGpKa4sKMMUX446aauNMwRNM54ILui3jIDhxpxveG9orZji45aVFTBiSIpT90yRFfDEppop73WBskAN8f0Rv/2BZ7AYy3v/hW+DUjsXJFELtioEtEZCNaaqA6WaxyWXZfNESv1c5En+fg0Rbd2h6uR24mUlNcmD+pGLevjFzZQAJw5fDe+NO7u+N+PdE0CZGJept2HYQnLzNkqeNIXABcro4pF2pZsSKIXTFHl4jIJsqranH+oxswfdlm3LG6EtOXbcb5j25I2pw+pVxSp+U6KuXsetyZqlZ4E3keIypRuHAyX7a8qhY/ezXywhOyvCzl+sBqiL4XkYl6dY0tmD6yb8znknAyyI3n9tOKFUHsyiVJaot8OFtjYyPcbje8Xi/y8vLMbg4REQDloVX5y1TLkrZ25fNLeG/XQdz6wlY0tfgibiMPxW+aOzZm2S279JDr1dZoz+PzSzj3l+uES2sBEKoO8cx1IwAg4hLC4c/VMy8DgAv7G5VLrEV7TdHPXramci/uWF0Zc7snpw3Hx18fFuptvmlMf/yzqk51Gojatoez0/EcL9F4jakLREQWZ+SStnZTXlWL+1/+JObkIJFcR62rhJlFrzSMaM+TmuJSnTrQJSc9ak5tfnYa/H5g8drtMZ9L7j2dM+40PLH+C8WANlqQC6irqqFmot64Yo/QvhlX7MHPJhVjS00D6rzHsXjtDhxqahWa5Ke1IojdjudEYeoCEZHFqamB6mTlVbW4dcU24RnwgHKuo13SHuRyV2sq96Kiuh6+eJM/BYwr9qjafv6kwVg1czR+NKZ/xN8fPtaG21eqK1fWv1u26hJrgPp0DuDkRL1YoeWsVdtwqKk16rZymsbZ/boEelY97iw8NKUk8HslhRraLrPL8WwG9ugSEVmcnZe0FSEy3OrzS1j4WuwewXCReuvs0kNuVg+dHPiJBqYedxZGFhXgrr9X6taGHrmZKB3YFeOLPdj8ZT1mvbANh6OszpaflYal147A6AHqazCLLv0rB+w//m4FNKVJfVcMK8RFv9rY4XO75cIivPZRbcjjXXPSMWV4L4wv9sSVimKH49ksDHSJiCzOzkvaxiIazG2paYg54z1YtLJbalcJM4NSTnb4SmNGCA78YvUfd81JD/Re6lWWLHihh9QUFyAhapALtP8+xeXSHMiVlRRi6TVnYdbKD2O+59c+qsXSa87C4rU7OtRovmJYIf7wTk3Ez+0P79Rg6TVnoUtOhuJNnZYcWzscz2ZioEtEZHF2XtI2GjXBnJbeaqVcR6v3kFuhh06u0BArH7q+qRUX/WojJpaoS3dQ4kLo51ZeVYv7X4pepUEW7+fVJSdDKIe21tuMLjkZ2DR3bEhQena/LrjoVxujfm6L1+5QnGimtQff6sez2ZijS0RkcXZf0jaSWMEccHLZWEBdb3VBTlrUHk+zeshF822tkpNdVlKIrQ+Mx5xxpyI/SsmvOm+zLnVvu2SHfm7yjVCs3lxZvJ+XmkAw0rb/jeNziyfH1skjPnpgjy4RkQ3YfUnbcGqHW0cWFQgV7C/ITsPmeeOQ3km5H8eMHnI1vXVW6qFLTXHhjnGn4baLB2H0kvURy47J+1CkzBjQnk8bHLzmZ6XhxjH9MXvsqSFlzpRuhMIFf17xlNdSEwjuPtiE8x/dELpcsGD93/DPLd4efKeO+OiFgS4RkU3YfUnbYGqDudQUFxZeURyzDuvDVw2JGuTKz6XHamOi1ObbWrGHbutXh2LW1hWtB7H0mhFISXFFPYbV5vwumFyMddvr4pq8134zlYG6xuirw3XJTsPj63d2eFxrz3O8ObaJPp7thqkLREQ2ItdAnTK8N0oHqp9hbhVagrmykkI8c90I5Gd37DnLz07DMzqvEqYHtSkaQOxyV8ErjSWK6I1Jdnqq4u/kdo8e2DXmMSz6evlZ7ekOAOIur9V+M3VmzO20FnhT+tz06MFP1PFsR+zRJSKihNM63Cr3am/+sh4V1fUAJJQO6IbRGoL+RPSQa+mtU9tDZ+RqWPJz79x/VGj7H184EI+v/6LD43Jr5k8aLNRW0RshuaTY+Y9u0GXynnwzFWkSXpfsNMw4ryji+4slWs+qXj34Thrx0RMDXSIiB7L6UqDxDLemprgwZlA3jBnUTZd2GFlySWtvnWhOtpG1diM9txL5xmT22EE43dM5YruvGFbYoSSXUltFb4RGD+iqe3mtwM1UdT0qvjwIoP0YGT2gK974eF/Mv4/EnZ2GR64aEvEz0TPH1ujj2Y4Y6BIROYxdlgJ12gS7SOLprYvVQ2dkrV2l544k/MYkUrsPNbVi1krxtqq5ETJi8l5qigtjTu2GMaeG3kxpzYvOSkvFeIUV55hjaywGukREDmLmQgNaOH24Nd7eOqUeOiNr7aqpeABEvjEJbrfPL2lKLRC9EUrk5L1Yn6eSWD3KyXDTZxYGukREDmGFhQa0cPJwq1G9dUauhiVa8WD2JQMxZlD3mDcm8bRV5EYokeW1on2esUQqKxb8vsYXexx902cWBrpERA7BpUCtyYjeOjXD9WrztUWf+9SeuULHUbypBbFuhBI99K/0ecYS3KNsl/QiJ2CgS0TkEKIBRZ33uMEtoXB6p2iIDsNHWtggVkCldypAIlIL9LiZUHNDEPx51nmPY/HaHTjU1CrUoyySXsSeXf0w0CUicgjRQGHx2h3ISk9lz1GC6ZmiITJcn6+wsEGsfG29UwESlVoQz82Elh7W4M8zKz1VqEdZJL3o/pc/wcLXtoesAsjeXu24YAQRkUPEWmhAdqipVbiIPlmTPFwPoMPnLf+slD+qtFCFmudWkwqg9/PFei21C6rIPazxLDYRa8GG8cUeVFTX4/F1X8RMLzp8rK3DUtdq2kKhXJIkaV3kw5EaGxvhdrvh9XqRl5dndnOIiFQRLQsl96JtmjuWQ6Imi6fmsVJP5LRz+wotbLBq5mjFXma980itmJcqV4RQCj7VnieRPstISxNrwXM2lGi8xtQFIiIFVl90IRK5Z+lnr3yChqY2xe04Mc0a4g3+xhd7kJuRpnlhg1jLyuqZK2pkKTmt56reEzjD01PU1COOheesNgx0iYgisGLvk6iykkIcb/Njzt8qY26rpoi+CC0Bhx1vKPQQb83jSMfoS9u+wYLJxbpNANO79JsRpeTiOVeNWGxCprYesSi9z1mnY6BLRBTGbosuROLJS1wRfZmWgMPONxTxiLfmcaxjdOk1ZyWstqyZ4j1XjawIIVqPWC09z9lkwMloRERBYgUggPIkHiuJNTHNhfaAUq9AR8uEHj0mAdmVmiHzcCLH6OK1OzB/UmImgJlFj3PVyPNEbc+rJy8D+dlpCTtnkwUDXSKiIPEEIGbw+SVUVNdjTeVeVFTXB77UEznTXUvA4ZQbCq3iGTIXPUa75KRHrQRg9x5zPc5VI88T0Z7X2ZcMxKqZo/Hu/ZfikauGGNKWZMbUBSKiIEbm7Okt1rC/EStyRaJlQk+yr+IWz5C5mmN0yvDejl18QK9z1ajzRLR+8Jzxpwc+j0Sds8mEgS4RUZBErOKkB9HcRCNnusu0BBx2uqEwQjyLKKg9Ro2YAGYFep6rRpwnWpcmTsQ5m0wY6BIRBUnUKk7xUDuRyehAR0vAYZcbCqNoDYIAexyjiaD3fjDiPNHaQ+vUmxMzMEeXiChIInNbtbJaHrGWCT2JnixnRbFW04oWBFn9GE0Eu+yHspJCbJo7FqtmjsaT04Zj1czR2DR3LNMQEoSBLhFRGK0BSKJYbdhfS8BhlyDFaFqDIKsfo4lil/2gZWli0geXAA7DJYCJSGbVhQwqqusxfdnmmNtFW97VCHaso2vVz1iU3duvF+6H5CMarzHQDcNAl4iszueXcP6jG2LmJm6aOzbhX/Z2WhnN7CCb9MVgN7kw0NWIgS4R2YFcdQGIPJHJSsO2VqRUtYL7z55405J8ROM15ugSEdmQXXITrSjZF6twGiuusKe0kAslHsuLERHZFOttapPsi1U4hc8vYfOX9bj/pU+ES+0lAnuXrYWBLhGRjbHepnpWq1pB6kUKJiNJ9E2L6EIulDhMXSAioqSS7ItV2J1SqkI0ibhpYUqMNTHQJSKipMLFKuwrWjAZTSJuWqy2kAu1Y6BLRERJhYtV2FesYDJcIm9amBJjTQx0iYgo6bBqhT2pCRITfdPClBhr4mQ0IiJKSqxaYT9qgkRPgisdyCkxsRZyYUpMYjHQJSKipMWqFfYSK5gEgPzsNCydPgKjB3ZN6E2LnBJz24ptcCHyQi5MiUk8pi4QERGRLcTKr3YBeOSqIRhzajdTAkqmxFgPlwAOwyWAiYiIrM3qizL4/BJTYgwmGq8x0A3DQJeIyDkYcDgXP9vkJhqvMUeXiIgcyeq9fhQf5leTCOboEhGR4yitniUvxVpeVWtSy4gokRjoEhGRo3ApViKSMdAlIiJH4VKsRCRjji4RETkKl2K1P040I70w0CUiIkcxYilWBl6Jw0mEpCcGukRE5Ch6L8XKwCsyI4J/eRJh+OcmTyLkogukFgNdIiJyFD2XYmXgFZkRwX+sSYQutE8iHF/sYW86CbPNZLRf/vKXOO+885CdnY38/PyI2+zZsweTJk1CdnY2evTogXvvvRcnTpxIbEOJiMh0eizFyuoNkRlVuo2TCMkItunRbW1txQ9+8AOUlpbiT3/6U4ff+3w+TJo0CR6PB++99x5qa2tx/fXXIy0tDQ8//LAJLSYiIjOVlRRifLFH8/C6msArWRYuMLLXlZMIyQi2CXQXLVoEAHj++ecj/v7f//43tm/fjvXr16Nnz54YPnw4Fi9ejLlz52LhwoVIT09PYGuJiMgK4lk9i4FXR0YG/0ZMIiSyTepCLBUVFRgyZAh69uwZeOzyyy9HY2MjPv30U8W/a2lpQWNjY8g/IiIiBl4dGRn8y5MIlfqBXWjPAxadREgEOCjQraurCwlyAQR+rqurU/y7JUuWwO12B/716dPH0HYSEZE9MPDqyMjgX55ECKDDPlc7iZBIZmqge//998PlckX999lnnxnahnnz5sHr9Qb+ff3114a+HhER2QMDr46MDv71mERIFMzUHN27774bM2bMiLrNgAEDhJ7L4/Fgy5YtIY/t378/8DslGRkZyMjIEHoNIiJKLnLgFV5Ky5OkdXT1LN2mJN5JhETBTA10u3fvju7du+vyXKWlpfjlL3+JAwcOoEePHgCAdevWIS8vD8XFxbq8BhERJR8GXqESEfzHM4mQKJhtqi7s2bMHDQ0N2LNnD3w+HyorKwEAgwYNQufOnXHZZZehuLgY//u//4vHHnsMdXV1eOCBBzBr1iz22BIRUVwYeIVi8E924ZIkyRaVrmfMmIE///nPHR7fuHEjLr74YgDAV199hdtuuw1vvfUWcnJycMMNN+CRRx5Bp07i8XxjYyPcbje8Xi/y8vL0aj4RERER6UQ0XrNNoJsoDHSJiIiIrE00XnNMeTEiIiIiomAMdImIiIjIkRjoEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIiciQGukRERETkSAx0iYiIiMiRGOgSERERkSMx0CUiIiIiR2KgS0RERESOxECXiIiIiByJgS4RERERORIDXSIiIiJyJAa6RERERORIDHSJiIiIyJEY6BIRERGRIzHQJSIiIiJHYqBLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBrpERERE5EgMdImIiIjIkRjoEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIiciQGukRERETkSAx0iYiIiMiRGOgSERERkSMx0CUiIiIiR2KgS0RERESOxECXiIiIiByJgS4RERERORIDXSIiIiJyJAa6RERERORIDHSJiIiIyJEY6BIRERGRIzHQJSIiIiJHYqBLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBrpERERE5EgMdImIiIjIkRjoEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIiciQGukRERETkSAx0iYiIiMiRGOgSERERkSPZJtD95S9/ifPOOw/Z2dnIz8+PuI3L5erwb/Xq1YltKBERERFZQiezGyCqtbUVP/jBD1BaWoo//elPitstX74cZWVlgZ+VgmIiIiIicjbbBLqLFi0CADz//PNRt8vPz4fH40lAi4iIiMgKfH4JW2oacOBIM3rkZmJkUQFSU1xmN4sswDaBrqhZs2bh5ptvxoABA3DrrbfixhtvhMulfLC3tLSgpaUl8HNjY2MimklEREQ6KK+qxaLXt6PW2xx4rNCdiQWTi1FWUmhiy8gKbJOjK+IXv/gF/v73v2PdunW4+uqrcfvtt+N3v/td1L9ZsmQJ3G534F+fPn0S1FoiIiKKR3lVLW5bsS0kyAWAOm8zbluxDeVVtSa1jKzC1ED3/vvvjziBLPjfZ599Jvx88+fPx5gxY3DWWWdh7ty5uO+++/CrX/0q6t/MmzcPXq838O/rr7+O920RERGRwXx+CYte3w4pwu/kxxa9vh0+f6QtKFmYmrpw9913Y8aMGVG3GTBggObnHzVqFBYvXoyWlhZkZGRE3CYjI0Pxd0RERGRNW2oaOvTkBpMA1HqbsaWmAaUDuyauYWQppga63bt3R/fu3Q17/srKSnTp0oWBLBERkcMcOKIc5GrZjpzJNpPR9uzZg4aGBuzZswc+nw+VlZUAgEGDBqFz5854/fXXsX//fowePRqZmZlYt24dHn74Ydxzzz3mNpyIiIh01yM3U9ftyJlsE+g++OCD+POf/xz4+ayzzgIAbNy4ERdffDHS0tKwdOlSzJkzB5IkYdCgQfjNb36DmTNnmtVkIiIiMsjIogIUujNR522OmKfrAuBxt5cao+TlkiSJWdpBGhsb4Xa74fV6kZeXZ3ZziIiISIFcdQFASLArFxV9+roRLDHmUKLxmqPKixEREVHyKCspxNPXjYDHHZqe4HFnMsglADZKXSAiIiIKV1ZSiPHFHq6MRhEx0CUiIiJbS01xsYQYRcTUBSIiIiJyJAa6RERERORIDHSJiIiIyJEY6BIRERGRIzHQJSIiIiJHYqBLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBrpERERE5EgMdImIiIjIkRjoEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIiciQGukRERETkSAx0iYiIiMiRGOgSERERkSN1MrsBViNJEgCgsbHR5JYQERERUSRynCbHbUoY6IY5cuQIAKBPnz4mt4SIiIiIojly5Ajcbrfi711SrFA4yfj9fuzbtw+5ublwuVxmN0cXjY2N6NOnD77++mvk5eWZ3Rxb4j7UB/ejPrgf9cH9qA/uR31wP6ojSRKOHDmCXr16ISVFOROXPbphUlJScMopp5jdDEPk5eXx5IkT96E+uB/1wf2oD+5HfXA/6oP7UVy0nlwZJ6MRERERkSMx0CUiIiIiR2KgmwQyMjKwYMECZGRkmN0U2+I+1Af3oz64H/XB/agP7kd9cD8ag5PRiIiIiMiR2KNLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBroOt3TpUvTv3x+ZmZkYNWoUtmzZYnaTbGXhwoVwuVwh/8444wyzm2V577zzDiZPnoxevXrB5XLh1VdfDfm9JEl48MEHUVhYiKysLIwbNw47d+40p7EWFms/zpgxo8PxWVZWZk5jLWrJkiU499xzkZubix49emDq1Kn4/PPPQ7Zpbm7GrFmz0LVrV3Tu3BlXX3019u/fb1KLrUlkP1588cUdjsdbb73VpBZb09NPP42hQ4cGFoUoLS3Fv/71r8DveSzqj4Gug/3tb3/DXXfdhQULFmDbtm0YNmwYLr/8chw4cMDsptnKmWeeidra2sC/TZs2md0ky2tqasKwYcOwdOnSiL9/7LHH8Nvf/hbPPPMM3n//feTk5ODyyy9Hc3NzgltqbbH2IwCUlZWFHJ+rVq1KYAut7+2338asWbOwefNmrFu3Dm1tbbjsssvQ1NQU2GbOnDl4/fXX8eKLL+Ltt9/Gvn37cNVVV5nYausR2Y8AMHPmzJDj8bHHHjOpxdZ0yimn4JFHHsHWrVvxwQcfYOzYsZgyZQo+/fRTADwWDSGRY40cOVKaNWtW4Gefzyf16tVLWrJkiYmtspcFCxZIw4YNM7sZtgZAeuWVVwI/+/1+yePxSL/61a8Cjx0+fFjKyMiQVq1aZUIL7SF8P0qSJN1www3SlClTTGmPXR04cEACIL399tuSJLUfe2lpadKLL74Y2GbHjh0SAKmiosKsZlpe+H6UJEm66KKLpDvuuMO8RtlUly5dpD/+8Y88Fg3CHl2Ham1txdatWzFu3LjAYykpKRg3bhwqKipMbJn97Ny5E7169cKAAQNw7bXXYs+ePWY3ydZqampQV1cXcmy63W6MGjWKx6YGb731Fnr06IHTTz8dt912G+rr681ukqV5vV4AQEFBAQBg69ataGtrCzkezzjjDPTt25fHYxTh+1H2wgsvoFu3bigpKcG8efNw7NgxM5pnCz6fD6tXr0ZTUxNKS0t5LBqkk9kNIGMcPHgQPp8PPXv2DHm8Z8+e+Oyzz0xqlf2MGjUKzz//PE4//XTU1tZi0aJFuOCCC1BVVYXc3Fyzm2dLdXV1ABDx2JR/R2LKyspw1VVXoaioCNXV1fjZz36GCRMmoKKiAqmpqWY3z3L8fj/uvPNOjBkzBiUlJQDaj8f09HTk5+eHbMvjUVmk/QgA11xzDfr164devXrh448/xty5c/H555/j5ZdfNrG11vPJJ5+gtLQUzc3N6Ny5M1555RUUFxejsrKSx6IBGOgSRTFhwoTA/4cOHYpRo0ahX79++Pvf/46bbrrJxJYRAdOmTQv8f8iQIRg6dCgGDhyIt956C5deeqmJLbOmWbNmoaqqinn2cVLaj7fcckvg/0OGDEFhYSEuvfRSVFdXY+DAgYlupmWdfvrpqKyshNfrxT/+8Q/ccMMNePvtt81ulmMxdcGhunXrhtTU1A6zNffv3w+Px2NSq+wvPz8fp512Gnbt2mV2U2xLPv54bOpvwIAB6NatG4/PCGbPno033ngDGzduxCmnnBJ43OPxoLW1FYcPHw7ZnsdjZEr7MZJRo0YBAI/HMOnp6Rg0aBDOPvtsLFmyBMOGDcOTTz7JY9EgDHQdKj09HWeffTbefPPNwGN+vx9vvvkmSktLTWyZvR09ehTV1dUoLCw0uym2VVRUBI/HE3JsNjY24v333+exGadvvvkG9fX1PD6DSJKE2bNn45VXXsGGDRtQVFQU8vuzzz4baWlpIcfj559/jj179vB4DBJrP0ZSWVkJADweY/D7/WhpaeGxaBCmLjjYXXfdhRtuuAHnnHMORo4ciSeeeAJNTU248cYbzW6abdxzzz2YPHky+vXrh3379mHBggVITU3F9OnTzW6apR09ejSkF6empgaVlZUoKChA3759ceedd+Khhx7CqaeeiqKiIsyfPx+9evXC1KlTzWu0BUXbjwUFBVi0aBGuvvpqeDweVFdX47777sOgQYNw+eWXm9hqa5k1axZWrlyJNWvWIDc3N5Dr6Ha7kZWVBbfbjZtuugl33XUXCgoKkJeXh5/85CcoLS3F6NGjTW69dcTaj9XV1Vi5ciUmTpyIrl274uOPP8acOXNw4YUXYujQoSa33jrmzZuHCRMmoG/fvjhy5AhWrlyJt956C//v//0/HotGMbvsAxnrd7/7ndS3b18pPT1dGjlypLR582azm2QrP/zhD6XCwkIpPT1d6t27t/TDH/5Q2rVrl9nNsryNGzdKADr8u+GGGyRJai8xNn/+fKlnz55SRkaGdOmll0qff/65uY22oGj78dixY9Jll10mde/eXUpLS5P69esnzZw5U6qrqzO72ZYSaf8BkJYvXx7Y5vjx49Ltt98udenSRcrOzpauvPJKqba21rxGW1Cs/bhnzx7pwgsvlAoKCqSMjAxp0KBB0r333it5vV5zG24xP/rRj6R+/fpJ6enpUvfu3aVLL71U+ve//x34PY9F/bkkSZISGVgTERERESUCc3SJiIiIyJEY6BIRERGRIzHQJSIiIiJHYqBLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBrpERAa7+OKLceeddybs9Z5//nnk5+cb+hpvvfUWXC4XDh8+bOjrEBHFg4EuEZEOZsyYAZfL1eHfrl278PLLL2Px4sWBbfv3748nnngi5O8TEZwSESWbTmY3gIjIKcrKyrB8+fKQx7p3747U1FSTWkRElNzYo0tEpJOMjAx4PJ6Qf6mpqSGpCxdffDG++uorzJkzJ9Dr+9Zbb+HGG2+E1+sNPLZw4UIAQEtLC+655x707t0bOTk5GDVqFN56662Q133++efRt29fZGdn48orr0R9fX3Udp533nmYO3duyGPffvst0tLS8M477wAA/vrXv+Kcc85Bbm4uPB4PrrnmGhw4cEDxORcuXIjhw4eHPPbEE0+gf//+IY/98Y9/xODBg5GZmYkzzjgDv//976O2lYgoHgx0iYgS6OWXX8Ypp5yCX/ziF6itrUVtbS3OO+88PPHEE8jLyws8ds899wAAZs+ejYqKCqxevRoff/wxfvCDH6CsrAw7d+4EALz//vu46aabMHv2bFRWVuKSSy7BQw89FLUN1157LVavXg1JkgKP/e1vf0OvXr1wwQUXAADa2tqwePFifPTRR3j11Vexe/duzJgxI673/sILL+DBBx/EL3/5S+zYsQMPP/ww5s+fjz//+c9xPS8RkRKmLhAR6eSNN95A586dAz9PmDABL774Ysg2BQUFSE1NDfSUytxuN1wuV8hje/bswfLly7Fnzx706tULAHDPPfegvLwcy5cvx8MPP4wnn3wSZWVluO+++wAAp512Gt577z2Ul5crtvN//ud/cOedd2LTpk2BwHblypWYPn06XC4XAOBHP/pRYPsBAwbgt7/9Lc4991wcPXo05D2qsWDBAvzf//0frrrqKgBAUVERtm/fjmeffRY33HCDpuckIoqGgS4RkU4uueQSPP3004Gfc3Jy4nq+Tz75BD6fD6eddlrI4y0tLejatSsAYMeOHbjyyitDfl9aWho10O3evTsuu+wyvPDCC7jgggtQU1ODiooKPPvss4Fttm7dioULF+Kjjz7CoUOH4Pf7AbQH38XFxarfS1NTE6qrq3HTTTdh5syZgcdPnDgBt9ut+vmIiEQw0CUi0klOTg4GDRqk2/MdPXoUqamp2Lp1a4cJbVp7VWXXXnstfvrTn+J3v/sdVq5ciSFDhmDIkCEA2oPSyy+/HJdffjleeOEFdO/eHXv27MHll1+O1tbWiM+XkpISkgoBtKc/BL8XAFi2bBlGjRoVsh0n6xGRURjoEhElWHp6Onw+X8zHzjrrLPh8Phw4cCCQYhBu8ODBeP/990Me27x5c8w2TJkyBbfccgvKy8uxcuVKXH/99YHfffbZZ6ivr8cjjzyCPn36AAA++OCDqM/XvXt31NXVQZKkQPpDZWVl4Pc9e/ZEr1698OWXX+Laa6+N2T4iIj1wMhoRUYL1798f77zzDvbu3YuDBw8GHjt69CjefPNNHDx4EMeOHcNpp52Ga6+9Ftdffz1efvll1NTUYMuWLViyZAnWrl0LAPjpT3+K8vJy/PrXv8bOnTvx1FNPRU1bkOXk5GDq1KmYP38+duzYgenTpwd+17dvX6Snp+N3v/sdvvzyS7z22mshdYAjufjii/Htt9/iscceQ3V1NZYuXYp//etfIdssWrQIS5YswW9/+1t88cUX+OSTT7B8+XL85je/UbsLiYiEMNAlIkqwX/ziF9i9ezcGDhyI7t27A2gv+XXrrbfihz/8Ibp3747HHnsMALB8+XJcf/31uPvuu3H66adj6tSp+O9//4u+ffsCAEaPHo1ly5bhySefxLBhw/Dvf/8bDzzwgFA7rr32Wnz00Ue44IILAs8HtPfOPv/883jxxRdRXFyMRx55BL/+9a+jPtfgwYPx+9//HkuXLsWwYcOwZcuWQOUI2c0334w//vGPWL58OYYMGYKLLroIzz//PIqKioT3HRGRGi4pPKmKiIiIiMgB2KNLRERERI7EQJeIiIiIHImBLhERERE5EgNdIiIiInIkBrpERERE5EgMdImIiIjIkRjoEhEREZEjMdAlIiIiIkdioEtEREREjsRAl4iIiIgciYEuERERETkSA10iIiIicqT/DxcvOIC7g093AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = subplots(figsize=(8,8))[1]\n", "ax.scatter(results.fittedvalues, results.resid)\n", "ax.set_xlabel('Fitted value')\n", "ax.set_ylabel('Residual')\n", - "ax.axhline(0, c='k', ls='--');\n" + "ax.axhline(0, c='k', ls='--');" ] }, { @@ -1549,46 +638,17 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "82673b80", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.188851Z", - "iopub.status.busy": "2023-07-25T23:59:12.188712Z", - "iopub.status.idle": "2023-07-25T23:59:12.286175Z", - "shell.execute_reply": "2023-07-25T23:59:12.285838Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "374" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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coefstd errtP>|t|
intercept33.22280.73145.4580.000
lstat-1.03210.048-21.4160.000
age0.03450.0122.8260.005
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coefstd errtP>|t|
intercept41.61734.9368.4310.000
crim-0.12140.033-3.6780.000
zn0.04700.0143.3840.001
indus0.01350.0620.2170.829
chas2.84000.8703.2640.001
nox-18.75803.851-4.8700.000
rm3.65810.4208.7050.000
age0.00360.0130.2710.787
dis-1.49080.202-7.3940.000
rad0.28940.0674.3250.000
tax-0.01270.004-3.3370.001
ptratio-0.93750.132-7.0910.000
lstat-0.55200.051-10.8970.000
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 41.6173 4.936 8.431 0.000\n", - "crim -0.1214 0.033 -3.678 0.000\n", - "zn 0.0470 0.014 3.384 0.001\n", - "indus 0.0135 0.062 0.217 0.829\n", - "chas 2.8400 0.870 3.264 0.001\n", - "nox -18.7580 3.851 -4.870 0.000\n", - "rm 3.6581 0.420 8.705 0.000\n", - "age 0.0036 0.013 0.271 0.787\n", - "dis -1.4908 0.202 -7.394 0.000\n", - "rad 0.2894 0.067 4.325 0.000\n", - "tax -0.0127 0.004 -3.337 0.001\n", - "ptratio -0.9375 0.132 -7.091 0.000\n", - "lstat -0.5520 0.051 -10.897 0.000" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS(terms).fit_transform(Boston)\n", "model = sm.OLS(y, X)\n", "results = model.fit()\n", - "summarize(results)\n" + "summarize(results)" ] }, { @@ -1931,159 +746,15 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "0a2714b1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.323903Z", - "iopub.status.busy": "2023-07-25T23:59:12.323760Z", - "iopub.status.idle": "2023-07-25T23:59:12.339122Z", - "shell.execute_reply": "2023-07-25T23:59:12.338823Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept41.52514.9208.4410.000
crim-0.12140.033-3.6830.000
zn0.04650.0143.3790.001
indus0.01350.0620.2170.829
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rad0.28790.0674.3220.000
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lstat-0.54740.048-11.4830.000
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 41.5251 4.920 8.441 0.000\n", - "crim -0.1214 0.033 -3.683 0.000\n", - "zn 0.0465 0.014 3.379 0.001\n", - "indus 0.0135 0.062 0.217 0.829\n", - "chas 2.8528 0.868 3.287 0.001\n", - "nox -18.4851 3.714 -4.978 0.000\n", - "rm 3.6811 0.411 8.951 0.000\n", - "dis -1.5068 0.193 -7.825 0.000\n", - "rad 0.2879 0.067 4.322 0.000\n", - "tax -0.0127 0.004 -3.333 0.001\n", - "ptratio -0.9346 0.132 -7.099 0.000\n", - "lstat -0.5474 0.048 -11.483 0.000" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "minus_age = Boston.columns.drop(['medv', 'age']) \n", "Xma = MS(minus_age).fit_transform(Boston)\n", "model1 = sm.OLS(y, Xma)\n", - "summarize(model1.fit())\n" + "summarize(model1.fit())" ] }, { @@ -2114,127 +785,21 @@ "lists of `Python` objects. The language also supports\n", "dictionary and *generator* comprehension, though these are\n", "beyond our scope here. Let's look at an example. We compute the VIF for each of the variables\n", - "in the model matrix `X`, using the function `variance_inflation_factor()`.\n" + "in the model matrix `X`, using the function `variance_inflation_factor()`." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "961c9128", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.340898Z", - "iopub.status.busy": "2023-07-25T23:59:12.340771Z", - "iopub.status.idle": "2023-07-25T23:59:12.348463Z", - "shell.execute_reply": "2023-07-25T23:59:12.348166Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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vif
crim1.767486
zn2.298459
indus3.987181
chas1.071168
nox4.369093
rm1.912532
age3.088232
dis3.954037
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lstat2.870777
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" - ], - "text/plain": [ - " vif\n", - "crim 1.767486\n", - "zn 2.298459\n", - "indus 3.987181\n", - "chas 1.071168\n", - "nox 4.369093\n", - "rm 1.912532\n", - "age 3.088232\n", - "dis 3.954037\n", - "rad 7.445301\n", - "tax 9.002158\n", - "ptratio 1.797060\n", - "lstat 2.870777" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "vals = [VIF(X, i)\n", " for i in range(1, X.shape[1])]\n", "vif = pd.DataFrame({'vif':vals},\n", " index=X.columns[1:])\n", - "vif\n" + "vif" ] }, { @@ -2251,22 +816,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "4886f9e9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.350254Z", - "iopub.status.busy": "2023-07-25T23:59:12.350123Z", - "iopub.status.idle": "2023-07-25T23:59:12.356093Z", - "shell.execute_reply": "2023-07-25T23:59:12.355816Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "vals = []\n", "for i in range(1, X.values.shape[1]):\n", - " vals.append(VIF(X.values, i))\n" + " vals.append(VIF(X.values, i))" ] }, { @@ -2284,97 +841,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "b54d2da1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.357794Z", - "iopub.status.busy": "2023-07-25T23:59:12.357702Z", - "iopub.status.idle": "2023-07-25T23:59:12.369302Z", - "shell.execute_reply": "2023-07-25T23:59:12.368984Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept36.08851.47024.5530.000
lstat-1.39210.167-8.3130.000
age-0.00070.020-0.0360.971
lstat:age0.00420.0022.2440.025
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 36.0885 1.470 24.553 0.000\n", - "lstat -1.3921 0.167 -8.313 0.000\n", - "age -0.0007 0.020 -0.036 0.971\n", - "lstat:age 0.0042 0.002 2.244 0.025" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS(['lstat',\n", " 'age',\n", " ('lstat', 'age')]).fit_transform(Boston)\n", "model2 = sm.OLS(y, X)\n", - "summarize(model2.fit())\n" + "summarize(model2.fit())" ] }, { @@ -2392,96 +868,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "1b71633a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.371166Z", - "iopub.status.busy": "2023-07-25T23:59:12.371022Z", - "iopub.status.idle": "2023-07-25T23:59:12.382869Z", - "shell.execute_reply": "2023-07-25T23:59:12.382565Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept17.71510.78122.6810.0
poly(lstat, degree=2)[0]-179.22796.733-26.6200.0
poly(lstat, degree=2)[1]72.99085.48213.3150.0
age0.07030.0116.4710.0
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 17.7151 0.781 22.681 0.0\n", - "poly(lstat, degree=2)[0] -179.2279 6.733 -26.620 0.0\n", - "poly(lstat, degree=2)[1] 72.9908 5.482 13.315 0.0\n", - "age 0.0703 0.011 6.471 0.0" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n", "model3 = sm.OLS(y, X)\n", "results3 = model3.fit()\n", - "summarize(results3)\n" + "summarize(results3)" ] }, { @@ -2510,83 +905,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "6d30a306", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.384859Z", - "iopub.status.busy": "2023-07-25T23:59:12.384717Z", - "iopub.status.idle": "2023-07-25T23:59:12.390818Z", - "shell.execute_reply": "2023-07-25T23:59:12.390400Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
0503.019168.1286090.0NaNNaNNaN
1502.014165.6132511.05002.515357177.2787857.468491e-35
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" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 503.0 19168.128609 0.0 NaN NaN NaN\n", - "1 502.0 14165.613251 1.0 5002.515357 177.278785 7.468491e-35" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "anova_lm(results1, results3)\n" + "metadata": {}, + "outputs": [], + "source": [ + "anova_lm(results1, results3)" ] }, { @@ -2615,50 +939,21 @@ "The function `anova_lm()` can take more than two nested models\n", "as input, in which case it compares every successive pair of models.\n", "That also explains why their are `NaN`s in the first row above, since\n", - "there is no previous model with which to compare the first.\n" + "there is no previous model with which to compare the first." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "9a5ec13f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:12.392629Z", - "iopub.status.busy": "2023-07-25T23:59:12.392494Z", - "iopub.status.idle": "2023-07-25T23:59:12.503315Z", - "shell.execute_reply": "2023-07-25T23:59:12.502922Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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intercept6.57561.0096.5190.000
CompPrice0.09290.00422.5670.000
Income0.01090.0034.1830.000
Advertising0.07020.0233.1070.002
Population0.00020.0000.4330.665
Price-0.10080.007-13.5490.000
ShelveLoc[Good]4.84870.15331.7240.000
ShelveLoc[Medium]1.95330.12615.5310.000
Age-0.05790.016-3.6330.000
Education-0.02090.020-1.0630.288
Urban[Yes]0.14020.1121.2470.213
US[Yes]-0.15760.149-1.0580.291
Income:Advertising0.00080.0002.6980.007
Price:Age0.00010.0000.8010.424
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 6.5756 1.009 6.519 0.000\n", - "CompPrice 0.0929 0.004 22.567 0.000\n", - "Income 0.0109 0.003 4.183 0.000\n", - "Advertising 0.0702 0.023 3.107 0.002\n", - "Population 0.0002 0.000 0.433 0.665\n", - "Price -0.1008 0.007 -13.549 0.000\n", - "ShelveLoc[Good] 4.8487 0.153 31.724 0.000\n", - "ShelveLoc[Medium] 1.9533 0.126 15.531 0.000\n", - "Age -0.0579 0.016 -3.633 0.000\n", - "Education -0.0209 0.020 -1.063 0.288\n", - "Urban[Yes] 0.1402 0.112 1.247 0.213\n", - "US[Yes] -0.1576 0.149 -1.058 0.291\n", - "Income:Advertising 0.0008 0.000 2.698 0.007\n", - "Price:Age 0.0001 0.000 0.801 0.424" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "allvars = list(Carseats.columns.drop('Sales'))\n", "y = Carseats['Sales']\n", @@ -2909,7 +1022,7 @@ " ('Price', 'Age')]\n", "X = MS(final).fit_transform(Carseats)\n", "model = sm.OLS(y, X)\n", - "summarize(model.fit())\n" + "summarize(model.fit())" ] }, { @@ -2935,20 +1048,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch4-classification-lab.ipynb b/docs/source/labs/Ch4-classification-lab.ipynb index e5ab894..b8e8dd8 100644 --- a/docs/source/labs/Ch4-classification-lab.ipynb +++ b/docs/source/labs/Ch4-classification-lab.ipynb @@ -2,20 +2,15 @@ "cells": [ { "cell_type": "markdown", - "id": "2b1e8bf1", + "id": "e6cc127e", "metadata": {}, "source": [ + "# Logistic Regression, LDA, QDA, and KNN\n", "\n", + "\n", + " \"Open\n", "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "84b9c86a", - "metadata": {}, - "source": [ - "# Logistic Regression, LDA, QDA, and KNN\n" + "" ] }, { @@ -41,16 +36,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "71523a7f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:13.693335Z", - "iopub.status.busy": "2023-07-25T23:59:13.692982Z", - "iopub.status.idle": "2023-07-25T23:59:14.692387Z", - "shell.execute_reply": "2023-07-25T23:59:14.692045Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -72,17 +60,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "901ed7c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.694539Z", - "iopub.status.busy": "2023-07-25T23:59:14.694353Z", - "iopub.status.idle": "2023-07-25T23:59:14.710282Z", - "shell.execute_reply": "2023-07-25T23:59:14.709950Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "from ISLP import confusion_table\n", @@ -94,7 +74,7 @@ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n" + "from sklearn.linear_model import LogisticRegression" ] }, { @@ -107,210 +87,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "8b56ee0f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.712197Z", - "iopub.status.busy": "2023-07-25T23:59:14.712071Z", - "iopub.status.idle": "2023-07-25T23:59:14.723241Z", - "shell.execute_reply": "2023-07-25T23:59:14.722941Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLag1Lag2Lag3Lag4Lag5VolumeTodayDirection
020010.381-0.192-2.624-1.0555.0101.191300.959Up
120010.9590.381-0.192-2.624-1.0551.296501.032Up
220011.0320.9590.381-0.192-2.6241.41120-0.623Down
32001-0.6231.0320.9590.381-0.1921.276000.614Up
420010.614-0.6231.0320.9590.3811.205700.213Up
..............................
124520050.4220.252-0.024-0.584-0.2851.888500.043Up
124620050.0430.4220.252-0.024-0.5841.28581-0.955Down
12472005-0.9550.0430.4220.252-0.0241.540470.130Up
124820050.130-0.9550.0430.4220.2521.42236-0.298Down
12492005-0.2980.130-0.9550.0430.4221.38254-0.489Down
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1250 rows × 9 columns

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" - ], - "text/plain": [ - " Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume Today Direction\n", - "0 2001 0.381 -0.192 -2.624 -1.055 5.010 1.19130 0.959 Up\n", - "1 2001 0.959 0.381 -0.192 -2.624 -1.055 1.29650 1.032 Up\n", - "2 2001 1.032 0.959 0.381 -0.192 -2.624 1.41120 -0.623 Down\n", - "3 2001 -0.623 1.032 0.959 0.381 -0.192 1.27600 0.614 Up\n", - "4 2001 0.614 -0.623 1.032 0.959 0.381 1.20570 0.213 Up\n", - "... ... ... ... ... ... ... ... ... ...\n", - "1245 2005 0.422 0.252 -0.024 -0.584 -0.285 1.88850 0.043 Up\n", - "1246 2005 0.043 0.422 0.252 -0.024 -0.584 1.28581 -0.955 Down\n", - "1247 2005 -0.955 0.043 0.422 0.252 -0.024 1.54047 0.130 Up\n", - "1248 2005 0.130 -0.955 0.043 0.422 0.252 1.42236 -0.298 Down\n", - "1249 2005 -0.298 0.130 -0.955 0.043 0.422 1.38254 -0.489 Down\n", - "\n", - "[1250 rows x 9 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Smarket = load_data('Smarket')\n", "Smarket" @@ -327,31 +107,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "62d49b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.725047Z", - "iopub.status.busy": "2023-07-25T23:59:14.724902Z", - "iopub.status.idle": "2023-07-25T23:59:14.727346Z", - "shell.execute_reply": "2023-07-25T23:59:14.727072Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Year', 'Lag1', 'Lag2', 'Lag3', 'Lag4', 'Lag5', 'Volume', 'Today',\n", - " 'Direction'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Smarket.columns" ] @@ -371,171 +130,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "bc34e7b7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.729048Z", - "iopub.status.busy": "2023-07-25T23:59:14.728923Z", - "iopub.status.idle": "2023-07-25T23:59:14.734089Z", - "shell.execute_reply": "2023-07-25T23:59:14.733820Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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YearLag1Lag2Lag3Lag4Lag5VolumeToday
Year1.0000000.0297000.0305960.0331950.0356890.0297880.5390060.030095
Lag10.0297001.000000-0.026294-0.010803-0.002986-0.0056750.040910-0.026155
Lag20.030596-0.0262941.000000-0.025897-0.010854-0.003558-0.043383-0.010250
Lag30.033195-0.010803-0.0258971.000000-0.024051-0.018808-0.041824-0.002448
Lag40.035689-0.002986-0.010854-0.0240511.000000-0.027084-0.048414-0.006900
Lag50.029788-0.005675-0.003558-0.018808-0.0270841.000000-0.022002-0.034860
Volume0.5390060.040910-0.043383-0.041824-0.048414-0.0220021.0000000.014592
Today0.030095-0.026155-0.010250-0.002448-0.006900-0.0348600.0145921.000000
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" - ], - "text/plain": [ - " Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume \\\n", - "Year 1.000000 0.029700 0.030596 0.033195 0.035689 0.029788 0.539006 \n", - "Lag1 0.029700 1.000000 -0.026294 -0.010803 -0.002986 -0.005675 0.040910 \n", - "Lag2 0.030596 -0.026294 1.000000 -0.025897 -0.010854 -0.003558 -0.043383 \n", - "Lag3 0.033195 -0.010803 -0.025897 1.000000 -0.024051 -0.018808 -0.041824 \n", - "Lag4 0.035689 -0.002986 -0.010854 -0.024051 1.000000 -0.027084 -0.048414 \n", - "Lag5 0.029788 -0.005675 -0.003558 -0.018808 -0.027084 1.000000 -0.022002 \n", - "Volume 0.539006 0.040910 -0.043383 -0.041824 -0.048414 -0.022002 1.000000 \n", - "Today 0.030095 -0.026155 -0.010250 -0.002448 -0.006900 -0.034860 0.014592 \n", - "\n", - " Today \n", - "Year 0.030095 \n", - "Lag1 -0.026155 \n", - "Lag2 -0.010250 \n", - "Lag3 -0.002448 \n", - "Lag4 -0.006900 \n", - "Lag5 -0.034860 \n", - "Volume 0.014592 \n", - "Today 1.000000 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Smarket.corr()\n" + "metadata": {}, + "outputs": [], + "source": [ + "Smarket.corr()" ] }, { @@ -547,36 +147,17 @@ "today’s return are close to zero. The only substantial correlation is between `Year` and\n", " `Volume`. By plotting the data we see that `Volume`\n", "is increasing over time. In other words, the average number of shares traded\n", - "daily increased from 2001 to 2005.\n" + "daily increased from 2001 to 2005." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "9f075144", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.735695Z", - "iopub.status.busy": "2023-07-25T23:59:14.735602Z", - "iopub.status.idle": "2023-07-25T23:59:14.838151Z", - "shell.execute_reply": "2023-07-25T23:59:14.837685Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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LinearDiscriminantAnalysis(store_covariance=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "LinearDiscriminantAnalysis(store_covariance=True)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X_train, X_test = [M.drop(columns=['intercept'])\n", " for M in [X_train, X_test]]\n", - "lda.fit(X_train, L_train)\n" + "lda.fit(X_train, L_train)" ] }, { @@ -1623,32 +678,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "8917085e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:14.996668Z", - "iopub.status.busy": "2023-07-25T23:59:14.996540Z", - "iopub.status.idle": "2023-07-25T23:59:14.999375Z", - "shell.execute_reply": "2023-07-25T23:59:14.999083Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.04279022, 0.03389409],\n", - " [-0.03954635, -0.03132544]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lda.means_\n" + "metadata": {}, + "outputs": [], + "source": [ + "lda.means_" ] }, { @@ -1664,31 +699,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "efb3e9bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.001036Z", - "iopub.status.busy": "2023-07-25T23:59:15.000898Z", - "iopub.status.idle": "2023-07-25T23:59:15.003395Z", - "shell.execute_reply": "2023-07-25T23:59:15.003049Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Down', 'Up'], dtype='\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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QuadraticDiscriminantAnalysis(store_covariance=True)
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" - ], - "text/plain": [ - "QuadraticDiscriminantAnalysis(store_covariance=True)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "qda = QDA(store_covariance=True)\n", - "qda.fit(X_train, L_train)\n" + "qda.fit(X_train, L_train)" ] }, { @@ -2086,32 +915,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "296fdd89", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.041544Z", - "iopub.status.busy": "2023-07-25T23:59:15.041451Z", - "iopub.status.idle": "2023-07-25T23:59:15.043958Z", - "shell.execute_reply": "2023-07-25T23:59:15.043703Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[ 0.04279022, 0.03389409],\n", - " [-0.03954635, -0.03132544]]),\n", - " array([0.49198397, 0.50801603]))" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qda.means_, qda.priors_\n" + "metadata": {}, + "outputs": [], + "source": [ + "qda.means_, qda.priors_" ] }, { @@ -2125,32 +934,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "7fbbefb0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.046394Z", - "iopub.status.busy": "2023-07-25T23:59:15.046278Z", - "iopub.status.idle": "2023-07-25T23:59:15.048574Z", - "shell.execute_reply": "2023-07-25T23:59:15.048306Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1.50662277, -0.03924806],\n", - " [-0.03924806, 1.53559498]])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qda.covariance_[0]\n" + "metadata": {}, + "outputs": [], + "source": [ + "qda.covariance_[0]" ] }, { @@ -2167,78 +956,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "03094dcd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.050232Z", - "iopub.status.busy": "2023-07-25T23:59:15.050116Z", - "iopub.status.idle": "2023-07-25T23:59:15.054828Z", - "shell.execute_reply": "2023-07-25T23:59:15.054541Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GaussianNB()" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "NB = GaussianNB()\n", - "NB.fit(X_train, L_train)\n" + "NB.fit(X_train, L_train)" ] }, { @@ -2349,31 +1032,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "8e860cc3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.065952Z", - "iopub.status.busy": "2023-07-25T23:59:15.065853Z", - "iopub.status.idle": "2023-07-25T23:59:15.068116Z", - "shell.execute_reply": "2023-07-25T23:59:15.067830Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Down', 'Up'], dtype='\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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"execution_count": 49, + "execution_count": null, "id": "8ec72efc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.118824Z", - "iopub.status.busy": "2023-07-25T23:59:15.118699Z", - "iopub.status.idle": "2023-07-25T23:59:15.125616Z", - "shell.execute_reply": "2023-07-25T23:59:15.125343Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5317460317460317" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "knn3 = KNeighborsClassifier(n_neighbors=3)\n", "knn3_pred = knn3.fit(X_train, L_train).predict(X_test)\n", @@ -2895,35 +1271,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "98fef4ed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.127395Z", - "iopub.status.busy": "2023-07-25T23:59:15.127266Z", - "iopub.status.idle": "2023-07-25T23:59:15.145746Z", - "shell.execute_reply": "2023-07-25T23:59:15.145440Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "No 5474\n", - "Yes 348\n", - "Name: Purchase, dtype: int64" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Caravan = load_data('Caravan')\n", "Purchase = Caravan.Purchase\n", - "Purchase.value_counts()\n" + "Purchase.value_counts()" ] }, { @@ -2939,31 +1294,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "1af72c0b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.147502Z", - "iopub.status.busy": "2023-07-25T23:59:15.147373Z", - "iopub.status.idle": "2023-07-25T23:59:15.149578Z", - "shell.execute_reply": "2023-07-25T23:59:15.149324Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.05977327378907592" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "348 / 5822\n" + "metadata": {}, + "outputs": [], + "source": [ + "348 / 5822" ] }, { @@ -2976,19 +1312,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "9ea841e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.151176Z", - "iopub.status.busy": "2023-07-25T23:59:15.151061Z", - "iopub.status.idle": "2023-07-25T23:59:15.153606Z", - "shell.execute_reply": "2023-07-25T23:59:15.153341Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "feature_df = Caravan.drop(columns=['Purchase'])\n" + "feature_df = Caravan.drop(columns=['Purchase'])" ] }, { @@ -3025,22 +1354,14 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "6e26b14f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.155200Z", - "iopub.status.busy": "2023-07-25T23:59:15.155088Z", - "iopub.status.idle": "2023-07-25T23:59:15.156876Z", - "shell.execute_reply": "2023-07-25T23:59:15.156624Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler(with_mean=True,\n", " with_std=True,\n", - " copy=True)\n" + " copy=True)" ] }, { @@ -3064,21 +1385,13 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "bd178f38", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.158484Z", - "iopub.status.busy": "2023-07-25T23:59:15.158369Z", - "iopub.status.idle": "2023-07-25T23:59:15.164862Z", - "shell.execute_reply": "2023-07-25T23:59:15.164529Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "scaler.fit(feature_df)\n", - "X_std = scaler.transform(feature_df)\n" + "X_std = scaler.transform(feature_df)" ] }, { @@ -3092,44 +1405,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "18929b37", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.166628Z", - "iopub.status.busy": "2023-07-25T23:59:15.166522Z", - "iopub.status.idle": "2023-07-25T23:59:15.172686Z", - "shell.execute_reply": "2023-07-25T23:59:15.172394Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "MOSTYPE 1.000086\n", - "MAANTHUI 1.000086\n", - "MGEMOMV 1.000086\n", - "MGEMLEEF 1.000086\n", - "MOSHOOFD 1.000086\n", - " ... \n", - "AZEILPL 1.000086\n", - "APLEZIER 1.000086\n", - "AFIETS 1.000086\n", - "AINBOED 1.000086\n", - "ABYSTAND 1.000086\n", - "Length: 85, dtype: float64" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "feature_std = pd.DataFrame(\n", " X_std,\n", " columns=feature_df.columns);\n", - "feature_std.std()\n" + "feature_std.std()" ] }, { @@ -3148,17 +1432,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "9d439d0b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.174341Z", - "iopub.status.busy": "2023-07-25T23:59:15.174217Z", - "iopub.status.idle": "2023-07-25T23:59:15.178004Z", - "shell.execute_reply": "2023-07-25T23:59:15.177680Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "(X_train,\n", @@ -3167,7 +1443,7 @@ " y_test) = train_test_split(feature_std,\n", " Purchase,\n", " test_size=1000,\n", - " random_state=0)\n" + " random_state=0)" ] }, { @@ -3182,33 +1458,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "9b8c0171", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.179661Z", - "iopub.status.busy": "2023-07-25T23:59:15.179563Z", - "iopub.status.idle": "2023-07-25T23:59:15.228740Z", - "shell.execute_reply": "2023-07-25T23:59:15.228255Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.111, 0.067)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "knn1 = KNeighborsClassifier(n_neighbors=1)\n", "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n", - "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")\n" + "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")" ] }, { @@ -3235,76 +1492,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "f31b6e26", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.231007Z", - "iopub.status.busy": "2023-07-25T23:59:15.230866Z", - "iopub.status.idle": "2023-07-25T23:59:15.236660Z", - "shell.execute_reply": "2023-07-25T23:59:15.236308Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Truth No Yes\n", - "Predicted \n", - "No 880 58\n", - "Yes 53 9" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_table(knn1_pred, y_test)\n" + "metadata": {}, + "outputs": [], + "source": [ + "confusion_table(knn1_pred, y_test)" ] }, { @@ -3320,29 +1513,10 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "4a8c37aa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.238783Z", - "iopub.status.busy": "2023-07-25T23:59:15.238652Z", - "iopub.status.idle": "2023-07-25T23:59:15.240970Z", - "shell.execute_reply": "2023-07-25T23:59:15.240677Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.14516129032258066" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "9/(53+9)" ] @@ -3364,30 +1538,10 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "153662f8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.243142Z", - "iopub.status.busy": "2023-07-25T23:59:15.242998Z", - "iopub.status.idle": "2023-07-25T23:59:15.361575Z", - "shell.execute_reply": "2023-07-25T23:59:15.361121Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "K=1: # predicted to rent: 62, # who did rent 9, accuracy 14.5%\n", - "K=2: # predicted to rent: 6, # who did rent 1, accuracy 16.7%\n", - "K=3: # predicted to rent: 20, # who did rent 3, accuracy 15.0%\n", - "K=4: # predicted to rent: 4, # who did rent 0, accuracy 0.0%\n", - "K=5: # predicted to rent: 7, # who did rent 1, accuracy 14.3%\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "for K in range(1,6):\n", " knn = KNeighborsClassifier(n_neighbors=K)\n", @@ -3401,7 +1555,7 @@ " K,\n", " pred,\n", " did_rent,\n", - " did_rent / pred))\n" + " did_rent / pred))" ] }, { @@ -3431,80 +1585,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "55b99a3f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:15.363863Z", - "iopub.status.busy": "2023-07-25T23:59:15.363724Z", - "iopub.status.idle": "2023-07-25T23:59:15.977575Z", - "shell.execute_reply": "2023-07-25T23:59:15.976720Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept-27.20686.715-4.0520.000
mnth[Aug]11.81814.6982.5150.012
mnth[Dec]5.03284.2801.1760.240
mnth[Feb]-34.57974.575-7.5580.000
mnth[Jan]-41.42494.972-8.3310.000
mnth[July]3.89965.0030.7790.436
mnth[June]26.39384.6425.6860.000
mnth[March]-24.87354.277-5.8150.000
mnth[May]31.13224.1507.5010.000
mnth[Nov]18.88514.0994.6070.000
mnth[Oct]34.40934.0068.5890.000
mnth[Sept]25.25344.2935.8830.000
hr[1]-14.57935.699-2.5580.011
hr[2]-21.57915.733-3.7640.000
hr[3]-31.14085.778-5.3890.000
hr[4]-36.90755.802-6.3610.000
hr[5]-24.13555.737-4.2070.000
hr[6]20.59975.7043.6120.000
hr[7]120.09315.69321.0950.000
hr[8]223.66195.69039.3100.000
hr[9]120.58195.69321.1820.000
hr[10]83.80135.70514.6890.000
hr[11]105.42345.72218.4240.000
hr[12]137.28375.74023.9160.000
hr[13]136.03595.76023.6170.000
hr[14]126.63615.77621.9230.000
hr[15]132.08655.78022.8520.000
hr[16]178.52065.77230.9270.000
hr[17]296.26705.74951.5370.000
hr[18]269.44095.73646.9760.000
hr[19]186.25585.71432.5960.000
hr[20]125.54925.70422.0120.000
hr[21]87.55375.69315.3780.000
hr[22]59.12265.68910.3920.000
hr[23]26.83765.6884.7190.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept -27.2068 6.715 -4.052 0.000\n", - "mnth[Aug] 11.8181 4.698 2.515 0.012\n", - "mnth[Dec] 5.0328 4.280 1.176 0.240\n", - "mnth[Feb] -34.5797 4.575 -7.558 0.000\n", - "mnth[Jan] -41.4249 4.972 -8.331 0.000\n", - "mnth[July] 3.8996 5.003 0.779 0.436\n", - "mnth[June] 26.3938 4.642 5.686 0.000\n", - "mnth[March] -24.8735 4.277 -5.815 0.000\n", - "mnth[May] 31.1322 4.150 7.501 0.000\n", - "mnth[Nov] 18.8851 4.099 4.607 0.000\n", - "mnth[Oct] 34.4093 4.006 8.589 0.000\n", - "mnth[Sept] 25.2534 4.293 5.883 0.000\n", - "hr[1] -14.5793 5.699 -2.558 0.011\n", - "hr[2] -21.5791 5.733 -3.764 0.000\n", - "hr[3] -31.1408 5.778 -5.389 0.000\n", - "hr[4] -36.9075 5.802 -6.361 0.000\n", - "hr[5] -24.1355 5.737 -4.207 0.000\n", - "hr[6] 20.5997 5.704 3.612 0.000\n", - "hr[7] 120.0931 5.693 21.095 0.000\n", - "hr[8] 223.6619 5.690 39.310 0.000\n", - "hr[9] 120.5819 5.693 21.182 0.000\n", - "hr[10] 83.8013 5.705 14.689 0.000\n", - "hr[11] 105.4234 5.722 18.424 0.000\n", - "hr[12] 137.2837 5.740 23.916 0.000\n", - "hr[13] 136.0359 5.760 23.617 0.000\n", - "hr[14] 126.6361 5.776 21.923 0.000\n", - "hr[15] 132.0865 5.780 22.852 0.000\n", - "hr[16] 178.5206 5.772 30.927 0.000\n", - "hr[17] 296.2670 5.749 51.537 0.000\n", - "hr[18] 269.4409 5.736 46.976 0.000\n", - "hr[19] 186.2558 5.714 32.596 0.000\n", - "hr[20] 125.5492 5.704 22.012 0.000\n", - "hr[21] 87.5537 5.693 15.378 0.000\n", - "hr[22] 59.1226 5.689 10.392 0.000\n", - "hr[23] 26.8376 5.688 4.719 0.000\n", - "workingday 1.2696 1.784 0.711 0.477\n", - "temp 157.2094 10.261 15.321 0.000\n", - "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", - "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", - "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = MS(['mnth',\n", " 'hr',\n", @@ -4091,7 +1700,7 @@ " 'weathersit']).fit_transform(Bike)\n", "Y = Bike['bikers']\n", "M_lm = sm.OLS(Y, X).fit()\n", - "summarize(M_lm)\n" + "summarize(M_lm)" ] }, { @@ -4115,21 +1724,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "064b24d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.142403Z", - "iopub.status.busy": "2023-07-25T23:59:16.141745Z", - "iopub.status.idle": "2023-07-25T23:59:16.146332Z", - "shell.execute_reply": "2023-07-25T23:59:16.145086Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "hr_encode = contrast('hr', 'sum')\n", - "mnth_encode = contrast('mnth', 'sum')\n" + "mnth_encode = contrast('mnth', 'sum')" ] }, { @@ -4142,378 +1743,10 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "27c1aa54", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.150801Z", - "iopub.status.busy": "2023-07-25T23:59:16.150164Z", - "iopub.status.idle": "2023-07-25T23:59:16.223980Z", - "shell.execute_reply": "2023-07-25T23:59:16.222963Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept73.59745.13214.3400.000
mnth[Jan]-46.08714.085-11.2810.000
mnth[Feb]-39.24193.539-11.0880.000
mnth[March]-29.53573.155-9.3610.000
mnth[April]-4.66222.741-1.7010.089
mnth[May]26.47002.8519.2850.000
mnth[June]21.73173.4656.2720.000
mnth[July]-0.76263.908-0.1950.845
mnth[Aug]7.15603.5352.0240.043
mnth[Sept]20.59123.0466.7610.000
mnth[Oct]29.74722.70011.0190.000
mnth[Nov]14.22292.8604.9720.000
hr[0]-96.14203.955-24.3070.000
hr[1]-110.72133.966-27.9160.000
hr[2]-117.72124.016-29.3100.000
hr[3]-127.28284.081-31.1910.000
hr[4]-133.04954.117-32.3190.000
hr[5]-120.27754.037-29.7940.000
hr[6]-75.54243.992-18.9250.000
hr[7]23.95113.9696.0350.000
hr[8]127.51993.95032.2840.000
hr[9]24.43993.9366.2090.000
hr[10]-12.34073.936-3.1350.002
hr[11]9.28143.9452.3530.019
hr[12]41.14173.95710.3970.000
hr[13]39.89393.97510.0360.000
hr[14]30.49403.9917.6410.000
hr[15]35.94453.9958.9980.000
hr[16]82.37863.98820.6550.000
hr[17]200.12493.96450.4880.000
hr[18]173.29893.95643.8060.000
hr[19]90.11383.94022.8720.000
hr[20]29.40713.9367.4710.000
hr[21]-8.58833.933-2.1840.029
hr[22]-37.01943.934-9.4090.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 73.5974 5.132 14.340 0.000\n", - "mnth[Jan] -46.0871 4.085 -11.281 0.000\n", - "mnth[Feb] -39.2419 3.539 -11.088 0.000\n", - "mnth[March] -29.5357 3.155 -9.361 0.000\n", - "mnth[April] -4.6622 2.741 -1.701 0.089\n", - "mnth[May] 26.4700 2.851 9.285 0.000\n", - "mnth[June] 21.7317 3.465 6.272 0.000\n", - "mnth[July] -0.7626 3.908 -0.195 0.845\n", - "mnth[Aug] 7.1560 3.535 2.024 0.043\n", - "mnth[Sept] 20.5912 3.046 6.761 0.000\n", - "mnth[Oct] 29.7472 2.700 11.019 0.000\n", - "mnth[Nov] 14.2229 2.860 4.972 0.000\n", - "hr[0] -96.1420 3.955 -24.307 0.000\n", - "hr[1] -110.7213 3.966 -27.916 0.000\n", - "hr[2] -117.7212 4.016 -29.310 0.000\n", - "hr[3] -127.2828 4.081 -31.191 0.000\n", - "hr[4] -133.0495 4.117 -32.319 0.000\n", - "hr[5] -120.2775 4.037 -29.794 0.000\n", - "hr[6] -75.5424 3.992 -18.925 0.000\n", - "hr[7] 23.9511 3.969 6.035 0.000\n", - "hr[8] 127.5199 3.950 32.284 0.000\n", - "hr[9] 24.4399 3.936 6.209 0.000\n", - "hr[10] -12.3407 3.936 -3.135 0.002\n", - "hr[11] 9.2814 3.945 2.353 0.019\n", - "hr[12] 41.1417 3.957 10.397 0.000\n", - "hr[13] 39.8939 3.975 10.036 0.000\n", - "hr[14] 30.4940 3.991 7.641 0.000\n", - "hr[15] 35.9445 3.995 8.998 0.000\n", - "hr[16] 82.3786 3.988 20.655 0.000\n", - "hr[17] 200.1249 3.964 50.488 0.000\n", - "hr[18] 173.2989 3.956 43.806 0.000\n", - "hr[19] 90.1138 3.940 22.872 0.000\n", - "hr[20] 29.4071 3.936 7.471 0.000\n", - "hr[21] -8.5883 3.933 -2.184 0.029\n", - "hr[22] -37.0194 3.934 -9.409 0.000\n", - "workingday 1.2696 1.784 0.711 0.477\n", - "temp 157.2094 10.261 15.321 0.000\n", - "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", - "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", - "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X2 = MS([mnth_encode,\n", " hr_encode,\n", @@ -4552,30 +1785,12 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "dd0114c8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.228972Z", - "iopub.status.busy": "2023-07-25T23:59:16.228601Z", - "iopub.status.idle": "2023-07-25T23:59:16.238595Z", - "shell.execute_reply": "2023-07-25T23:59:16.237798Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.090076132857841e-19" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)" ] }, { @@ -4589,31 +1804,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "292585a1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.243655Z", - "iopub.status.busy": "2023-07-25T23:59:16.243153Z", - "iopub.status.idle": "2023-07-25T23:59:16.252786Z", - "shell.execute_reply": "2023-07-25T23:59:16.252026Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n" + "metadata": {}, + "outputs": [], + "source": [ + "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)" ] }, { @@ -4632,43 +1828,13 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "e05e0575", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.257683Z", - "iopub.status.busy": "2023-07-25T23:59:16.257311Z", - "iopub.status.idle": "2023-07-25T23:59:16.267187Z", - "shell.execute_reply": "2023-07-25T23:59:16.266452Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.0871\n", - "mnth[Feb] -39.2419\n", - "mnth[March] -29.5357\n", - "mnth[April] -4.6622\n", - "mnth[May] 26.4700\n", - "mnth[June] 21.7317\n", - "mnth[July] -0.7626\n", - "mnth[Aug] 7.1560\n", - "mnth[Sept] 20.5912\n", - "mnth[Oct] 29.7472\n", - "mnth[Nov] 14.2229\n", - "Name: coef, dtype: float64" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", - "coef_month\n" + "coef_month" ] }, { @@ -4681,41 +1847,10 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "9f04a25e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.272068Z", - "iopub.status.busy": "2023-07-25T23:59:16.271356Z", - "iopub.status.idle": "2023-07-25T23:59:16.283167Z", - "shell.execute_reply": "2023-07-25T23:59:16.282005Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.0871\n", - "mnth[Feb] -39.2419\n", - "mnth[March] -29.5357\n", - "mnth[April] -4.6622\n", - "mnth[May] 26.4700\n", - "mnth[June] 21.7317\n", - "mnth[July] -0.7626\n", - "mnth[Aug] 7.1560\n", - "mnth[Sept] 20.5912\n", - "mnth[Oct] 29.7472\n", - "mnth[Nov] 14.2229\n", - "mnth[Dec] 0.3705\n", - "dtype: float64" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "months = Bike['mnth'].dtype.categories\n", "coef_month = pd.concat([\n", @@ -4724,7 +1859,7 @@ " index=['mnth[Dec]'\n", " ])\n", " ])\n", - "coef_month\n" + "coef_month" ] }, { @@ -4738,28 +1873,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "20c7c054", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.292541Z", - "iopub.status.busy": "2023-07-25T23:59:16.292061Z", - "iopub.status.idle": "2023-07-25T23:59:16.444617Z", - "shell.execute_reply": "2023-07-25T23:59:16.443792Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig_month, ax_month = subplots(figsize=(8,8))\n", "x_month = np.arange(coef_month.shape[0])\n", @@ -4767,7 +1884,7 @@ "ax_month.set_xticks(x_month)\n", "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20);\n" + "ax_month.set_ylabel('Coefficient', fontsize=20);" ] }, { @@ -4780,23 +1897,16 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "3f83f4bf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.449734Z", - "iopub.status.busy": "2023-07-25T23:59:16.449373Z", - "iopub.status.idle": "2023-07-25T23:59:16.458325Z", - "shell.execute_reply": "2023-07-25T23:59:16.456791Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n", "coef_hr = pd.concat([coef_hr,\n", " pd.Series([-coef_hr.sum()], index=['hr[23]'])\n", - " ])\n" + " ])" ] }, { @@ -4809,28 +1919,10 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "86e9a741", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.463694Z", - "iopub.status.busy": "2023-07-25T23:59:16.463309Z", - "iopub.status.idle": "2023-07-25T23:59:16.589630Z", - "shell.execute_reply": "2023-07-25T23:59:16.589275Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig_hr, ax_hr = subplots(figsize=(8,8))\n", "x_hr = np.arange(coef_hr.shape[0])\n", @@ -4838,7 +1930,7 @@ "ax_hr.set_xticks(x_hr[::2])\n", "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" + "ax_hr.set_ylabel('Coefficient', fontsize=20);" ] }, { @@ -4855,19 +1947,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "a9448278", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.591638Z", - "iopub.status.busy": "2023-07-25T23:59:16.591489Z", - "iopub.status.idle": "2023-07-25T23:59:16.661619Z", - "shell.execute_reply": "2023-07-25T23:59:16.660835Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n" + "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()" ] }, { @@ -4880,17 +1965,9 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "ec8505be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.666429Z", - "iopub.status.busy": "2023-07-25T23:59:16.666119Z", - "iopub.status.idle": "2023-07-25T23:59:16.691878Z", - "shell.execute_reply": "2023-07-25T23:59:16.690343Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "S_pois = summarize(M_pois)\n", @@ -4914,37 +1991,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "0b8a5edf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.696339Z", - "iopub.status.busy": "2023-07-25T23:59:16.696023Z", - "iopub.status.idle": "2023-07-25T23:59:16.992754Z", - "shell.execute_reply": "2023-07-25T23:59:16.992369Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_33380/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", @@ -4955,7 +2005,7 @@ "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" + "ax_hr.set_ylabel('Coefficient', fontsize=20);" ] }, { @@ -4971,28 +2021,10 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "d487c7ed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:16.994758Z", - "iopub.status.busy": "2023-07-25T23:59:16.994651Z", - "iopub.status.idle": "2023-07-25T23:59:17.107603Z", - "shell.execute_reply": "2023-07-25T23:59:17.107255Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax.scatter(M2_lm.fittedvalues,\n", @@ -5001,7 +2033,7 @@ "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n", "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n", "ax.axline([0,0], c='black', linewidth=3,\n", - " linestyle='--', slope=1);\n" + " linestyle='--', slope=1);" ] }, { @@ -5027,20 +2059,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch5-resample-lab.ipynb b/docs/source/labs/Ch5-resample-lab.ipynb index 86162e6..8d35e84 100644 --- a/docs/source/labs/Ch5-resample-lab.ipynb +++ b/docs/source/labs/Ch5-resample-lab.ipynb @@ -2,19 +2,16 @@ "cells": [ { "cell_type": "markdown", - "id": "0c11a71f", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ad36451e", + "id": "3a3f2f85", "metadata": {}, "source": [ "# Cross-Validation and the Bootstrap\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", "In this lab, we explore the resampling techniques covered in this\n", "chapter. Some of the commands in this lab may take a while to run on\n", "your computer.\n", @@ -24,17 +21,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "60fad148", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:18.516196Z", - "iopub.status.busy": "2023-07-25T23:59:18.515741Z", - "iopub.status.idle": "2023-07-25T23:59:19.379955Z", - "shell.execute_reply": "2023-07-25T23:59:19.379486Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -43,7 +32,7 @@ "from ISLP.models import (ModelSpec as MS,\n", " summarize,\n", " poly)\n", - "from sklearn.model_selection import train_test_split\n" + "from sklearn.model_selection import train_test_split" ] }, { @@ -56,17 +45,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2478aeb4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.382153Z", - "iopub.status.busy": "2023-07-25T23:59:19.381970Z", - "iopub.status.idle": "2023-07-25T23:59:19.384142Z", - "shell.execute_reply": "2023-07-25T23:59:19.383878Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "from functools import partial\n", @@ -75,7 +56,7 @@ " KFold,\n", " ShuffleSplit)\n", "from sklearn.base import clone\n", - "from ISLP.models import sklearn_sm\n" + "from ISLP.models import sklearn_sm" ] }, { @@ -95,27 +76,20 @@ "when performing operations like this that contain an\n", "element of randomness, so that the results obtained can be reproduced\n", "precisely at a later time. We set the random seed of the splitter\n", - "with the argument `random_state=0`. " + "with the argument `random_state=0`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "99c95faf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.385847Z", - "iopub.status.busy": "2023-07-25T23:59:19.385721Z", - "iopub.status.idle": "2023-07-25T23:59:19.391245Z", - "shell.execute_reply": "2023-07-25T23:59:19.390937Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "Auto = load_data('Auto')\n", "Auto_train, Auto_valid = train_test_split(Auto,\n", " test_size=196,\n", - " random_state=0)\n" + " random_state=0)" ] }, { @@ -128,23 +102,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "41b0717d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.393063Z", - "iopub.status.busy": "2023-07-25T23:59:19.392958Z", - "iopub.status.idle": "2023-07-25T23:59:19.396896Z", - "shell.execute_reply": "2023-07-25T23:59:19.396495Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "hp_mm = MS(['horsepower'])\n", "X_train = hp_mm.fit_transform(Auto_train)\n", "y_train = Auto_train['mpg']\n", "model = sm.OLS(y_train, X_train)\n", - "results = model.fit()\n" + "results = model.fit()" ] }, { @@ -158,33 +125,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d7ea3c0d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.398586Z", - "iopub.status.busy": "2023-07-25T23:59:19.398485Z", - "iopub.status.idle": "2023-07-25T23:59:19.403011Z", - "shell.execute_reply": "2023-07-25T23:59:19.402741Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "23.61661706966988" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X_valid = hp_mm.transform(Auto_valid)\n", "y_valid = Auto_valid['mpg']\n", "valid_pred = results.predict(X_valid)\n", - "np.mean((y_valid - valid_pred)**2)\n" + "np.mean((y_valid - valid_pred)**2)" ] }, { @@ -202,16 +151,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "a02a2d05", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.404921Z", - "iopub.status.busy": "2023-07-25T23:59:19.404788Z", - "iopub.status.idle": "2023-07-25T23:59:19.408021Z", - "shell.execute_reply": "2023-07-25T23:59:19.407664Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def evalMSE(terms,\n", @@ -229,7 +171,7 @@ " results = sm.OLS(y_train, X_train).fit()\n", " test_pred = results.predict(X_test)\n", "\n", - " return np.mean((y_test - test_pred)**2)\n" + " return np.mean((y_test - test_pred)**2)" ] }, { @@ -245,28 +187,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "51d93dea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.409619Z", - "iopub.status.busy": "2023-07-25T23:59:19.409514Z", - "iopub.status.idle": "2023-07-25T23:59:19.420772Z", - "shell.execute_reply": "2023-07-25T23:59:19.420495Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([23.61661707, 18.76303135, 18.79694163])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "MSE = np.zeros(3)\n", "for idx, degree in enumerate(range(1, 4)):\n", @@ -274,7 +198,7 @@ " 'mpg',\n", " Auto_train,\n", " Auto_valid)\n", - "MSE\n" + "MSE" ] }, { @@ -289,28 +213,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "83432f06", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.422551Z", - "iopub.status.busy": "2023-07-25T23:59:19.422418Z", - "iopub.status.idle": "2023-07-25T23:59:19.433976Z", - "shell.execute_reply": "2023-07-25T23:59:19.433683Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([20.75540796, 16.94510676, 16.97437833])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Auto_train, Auto_valid = train_test_split(Auto,\n", " test_size=196,\n", @@ -372,29 +278,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "bcfc433f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:19.435742Z", - "iopub.status.busy": "2023-07-25T23:59:19.435616Z", - "iopub.status.idle": "2023-07-25T23:59:20.107186Z", - "shell.execute_reply": "2023-07-25T23:59:20.106844Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "24.23151351792924" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hp_model = sklearn_sm(sm.OLS,\n", " MS(['horsepower']))\n", @@ -404,7 +291,7 @@ " Y,\n", " cv=Auto.shape[0])\n", "cv_err = np.mean(cv_results['test_score'])\n", - "cv_err\n" + "cv_err" ] }, { @@ -440,29 +327,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "f951ffc8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.109173Z", - "iopub.status.busy": "2023-07-25T23:59:20.109038Z", - "iopub.status.idle": "2023-07-25T23:59:20.813810Z", - "shell.execute_reply": "2023-07-25T23:59:20.813473Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([24.23151352, 19.24821312, 19.33498406, 19.42443033, 19.03323827])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cv_error = np.zeros(5)\n", "H = np.array(Auto['horsepower'])\n", @@ -474,7 +342,7 @@ " Y,\n", " cv=Auto.shape[0])\n", " cv_error[i] = np.mean(M_CV['test_score'])\n", - "cv_error\n" + "cv_error" ] }, { @@ -492,39 +360,19 @@ "It has two arrays as\n", "arguments, and then forms a larger\n", "array where the operation is applied to each pair of elements of the\n", - "two arrays. " + "two arrays." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "e3610b5a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.815663Z", - "iopub.status.busy": "2023-07-25T23:59:20.815525Z", - "iopub.status.idle": "2023-07-25T23:59:20.818253Z", - "shell.execute_reply": "2023-07-25T23:59:20.817958Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 5, 7],\n", - " [ 7, 9],\n", - " [11, 13]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "A = np.array([3, 5, 9])\n", "B = np.array([2, 4])\n", - "np.add.outer(A, B)\n" + "np.add.outer(A, B)" ] }, { @@ -539,29 +387,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "1627460d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.819903Z", - "iopub.status.busy": "2023-07-25T23:59:20.819780Z", - "iopub.status.idle": "2023-07-25T23:59:20.842970Z", - "shell.execute_reply": "2023-07-25T23:59:20.842686Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([24.20766449, 19.18533142, 19.27626666, 19.47848403, 19.13720581])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cv_error = np.zeros(5)\n", "cv = KFold(n_splits=10,\n", @@ -574,7 +403,7 @@ " Y,\n", " cv=cv)\n", " cv_error[i] = np.mean(M_CV['test_score'])\n", - "cv_error\n" + "cv_error" ] }, { @@ -604,29 +433,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "8a636468", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.844695Z", - "iopub.status.busy": "2023-07-25T23:59:20.844573Z", - "iopub.status.idle": "2023-07-25T23:59:20.850336Z", - "shell.execute_reply": "2023-07-25T23:59:20.850052Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([23.61661707])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "validation = ShuffleSplit(n_splits=1,\n", " test_size=196,\n", @@ -635,7 +445,7 @@ " Auto.drop(['mpg'], axis=1),\n", " Auto['mpg'],\n", " cv=validation);\n", - "results['test_score']\n" + "results['test_score']" ] }, { @@ -648,28 +458,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "746aeccd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.852176Z", - "iopub.status.busy": "2023-07-25T23:59:20.852041Z", - "iopub.status.idle": "2023-07-25T23:59:20.873556Z", - "shell.execute_reply": "2023-07-25T23:59:20.873271Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(23.802232661034168, 1.421845094109185)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "validation = ShuffleSplit(n_splits=10,\n", " test_size=196,\n", @@ -678,7 +470,7 @@ " Auto.drop(['mpg'], axis=1),\n", " Auto['mpg'],\n", " cv=validation)\n", - "results['test_score'].mean(), results['test_score'].std()\n" + "results['test_score'].mean(), results['test_score'].std()" ] }, { @@ -722,24 +514,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "daa53d0c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.875275Z", - "iopub.status.busy": "2023-07-25T23:59:20.875145Z", - "iopub.status.idle": "2023-07-25T23:59:20.878569Z", - "shell.execute_reply": "2023-07-25T23:59:20.878269Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "Portfolio = load_data('Portfolio')\n", "def alpha_func(D, idx):\n", " cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n", " return ((cov_[1,1] - cov_[0,1]) /\n", - " (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n" + " (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))" ] }, { @@ -756,28 +540,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "578c9564", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.880246Z", - "iopub.status.busy": "2023-07-25T23:59:20.880127Z", - "iopub.status.idle": "2023-07-25T23:59:20.883007Z", - "shell.execute_reply": "2023-07-25T23:59:20.882752Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.57583207459283" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "alpha_func(Portfolio, range(100))" ] @@ -795,29 +561,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "5754d6d5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.884729Z", - "iopub.status.busy": "2023-07-25T23:59:20.884605Z", - "iopub.status.idle": "2023-07-25T23:59:20.887853Z", - "shell.execute_reply": "2023-07-25T23:59:20.887569Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6074452469619004" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(0)\n", "alpha_func(Portfolio,\n", @@ -838,17 +585,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "8320a49c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.889568Z", - "iopub.status.busy": "2023-07-25T23:59:20.889470Z", - "iopub.status.idle": "2023-07-25T23:59:20.892197Z", - "shell.execute_reply": "2023-07-25T23:59:20.891914Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def boot_SE(func,\n", @@ -878,39 +617,21 @@ "unimportant and simply makes sure the loop is executed `B` times.\n", "\n", "Let’s use our function to evaluate the accuracy of our\n", - "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications. " + "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "e656aa1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:20.894076Z", - "iopub.status.busy": "2023-07-25T23:59:20.893953Z", - "iopub.status.idle": "2023-07-25T23:59:21.209418Z", - "shell.execute_reply": "2023-07-25T23:59:21.209114Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.09118176521277699" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "alpha_SE = boot_SE(alpha_func,\n", " Portfolio,\n", " B=1000,\n", " seed=0)\n", - "alpha_SE\n" + "alpha_SE" ] }, { @@ -949,17 +670,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "c5d14195", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:21.211286Z", - "iopub.status.busy": "2023-07-25T23:59:21.211158Z", - "iopub.status.idle": "2023-07-25T23:59:21.213339Z", - "shell.execute_reply": "2023-07-25T23:59:21.213042Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def boot_OLS(model_matrix, response, D, idx):\n", @@ -984,20 +697,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "7e0523f0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:21.214975Z", - "iopub.status.busy": "2023-07-25T23:59:21.214858Z", - "iopub.status.idle": "2023-07-25T23:59:21.216686Z", - "shell.execute_reply": "2023-07-25T23:59:21.216408Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n" + "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')" ] }, { @@ -1017,44 +722,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "32836e93", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:21.218240Z", - "iopub.status.busy": "2023-07-25T23:59:21.218130Z", - "iopub.status.idle": "2023-07-25T23:59:21.234117Z", - "shell.execute_reply": "2023-07-25T23:59:21.233834Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[39.88064456, -0.1567849 ],\n", - " [38.73298691, -0.14699495],\n", - " [38.31734657, -0.14442683],\n", - " [39.91446826, -0.15782234],\n", - " [39.43349349, -0.15072702],\n", - " [40.36629857, -0.15912217],\n", - " [39.62334517, -0.15449117],\n", - " [39.0580588 , -0.14952908],\n", - " [38.66688437, -0.14521037],\n", - " [39.64280792, -0.15555698]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(0)\n", "np.array([hp_func(Auto,\n", " rng.choice(392,\n", " 392,\n", - " replace=True)) for _ in range(10)])\n" + " replace=True)) for _ in range(10)])" ] }, { @@ -1068,37 +745,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "14ce3afa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:21.235834Z", - "iopub.status.busy": "2023-07-25T23:59:21.235710Z", - "iopub.status.idle": "2023-07-25T23:59:22.536815Z", - "shell.execute_reply": "2023-07-25T23:59:22.536478Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 0.848807\n", - "horsepower 0.007352\n", - "dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hp_se = boot_SE(hp_func,\n", " Auto,\n", " B=1000,\n", " seed=10)\n", - "hp_se\n" + "hp_se" ] }, { @@ -1118,35 +774,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "6b1213ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:22.538715Z", - "iopub.status.busy": "2023-07-25T23:59:22.538582Z", - "iopub.status.idle": "2023-07-25T23:59:22.627465Z", - "shell.execute_reply": "2023-07-25T23:59:22.627170Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 0.717\n", - "horsepower 0.006\n", - "Name: std err, dtype: float64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hp_model.fit(Auto, Auto['mpg'])\n", "model_se = summarize(hp_model.results_)['std err']\n", - "model_se\n" + "model_se" ] }, { @@ -1190,37 +825,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "af99b778", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:22.629295Z", - "iopub.status.busy": "2023-07-25T23:59:22.629160Z", - "iopub.status.idle": "2023-07-25T23:59:24.704262Z", - "shell.execute_reply": "2023-07-25T23:59:24.703883Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 2.067840\n", - "poly(horsepower, degree=2, raw=True)[0] 0.033019\n", - "poly(horsepower, degree=2, raw=True)[1] 0.000120\n", - "dtype: float64" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "quad_model = MS([poly('horsepower', 2, raw=True)])\n", "quad_func = partial(boot_OLS,\n", " quad_model,\n", " 'mpg')\n", - "boot_SE(quad_func, Auto, B=1000)\n" + "boot_SE(quad_func, Auto, B=1000)" ] }, { @@ -1233,55 +847,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "0206281e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:24.706236Z", - "iopub.status.busy": "2023-07-25T23:59:24.706087Z", - "iopub.status.idle": "2023-07-25T23:59:24.716168Z", - "shell.execute_reply": "2023-07-25T23:59:24.715845Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "intercept 1.800\n", - "poly(horsepower, degree=2, raw=True)[0] 0.031\n", - "poly(horsepower, degree=2, raw=True)[1] 0.000\n", - "Name: std err, dtype: float64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "M = sm.OLS(Auto['mpg'],\n", " quad_model.fit_transform(Auto))\n", - "summarize(M.fit())['std err']\n" + "summarize(M.fit())['std err']" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch6-varselect-lab.ipynb b/docs/source/labs/Ch6-varselect-lab.ipynb index 925821d..7d79f3a 100644 --- a/docs/source/labs/Ch6-varselect-lab.ipynb +++ b/docs/source/labs/Ch6-varselect-lab.ipynb @@ -2,33 +2,53 @@ "cells": [ { "cell_type": "markdown", - "id": "cf2eba05", + "id": "1e9ee9d8", "metadata": {}, - "source": [] + "source": [ + "# Linear Models and Regularization Methods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "394db52b", + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.simplefilter('ignore') " + ] }, { "cell_type": "markdown", - "id": "6e1cafa3", + "id": "98c1f26d", "metadata": {}, "source": [ - "# Linear Models and Regularization Methods\n", + "```{attention}\n", + "Using `skl.ElasticNet` to fit ridge regression\n", + "throws up many warnings. We have suppressed them below.\n", + "```\n", + "\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", "In this lab we implement many of the techniques discussed in this chapter.\n", "We import some of our libraries at this top\n", - "level. " + "level." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "564883b2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:25.902673Z", - "iopub.status.busy": "2023-07-25T23:59:25.902240Z", - "iopub.status.idle": "2023-07-25T23:59:26.930903Z", - "shell.execute_reply": "2023-07-25T23:59:26.930443Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -40,7 +60,7 @@ "from sklearn.preprocessing import StandardScaler\n", "from ISLP import load_data\n", "from ISLP.models import ModelSpec as MS\n", - "from functools import partial\n" + "from functools import partial" ] }, { @@ -54,32 +74,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9aa8d3ee", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:26.933137Z", - "iopub.status.busy": "2023-07-25T23:59:26.932936Z", - "iopub.status.idle": "2023-07-25T23:59:28.365522Z", - "shell.execute_reply": "2023-07-25T23:59:28.365174Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: l0bnb in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (1.0.0)\r\n", - "Requirement already satisfied: numpy>=1.18.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.21.6)\r\n", - "Requirement already satisfied: scipy>=1.4.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.10.1)\r\n", - "Requirement already satisfied: numba>=0.53.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (0.57.1)\r\n", - "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from numba>=0.53.1->l0bnb) (0.40.1)\r\n", - "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\r\n", - "\u001b[0m" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.decomposition import PCA\n", @@ -89,7 +87,7 @@ " sklearn_selected,\n", " sklearn_selection_path)\n", "!pip install l0bnb\n", - "from l0bnb import fit_path\n" + "from l0bnb import fit_path" ] }, { @@ -120,31 +118,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "05371e33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.367608Z", - "iopub.status.busy": "2023-07-25T23:59:28.367471Z", - "iopub.status.idle": "2023-07-25T23:59:28.374856Z", - "shell.execute_reply": "2023-07-25T23:59:28.374563Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "59" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Hitters = load_data('Hitters')\n", - "np.isnan(Hitters['Salary']).sum()\n" + "np.isnan(Hitters['Salary']).sum()" ] }, { @@ -159,32 +139,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "aa5bc5b8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.376501Z", - "iopub.status.busy": "2023-07-25T23:59:28.376391Z", - "iopub.status.idle": "2023-07-25T23:59:28.379413Z", - "shell.execute_reply": "2023-07-25T23:59:28.379133Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(263, 20)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Hitters = Hitters.dropna();\n", - "Hitters.shape\n" + "Hitters.shape" ] }, { @@ -200,17 +161,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "10494837", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.381043Z", - "iopub.status.busy": "2023-07-25T23:59:28.380935Z", - "iopub.status.idle": "2023-07-25T23:59:28.383115Z", - "shell.execute_reply": "2023-07-25T23:59:28.382819Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def nCp(sigma2, estimator, X, Y):\n", @@ -218,7 +171,7 @@ " n, p = X.shape\n", " Yhat = estimator.predict(X)\n", " RSS = np.sum((Y - Yhat)**2)\n", - " return -(RSS + 2 * p * sigma2) / n \n" + " return -(RSS + 2 * p * sigma2) / n " ] }, { @@ -232,22 +185,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "b8173cef", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.384748Z", - "iopub.status.busy": "2023-07-25T23:59:28.384661Z", - "iopub.status.idle": "2023-07-25T23:59:28.397967Z", - "shell.execute_reply": "2023-07-25T23:59:28.397658Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n", "Y = np.array(Hitters['Salary'])\n", "X = design.transform(Hitters)\n", - "sigma2 = OLS(Y,X).fit().scale\n" + "sigma2 = OLS(Y,X).fit().scale" ] }, { @@ -260,20 +206,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0cc8d7bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.399611Z", - "iopub.status.busy": "2023-07-25T23:59:28.399511Z", - "iopub.status.idle": "2023-07-25T23:59:28.401406Z", - "shell.execute_reply": "2023-07-25T23:59:28.401144Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ - "neg_Cp = partial(nCp, sigma2)\n" + "neg_Cp = partial(nCp, sigma2)" ] }, { @@ -281,7 +219,7 @@ "id": "3ff5a859", "metadata": {}, "source": [ - "We can now use `neg_Cp()` as a scorer for model selection.\n" + "We can now use `neg_Cp()` as a scorer for model selection." ] }, { @@ -298,21 +236,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "440b4c50", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.403063Z", - "iopub.status.busy": "2023-07-25T23:59:28.402971Z", - "iopub.status.idle": "2023-07-25T23:59:28.404782Z", - "shell.execute_reply": "2023-07-25T23:59:28.404512Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "strategy = Stepwise.first_peak(design,\n", " direction='forward',\n", - " max_terms=len(design.terms))\n" + " max_terms=len(design.terms))" ] }, { @@ -329,51 +260,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "b33a8617", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:28.406436Z", - "iopub.status.busy": "2023-07-25T23:59:28.406315Z", - "iopub.status.idle": "2023-07-25T23:59:29.209115Z", - "shell.execute_reply": "2023-07-25T23:59:29.208717Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('Assists',\n", - " 'AtBat',\n", - " 'CAtBat',\n", - " 'CHits',\n", - " 'CHmRun',\n", - " 'CRBI',\n", - " 'CRuns',\n", - " 'CWalks',\n", - " 'Division',\n", - " 'Errors',\n", - " 'Hits',\n", - " 'HmRun',\n", - " 'League',\n", - " 'NewLeague',\n", - " 'PutOuts',\n", - " 'RBI',\n", - " 'Runs',\n", - " 'Walks',\n", - " 'Years')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hitters_MSE = sklearn_selected(OLS,\n", " strategy)\n", "hitters_MSE.fit(Hitters, Y)\n", - "hitters_MSE.selected_state_\n" + "hitters_MSE.selected_state_" ] }, { @@ -386,43 +281,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "e7f554cf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:29.211384Z", - "iopub.status.busy": "2023-07-25T23:59:29.211235Z", - "iopub.status.idle": "2023-07-25T23:59:29.694768Z", - "shell.execute_reply": "2023-07-25T23:59:29.694467Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('Assists',\n", - " 'AtBat',\n", - " 'CAtBat',\n", - " 'CRBI',\n", - " 'CRuns',\n", - " 'CWalks',\n", - " 'Division',\n", - " 'Hits',\n", - " 'PutOuts',\n", - " 'Walks')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "hitters_Cp = sklearn_selected(OLS,\n", " strategy,\n", " scoring=neg_Cp)\n", "hitters_Cp.fit(Hitters, Y)\n", - "hitters_Cp.selected_state_\n" + "hitters_Cp.selected_state_" ] }, { @@ -452,22 +320,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "fb7091b8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:29.696711Z", - "iopub.status.busy": "2023-07-25T23:59:29.696579Z", - "iopub.status.idle": "2023-07-25T23:59:29.698718Z", - "shell.execute_reply": "2023-07-25T23:59:29.698431Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "strategy = Stepwise.fixed_steps(design,\n", " len(design.terms),\n", " direction='forward')\n", - "full_path = sklearn_selection_path(OLS, strategy)\n" + "full_path = sklearn_selection_path(OLS, strategy)" ] }, { @@ -480,33 +341,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "56adc728", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:29.700418Z", - "iopub.status.busy": "2023-07-25T23:59:29.700293Z", - "iopub.status.idle": "2023-07-25T23:59:30.144070Z", - "shell.execute_reply": "2023-07-25T23:59:30.143731Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(263, 20)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "full_path.fit(Hitters, Y)\n", "Yhat_in = full_path.predict(Hitters)\n", - "Yhat_in.shape\n" + "Yhat_in.shape" ] }, { @@ -525,29 +367,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "387267d9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:30.146111Z", - "iopub.status.busy": "2023-07-25T23:59:30.145965Z", - "iopub.status.idle": "2023-07-25T23:59:30.281524Z", - "shell.execute_reply": "2023-07-25T23:59:30.280654Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "mse_fig, ax = subplots(figsize=(8,8))\n", "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n", @@ -562,7 +385,7 @@ " fontsize=20)\n", "ax.set_xticks(np.arange(n_steps)[::2])\n", "ax.legend()\n", - "ax.set_ylim([50000,250000]);\n" + "ax.set_ylim([50000,250000]);" ] }, { @@ -594,28 +417,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "0047ba88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:30.285990Z", - "iopub.status.busy": "2023-07-25T23:59:30.285674Z", - "iopub.status.idle": "2023-07-25T23:59:32.494537Z", - "shell.execute_reply": "2023-07-25T23:59:32.494207Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(263, 20)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "K = 5\n", "kfold = skm.KFold(K,\n", @@ -625,7 +430,7 @@ " Hitters,\n", " Y,\n", " cv=kfold)\n", - "Yhat_cv.shape\n" + "Yhat_cv.shape" ] }, { @@ -646,35 +451,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "27b0eb63", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:32.496480Z", - "iopub.status.busy": "2023-07-25T23:59:32.496340Z", - "iopub.status.idle": "2023-07-25T23:59:32.499706Z", - "shell.execute_reply": "2023-07-25T23:59:32.499405Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20, 5)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cv_mse = []\n", "for train_idx, test_idx in kfold.split(Y):\n", " errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n", " cv_mse.append(errors.mean(0)) # column means\n", "cv_mse = np.array(cv_mse).T\n", - "cv_mse.shape\n" + "cv_mse.shape" ] }, { @@ -683,34 +470,15 @@ "metadata": {}, "source": [ "We now add the cross-validation error estimates to our MSE plot.\n", - "We include the mean error across the five folds, and the estimate of the standard error of the mean. " + "We include the mean error across the five folds, and the estimate of the standard error of the mean." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "a45fe587", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:32.501370Z", - "iopub.status.busy": "2023-07-25T23:59:32.501275Z", - "iopub.status.idle": "2023-07-25T23:59:32.592962Z", - "shell.execute_reply": "2023-07-25T23:59:32.592640Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax.errorbar(np.arange(n_steps), \n", " cv_mse.mean(1),\n", @@ -719,7 +487,7 @@ " c='r') # color red\n", "ax.set_ylim([50000,250000])\n", "ax.legend()\n", - "mse_fig\n" + "mse_fig" ] }, { @@ -734,17 +502,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "c71c2079", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:32.594717Z", - "iopub.status.busy": "2023-07-25T23:59:32.594588Z", - "iopub.status.idle": "2023-07-25T23:59:33.030365Z", - "shell.execute_reply": "2023-07-25T23:59:33.030053Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1, \n", @@ -755,7 +515,7 @@ " Y[train_idx])\n", " Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n", " errors = (Yhat_val - Y[test_idx,None])**2\n", - " validation_mse = errors.mean(0)\n" + " validation_mse = errors.mean(0)" ] }, { @@ -768,30 +528,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "2ff34edf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:33.032304Z", - "iopub.status.busy": "2023-07-25T23:59:33.032173Z", - "iopub.status.idle": "2023-07-25T23:59:33.128471Z", - "shell.execute_reply": "2023-07-25T23:59:33.128143Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax.plot(np.arange(n_steps), \n", " validation_mse,\n", @@ -800,7 +540,7 @@ "ax.set_xticks(np.arange(n_steps)[::2])\n", "ax.set_ylim([50000,250000])\n", "ax.legend()\n", - "mse_fig\n" + "mse_fig" ] }, { @@ -816,27 +556,19 @@ "best subset selection.\n", "Instead of constraining the subset to be a given size,\n", "this package produces a path of solutions using the subset size as a\n", - "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate. \n" + "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "845f3fa0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:33.130280Z", - "iopub.status.busy": "2023-07-25T23:59:33.130150Z", - "iopub.status.idle": "2023-07-25T23:59:33.142290Z", - "shell.execute_reply": "2023-07-25T23:59:33.141982Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "D = design.fit_transform(Hitters)\n", "D = D.drop('intercept', axis=1)\n", - "X = np.asarray(D)\n" + "X = np.asarray(D)" ] }, { @@ -850,49 +582,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "e9f943e7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:33.143916Z", - "iopub.status.busy": "2023-07-25T23:59:33.143817Z", - "iopub.status.idle": "2023-07-25T23:59:35.350804Z", - "shell.execute_reply": "2023-07-25T23:59:35.350454Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preprocessing Data.\n", - "BnB Started.\n", - "Iteration: 1. Number of non-zeros: 1\n", - "Iteration: 2. Number of non-zeros: 2\n", - "Iteration: 3. Number of non-zeros: 2\n", - "Iteration: 4. Number of non-zeros: 2\n", - "Iteration: 5. Number of non-zeros: 3\n", - "Iteration: 6. Number of non-zeros: 3\n", - "Iteration: 7. Number of non-zeros: 4\n", - "Iteration: 8. Number of non-zeros: 9\n", - "Iteration: 9. Number of non-zeros: 9\n", - "Iteration: 10. Number of non-zeros: 9\n", - "Iteration: 11. Number of non-zeros: 9\n", - "Iteration: 12. Number of non-zeros: 9\n", - "Iteration: 13. Number of non-zeros: 9\n", - "Iteration: 14. Number of non-zeros: 9\n", - "Iteration: 15. Number of non-zeros: 9\n", - "Iteration: 16. Number of non-zeros: 9\n", - "Iteration: 17. Number of non-zeros: 9\n", - "Iteration: 18. Number of non-zeros: 17\n", - "Iteration: 19. Number of non-zeros: 19\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "path = fit_path(X, \n", " Y,\n", - " max_nonzeros=X.shape[1])\n" + " max_nonzeros=X.shape[1])" ] }, { @@ -905,38 +602,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "657ac171", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.352837Z", - "iopub.status.busy": "2023-07-25T23:59:35.352703Z", - "iopub.status.idle": "2023-07-25T23:59:35.355490Z", - "shell.execute_reply": "2023-07-25T23:59:35.355210Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'B': array([0. , 3.25484367, 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0.67775265, 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. ]),\n", - " 'B0': -38.98216739555551,\n", - " 'lambda_0': 0.011416248027450178,\n", - " 'M': 0.5829861733382012,\n", - " 'Time_exceeded': False}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "path[3]\n" + "path[3]" ] }, { @@ -970,435 +641,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "e576ad54", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.357157Z", - "iopub.status.busy": "2023-07-25T23:59:35.357034Z", - "iopub.status.idle": "2023-07-25T23:59:35.424501Z", - "shell.execute_reply": "2023-07-25T23:59:35.424223Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120446, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.685659595, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.02159835, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.30893663, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435659, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.34364891, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107168, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.92275897, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.09233121, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.277090184, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.98847021, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376922, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.251295805, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761593, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955906, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546297, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.6204245, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.32609803, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.002990715, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601913, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047491, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683641, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.273268215, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.981010474, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539254, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087247, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.15213543, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450393, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126286, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247456, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.0816231, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213055, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.950182974, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.08142715, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032734, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.375795275, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.65542218, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721766, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.65921827, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01861009, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520982, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.66025478, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.54819732, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147845834, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362815, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553557, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499264, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408341, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.272819825, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300109, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638604, tolerance: 12885.7065737425\n", - " model = cd_fast.enet_coordinate_descent_gram(\n" - ] - }, - { - "data": { - "text/plain": [ - "(19, 100)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Xs = X - X.mean(0)[None,:]\n", "X_scale = X.std(0)\n", @@ -1408,7 +654,7 @@ " Y,\n", " l1_ratio=0.,\n", " alphas=lambdas)[1]\n", - "soln_array.shape\n" + "soln_array.shape" ] }, { @@ -1437,414 +683,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "3ef15192", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.426658Z", - "iopub.status.busy": "2023-07-25T23:59:35.426532Z", - "iopub.status.idle": "2023-07-25T23:59:35.436847Z", - "shell.execute_reply": "2023-07-25T23:59:35.436537Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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AtBatHitsHmRunRunsRBIWalksYearsCAtBatCHitsCHmRunCRunsCRBICWalksLeague[N]Division[W]PutOutsAssistsErrorsNewLeague[N]
negative log(lambda)
-12.3108550.0008000.0008890.0006950.0008510.0009110.0009000.0008120.0010670.0011130.0010640.0011410.0011490.000993-0.000029-0.0003900.0006090.000052-0.000011-0.000006
-12.0782710.0010100.0011220.0008780.0010740.0011500.0011350.0010250.0013460.0014040.0013430.0014390.0014500.001253-0.000037-0.0004920.0007690.000065-0.000014-0.000007
-11.8456860.0012740.0014160.0011070.0013550.0014510.0014330.0012930.0016980.0017720.0016940.0018160.0018300.001581-0.000046-0.0006210.0009700.000082-0.000017-0.000009
-11.6131020.0016080.0017870.0013970.0017100.0018310.0018080.0016320.0021430.0022360.0021380.0022920.0023090.001995-0.000058-0.0007840.0012240.000104-0.000022-0.000012
-11.3805180.0020290.0022550.0017630.0021580.0023100.0022810.0020590.0027040.0028210.0026980.0028920.0029140.002517-0.000073-0.0009900.0015440.000131-0.000028-0.000015
............................................................
9.784658-290.823989336.92996837.322686-59.748520-26.507086134.855915-17.216195-387.77582689.573601-12.273926476.079273257.271255-213.12478031.258215-58.45785778.76126653.622113-22.208456-12.402891
10.017243-290.879272337.11371337.431373-59.916820-26.606957134.900549-17.108041-388.45840489.000707-12.661459477.031349257.966790-213.28089131.256434-58.44885078.76124053.645147-22.198802-12.391969
10.249827-290.923382337.26044637.518064-60.051166-26.686604134.936136-17.022194-388.99747088.537380-12.971603477.791860258.523025-213.40574031.254958-58.44168278.76123053.663357-22.191071-12.383205
10.482412-290.958537337.37745537.587122-60.158256-26.750044134.964477-16.954081-389.42341488.164178-13.219329478.398404258.967059-213.50541231.253747-58.43598378.76123053.677759-22.184893-12.376191
10.714996-290.986528337.47064837.642077-60.243522-26.800522134.987027-16.900054-389.76013587.864551-13.416889478.881540259.321007-213.58486931.252760-58.43145478.76123553.689152-22.179964-12.370587
\n", - "

100 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " AtBat Hits HmRun Runs RBI \\\n", - "negative log(lambda) \n", - "-12.310855 0.000800 0.000889 0.000695 0.000851 0.000911 \n", - "-12.078271 0.001010 0.001122 0.000878 0.001074 0.001150 \n", - "-11.845686 0.001274 0.001416 0.001107 0.001355 0.001451 \n", - "-11.613102 0.001608 0.001787 0.001397 0.001710 0.001831 \n", - "-11.380518 0.002029 0.002255 0.001763 0.002158 0.002310 \n", - "... ... ... ... ... ... \n", - " 9.784658 -290.823989 336.929968 37.322686 -59.748520 -26.507086 \n", - " 10.017243 -290.879272 337.113713 37.431373 -59.916820 -26.606957 \n", - " 10.249827 -290.923382 337.260446 37.518064 -60.051166 -26.686604 \n", - " 10.482412 -290.958537 337.377455 37.587122 -60.158256 -26.750044 \n", - " 10.714996 -290.986528 337.470648 37.642077 -60.243522 -26.800522 \n", - "\n", - " Walks Years CAtBat CHits CHmRun \\\n", - "negative log(lambda) \n", - "-12.310855 0.000900 0.000812 0.001067 0.001113 0.001064 \n", - "-12.078271 0.001135 0.001025 0.001346 0.001404 0.001343 \n", - "-11.845686 0.001433 0.001293 0.001698 0.001772 0.001694 \n", - "-11.613102 0.001808 0.001632 0.002143 0.002236 0.002138 \n", - "-11.380518 0.002281 0.002059 0.002704 0.002821 0.002698 \n", - "... ... ... ... ... ... \n", - " 9.784658 134.855915 -17.216195 -387.775826 89.573601 -12.273926 \n", - " 10.017243 134.900549 -17.108041 -388.458404 89.000707 -12.661459 \n", - " 10.249827 134.936136 -17.022194 -388.997470 88.537380 -12.971603 \n", - " 10.482412 134.964477 -16.954081 -389.423414 88.164178 -13.219329 \n", - " 10.714996 134.987027 -16.900054 -389.760135 87.864551 -13.416889 \n", - "\n", - " CRuns CRBI CWalks League[N] \\\n", - "negative log(lambda) \n", - "-12.310855 0.001141 0.001149 0.000993 -0.000029 \n", - "-12.078271 0.001439 0.001450 0.001253 -0.000037 \n", - "-11.845686 0.001816 0.001830 0.001581 -0.000046 \n", - "-11.613102 0.002292 0.002309 0.001995 -0.000058 \n", - "-11.380518 0.002892 0.002914 0.002517 -0.000073 \n", - "... ... ... ... ... \n", - " 9.784658 476.079273 257.271255 -213.124780 31.258215 \n", - " 10.017243 477.031349 257.966790 -213.280891 31.256434 \n", - " 10.249827 477.791860 258.523025 -213.405740 31.254958 \n", - " 10.482412 478.398404 258.967059 -213.505412 31.253747 \n", - " 10.714996 478.881540 259.321007 -213.584869 31.252760 \n", - "\n", - " Division[W] PutOuts Assists Errors \\\n", - "negative log(lambda) \n", - "-12.310855 -0.000390 0.000609 0.000052 -0.000011 \n", - "-12.078271 -0.000492 0.000769 0.000065 -0.000014 \n", - "-11.845686 -0.000621 0.000970 0.000082 -0.000017 \n", - "-11.613102 -0.000784 0.001224 0.000104 -0.000022 \n", - "-11.380518 -0.000990 0.001544 0.000131 -0.000028 \n", - "... ... ... ... ... \n", - " 9.784658 -58.457857 78.761266 53.622113 -22.208456 \n", - " 10.017243 -58.448850 78.761240 53.645147 -22.198802 \n", - " 10.249827 -58.441682 78.761230 53.663357 -22.191071 \n", - " 10.482412 -58.435983 78.761230 53.677759 -22.184893 \n", - " 10.714996 -58.431454 78.761235 53.689152 -22.179964 \n", - "\n", - " NewLeague[N] \n", - "negative log(lambda) \n", - "-12.310855 -0.000006 \n", - "-12.078271 -0.000007 \n", - "-11.845686 -0.000009 \n", - "-11.613102 -0.000012 \n", - "-11.380518 -0.000015 \n", - "... ... \n", - " 9.784658 -12.402891 \n", - " 10.017243 -12.391969 \n", - " 10.249827 -12.383205 \n", - " 10.482412 -12.376191 \n", - " 10.714996 -12.370587 \n", - "\n", - "[100 rows x 19 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "soln_path = pd.DataFrame(soln_array.T,\n", " columns=D.columns,\n", " index=-np.log(lambdas))\n", "soln_path.index.name = 'negative log(lambda)'\n", - "soln_path\n" + "soln_path" ] }, { @@ -1859,35 +707,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "40b67c85", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.438537Z", - "iopub.status.busy": "2023-07-25T23:59:35.438413Z", - "iopub.status.idle": "2023-07-25T23:59:35.665568Z", - "shell.execute_reply": "2023-07-25T23:59:35.665151Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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Pipeline(steps=[('scaler', StandardScaler()),\n",
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" - ], - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n", "scaler = StandardScaler(with_mean=True, with_std=True)\n", "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n", - "pipe.fit(X, Y)\n" + "pipe.fit(X, Y)" ] }, { @@ -2093,31 +815,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "a89989c3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.695813Z", - "iopub.status.busy": "2023-07-25T23:59:35.695707Z", - "iopub.status.idle": "2023-07-25T23:59:35.698261Z", - "shell.execute_reply": "2023-07-25T23:59:35.698013Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "160.4237101772591" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "np.linalg.norm(ridge.coef_)\n" + "np.linalg.norm(ridge.coef_)" ] }, { @@ -2140,36 +843,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "3eb41945", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.699892Z", - "iopub.status.busy": "2023-07-25T23:59:35.699805Z", - "iopub.status.idle": "2023-07-25T23:59:35.707120Z", - "shell.execute_reply": "2023-07-25T23:59:35.706784Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/plain": [ - "array([134214.00419204])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", " test_size=0.5,\n", @@ -2180,7 +857,7 @@ " Y,\n", " scoring='neg_mean_squared_error',\n", " cv=validation)\n", - "-results['test_score']\n" + "-results['test_score']" ] }, { @@ -2198,37 +875,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "635bc363", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.708905Z", - "iopub.status.busy": "2023-07-25T23:59:35.708772Z", - "iopub.status.idle": "2023-07-25T23:59:35.716331Z", - "shell.execute_reply": "2023-07-25T23:59:35.716004Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/plain": [ - "array([231788.32155285])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ridge.alpha = 1e10\n", "results = skm.cross_validate(ridge,\n", @@ -2236,7 +886,7 @@ " Y,\n", " scoring='neg_mean_squared_error',\n", " cv=validation)\n", - "-results['test_score']\n" + "-results['test_score']" ] }, { @@ -2255,242 +905,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "e117267f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:35.718146Z", - "iopub.status.busy": "2023-07-25T23:59:35.718021Z", - "iopub.status.idle": "2023-07-25T23:59:36.172012Z", - "shell.execute_reply": "2023-07-25T23:59:36.171718Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "param_grid = {'ridge__alpha': lambdas}\n", "grid = skm.GridSearchCV(pipe,\n", @@ -2499,7 +917,7 @@ " scoring='neg_mean_squared_error')\n", "grid.fit(X, Y)\n", "grid.best_params_['ridge__alpha']\n", - "grid.best_estimator_\n" + "grid.best_estimator_" ] }, { @@ -2512,1043 +930,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "0037e9e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:36.173832Z", - "iopub.status.busy": "2023-07-25T23:59:36.173705Z", - "iopub.status.idle": "2023-07-25T23:59:39.246833Z", - "shell.execute_reply": "2023-07-25T23:59:39.246526Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "grid = skm.GridSearchCV(pipe, \n", " param_grid,\n", @@ -3556,7 +941,7 @@ " scoring='neg_mean_squared_error')\n", "grid.fit(X, Y)\n", "grid.best_params_['ridge__alpha']\n", - "grid.best_estimator_\n" + "grid.best_estimator_" ] }, { @@ -3570,28 +955,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "71f0f5c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:39.248632Z", - "iopub.status.busy": "2023-07-25T23:59:39.248534Z", - "iopub.status.idle": "2023-07-25T23:59:39.354727Z", - "shell.execute_reply": "2023-07-25T23:59:39.354370Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ridge_fig, ax = subplots(figsize=(8,8))\n", "ax.errorbar(-np.log(lambdas),\n", @@ -3599,7 +966,7 @@ " yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n", "ax.set_ylim([50000,250000])\n", "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n" + "ax.set_ylabel('Cross-validated MSE', fontsize=20);" ] }, { @@ -3614,1084 +981,15 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "fb265379", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:39.356640Z", - "iopub.status.busy": "2023-07-25T23:59:39.356501Z", - "iopub.status.idle": "2023-07-25T23:59:42.482886Z", - "shell.execute_reply": "2023-07-25T23:59:42.482602Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/html": [ - "
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
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-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", - " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('ridge',\n", - " ElasticNet(alpha=10000000000.0,\n", - " l1_ratio=0))]),\n", - " param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n", - " 8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n", - " 3.45506012e+04, 2.73807606...\n", - " 4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n", - " 1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n", - " 7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n", - " 2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n", - " 1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n", - " 4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "grid_r2 = skm.GridSearchCV(pipe, \n", " param_grid,\n", " cv=kfold)\n", - "grid_r2.fit(X, Y)\n" + "grid_r2.fit(X, Y)" ] }, { @@ -4704,36 +1002,17 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "79616f48", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:42.484749Z", - "iopub.status.busy": "2023-07-25T23:59:42.484621Z", - "iopub.status.idle": "2023-07-25T23:59:42.586564Z", - "shell.execute_reply": "2023-07-25T23:59:42.586230Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "r2_fig, ax = subplots(figsize=(8,8))\n", "ax.errorbar(-np.log(lambdas),\n", " grid_r2.cv_results_['mean_test_score'],\n", " yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n", "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n" + "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);" ] }, { @@ -4753,2096 +1032,17 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "11e55883", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:42.588593Z", - "iopub.status.busy": "2023-07-25T23:59:42.588455Z", - "iopub.status.idle": "2023-07-25T23:59:42.930873Z", - "shell.execute_reply": "2023-07-25T23:59:42.930547Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795326.355502333, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795268.885511458, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795196.367825005, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795104.862821113, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794989.399687696, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794843.706650957, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794659.87071198, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794427.908521358, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794135.22526347, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793765.932449568, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793299.98803079, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18792712.112872534, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791970.425932087, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791034.72591697, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18789854.32913581, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18788365.350956466, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18786487.290938053, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18784118.748442672, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18781132.05553399, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18777366.566605024, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18772620.289297033, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18766639.479676694, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18759105.758860495, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18749620.243803147, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18737684.132153213, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18722675.157982755, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18703819.37168406, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18680157.84067929, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18650508.189617783, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18613421.503628485, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18567136.14871325, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18509531.699850053, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18438088.608600505, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18349862.649110064, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18241487.557216965, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18109224.25083878, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17949079.523028806, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17757018.994714484, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17529294.98190815, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17262895.457700975, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16956091.882983487, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16609021.736273043, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16224194.650997939, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363884, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15364525.127389485, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14907268.75187378, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14446023.624531085, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13991857.160644894, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13554773.727504015, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13142847.182203237, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12761747.456957739, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12414679.232309299, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12102642.724649917, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11824874.692517474, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11579334.50630629, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11363143.416383019, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11172936.696242273, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.92643167, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10856105.032984463, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10722381.625233045, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10600721.735570516, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10488247.552619573, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10382531.68105097, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078632, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10184320.545404715, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089716.55059902, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9997617.850835908, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9908230.155360885, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9822083.085401118, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739888.930170696, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9662401.666184625, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9590296.226307327, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9524082.854699288, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9464062.902306747, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9410323.196208755, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991764, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9321112.753117379, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285016.290065145, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9254029.627395952, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9227672.214767914, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9205447.27460862, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9186860.578098293, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133288, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9158722.527650403, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396464, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9139831.50202173, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055235, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9127409.145408802, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9122944.972944392, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9119363.705526328, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9116497.490587894, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834428, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.008592516, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9110927.575648237, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829819, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108850.148759764, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108119.08491204, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107538.538969103, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962065, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9106712.046135923, tolerance: 3759.109166869193\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005651.632865302, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005578.608102243, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005486.463074774, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005370.192059726, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005223.47917251, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005038.355660334, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004804.76767336, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004510.03120046, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004138.144828446, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003668.923421204, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003076.906345215, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21002329.98203154, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21001387.655909717, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21000198.8704182, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20998699.26312138, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20996807.72107362, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20994422.05552329, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20991413.57989597, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20987620.324921425, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20982838.567338496, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20976812.283196613, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20969220.065253027, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959658.970863715, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20947624.701018073, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20932487.468798272, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20913462.923603535, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20889577.599545892, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20859628.61984418, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20822137.913488373, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20775302.126054227, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20716940.917180095, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20644448.64953633, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20554757.795455974, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20444326.815649558, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20309170.5956441, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20144956.94257016, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19947196.308887925, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19711550.604615457, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19434276.168588594, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19112791.023677077, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18746315.49762964, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18336483.416578818, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17887774.82963546, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17407607.14883928, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16905965.499829993, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16394560.80209675, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15885645.94315279, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15390736.734407002, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14919517.25785277, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14479140.715843389, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14074002.01810337, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13705921.512677444, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13374594.126102015, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13078142.079861483, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12813645.639316088, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12577583.791150972, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.387483226, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175587.27845306, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12002182.958268248, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11842589.470659975, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11693840.031875866, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.60800361, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11419454.0438313, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11290441.388440857, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11165501.742342338, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11044168.420816425, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926319.289729377, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10812069.210340973, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10701669.403435929, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595426.714498514, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10493648.013477515, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10396608.203056702, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10304536.713966034, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012153, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876721, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059761.060749074, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9989004.697692012, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593688, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9863986.795334544, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9809627.884194935, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9760531.052715844, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9716491.487344079, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9677258.06531545, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642549.951165989, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835175, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134583, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908714, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681038, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9526251.156759001, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9512226.472533092, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319627, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9490849.431706948, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9482895.334901826, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9476399.71781569, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471123.439398324, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9466857.004635958, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9463420.20844639, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9460660.409301298, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484353, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9456683.22035802, tolerance: 4201.186103419478\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331946.25629055, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331864.018678214, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331760.248581372, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331629.308755428, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331464.086506005, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331255.607747704, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330992.550247405, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330660.62979839, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330241.82628314, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329713.40806704, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329046.702501133, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22328205.546983715, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22327144.338416774, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22325805.578253012, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22324116.784799173, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22321986.613041975, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22319299.9839329, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22315911.97874348, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22311640.198869713, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22306255.226839963, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22299468.750693016, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22290918.833475478, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22280151.72747749, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22266599.559077755, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22249553.162800502, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22228129.35292585, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22201232.036903117, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22167506.872833706, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22125289.76574775, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22072550.542125095, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22006834.845984127, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21925209.906269174, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21824223.56629905, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21699890.94922881, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21547729.124614064, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21362866.213577304, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21140255.446179498, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20875023.13975618, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562967.32341789, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201195.56502676, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19788844.32939185, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19327763.89751004, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18823001.04313301, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18282896.08461045, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17718660.4886989, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17143422.40324079, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16570887.230051238, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16013892.090309372, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15483171.861886727, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14986579.129588084, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14528848.289413737, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.239774454, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13735069.935277399, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13396407.639332836, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13092660.916831579, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12820093.900344713, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12574781.90922219, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.175078167, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12150651.369793259, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11964850.854771722, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11792543.263225015, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11631302.416094316, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11479227.7561279, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334963.041737791, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11197685.003164051, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11067056.224580359, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10943139.511030385, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10826278.220752902, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10716956.341549171, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10615659.18706467, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10522756.819315987, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10438426.844454892, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10362623.27115231, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10295087.38179537, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414705, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10182978.74114095, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10137247.95260475, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10097567.748922419, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10063321.789749103, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10033922.656392608, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10008819.486834103, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9987500.645290056, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9969494.453323793, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9954369.32479691, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9941733.515465437, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9931234.335989065, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9922556.777457738, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9915421.679110043, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9909583.627876062, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904828.718921196, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900972.216495434, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9897856.106707212, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9895346.540855931, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755055, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948297, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9890425.865192825, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9889395.604661888, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116646, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.674489552, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887400.660169175, tolerance: 4466.452064951529\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225193.80408011, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225110.813517075, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225006.093373984, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224873.954836704, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224707.22016197, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224496.83322094, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224231.36831536, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223896.410779048, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223473.77603032, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222940.525154293, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222267.72434169, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22221418.88207672, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22220347.981225494, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22218997.002387535, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22217292.809172478, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22215143.234477364, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22212432.168317866, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22209013.40126823, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22204702.922219783, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22199269.304569546, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22192421.741654057, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22183795.21258787, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22172932.17909693, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159260.14304964, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22142064.35203175, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22120454.95809202, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22093328.06334204, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22059320.403233726, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016758.03845356, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21963600.508906867, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21897383.65478575, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21815166.968429696, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21713495.123522323, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21588388.30984886, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21435381.888817236, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21249641.65996918, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21026184.505123127, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20760231.655652393, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20447708.31167379, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.018901825, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19674021.850113407, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19214128.3053442, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18711289.424232315, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18173771.014405873, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17612557.62934444, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17040407.03219554, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16470567.131662391, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15915425.819018744, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15385384.27148134, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14888160.528800251, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14428585.410549453, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14008814.608291918, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13628800.43657365, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13286857.361730725, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12980197.224087035, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.410345197, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12458600.903357476, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.77957509, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12033913.49389702, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11848733.596731743, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11677333.403468838, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11516981.070353946, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11365424.704670552, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073826, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11082243.920528807, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10948669.457422748, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819942.016223667, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10696213.317397818, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10577957.546131799, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929628, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10360715.842557454, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10263264.873279892, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10174122.558233691, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10093678.084935276, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10022055.90958463, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252601, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904471.381944738, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9857566.988895562, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9817713.479318874, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9784158.875562938, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9756135.4293958, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9732897.547924208, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.933154197, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9698053.28484727, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9685251.610393653, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9674853.346299471, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9666438.328081315, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9659650.291029936, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654190.159247063, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649808.977198932, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646301.012972398, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643497.331329834, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9641259.984843817, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9639476.879484767, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691978, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636928.172691077, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636030.684258943, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635317.743812287, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914779, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158696, tolerance: 4445.102149685068\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182535.705905367, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182443.31748153, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182326.738805104, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182179.636849403, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181994.021044992, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181759.809716668, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181464.28327085, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181091.39464482, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180620.89990636, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180027.262331244, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22179278.27131426, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22178333.30250969, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22177141.126954924, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22175637.153777506, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22173739.962460298, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22171346.945451487, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22168328.83898362, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22164522.86814139, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159724.170510717, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22153675.090679448, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22146051.856030278, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22136448.05522982, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22124354.250566233, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22109132.97552027, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22089988.320511386, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22065929.327634364, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22035726.555516478, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21997861.52451259, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21950469.43740475, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21891276.769023754, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21817537.260214396, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21725972.80421009, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21612729.92224466, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21473368.08108258, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21302902.69437747, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21095932.158423126, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20846882.286273133, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20550398.911674757, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201904.639180813, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19798303.254432607, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19338763.60317261, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18825451.629099563, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18264026.303933263, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17663705.112665202, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17036766.85903686, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16397496.223623449, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15760744.92149185, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140415.226936534, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548197.66197113, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13992801.316187331, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13479749.374918321, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13011650.716625076, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12588761.536151327, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12209637.009462353, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11871722.016013274, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11571805.12738007, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11306328.206388844, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11071585.798533637, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10863860.419768564, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10679529.96999051, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10515164.967978276, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10367617.395431116, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10234094.932508666, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112213.557229094, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10000024.23575536, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9896012.57105465, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9799072.614562154, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9708457.890308099, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9623714.619163413, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9544604.25348744, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471024.212762302, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9402936.228999598, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9340310.144654753, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9283087.29859646, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9231162.854348717, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9184382.359520858, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9142546.024753645, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890352, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093456, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9044152.703403218, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019396.685310023, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8998109.575324507, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8979944.333787149, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8964556.387394711, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.3695335, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8940797.002727017, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8931817.822045382, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8924407.976697778, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8918327.548498938, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8913363.779400796, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909330.473245148, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906066.743651448, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8903435.248435475, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8901320.0564094, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8899624.301448012, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.772619218, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8897184.565959448, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152896, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895633.08348321, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895085.869018715, tolerance: 4436.577708196869\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('ridge',\n", - " ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n", - " 8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n", - " 3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n", - " 1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n", - " 5.37495461e+03, 4.25955961e+03,...\n", - " 1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n", - " 7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n", - " 2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n", - " 1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n", - " 4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n", - " cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", - " l1_ratio=0))])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n", " l1_ratio=0,\n", " cv=kfold)\n", "pipeCV = Pipeline(steps=[('scaler', scaler),\n", " ('ridge', ridgeCV)])\n", - "pipeCV.fit(X, Y)\n" + "pipeCV.fit(X, Y)" ] }, { @@ -6856,28 +1056,10 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "d107f961", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:42.932916Z", - "iopub.status.busy": "2023-07-25T23:59:42.932770Z", - "iopub.status.idle": "2023-07-25T23:59:43.043431Z", - "shell.execute_reply": "2023-07-25T23:59:43.043097Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "tuned_ridge = pipeCV.named_steps['ridge']\n", "ridgeCV_fig, ax = subplots(figsize=(8,8))\n", @@ -6887,7 +1069,7 @@ "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n", "ax.set_ylim([50000,250000])\n", "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n" + "ax.set_ylabel('Cross-validated MSE', fontsize=20);" ] }, { @@ -6903,30 +1085,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "21d65d1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.045221Z", - "iopub.status.busy": "2023-07-25T23:59:43.045098Z", - "iopub.status.idle": "2023-07-25T23:59:43.047578Z", - "shell.execute_reply": "2023-07-25T23:59:43.047310Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "115526.70630987917" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "np.min(tuned_ridge.mse_path_.mean(1))\n" + "np.min(tuned_ridge.mse_path_.mean(1))" ] }, { @@ -6942,35 +1106,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "11373de9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.049360Z", - "iopub.status.busy": "2023-07-25T23:59:43.049242Z", - "iopub.status.idle": "2023-07-25T23:59:43.051656Z", - "shell.execute_reply": "2023-07-25T23:59:43.051383Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-222.80877051, 238.77246614, 3.21103754, -2.93050845,\n", - " 3.64888723, 108.90953869, -50.81896152, -105.15731984,\n", - " 122.00714801, 57.1859509 , 210.35170348, 118.05683748,\n", - " -150.21959435, 30.36634231, -61.62459095, 77.73832472,\n", - " 40.07350744, -25.02151514, -13.68429544])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "tuned_ridge.coef_\n" + "tuned_ridge.coef_" ] }, { @@ -7007,16 +1148,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "72181964", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.053342Z", - "iopub.status.busy": "2023-07-25T23:59:43.053221Z", - "iopub.status.idle": "2023-07-25T23:59:43.055713Z", - "shell.execute_reply": "2023-07-25T23:59:43.055414Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "outer_valid = skm.ShuffleSplit(n_splits=1, \n", @@ -7029,2057 +1163,22 @@ " l1_ratio=0,\n", " cv=inner_cv)\n", "pipeCV = Pipeline(steps=[('scaler', scaler),\n", - " ('ridge', ridgeCV)]);\n" + " ('ridge', ridgeCV)]);" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "4c3054b5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.057432Z", - "iopub.status.busy": "2023-07-25T23:59:43.057306Z", - "iopub.status.idle": "2023-07-25T23:59:43.365804Z", - "shell.execute_reply": "2023-07-25T23:59:43.365508Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.893047336, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721532, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898538, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.16890147, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324104, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170538, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507718, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836109, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040235, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.727331886, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.28977405, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442707, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647634, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416634, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094172, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783943, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788342, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472542, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502639, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835295, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395373, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356627, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495732, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501963, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.320705947, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581451, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469924, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.97009871, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491088, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622943, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.961932577, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781398, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964379, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216344, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326858, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333132, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243072, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988702, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148528, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599191, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660212, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.23862632, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.88143778, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592549, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.88091306, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576018, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592053, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559342, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773343, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569963, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.312997252, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.88124088, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431445, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573516, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192497, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409387, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950285, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093235, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631114, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589174, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.107432563, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020331, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113679, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072118, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745581, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013418, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.8293056125, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206969, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.645216511, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947152, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247044, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469164, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564693, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.1469736025, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.63533624, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616933, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404535, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.48376607, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.76970095, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858365, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.911777701, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.571027264, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528801, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.79244657, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319955, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039895, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.05095333, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534292, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908728, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345776, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998693, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629447, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630652, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791553852, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512218, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783063, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.892213011, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162482, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967933, tolerance: 3200.6325551004925\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.82487654, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.33151807, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703294, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.51311625, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649913, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059805, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268812, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.193566969, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.866617758, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748763, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.00720005, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.090500388, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.71354342, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771877, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.213598879, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179863, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224856, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.11903821, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821936, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191706, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135195, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.6946986, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.628850497, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.241777299, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980713, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.850599289, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.41286841, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066573, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110113, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.208955988, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870235, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844737, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850953, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151276, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.41690654, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015966, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016687, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940995, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934752, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094303, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.205683708, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702371, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.573228309, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217315, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401602, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.578529166, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698365, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.230672905, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233683, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070565, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535858, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.19434187, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689679, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287674, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.280234471, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660187, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690647, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.1056430675, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.35524954, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.53846852, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275469, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.535335509, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.802084766, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684168, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666606, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521793, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162683, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117484922, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.041596897, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674365, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695727, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456673, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.012726546, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405474, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238734, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036072, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610088, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073966, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.4663270125, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661856, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.977539104, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.2920428645, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171555, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603645, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569127, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.97498893, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.207170451, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867455, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.853912757, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852352, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345377, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501478, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.94700243, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652601, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999971, tolerance: 3034.7626598069196\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776321, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791689, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780785, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.242584623, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469946, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.14527111, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.128467944, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.892430544, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207122, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140417, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968959, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532348, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307216, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220387, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893765, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962447, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599917, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.060097354, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588472, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383827, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.042096594, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194367, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.47325135, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912459, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906962, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558565, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.990428165, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220897, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036042, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371142, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116722, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098895, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258615, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385439, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455775, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.07465526, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592412, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.27386727, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.36384661, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940228, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143818, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873377, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990832, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361462, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.79937819, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668119, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.36369123, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169304, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702794, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200799, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.2675956, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.64783793, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.819731826, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.688773429, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.61072085, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433397, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351493, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835387, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905487, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109628, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262948, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199782, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107317, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355204, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804332, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980087, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847987, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.81877588, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.491724883, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.53569288, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006125, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576015, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131106991, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931618, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364969, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578147, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267373, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337014, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.53934286, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.0266303, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473388, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602925, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.407961924, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412938, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325930048, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782039, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061372, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284237, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612171, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205515, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.584421616, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672326, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096559, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096591, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784204, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014641162, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080780019, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668608, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092072, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.805305656, tolerance: 3200.070250165819\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.844425442, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219734, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.655993313, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938994, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082258, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.13980752, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748439, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.08077331, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848666, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612368, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286435, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.12581003, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.894413952, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859002, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.98172164, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.01686267, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.406945651, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966294, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.31968939, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396805, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.257695232, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583746, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.49455343, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.749109622, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.02436845, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124275, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.03569032, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086988, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015218, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622873, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156208, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.48980598, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488008, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455977, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389356, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404455, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886174, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820404, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.752768412, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278118, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379244, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.98420337, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464427, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969216, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900381, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.138994666, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742979, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.239570614, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329573, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594905, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591967, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.99395707, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983279, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.9445123505, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915861, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.8174845725, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.0523840925, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150196, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.436922483, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368484, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584399, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.9732326735, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369866, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.567763316, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.2241215855, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985473, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757254, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128507, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388530005, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167879, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628094, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966857, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857503, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839442, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273544, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385814, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983606, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225937976, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329608, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.873515937, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701696, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567688, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.7780512385, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079299, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.176477653, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794392, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473216, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.3336993465, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.074242126, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697735, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244275, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028902, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897053, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972435, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142942, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.8736711275, tolerance: 2753.321903486231\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658319, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447501, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.200043006, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596577, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871313, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543608, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935372, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587788, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986472, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.55809336, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.663752725, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.060052717, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.779778486, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.168518286, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996723, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918179, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036688, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.47434148, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294181, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470121, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017829, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.46580684, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.35332688, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018394, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450969, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.476241706, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872495, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394275, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596483, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.11469827, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060235, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340755, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206709, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.420748936, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182043, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.908593165, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.453317674, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.177746587, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.082835246, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678281, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817537, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286602, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.869677957, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.659031235, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043081, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912804, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176824, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656613, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610261, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946363, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.25861056, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.101396974, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097185, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072414, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051512, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382325, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.9531656, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803495, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990706, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955583, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532658, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.8422521455, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908956, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898104, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651163, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854576, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079197, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818847, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832884, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831903, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035702, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235791, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998971, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001433, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484867, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.93844572, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.96370539, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.339338534, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895556, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319144, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.533593078, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.495343714, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579567, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622955, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.7733192, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915036, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645212, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.662147184, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.581406483, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.34181063, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.2530382, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.7760819215, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037627, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491712, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.90595999, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146216, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.23751574, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.038791819, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992944, tolerance: 3224.823681413525\n", - " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", - " model = cd_fast.enet_coordinate_descent(\n" - ] - }, - { - "data": { - "text/plain": [ - "array([132393.84003227])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "results = skm.cross_validate(pipeCV, \n", " X,\n", " Y,\n", " cv=outer_valid,\n", " scoring='neg_mean_squared_error')\n", - "-results['test_score']\n" - ] - }, - { - "cell_type": "markdown", - "id": "68921cdc", - "metadata": {}, - "source": [ - " " + "-results['test_score']" ] }, { @@ -9100,29 +1199,10 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "72bb0c1d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.368185Z", - "iopub.status.busy": "2023-07-25T23:59:43.368043Z", - "iopub.status.idle": "2023-07-25T23:59:43.419802Z", - "shell.execute_reply": "2023-07-25T23:59:43.419498Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3.1472370031649866" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "lassoCV = skl.ElasticNetCV(n_alphas=100, \n", " l1_ratio=1,\n", @@ -9131,22 +1211,14 @@ " ('lasso', lassoCV)])\n", "pipeCV.fit(X, Y)\n", "tuned_lasso = pipeCV.named_steps['lasso']\n", - "tuned_lasso.alpha_\n" + "tuned_lasso.alpha_" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "69adc7ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.421483Z", - "iopub.status.busy": "2023-07-25T23:59:43.421361Z", - "iopub.status.idle": "2023-07-25T23:59:43.431280Z", - "shell.execute_reply": "2023-07-25T23:59:43.431010Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "lambdas, soln_array = skl.Lasso.path(Xs, \n", @@ -9155,7 +1227,7 @@ " n_alphas=100)[:2]\n", "soln_path = pd.DataFrame(soln_array.T,\n", " columns=D.columns,\n", - " index=-np.log(lambdas))\n" + " index=-np.log(lambdas))" ] }, { @@ -9170,35 +1242,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "2aa75789", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.432981Z", - "iopub.status.busy": "2023-07-25T23:59:43.432885Z", - "iopub.status.idle": "2023-07-25T23:59:43.617766Z", - "shell.execute_reply": "2023-07-25T23:59:43.617396Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "path_fig, ax = subplots(figsize=(8,8))\n", "soln_path.plot(ax=ax, legend=False)\n", "ax.legend(loc='upper left')\n", "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n" + "ax.set_ylabel('Standardized coefficiients', fontsize=20);" ] }, { @@ -9213,30 +1266,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "e38c9bed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.619681Z", - "iopub.status.busy": "2023-07-25T23:59:43.619553Z", - "iopub.status.idle": "2023-07-25T23:59:43.621973Z", - "shell.execute_reply": "2023-07-25T23:59:43.621711Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "114690.73118253548" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "np.min(tuned_lasso.mse_path_.mean(1))\n" + "np.min(tuned_lasso.mse_path_.mean(1))" ] }, { @@ -9244,33 +1279,15 @@ "id": "d091d74c", "metadata": {}, "source": [ - "Let’s again produce a plot of the cross-validation error.\n" + "Let’s again produce a plot of the cross-validation error." ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "53c42724", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.623732Z", - "iopub.status.busy": "2023-07-25T23:59:43.623610Z", - "iopub.status.idle": "2023-07-25T23:59:43.735692Z", - "shell.execute_reply": "2023-07-25T23:59:43.735334Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "lassoCV_fig, ax = subplots(figsize=(8,8))\n", "ax.errorbar(-np.log(tuned_lasso.alphas_),\n", @@ -9296,34 +1313,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "5f4942ba", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.737708Z", - "iopub.status.busy": "2023-07-25T23:59:43.737572Z", - "iopub.status.idle": "2023-07-25T23:59:43.740456Z", - "shell.execute_reply": "2023-07-25T23:59:43.740061Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-210.01008773, 243.4550306 , 0. , 0. ,\n", - " 0. , 97.69397357, -41.52283116, -0. ,\n", - " 0. , 39.62298193, 205.75273856, 124.55456561,\n", - " -126.29986768, 15.70262427, -59.50157967, 75.24590036,\n", - " 21.62698014, -12.04423675, -0. ])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "tuned_lasso.coef_\n" + "tuned_lasso.coef_" ] }, { @@ -9366,35 +1361,17 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "72a876bb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.742347Z", - "iopub.status.busy": "2023-07-25T23:59:43.742212Z", - "iopub.status.idle": "2023-07-25T23:59:43.746741Z", - "shell.execute_reply": "2023-07-25T23:59:43.746433Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09846131, 0.4758765 ])" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "pca = PCA(n_components=2)\n", "linreg = skl.LinearRegression()\n", "pipe = Pipeline([('pca', pca),\n", " ('linreg', linreg)])\n", "pipe.fit(X, Y)\n", - "pipe.named_steps['linreg'].coef_\n" + "pipe.named_steps['linreg'].coef_" ] }, { @@ -9410,34 +1387,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "e0e821c6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.748488Z", - "iopub.status.busy": "2023-07-25T23:59:43.748344Z", - "iopub.status.idle": "2023-07-25T23:59:43.752896Z", - "shell.execute_reply": "2023-07-25T23:59:43.752570Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([106.36859204, -21.60350456])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "pipe = Pipeline([('scaler', scaler), \n", " ('pca', pca),\n", " ('linreg', linreg)])\n", "pipe.fit(X, Y)\n", - "pipe.named_steps['linreg'].coef_\n" + "pipe.named_steps['linreg'].coef_" ] }, { @@ -9453,54 +1412,17 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "1ac6886c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.754728Z", - "iopub.status.busy": "2023-07-25T23:59:43.754585Z", - "iopub.status.idle": "2023-07-25T23:59:43.889511Z", - "shell.execute_reply": "2023-07-25T23:59:43.889157Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                                       ('pca', PCA(n_components=2)),\n",
-       "                                       ('linreg', LinearRegression())]),\n",
-       "             param_grid={'pca__n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')
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" - ], - "text/plain": [ - "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", - " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n", - " ('pca', PCA(n_components=2)),\n", - " ('linreg', LinearRegression())]),\n", - " param_grid={'pca__n_components': range(1, 20)},\n", - " scoring='neg_mean_squared_error')" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "param_grid = {'pca__n_components': range(1, 20)}\n", "grid = skm.GridSearchCV(pipe,\n", " param_grid,\n", " cv=kfold,\n", " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)\n" + "grid.fit(X, Y)" ] }, { @@ -9513,28 +1435,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "5e0c5a96", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.891415Z", - "iopub.status.busy": "2023-07-25T23:59:43.891279Z", - "iopub.status.idle": "2023-07-25T23:59:43.997785Z", - "shell.execute_reply": "2023-07-25T23:59:43.997406Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "pcr_fig, ax = subplots(figsize=(8,8))\n", "n_comp = param_grid['pca__n_components']\n", @@ -9544,7 +1448,7 @@ "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n", "ax.set_xlabel('# principal components', fontsize=20)\n", "ax.set_xticks(n_comp[::2])\n", - "ax.set_ylim([50000,250000]);\n" + "ax.set_ylim([50000,250000]);" ] }, { @@ -9568,29 +1472,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "9df0fb7f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:43.999739Z", - "iopub.status.busy": "2023-07-25T23:59:43.999596Z", - "iopub.status.idle": "2023-07-25T23:59:44.006697Z", - "shell.execute_reply": "2023-07-25T23:59:44.006373Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "204139.30692994667" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Xn = np.zeros((X.shape[0], 1))\n", "cv_null = skm.cross_validate(linreg,\n", @@ -9598,7 +1483,7 @@ " Y,\n", " cv=kfold,\n", " scoring='neg_mean_squared_error')\n", - "-cv_null['test_score'].mean()\n" + "-cv_null['test_score'].mean()" ] }, { @@ -9614,30 +1499,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "458c21a4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:44.008508Z", - "iopub.status.busy": "2023-07-25T23:59:44.008380Z", - "iopub.status.idle": "2023-07-25T23:59:44.010723Z", - "shell.execute_reply": "2023-07-25T23:59:44.010438Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.3831424 , 0.21841076])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ - "pipe.named_steps['pca'].explained_variance_ratio_\n" + "pipe.named_steps['pca'].explained_variance_ratio_" ] }, { @@ -9650,9 +1517,7 @@ "that is captured using $M$ principal components. For example, setting\n", "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n", "By $M=6$ it increases to\n", - "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all 100% of the variance.\n", - "\n", - " " + "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all 100% of the variance." ] }, { @@ -9662,42 +1527,19 @@ "source": [ "### Partial Least Squares\n", "Partial least squares (PLS) is implemented in the\n", - "`PLSRegression()` function.\n", - "\n", - " " + "`PLSRegression()` function." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "f49d464e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:44.012454Z", - "iopub.status.busy": "2023-07-25T23:59:44.012320Z", - "iopub.status.idle": "2023-07-25T23:59:44.016175Z", - "shell.execute_reply": "2023-07-25T23:59:44.015867Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
PLSRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "PLSRegression()" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "pls = PLSRegression(n_components=2, \n", " scale=True)\n", - "pls.fit(X, Y)\n" + "pls.fit(X, Y)" ] }, { @@ -9711,47 +1553,17 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "8e118b5b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:44.017881Z", - "iopub.status.busy": "2023-07-25T23:59:44.017786Z", - "iopub.status.idle": "2023-07-25T23:59:44.123851Z", - "shell.execute_reply": "2023-07-25T23:59:44.123517Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={'n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" - ], - "text/plain": [ - "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n", - " estimator=PLSRegression(),\n", - " param_grid={'n_components': range(1, 20)},\n", - " scoring='neg_mean_squared_error')" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "param_grid = {'n_components':range(1, 20)}\n", "grid = skm.GridSearchCV(pls,\n", " param_grid,\n", " cv=kfold,\n", " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)\n" + "grid.fit(X, Y)" ] }, { @@ -9764,28 +1576,10 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "0f98407c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:44.125903Z", - "iopub.status.busy": "2023-07-25T23:59:44.125763Z", - "iopub.status.idle": "2023-07-25T23:59:44.234276Z", - "shell.execute_reply": "2023-07-25T23:59:44.233879Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "pls_fig, ax = subplots(figsize=(8,8))\n", "n_comp = param_grid['n_components']\n", @@ -9795,7 +1589,7 @@ "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n", "ax.set_xlabel('# principal components', fontsize=20)\n", "ax.set_xticks(n_comp[::2])\n", - "ax.set_ylim([50000,250000]);\n" + "ax.set_ylim([50000,250000]);" ] }, { @@ -9811,20 +1605,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch7-nonlin-lab.ipynb b/docs/source/labs/Ch7-nonlin-lab.ipynb index 798a73a..54807a8 100644 --- a/docs/source/labs/Ch7-nonlin-lab.ipynb +++ b/docs/source/labs/Ch7-nonlin-lab.ipynb @@ -2,16 +2,16 @@ "cells": [ { "cell_type": "markdown", - "id": "6c5aa81b", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "88fced67", + "id": "8d006b12", "metadata": {}, "source": [ "# Non-Linear Modeling\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", "In this lab, we demonstrate some of the nonlinear models discussed in\n", "this chapter. We use the `Wage` data as a running example, and show that many of the complex non-linear fitting procedures discussed can easily be implemented in \\Python.\n", "\n", @@ -20,16 +20,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "77af2dc4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:45.681484Z", - "iopub.status.busy": "2023-07-25T23:59:45.681169Z", - "iopub.status.idle": "2023-07-25T23:59:46.716705Z", - "shell.execute_reply": "2023-07-25T23:59:46.716226Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np, pandas as pd\n", @@ -54,16 +47,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b57d1d51", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.718874Z", - "iopub.status.busy": "2023-07-25T23:59:46.718688Z", - "iopub.status.idle": "2023-07-25T23:59:46.729795Z", - "shell.execute_reply": "2023-07-25T23:59:46.729463Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from pygam import (s as s_gam,\n", @@ -78,7 +64,7 @@ "from ISLP.pygam import (approx_lam,\n", " degrees_of_freedom,\n", " plot as plot_gam,\n", - " anova as anova_gam)\n" + " anova as anova_gam)" ] }, { @@ -93,21 +79,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "65f0e562", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.731616Z", - "iopub.status.busy": "2023-07-25T23:59:46.731518Z", - "iopub.status.idle": "2023-07-25T23:59:46.739064Z", - "shell.execute_reply": "2023-07-25T23:59:46.738783Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "Wage = load_data('Wage')\n", "y = Wage['wage']\n", - "age = Wage['age']\n" + "age = Wage['age']" ] }, { @@ -123,103 +102,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a391fae6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.740875Z", - "iopub.status.busy": "2023-07-25T23:59:46.740748Z", - "iopub.status.idle": "2023-07-25T23:59:46.814762Z", - "shell.execute_reply": "2023-07-25T23:59:46.813114Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept111.70360.729153.2830.000
poly(age, degree=4)[0]447.067939.91511.2010.000
poly(age, degree=4)[1]-478.315839.915-11.9830.000
poly(age, degree=4)[2]125.521739.9153.1450.002
poly(age, degree=4)[3]-77.911239.915-1.9520.051
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 111.7036 0.729 153.283 0.000\n", - "poly(age, degree=4)[0] 447.0679 39.915 11.201 0.000\n", - "poly(age, degree=4)[1] -478.3158 39.915 -11.983 0.000\n", - "poly(age, degree=4)[2] 125.5217 39.915 3.145 0.002\n", - "poly(age, degree=4)[3] -77.9112 39.915 -1.952 0.051" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n", "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n", - "summarize(M)\n" + "summarize(M)" ] }, { @@ -241,10 +131,7 @@ "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n", "on the training data, and these will be used on all subsequent\n", "evaluations of the `transform()` method. For example, it is used\n", - "on the second line, as well as in the plotting function developed below.\n", - "\n", - "\n", - " \n" + "on the second line, as well as in the plotting function developed below." ] }, { @@ -258,22 +145,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d672f40e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.819794Z", - "iopub.status.busy": "2023-07-25T23:59:46.819458Z", - "iopub.status.idle": "2023-07-25T23:59:46.827533Z", - "shell.execute_reply": "2023-07-25T23:59:46.826805Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "age_grid = np.linspace(age.min(),\n", " age.max(),\n", " 100)\n", - "age_df = pd.DataFrame({'age': age_grid})\n" + "age_df = pd.DataFrame({'age': age_grid})" ] }, { @@ -288,22 +168,14 @@ "transform), as well as a grid of `age` values. The function\n", "produces a fitted curve as well as 95% confidence bands. By using\n", "an argument for `basis` we can produce and plot the results with several different\n", - "transforms, such as the splines we will see shortly. " + "transforms, such as the splines we will see shortly." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "56174265", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.833075Z", - "iopub.status.busy": "2023-07-25T23:59:46.832741Z", - "iopub.status.idle": "2023-07-25T23:59:46.848016Z", - "shell.execute_reply": "2023-07-25T23:59:46.842611Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def plot_wage_fit(age_df, \n", @@ -328,7 +200,7 @@ " ax.set_title(title, fontsize=20)\n", " ax.set_xlabel('Age', fontsize=20)\n", " ax.set_ylabel('Wage', fontsize=20);\n", - " return ax\n" + " return ax" ] }, { @@ -350,43 +222,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "1a10422f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:46.853147Z", - "iopub.status.busy": "2023-07-25T23:59:46.852739Z", - "iopub.status.idle": "2023-07-25T23:59:47.050039Z", - "shell.execute_reply": "2023-07-25T23:59:47.048574Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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df_residssrdf_diffss_diffFPr(>F)
02998.05.022216e+060.0NaNNaNNaN
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coefstd errtP>|t|
intercept111.70360.729153.2830.000
poly(age, degree=4)[0]447.067939.91511.2010.000
poly(age, degree=4)[1]-478.315839.915-11.9830.000
poly(age, degree=4)[2]125.521739.9153.1450.002
poly(age, degree=4)[3]-77.911239.915-1.9520.051
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 111.7036 0.729 153.283 0.000\n", - "poly(age, degree=4)[0] 447.0679 39.915 11.201 0.000\n", - "poly(age, degree=4)[1] -478.3158 39.915 -11.983 0.000\n", - "poly(age, degree=4)[2] 125.5217 39.915 3.145 0.002\n", - "poly(age, degree=4)[3] -77.9112 39.915 -1.952 0.051" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summarize(M)\n" + "metadata": {}, + "outputs": [], + "source": [ + "summarize(M)" ] }, { @@ -663,36 +317,17 @@ "source": [ "Notice that the p-values are the same, and in fact the square of\n", "the t-statistics are equal to the F-statistics from the\n", - "`anova_lm()` function; for example: " + "`anova_lm()` function; for example:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "0b576607", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.136679Z", - "iopub.status.busy": "2023-07-25T23:59:47.136366Z", - "iopub.status.idle": "2023-07-25T23:59:47.143007Z", - "shell.execute_reply": "2023-07-25T23:59:47.141901Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "143.59228900000002" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(-11.983)**2\n" + "metadata": {}, + "outputs": [], + "source": [ + "(-11.983)**2" ] }, { @@ -709,97 +344,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "7242df77", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.147738Z", - "iopub.status.busy": "2023-07-25T23:59:47.147429Z", - "iopub.status.idle": "2023-07-25T23:59:47.178407Z", - "shell.execute_reply": "2023-07-25T23:59:47.177311Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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df_residssrdf_diffss_diffFPr(>F)
02997.03.902335e+060.0NaNNaNNaN
12996.03.759472e+061.0142862.701185113.9918833.838075e-26
22995.03.753546e+061.05926.2070704.7285932.974318e-02
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coefstd errzP>|z|
intercept-4.30120.345-12.4570.000
poly(age, degree=4)[0]71.964226.1332.7540.006
poly(age, degree=4)[1]-85.772935.929-2.3870.017
poly(age, degree=4)[2]34.162619.6971.7340.083
poly(age, degree=4)[3]-47.400824.105-1.9660.049
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" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept -4.3012 0.345 -12.457 0.000\n", - "poly(age, degree=4)[0] 71.9642 26.133 2.754 0.006\n", - "poly(age, degree=4)[1] -85.7729 35.929 -2.387 0.017\n", - "poly(age, degree=4)[2] 34.1626 19.697 1.734 0.083\n", - "poly(age, degree=4)[3] -47.4008 24.105 -1.966 0.049" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X = poly_age.transform(Wage)\n", "high_earn = Wage['high_earn'] = y > 250 # shorthand\n", @@ -919,7 +384,7 @@ " X,\n", " family=sm.families.Binomial())\n", "B = glm.fit()\n", - "summarize(B)\n" + "summarize(B)" ] }, { @@ -932,21 +397,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "84f172f2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.217196Z", - "iopub.status.busy": "2023-07-25T23:59:47.216538Z", - "iopub.status.idle": "2023-07-25T23:59:47.224306Z", - "shell.execute_reply": "2023-07-25T23:59:47.223577Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "newX = poly_age.transform(age_df)\n", "preds = B.get_prediction(newX)\n", - "bands = preds.conf_int(alpha=0.05)\n" + "bands = preds.conf_int(alpha=0.05)" ] }, { @@ -959,29 +417,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "fcf376bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.229023Z", - "iopub.status.busy": "2023-07-25T23:59:47.228288Z", - "iopub.status.idle": "2023-07-25T23:59:47.431859Z", - "shell.execute_reply": "2023-07-25T23:59:47.430536Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "(17.999, 33.75] 94.1584 1.478 63.692 0.0\n", - "(33.75, 42.0] 116.6608 1.470 79.385 0.0\n", - "(42.0, 51.0] 119.1887 1.416 84.147 0.0\n", - "(51.0, 80.0] 116.5717 1.559 74.751 0.0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "cut_age = pd.qcut(age, 4)\n", - "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n" + "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())" ] }, { @@ -1153,33 +511,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "d3817102", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.462938Z", - "iopub.status.busy": "2023-07-25T23:59:47.462123Z", - "iopub.status.idle": "2023-07-25T23:59:47.472495Z", - "shell.execute_reply": "2023-07-25T23:59:47.471619Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3000, 7)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n", "bs_age = bs_.transform(age)\n", - "bs_age.shape\n" + "bs_age.shape" ] }, { @@ -1191,125 +530,20 @@ "We can form this same matrix using the `bs()` object,\n", "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.\n", "\n", - "We now fit a cubic spline model to the `Wage` data. " + "We now fit a cubic spline model to the `Wage` data." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "c06c0f27", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.478643Z", - "iopub.status.busy": "2023-07-25T23:59:47.478133Z", - "iopub.status.idle": "2023-07-25T23:59:47.545629Z", - "shell.execute_reply": "2023-07-25T23:59:47.543199Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept60.49379.4606.3940.000
bs(age, internal_knots=[25, 40, 60])[0]3.980512.5380.3170.751
bs(age, internal_knots=[25, 40, 60])[1]44.63109.6264.6360.000
bs(age, internal_knots=[25, 40, 60])[2]62.838810.7555.8430.000
bs(age, internal_knots=[25, 40, 60])[3]55.990810.7065.2300.000
bs(age, internal_knots=[25, 40, 60])[4]50.688114.4023.5200.000
bs(age, internal_knots=[25, 40, 60])[5]16.606119.1260.8680.385
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 60.4937 9.460 6.394 0.000\n", - "bs(age, internal_knots=[25, 40, 60])[0] 3.9805 12.538 0.317 0.751\n", - "bs(age, internal_knots=[25, 40, 60])[1] 44.6310 9.626 4.636 0.000\n", - "bs(age, internal_knots=[25, 40, 60])[2] 62.8388 10.755 5.843 0.000\n", - "bs(age, internal_knots=[25, 40, 60])[3] 55.9908 10.706 5.230 0.000\n", - "bs(age, internal_knots=[25, 40, 60])[4] 50.6881 14.402 3.520 0.000\n", - "bs(age, internal_knots=[25, 40, 60])[5] 16.6061 19.126 0.868 0.385" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n", "Xbs = bs_age.fit_transform(Wage)\n", "M = sm.OLS(y, Xbs).fit()\n", - "summarize(M)\n" + "summarize(M)" ] }, { @@ -1322,121 +556,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "8cb32db0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.550992Z", - "iopub.status.busy": "2023-07-25T23:59:47.550477Z", - "iopub.status.idle": "2023-07-25T23:59:47.578217Z", - "shell.execute_reply": "2023-07-25T23:59:47.577450Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept60.49379.4606.3940.000
bs(age)[0]3.980512.5380.3170.751
bs(age)[1]44.63109.6264.6360.000
bs(age)[2]62.838810.7555.8430.000
bs(age)[3]55.990810.7065.2300.000
bs(age)[4]50.688114.4023.5200.000
bs(age)[5]16.606119.1260.8680.385
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 60.4937 9.460 6.394 0.000\n", - "bs(age)[0] 3.9805 12.538 0.317 0.751\n", - "bs(age)[1] 44.6310 9.626 4.636 0.000\n", - "bs(age)[2] 62.8388 10.755 5.843 0.000\n", - "bs(age)[3] 55.9908 10.706 5.230 0.000\n", - "bs(age)[4] 50.6881 14.402 3.520 0.000\n", - "bs(age)[5] 16.6061 19.126 0.868 0.385" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bs_age = MS([bs('age',\n", " internal_knots=[25,40,60],\n", " name='bs(age)')])\n", "Xbs = bs_age.fit_transform(Wage)\n", "M = sm.OLS(y, Xbs).fit()\n", - "summarize(M)\n" + "summarize(M)" ] }, { @@ -1458,31 +588,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "f26b715f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.583782Z", - "iopub.status.busy": "2023-07-25T23:59:47.583349Z", - "iopub.status.idle": "2023-07-25T23:59:47.596998Z", - "shell.execute_reply": "2023-07-25T23:59:47.595684Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([33.75, 42. , 51. ])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BSpline(df=6).fit(age).internal_knots_\n" + "metadata": {}, + "outputs": [], + "source": [ + "BSpline(df=6).fit(age).internal_knots_" ] }, { @@ -1498,102 +609,21 @@ "When using B-splines we need not limit ourselves to cubic polynomials\n", "(i.e. `degree=3`). For instance, using `degree=0` results\n", "in piecewise constant functions, as in our example with\n", - "`pd.qcut()` above.\n" + "`pd.qcut()` above." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d6109816", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.608918Z", - "iopub.status.busy": "2023-07-25T23:59:47.608409Z", - "iopub.status.idle": "2023-07-25T23:59:47.641795Z", - "shell.execute_reply": "2023-07-25T23:59:47.640942Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept94.15841.47863.6870.0
bs(age, df=3, degree=0)[0]22.34902.15210.3880.0
bs(age, df=3, degree=0)[1]24.80762.04412.1370.0
bs(age, df=3, degree=0)[2]22.78142.08710.9170.0
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 94.1584 1.478 63.687 0.0\n", - "bs(age, df=3, degree=0)[0] 22.3490 2.152 10.388 0.0\n", - "bs(age, df=3, degree=0)[1] 24.8076 2.044 12.137 0.0\n", - "bs(age, df=3, degree=0)[2] 22.7814 2.087 10.917 0.0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bs_age0 = MS([bs('age',\n", " df=3, \n", " degree=0)]).fit(Wage)\n", "Xbs0 = bs_age0.transform(Wage)\n", - "summarize(sm.OLS(y, Xbs0).fit())\n" + "summarize(sm.OLS(y, Xbs0).fit())" ] }, { @@ -1613,16 +643,7 @@ "of ones, so the second, third and fourth coefficients are increments\n", "for those bins. Why is the sum not exactly the same? It turns out that\n", "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n", - "deciding bin membership.\n", - "\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - " " + "deciding bin membership." ] }, { @@ -1637,107 +658,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "7c73a3da", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.647140Z", - "iopub.status.busy": "2023-07-25T23:59:47.646766Z", - "iopub.status.idle": "2023-07-25T23:59:47.682939Z", - "shell.execute_reply": "2023-07-25T23:59:47.682146Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept60.47524.70812.8440.000
ns(age, df=5)[0]61.52674.70913.0650.000
ns(age, df=5)[1]55.69125.7179.7410.000
ns(age, df=5)[2]46.81844.9489.4630.000
ns(age, df=5)[3]83.203611.9186.9820.000
ns(age, df=5)[4]6.87709.4840.7250.468
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 60.4752 4.708 12.844 0.000\n", - "ns(age, df=5)[0] 61.5267 4.709 13.065 0.000\n", - "ns(age, df=5)[1] 55.6912 5.717 9.741 0.000\n", - "ns(age, df=5)[2] 46.8184 4.948 9.463 0.000\n", - "ns(age, df=5)[3] 83.2036 11.918 6.982 0.000\n", - "ns(age, df=5)[4] 6.8770 9.484 0.725 0.468" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ns_age = MS([ns('age', df=5)]).fit(Wage)\n", "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n", @@ -1754,32 +678,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "d73789b4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.692145Z", - "iopub.status.busy": "2023-07-25T23:59:47.691762Z", - "iopub.status.idle": "2023-07-25T23:59:47.897115Z", - "shell.execute_reply": "2023-07-25T23:59:47.891816Z" - } - }, - "outputs": [ - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "plot_wage_fit(age_df,\n", " ns_age,\n", - " 'Natural spline, df=5');\n" + " 'Natural spline, df=5');" ] }, { @@ -1802,34 +708,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "3e70b87d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.906535Z", - "iopub.status.busy": "2023-07-25T23:59:47.906039Z", - "iopub.status.idle": "2023-07-25T23:59:47.942756Z", - "shell.execute_reply": "2023-07-25T23:59:47.941983Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n", - " max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n", - " verbose=False)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "X_age = np.asarray(age).reshape((-1,1))\n", "gam = LinearGAM(s_gam(0, lam=0.6))\n", - "gam.fit(X_age, y)\n" + "gam.fit(X_age, y)" ] }, { @@ -1848,28 +734,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "efe03b12", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:47.948147Z", - "iopub.status.busy": "2023-07-25T23:59:47.947788Z", - "iopub.status.idle": "2023-07-25T23:59:48.314582Z", - "shell.execute_reply": "2023-07-25T23:59:48.314258Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", @@ -1881,7 +749,7 @@ " linewidth=3)\n", "ax.set_xlabel('Age', fontsize=20)\n", "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='$\\lambda$');\n" + "ax.legend(title='$\\lambda$');" ] }, { @@ -1894,36 +762,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "acff2af2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:48.316489Z", - "iopub.status.busy": "2023-07-25T23:59:48.316348Z", - "iopub.status.idle": "2023-07-25T23:59:48.751690Z", - "shell.execute_reply": "2023-07-25T23:59:48.750894Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time: 0:00:00\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "gam_opt = gam.gridsearch(X_age, y)\n", "ax.plot(age_grid,\n", @@ -1931,7 +773,7 @@ " label='Grid search',\n", " linewidth=4)\n", "ax.legend()\n", - "fig\n" + "fig" ] }, { @@ -1949,34 +791,15 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "a2d25550", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:48.756958Z", - "iopub.status.busy": "2023-07-25T23:59:48.756585Z", - "iopub.status.idle": "2023-07-25T23:59:48.783496Z", - "shell.execute_reply": "2023-07-25T23:59:48.782677Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4.000000100001664" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "age_term = gam.terms[0]\n", "lam_4 = approx_lam(X_age, age_term, 4)\n", "age_term.lam = lam_4\n", - "degrees_of_freedom(X_age, age_term)\n" + "degrees_of_freedom(X_age, age_term)" ] }, { @@ -1991,28 +814,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "bc6322de", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:48.795407Z", - "iopub.status.busy": "2023-07-25T23:59:48.792243Z", - "iopub.status.idle": "2023-07-25T23:59:49.191938Z", - "shell.execute_reply": "2023-07-25T23:59:49.177094Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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EIgHKEHliDqTNZkMsFptyPURRRCwWg81mg8FgmKMWkvmC+hohxejrHyFkTsznNFDVUCgUcLlciMfjCAaDRRkBYrEY9Ho9XC5Xwy58IvWD+hohxSgoJoTU3HxPA1Uti8WCnp6eKV8anE7ngv/SQORFfY2Qv6KgmBBSUwshDZQcLBYLzGYzVbQjs476GiEn0JxiQkhNURoo6RQKBYxGI6xWK4xGIwUpZNZQX5s/XnrpJbzvfe+D2+2GQqHA73//+7luUsOgoJgQUlOFaaBEUUQ6nUYikUA6nYYoipQGihBCqhCPx7Fu3To89NBDc92UhkPTJwghNcXSQEWjUcRiMSQSCT6P0Wg0wmw2UxooQkhd+crT7+DAWHTOjr+izYLvXLNW0rabN2/G5s2bZ7lF8xP91SGE1JTBYIBWq8WhQ4eg0Wig1+thMBiQy+UQjUYxMTGB5cuXUxooQkjdODAWxa7h0Fw3g8wymj5BCJkToihCFEU+d1GhUPCfEUIIIbVGI8WELBCiKNbF6vJkMolMJoMlS5YgHo8jHo9DEAQolUrYbDaYTCZkMhkkk0kYjcaat4+QatTLc0YImTkKiglZAOqpUAZbaGez2WC1WpHJZHibtFotRFFEOBymhXak4dTTc0YImTkKigmZ5+qtUAZbaJfL5aDVaqHT6Yp+n81maaEdaTj19pwRea1om9t7N9fHXyjorw4h81g9FsowGAyw2WwIBAJFbWLtjcVicDqdtNCONIx6fM6IvKRmfqgHsVgMAwMD/N9HjhzBW2+9haamJixevHgOW1b/aKEdIfNYPRbKUCgUcLlc0Ov1CAaDyGQyEAQBmUwGwWAQer0eLpeLggfSMOrxOSML186dO7F+/XqsX78eAPDFL34R69evxze+8Y05bln9o5FiQuaxwkIZ05mrQhkWiwU9PT1T5l86nU6af0kaTr0+Z2RhOu+88yiLz0mioJiQeWzy/N3JcrncnM3ftVgsMJvNtFKfNLx6fs4IIdLR9AlC5jE2fzcWi00ZOWDzd20225zN31UoFDAajbBarTAajRQQk4ZU788ZIUQaCooJmcdo/i4hs4+eM0LmB3qXQ8g8R/N3CZl99JwR0vgoKCZkAaD5u4TMPnrOCGlsFBQTskCw+buEkNlDzxkhjYvmFBNCCCGEkAWPgmJCCCGEELLgUVBMCCGEEEIWPAqKCSGEEELIgkdBMSGEEELIPPHwww9j7dq1sFqtsFqt2LRpE5555pm5blZDoKCYEEIIIWSe6OzsxHe+8x288cYb2LlzJy644AJceeWV2Lt371w3re5RSjZCCCGEkHL+cBvg2z93x29dCfzNjyRt+r73va/o3/fddx8efvhhvPbaa+jr65uN1s0bDRkUP/zww3j44YcxNDQEAOjr68M3vvENbN68GQCQSqXwpS99CU899RTS6TQuvfRS/PjHP0ZbWxvfx/DwMG6++Wb85S9/gdlsxvXXX4/7778fanVDXhJCCCGEzBbffuD4jrluxYzl83n85je/QTwex6ZNm+a6OXWvISNA9mpg+fLlEEURjz/+OK688krs2rULfX19+MIXvoD/+Z//wW9+8xvYbDbceuutuPrqq/HKK68AONFJLr/8crS3t+PVV1+Fx+PBddddB41Gg29/+9tzfHaEkJkSRZGqiBFCyP9v9+7d2LRpE1KpFMxmM373u99h1apVc92suqcQRVGc60bIoampCQ888ADe//73o6WlBU8++STe//73AwD6+/uxcuVKbNu2DWeeeSaeeeYZXHHFFRgdHeWjxz/5yU9wxx13wO/3Q6vVSjpmJBKBzWZDOByG1WqdtXMjhJQWjUbh8XgQDoeRz+ehUqlgs9ngcrlgsVjmunmEkElSqRSOHDmC7u5u6PX6uW6ONP9+0dyOFHduAD7+guTNM5kMhoeHEQ6H8dvf/hb//u//jq1bt87rwLhcv5IarzX8Qrt8Po+nnnqKvxp44403kM1mcdFFF/Ftent7sXjxYmzbtg0AsG3bNqxZs6ZoOsWll16KSCRSdiJ6Op1GJBIp+h8hZO5Eo1EMDAwgEAhAr9fDZrNBr9cjEAhgYGAA0Wh0rptICCE1p9Vq0dPTg9NPPx33338/1q1bhx/84Adz3ay615DTJ4DSrwbeeustaLVa2O32ou3b2trg9XoBAF6vtyggZr9nvyvl/vvvx1133SXviRBCToooivB4PEilUnA4HHy6hFarhcPhQDAYhMfjgdlspqkUhJDqtK5s6OMLgoB0Oi1TY+avhg2KV6xYgbfeeou/Grj++uuxdevWWT3mnXfeiS9+8Yv835FIBIsWLZrVYxJCppdMJhEOh6cNehUKBcxmM8LhMJLJJIxG4xy1khAyL0jM/FAP7rzzTmzevBmLFy9GNBrFk08+iS1btuC5556b66bVvYYNitmrAQA4/fTTsWPHDvzgBz/ABz/4QWQyGYRCoaLR4rGxMbS3twMA2tvbsX379qL9jY2N8d+VotPpoNPpZD4TQsjJyOVyyOfzJTPGqNVq5PN55HK5GreMEELmjs/nw3XXXQePxwObzYa1a9fiueeew8UXXzzXTat7DRsUT8ZeDZx++unQaDR48cUXcc011wAADhw4gOHhYZ6OZNOmTbjvvvvg8/nQ2toKAHj++edhtVrn9SR0QhpRqcwSarUaKpUKuVxu2sWxuVwOKpWK0iwSQhaURx55ZK6b0LAa8q9FuVcDNpsNH/vYx/DFL34RTU1NsFqtuO2227Bp0yaceeaZAIBLLrkEq1atwkc/+lF897vfhdfrxde+9jXccsstNBJMSB0pl1nCbDbDZrMhEAgUzSkGTgTSsVgMTqcTBoNhDs+AEEJIo2jIoLjSq4Hvfe97UCqVuOaaa4qKdzAqlQp//OMfcfPNN2PTpk0wmUy4/vrrcffdd8/VKRFCJmGZJdhiWrVajVwuh0AggHg8jp6eHrhcLsTjcQSDwaJtYrEY9Ho9XC4XLbIjhBAiybzJUzwXKE8xIbNDFEUcOnSo5ChwMBiE0+nE8uXLEYvFKE8xIQ2kIfMUk7onR57ihhwpJoTMbzPJLGGxWGA2m6miHSGEkKpQUEwIqTszzSyhUCgo7RohhJCqNHxFO0LI/FOYWWI6lFmCEEKI3CgoJoTUHYPBAJvNhlgshsnLHlhmCZvNRpklCCGEyIaCYkJI3VEoFHC5XNDr9QgGg8hkMhAEAZlMBsFgkDJLEEIIkR29eySE1CWLxYKenp4pmSWcTidlliCEECI7CooJIXWLMksQQgipFZo+QQipayyzhNVqhdFopICYEEIk+s53vgOFQoHPf/7zc92UhkBBMSGEEELIPLNjxw789Kc/xdq1a+e6KQ2Dpk8QQgghhJTxrVe/hUOhQ3N2/OX25fjWu78leftYLIZrr70W//Zv/4Z777139ho2z1BQTAghhBBSxqHQIbzjf2eumyHZLbfcgssvvxwXXXQRBcUzQEExIYQQQsg88dRTT+HNN9/Ejh075ropDYeCYkIIIYSQeeDYsWP43Oc+h+effx56vX6um9NwKCgmhBBCCJkH3njjDfh8Ppx22mn8Z/l8Hi+99BL+5V/+Bel0GiqVag5bWN8oKCaEEEIIKWO5fXlDHP/CCy/E7t27i3524403ore3F3fccQcFxBVQUEwIIYQQUsZMMj/MJYvFgtWrVxf9zGQywel0Tvk5mYryFBNCCCGEkAWPRooJIQuCKIpULpoQsuBs2bJlrpvQMCgoJoTMe9FoFB6PB+FwGPl8HiqVCjabDS6XCxaLZa6bRwghpA5QUEwImdei0SgGBgaQSqVgNpuhVquRy+UQCAQQj8fR09NDgTEhhBCaU0wImb9EUYTH40EqlYLD4YBWq4VSqYRWq4XD4UAqlYLH44EoinPdVEIIIXOMgmJCyLyVTCYRDodhNpunzB9WKBQwm80Ih8NIJpNz1EJCCCH1gqZPEELmrVwuh3w+D7V6+o86tVqNfD6PXC4neZ+1XLAnCAKCwSDS6TR0Oh0cDgeUyvofy5Cr3bQ4sjK6RoTIh4JiQsi8pVaroVKpkMvloNVqp/w+l8tBpVKVDJonq+WCPY/Hg/7+fgQCAR7wOJ1O9Pb2wuVyyXosOcnVblocWRldI0LkRUExIWTeMhgMsNlsCAQCcDgcRSNooigiFovB6XTCYDBU3FctF+x5PB5s374diUQCNpsNWq0WmUwGXq8XkUgEGzdurMvAWK520+LIyugaESK/+n8PRwghJ0mhUMDlckGv1yMYDCKTyUAQBGQyGQSDQej1erhcroqvm2u5YE8QBPT39yORSKClpQV6vR5KpRJ6vR4tLS1IJBLo7++HIAhVH0tOcrWbFkdWRteIkNlBQTEhZF6zWCzo6emB0+lEKpVCOBxGKpWC0+mUPJpWywV7wWAQgUAANpttyjxcpVLJR76DwWDVx5KTXO2mxZGV0TUiZHbQ9AlCyLxnsVhgNptPekHSbCzYKyWdTpecAw0AWq0WkUgE6XS66mNNVs2iLbnaXctr3ahmeo1oMR4h0lBQTAhZEBQKBYxG40n9t3Iv2CtHp9NBrVYjk8lAr9dP+X0mk4FarYZOp5vRfisFRtUu2pKr3bW81o1qJteIFuMtPPl8Ht/61rfwxBNPwOv1wu1244YbbsDXvvY1+jJUwcL9VCGEEInkXLBXicPhgNPphNfr5XNFGUEQEA6H0d7eDofDIXmflQIjORZtydXuWl7rRiX1GuVyOQwODtJivAXmn/7pn/Dwww/j8ccfR19fH3bu3Ikbb7wRNpsNn/3sZ+e6eXWNgmJCyIzU+lWslJy3UtpUTbvZgr14PI6JiQlotVooFAqIoohMJgODwVC0YK+aYymVSvT29iISicDn88FsNkOlUiGfzyMWi8FkMqG3t1dy3l8W8CaTSWi1Wmg0GoiiiPHxccTjcSxbtgxerxepVAp2ux3ZbBapVAoqlQp2ux2hUAgej4fPXy11bnK1u/BaB4PBomAuFotNWRw52/d+puQ8Vql9SblG7e3t/L4WBs5sMV4wGCy6r6Q8z9e/jvTBQ3N2fN0py+G65x5J27766qu48sorcfnllwMAurq68Mtf/hLbt2+fzSbOCxQUE0Ikq/WrWCk5b6W0SY52WywWtLe3o7+/H6Ojo0Xt6e7ulvVYLpcLq1evxq5du+D1evmxmpqasHr1asnp2FiWgnA4DEEQMDExwdtkNBqRyWRw9OhRJJNJqFQqeL1exONxCIIApVIJk8kEk8nEF23l8/my5yZXu9niyMnHcjqdM76vteyzch6r0r4qXSOVSiV5Md7JTitaSNIHDyH59ttz3QxJ3v3ud+Nf//VfcfDgQZxyyil4++238X//93948MEH57ppdY+CYkLqXL2MhM1GXtRy7WY5b+PxOEwmEwwGA3K5HDweD895azabK7YJQNmRUqntjkajfGpAR0cHDxwFQYDX64XZbObHqvYaRaNRRCIRuFwutLS08IBHrVYjEokgGo3y/ZS7hslkEmNjY4hGoxAEAXq9nl/HaDQKpVKJXC4HURSRzWaRzWZhMBj4CG8kEuFBUygUwtjYWMVrzdo9+RpNbncllRZHSumPM7kf1T5Dcj4fUvdV7hpFIpG6XbAoiiIEQUA+n0c+n4dSqaTRahl95StfQSQSQW9vL3+W77vvPlx77bVz3bS6R0ExIXWsXkbCJudFleNVbLl2m0wm9Pf3IxKJQKfT8aCO5WKNRCLYv38/Ojo6yrZpdHQUCoWiaKSU7YeNlEpp9+QR10QiUbSfbDbL88JWe42kHstsNiMWi5W999lsFqFQCPl8HhaLhR9Xo9HwRVjhcJhPi7Db7XwbtVoNs9mMUCgEQRAQCAQkXWuW7m7yPNeT6SOlFkdK6Y+F7al0Pypdx0rkfD5muq9S16heFyxGo1EcP36cT9MBAJVKBY1GA5VKVdO2zFe//vWv8Ytf/AJPPvkk+vr68NZbb+Hzn/883G43rr/++rluXl2joJiQOiX3SFg1ZpIXVcqr2ErnxhZs5fN5pFIpvnBLEASkUikIgsCDnubm5pJtGh8fRzqd5lMCCkdB2Uipz+dDZ2dn2XYnk0n4fD4enE+3n+PHj0On08FqtVZ1jaQcy+fzwW63Y2RkpOy9z+VySKfTMBqN07aJfcHQaDRl71cul0M4HIbNZit7rQFUff5SSOmPUtvj9/srXsdKz5Ccz4dc+6rHBYuFz73RaIRKpYJCoUAul4MgCNDpdBQYy+D222/HV77yFXzoQx8CAKxZswZHjx7F/fffT0FxBRQUE1KH5B4Jq/bVpJy5Y6Wc2/HjxxGPx6FWq6HX6/k2KpUKer0eiUQC8XgcqVSqbJsymQwmJiagVquLRkrZKGg0GsXExASy2WzZNmezWT7KXG4/Tqez6msk9VgskCt371tbW3mpZZ1ONyUwYmnSzGYzXxCn1+t5YJhKpXhQns1my55bNpuFKIo1eV0vpT9KaU8ul8Po6GjVz5Ccz4dc+5rpgsXZVvjc22w25HI5vkBToVAgn88jm83W7VQK3SnLG+b4iURiyoJWlUpVd1Uw6xEFxYTUITlHwuQYmZPzVayUc/N4PMhms1MCObYN+4Cv1Cb2f8uNlCYSiYoBRi6XQyaTKbsf9iq42msk5ViRSAShUKjsKHk4HIbT6URTUxOCwSBisVjRqDNbXGe1WqHT6aDRaBCLxZBIJHj2CTZnlX1pKHdubLS5Fq/rpfRHKe0RBAHxeLzqZ0jO50POfUldsFgLlZ57pVKJfD7Pn+t6IzXzQz143/veh/vuuw+LFy9GX18fdu3ahQcffBA33XTTXDet7lFQTEgdkmskTK6ROTlfxUo5NzZCXDh1gmFTKEwmE1paWhCLxUq2yW63IxqNlh0pZUUnymFFJ8rtx2g0wm63l22PlGsk5VisvZXuvVqtRmtrKw8AC+cnWywWKJVKuN1uiKKIiYkJuFwuZLNZHjxpNJqi4Lvc/ZeyjVyv66X0RyntMZlMSCaTVT9Dcj4fck97qLaao1wqPfcKhYJGMmXyox/9CF//+tfxmc98Bj6fD263G5/61KfwjW98Y66bVvcoKCakDsk1EibXyJycr2KlnJtOp4PL5eKZJoxGIz9eIpGAWq1GR0cHOjo6MDIyUrJNHR0diMViCIVC004NYLl4K82p1Wg0PGdvuf1Uao+UayTlWFarFVqttuK912g0/L4lk0k+Ijo5vzJw4pVrKBSC2WyGXq9HLpdDKBSCXq+H2+0GgLL3X8o2cr2ul9Ifpbb56NGjVT9Dcj4fszHtodRivFoqfO6nu56iKNbltIlGZLFY8P3vfx/f//7357opDYeCYkLqkFwjYXIupJHrVazUc2tpaeGppWKxGH/db7fbYbVasWjRIrS0tMBgMJRsE8uewF7LTp4aoFQq0dbWVvEaGQwGtLW1VdxPpfZIuUZSjlU4ulvp3isUiqL7xgK95ubmojZJubdybSMHqf2x3Dasf8jxDMk5VaGepj3IpfC5t9lsRb9jKdrUarXkojSEzAYKigmpQ3KNhMm9kEaOV7GF51aqOlzhubE5iOy1vyiKMBqN/NwqtUnKSCnbVkoFMZbJIpvNQqPRQKvVwmQySW4PU6pSX+GxpIzuSqmwJ6VNtdxGqkq5gy0WC0wmU9mKh1L7hxzV86S0R6p6mfYgl8J+HQ6HYTQaeTDMnm2NRtOw50fmB4UoiuJcN6JRRSIR2Gw2hMNhWK3WuW4OmYfqJU/xbJCrWp0Ucl3H3bt3Y+fOnXz0mY1cn3HGGVizZk3Nz13KfhpVLfu+XMdq1Gexllie4nQ6jUWLFkGv11OeYiKLVCqFI0eOoLu7G3q9vuh3UuM1CoqrQEExqYV6qWgnJ5avlFWZmzzKKWelMabcfkrlTWajhT09PfD5fNi6dSsSicSUbYxGI84991wsW7asYjtYpb5EIgGbzcZTprHRs40bN/KAVkqbpVzDRiPlfgDT5+gu3GYm519t/5C7PfNZMpnE4cOH0dXVBb1eX7dp2EhjkSMopukThNQ5KYtk6mEhjVSF+UqbmpqmzOOUWrFrpkrtR0re5JGREezevRvJZBItLS38dbhOp4PBYMD4+Dh27dqFrq6usqNdgiCgv78fiUSiaD96vR5arRZ+vx/9/f1oa2vjgUKlNku5ho1EakU3OaoHFqqmf8xGe+YzloJNpVLR6DCpKzSjnRBSUzOp2FUv7Tl27BjGx8dhtVqnzA9VKpWwWq2YmJiAx+Mpe6xgMMgXGk23H7YQKRgMVt3mWl5DOUk5N7/fj/Hx8Zqcf721hxAyeygoJoTUlJzVv2rVnlQqVTJtFwCeHq1S0JNOpyXtJ51OV93mWl5DOUk5t0wmU7HCXi2r59WyPYSQ2UNBMSGkpgrzlU5HzvzKcrWH5QrOZDLTbsMKalRK3cUKhVTaj06nq7rNtbyGcpJyblqtFhqNpibnX2/tIYTMHgqKCSE1xfKVxmIxTF7ny3LD2mw22fIry9GeRYsWobm5GZFIZErVLUEQEIlE0NTUVDHjg8PhgNPpRDgcnnY/rDSzw+Gous21vIZyknJuLS0taG5ursn511t7CCGzh762knmj0TIwyKlUztvZ2I/U6ywl56+UHLtSVHPvpeSE7ujogEajwdatW/ncYpY1IhKJwGAwYP369XzRUKn2KJVK9Pb2IhKJwO/3T5t9ore3t+K9m42qZ9VeR7n2I7V/ACfyNMuRX7jcNrPRnmrbNBv3YyF/fhLCUFBM5oWFnB9Urly1cuYNrrSdxWJBe3s7+vv7MTo6WnS87u5u2XPMViKlghjb165duzAxMYFoNAq1Wo22tjasX7+ep2Or1B6Xy4WNGzfyax2JRKBWq9He3j6jeyZ31bNa5vyVcm5S+oeU85cjv7Cc7ZGrTXLej4X8+TkfvfTSS3jggQfwxhtvwOPx4He/+x2uuuoq/vsbbrgBjz/+eNF/c+mll+LZZ5+tcUvrDwXFpOGVyiEaCAQQj8fndX7QUjlvvV4vIpFIUc7bavdjNpslXWcp9wMAvF4vdDodurq6ikbevF4vzGazpHsm572XUkFs2bJl6OrqgsfjQTKZ5KOEbIRYantcLhfa2tqqHt2Xq+qZXNdRzv1I6R+Vzl9qX5Srv0q5H3K1Sa77IdexSP2Ix+NYt24dbrrpJlx99dXTbnPZZZfh0Ucf5f+utI5hoaCgmDQ0qTlE52N+0JnmvK1mP/v370dHR0fF62wymWaU07WaHLuzce+l5ERWqVTo7Oysuj1KpRJOp1NSu6ptczlyXcfZ2E81eazlzC88k/5a7n7UMuexlGONjo5CoVAsyM/PmfrLz/cjMBqfs+M73Sac/9GVkrbdvHkzNm/eXHYbnU6H9vZ2OZo2r1BQTBraTPK1NkpxC6lmkvO2XPAlZT8+nw8KhQLNzc1lr3MwGJSU0xUArFZrVfes3u59vbVHKrna3Yj7kdIX5eqvcrZJrvMfHx+X7dzmu8BoHGNHInPdDNls2bIFra2tcDgcuOCCC3DvvffK8iW90VFQTBrafM7XWomUnLeRSKRizlsp+8lms0in0xWvczqdlpTTVaFQVH3P6u3e11t7pJKr3Y24Hyl9Ua7+Kmeb5Dr/bDYLURQbrs+S6lx22WW4+uqr0d3djcHBQXz1q1/F5s2bsW3btgVfYZCCYtLQCnOIThfUzef8oIU5byfXeQek57yVsh+NRgOdTlfxOut0uor3g/282ntWb/e+3tojlVztbsT9SOmLcvVXOdsk1/lrNBrZzo00jg996EP8/1+zZg3Wrl2LZcuWYcuWLbjwwgvnsGVzj/IUk4Y2n/O1ViJXzlsp+2ltbYXb7a54nR0OR81yutbbva+39kglV7sbcT9S+qKcOYhrmfNYyrGam5vR0tLScH2WyGvp0qVobm7GwMDAXDdlztHXP9LQZitfayOQK+etlP2sXLkSZrO5Yh5WpVIp6X4AM8vpOp16u/f11h6p5Gp3o+4HqNwXpWwj5b7K2SY5zt/tdgNAw/XZueB0m+bt8Y8fP45AIDCjFJ7zlUKc/PWQSBaJRGCz2RAOh2G1Wue6OQvaQs6z2Yh5imeyr0rq7d7XW3ukqrf7UW95euW8r/X2fNS6z6ZSKRw5cgTd3d3TTtki1YnFYnzUd/369XjwwQdx/vnno6mpCU1NTbjrrrtwzTXXoL29HYODg/jyl7+MaDSK3bt3N3RqtnL9Smq8RkFxFSgori8LuSJTI1W0O5l9VVJv977e2iNVvd2PeqvoJud9rbfno5Z9loLi2bVlyxacf/75U35+/fXX4+GHH8ZVV12FXbt2IRQKwe1245JLLsE999yDtra2OWitfCgonmMUFBNCCCEzQ0ExmQ1yBMW00I4QQgghhCx4FBQTQgghhJAFj4JiQgghhBCy4FFQTAghhBBCFjwKigkhhBBCyIJHQTEhhBBCao6SXxE5ydGfKCgmhBBCSM1oNBoAJ6r2ESIX1p9Y/zoZVOaZEEIIITWjUqlgt9vh8/kAAEajsSGK25D6JIoiEokEfD4f7HY7VCrVSe+LgmJCCCGE1FR7ezsA8MCYkGrZ7Xber04WBcWEEEIIqSmFQgGXy4XW1lZks9m5bg5pcBqNpqoRYoaCYkIImQFRFJFMJpHL5aBWq2EwGOjVL5kx6kcnqFQqWYIZQuRAQTEhhEgUjUbh8XgQDoeRz+ehUqlgs9ngcrlgsVjmunmkQVA/IqQ+UVBMCCESRKNRDAwMIJVKwWw2Q61WI5fLIRAIIB6Po6enhwIaUhH1I0LqF6VkI4SQCkRRhMfjQSqVgsPhgFarhVKphFarhcPhQCqVgsfjobyrpCzqR4TUNwqKCSGkgmQyiXA4DLPZPGXep0KhgNlsRjgcRjKZnKMWkkZA/YiQ+kZBMSGEVJDL5ZDP56FWTz/jTK1WI5/PI5fL1bhlpJFQPyKkvlFQTAghFajVaqhUqpLBSi6Xg0qlKhnsEAJQPyKk3lFQTAghFRgMBthsNsRisSnzPUVRRCwWg81mg8FgmKMWkkZA/YiQ+kZBMSGEVMAKDej1egSDQWQyGQiCgEwmg2AwCL1eD5fLtSDzzBLpqB8RUt/oHQ0hhEhgsVjQ09MzJb+s0+mk/LJEMupHhNQvCooJIUQii8UCs9lMlchIVagfEVKfKCgmhJAZUCgUMBqNc90M0uCoHxFSf2hOMSGEEEIIWfAaMii+//77sWHDBlgsFrS2tuKqq67CgQMHirY577zzoFAoiv736U9/umib4eFhXH755TAajWhtbcXtt99O+SEJIYQQQhaghpw+sXXrVtxyyy3YsGEDcrkcvvrVr+KSSy7Bvn37YDKZ+Haf+MQncPfdd/N/F76qyufzuPzyy9He3o5XX30VHo8H1113HTQaDb797W/X9HwIIYQQQsjcUojzoMi63+9Ha2srtm7dinPOOQfAiZHiU089Fd///ven/W+eeeYZXHHFFRgdHUVbWxsA4Cc/+QnuuOMO+P1+aLXaiseNRCKw2WwIh8OwWq2ynQ8hhBBCCJGH1HitIadPTBYOhwEATU1NRT//xS9+gebmZqxevRp33nknEokE/922bduwZs0aHhADwKWXXopIJIK9e/dOe5x0Oo1IJFL0P0IIIYQQ0vgacvpEIUEQ8PnPfx5nnXUWVq9ezX/+4Q9/GEuWLIHb7cY777yDO+64AwcOHMB//ud/AgC8Xm9RQAyA/9vr9U57rPvvvx933XXXLJ0JIYQQQgiZKw0fFN9yyy3Ys2cP/u///q/o55/85Cf5/79mzRq4XC5ceOGFGBwcxLJly07qWHfeeSe++MUv8n9HIhEsWrTo5BpOCCGEEELqRkNPn7j11lvxxz/+EX/5y1/Q2dlZdtt3vetdAICBgQEAQHt7O8bGxoq2Yf9ub2+fdh86nQ5Wq7Xof4QQQgghpPE1ZFAsiiJuvfVW/O53v8Of//xndHd3V/xv3nrrLQCAy+UCAGzatAm7d++Gz+fj2zz//POwWq1YtWrVrLSbEEIIIYTUp4acPnHLLbfgySefxH/913/BYrHwOcA2mw0GgwGDg4N48skn8d73vhdOpxPvvPMOvvCFL+Ccc87B2rVrAQCXXHIJVq1ahY9+9KP47ne/C6/Xi6997Wu45ZZboNPp5vL0CCGEEEJIjTVkSrZS9eEfffRR3HDDDTh27Bg+8pGPYM+ePYjH41i0aBH+9m//Fl/72teKpjwcPXoUN998M7Zs2QKTyYTrr78e3/nOd6BWS/uuQCnZCCGEEELqm9R4rSGD4npBQTEhhBBCSH1bUHmKCSGEEEIIqQYFxYQQQgghZMGjoJgQQgghhCx4FBQTQgghhJAFj4JiQgghhBCy4FFQTAghhBBCFjwKigkhhBBCyIJHQTEhhBBCCFnwKCgmhBBCCCELHgXFhBBCCCFkwaOgmBBCCCGELHgUFBNCCCGEkAWPgmJCCCGEELLgUVBMCCGEEEIWPAqKCSGEEELIgkdBMSGEEEIIWfAoKCaEEEIIIQseBcWEEEIIIWTBo6CYEEIIIYQseBQUE0IIIYSQBY+CYkIIIYQQsuBRUEwIIYQQQhY8CooJIYQQQsiCR0ExIYQQQghZ8CgoJoQQQgghCx4FxYQQQgghZMGjoJgQQgghhCx4FBQTQgghhJAFj4JiQgghhBCy4FFQTAghhBBCFjz1XDeAEEIImS9EUUQymUQul4NarYbBYIBCoZjrZhFCJKCgmBBCCJFBNBqFx+NBOBxGPp+HSqWCzWaDy+WCxWKZ6+YRQiqgoJgQQgipUjQaxcDAAFKpFMxmM9RqNXK5HAKBAOLxOHp6eigwJqTO0ZxiQgghpAqiKMLj8SCVSsHhcECr1UKpVEKr1cLhcCCVSsHj8UAUxbluKiGkDAqKCSGEkCokk0mEw2GYzeYp84cVCgXMZjPC4TCSyeQctZAQIgUFxYQQQkgVcrkc8vk81OrpZySq1Wrk83nkcrkat4wQMhMUFBNCCCFVUKvVUKlUJYPeXC4HlUpVMmgmhNQHCooJIYSQKhgMBthsNsRisSnzhkVRRCwWg81mg8FgmKMWEkKkoKCYEEIIqYJCoYDL5YJer0cwGEQmk4EgCMhkMggGg9Dr9XC5XJSvmJA6R+9yCCGEkCpZLBb09PRMyVPsdDopTzEhDYKCYkIIIUQGFosFZrOZKtoR0qAoKCaEEEJkolAoYDQa57oZhJCTQHOKCSGEEELIgkdBMSGEEEIIWfAoKCaEEEIIIQseBcWEEEIIIWTBo6CYEEIIIYQseBQUE0IIIYSQBY+CYkIIIYQQsuBRUEwIIYQQQhY8CooJIYQQQsiCR0ExIYQQQghZ8CgoJoQQQgghCx4FxYQQQgghZMGjoJgQQgghhCx4FBQTQgghhJAFj4JiQgghhBCy4FFQTAghhBBCFjz1XDeAEEJI4xBFEclkErlcDmq1GgaDAQqFYq6bRQghVaOgmBBCiCTRaBQejwfhcBj5fB4qlQo2mw0ulwsWi2Wum0cIIVWhoJgQQkhF0WgUAwMDSKVSMJvNUKvVyOVyCAQCiMfj6OnpocCYENLQaE4xIYSQskRRhMfjQSqVgsPhgFarhVKphFarhcPhQCqVgsfjgSiKc91UQgg5aRQUE0IIKSuZTCIcDsNsNk+ZP6xQKGA2mxEOh5FMJueohYQQUj0KigkhhJSVy+WQz+ehVk8/406tViOfzyOXy9W4ZYQQIh8KigkhhJSlVquhUqlKBr25XA4qlapk0EwIIY2AgmJCCCFlGQwG2Gw2xGKxKfOGRVFELBaDzWaDwWCYoxYSQkj1KCgmhBBSlkKhgMvlgl6vRzAYRCaTgSAIyGQyCAaD0Ov1cLlclK+YENLQ6F0XIYSQiiwWC3p6eqbkKXY6nZSnmBAyL1BQTAghRBKLxQKz2UwV7Qgh8xIFxYQQQiRTKBQwGo1z3QxCCJEdzSkmhBBCCCELHgXFhBBCCCFkwaOgmBBCCCGELHgUFBNCCCGEkAWPgmJCCCGEELLgUVBMCCGEEEIWPAqKCSGEEELIgkdBMSGEEEIIWfAoKCaEEEIIIQseBcWEEEIIIWTBo6CYEEIIIYQseOq5bgAhtSSKIpLJJHK5HNRqNQwGAxQKRcMfSy5S2iz1vKRsJwgCgsEg0uk0dDodHA4HlMr6/q4uV5vz+Tw8Hg+SySQMBgNcLhdUKtUstPiERuyPUjTqM11vfX++9g9CZoKCYrJgRKNReDwehMNh5PN5qFQq2Gw2uFwuWCyWhj2WXKS0Wep5SdnO4/Ggv78fgUCA/yF2Op3o7e2Fy+Wak2tQiVxtHhwcxK5duzAxMcH309TUhPXr12PZsmWyt7sR+6MUjfpM11vfn6/9g5CZqu8hmRLuv/9+bNiwARaLBa2trbjqqqtw4MCBom1SqRRuueUWOJ1OmM1mXHPNNRgbGyvaZnh4GJdffjmMRiNaW1tx++23I5fL1fJUSI1Eo1EMDAwgEAhAr9fDZrNBr9cjEAhgYGAA0Wi0IY8lFyltlnpeUrbzeDzYvn07vF4vjEYjmpubYTQa4fV6sX37dng8njm+IlPJ1ebBwUFs3boVY2NjMBgMcDqdMBgMGBsbw9atWzE4OChruxuxP0rRqM90vfX9+do/CDkZDRkUb926Fbfccgtee+01PP/888hms7jkkksQj8f5Nl/4whfw3//93/jNb36DrVu3YnR0FFdffTX/fT6fx+WXX45MJoNXX30Vjz/+OB577DF84xvfmItTIrNIFEV4PB6kUik4HA5otVoolUpotVo4HA6kUil4PB6IothQx5KLlDaPjo5KOi9BECTta//+/UgkEmhpaYFer4dSqYRer0dLSwsSiQT6+/shCMJcXxpOEAT09/dX3eZ8Po9du3YhmUyiubm5aD/Nzc1IJpPYtWsX8vm8LO1uxP4oRaM+03L1I7nM1/5ByMlqyKD42WefxQ033IC+vj6sW7cOjz32GIaHh/HGG28AAMLhMB555BE8+OCDuOCCC3D66afj0UcfxauvvorXXnsNAPC///u/2LdvH5544gmceuqp2Lx5M+655x489NBDyGQyc3l6RGbJZBLhcBhms3nKHDmFQgGz2YxwOIxkMtlQx5KLlDaPj4/D7/dXPK9gMFhxX6Ojo/D5fLDZbFPmUCqVSthsNgQCAQSDQQAn/nAnEglEIhEkEolp/0BL2UaKUvsJBoMIBAKS21yKx+PBxMQErFbrtPuxWq2YmJiQbbSwEfujFI36TMvVj+QyX/sHISdrXswpDofDAICmpiYAwBtvvIFsNouLLrqIb9Pb24vFixdj27ZtOPPMM7Ft2zasWbMGbW1tfJtLL70UN998M/bu3Yv169dPOU46nUY6neb/jkQis3VKREa5XA75fB5q9fTdXa1WI5/PyzJ1ppbHkouUNmezWYiiWPG80ul0xX2l02lks1lotdppt9FqtYhEIkin07LOc66k3H7S6TRyuZykNpfDFjKV2080GpUtCGnE/ihFoz7TcvUjuczX/kHIyWrIkeJCgiDg85//PM466yysXr0aAOD1eqHVamG324u2bWtrg9fr5dsUBsTs9+x307n//vths9n4/xYtWiTz2ZDZoFaroVKpSn6w53I5qFSqkn8Y6vVYcpHSZo1GA61WW/G8dDpdxX3pdDpoNJqSb2QymQzUajVyuZxs85wrqbQfthiqUpt1Ol3Z4xgMBkn7MRgMktpdSSP2Ryka9ZnW6XSy9CO5zNf+QcjJavig+JZbbsGePXvw1FNPzfqx7rzzToTDYf6/Y8eOzfoxSfUMBgNsNhtisdiU1+qiKCIWi8Fms8kSiNTyWHKR0ubm5ma0tLRUPC+Hw1FxX263G62trQiHw1PmTgqCgHA4jKamJiSTSVnmOVeaSiFlXmUymURTU1PZNjudTjgcjrLHcrlcaGpqQiQSmXY/kUgETU1NsmUgaMT+KEWjPtMOhwNOp7PqfiSX+do/CDlZDf3179Zbb8Uf//hHvPTSS+js7OQ/b29vRyaTQSgUKhotHhsbQ3t7O99m+/btRftj2SnYNpPpdLqafYMn8lEoFHC5XIjH4wgGgzCbzXwkMhaLQa/Xw+VyyZKTs5bHKlRNjlEpbXa73QBQ8byUSiXf18TEBLRaLRQKBURRRCaTgcFggNvthtVqRTQahd/vh81mg1arRSaTQTgchtFoRHd3N8bHxyvOcwYAq9VacT6k0Wgsef5S5lVGIhF0d3cjGo3C5/PBZDLx84/H4zCZTOjt7a2YZ1alUmH9+vXYunUrn6NdeB2NRiPWr18vW77iueqPs61Rn2mlUone3l5EIpGSfV9KPyo0289+I/YPQk5WQwbFoijitttuw+9+9zts2bIF3d3dRb8//fTTodFo8OKLL+Kaa64BABw4cADDw8PYtGkTAGDTpk2477774PP50NraCgB4/vnnYbVasWrVqtqeEJl1FosFPT09U+aMOp1O2XNx1vJYgDxzaqW2Wco2FosF7e3t6O/vx+joaFEe1u7ublgsFlgsFmzcuJHnao1EIlCr1Whvb0dvby9MJhPGxsZkmedcaT6k1HmVTqcTq1evxq5duzA2NlaUX3j16tWSR3eXLVuGRCKBnTt3IhgM8utot9txxhlnyJ6nuNb9sVZmcl7VFqaQ8xq6XK6yfX8mbwlq+ewTshAoxAbMtfKZz3wGTz75JP7rv/4LK1as4D8vfM1z8803409/+hMee+wxWK1W3HbbbQCAV199FcCJ1Einnnoq3G43vvvd78Lr9eKjH/0oPv7xj+Pb3/62pHZEIhHYbDaEw2FYrVaZz5LMhkatflUKmwubSqWmHeXp6emZ0R81OSrasTYlk8lpR4oL21SqqlcikcDevXuh1+unXZSUyWT4Qler1Vpym1Qqhb6+vrIjxVKOlUqlsGTJEoyMjCCRSEChUEAQBCiVSoiiCKPRKPlas+uTSCR4QM7mbc5kPzM1XyuWSemPchWmqKeKdnPx7BPSqKTGaw05Uvzwww8DAM4777yinz/66KO44YYbAADf+973oFQqcc011yCdTuPSSy/Fj3/8Y76tSqXCH//4R9x8883YtGkTTCYTrr/+etx99921Og0yBxQKRdkAqZGONXkuLPsDxubCBoNBeDyeaacFVNPmctsUtqmpqanouKIoTmmTUqmE0+mcsh821zEQCBSdG9sPm+esUCjKbsMKY5Qj5VhNTU0IhUJIpVJwOp0Vz6uUwutTzX5ORi37fi2VO69SgWMgEEA8Hp9x4CjnNSzV96WYq2efkPmuIYNiKYPber0eDz30EB566KGS2yxZsgR/+tOf5GwaITUzkxyjtfpjJ1ebZjrPudT8ZSnzIaUcy+Fw4OjRo1WfVz3es/lqNgLHekH9iJDZ0ZBBMSFkdnKMVvsKVc42SZ3rWGn+shSVjiWKoiznRXlhZ6aa/jifA0fqR4TMDgqKCWlQhTlGp5sLO9Mco3LMvZS7TRaLBWazuWRgFI1G4fV6odPp0NXVVTRS7PV6YTabZxQYlzpWIpGQ5bzkvj7zWbX9cT4HjtSPCJkdDZ+nmJCFSs4co3IVwZiNvKdsrqPVaoXRaOQB8eT5yyyotVgsaGpqkpynWMqx5DovygsrjRz9cT4XpqB+RMjsoKCYkAbF5sLq9XoEg0FkMhkIgoBMJoNgMCg5x6iU4hVSg0u52iTFTF6PV0uu86rl9WlUcvXH+Rw4Uj8iZHY03ldkQggnR45Ruede1irvaa1fjxeel9/vRyaTgVarRUtLy7zJCytXWq5qUvvVcrFmIweO9dyPCGlUFBQT0uAqzbutZDaCy2rbJMVczatko46F0zhmqhbXZ6bkyucrZT/ltpFrUSMw/wPHeuxHhDQyCooJmQeqyTE6W8HlbOc9lZJfWEqeYqkKc95arVY+6jgxMYFEIjErOW9rVVBBrny+UvYDoOw2HR0dNV2s2egovzAh8qGgmJAFrtbBpVxq+Xp8LnLeylmJrRy5zk3KfkZHR6FQKMpuEwqFYLVaMTExIVt/pMCRECIFBcWELHCzFVzWYpSzVq/Ha53zVu5KbOXIdW6F+wGAdDrN74dWq4XZbMb4+DiAE6W5yx1ryZIlSCQS83IuMENllecnuq+NjYJiQojswWWtRjlZ22f79XgtF/XVelRarnNj+8lms/D7/UgkEvzeG41G2O12ZLNZiKJY8Vh6vX5ezwWu5fNBaofua+OjoJgQAkC+4LKWo5zMbL8er+WivlqPSst1bmq1mgfEgiBAr9fDYDAgl8shGo0iHo/DZDJBp9NJOpbRaJyXc4Hn4vkgs4/u6/xAeYoJIVyp4hVSyZnzuJ7UMudtrVPNyXVuer2eT3EwmUzQaDRQKBTQaDQwmUyIxWIAgObmZsnHqrY/1pv5+nwsdHRf5w8KigkhsqllQY1aqmWxhFpXYpPr3FKpFDQaDcxmM+LxOHK5HERRRC6XQzweh9ls5kHCQi06MV+fj4WO7uv8QdMnCJklC3HBxUxHOWt5jaopKAHUblHfXGQDmcm5lbpG7N8dHR0Ih8OIx+MQBAFKpRJWqxU2mw2ZTGbezxcup9ZvAUht0H2dPygoJmQW1HrBRb0E4DOZn1rLa1RtQQm2TS0W9c1VJTYp51buGrF7r9Fo4HK5kMlkirJPZLNZHjjM1/nClcxVwRkyu+i+zh90hwiRWa0XXNTTimepo5y5XA6Dg4M1uUZyFJQobE8tct7OVSW2cudW6TouW7as6N7rdDr+3043wr0Qcwc3ak5wUh7d1/lD1qD40KFD+I//+A9s27YNXq8XyWQSzz33HP+jAwB79uzB8PAwTCYTzj33XDkPT8icq3U6rXpb8SxllLO9vR1er7cm10jq/RBFsaaFOaSwWCwwmUwIBoNIp9PQ6XRwOBxQKmu/FETKdfR6vWhvb6/5CHcjmau3AGR20X2dP2QJigVBwJe//GX84Ac/gCAIfIWlQqFAJpMp2nZ4eBhXXHEF1Go1jhw5go6ODjmaQEhdqGU6rbmosiZFpVFOlUpVs2sk5X74/X4AlQtKyJUCTSqpbwBqMXVGar/u7OxcsPOFpZqrtwBkdtF9nR9kCYo/9alP4Wc/+xlEUURHRwc2bdqE3/72t9Nu+973vhfd3d0YGhrCb3/7W3zuc5+TowmE1IVaLriodT7bmSg3PzUSidTsGkm5H5lMBgqFoq4WB0p9A1CrqTMz6ddWq3VBzheeiVrMTSe1R/e18VX9Hu7FF1/EI488AgD46le/iqGhIfz6178u+9/83d/9HURRxJ///OdqD09IXallOq16X/FcKsdsLa+RlGNptVpoNBpJ7YlGozh06BD27t2Lffv2Ye/evTh06BCi0WjVbWWk5jyNRCIYGBhAIBCAXq+HzWaDXq9HIBDAwMCArG2a6T2bb/mFZwNdo/mJ7mtjqzoo/td//VcAJ0aA7733XqhUqor/zcaNGwEAe/furfbwhNSVWhZ5qHU+W7nU8hpJOVZLS4ukghK5XK4mQajUNwBHjx6tWbGAWt4zQgiZK1UHxdu2bYNCocDHPvYxyf9NZ2cnAMDr9VZ7eELqSi2LPDRqoFLLayT1WG63u+w2kxcHzmYQKuUNQDKZRCgUqlmxgFreM0IImStVDyH5fD4AQFdXl+T/RqPRAAAlsiYNS0qRh9HRUYyPjyObzUKj0aC5uRlut1u2uZ71vuK5loUw5DhWuXs208WB1cw7lpLzVKFQQBRFqNVqiKI4JSfwbEydYddxZGQEHo8HmUwGWq0WLpcLHR0dtJBohuoltzgh5K+qDopNJhNCoRBfwS3F8ePHAQBNTU3VHp6QmpO6uIn9gSvMxiK3el3xXMtCGHIeq9Q9m8n87WoXv0nJeWq325FMJhGNRhGLxZBIJPixWGGM2Zg6E4vF4PF4MDY2xr84AIDNZqOgeAbqKbc4IeSvqv7EXLp0Kd58803s27cPF198saT/5plnngEA9PX1VXt4QmpqpoUgrFbrrOcOrrcVz+waJZNJvohNFEWMj4/PSWEOKUU3Cvcz3T3r6OiQVLEqlUphZGSkqrzRUt4ALFmyBEeOHMGhQ4eg0Wig1+thMBh4UD4xMYHly5fLOnXG4/Fg+/btSCQSsNls0Gq1yGQy8Hq9iEQi2LhxI1wul2zHm6/qLbc4IeSvqp5TfMkll0AURTz00EMQBKHi9vv27cNjjz0GhUKB9773vdUenpCakZIVYHR0VFLmALkWQDH1suKZXaNwOIxUKgWv14uRkRE+HzccDvPzrzaTg9QsDexai6KIRCKBSCSCRCJR9PNK+wkGgxXnb1utVoRCIVnuPXsD4HQ6+XVLpVJwOp1FQZMoihBFkd9vNq1C7v4lCAL6+/uRSCTQ0tICvV4PpVIJvV6PlpYWJBIJ9Pf3S/obsJDNtM8SQmqr6pHiz372s/jhD3+IwcFBfPrTn8aPf/zjkq/snn/+edx44438w/0Tn/hEtYcnpGakZAUYHx8HUH+FIGolmUzC5/MhGo1CEAQYDAaoVCrk83lEo1EolUr4fD7Y7XaMjIxUNZo8kzzN+Xy+5OtqKfOFI5EIlixZUnb01uFw4OjRo7LljS73BiCRSCCTyfA2xeNxCIIApVIJm80Gk8mETCYjWz8LBoMIBAKw2WxTKuqxYwYCAQSDQTidzqqPx8y3ebf1nFucECJDUNzW1oaf/OQnuO666/DII4/gueeew+WXX85//4Mf/ACiKOKVV15Bf38/RFGEUqnEY489BrPZXO3hCakZKfNKs9ksXwBVapu5zB0sh3KBSjabxcTEBARBgMViKcpNbDabEY1GEQgEMDo6inA4DEEQMDExUTQfNpPJSKrEJ3WebygUwtjYWMnX1W1tbZL2o9fry87fFkVR9rzRpaZ8sHO32WywWq1TFtqJoohwOCxbP0un0yWnjgAnqihGIhGk02lZjgfMz3m39Z5bnJCFTpZVGNdeey00Gg0+9alP4dixY/jpT3/K/5j9+7//O4C/Llwxm814/PHHiwJnQupJqaBPSlaAwswq5eae1lvuYKkqBSq5XA6ZTGbaKRwKhYIHT16vF9lsFoIgTJkPq1QqMTY2hs7OzrKjZVLuh0qlQiAQKFsKOxAIQKlUSrpnbBFbqdFbKe2RuyiJVquFTqcr+n02m5W1n+l0Ol79T6/XT/l9JpOBWq3m7ah2hHe+zruV2mcb9fOBkEYn25P3gQ98ABdeeCF+/OMf47//+7/x1ltvFX3b7evrw9/8zd/gc5/7HFpbW+U6LCGyKhf0mc3milkBmpuboVAoym7jdDrrLnewFFICFRYYZTIZ6HS6KefPgqdoNAqNRlM0mqzRaPjvQqEQstls2fZIydLAAthyr6tTqRQMBgNisZike1Zq9FZKe+S697U8FgA4HA44nU54vV4+D5YRBAHhcBjt7e1wOBxVj/BOnnc73RcZKW8S6lGt7xshZGZk/TrqdDrx9a9/HV//+teLXos2NTXxETRC6pWUoK9SVgC32w0AdZs7+GRJDVQ6Ojpgt9sRCoX4+bLzT6VSUKlUMBgMCAaDMJlMJUeTE4lExVfIUrI0OJ1ODA8PV3xd7XQ6kcvlqrpntcwbXesc1UqlEr29vYhEIvD7/UXZJ8LhMIxGI3p7exGPx6se4Z3P827rPbc4IQvdrL2jUSqVaG5unq3dEyIrqUHf8uXLJReCkJI7WBAEBINBpNNp6HQ6OByOKQuZ6kFhoAKcmGNaOIeVBSodHR18jm4ul+MjvhqNBna7HWq1GjabDel0mhd/iMfjfBu2QIwVoKiksFCKx+Ph15FVqVOpVBgZGeFTWybPvWWvq+12OywWi6TCFOXu2UwKt1R77wuPNTo6yvfjdrunLRIj5XjltnG5XNi4cSP2798Pr9fLr1F7eztWrlyJ9vZ2HDp0CKlUCna7Hdlsln8RYl+UCkd4Sx2rcN6tIAiIxWL8OrIgsnDebS0X48nxvNaycA0h1D9mhiYuEYKZjU5JyQssZRuPx4P+/n4EAgG+jdPpRG9vb93le2WBSjab5RkiWLYDk8kEm82GfD6PfD4Pl8vFA7VUKsW3SyQSWLx4MTo6OhCPx3H06FEcPXoUmUyGpxXTarVobm7GkiVLJL9disViGBkZgc/n48GTIAiwWq1ob2+HzWbD6OgoBEGYUuRCqVTC7XbDYDDA6/VWLEwh9Z5VKtwi172f7txFUYTVai0KrqQcT2qbWMq3wv8Bf32GVCoVvF7vlD5iMpn4MxQOh0sey2azQaVSwe/380wm7J5ZLBa0trbCYDDwqTa1Wown5/Nay8I1ZOGi/jFzFBQTgpmvCi9XCIIpt02jFUJgr3j9fv+UVGuRSITPx1Wr1QiHwxgfH0c6neYLi/L5PNLpNMbHx/m19vl8yGQy0Gg0UCqVEAQBqVQKPp8PnZ2dkuZVlrqOY2NjiEaj2LhxIywWC8LhMN9Gr9fzbYxGI1asWAGv11vxfgCouI3ZbK5YuCUWi8ly76Wcu8vlktTX2LnF43GYzWYYDAaexq7UNuzcxsbGEIvF0NfXh3g8jkQigWw2O6WPsOkOo6Oj2Lt3b8n2bNiwgedFZs8QO1YwGMTExATWrVuHXC6HwcHBmizGm43nVcpnSDnzdTEikQf1j5MjS0W7mVIoFNDr9bDZbFi+fDnOPPNMfPCDH6Syz2TO1HJV+ORCCOz1q16vh1arhd/vR39/P9ra2upmKoVer0c2m0UsFitqs1qthslkgt/vh9lshlarRX9/P7LZLLq7u5HP5/loIRv96+/vh8fjQT6fh8VigUql4sfJ5/NIJBJ8ZFelUpV8/SflOu7fvx8dHR18tDeRSCCRSECpVPLrG4lEMDIyUnY/+/bt4xkmyt0zl8tVdgrO6OhoxWNJufdS+1BLS0vF7di5RSIR6HQ6RCKRopH7SCQyZZtoNDplm8HBQWQyGWQyGdjt9inp+EKhEPL5PA4fPlyx3el0uujLl0Kh4FNekskkn85Ui8V49fi8zufFiKR61D9OXtV/4YeGhor+zSoqTWe6373++ut44okn8KUvfQlf//rXceedd1bbJEJmrJarwueqEEI1UqkUD27i8fiUBXRsJMLr9Rad2+Tzs9lsOH78OL+eoijy3M7sy7LBYOCv/Ww2W8nXf5lMpuJ19Pl8UCqVcDqd084pzmaz8Hg88Pl8Zffj9XqhUChgt9tLbjM2NgYAPANJITYFR8qxpNx7qX1oaGio4nZer5e/AUmlUjy7BBu5F0URx48fB3DiWZhuG0EQ4PP5Ko58ptNpxOPxsu3xeDwATuTAz+VyPEBWKpW8aiObn+x0Omd9MV49Pq/zeTEiqR71j5NXdVB8/fXXAwDeeecd7Nq1C6Iowul04tRTT0VLSwsAwO/346233kIgEIBCocCpp56K1atXIxKJYM+ePfwV2Ne+9jV4PB788Ic/rLZZhMxILVeFz0UhhGqxhWodHR0IhUJIJBJ8EZXFYoHdbucV1CqdWyaTQTab5QFNPp/nQTEbGQ4EApiYmIDf7y/5+s9sNlc8Vjab5dM4FArFlHy+arUa6XQa2Wy2YpvZqGi5Y7GUc9OReiwp976wD4miiFwuxwNHtVrN9xOPxyteo3Q6zdPW6fV63sdVKhX0ej2SySRisRgAwGQyTbtNIpFAPB6H3W6HSqWaNvMIm5LBqhiWag+71ixd3+RzY19S2X0tda3lKoJRj88rFQEh5VD/OHlVB8WPPvoo/uM//gO//OUvsXTpUnzve9/D5ZdfPu2q5j/+8Y/4whe+gL179+KWW27BTTfdBADYsWMHPvnJT+Ltt9/GQw89hA9/+MM488wzq20aITMi96rwUmZaCKEesOklGo0Gbrd72hHXfD7PF0CVOzcW+LBtJn9wp9NpqFQqxONxACj5+o8dv9yxNBoNdDpd2WkxOp2OjyKXa7NCoah4LJbRoppjSbn3rA+xRWhsRJeNtrOpPiaTSVJfA06Mek43qsRGhAHwqQyTt2FfZnQ6Hex2O2Kx2JQvTuwtQzwer3it2f+v1+unLLhMp9OS7mvhdKdqVuDX4/NKRUBIOdQ/Tl7VV2TXrl34xCc+gfb2drz22msl07AplUr8zd/8DTZt2oTTTz8dN998M9auXYszzjgDGzZswAsvvIC1a9fC6/XiX//1XykoJnNCrlXh5cykEEK9mDy9pDAAKJxe4nK5Kp5bZ2cnxsfH4ff7p90mEonA6XTy1+WlXv8lk0neplLHamtrg8vlwsTERMlpMS6XC4IgYGxsrOz9UCgUFbdxuVwIBoNVH6vSvXc4HLBYLDh8+DAMBgP0ej0PXmOxGJLJJJYuXYquri4cO3as7P1g0z2SyeS0BVeSySRMJhOA4ukVhftho/nNzc1Ip9NwuVz8ixL7MhUKhfhitHLtYduUu0ZS7iub7lTtCvx6fF6pCAgph/rHyat6VcCDDz6IXC6HO++8U1Je4paWFtx5553IZrN48MEH+c+dTiduvvlmiKKI//u//6u2WYScNLYq3Gq1ThuUVYsVQjAajXx6AAss/H4/L4RQL4vsgL9OL9Hr9QgGg8hkMhAEAZlMBsFgkE8vUalUFc9t5cqVOP3002EwGDA+Pl60zfj4OAwGA/r6+iCKYtnXf4IgYOnSpRWP5Xa7y7bb7XZj5cqVZfezatWqitv09vaio6Oj6mNJufdsfrNarebBpyiKPG2eWq3mUxmk3A+32w2NRoNwOMzneGezWYTDYf52wO12Q61WIxKJFJ1bJBKBWq2Gy+VCZ2cn9Ho9QqEQH7VWKBQIhULQ6/Xo6OiQ1J5K10jKfXW5XIjFYhgYGEAgEOCLu/V6PQKBAAYGBhCNRiv2/Xp8XqU+j7SIamGi/nHyFGKpVXESLVmyBMePH8frr7+OM844Q9J/s3PnTmzcuBGdnZ0YHh7mP9+yZQsuuOACmM1mRCKRappVE5FIBDabDeFwGFarda6b05AWcmLxRspTzEgddZNyboODg9i1axcmJib4Nk1NTVi/fj1cLhf27t3LV/hPlslkkEql0NfXVzbnLTuWlHbLlctXrmOVk0gksHfvXiSTSZ4Sje3HbDajra2Nf7kwGo1lj8eKbhw+fBjRaBSxWIy322w2w2KxYOnSpRBFEUNDQzwFX+HxrFYruru7sXz5csRisbq41mazGYcOHSo5WsYWxi1fvlzSZ049Pq+Uh5aUQ/3jr6TGa1VPn/D5fAAwo0UGbFu/31/0c/b6iSZ/LwwL/YF1uVxobW2Fx+NBMpmEwWDgo631Sur0EpfLhba2trLVv5YtW4YlS5ZgaGgI8XgcJpMJXV1dfDGV1Nd/RqOx4nWU0m4pbZZyz+Q6VjlsIU1LSwuam5un7EehUCAcDvPP0krHY4tMLRYLn4bB/q/RaORBXyKRKKpIp1QqIYoi34YtkJPrWlfaptyxEomEpCqMUlfgV3vPJpNjQEDO6V4LeYBivqrFdMD5puqguLm5GaOjo3j++edx1llnSfpvnnvuOf7fFgqHwwDAs1aQ+YsSi0//pSCRSNT9lwKpRQdYKrRSJp8/e8XHzl9qNhCp11FKu2fa5tk8VjlsIU00GuWL11jFvkwmA5PJNGUhTbnjlVpkOvlLqpRt5Dx/KduUOlZhFUa/3z+lmqHdbp/xCvxq7lkhOQcEqi0CInd7SH2Ro38sJFUHxeeffz6eeOIJ/PM//zOuuOKKilModuzYgQcffBAKhQLnn39+0e/eeecdAKjbV8dEHpRYnL4USD3/StlA5L6O5UbLZnKs2R51MxgM0Gq1OHjwIDQaDYxGI68exyoKnnLKKTNaSCNX+fJ6weZbsyqMLAd2LpfjXybYvOxamotnX65+Tch8V/WnwR133IFf/epXSCQSOOecc/DZz34WH/nIR9DX18cfOlEUsXfvXvz85z/Hj370I76C+ctf/nLRvn73u99BoVDgnHPOqbZZpI4t9MTiC/1LwUzOv1wQJvd1rDQ/VeqxpMyplYtCoSgqisTSsp1sv6m2fPlcKBXw6fV6/mahsBKdRqPh1RVZbuZatrXWz75c/Xo+fhYRMlnVQXFfXx8eeeQR3HjjjUilUnjggQfwwAMPQKfT8bLNExMTfB6xKIpQKpX4t3/7N6xevZrvZ3BwEIcPH8bixYvxvve9r9pmkTq20BOLL/QvBTM9/1JBmJzXsdJoWUdHh6Rj+f1+jIyM8AIVGo0GoihifHxc1lG3ZDKJTCaDxYsXl8wJzIqpzMc+xJQL+FgqOJYfmZWMZgVEzGYzNBoNUqlUza5RrZ991q9L9Uep/Xq+9yNCGFneG33kIx/B0qVLccstt+Dtt98GcCKf5ejo6JRt165di4ceemjK/ONly5bhyJEjcjSH1LmFnlh8rr4U1HohTanjyXX+cu1Hyujd6OgoP49Sx8rlchgdHUU4HIYgCJiYmCgqT5zJZGY86lbpGtpsNlit1inFVERRLFpoNx9V+iLT1tYGtVrNAz8271qpVMJqtcJmsyGTydT0GtXy2Wf9urA/Fs6pZpUDK/Xr+TxAQchkskUd7373u7Fr1y5s374dL7zwAvbs2YNgMAjgRFaJvr4+XHjhhVSUgyz4xOJz8aWg1gtpyh1PrvOXaz9SRu9Yishyx2KBBwu+Ckcmo9EolEolfD4fOjs7JY26zeQaTq6mls1m5/UXSylfZAKBAB8tdrlcJasw1vIa1fLZZ+n6otHotHOqWXaRmVQGJGS+k72nb9y4ERs3bpR7t2QeYYnFpWQWmI9m40tBPS2kqXS8ZcuWyXL+cl1HKaN3SqUSBoMBsVis5LFMJhPGx8chiiIsFgvfhuXyjUajmJiYQDabrZtr2KikfJFhKfPYPStVhbGW16iWAwLZbBahUAj5fL6oP2o0Gl4inF2bcv16PvcjQiajr39kTkjJLDBfyf2loJ4W0kgZwfN6vWhvb6/6/OW6jlJG79RqNdxuN0ZGRjAxMQGtVssXuGUyGRgMBjQ1NWFgYKBkaWqtVotEIiHLdA65rmGjkvJFRhAEOJ1O5HK5urlGtRwQyOVySKfTFfuj0+lEIBCom2tEyFyioJjMmUZK7yQ3ub4UyLVATK6FNFIXEnV2dspy/uw6jo6OYnx8HNlsFhqNBs3NzXC73ZL2I3X0rqWlBfl8Hv39/UVzjJ1OJ7q7u2E0GqHT6ZDJZKDT6absh/1cjukccl7DRiR1GoLdbofFYqmra1SrAQG1Wg2tVlu2P2q1WthsNjgcjrq6RoTMlVkJioeGhjA+Po5kMslTBZVC6dcWtnpL71RLFosFJpPppCtkzXSBGPtDWDivUu6FNDNZSGS1WiWdv5QFgqIo8gwMer2+4udOocLRu1KjwC6XC7FYDF6vFzqdDl1dXUXbeL1edHR0wG63IxQKIRKJTAlC1Go17HY7NBpN2fMqvIaCICAWi/Fgn33xKbyGcn6xlGsxZi3yNLMvMna7nc8PZnOIC1/7KxSKqp6z2VCLAQGNRoOmpiYEg0HEYrEp2TdUKhUcDgfPc71QBygIKSRbUHzgwAF8+9vfxh/+8Ae+KKUS9geAkIWo2sVvM1kgNrnymVKphMlkmrbyWTVmspBIyvlX2iYajeKNN97A8PAwMpkMz9Pr8XiwePFinH766ZKupcViQXt7e8lRYLPZjEOHDiGVSqGpqWlKwBsMBhEKhdDa2opQKMQDEbYfs9mMlpYWtLW1wWAwSFpE5/f74fP5EI1G+TYWiwWtra0wGAz8nsn1xVKuxZi1WNTJvsgEAgEcPHiQ33f2f1tbW8tWPKyHam2zPSBgMBjQ2trKF4EmEgn+7LNy3qwv1aI9hDQCWf4S/v73v8e1116LVCo1oxEaQhYqORa/SV0gplAocPToUdkqn5UjdSpCLpfD4OBg2fMHUHGx2f79+3Hw4EH+B51tk0gkcPDgQRgMBmzcuLHiiFc0Gi07CqxSqSRNaTAajbyksMVigUajQTabRTweh9/vx6pVqxCLxSqelyAI6O/vn3JewWAQExMTWLdunayLnyrls5W6GHMuqqMV3o/JhUsWcrW2wjcgyWQSVqt12jcgNBpMyF9VHRQfO3YMH/nIR5BMJtHR0YHbb78dRqMRn/zkJ6FQKPDCCy9gYmICO3fuxM9//nOMjo7i7LPPxre+9S2oVCo5zoGQhiJXVSupC8QA+SuflSJlIVF7ezu8Xi9SqRR/9c0KT7DpB6Ojo1AoFGWv0dDQEA4dOgRBEIpeh7NpIcFgEAMDA+jr64PZbC7Z5sL7UWoUWEqe4mw2i2PHjkGj0aC1tZWPXLN/p9NpDA8P8/Mtd++DwWBRWjeFQsGnvCSTSYRCIX4PqzVdPtuTya9cy2pt7FgAsHz58inTJ6T2oXqv1lbtNJTJ85fZm5rm5uY5HyknpB5VHRT/8Ic/5KMir7/+OtxuN/bu3ct/f/755wMArrnmGnzjG9/Axz72MfzqV7/CI488gl/84hfVHp6QhiNXVSspo7JsnmAtK59VWkjERl1VKhW8Xu+0UzrGx8cBgI9uTXeNRkZGEIlEYLPZpswPZa+Io9EoAoFA2aBYrjzF6XQaoVAITqeT535l56VWq5FOpzE2NgYAaG5uLnms0dFRhEIhtLW18f0WBqlWqxXRaBTBYBBOp1PiXSktmUzyaRrV5FeuZbW2wmMplcopeZrNZrOkPlTP1drkmvaxkBc0EzJTVQfFL7zwAhQKBT7zmc/A7XaX3dZgMOCJJ57AwYMH8dRTT+Hqq6/GNddcU20TCGkoclW1kjIq63Q6MTw8DJvNBovFMmXRFoAZVz4TBKHioqVyf4gjkQji8TgSiQSy2WxREBaJRPgiIDbiW2qBYDab5cGiIAhF/9ZoNFAqlRBFUZbKeIV5ikst7NLr9QAwbdDMfp7NZpHJZMqeVzqdRjab5YHz5OCaTWtIp9MApI8mlrpv2WyWjw5Xk1+5ltXaCo9Vrn+wBY5S2lPrio/lyD3tg+YLEyJN1UHx0NAQgBMV7ZjCD5LJrxyVSiU++9nP4oYbbsDPfvYzCorJgiNnVSspo7IjIyM8UX8ikeDbRKNRmM3mGS2083g86O/vRyAQKFqM1tvbC5fLVbRtqT/EKpUK8XicT5/I5XLIZDJQKpUwm80IhUJQq9UwmUxlFwiazWbo9XpeoKBwoZ1Wq4VKpYJer4fNZit7TjPJUzw4OIhDhw4VrZ1QKBRoaWlBR0cHv9b5fJ6vsVAoFNDr9TyA1mq1Zc9Lp9NBo9Egk8lAr9fzbBVMOp2GWq2GTqeTPJpY7r6xtF3V5leuZbU2dqzp+jXLpMCum1yLPmulltNQCCHFqv50isfjAIBFixbxnxX+IQyHw1Ne8fX19QEA3n777WoPT0jDkbuqVblRWVEUodVqcejQIWg0mimlXicmJrB8+XJJx/J4PNi+fTsSiQRsNhsPprxeLyKRCDZu3DglMC4nl8thYmIC6XSaB486nY6XntVqtWUXCPb09MDhcODQoUP8v2WjxvF4HKIooq2tDU1NTWXbIfV+sJFgURSLsh0wbDT+8OHDMBgM0Ov1vD2xWAzJZBLd3d2w2+0YGBgoe16iKGJsbAxarbZoFF4QBITDYbS3t0OtVksaTax031atWiVLfuVaVmszGAwV+3VPTw+MRqMsiz5rGRjXchoKIaRY1UGxzWbDxMQEUqkU/1lhEDw4ODglKA6HwwDA53wRspDMRlWrSq9HCwM5tj37mRQsG0IikUBLSwsP1PR6PbRaLfx+P/r7+9HW1sZ/V+p1NHvtnUql+AilRqNBLpdDKBTiBQUEQai4QJBlShAEAYIg8LYqlUo+jULKtZO6OBAATjnllGkXdo2NjcFms/FX9xqNhp9vNpuFWq3mwVm581Iqlejt7UU0GoXf7y8KZFmGixUrVmBsbKziaKLRaKx4344cOQKbzYZwOMzPl51/4QLIStdyNvp1JeX6tUKhqFj1r3DRZ72MytZyGgohpFjVQfGKFSuwbds2HD58GGeeeSaAEyNXS5YswfDwMP73f/8XGzduLPpvnn/+eQCA3W6v9vCENKRaVbVKJpPIZDJYsmTJlNf1NpsNJpNJ0kK7YDCIQCBQclEbGyFki7/KvY5mr9h1Oh2MRiPS6TQymQwUCgXsdjvy+TzS6TRyuVzZBYKhUAjxeByLFy/m0xEEQYBGo+GVzNLptKQFaVIXB5Zb2OX3+wGAB6ys3axoB1s4V+m8MpkM7HY7Nm7cyKc8RCIRqNVqtLe3o7e3FzabDSMjIxVHEz0eT8X7Fg6H0dTUxL9YTG6TUqnk+ZUrqbd+rVarJd/XehmVreU0FEJIsaqfqk2bNmHbtm147bXX8OEPf5j//IorrsBDDz2EBx54AGeddRbPQvHrX/8aP/jBD6BQKHDWWWdVe3hCGlYtVoWzUSebzQar1TplQZIoipIW2rEgtdwiskgkgnQ6XXGREFuQq9FoYLPZkM/neUDDghRBEJDP52GxWEq2OxgM8gVpTU1NSKVS/DqyqnaFC9IqqbQ4sNLoHQvs29ra0NLSMu2iRq/XC4VCgaampor3w+Vyoa2tbdrFcYXtKVepkJ1Lpftmt9uhUqlkyWdbb/26XNU/Kfe11qOytZyGQggpVnVQ/N73vhf//M//jP/8z//E9773PZ57+Pbbb8ejjz6KWCyGiy66iP/RSiQSEEURKpUKt99+e9UnQAhTy9Xjch1rtleFF446Tff6e/KoU6nzYnNK2eKvydionFarlbRIyGQy8aBEEAT+uptleWBzacu1m02dyGQyPBBi58ACOrYgTSqWpzeZTPJgkF2fwus4OeAtDDzZNWWLCfV6PUwmE/L5fNE2hZkmNBoNn0JSeD8UCgUMBgM0Gg3UanVRZgi20CwSiWBsbIxfh7a2NlitVqhUKl75jv0ulUrx4FGv1/Nr5HA40NbWhtHRUYyPj/M2NTc3w+12z8qcWqnlu6fbZvJo6uR7nM1mJY2mznRUVs7PmFL7muk0lHrKmiGn+XpepL5VHRSfd955+OY3v4lcLoeRkREsXrwYALB48WL85je/wbXXXotQKIRAIMD/G51Oh4cffphPtyCkWrVcPV5PK9UrYaNOo6OjU0q9Go1GKJVKuN3uiqWHHQ4HnE4nvF5v2cVfer1eUs5fVjFtdHR0SuaA7u5uWK1WGAwGBAKBku1mi/qGhoYgCAJSqRTfhi1y6+rqgsPhkHStBgcHsWvXLkxMTPA/xE1NTVi/fj2WLl0Km82Gw4cP84wHrM1msxkWiwVLly6FKIrYs2cPPB7PlDa7XC6sWbMGoihiaGgIkUhkSiloq9WK7u7uivfDbDZDq9Vix44dfMScHWt0dBQOhwMbNmyAy+WC0+mUdI3i8XhRoMXu2UzJUb670jZms1nyaGot9jPT577SvqROQ2mkz6KZmK/nRerfjILin/70pzjnnHOwcuVK/jOFQoFvfvOb026/efNmHDp0CL/97W+xd+9e5HI5LF++HB/4wAfQ0dFRXcsJ+f/VspRro5WNVSgUsFgsCIfDPPsAGyEcGxvji7YqlR7u6elBb28vIpFIycVfvb29fNpDpZy/7DoKgsDTwrG5xAMDAzCbzejs7MThw4fLtjubzWL37t18wZ5er0c2m0UwGIRWq0Vzc/OUubTTGRwcxNatW/n0AXZuY2Nj2Lp1K4ATqeRGR0f5NqzaGyt8sXz5cgQCARw+fBjZbLZowRrLv7xo0SI4nU6MjIyU3E9PT4+kUtDHjh2Dz+fjI9BswWEqlYLP58OxY8ewdu1aNDc3V7xG8XicH89qtZ50v5byfADly3dL3UbKaKqUfi3XfqQ+91I/QypNQ2m0zyKp5ut5kcYwo6D45ptvhkKhQHNzM84++2ycc845OOecc3DqqaeWHFFwOp341Kc+JUtjCZlsLkrL1tNK9UpEUUQ0GoXNZuOFGNjir9bW1qJX8JXOa/ny5WUXf7lcLiQSiYqvoxUKBfx+Py/codVqeRlj9sp0dHQUra2tPM1ZIpFAPB6HSqXiGS5YgG6xWHgwmEqloFQq+dzb8fFxPjJaSj6fx65du5BMJouCaJahYXx8HG+++Saampr4dIlMJsPnELe0tCCdTuPIkSMYHh6GKIowm818GgcbTU6lUti9eze6urrK7kdKKejDhw9jcHCQl5FmX0YUCgU0Gg3y+TwOHz7M07yVu0Z+v59PraimX0t5PqSUXpZannn58uVlR1PNZjMOHTpUs/1Iee5n+hlSanpVI34WSTFfz4s0jhlPnxBFEX6/H7///e/x+9//HsCJMprvfve7eZC8YcMGWhlLamKuSsvWy0r1SlibjUYjotEogL++GgdO5BRnWROklMMtt/gLkLZISBRFxONxNDU1QaFQFBXdMBqNMBgMCIVCOHbsGFwuF583XLiQKpvNYnR0FD6fD21tbdDpdEgmk3wbg8GAdDpdlBGj1BxFj8eDiYkJfv6FC/+USiWsVit8Ph8PmkuVcD5+/DgmJiZgNpuh0+kgiiLfhs2DDYVCGB4e5m0+2VLQAwMD/EtCqc/aeDyOvXv3IhAIlL1GPp8PSqUSTqezqn4t5fmQUnp5JuWZLRYLTCbTtP0xkUhIfl7LjcrOZD+Vnnu5PkMa8bNIivl6XqRxzChyfeyxx/Dyyy/j5ZdfxsGDB/nPw+Ewnn32WTz77LMAToywvOtd7+JB8qZNm2ilLJkVc1VadraPJZdcLsdHWdnIrMVi4a+H0+k0n19aKZMBOy8WQE1HyiIhtoDOZrPxhWSFQSgLmlnlNrbQr1DhIjU20jz5j+TkjBgejwd+v58vOmtpaYHL5eKBkEKhQDwen1IZjy2AS6fT/FiTF/9ptVqk02nk83men5gtHGQ0Gg2i0Sg//nSmKwU9eVEfy/EsCAJUKtW0o+AKxYny0PF4nI/al7pG2WyWX+vpSC2HLOX5kFJ6eSblmcvNPRVFcUbPa6lRWTmfe7n21YifRVLM1/MijWNGQfF1112H6667DgDg9/t5gPzyyy/j7bffRj6fB3Di297WrVv5XDyNRoPTTjuNB8lnn302rFarzKdCFqK5KC3bSPlDVSoVYrEYL6nMRl9YNoNQKMRf75crPTyT86q0SCgcDhdlslCpVDxrDVBcxrjctZ5cDnkyFljmcjns2bMHPp+vqBLd+Pg4AoEAHyFl5aVZIC6KItLpNOLxOM9NXO5YOp0OKpWKZz6YjBXwYGWeWaDNrjXL8MFKQY+Pj2NsbIyXjWZ5gwtzBk8uXjH5ZxaLBZFIpGy7NRpNxWstpRyylOdDSullqeWZU6kURkZGSs497ejokOV5nZx5ZPKXxpk893J9hjTiZ5EU8/W8SOM46Z7V0tKCq6++GldffTUAIBaL4dVXX+VB8vbt23mVu0wmg9dffx2vv/46HnjgASiVSqxZswbnnnsuvve978lzJmRBqnVp2fmYP5QFYeVKKp9yyikzOq9yr6ONRiOamppKljGORCJoa2vDokWLEAqFSl5rt9tdsRxyW1sbAoEAjh8/PuXcEokEjh8/zlN6TUxMFBW5YPOcY7EYmpqa4Ha7+fzb6Y7V2dnJU7ppNJqiwJgdj6U+Gx4ehsFgKCpNnUgkeClovV6PPXv28PnJLOCbmJhAMBjE0qVLYTKZkEgk+NQMho22GY1GLF++HKlUqmzWkLa2NrhcLkxMTFRVDnnZsmUVnw82JaTabZqamhAKhcrOPQ2FQrBarRXPq1K/ni6DS2G2lMIMLpXI9RkyXz+L5ut5kcYh29cts9mMSy65BJdccgmAE6MiO3bswMsvv4yXXnoJr776Ki/vnM/n8dZbb+Htt9+moJhUZaY5PRvlWHLJ5/M8J3AsFoPBYOBBIcvFy6YvsFf+pUoqz1Sp19EqlQrr16/H1q1bMT4+XpTtIRKJwGAwYP369WhtbUUqlcLExAR//V9YUMLtdsNqtZYth9zV1YX9+/dDqVTCYrEU5fm1WCwIhUI4fvw4nE4n4vH4tNdIp9Ohvb0dy5YtQzweh8/ng8lk4vc+Ho/DZDKhr68PbW1t+Mtf/sKPr9FokM1mkUgkoNVqsXHjRh6Ms5FsNo85k8nwOcyhUAj5fB4Gg4EHsmw0OZlMIpFIoLu7GwcPHuTTDVhwzaZwLF26lGcFKZc1ZOXKlTCbzUgkElWVQ/Z6vRXLKrPCLdVu43A4cPTo0YpzT5csWVL2vKQ8r1IzuEh5TuT6DGnEzyIp5ut5kcahEAtX3cyiTCaDxx57DN/97ndx5MgR/geXTbloRJFIhJdJpekgc4vyFE8vkUhg7969fJ7udFMjstksAPCiFJNHwljg0dfXJ+vilnJ5gZctWwYA8Hg8PNsF28bpdPJsF5W2USqV2Lp1K2w227RFQLLZLAKBAFpaWqBWq3HkyJEpuYNZ3uBVq1bB7/dXbPPu3buxc+dOHtiqVCrY7XacccYZWLZsGfbu3YtkMgmfz4dQKMT3Y7fb0draCkEQ4PV6eeq0dDrNPy/ZlJFsNovu7m4cPXqU53ouzInsdruxZs0aLF++nC8mrHQdK5Xm3rt3L8/IMVkmk0EqlUJfXx/y+byseYqnmwcuiiL27ds3bflq4K+j4KtWreLnf7LPqyiKOHToUMVc3+xaSyHXZ0gjfRbNxHw9LzJ3pMZrszYxJ51O47XXXsNLL72El19+Ga+99hri8TiA4tXvhMihFqVl5+JY1Sp8Hdne3o5sNsv/yGg0GoRCIZhMJr4C32KxTFueWEop6JlatmwZurq64PF4plSQA078YfR6vdDpdOjq6ioaKfZ6vbxoRrmMGCyTQyUqlQrNzc3T7iefz/NUZpFIBC6XC263mwdGoigiEokgGo3CYrFgzZo1WLlyJYaGhvgocldXF9RqNS8rbDab+ZcRdq0dDgfMZjNPV8emEUzOUMHmQ7PSzK2trUin0zxw1Ol0MJvNRSNqlbKGANWXuWYLoMqVVZZyrEKTi4iwf89k7in7YneyzyvLiOB0OktmQplpRgS5PkMa6bNoJubreZH6J1tQHA6H8corr/DpEm+88Qb/0GcfZCqVCmvWrMHZZ5+Ns88+G+95z3vkOjwhJV/X1/OxZruUaeHryFAoBLPZDL1ej1wuh1AoxF9XHz16FNFolP+PBWosUJ6txS1KpRJNTU38/FmQVpivlKVuY0RRnJKvlF3HZDIJQRBgt9sBnJjWZTKZEIvFYLPZitKtsfnCNpsNzc3NiMVicDgcaG5uLjpWOBwumsNqt9sRCoX44jq73Y5IJFLUHpVKBbfbzc+LBfrsVbDf7+fTHFgGCZZDurB8tV6vnzLCzRYisvnJUkfUymUNKTzfZDLJA2e9Xs/nLM9kAVS1z0dhAYfCYiITExNIJBJF85ftdvuUL3uT555W057CjAilMqFIyc4xmZQ2SdlXLT/3amm+nhepbyf9V87r9fJFdS+99BJfFAL8NQg2Go3YuHEjD4I3bdpErz7IglHpD1qtXhFWygZhNpvh8Xiwb98+Pj+V5WdlJZlXrVol++KWSq/rpeYr9Xg8Zcszd3Z2Yt++fRgdHS16S8VSrvX09KCrqwuDg4MV57DGYjH09/dPyQjR2dkJtVrN8wCXOi82XYUVFZmu7PKSJUvQ0tICn89XtqQ2G+mVa0St3BSL9vZ2WRdAVSq9XKmAA5u/HAgEcPDgwaKsIgqFAq2trTOee1rqeZ3JF4JaloImhMjvpPMUDw4O8p+zPzTNzc0466yz+CjwaaedRqlTyIJU6Q9arUuZlnsdKYoiUqkUotEoH51h7UkkEnzqgJwqnX9bW5uk1/WDg4PYsWNH2fLMra2tOHToEGKxGB+NFgQBuVwORqMRra2tsFqtZb84iKIIr9eLo0eP8nzPbDFeKBRCPB7HkiVL0NHRgbGxsbJpwlKpFJ+3zCr65fN5XiEwnU6jt7cXsVisbEntwiwZ1Y6oeTwebN++nS8kY8fzer2IRCLYuHEjf+NQauFjYRBa7gthpXvf0dEh6QsRextQuM3JLgytFKRL+UJQKTvHyZSCTiaT/M0BmzZDpY4JmT0zilhvuummotXpy5Yt46PAZ599NlasWDErjSSkkVT6o79s2bKKK/lrWco0kUhgfHycL1pi81MVCgXsdjsEQcD4+DgSiQRMJlPVx5NSyjUQCPAiH6VG5wDwRWvlyjOvWbMGzc3NcDgcPD8wyz7Byly3t7eX/eLAgqZ0Os0rrQmCwBfjRSIRjI6OYvHixWXPa2RkBH6/n6eHY6/mFQoFrFYrH0V2OBwVS2oXXs9qRooFQUB/fz8SiQRaWlqmXEe/34/+/n6ce+65aG9vR39/P0ZHR4tGk7u7uyUtopMyCly47+mw52l0dBQAsHz58mnnykt9hqR8Qa2UEUFKdo6ZloIOh8M8xV/hwr5MJkOljgmZJSc1jKtWq/F3f/d3eP/734+zzz4bLS0tcreLkIYkJeA7evQokslkTUuZlgtUWMU7u93O/9gXLuzK5XIIh8OIxWKyBMVSSrmmUikYDAY+z3e60Tm2wM1qtU7JQMBSm42Pj+PYsWNob2+XtEiq1KhrOBxGKpWCSqXilesYVnwkmUxibGxs2tf27Lw8Hg//cqHT6ZDP5/krf7bvZDKJWCyGzs7Oiovj5HjFzr6EsAqD2Wy26P6zUdLjx48jEAiUXfgIoOpR4EgkAqB88Q5BEBCPx/m9nzzPV+ozJOV59Xg8WL58edk3CTOZ7iOlFLTP50M0GoUgCEVvJaLRKJRKJXw+Hzo7O2nOLSEym1FQzD4kcrkcnnrqKTz11FMATnxTZ1Mmzj77bJ6aiJCFRkrAFwqFIIpiyaBF7lKmlUbCbDZbURunS10mJ6mlXNkr6UrlosuVTM7lckilUpIXSZWSTCYhiiIEQUA6nealnFnVO/bavlK5ZJYPmgXVhduyssRKpZL3nXKL42Y6BUcQhGkD7HQ6zc9/YmJiSgo4Ng+azckutfBxdHQUCoWi6lFgpVLJvxCVWkTHMqZIyYZRjpTnlQWzUrNzSCmVXk42m+Wjw5Nza7PKkxMTE3whOyFEPjMKigOBAPbu3csX17388ssYGRnBwYMHcfDgQTz66KMAgLa2tqJpFevXr6fXPGRBkBLwsYBDzlKmpV6hF46EsQCDjXiyLAosHzELQqYblTWZTHwksNo2SV24ZLfbYbFYJJeLnowVx2DZNqq51qztSqUSGo0GuVyOTyNj+YQBVCyXrNfrYTKZeLDOFtIJgoBMJgMAMJlM057P5Gtb6b4WvmIvt4hOp9NBFEX4/X4+Ys36ACsUwsoqs5LYhVjwOD4+DgB8esl020gZBVar1XC73RgcHMShQ4emLI5saWnhGVOqva9Sv6CxYLbUmwTWp2dSKr3U85HL5ZDJZPibi8nXUavVIpFIyJ4isd7MdmYeQqYz4+kTfX196Ovrw6c//WkAwNDQEA+QX375ZRw8eBBerxe//e1v8fTTTwM48SrrzDPP5KPJZ555ZsUPfUIakZSAz2AwQK/Xl50aINdKfvZaV6VSwev1TvvHOp1Oo62tDUNDQ4hGo1NKIQuCUPSqVsofKzkWLrH9VlMuurW1FYsWLUIwGKzqWrMqZtFolE99KMxTzEZr29rayt5Xl8sFQRAwMjICnU6HTCZTlH0inU7zzBLlsBHOcveVjXCGw+Gyi+jOOOMMaDQajI+Pw2AwTJnSweZsazSassEjy1xSbrS0cBS43P1gfx9EUSzKLMHo9XpZsmHMNNVcKWzB5MGDByWVSi/3fKjVat43dDrdlHNjP5/Pi9gp8waZK1U/VV1dXejq6sJ1110HAPD7/UVB8ttvv41oNIrnn38eL7zwwomDqtVYv3493vOe9+CBBx6otgmE1I3CYhnl/li3t7eXTQEmNZ2UlCwO8XgciURiStaESCTC5ziecsopyOVy8Pl8SCQSRemtOjs7sXTp0qIFZ5UqkVW7cKnw/EuNzkkpF33aaaehtbUVyWSyqmstiiJcLhcymUxRKehcLsdLQbvdbrS0tGBsbKxsCWNWmjoej8NisfBt2BzZwswS5UYTpdzXTCZTcRHd3r17ebAWiUSKso+wlHzs+pcLHtm0m3KjpWwUeGRkpOKiNQA45ZRTpl1EJ6WktJT7KvV5lfoFlU2jKVcqXUr2DTbaz86FbVP4NmC2pznNlVpn5iGk0KyXeY5Go0VFPXbu3Il0On3i4AVz6xoRlXkm0yn1oc7+wLEP9WpHQ1j52VJ/0IPBIEwmE4aHh5FOp6edGsEKeJx33nkQBKFkWd1yaeQKz8tsNldsk9PpxPLlyxGLxWQZDZJSLrraa83KZYdCIRw7dgyRSIQHfFarFYsWLYLdbpdc5phNZxgbG+OFUtra2iSXXVYqldiyZQufPjF5cSS7r+vWrcNrr70Go9E47du5VCqFUChUFHSxAihsVFev18NsNmPRokVT5gsX3tempiYkk0kcOnRoymgpC95POeUUrF27FrFYDKOjoxgfH+fn39zcDLfbLXtJ6UqkPq9S+gcLpCORCO+LrMKfQqHAqlWrMDIyUvH5YNNjWFnpwrLrJ1NWulFI+Uxjnx/z7dzJ7JrzMs+MxWLB0qVLMTIygmPHjmFoaGhKEv2Zeumll/DAAw/gjTfegMfjwe9+9ztcddVV/Pc33HADHn/88aL/5tJLL8Wzzz7L/z0xMYHbbrsN//3f/w2lUolrrrkGP/jBD2Y8b5KQySoVy2B/YKstZSp1kZDUL56sPZ2dnWXnJpdbpS81x2ylhUszUalcdOG5neyx2IhiPB7HokWLEAgE+BcHVv7XZrNVnPLBsDLMAPh+2NQSoPJomdvtBgD+s8JpGFqtln++skV0lRYjsoWNOp0OqVSKt5tN6UgkEnA6nQgEAmVHZo8cOSJptBSYWrqZ/Xsm83yllJSuROrzWg5rc+G1L/y/bO57LBaT9HwsWbIEiUSC598uzPQxOSf0fDKThY+UeYPMBtmDYlEU8dZbb/HpEy+//DL8fv+UbaoRj8exbt063HTTTbj66qun3eayyy7jC/8ATFl1fu2118Lj8eD5559HNpvFjTfeiE9+8pN48sknq2obIUD1QZgUUoIH9mq9MHgpHBU0GAwwmUw8cC41VUHqHytWSrnahUszpVKp0NnZWfV+SlEoFLBYLAiHw1Pm5o6Pj8NoNGLFihUVp3wAxQFvc3Mzvx/BYBDJZFJyHmu1Wo1UKsUXZbFFfuFwmG+rVqslLUZk6ep0Ol3RVIHCOaw2mw0Oh6NsWrJMJoPFixcjFovxwi+s6p/ZbEYmk4Hf78fIyAifdqLX64sKU3R0dNS0pDRQ/fPKnjW/38/TqFksFv7cJRIJXmxEyvPBRqjZtWbn3NzcPK/n1c504SMhcqs6KM5ms3j99dd5APzqq68iGo3y308OgJctW4b3vOc9OOecc3DOOeec1DE3b96MzZs3l91Gp9Ohvb192t/t378fzz77LHbs2IEzzjgDAPCjH/0I733ve/H//t//46MwZPbV4wrjWrWJvR4vNV2BKZVKS8oiIa1WyytiRSKRKa+r2SgU+yNUbg4r+2M1XXvYHysAvE1qtRqxWIwfz2w2TwlocrkchoaGEI/HYTKZ0NXVNeUPYjabxcGDBxGNRmGxWHDKKadMmU/JXqOXGimORqMYHR3lBTh0Oh1cLhfcbnfRtS7VHlEUEY1GYbPZeFqsRCIBtVqN1tbWoiIgbFrYdO0pHHE3Go04fPhw0bESiQSOHj3KR4gB8LzIbMEa+wKSSqWg0+mg0+l4hTxWlIRdt6amJjidTni9Xmi1Wj5yzBZzhcNhNDc3Q6/XIxwOF312M4VzWI1GI4xG47TnxtKS2Ww2GI1GjI6O8ops7BqFQiGMjo7yoNrn8xW1mwX4bJ4vG51nfchkMk2Z5yulf8j1TJd6FlmGk1gsxqeRsDYbDAZMTEzAbDbzLBRsDvbkxYiFz4fRaITJZCqbp1pK35fz/OUiR0ntao9V7xq13Y1uxkFxLBbDq6++yhfT7dixg88RBoqDYIVCgdWrV+Occ87hgXBhJabZtGXLFrS2tsLhcOCCCy7Avffey/N9btu2DXa7nQfEAHDRRRdBqVTi9ddfx9/+7d9Ou890Ol10riy9EDk59bjCWK42SSnzvGfPHvh8vqLV9ePj4wgEAli9ejVPR1YqlVZ7e3vFRUItLS0QRREHDx7kBQFYe+LxOFpbW7FixQoYDIaKK+JVKhWfBlW4H4vFgkWLFvGA0Waz4ciRI4hEIkXBmtlshtVqRXd3NwwGA3bv3o2dO3ciFArxfbHncs2aNQCA7du3Y8eOHbyQgVKphMViwYYNG7Bx40YAlecUR6NR7Ny5E8eOHSsqTDE8PIxFixbhjDPOgMViwe7du7Fjxw4Eg0HeHofDgQ0bNmDZsmW8xHIsFiu612ykkr3W9Xg8JdvDUskdPnyYl4xm9u7diyVLlmDx4sUQRREqlQp+v3/KnFK73Y58Pg9BEHjJ6MJ5wCygtlgsUCqV6O3thcfjwf79+6d8Pjc3N2P16tX8tf7ExARisRg/ntlsRktLC9ra2mAwGMr2R5vNBpVKhcOHD+PIkSNF7T548CC6u7tht9sxMTGBYDCIeDxe1J5IJMKDxhUrVmB0dBQ7d+5EJpPhz4hWq8XixYv59AEp/UPq4lCp88BLnbtGo4EgCNi/fz/PxMHyfre0tPDc1jabDaOjo2XnC1d6HlmbajGfXm5yZaap9lj1PNreqO2eD2YUFJ9xxhl4++23IQgC/1nhhxrLKsGC4Pe85z0VUwvNhssuuwxXX301uru7MTg4iK9+9avYvHkztm3bxlMYtba2Fv037MOErXqezv3334+77rprtpu/INTjCmO52iSlzPORI0dw/PjxaRckHT9+HEajES0tLdixY0fJVFobN26UlMVhaGgIR44cQTqd5guncrkcJiYmEI/H0dXVhVgsVrHNbDFRPp8vylAQDAYRiURw6qmn8nNhr8etVivPgsCC8p6eHuzZswd/+ctf+MigRqNBNptFIBDAX/7yFwAnpmxs3boV2Wy2aNpHOBzG1q1bAQBOpxNbt27lx2LXaGxsDFu3boUoiggEAhgYGIAoilOu0cDAAB/9fPHFF3mhDzYi7vP58OKLLyKZTCKZTPJRS4PBwPcTjUaRSqVgMpkwODiIHTt2lGzPhg0bsG/fPhw9erRorq0oishmsxgcHEQ6ncbixYsxMjICQRCg1+v5FBiW2cFgMPCS0LlcDjqdjvehWCyGVCoFq9VaNC1GEIQpQTH7v4UBuNVqLbpGfr8fq1atgtfrLZvabcOGDQgGg9i7dy8EQeBvKQRBQCwWw969e7Fy5UpkMhmeTk6n0/E8zel0GuFwGADQ3t6O8fFxXgylcJvx8XHEYjHs37+/Yv9YuXIlBgYG+Ii1RqMpmqrR09MDoHwVvp6eHsRisbLn3tfXh1AohPHxcWQyGahUqqL806yMej6fnzINR6/X8z7CpuGwvlmu3T6fr2zfB4DW1ta6+pyVOzNNtceqxwCzUds9X8woKH7zzTeL/q3X67Fx40Y+FWLTpk2ylIGt1oc+9CH+/69ZswZr167FsmXLsGXLFlx44YUnvd8777wTX/ziF/m/I5EIFi1aVFVbFyKpi7amm79a722Ssp+hoSEcO3aMj2oVVqyyWCwIhUIYHh6G1+stm0qrv78f5557btlFQkajEQcOHIAoitDpdLyMMCuNK4oi+vv7odFoKlYiY9XIWEDIXnfq9Xokk0n+++HhYT5dIpPJIJPJ8KIL6XQaQ0NDGBoaQiaT4aOLAHjKrXA4jNdeew3pdBrZbBZms7loGzYtY/v27WhubuZ5dCdfo/HxcWzfvh2CIEAQhKJz02g0sNvtCAaDOHDgAF/UxIIrFqiyPL1vvvkm3G43stlsURYPlruXjXazKQOl2vPWW2/h2LFjEEWRT4FhWAaJ0dFRnu+48N5rNBoevGo0GgSDQQCA0WiEIAjI5XJQKpX8S8j4+Di/v7lcDm1tbVNex2azWezbt4+PaLa2tvJFe2xqSDqdxtGjRwGgbH/cv38/hoaG+CvufD5fFJTncjkcP36cvxYvfB2sUqn46Gg4HMbBgweRzWbR3d3NR8WVSiU//z179uDAgQMV+webaiIIAq8SV3iNpFbhGxkZKXvuhw4dwsjICHK5HN9PYR7rSCSC0dFRAODTcCwWCxKJBBKJBJRKJdra2qBUKhGNRhGJRIraXTianMlkMDIygt27d5fta2+++SbWrFlTN5+zcpXUlpqZp97+xkjRqO2eT2YUFJvNZpx11lk8CN6wYUPJVc31ZOnSpWhubsbAwAAuvPBCtLe3w+fzFW3DRs5KzUMGwOfvkerU4wpjudokZT8+nw+RSKRsdTDWP51O55Q5hEqlkr9iZCmKSi0SOn78OK9UxgKxwtFJhUIBn8+HY8eO8fmw07Xn2LFjCIVC/M1P4ethvV7PC1scPHgQgUCAZzKYnCosnU5jeHgYwWCQjyoXYn/42etgln938jZ6vR6RSATZbLYoKCi8RlarFYFAAACmlCYuPDc2QspG5ApHUDUaDS/AYLFYyhYdSiQSCIVCsFqtJdvj8/mmLfHM/s3mmLL9sFFhFmQmk0k+N5uVOVapVEVv71hgmEqlcPz4cXi9Xj5iaTAYikYwRVHkbyzK3TOPxwMA085pZf1xeHgY4XC4qAR2YZtYwGcymUq+/lYoFLzMMev7k49ns9lw9OhRRKNR6PX6kv0jGo1i3759vNBK4XWMRqM8YNVqtWWr8Hk8Hvh8PthstpLnzvqQRqPhz1hhrmn2pdPn8yEcDvOMJZPnFLPFeul0mud5nvymQKlUIh6P89zcpfra+Ph4xee6lp+zcpXUlvtY9ZTFolHbPZ/MKCgOhUJTHsBGcPz4cQQCAT6fedOmTQiFQnjjjTdw+umnAwD+/Oc/QxAEvOtd75rLpi4Ic7nCWMpCsmraJHU/lTKwsFfd5VJpRSKRopzf031IspEoNupZOArK2gqAB1il2szSdLHgko2+sqCFTVOIRqN8kQwLKie3my0cK1V8gAWibLSyVJsEQUA2m+VpsCanJWMLlwoDlMmUSiUPAgsDYoadA7vOrBrb5EDVYDDwa1Qp/Rk7riAIRV9Q2JcWFrCy9HaFRTCsVitsNhtGRkb4f5NKpfiINZtnzPYbiUQQj8eh0Wig1+uLRmbZCH8sFoPRaCx7z9iXoHLnlslk+FQGtg/2xQkAL0XNSmVPV+aaTRNg0y/KHatS/2AFW1paWqa8kWGLJdkXuHL7YW8syrWH9Q+dTodsNjvlOWMV6uLxeMXPh3Q6jWAwyOfrT34rwUbTK7WJZZipl0wOM/2MrSarSKNmsWjUds8nMwqK6yUgZvOtmCNHjuCtt95CU1MTmpqacNddd+Gaa67hVcO+/OUvo6enB5deeimAE/PMLrvsMnziE5/AT37yE2SzWdx666340Ic+RJknamA2VhhLIWUhWbVtkrIfo9EIs9mMWCw2bUGNWCwGs9mMbDZbMZUWe3NRalU8y4TAXj2zYJvNI2WvuFlwWKrNbFoBS89Vqj2sQlu5drP5r9lslgfULOhjI4UsUwMLagvbzdrKgqtoNMoXwbJt2FsdFnSxEdbJ15rN2SwcsSssLVx4/YxGI5qbmxGPx3maLLVaDZvNBpPJhFAoxEf/yt0zAPw8Cuf5smsBgM+zdrlcUzJ4ZLNZXv6X3cfCBYRarRb5fJ5fB/Zzdi8L7z/7OUunNt1IMQtW2TmUOjeVSsWvb6m/FQqFAiaTifezyQvNAPBR0XLHYveVPZeTsf7Brsd0X3a0Wi3/AlGu7+t0uor3lQXD7ItVKpUqKt/NrrnJZEImk0E0GuWp2grPn/VRNm+9VLsFQSi6Z5O/pLK+xtYP1PJztpRafu7P1d+YajVqu+eThryyO3fuxPnnn8//zeb5Xn/99Xj44Yfxzjvv4PHHH0coFILb7cYll1yCe+65p+iP+S9+8QvceuutuPDCC3nxjh/+8Ic1P5eFSO7SqlJIWfzG2mS326eUlpXaJinn1traCrPZjIGBgWlzB4uiiGXLlvFytiwAYARBQDgcRnt7O88bW2pVPCutm0ql+OhrYXCZz+eh0+nQ3NzMg/Tpzn3RokW8PQaDgY92s+AqmUzC5XLhlFNOgc/nK9vuxYsXQxAE3t7Ji79SqRSampqQTqcRCoVKBqp2ux1msxler5cv2mKjc4lEAtFoFC6XC21tbRgaGpoyGs4Cr66uLhw5coSPyE6WyWRgMpnQ3d2NUCg0ZRuWRWDx4sUIh8Pw+Xw8iCwMSCORCNxuN4aHh/l9Lrw+LBjV6/VYvXo1/H4/f9VfmLZMpVKhra0NR44cQTgc5sEhuz4sQLLb7TxrRCwWQyaTKdqOBbwWiwUtLS0YHx/nI5qFAXY6nUZHRwcAYGxsrOR9dblc/AsK++PO5PN5Pn1j2bJlGBwcRDweL8oswRYrrl27FhMTE2WPtWTJEqRSKUQikWmPxZ5zh8PBA8fJz2Jh+rdYLFbyeXW5XBAEoWx73G43gsEgvF7vlMCZPdculwtdXV2Ix+O86t/kqRETExPo7OyEwWAo226bzQa1Wo1AIFDyeWxvb8eiRYsQDAZr9jlbTi0/9+fib4wcGrXd80lDBsXnnXde2dfPzz33XMV9NDU1UaGOOaJQKGRbYSyFlMULXq8X7e3tCAQCOHTo0JRAjeUPrtQmqecGnJiy4Pf7kUqliv77jo4OvuI9EonA7/cXrXhnqcF6e3sxNjZWdlV8T08PjEYjf23LRvPYH1c2etXa2oqxsbGS597R0YFsNguPx4NQKFSUMSIajUKr1WLFihXQaDTo7e0t2+5Vq1bBYrFg69at034p0Gg0WLduHV9wx9pZeD+VSiVWrFiBaDSKsbExHgiy0XA2NcFisWDt2rUIBoPw+/1F6e/YuZ1xxhlQKpV45513eEGJwmwHKpUKy5YtQ2trK44ePcqvNVtI6PP5eNaA0047DX/+8595cQ02Gp/L5WA2m7Fx40ZYrVbs3Lmz6AsKC/oBYPXq1WhqasL+/fsRDAaLsi/4/X44HA50dnbC4XDwLxbsvrJpCGq1Gna7HWq1Gs3NzQiFQrxYC7vWLKBsaWnB4sWL+VuUwj7L7vWSJUtgNBr59Z48PcBsNmPVqlXI5XI4ePAgEolEUUYMFiiztGzpdJrfa7YNe9VvsVjQ1taGaDRasg+tXr0aNputbB867bTTIIonSplPtw1LAdjR0YGRkZGSz6vb7YbVai3bnr6+PoyPj/NryKajZLNZXkxlxYoVPHhnb0fY63L2bzbNqampibd78lQdliqwo6MDr776KoLBIJ+ywkpg63Q69Pb2oqOjg08jme3P2Upq+blf678xcmnUds8nDRkUk8YnR2lVqaQuXmAVpwpHJU+m+qLUc1u9ejVGR0enFNRgBSUsFgs2btzIR4FZENPe3o7e3l60tbVh69atZVfFDw8P81euAIpGZtnPlEolH3kode4se0V3dzf8fj9isRgPGJuamtDS0sKzWbhcrrLtbm9vRzQaxdKlS+HxeJBIJJDJZPicWZfLBZvNhomJCRiNRp6Dl2GZCligz0bDkskk3w8bJWSBh8vlQjabRTKZ5OfHCh2YzWYsWrQIXq8XwWCwaCqCXq+Hw+HAokWLpmQNiMfjfNSWLSJjo3NHjhzhc2zZ/N1FixahpaUF+Xwe4+PjGBkZ4XN1gRNzRtkXIp/Px6dJFM79Zq/pA4FA0ch3YSlvllKOzXXW6/UwmUx8/jGbtsIWl7HFai0tLfD7/bwIR2Ge4nw+j/b2drhcrmnzAi9fvhxdXV3IZrPI5XL8vqbTaX4/XC4Xent7MTIyAr1eD6vVOmWaQSaTwfDwMM4999yyfcjlcvEvl6w9bL6yzWbDhg0bsGHDBhw6dIi/IZlcYY9lfGhpaeE5mEs9r5WeRdanC58PFuQXPh+sr7e1tcHn8/GFl6xNbW1tEEWxKBd1IpEoutZKpRKtra0QRRGdnZ0YHR3l/ZFN83G73dDpdDCbzTX7nJWilp/7tTyWnBq13fOFQjyZv/oEwInXoTabDeFwGFarda6b05BqUbUnEolg3759064eB068Ag2FQjAYDEgmk9NOIQiFQnA6nVi+fLnk9kk5NynblKpYxXL6Go3Gaec6plIpTExMIBqNFi1iKpyGkM1mYTQaceqppyKXy5U8d7YSnI1aTZ6/zEb6+vr6+NzQUvOcWb5jFoz5fD6kUino9Xq0trbyoHFwcJAvtolGo/yLAwtK2bxL9mqfvY7XarU8NeT4+DiWLFkCANNWR2Nflth0hkgkgrGxMb6ftrY2WK1WXmTDarWWzBrArk8sFoPVauVvAfR6PVpaWhCJRIquoyAIGBgY4BXtenp6oFQqEQ6HeY5ehUJRVJjDYDDwkXKPx4Nc7q+V0QoLXLCA+m/+5m9w9OhRBINBPj2jcO6pWq3m1yoej5fcxm63o6mpCa+//jri8fiU0XSTyYRzzz2X58VlX5rYdWQBmtPpxPbt24vKUk/OdJFIJHD++efD6XRKqtZWrqIdmzbF5o4XviUxGAxFOV+lPItS+3Sp52Px4sXo7+/n/XDyiDvrl8uXL8fY2FjJdnd0dODgwYNF95X1R3YtHQ4HTjvtNBiNxpp8zs5ELdtTb+cuVaO2u15JjddopJjMqWpWGEslZfGCIAiIx+M8xdHkxWQnkwpHyrlV2ma6xYGJRAIul4uX7C23Ap0FgGzEdfLUCDY6Hg6H+YjndOceCoUgiiIfqWpubp5yHpNXRSuVSl5FslDhCmulUjmlyqVCoSgqQMHazeYWJ5NJaDQaJBIJPrqo1+unjKCwaSmpVArNzc1QqVRTPgwLz62lpQVWq5UH5izgFUURXq+Xz8NlC9gKsXnbLBAaGxvji6hY/yo8llqt5hkG2BeN8fFxOBwO/maDLbwrDEDZwjCW6QIA/8IzeVoMSyXH5piPjo5OqUTY1taGdDrNc/gCmLJgkc29P3bsGE8Rx86LjbjH43Hs2rULf/u3fzvtKBdb0ColOwnLqlKu7xfea41Gg76+vin9DPjrqFu5NzIzIbVPl3o+2LzQVCo17SJb1j/YGwl2/myKTHNzM5/jzO7Z5HRyoijy+cnsy1wtPmdnopbtqbdzl6pR293oKCgmU8y3b6hSFi+YTCb+Onq60aCTSYVTalRJqkqLA1kqqUqr9NmHq9FoLHoVy+YFJ5NJHvCxoKowKGQ/r7RKX+qq6MIvKSw4LLxGbMSM3Rs28lUY9LFX5Xa7nQd+bPSYfQkIh8NwOBzQ6XRlUxwVntt0aeIKz5m1eXJGCBYgplIpvtiscPETS0fGgtehoSHEYjH++0QiwQs2sECfZSQoHE3VarX8FTxwYioJu5ZsG51Ox4/N5n2zzBhNTU1FadICgQC0Wi2SySSfglE4Ulz4s1gsxs+FYXNhFQoF/H4/PB4POjs7S+aYZfOdK2XoyOVyFSu6zWSEFzgxosxSp00WjUZ5gRrWF91u95TAudSxTjZrQOFUncm/K5erNxAI8IWC0+2H9RNK3UXIzFBQTIrMx5rrUhYvuN1uHD16FCMjIzh27NiUEbVFixbxFd9SlMsIMXlkdDpSFgcmk0k0NTWVXRXPEvePjY2hubkZVqu16NX4+Pg4nE4nLBYLLyFcmBfXZDLxYgt6vb7sKn2pq6LZl5T+/n5esrdwDmtzczMWL16M4eFhRKPRogIFbHV9PB6HxWLB6tWrsW3bNrzzzjtF847ZfNEVK1bwUsjl0s0ZDAYEAoEpcziNRiOUSiXcbjdEUcTQ0BAikciUNrO50GyBIRsNLJwvy85NFEX4fD4YDIYp0xB8Ph/a2tr4yDMLgAunRrDgio1OTpeSjGUVsdvtGB0dRSgU4pklCveVTqfhdDohCAIikQifB8220Wg0PChli+Wmy2KhVCqRzWaRSCT4fZpulMvhcMDpdJbNTsIq75WrRMeqesVisbKfV9FoFHv27IHP5/v/2PvTIEnS9DwQe9zj8Dg87jvyrsy6q4/q6a7pnsHMACQIDrAkAdsVltcCkCiJy+WaSatdW5Np9UemlZnWZJKoXdNhNBIAQXIJcAECIAgSJGYwQM9wZnr67rqrsrLyzrjv8DPC3fUj6v3qizOjuqqre7rjNUurysxIPz7/3P353vd5n4cdL227Xq/jypUrAID33nsPBwcHQ+NzeHiI1dVVfOlLX2LbmrYvWZbnUg0gekSv18PJyclY5SYUCiEYDA45AU4aR5JjpPt1NLsPgDUWLmIRi5g/FnfMIlh8nj3XT2tekGUZd+7cwa1bt2BZFgKBADv/RqOBdruNl19+eS7QVygUZipCXLt27VRgPE9zYLvdxsbGxsyu+EuXLgEYXNtqtTr0GWpkI/mv+/fvsywrdbsTv/XcuXNYW1vDzs7OU3dFC4LA7J4p20WWw/V6Hd1uF/l8HtFoFJqmMftlnkJAPFdd1xmI5/dNdBjLsuYCK7Is4+HDh+yaUba2VCoxZQlVVXF8fAxN0xAOhxlAK5fL6HQ6WF5eRrPZZG6EvDEFb+dLChCTgkCnKIrodruwbXsIOFMzVTKZZHa+iqIw9QH+M5FIZEjlgCTP6LpRhp34rp1Oh2WX+bE2DAOhUAiWZbEmPR6IU5aS/m5WiKJ4qjrJxsYGjo+PWTPfJCe6crmMaDSK4+PjmTKLu7u7zLGPn9eqquLo6IiZrty/f58BUNqOqqq4f/8+/H4/Ll68iJ2dnZnPxnlUA2hBQ5n80aZWomCcBmY9Hg/8fj9b8FHVhxRDiFM8zSBnEYtYxORYgOJFAPhieK7PKkdaloWTkxP0+30GWigLRs5fJycnTPZrWti2jbt3785UhLh79y7j706LeZ2NEonEqV36AE7tnCcraP4FzTfk0fhtbW3h6OgIh4eHrMy8srKC5eXluRdMlmXh3r17jNNMBiIkkaVpGra3t5HJZCAIAms6oyBOaDwex40bN5gCxSjto16v48MPP8RP//RPzwQr2WwWxWJxprJEu93G8fExy/QRGCeep2ma2NvbYzrGJEdG40eZ1mazCUEQkE6nWVMZnwWVJIllbPmSOmXBybjBcRw2l0czt2RaQooTiqJAlmUAYFJoRD0BBpzrbrcLAENcaaJiUPaY5iVRTij4JjGyAZ8Vp6mTBAIB3LhxA7ZtT3Wiq9frDBBPe17t7e3h8PCQqTbw2wmFQmg2myzzT7rOJB9HFaJms4kHDx4w8Dzr2Xj27NlTVQPIhdE0TeTzeaYwQQuNSqXCXAFnBTX0UaOkaZpM6SMSibBzOG07i1jEIoZjAYoXAeCL47k+rRxZKBTQ6XTYS90wjKHSt8/nY+XT5eXlqdtvNBqo1WoTlS7ohVWr1dBoNJBIJJ4JRzGXyyGdTs/s0icTi2md86ZpYnV1lbls8dJVpMeraRrK5TJu3bqFSqXCjrndbsPr9c4NiguFAur1OqLR6EQ3LkmS0Gw2kUwmsba2hkQiwZqGiBdLTWu8aghf9rcsC36/n2WeZ4EVl8uFVqvFytuTlCVOTk5QLpcZf5jmEl1XWZZRLpehaRqCwSAz1KAgbjYpMvB/T0HfU3aXjDpGgTMBKZLDE0WRKUcQSCKbZDLNCAaDU9UeSBaMuNej2XTiWxPAJ4c/yiZTlp5k4OaJWXOW58tOGiNqxqM5Mu15VS6X0W63kUgkpn7m5OSEaQFXq9UxeonP50Or1cLJyQmWlpZOfTbOWngDYFUOWZZZhp8smWnxQtSZWc9Z4qdHo1HYto1gMDikYuH3+5mV9o/z83oRi3jesQDFiwCw8FynlxhlAflwHAc+nw+6rp/60p9HEWJWd/2TchT9fv/cXfqndc5TpnS0iQwYqFPs7OzgnXfeYfQBKnuXSiW8+eabAIDNzc25x3rWGFE53rIs5HI5JJPJMZk4KheTSsNoMx41hWmaNrP5i3R5ZylLUIaVsto8pYEMFSibTWB61GKZsr8kQ+fxeMaoEQTyqVmOrgUFqVjQPsj5jLjPRJWgTDGdD7mj8eV0Gid+MULzgQfOLpeL3RN0Trx2MtEm+EXYac1vs+Ys8WVnObrRc2pWcyhRFGYFgch2u80UTmgO0QKGbLSfxbORMupLS0toNpuo1+tsjOLxOKLRKMv8n7Ydt9uNpaUltFotRhUi/elIJDLXdhaxiEUMxwIULwLAwnOdXtpkK8xLXJFyAG9yMS0o2zZvd/3TchS73e5T88Dp2k9qtCNlDgC4desWNE1DMpkco4VUq1V88MEHWF9fn0kvobF2u91QFAUAxhq7gAHQyufzDCjKsgyfz8c4kz6fj9EnSCfXcRwGDDweD9s+XbNZTUunzX3eRIMvxVOJmvi4ZGBCoIVk5IjeQA131Wp1qtJFMBhEp9NBu91moJ8HvIqiMEe9YDDIsvtkFBIOh1nFx+fzIRqNznR1i8fjjEJAQJf4rrRNOla6/yljTOcHgM2J05p1T+tdWFpaOvWYaVHW6XTY+dO+AoEAZFlm/5J9+TTVGTJsoX3wwauZ8JrQPADnn42nnTvNCUVRUKvVhj5HfO55GuRoOx6PB7lcbmJ1Y1aSYxGLWMTkWNwxiwCw8FzPZrPw+Xxj1AdSO6Dzz2azAKZnwvjueuKR0stKkqSh7vp5OYrTNFZlWcb29jbTPCXwTtzcZrM5Fw/c7/fD6/Xi/v37TM6JMnDNZhPVahXJZBLtdptpovJcSMpO1ev1U+klABhAODk5YfumBQhl4/P5PDY3N6Gq6lTaAzAA5e12G4IgjKlPmKbJsmazgp/7kww+ut0u4vE40/klTu+oCYosy/B4PKhUKkOyaEQt6PV6SKVSrORPYI4+Q65ykUiElfIjkQg7N8qIkypDJpNBq9Vibn2TzGZisRgymQzLcJNOMPFqXS4Xstks40zzoJfCMAzmtEYa0QTW+XOjeTZLSm1zcxPFYnHmnG00GmPHzBu3uFwu5PN5qKqKBw8eMCDp9/vR7/cZ5/js2bOQZZmZiYyCa8dxsLS0xI4HwFCmmPbv9/sRDoeH1El4AE7qJP1+Hzs7OzNl5GRZZn0HxAvnG3qbzSZefPFF9pydZl4y+rzmqxs/Ds/rz5vs5yI+P7EAxYsAsPBcN00T2WwW3W6XcQzp/DVNgyRJyGazjFs7ywzgwoULKBaLuHv3LuNbkgtZKpXCxsYGqtXqXPxt+hkAlpGj74kH7nK5UCwWJ0qp8Tzw015E1HHPa56SJBeVYgVBYM5xPPeSgNI8nFICEeRkR2V6Aitutxv5fJ41SM2iPRBYJ5c1UhcgzWmSQJsVNPdPTk7w7rvvjp3b6uoq1tbWkEwmoes6yuUycw6j7GowGEQ6nUY+n8c777wDVVXH7qFgMIjLly9DURTkcjkcHx8zYwYCqblcjtExiLIzKttG4E6WZda8NymTnsvlmDlKrVZDqVRiGWsCj5lMBvl8HqVSCcVikcmuERCn7DlJ0h0dHTEnQV6hQpIkZDIZ1Ov1mVJq+/v77PxokcED3lAohHa7jbW1NdRqNZTLZZa9pXFMp9PIZrPY3d0dWpzQtaSfAcCZM2cYF57UPGi+LS0tIZlMwuPxDEnc8eF2u9k9QHbKk9RJzp07h2KxeKqM3ObmJhqNBgPbtPgm+oqmaWg0GnAcBw8fPsQHH3wwRrG4evUqNjc3f2yf159H2c9FPHkYfQuHdRXVronXz4zT+j6tWIDiRbD4Inuu9/t9xGIxXLlyBbu7u4xnSg/sjY0N+P1+NJtN7O/vo1KpDIGtWq02pHsKYOrLmjJQp3EUm80mSqUSc3YbLTNnMhkoisIa5ejlTtlEAsMEgKa9iFwuFzvfZrMJ0zSHzCISiQSjOJDcE8/fNQyDKTHMk5mi47p8+TIODg6YYgMpIqyurjIHPmq2mkR7oOw7WShrmsY68IPBIMLhMAPKp0W322UqF6S7SyCUrKIDgQDTmKXMLAHnYDCIQCCAzc1NBAIBBmZUVWXKClevXkUul8N7772HZrMJr9fLgAsB1VqtxsY9FouhXq8P2TwHg0HE43G2eHiS+5UWORR88xcpbVCGmhZzfr8fyWSSaR6TnjHZOJP0VzgcRjabRbPZnCmlRgC32+2yY6ZotVqIRCLMwGVW6LoO0zSxtrY2RvmJRCJMkcHtdmN9fZ0BYwLgsVgM6+vr7BrSwoM/Hp42QpQVUicheT1SJ6nVaqeee7lcRiAQQLfbRTabZWYi9JwIBAKIRCLodrv46KOP8MEHH5zK33+Wz+vnkb39PMt+LmI8+paN46aG3aqCvaqC3aqC3ZqK3WoXxw0NtgOEJDeu/59+5jOzgFuA4kUMxWnd05/XII5eMBjE2toaAwderxfJZJLxao+Ojpg012g59vj4mCkeAMDFixfH6BPVahUPHz5ENBo9lb9dq9VmUizI+KLb7TKKBy/LRSViymxPK+um02kcHx+jXq8zAwEChaRnTHxf8o8/jV4yKygjR5xPkvyixj5SWJi3SYjK2Ly0G2nSzhNUzu71etjY2Jgok7W7uwtgABw2NzcnXlcyAtnc3MT6+vrEsjdlX7vd7pBkHx1HpVJhIIiy1NTgR9ljytpLknTq/UpSiwBw9uzZiTSL4+Nj1Go1lu0epY9QNnJzcxP9fp9VOHhKRyAQQCKRwO7u7kwptXa7jV6vx7Kho0ELgFgsBsMw2N/RuVF2nOQTo9EowuHwGKeWFoa0sPT5fNja2hpqxCwWi+waOI6DaDTKlD+IGkJSdaZpzlQnoftxkvwbLyOXTqfZcZOqB5+9dhwHpVJpbv7+s3peP4/s7RdB9vOLGLbtoNDWH4NeAsA1BYd1FT1rdqWuY/RRU0wkZWnm555XLEDxIsZiWmbu8xzEbd3e3mZSR3w5stFoYHV1len58g9uytA2m03s7OwwLiiBMz4ikQharRbi8fhMdzh60c1Dseh0OowHTJ+l7LHX60WpVJpZ1qVMDTWR8UCNmofq9Try+TwMw2D0Ep6qQOVzXhlgWlCT3cHBAfr9PmtAIkMJRVGwurp66nYIvDabTdYoRQYW7XYbHo8HkUhkrCQ+GqMyeqNSepFIBOVyGbFYDLIsQ1XVofMnqgQvgSWKIuLxOAMqtE2S0iJJrtFxlGUZoVAIHo8HtVoNqVRqaA7xToXzaALzUou8wgSFLMuo1WosI0lNbHyQZbAkSRMzkwSeTNM8VUqNri+N4WhDq6qqaDabqFQqTIqON90g22/SgqaF5eh5UYMgLSzj8fjYfdZoNFAsFplxDNFA3G43o4WQScZp6iSU9SUO+KRzp+wy34g72mxJi61utzvk5EjxpPz9eYKyt/NYaj9NfFFkPz+P4TgOKl0De9VBlne3qjIQvFdTYPTt0zcyI/aqygIUL2IRn8WYxVEkk4PRTnb6nCzLKBaLcBznVEm2aDSKTqczlQ+YSCRwcHAwk2JBWT8AzF530mdIP3daWZfMIkY1aum8aDs+n4/RS7rdLgN8PL1knuyuJEloNBpQVXUoE0bAvlqtotFojIGP0aBjJ61cRVGGVCwIHJ/msjaPjB7JovESWLS4GJXAmpV1I+m407aTz+ehadpU17cLFy5AFMVTM3zzSC2S6sEsCTQCi7Myk61W61QpNeCxosOkoObUSqUCn883Neva6XSeycKSv/+oyZOnqpBGOalMTKvsUOWIFqKj1QYak0QicarNdSAQYAvdafOx0+mwBfHTZHgpe8svmvlqC2+p/bTZ2y+67OePQzRVcyTbOwDBe1UVXeOTuy4PqwpeXY9/Ytt/kliA4kUsAmDOYLM4ip1O59SsI69JO0uSjVQBZhlKHB8fz3wRU/mVSr28ZawgCBBFEYZhoF6vM3ONSQCj2+2yLCtlXHmeKwEYsheeZAJCVsHzSEBRZjcYDMIwjCE7ZDKa6PV6TEFhWpA1MXFiCZzRcfO/l2V5KmdyHhk90hQ+TQKLaDSz5MbmkdLK5/MIBoMznQrn4WfOIzdH5WtFUaZKoEWjUTYPplWSqMIyS0qNJNBCoRADyjRffT4foywYhsGUTvjgs67xeJwZ4XzchSUAhMNhlhEmQx2ih7jdAzvxaDQ6E4BT1r5areLk5GTsXpRlGYlEApIknWpzvba2hnq9fuozxHGcp+bnapqGUqnEuNCjKh6iKKJUKmF5efmps7dfdNnPz0ooRn8Y+D6iOuxVFTTU3ukb+ARir6p8KvudFIvZt4hFYNjAYhpHkfihs3RPY7EYPB4PA6KTMkFU+qZs1CSXOcdxmOQSSVfxXNBut4tIJIKDgwN23KPH3Ol0ADwGwACGuv1lWWYvJ8rK0kuLz7iSukIymWT7JRk1AkqTJKCmgVDDMCAIAlKpFANidNykvdvpdJgZxjRZKl3Xhwwz+MUBZcPJdGNWRo2X0XO73UyBg/RpSUYvl8uhXq8jFAqhVCoNHQ+df6PROFVujL+ufIxKaQUCAaRSKezt7UFRFASDQayvrzNARPzMSCSCZrPJ5lA0GkWr1UKhUMDW1tapUoupVIptj9Qr6PyJRpTJZOaSCSMpNZIu450RibJTqVQYLQJ4nDn2+/1QVZUtQCjrOolTLUkSa8o7OTlBoVBg55/L5ZDP54cWltP0hf1+P2KxGGq1Gvr9/piiDJ17NpvFzs4Ou695brLf78fa2hps28bx8TFrpuWl9gjk03WdZXOdTqfx4MEDlEqlic+QdruNdDrNKB5Pw8+lxSfd43S/UKaYdMJ5A5mPG1902c/nGXrPwkFdxcPKgN6wW3kMfMsd41M9tkxYwnoiiI3k4Gs9GcTlfPj0P3xOsQDFi1gExrMYkziKgUAAgUAAe3t76HQ6Q1xH4j+urKwglUrhnXfeeerSN0lp3b9/fyzzlE6nkUgkGGggq17+RUycTY/Hg2q1ina7DV3Xh+yrqWQfCoVQKBQgSdJQdqrf76PX6yGfz+PcuXP44IMP8N577w11zUuShJWVlSEJqFnnRplZRVHQ6XSGbIyp+crtHria7ezsTJWloqYokrbiy64ej4ddz0ajgUqlMjOjduHCBRwdHeHGjRtD2yFQdPHiRciyjFu3buH73//+kA24JEk4c+YMNjc3sb+/D5fLhUKhMNFQgpcbm3ZdaRwLhQIDTnTuh4eHuHDhAuOmK4qCu3fvsioGAdCVlRVGCZhHugsY2G+fnJwMzRFVVbG6usqOadb14GXCiKM8Ch7z+TyOj49xdHTEzovXcvb5fMhms4jH46hUKgyo85+JxWJYWlqCx+NBq9XC8fHxkLKEbdtMDSMSieDk5GSmvnA2m8Xx8TF2d3eHAHij0cDGxga7H7PZLO7evcua/NxuNxKJBDY2NlhGlteuBsD+P9pUOMt2HQCuXr2KN998E9VqdUh9ot1uw+/349KlS+h0Ok/Nz+33+2wuE6WJ7zkYtSx/GoWKL7rs57OOnmXjsK4OQG/1Mc1ht6rgpKXhFCXKTzTiQS/WEwGsJ4M48wj4biSDWE8EEZQ+27Dzs310i1jEM45pD/V5sxjZbBb9fh/lchmqqg4BmuXlZZw5cwahUGhmJogvfdM2iBdbqVQYUOOD5yhSeZHUFTRNY41FBCwps0NOb6VSidkTE+ex2+2yTOOLL77IuuhpP6SUkEwm8dJLL0FVVVSrVZZJogWBrutMCWMex7IzZ85AkiRsb28zIMRbHbdaLZw9exb1eh3f+973pspSXbp0aYgTS9sgHicdZ6fTgSAIMw1OeG41D2Aou6+qKg4PD/HgwQNWvqZ967qOBw8eME1kUm8YLUXruo5gMDgEukez8hSFQgFvv/02FEWBLMus2atQKKDdbuPy5csoFovY399nzW28CYSiKFhbW8P58+cRDodZg1ylUmEZ2FQqxQBfoVBgknT8WJIkXbfbRblcxptvvglVVZkEXa/XQ7FYnCgTNmlfVAkgrWv+PqNqCNGVtre30ev1GBWI5nWz2cTGxgaazSbeeecdlkUnDnWxWESn08G1a9cQCoXY9ZukL3z+/HlUKhUcHh4y+g8Zr+i6jsPDQ5TLZQBAsViEJElYX18fU7EgWUNq/uOrFsCgEkOygQRSp9mu0zgCYAuQTqfDFmhXr15FKpXC7du3n5qfS3OmVCoxCgtde1ILyWQyrOH2aRUqvsiynx8nLNvBySNJs12usW2vquCwocGyPz3kG5Lcj8FuMoiNZADriSDOJGVEApN7Bn4cYgGKF/GFiXkys6dlMUKhEK5cuYKTkxOcnJywLE8+n2fmHcDsTBCVqsvlMtN85ekD4XCYgVlelopoASRLRSCGStH8C5IyQKZpMtc0MvAgI5FAIABd11Gv17GysgKfz4c7d+6wrJskSUin07h48SIymQzefPPNmbJld+/eRTqdPlV2iZoRZ4Vt2/jwww9nylLt7OywTvxRiS/insZiMQYapxmcNBoN1n1PShj8OGqahh/+8IdoNBpMLWM0FEXBhx9+iLW1tSHJLWBYnYS6+k+TGzs+PmYgq91uD5mJtNttPHjwgM0/XiKPmhWJPjG6uKOx4b+fR5Lu9u3bqNfrTDHDNE1Gg/H5fFAUhcmEnbYvqmqMNkDS4rBYLI41ZdGcBQYLxHq9jm63y8aIOLGk8dxut3Hnzh0sLS3N1BdutVq4ceMGTNNkGXPaDjDQH3///ffxwgsvzFSxOD4+RqPRYG57o2PY7XafmIYwS9pPVdVnws8lagfxiQnMk6Y1UZQ0TWP39dPqC39RZT+nheM4KLWNIdBL/z+oqTCtp1N2eJrwecQxqgNlfJOy93N5zRageBFfiJhXNH7eLAYBFACMYzga0zJBmqbh8PCQlWH5LB81KZGCBYE43mGPZKkMw2ANa6QQwTctUfm83++zMiifwbJtm2VUi8UilpeXpwL5Wq3GZMsmlWspy05jN6usS9SClZUVZnVMAIJemK1WC51OZ6YsVafTQSQSYdlW4HH5mprtwuEwA6LUREgZXsrcUebf6/WOvZypHF8oFNDr9YaUBfjjoWvVbrfZ4oPXoKWsPvFW6bpOkhsj9QXijPKNiKQ9fXx8DFVVJ849mpu6rqPZbAIAm/u8CQyZiyQSiVMl6ch9TxAGltb0OcreCoKASqWCnZ0dKIoydV+BQADVapVlxUcXMgBQqVSY5jPNadoXZdQpe0uZegKA9L1t2zg5OWH34DR94cPDQ0ZRoDEenWe1Wg0HBwcTS/s0p2u1GlRVZdsZHUNqDnxSZQWXyzVRdu1Z8XNprILB4Fg/gdfrZdrah4eHzODoWegLf9FkPx3HQV0xsVdTHvN8qwPaw35NgWrObuD+JMPjErAaDzDgu5GUsZ4cfJ8J+SCKnz/gOysWoHgRn/t4EtH407IYPLie5DLHZ0ymUTVM02SAmAd9lOVrt9vseMj+djTrSHJuxB0mMwceEEqSxKxt4/E4A9HkViZJEiRJGrKUnhYkWwaAAZxR7iFlVSnDR+VlHoi43W52HMlkErFYDN1ud6j5DxgoVJwmk0YufKScwZt1EL8aAFMTIR73qMEJnVswGJwIenw+H8vm27bNQBqNNe2LxpUavOizlHmj8n29XmfyZpPUQGq1GtOCJkUGAKzRTNM0lv0nSsmo3i/9XNM0aJo2c+7PUjmhzxqGwRZKlmUxeTU6LgKjR0dHrHlt0r6I281ngEd5uIZhsAVPKBRi1CICm6qqMlMNn8/HNJ553jFth/ZF15sPftFIqhf8wpLsnUmD+DRZO1IwmSZHR/P/WcSz4ufS3KHFHs03mtdkHU7Npk/DX/4iRFvvDZlY8AoPbf3Tk5oTBWAlHhjL+p5JBpGP+uH6ggHfWbEAxYv43MeTisZPy2I8CbjudrtTqRrdbpeVQydlQX0+H9rtNpOwmpYJVBQFXq+XublR1kcQBJb5IV6hbduQZXkMYNDL3u/3T2zsSiQSuHDhAiRJguM4DMzzGT4a32AwyHSQO53OmLQd0RVI2oxACDXtEVjm1QdmyVIBg1I3Zbd5jVkCZlTmVxSFbZcygrzKBJXuJwVtkxzzeHDFqwsAYEB7NCtIXFVawMwyuCCwT8B79DM0ZwjMkJQdTx/gy+AEmqbN/Wq1ysaUOLE8f5041LQf4pPzCzBaMGiahlQqNXVfpVIJtm2ze4JfXBAQozlK+6D9UVD2kvZH2X9+UUBAl5rEplEMiDIwyb6cqEfUTHmarF0ikWAOk6OmLC6XiynTPKt4FvxcaqylLDGZh9A9RAsHfrExGl80fWHV7GOvqg7RHAj41hTz9A18gpGP+LDOAd71RBAbqSBWYgF43bP12hcxiAUoXsRnPubteJ72uScVjZ+2HR5cA4/dpygLSuC6Uqng+Ph4qkMUuYvxahH8OdA2T8s8kblAo9FAKpWayAVdXl5GtVpFpVIZo3KQvBNlgGY1dr366qsQBAHNZhN+v38oK00ud7IsI5vNMmUFkjQjcNBqtVCtVrG1tQVN07C/v89cvej8qPS+trYGv9+PcrnMxoEf63a7zTKtxJcm4EbAkK4jD+Aow0qAiYAoNX9N0nxWFAWhUIiBeMuyxj5DWTWv14tCocAWPLzmLY0dcXJFUUS5XIau6/D5fEin00ylgXcKHF3IaJqGSCQCt9uNWq02BmSJc5rL5ZBIJFCtVmdm7knGrVKpMNoFr1BimiZSqRRarRZzeaP5Q+NIbnCkLDJrXy6XizWG0nXnAS0tBok6Qll4ujbkGkiujQRsaTskq+f3+5mM4DRZw+XlZRweHqJeryMSiQAAA4XEBY7H41hdXUWj0Zi6nXlk7dLp9DOXGwuFQvD7/RNl+/iYJqPHyxFOe4YkEgnIsvyF0hc2+hYO6ypTdeAd3Ipt/VM9tqQsYeMRvWE9GcRGYvDveiIIv3c+a/tFTI/PzyxexOcy5u14nvW5JxGNP82NzLIs9Hq9IV4oZUGJ33pycjLTIcqyLPb/Uc4oZUBJ7N80zZmZp6WlJVy/fn1M/q1eryMQCODixYtQVRXf+c53UCgU2FjQIkCWZbz00ku4f//+zMauW7duseYbHqTR/0mxQNcHLwwqh49yfekrFothf3+fKXPwWUev14t4PI6zZ8/i3/27f4fd3d2xaxaJRHD58mVcv36dqT3QvgEwsE2LErfbjXq9Pgb44vE4PB4P1tfXcffuXZYx5OeGx+PB2bNnWUMeKYZQkPpFLpdjzWeT6CiiKMI0TcTjcdy/fx/VanUoO72/v49kMonz588jFArh+PgYpVKJgWEa60AgwBrIvvvd74659pFs1/nz5xkIJZOWSTJxHo8HKysrjF/Lz5F6vQ5ZlpHP51GpVNDr9dDtdocWGpZlwePxMJtocpzjNbFDoRBCoRB8Ph+CwSDa7fZEoxjilWezWZRKJZTL5bHz9/v9SKVSaDQajBfOVwBIQSMUCiGZTOL4+Bj3798fu/Yka0hqMERHou1QVSCXyyEej6PRaMyU0QPmk7V7lnGaRN48n5lmJkLPkCtXrqDb7X7u9IX7lo2jhobdRzq+fOb3pKnhUxR2QNjnxkZKZtne9WQAZ5Iy1pIBhH0/vsoOPw6xAMWL+MzGvM1xp31uc3NzrqaUfr+PnZ2dmW5k/X6fNUHxQJVUJILBIDRNY4B5kkOUKA6c4UqlEqMJ8C9rwzCQz+extLSEQqHASv18M5ooikin01hfX4fP5ztV/m1lZQW7u7swTZNl43w+H1ZWViBJEorF4qmNXW63G8vLy4xqMrogsCwLtVoNpmlidXWVgTDewEGWZSiKAsMwEIvFmHMXD8KJ9sDzVic1ZEmShF6vxxrAeF4t8W7j8ThbxBDI4cFTuVxGLpfDSy+9hGq1inK5PNRs5HK5kEwmcfbsWezs7KBUKrEFD4UoipBlmVUK+Gwrn5UmsEG6uqNBygzxeJxJhZF1NO3P7XazUj1lbWn+8Mfs8XigqirLXm9vbzMrYn4+1ut1nD17lknI9ft99kV8apqT4XAYijJwnhrVhPb7/YhGo/D5fNjZ2Rni5qqqina7zRYfBCBHKSYE1HO5HFKpFOOVTzr/VCrF9j3NnpmUREjLmK/sUFabDDxSqRTK5TIbb5qLqVRqyL6cf3bwPGhgYGBzmqzdk0qOzaqS7ezs4M0335wqWUhx2mc2NzfnkpB83vrCT6OJTGHbDgptHXtVBQ8f0Rwo43tQV9H/FJFv0OsaozrQ/2PByfz+RXzysQDFi/hMxrz83WAwOJcEWDabnflQz2azKBaLM7fTaDRY5jaVSrHsnNvtRjAYRKVSYaV+x3GmNsi1Wi1cuHCBgWd6eff7fSiKgnA4zMwiVFWdaoRAL6J55N8ikQi+8Y1vTHQ+Ozo6YpzbaY1dxLeWZRnRaJQ11PGNfpRdP80ZsFgsol6vM2UARVHYZ4hDXa/XcXh4CNu2kUqlxrJ8uq7jww8/ZFxePgtN39NY0TFQaZ/P8hmGgXa7zdQwcrncRFczwzDQaDQgCAIymczYMdN26HgIfPB0DgJ4lUqFHeOk5sDd3V1sbm7C7/ePcYEpM2uaJh48eMCyfqNmIpZl4cGDB7h06RL7O56qw/N4dV1HpVJBIBBANpsdA4/tdptxgX0+H7PM5ikNlOXt9XpMF5pXVaHFUa/XY4C20+mMzY9QKIR0Os0AMn8P8edPlRLazyidhSo8Ozs72NvbY9eTqB+9Xg97e3sIBAJM9SObzaLVajEQRnQKypICYAsInj7RbDZxfHzMFEpmSRaSFNw8MatqFQgE8MEHH8yULHz33XcZ3WbaZ0hG7zQzkeetL/wkmsiO46DSNR4ZV3SHjCz2agqM/qcnaeZ1i1hPBIYa24jnm5KlZ76QWMTTxwIUL+IzGfM2xzUajbk+t7y8PPOh7nK5Tt0ONSRRtnO09Et/y4PY0e2QNFM8Hse1a9eGdIE9Hg+y2SwuXrzIsmn8MRPNI5lMjr0cKKtCGTOyEObHkTKalOGm7+llPspdpmMm4EMqDOFwGP1+f8hBjLLkJGFFNIhJzoAAWNc/AUYaRwK/1LBEQGt0G6IoMukuWZYZHYRXlnC5XMwggiTq+G1RZpkyZ7lcjnFAeTUMMqigLKlhGEONjASECeCR0sCoIgRlIXlJMz7jTA1kpmni4cOHWF9fn0p5qNfraDabbBv8OFPWuNVq4eTkBKZpYm1tDe12G9VqlR1fMplEOBxmer+JRIJRZGh+0BypVqsMtFKDJj/WxC8mbq4gCOzYCdA4jsOqFrZtM3vqUe4tLbpWV1fHaBjhcJjN+2g0OmQpPepWF41G8f7770PTtCHuOl0PTdOwvb2NcDg8lP2nMW00GgzUtlqtqXKEdA+VSqWZsna1Wg2NRmOqYQcfVP2a1pcQDAZRr9fZc4YH4SQlR9UI0sye9Jl6vY5CoYDl5WW2SJ0Wz0tfeFrlb79Qwfv7DdjBBIpda5D5rSnYq6roGp9ek59bHEiaEa93wPeVsZEKIhf+4kma/bjHAhQv4jMZ8zbHUVZrnia6cDg89aHOy3tN2w41RyUSCRwfHw9x9GRZxtLSElNzmEeayev1YmlpiWUsyQSEGvmAwYuIDCYmZXCA2ZxBap7p9XqoVCpj4IGACGWEJx2zpmmQZRmrq6u4d+8eDg8Px7KgkUgEW1tbiMfjqNfrM6kqkUgER0dH7IU/KqdFQFbXdQbALOuxjidlMKnMTZn6ScoahmGwY6RsKu2LFgKkgDGLK07SbZSV7PV6TJ+XjFbo+1EdYwCMo8rTQABMzBryzX3Tsu2lUompQowuHHjeN/2tpmk4ODhg85ya8SirCWAo281nnQlwU/MgWZ7zC0LaZ6vVgs/nYxbUvBFGJBJhjZxUtaGFDNE5stksEokEDg4OGP+fp3Pw9yJRnqZVUog6QdxyPktOEmTtdpudO4CJ/H4ywZl1D9HvT5MQpP3MCqruzOpLIGqJKIoTpeTo/qDznSY3R+NH+z0N8FJm/pOKjt7Dd2/s4t5JC01LwnG7gZO2iaN2Dx2D7qnqJ7b/aSEIwFLUz4wreFmz5ZgfHtdC2eHzEgtQvIjPZMzbHEcZwXk7o6c91OfZHwGrWq0Gj8fDKBS2bTPucTAYRDweh6IoMxvkDMPA8fExdF1nJXre5IDnS88qI57GK/zyl7/MXuaTOM6Udcrlcmw/ZAZCdA6Px4N8Pg+v14tms8lkqoBByV9RFKbdK4riqfqplI0isMPzL1VVBQD2Uiewx6tC0N+53e4howy6hvS94zjw+XxDMl2jPF+SHRNFkWkLT+KKExWFst+TALiu6/D7/QiHw0OgmcYmHA5D13VmzjIJEBNADQQCM7PtJJFHclmjc5Wa//x+Pw4PD7Gzs8P4s7wayM2bN7G2tgaPx4NKpcLAEl0PTdPQbrcRDocRjUbh9XpZBpjuLcogdjodVCoVNJtNlr2m7SiKwhwDATDFEj7jzOsB93o9nJycsLGijL6qqmi1WkilUohGo8yielIl5ejoiMmuTQJ3Ho+HVVcI1PPqJHT8uq4zLvi0e4iyubMkBEl947TQNA2lUom59E3qS6B51Ww22fONXxRomsY+N0tujuhPz8LCed7Qexb2ayOqDo+a3CodftGgPNP9zhMxn4hsUEQ+NGh0u7KWxuWVJFbiAfg8C2WHL0IsQPEiPrGYJ/Mw7TO8Y9M0GaREIoFYLPZEzk7z7G/adqiTfZRTDAwygZVKBcFgkDXI9fv9ifzUdDqNRqPBDEBGeb5k3uE4zszGv42NDcYrJKBJSgDkUnb79m3E4/GxY6YSOR3zxsYGADDOM40PAY+lpSV88MEHTHpsFBRaloVbt27h0qVLjH94fHyMg4MDdm6rq6tYWlpiwI/GmAcitCAhfVnih/LcWzrPfr+PVCqFer3OZL54fd1er4dcLodarYZ6vc4ACX/ciqIgkUjA5/OhXC6zzDpVBYgrHolEmC22x+MZ492STBhxoinbO8qVppI1r/nLnxMwWKBtbW3NlBJLJpOsyYwAPj8XHcdhc7pUKrFGOX6/pAJRqVQYl5oAM22DPh8KhdhYZzIZZsLh8/mQSqXQbreRTCZZ2Zv4uLxuNGVty+UyqtUqszTnZeuq1SqOj4+hKArK5fKQmQRtr1wuIxAIMCm2aZJklC2mxQm/mKKf09yl4yRwTIsgakol1Y1kMsnGRBRF5tCXTqeRTqdRKpXYooK/Hq1WC9lsFrFY7NTnZq/XQ7PZhGVZjBrEz0cyLvF4PGi320M238RTp7kjCAIajQbC4fAYfULXdSQSCQSDwbkamp8kzL6Nw4Y6ZGSx90jl4aT16UqaxYNerCcCjOOblV0Qu1XEPH0ko6HhRbyriWwguQDEX6BYgOJFfCIxT+bhtM8QoNne3h5TH0ilUsjlcnNlJqkhbZ79KYqCer0+xHOlcmwsFkOlUmHl39EssCzLrDFvb28PDx8+hK7rbF+NRgNnzpxBNBrF/v4+ut0u7t69i06nwz4TCoWwvLzMDCFmNf7dvn0btVoNfr+fGVHw5VG/349qtcoatk475nq9DsMwmIsYT40gDi8PLnjKg9vtRrlcZvzEcrmMGzduoFarsUUBgSLiO1OjF1+KJ+oEARb6DE9H4FUWstksczgjmTAyMpFlGRcvXsT169eZS9xoULaZAPnJycmY5BapGaTTaTx8+BB7e3tji4JwOIyVlRV4vV40Go2h7DlldImKsbS0hMPDQwCPgTAfy8vLyOVy2N/fx/3798euK0mJ0Rwd5VwDYE1lpVKJ0VCoDE9BdAziccdiMTSbzSHXP4/Hg1gshmAwiFgsht3dXXz44YdD89rn82FjYwP5fJ6Nx6SxprE4PDxkVJJRbrJlWdjf32fnTOCQP2ZahGiahmKxOJU6lMlkEI1GUSwWWfaarxLQuVEme1TFg+Y/2R1LkoRCoTB2XrIsQ5IkLC0tsWw5L23WarUQCARw4cKFuZrsyETD7XYz2hQ/RrQYSiaTLOPO28ATDSqdTgMA45JTIyrdU36/H5lMBkdHR3MZEo0mNCzbwUlTG3JvI/B71NBgfYrKDiGf+zHFYYTuEPE/ljRzHAfb29uoWRZisfgTnf8iPp+xAMWLeOYxj5QagLk+Awx3zo9yMoH5OqPnlXfLZrO4e/cuc24jV7eNjQ3WrLW0tDTGmQyHw4hEIoy6cHBwwLKKZEWr6zoODg6Qz+dRLBbZy58Hqs1mk8m/JZNJlu3hg5p7KAtLYzRaHqWSs23bTEpt2jHz/EO+/Ev7Jhk2PntLpXGiJvT7ffYSn0TpKJfLePPNN5kRCAE3HmAHg8EhRzICSqPAiGgSwWAQq6urePjw4RAX1ufzYXV1FYFAgPFoJwVRJGKxGMva8txT2h+BJNJEpnEmrrCiKPD5fAyEARjjp1O2NpPJoFwuT+SXSpKEVCrFnMRardZQBpyazWhMRrnLFHRcpPrAO5VR0LgTOPX5fMwdkV94UGWgVCphb29vSBe53++j3W5jb2+PaT6TWscoCCO+eqfTYVSDUaqGIAiMs0t9BTT2dE60zzt37uD69etTqUNf//rXEQ6HcXBwMLaoonMMhUKo1WpMepAoBzQ3SKIQeMz/Hl00UbY5kUicKm02T9B9TDStSXSWQCCAWCzGFuCdTofRs6LRKNbX1wGAJQaIW07PT7qutPCY9pwJBoN4WKzjpH+Ck46FvZqCh480fQ9qKkzr01N28HsGkmYbycCQnNlGMoh40DsXiH1St9NFfP5jAYoX8UxjHim1k5MT1kg1KztBD/Bz585NlEHiV/CzOqPnlXcjuTBJkrC+vj4EMIvFIpaWltj+s9ksa14hlQLiwN66dQumaSKbzY5JM9Xrddy8eZNRJsjwAHgM9trtNorFImKx2Ex3MALSlMkmkEbZW2pgo+xSLpcb2w6NK0lObW1tMeDn8XgQDAbRarWY2QJxVXmuLN94JoriqVJRN27cYJQV+h0FXTvTNBnXmEAJLylGGT0aw69//esTaSilUgn1ep3xgUe3Q01LRG3J5/NjWdBWq8Vkufr9PuOxjwK1w8NDBs5IjoznnBPnlVz6YrEYkymjxVOn00GxWGRGGuFweEw1oVqtMoBu2/YYvYRXZKCKA9FeRoP4sgRQ8/n82Jyl6394eMhURnjePckU3r17lzWrUvMozUeyEibqAQEufuHh8/mYhjUtGKLR6Jj8G1F8bt++faokWaPRADCuKUzPllqtxu4TohTx40g8Z2pGmzU/XC7XqdJm8wTPjR9NAtDP3G43PB4PIpEIstns2P76/YGrHskIrq2tjVF+ms0mqtXqQJauL+CkruKgoeOoZaLQtVDsWjju9GD0HQC1uY//WYZbBHIhD/IhN5KSg9WYhFfPr+HSahLZ8DhX/Elj3oZuXk97EZ/vWIDiRTzTmGflTdJm02TLZFlmWq7hcJh1wfMxaQU/rYlunmMiQKXrOuLx+NgLtNFooNlsIhwODxlq0EuGGmC8Xi8rl2qaNlb6Jg4iD/r4oGwgdbxTI8+oIgJ9kUwagDFDCeIsE4CPxWJD40hcaVpMuFwuFAqFiU5kJEdGWbbRoHNsNptMKmrSuZH8VTQaZdeKLw/Tz0KhEMtieb3esXPTNA3xeBySJLHGwGQyOTZHiH5AgIw/XgCsuVJVVQQCAWYmwtNHaFHSaDRYmZrGgOYmVR0qlcpEKTEax1qtxkw1qIRNiwNqqtI0jWkQj+r0+nw+NJtN1kRGizT+3Gix0+/3h1z+RtUvJoFEmqejP1cUhdlAj/YG0NjWajU27yVJYudIn6WM7igdhg/6uW3b7Dh4iTOqUtBihha5k+TGSqUSVFVl+tSjlB++cY0aF/m5TZUKWnwRBWpUnWP0XE6TNpsnaM6Myt8FAgHY9sDVkOT0yKqZv2ZEqaAeA1EUIXgDaBoiqrqAckNAUUmibrrQ2Fah9fef6nifJkQByMhu5GQ3MgEBr5xdxkpUgt/qQrIU4NHC75No/HsSt9NFfDFicaUX8UxjnpU3AcXT5M/o5TXtM/Ou4Oc5Jioxz6IrtFotJJNJtFotqKqKSCTCSpOlUomZH5CUE73YKctH8mB0biQHNqp3TC53wMD6l8A0rxpQrVaxsrLCjCQURWEZTOILU1NfKpVCqVSaqQaxvb3NbHPpBUxOZKSmQNeFyvA8hQAAK4lPe8EAYACXB5c8p5gAbCwWw+rqKn7wgx8wVQeeM+nz+fDiiy/Ctu1T5xpdi2lB15uypoFAgAFdcqijfUciEZbF47OXdF1UVUUqlRqjYNBcJkBFC41RtQNZltFutxmFZtpcpMw+UTv4MSDgCAyaGEl6ixQQKIgGQhJefr9/omIKKR/0+/2h7C5/TJQxpWi322Ogm+Td3G43M6UZdRgEwOT1aDEymimn86Us9DS5MaKd0L1DWr+UNaaGNPo/r1BCzZpUqeFVKChTTtlYqjhNo+g8aViWxcZ5mvxdIBBAMpmEZVlD93VXN/Gg1EbNdKFm9PFhKfLo/yIU69OVDcvIHiR9DtbifixHvFgKe7EU8SAre+BxCawh8dKlLFs8fdKayPM0WP84Wlgv4uPHAhQv4pnGPCtvygCdJn922mfmXcHPc0z00pkFsAgkRSIRhEIhqKrK5MnIqYoyVgSueFBExh3A40YoAEMvdP74CAzwXGr+pU5AKpVKodPpsFK8KA4MF0KhEGuU46WrRjnX1IRVrVYZcOG1WiuVCitL83xMOiYqs3u9XoTDYQaep0lTeb1eZDIZlpGljDtl+ERxYGF99uxZSJKEd999F81mk9lFJ5NJvPrqq9jc3MStW7dmXleeQjOphM5zZykLSmCVXM2I/kDgd1QJAwDjvjqOw6S7CJw5jsMy/sRB7Xa7TN+agkxL6DOzguY+n0Gl4EEy0Tj8fj+juPDziygDoVAI0WiUGXkQECFtbzq/0zK8PM+WPyZ+8ZBOp+H1ehlVgr8+0WiUGc3w+sB80DVVFGWm3Bhdb1o4jQIqAtVEMQEwNI+o4Y1ULohzTfQOOl7i1U+aFx8nSHc7EAiwBS9/f3h9AZRUG++VetitSLhz1MV+vYBCt4+GPtpzcboE3LOMiCRgJSphKewZgN6wB/mwG/6+gkQ0xBZZTyOf+SxDEIS5G7UX8cWIBShexDONeaXNqJFk2mdSqRQcx0G9Xp97BT8tszDPMUWjUeayNu2BTV3qiUSCZUVH+bmtVguSJDGlBz6jCIBlmsLhMHO3Gs1O1Wo1JBIJJvHV7XbHuutlWWZZWVmWkc/nWVMWcQ3JmACYbQLCu6ZR9povD6uqyrK7lHkbbf7q9XoIBoPY3NzE7u4uSqUSPB4Pawjj+dKZTAaXLl3C7u7uqRbWL7zwAs6dO4cPPviALUiuXr3Kytq8bN8oF7rb7WJ1dRW3b99mmXcegBFoo8wbmVeQPqwoiuy86FooisIqDzzvllQ7AoEAoxmMck9N00Qul4Pf7x9yfuMzzjQXSUZvtHJB85WsnalCMEk3ORKJ4MyZM7h16xZbUPAUAsoGy7LMeLCUsaXFCy1aVldXmW4wrwpCwFNRFNakZ9s24vH42Lxut9vQdR2pVIrNv1EATmoyHo8Hx8fHzOqc16WmcTw4OGBUHZ5PT1ldsk+nhSsdN405yejRfckbfACPK1Z0/Xq9HiKRCAzDYMcjSdJQhhwYgO2n4RT7/X7IoTDuHlbQdkJ40HRw3OqhogNlzUS5q2Iwc+tzb/NZRjTgwXrikWXxo6+87IJS2kM8FJj4/DRNkVV8iPJx2jP9eWSKgedvYb2Iz3YsQPEinmnMs/LO5/MAcOrqHBiUf+dZwc8rtzZtW2traygWizOBczAYZLQEytLyQeX6VCoF0zSZfi7fkOXz+bC0tIT19XW89dZbKBQKQ01zBHIvX77MMkS1Wm3ovBxnYEpBxgSaprFGQUF4bHIQDocRDAZhWdbM8aGsmtvtRrFYHKKkUNZKEAQkEglmj8srQNDLjiS5rl69im9961s4ODgYynQ7joNwOIyrV68iGo3OZWE9ya3v6OgIV69exebmJnK5HI6Pj/HOO++MAXWil8RiMZTL5SHgROV5yr6RugJlcylUVUUwGMTW1hZWVlZw8+ZN6Lo+1kTndruxsrLCgBRZMNNnSDGg3+8jmUyi0+mg0WiMzQ9JkpDL5ZBKpXBwcMDmJ81XUknY2NhAPB7HBx98wOYBbYdUT86fP88c18rl8lh5nxrxSHLt9u3bqFarbIwcx0G5XEYymcS5c+dw4cIFvPPOO0zei78/vF4vVldXcXR0BI/HwyoiFESDcBxnoh06vyDK5/NMz7lerw9pYbdaLcTjcWxubqLX66HT6bBsMQVRkFZWVgAA169fZ5l5/jMulwsbGxtsblFGmVdVkSQJgUCA8bYnSfbx2fRCocDUJ3j1mknqE7btoNDWsVsZmFeQpu9eVcFBXUXfdgAMA+7nFZLoIBsUsRyVkPYLePX8Ci6uJLGRCCIWHAe97XYbtyuzK22k0EEqNU8jn/ms43lZWC/isx8LULyIZx7zrryf1WfmlVvb2trCycnJmKFGPp9nDU2ngfn9/f1TaR/xeBy6rrOGOio9S5LEDEcymQxWVlawu7vLSsSUkSIwd+PGDRwcHDAuJx1Ps9lEt9tFLpdjDmPAY0UGPgtIfGlyz5s0PpFIhHF1iSbAAz4CwaFQiKkfkGoFlZUTiQSzjOYz3JTBpG1S1pXmyawX0WlufcCAHlCr1ZiuK3FNyXkwlUoxTjRJs/EKHQRoyL6Xp6ZQJllVVRSLRZw/f55liwnE0nbovGq1Gmtu4qkEvOReLBZDKpVCuVxm40jUmVQqBVmWsby8DMuymEkGhSAIWFpawubmJoABD3pUjs7v9+PMmTN45ZVXWEVmFu2hVqvh+PgYrVaLUYh4oN5qtXB4eIilpSXcvHkTpmmOqSL4/X6k02kcHx8PUWv4caTveQfFSqXCsrSkPU73dTKZhKqqjGZD3OVkMsnAaiqVYtvgqxupVIoZ6ZRKJZRKpaHFnsvlQiaTwcbGBjRNw9raGsrlMmuOpbmaTqfZtmkMR/nipABSq9Vw/fp11m9ADpjbhyW8f9SGnD2Det+NvaqCvaqKvZoCo//pSZp5XQJW4wFERQNRdw9JycZSxIulkBsBsf+oB8JCNBrFl64uzaQyzNuwNupC+DTymc86ngddYxGf/ViA4kV8IjHPyvtZfGZeuTXibvJ/x39P+5oFwkmlYh7XO1EUceHCBXQ6HfbSD4VCTN2g0WggEongG9/4xpiUWKvVYlkyVVWHJKdIp7ZaraLT6SAej7MOeyrX87QH2tes8SE1B9M0x5zIgIFGMTUJiqKIS5cusW2T/BgBZa/Xi7t378LtduPFF19kUm503LVaDXfv3mUc7GkvIsuyTpV2e//99xGPx9Hr9bC+vj5GISB3NMuy4PV6sb6+PnY9ut0uUyEAwPjUfJndMAzU63UUi0WkUimk02l0Oh02H0kpo1qtsuwvcU354yFFCiq70zjS+AQCAXQ6HXS7XUQiEVy5cmXmIg4AvvKVrzCdZuJrnjlzBsvLywiFQsy4ghrdRgGqYRhoNBrY3t6GIAjIZrNjx91oNHD//n2k02mEw2EkEgl0u112f8iyjF6vh1qtxni11PRJ40jSgXTuk+5BnjdfKBQgSRJeffXViRKB1WoV3W6XjePoZ4gGo6oqNjY2mKEMXft0Os0oUOTud/78+TGZRX4BYBgGcrncxHlWVwzc+WAH9ws9aO4YyiWgpJgoqoBhPQJwtw4mPyw/wXCLA+C7lgggL7uQC7mxmQ7hynoGS9EAAAdvvvkmisX6iEvnoLmUzIom9Qfw8SQNa9Qs+jTymQszjUV8UrEAxYv4xGKelffTfmZe8fVKpcKypdQMNin7cBoIP42GQaV6SZJQKpUYKOj1elBVlYFz0qAl0M2HLMsoFoswDAOyLMMwjCEBf9M02c+Jb0n8SKJVkHGAaZpsX9PGhyTyiBtMzVfU7c93/xPH0+fzMW1mOi+iX9RqNfh8PsYh5aWufD4farUa41NP4w0WCoVTpd0os0egeVS2LRKJoNFoMG5zpVJh2XQaM1KWoOwujREPjGlcWq0W1tbW4Ha7Geglvmy/32fUEwIQvH4wUTUoU02/C4fD7JhHs6+hUAjnzp3DysrKzJIugfBAIMDc1yioskE/41VDSKWCMthUuh7VPZZlmfGNiQ8cCoXYMfl8PhiGgVarxbL1pM5A58SrjpCm8+i9SItAMseRZXlsjOh4iHLDz4fRcSRjFuoByGQyYz0AmqYxMJdKpeD3+9nCCBgsCMkGXDFt3CuraFpeVA1xoOWr9FDRQ1D7lM33A3g2KhTzhgAgFXRhKexFXhaRkBysxn144/Imzi8n4XZN5zOrqsYWANMcL8lBb9Zz+kkb1qY90xdmGov4tGMBihfxYx3zyK31+32cnJxA13VEo1H0ej3WeBSNRseMQGaB8NOyyaONdcAw2KHmLV4hYNIxkzZpKpViXfoE0vx+P4LBIAP0siwzcGmaJgOERAchGaxp+6Lsmc/nY5lLPkiBAQCWlpbQbDaZUxqvXGCaJjRNg6ZpDADyYJ44rJR9ncUbJKA8S9qN1AEcx2HWyryWMykuEACirCC/uKCSPH89Rh3daA7NknUDHuvaEmVl1AiCaAmBQGBIgYJAKqllUMPaaUFl5lFd4Gq1yhZ6RIchwE+LAmBYE5tA+6TgObbU/ErNl3Q+xJemRYxhGPD7/UM8Z1mWkUwmUSwWTzX3IcBNXONR4xpafFiWNVVGjmTWZvUA2LaNM2fOoN1uY3d3F47oRrPvQdUQUdEFKGIAatGNw2YXLUMEIAKwH30BA0j6yYbgAGFbwLIALAsC0oILsukg6PLAbQMuiLCrDqyiA8ey4ZYEiEEdt28/QGOtiXDSj3DCj8xGGP7Q8P1E43yaS+c80pfPomFtYaaxiE87FqB4ET/WMQ+XjVQjPB4PCoXCkOlGIBB44uzDrGyyoiistDzNHUzTNASDwZnHTC50ACZ28hOdgZqzRl8SBPJIOuq0ffn9fiY5NQp4KFNEx5TP56c641EmkLR4CfQQX5rMEnq93kzeIHGoZ0m70bUvFotDsna0QCBJNFVVWVl9tBmP+KIEoKjUz2+LGsloAUWgb9TAgbLx3W6XgX6e5ytJEsLhMHNpI7c9GsdQKIR4PM4a62YtGmRZZr+zbXtoO6TQUCgUWEmcFig8pYEAJ/HCKTM4WvomXWi3241CocDGkILG1+fzIR6PM11sGge328149C6XC4qizKxckGoKUUkm3a8kJ+fxeMbMbajBlBZ3o3O/ZzkodnvYr6nYrxvoPqzjzlEQJ20vWv1JsmrahJ99MpEIeHEu4EW82UGqJyBiuOEx3BCcyeDbAmBheLHWNxz0DUCr6yg/OGI/FwQgtxXF5ispnHk5DTkmsXvoNMfLeaQvgadvWJuXmzzv8SxiEU8ai5m1iB/rmIfLFgwG0Ww2mWSZz+dj4I30fQmkzhvz0j4muYOR/i1Jbo1aWFMTHSkipFKpoe2QyH0ikUCn00G9XmdcTT4rS2YTtK9p48PvK5lMMjUGnjOZyWSQz+eZRN4kZ7xEIsEajDqdDgNf/Od6vR5CoRDTHZ6WLfT7/YyKQtlm/vzb7TZrgioUCgx0EtgzDINJ+9m2zUr+k3i+pCBALorkAEbbAQa0mbW1Ndy8eZMtRAZGLD10mhpEy4u1/BJMxUF7vwen74NHSAGWCLsvQOsBuiPA8PlQFywYpgEBEQhCFKILsN0OWi4brVsdxJIuaHv7UIwWBG8fkWQQwWgAcFts0UANZAS8R+e0KIoolUp46aWXkEgkUKlUGEjhM9O2bSOZTGJlZQWHh4cTDTVM08Ta2hoajQYqlcqQjizN6W63i6WlJeRyOTQaDaaeMsoF9vv9TMFlWhaYKB4HBwcMbPPnVq/XsbW1xRobs9ns2D1UbzTQlyI4bvdw/3oV9b4HJ+0ejts9lLo92ENMle6jf5+NzvBpEfK5sZEMDr4ifuT6IvztPvplHfXDLoyTPoBnTw1wHOBku4mT7Sa+98+3kdkI48zVFHwpGd1uc+Z9/STmFU/TsLYw01jEpx0LULyITyzm0ZmcR9Nz1md4LlutVmMlYgI1gUCAyXYRfYKOwePxwO12s0YkKneTtBKVYXO53Jgw/7Rz492oWq0WM9TgX+6BQIA1K92/f39M4imdTjNZqk6ng5OTkzGt1kgkgnPnzuGjjz5i48AHn6HN5/OsYWv0eILB4NC+yuXy2BjKsoyLFy8yPjEpIvC0C1mWGX0klUoxlYxRGSwCu+12e6Z7YKfTwaVLl9DpdJjuL5/dDAQCuHjxIm7fvs04v/yixnEctpAgoKQoCnqqCreiQNINeCwLWasPQVXhMUzkyiWIhgG3ZcPV78NlWXD1e3BbgCin0fujD5G1g9DcURjeBAwpDtMThiN6YAPYuWM+2nscADBMQgEcAJrmYJDfm/TodQHwoFDQUUCB+/lgu6JoIxC04A8WUYrcB5wyQv4OwiETnZMW+qYJv9eDzCOVELEuwi3t4WdXNXzU3INuGIApAIII2wEsCJB8AVw9u4RItIdAtYFm5wi9pgPLEQDRDcntRTKawKXVBN6uHMJtG+gbPYhuL8RHVBje/CYWi0HTNNYIR/espmkIhUJMwYWaDhuNBpuPsViMaQvTNaR5RHOfwLQgCMhkMjiotHHjfgklxUGh20dJdVDoWiirNvp2Y8IYf7LhsfqImF0kzC6W7DayloKYrSBq6ViLBJCN5qAoXtTrQdRuRdGxZVQ/pYax0m4bpd02RLeA9Hkv9PNVSLI48fnJN0Oe9kx/ms88KTf5ecez0k5+XhrMi3jyWIDiRXwiMY/O5DyanvN8JhQKIRwOY2dnZ8zk4urVq0OuYafFJF1c2g7JYM06N5LnqlQq2N3dHeICS5KEjY0NpFKpoWwbBb0UKHK5HLrdLnZ3d8eOc2trC5lMBl6vl5W8eVkqn88HSZLY73u9Hj766KOhkn4oFMJrr73GGgxjsRgePnwIRVHYMQeDQaysrLCx7vV6uH79+tTtqKqKWCzGFimapg1RCNLpNFNrmJUttCwLKysrqNVqeO+999jChY5pa2sLqVQKwKDxqlQsQlRV+LsKgpoKWdORhAO/osDTVeDqduHuduE1zbGxpOi7fFADGSiBDNRAFk158K/mT8ERn08WcVbYtohuR0S3E0GlCACD83eEHvrSMezAAwj+bbiUe5DcDXgdB/e+50ByHHhFBxGfA5/jwOsMfiY5Dnw9B9IPfxOO7eBVZ/B7N0aYsmUA94G1kePpw4U+3LDghi26Ye96Efjn/y/4ew7aqgHTFtBz3OgLbsDtQyydR6q0AafRwUmlAZ/pIO640Bc86MML/ciHfiCM5fWzsFw+hJ0WdnaLKKkCarYfbfihikH0fDE0HxRRVk+g9Z6PpJno2EhoLaS1JpJaEwmthZTeQr7XRq6vIKZ3ICktuA0dPY8MQ4rC8EagBrLoykvoykt4oGexXR555X4GcJDdd1C8ZaB424GYaMNOVuEJOOy5x0tfnvZMfxaf+ayaaTwr7eTnrcG8iCcLwRlteV7E3NFutxGJRNBqtcY6pL/IMU1nklb6W1tb6Ha7ePvtt4c0PU3TRKvVQiAQwLVr1wAAb7/99hDHtN/vs++vXbvG9E5nfe7y5cs4OjpiTWSjTTkkh5VIJPDOO++M6eK22234/X584xvfQDqdnnlum5ub+O53v4vr168zRQPaF2kRv/jiizh79izq9fpE+kSz2UQikcCtW7dw/fr1qeN88eJFZDIZVqYeLY1T2drv9+Ott95izU90zFQq/6mf+ikAwJ/+6Z8y7jB9hmxu+c/QzyhDS3/zUz/1U7hy5Qq++93v4sMPP5xYrne73Lh48RLCcgRW30G3o6DT6qJv9eHxuhCOhhCKBuFyDfjMP/rRj1izoCiKA3BbKiGq6Vj1uNG6ew+eWhXBdgeeU+gvtiDC9IZhSFHoUhxqIA3Nn4LqH/zb835+XkgdqYZiaBcn4Qc4jN5FV5o/ayoSYH4EoH2OA8l+DKT5L+/Yv4DXceB59DOv48ALBx4H8Dz6ucdx4MHj790O4AH3f8eB+9HfuB+BdMcRoMAHFT4ojm/o/1342b9dxw8FPnQe/b+DALqOH+1H/3adAGxHgtcR4QXgdQR2bGFTQ9zoImooiJoqwj0NIVNHuK/D3zcBQYAjuGCLXlguaeir5/bDlCIwvIPKwScdPr0GSW/AbRkQbRMuy4DL6kFwLBjeMHR/ArovgZ5n/oQAC8GBP2fCs9REODl4FsuyfOozHcAz+QwBw2eVlX4WMc87bR5A+6y2s4gnj3nx2gIUP0UsQPF4OI6D7e3tqZywRqOBeDyO4+Njxpcd5YtWKhVkMhkAwPHxMSRJGsqCkij+8vIyvva1r+F73/se05AdbWyrVCrMWALAWFNOMBhEMBiE4zjY3d1FrVZDMpkcUpEQBIHxai9fvoxmszkVzEYiEXz7299Gq9Vi9AzKcPb7ffR6PYTDYfaimWyJOnBE+7M/+zPW0DSq+QoMVAF+8id/EpIksXI0laIp42LbNm7evMmObVQRodVqIRaLAQAajQazzKV9CYIwRHUgfeVJ20kkEvgbf+Nv4Pd+7/dwtFeE2wpA7PkhGBJgSoN/+x4Ic6THBBEA+hBtEx5bh8dU4NE68BhduC0Not2DaFsQ7T4EZ/B/QBgCKn2XBMstwfRGoEtRmN4wbfgLF11PEYeRO3iYvIuT8A4s1yi547MdLseB+xGYdj8Cz+4JQNrXCyKs5hEwk/CZcUhmHF4zDncvBncvBsGRTt3XZykkvY5wZx9y9wQBtYiAWkJAK8Nlz3f9+i4Jui+BeuwCKqmX0Ypszr9z0YG00saZaxHkl/JoNBozn+mCIJz63D/tM4lEAmfPnp0L2D6vjOs877R5jvtZbWcRHy/mxWsL+sQinmnMozN5cnKCcrmMSCQyUYM2EomgUCgwnVPSk+XlnWzbRrFYxN7eHtPFpeYtAnQ+nw8+n49Zw/Z6vYlNOVSap0YgMqag7Xi9Xvj9flQqFRweHiIajU5Vsdje3maZTQBDqggul4uZahSLRVy4cGHiGLrdbjx8+JABYl6Dl6SzqGGsUCggnU4PjTEFWe2SxfAoL5qOu1arAQAD16NKFx6PB/V6HQCYduyk7bRKGv71r/0ItbsR+I00niYcG8Cj0rwlBqD74sBs/4DPXLgsA66+BvejL9HuA3AeLQkoFyHAEj2w3H70XT703T5YLumZg3e5l8XFahYXqz8FwTYhmNtQxVsoybdxEquhFBVQiQCW67P5MrYEAZYgwOB+FjQiSCmrSChLSCjLSHWXIPfin9oxPm2IloFwex/hzh4i7V2E23uQzPZTbdNtGZCVE8jKCVaPvgPDG0El+SIqqatoRM/Onme2AGM/gnuVHrTXT7B6MXWq1vk8euizPjOvCtDzdL17VtrJCw3mH49YgOJFPNOYR2fSMAymHzspiLagqiqCwSDTkQUeS3upqsrkz0gXl0po1CSiKArTIiUFhkajwZq2RjVOzUd8U+K78koGxNkl0DhNxYKyFnT+vN0vFWVI85UoDqOcWqItUExScaAg0wQqvxFVo1gsQpZlZjLBq1fw4fF42OKDbH0BsMw2SSCRU97odmzNDaseBGp5eHQvCugD+PHKxn2cEPtNiP0KXL0aXP0GPL0G3L0mvGYDPqMFv67BZ9rw9QBvb6BuO284ENB3B6BLURi+2CO6R+wR5SMDJZiD7Zp878y1fdELx3cZPlzGmglcfFhEon4T8dotONZD1EN9VCICqmGgGhFQDwGNoICGDDTl5w+cRdtBWBGxUs9jqb2BuHYGwf4GXMKPLwAGAK9RRbC7i6Cyh4C2B59egtu24e47cDsCXCJgu90Qn6Emr2S2sHzyPSyffA9KIIP91b+IYua1meDYVj3Y+w5gFLs4+/UIvP7hRbHb7WaL/1nP/Xk+M48G8fN2vXtW2skLDeYfj1iA4kU805hHZ5L0bmdp0FI20uVyTVxVE4glUEecWj5TTMYE1NkuyzLu3r07ZA6QSCSwsbHBtGkNw2DqEbQvUi4gSTEAM1UsAIw5pAGPJckcx0E0GkWtVmPyaXzGWRRFlo2mYxg9f94pTBAGdr21Wm3IepcyJaQLPJrhBQaZbFpQ8OPOA2/SshUEgW3H7nrRO4rBbn9+pZEMl4amv/Toq4yWr/LoqwpBNOC37QHflvFnAYl4snDgcgZlf8ER4emL8JoiPDrg1UV4DQFeQ4BPByQNCKiAX3MQUB0ENQeyoiCkKAgpx2PH5UCA5ktACeahBHPohFbQCp+BKUU+1nmqwSzUYBaHKz8N0TIQbT7ASuMOXjq4i6BSGCO7tP1AMwh0/YDiE6D4AMUHdH0CdC/QdwG9R1999+B7wQFEG3DZgPjo/24L8PUAnwn4TAd+c/B/WQcChgS3sI6+tAktsIF2eAOWm3tWfDYT2hPDhoWOVEbdf4xy6ATV4AlqwWOo3vmywC7bjawRQNYMI9OTEdM8iHRFXAykkVYAqdoBimX0TgpwtPk1lYNqCZfu/mOs7/8R9ld/BsXMl2c2lBZua6g+NHDhz0WQOf84k0mLe/r/tOf+PJ+ZR4P4eWdcn5V28kKD+ccjFqO/iGca8+hM5vN5Zl4wSYO21WohmUwCAHRdZ9lWnueq6zr8fj+i0ShEUYSiKKz5i8/w6rrOzC+KxSIkScL6+vqQvFexWEQ2m2XmEmTmwB9Tr9djNrp0LDzNgD4vyzIkSYKu60PSUrQ/avTb2NjA7du3WaOhz+eDaZoolUoIBAJ44YUXcOfOHbZ/XrKNB6x+v5+B4Wg0yrK6BJJ9Ph/C4TCazSZr+qOwLIvtv16vs2wxLUQcxxkysohEIlCbPQiHCdiNz0cziC30oXur6EsliJ4SvJ4i/J5jhNzHiAgNRBwbIduGbDvwqyIE1Q8IMmxPBIY7DE0IQHUkaEIA0dwG7uyX0DYFWJ4Qei4/TEjAI4k70zTh8Xqg2zp0L2CEBXRHritdW7/PB5euI2rbkLoKJEWBT1Hg63bhbbUQUBTEmreQqg0aMR0Ami+JVuQMWpFNNCObUIO5Jx8Pl4R64jLqicsAAK/RRLT5AJHOHsLtPcjdQ4S1PsIMe/EtKR+vPcUW3ANwLy+jE1pFK3YGJ8H8Z5r/7cBGz2WgJ5qDf10GNE8Hirf16KsJxduG4m2i6S/BEj9+9s8SgWO/imO/OvKbW+x/AZcP+UAeZ5HF+WYAq2UHyRMFrvtHcB8VIMzIPga0Ci7e+x+xvv9H2Fv7ORRyb0z9bE+3cePfNFB5qOPCn4vC5R2Y1SSTyZl84Xk/M48G8fPOuD4r7eSFBvOPRyxA8SI+VjyNziSvi1upVCaqT1y8eBEPHz7E7u4uyuXykD2vZVkIBALI5/OMp8s7kvEAlH7Pl9t4TjEZe5RKJeboNMk2VpIkZDIZBINBaJqGk5OTsTEJh8Pw+/1Mi1VV1bEHn9vtxvLyMhRFQSQSYTJmqqpCFEVkMhnmmJbNZlEsFtnf8v8CQCqVYlJ1oVAIhmHAMAzmjtbpdFAqlfDCCy/gRz/6EaOO8DbHPp8PZ8+exfvvv89APr8P+r9H9COinoO178B+IjLAILxGa9AopJXh7uuPGuX6j/7tYdAkR139Xtbh33f70Hf7oXv9ML1+WC4/IPggnGK0YAk9WKIBiAZEVxcedx1+sYaIq4aEUEVWqCAjFBF2VSAKNnQxgLYQQUeIQHFF0BUuQREjKDsB7HoiCOW28JWf+ibu3L2Lg4MDxmsn+bvV1VUkz55Fu/2vB1rPlgC34IYgOHAeafkCYNxuqjwQIOYd4gKBAILBIFSXC0XDgJCQ4MRjbF6TEYkAoL63h4CiQlYVBLsKgt06ou0DLBX+EO6eG43oedTjF1GPXUD/YygRmFIU5cyrKGdeBQAItgVZOUK4fQC/VoZPr8Gv1+HTa3D31akJ3MfKH7EBFcQXhxLIohtagRLIwhE/mVeRYPfgM5qQ9Dp8RgNeswOXpQ/moKVD8xjo+A00gn00ZBuNkIVayEY1bKEdsGEJNmzRggMb1qN/+2IPltj7TGWrVUvHg84+HmAff+QCkHv09SXAbQMvVaP4UjGIs4c2cg8b8Hb1sW349Tou3vunyBV+gHvn/hoUeWnq/op3NdR3O1j9ug/pMwP9aQCnPvdP+8w8GsRPmnF9WoWKZ6Wd/FnXYF7EIBageBFPHM9CZzIUCuHatWtMg7jdbsPtdiObzeLChQvIZrNot9s4OTlh/FvLshidQZIkJJNJJuifTCZRr9fHrHXj8QHvsNlsIhgMTm2QUxSFWeweHh6i2+2yh2gkEsHKygoCgQBzSgOGM8DEUSa71FqthkajMQQwgUEmOZ1OQ1EUJBKJiZziXq+HdrvNXMR4fjGFJEnI5XIoFosQBAEnJydjzYGBQACapmFpaQkXL17EBx98gHa7PdSIePHiRaysrOD69etsYTJ0zA4QNFbgK62gYQtzKUeIdg/x2i0kazcRVAsIqCV4+vOVdW0BOEoAu1kBOzkBexkBh0lA8Y+USR0Bwb6IdRNYM4GVHrDc62PJ7mLFVpC2zaEjtSCigQjqiKKOKB4ijvewga47BiG6Bl8kCcuyUC6Xx8YxHU+j5woAgoDz58+zBkcyL8nlcjh//jy8Xi/C4TB0XUen0xm6bi6Xi9kPExWFn0f8/KAGUcMwJma7aM7m83kc+v3Y399HyUqi7fjQtn3owAcrkIAKP6x6A6F6GTnlHayYNuJiBC5/Hr1A9mNlYh3RhU5oDZ3QqGoxWFOhMJIxtgUXet4QHOGT03oWLQNy9xih7hHk7hFk5Rg+vQ6324IektH0etENBNCOBdGRZahhGZELL2H57AbCTg9Wu4H60R4ktYklKFgTDEiWhUjEC0CHotbh2CaMvgLDBnoC0BP6MAQbumBDFQV0RREdUURXEGGKnx1g0xeB99JdvJfuAi8CcBzk6i5cOnBwbVfE5V0LXvPxNYu2H+K19/47HC39FHY3/oNB4+eEMA03HvxxD73oHVz6L/KQcpm59IWfVoP4STKuz0qh4llpJ39WNZgX8TgWoHgRTxTzdv2GQiHIsjxzhZ7L5ZDJZCa61RFQILc0ym7SvwQWqCGu2+0yEENBPw+HwzBNE5qmTW2QCwQCjIpx7do15trm8/mQSqXQ6XQQi8UYGMrn8xPl3wAw+12yEibgbNs2c6g7c+bMzPKfIAgoFArMmY0yuIIgsPGpVCro9XoMXPHNgbquQ9d1hMNhHB8f4/DwEH6/f0iKptfrMTUN4hTzIVpeyM1z8JqxU+eFYPcRb9xBuvw+UtXrcFvjmahJ0QoAd1cE3F0W8CAnYC8DGN5hQCHZNi4ZJs6aPfZ1ptdDxrLGILoKP+quDD5yJVATEnASZ1FFDNtlHTaEsesh2AJSrgEAJU44z0Ps9XqoVquIx+NQFAWtVgs+nw/nz58fo+CQCohlWUM0G6peUMmXslw+n2/sM3zzJc1pohf1HQFNy4P9phtOX4TQcbBbjaJixNHtj4xECwBswBUBUhF8lDo79OuAbeCC3sd5E8g6friFp38VWG4/LPdzKP06NoJKAZH2LgLaAVxWDbZHhxbwQ40GUcsHoAYuwLe2hitf/jJu3ryJXq/Hqkwulws+UYRtuxDxRhCLxVD31vHKmVfG7vt2uw2Xy8WcJ3nnS2BwrUU48EHHl69sQq8dwuqUIBh1GGYFRq8JrdeEYXegORravS5agoOGS0Td5UJDFNn/m6II53lkCQUBhQRQSAj4k6uAuy/i4qGDV3YcXH3gIN8YmJWsHv0J0pX3sL31i6ikXp66rf1WFr/1X/8xrsXvY/Vv/CVsXXsNuq5Pfe7P826YffjzZVy73e4zVah42uN+1ttZxCcTC1C8iLnjSbt+BUE4tdFBFEUkEomxn2uaBtM0sba2xrSFCSBQxs00TZZBJhtk/oVFIFiWZfaQntUgR5SNhw8fMlBOihOpVArxeBzVapVllv1+P7xeLyzLgqIokGWZZREdx2HubBTUEFgul7G2toZOp4Nutzsxc00gjTi+vM0xnXOn0xkC5qQcQUHydffv34emaUin02P87Wq1inv37rEGQgAQBRFeLYVA8wxEZ/YjwmUZWD34NpaP/wye/ijncTyqYeDmmoA7j4BwIQ6AexkEbRsvagYumSYuGyYumCZWe/0xsoQleNCQllD35nDUi6KMFOqeLDRxQBGg8UyH06hWqxBcg23w50/zwDRNZn8dDAaH5pDb7YaiKOh0Omi324yjPkljtFqtMgdDuu50behz/X6fLex4wEuf7dtA1XDhpOOgbKbQdvzowIemJUFxOOUPA0BNA06hkUwLVQTeD7jxfgAQnR5yloX1noi1voicJUL8DHEDBKcHv9CAz9uG4FdgyQbEZAT3mx5Yzhm4XGfHgKplWdBME0dHR6xR1OVyDX2O9MAvXrwIVVXRbreRSCQYeGq32wxgbW9vo9vtjtnQkwW9EAqh7kpBlYOIrbwBCAIkDHRYQo/mx1oigbNbW+jWTnB451047ROEBRVeow6XWobVOYHaq0Ox26iaTVRdQMXlQsXlQsntRsnlQtHtQmtCw+zTRN8t4MaGgBsbwG/8NLBccfDGHRtfueNgqd7EC7f+AUrpL+He2b+Gvmfy87wrL+HPtCTO/+//v1gLVBD7a38VkZ//ebimPP/neTfMitMyriSP+awVKp72uJ/1dhbx7GMBihcxdzzPrl8CfpFIhGV6eYoB6Qr3ej1omsZ+xx8XcYKpKW/e4LN3fGmbVvVLS0toNpuo1+vMLCMejyMajWJvb4/tbxSoEkjWdR3NZpMpY4xmruv1Ovx+P8ueU5aRABaBed5CelJQ5pFE9ydpQofDYVQqlccgzfYg2NiEV0/OHCPBsZA/+T7W9/9oppaq7gFurQq4viHgwzPDIFh0HFwwe3jZMPCibuAFw8Rqvz/GWLZECU15Ax15Ex35DFrBM9hXvJDDUSa71240YCgKwFlPx2IxNJtNmKaJSCSCXq/HMobEBfZ4PIzPTfJ9fNi2zRp3yuXyRM4fr8NqGAYbZ2pS5JsXdV2H7QC6W0bddKFheNC0vGjZElq2hI7tgdN+voDUFoBjt41jt4333UDeY2PTFrHccyFqeOBSnu/xWC4DLtlAdEmCKOvouxVAcGAJAfh8cQS83oGCjG0jEAiwMabweDzwer1QVRXVapU1sY6Gz+dDo9FAv9+fCbBEceCuqOs6DMMY4+XT/qgqNfPZqOsIpZax4htosRcnlPUzLhe0m9eRgYZAvwm3WoJbLcGjluBWiui3CqjqZVTtLo49Lpy43Th+9HXkcaP+lKD5KCXgt1Mu/PbXHKxUgK/csfGVO+/h2rs7uHPhl9GInZ/4d7ZLwp2Lv4JG8S2c+7/9PZT/n38P4b/8lxD/pV+G7/y5pzqmSTEr46qq6kITeBEfKxageBFzx/Ps+h1tpiAzDApqoqPmMgKcoxkj0vyl33U6nSGFClKD8Pv9TALt3LlzEw0+arUaXC4Xut0uarUaWq0WexhTdpBoDrQ/3tGOwC1lrXnwDTyWWuNVH/jz4F/+RKEgAEYydfy5838/SxOaNYHZKUjlDYj2bA3cVOUDnHn4Bwhq5Ym/r4SBd84JeOesgHvLAvruwfn5bBuv6wa+pBt4+REIDo5wrh0IaHpzOHEtw3/2a9g1EyhZEQTk0Jh99/r6Om7fvs24vDRf3G43gsEgy+JTc5rf7x+jvJAkniAITDVklKri8/lY1v00/W26TqbZQ8dyoWVJaNoS2o6Etu1Dy/ai2ffA/hQzsW7BRszdQ8zdR0TUEbRVXF5N4dXzq7C19iMt7sE9Ew57EQpGsHPzGMfbdXj6IYi6DKH/9FbGDhw4HgOOT4MjabAlDX1PF3pvkJFNnj0LtzuMfj8wZCbT7/fZ/UQ9BpNoKMBAx5uqL6MulbquQ1EU6LqORCIxFWC1222Wbex0OkMAWxAExl8lWciJYz7ybJwF6NrtNiwbQCQLXcwDiUtD26JqVLNWQcRuYtOlwm9W4NPL8OkVOHoRlX4ZJWg48Hhw4HFj3+PGrseDxpMAZkHAYRr452kX/vnXHZw77uAnb/y/sbH7DRyu/TzsKXbWxezraIfWceX2r8L5nX+B1u/8CwReOo/4L/0K5J/9KxCeYaZ7WsZ1oQm8iI8bC1C8iLnjeeoszttMAQxeEmRpzDvIERBSVRUejweBQADlchnVapW9iKLRKOLxOFMEmJXp0TQNuq7j7t277GFML+lms4lGo4FcLseE6kczswTC3W43JEnC0tLSmO10JBJBMBhkDnKqqrKyLw/k6YHPgzbeCluSJIiiyErHpmlCkqQxsG+aJjxuD7z1ONzq+hCVYTQkvY6Ld/8p4s17Y787SA6A8NvnRexmMCgf2za+ZBh4raPjNc3AC4aB0ddoT5BQ859B2b+FWmATdf8GWvoga/2Xv/6XsdZqofb++0Pa0slkEleuXEE2m8X29jaKxSLL3tG8pKpGIBCAqqqMN06AiK4xAV1avNA40zjS4sPtdsPn8w3Nfcdx0NQtHLd72K9ruF9wsFNOoN5zo2V5MZ7zfn7hFhxkgi5spGRopT0EHRWZgICIaMCPHlyuwbnRvXEpu4ZurciaWYmf3m63B4uQtAC7XoHulCFJEhwLcEwPxL4XQl+CpYsQ+1543T4I4qAhUxSFwXxyHDiuPgSvhb6goaVWAU8PnoAAiMMLw96jTHs4HEa320U0Gh0yjKF7Px6Po9vtQtd1BIPBMcdHcsEk1Rq6F/mFI2V7afE4DWDRIisQCKDb7aLdbrO5GA6HIcsyox/NMuQZfTbO2h89Z6dtSxAEmJaDfngFDUlCZcSF0jAM6N0W/qOrm4g5TTiNXfTL22jUH+BQOcCeXsWuW8ADjwcPvB6UTntmCwLuLwP3lwV4e9/FT9y5j4uNX4blXZ74cTWYxbuv/Nc4++B3kC98Dv2dfAABAABJREFUH+pH96B+9N/A/D//H+B/LY4zP/0G3GsvA5lLQOoi4H222dqFJvAiPm4sZsQi5o7nqbM4bzOFoiisEY04vTyg6Xa7TInh+PiYgVV6UKqqiuPjY2xsbDDAWalUxni+1IzWarVg2zaTbKMMrdfrhaZpcLlciMViqFarzAWOghp94vE4k2ObRg2h8+K5vqNBGXTKlNM4UBbUNE1m4lGtVplsHTM3cbsh75cRa5xD17s8U2IqW3wLZx/8zpCKRDkCfO+ygH9/WcRxcvDHW6aJX2nr+Kqq4RXDgDQiXdtzB2FkXsGRaw032iHUvcvoO48UPHQBoqnBtm1sbm7C4/GgXC4z8xWePlIul5FMJllZu9vtDpXQCRwsLy9DFEUcHR0N0Rl4M5J8Pg/DMFCpVNjv+UxxR+9DjGSxbUbxb98to2a6cdLu4bjdg9obplsATy579nFDFICox0La5yDm6SPhtZCUbGSDLvgdHasry/ja197AH/xBBXfuHMDSLNgAuhwIdbvdWFtbgyRJKBaLLHNOY0QLiWw2i2g0ikqlwlQ1GJh1Aa6wC6lUCsFgEI1GY2jBSvdArVZDMpmEWXTQbquwTHHs/iD3yUuXLmF3dxedTofJ2FHvgG3b2NragiiKuHfvHprN5th23G43VlZWWIOtJEljzwZN0+D3+yHLMvu7SU2//HMvm80iFAox2lQwGESr1UIqlWI9F9MMefL5/FzPRtrfycnJ1OsRi8XYgkVRlCEqFVXVJF8QYvo8OqKIAtbREl+BlRoc0wvhEP6iLAD1HTx854/hdI9Qd0o4smvYFzTc97pxz+uFKo4v7EyPgO+8WMKb9t/Dn7v/C9hsfG3iedguL+6d/xtoRM/hwv3fhNvS4W0LsP6kgevf+ze4d/lfwb6k4bLbwOVgHp7MFSDzApC5PPiKrgET9j9PLDSBF/FxYwGKFzF3PG+dxXnka6hRr1qtspcf0Rg0bQCw6AXd7XbhOM7YcZPttOM4qFarsG17jOerKAq8Xi8URUEmk2G0DP5lFQ6Hoaoqzp49y7isvCIEZS2uXLnCMj7TqCHAQHqLyoCjTXRerxfBYBCxWAyHh4dQVRWSJDHKgKqqcLvdyOfzSCaTTIrO5/PB5zjI3L6N0H4HByt/faYTmqfXxfl7v4l09UMAQMcH/OCigH9/RcS9JSDgOPiqpuE/rWh4Q9ORHTlOS/CgGbmIinwJ++I69PgFXPvy6/CoKmrf+hba7TbL3FIJPBwOY2trC3t7ezg+PmagmK6Zrus4Pj5mJVCqEPBa1rT4cRwH586dw8nJyZg2MMnSXbhwATfvbqOou9G0JXThR8uR0LIktG0vdHiANoDDylPO6I8TDkJiH1GXiYjLRAga0n7gp6+9AG+/i5OjQ/T7fa5qYUFVO7Af3QOmaWJ5eRn37t0bKhXz2dFsNssaCUnnmlwO6Z5KJBIIhUIol8uMf07boexsKBRCNBqFpmmoVCoMfBIYDQQCSCaTSCaTeOutt4aMYWgx4na78fLLL+P8+fOsKZXoLfTZ5eVlbG1twe12Y3d3l93nfHg8HnYf7u3toVQqTTyefD4Pr9eLQqHA5CF5t8sLFy4gl8sxmcUHDx4M9RkIgoBUKsXUDu7evTvVkIcUS04LQRAQCoXQarWmbmttbQ2yLOPg4AD9fh/BYHCoauV2uxGPx2EYBo6Pj8cVGOoNKKoPS0uvonV2cF3p/lh1LFzq1xDSy4BYRMNVxrHTwD2rjbseETX3INtuiX1868LvYKe2jZ/c+evwWpMBZjnzKjrhNVy59asIdQ8BAH4TePkDF/ofyfjhhRD+29dMhBs/wpcK38WXdB0vGSYCHnmQSc6+AGSuANkXgfR8WeWFJvAiPm4sQPEiniietc7itOwMv79Z8jWBQAAbGxtMaUJVHysguN1uyLKMpaUllMtlRo3QNI01REWj0UEZvNmEJEnMeYnc8CjTU61WEQwG4TgOQqEQa/QjrdpIJAJBEFhjz9bWFk5OTphCBO0rn88jlUpBEAbOTtToxstAkXlIKBSCJElot9tD1Aji0FIGPBaLQdM0Ru8gUxLaBnVgo1BE6gd/hpX7uyhl/zwebP71mVq1idoNXLj3z+Ax2/jgjIDvvCTg3bMCko6Fn1S7+N+VNLym6RgtThrRsyiFXkA5fAXbegJaz4YkSUilUoh4vWi329A0DcFgkHGsmVSWz4dgMIharYZKpcJKzIqiDGXn2u02Dg4O0Ol02AKBxpqalnq9Hmq1Gvx+PyRJgqKbaNketGwJbVtCG34oZgD/5F+V0DLCAMKThuG5hOyykPIDedmNi8txvHphDf3GCU7ufYR2s87OKx6P47XXXsPq6iq++93vMq6soihsIURzkWgjOzs7jC4zyql2uVzY2dlhCy+qNvCa4JZloVKpDEnKjW6H+LbpdBrhcHisAZbkACnLubm5iaOjI8bhpuu8vLyMbDYLWZZx5coVHB0d4eHDh+z+OHPmDJaXlxEMBlGtVpFIJNjClY4nFBrwz1utFuLxONPwHjVc8Xq9SCaTaDabeOeddxhYoqzyyckJ2u02rl27xrLJJHfIz1f6eafTYfSnZrOJVqsFj8eDVCoFt9uNTqeDbDZ7KhDjt0XmPoqiwOVyDZn7UDWEbwIk6gnRpur1OnRdRyQSGbqHIpEIWq0Wms0mQqEQjo6OhihYuhhB25+CJL2CS5cu4RdffBHFYhH3P3oLlZN3UDEfoCyUceTq4F7sQ/z2i0f4C/d/BWllXL8aADR/Cu++8l9ha+f3sHz8JitKuW3ga7cdfO02cGPNgz943Yu/vxGGG8Alw8SXuvfw2s2P8Mq7xqD/QBCBxNYAKGdfgJN9EVrkLPpSdOzdsNAEXsTHiQUoXsQTx7PSWTwtO0MxS75GEAScOXMGjUaDZU14bl02m0UymcTe3h5TdBj9e7J3Jo7uzs7OmIFDLBaDJElQVRXlchmNRoNlqKjESoBe13Ukk0mWrSTgnEgkkEwmmTHHvXv38Pbbb4+9rM+cOYOXXnoJR0dHcLlcDPAz2oPHw9QtyN2MMnzEmSRw0DMMGO++jTPvfBeZ+yfoe2Tcvvifoh6/NGk4B2NiW9ja+V1InT/Dv7wm4k9fdCHk7+MvqCr+y6KKi2Zv2BjDI8Pe+El4LnwT2tJXcfOghnK5jN3dXShKiZ1bo9HAxsYGKpUKs6ampkKeE2pZFg4ODlj2lyTl+LFOJpPQNI3xsUlFwHaAjiNBaWvQvWG0ah40HzZQNVbQsd34NG3IJKGPmLuHK6tphAQdbq2OoK0MeL6eQbVhZWUFr756+ZEjoYyjZIBlQ8kefHl5GaZpsrnc7XaHeOm0cDMMAycnJ0w/m5oAAQw1hFarVQCPmzfpczTXBEFAq9ViGWSar7QdqqwYhoFyucwaSUnpw7ZteDweVm3xeDzI5/MQRRGlUond+5lMBtlslqkClMtl3Lx5c6gHQFVVeL1eRKNR1Go1RKNRNg70GVpM1mo1VpHRdX2IpgSA6UTfvXsX9Xodtm0zGUS6F03TxJ07d7C0tIRWqwXDMB41IvZZFajVamF/fx+aprF5PtqEm8lk5lY7ID78LHOfWq3GNLUVRWH3kuMM7O1pAU/32a1bt4YMiWRZRiaTQbPZBACmiDP6nIlGo+y4ZFlGduMSbCkK2fg6LkgS8vn8IEtulXH9wbdx70/vwP3w4sTzckQPts/+x2hEz+HivX86Zujzwr6DF/Yd7KWBf/m6iB9e9OK6T8KvIwyX4+CSYeI1XcdrygGu3tpG8Oa/gAAgAMCUElDDm9DSVxA482X4N74MxNYXmsCLeOJYgOJFfKx4Wp3FQqGAt99+m5UHyU2tWCyy7AwPjE8LSZJYloYHxURNID4wKRFQGZWyq5QBarVa7CXDN7ZRExwAHB4OSoA+n49tp9vtotvtYm1tDYZhoFQqMSkwXvdU13Xkcjk4joOdnR1omsYk1hzHgaqq2NnZYTzP/f19JulFn9E0DYVCAevr64wfR8CElZl1A8L3/h3kDz7Eam3w8mlEtnDr0v8CphSdPo56Ha72r+Eff3Uf7SUHf0Hr4O93VJyrDwNhM5BDd+nraGa/grJvE1JAxtbWFhzHwdHRR9jZ2WH0EH6M7ty5g3w+z4wwbNtmDVFE+yDjEQDs3Phr1ukqKHX70N0hFFUfqrqAthNH2/Gh40hw6EjnM9F7puEVLISgIywaiIoGQoKBqMtACBokwUIoFMJPv34e9+7dQ8OhhdhgzhqGgVqtxsxYyHhgZWVlqDSu6zqj8JRKJbZwo2vf7XbR6XSQyWSgqiq63e5EbjrNJeJpExjiKT+0OKTF3ZjjIcDuI8dx0G63me43f39QJps4+LVaDaZpIh6Ps3Mj0JjNZrGzs4N33nkHqqoiGAwyfn2pVMKbb76JK1eusIwtzREy9SEuPTB4zrTbbZYhJc4+uUY+fPgQ7XabjTk/z6jqdHBwAEVRGDjnqQr0vKBFRrVahWmaY024qqoil8uNUVgmgTVeOYHnCFNQI69pmgzg84249L1pmlBVlY11IBBglCw6plQqBdM02RiNUpDIVTSXyzEaBjmJ9vt91Ot1qKqKra0tfPO1v4NvvgbsXa/i279xG4YyWdmhmnoZb4dWcenObyDWejD2+/Uy8L/9Axt//U3gD6+J+NMXBRheATd8Em74JPwaALfj4Iph4pqm48u6jpeMGqKVGlB5G7j1a4MN+SJA9kUIuZcQyL00oF8kz85sJl7EIgRn9Am3iLmj3W6zMhTvFLaI2WHbNt58800Ui0WkUqmxJplKpYJsNotvfOMbYwoOo+E4Dra3t1Gr1cZKhNQEEwwGcf369Zni+yTX1Wg0mFQXBekNk2V0sVhkQJbnC/d6PeRyOaZkMW1fgUAApmmiXq/D5/MN8YVdLheTiAqHw3j48CGAwcuagCNlxtbW1hhHLhaLDUBNtQLvu9/G2gcP4DceZV8hYH/1Z/Bw4y/NpEv07Jv40dl/jGveMn6uq+JcbxgIa7GLqCSuob/1F9GLbrKXC5lXJBIJZDIZ/MZv/AZrcBwNyppTRphsj/kxGpSK3VAdD6qGAN0TRtPyotH3oNFzo9l3w/pUlR1sRN2DrG/c3UPEZUIy28gGRYimgl5vINk1el7A4DpevHgRiqIgmUyONX9Vq1VkMhksLS2hXq9PbBJqNBoIBoP44IMPWAPlaFiWxVQ6/vAP//D0c3o0l4keQfOalwMkQEfAiUA00S1EUUQwGIRhGPD5fGP3BymAhMNhGIYx9f6QZRkulwvVapXdl7QvqpyQ+gQPiCloLP1+P6MEUKmcV7qg6hB/7JPmouM4LPPKm//Q9ojDS1n0aecVCoXw8z//85Bleab9sMvlwq1btxjFYzRM00Sr1WLNvJFIZIzO0mq1Bk2inQ5UVZ16TFT9MgyDvcP45sh2u41AIIBr166xazZpPiYSCZw9e/bx2NZ1fOtXb6Gw05o+6Rwbawd/jI29fw3RGW1YfRxtP/BHr4r4t18SxuzeKby2g6uGgS9rOl7XdFwyzcm2Np7AgJ+ce2nwlX8ZSF0AXE8vMbiIz3bMi9cWmeJFPPdoNBoMxE4ylKCuYb6LfVpQqdHlcqFUKg2VkYPBIOOfSpLEuMSj4vtut5tpEZNUD69lTMCg2Wwyow5VVaFpj1ORPp8PoVAIiqIMqVvwpWjKyNGLisA3Dx7oBUlcwHA4DMuyWAlVEATmutZut+E4DjxuN4Sdmwi89R2c2a5D5Ja5pkfG7Yu/MpMu4cCCmvpd/GTgd/G/UXSIyuPfnbjXcN91HrecTSyvv8HsqbudDlt8yLLMxPB5XjdlGPlGKmDwUh8AMA9US0TT8KDe96BpedDoe1A33WhaHoz72D2/EGEjLJqIiCZCgo6woCPqMpEJCEj4XWPJJsOwoGld2BgGw/y1BwaAo1arMW7oKJiLRCIol8sQBAHJZPLRto2h8jkZhRB/nLKUvKoGKY20WjNAyeg5PwJUVIYHwLbPHyevEUzf07nxWdpRKhM5S1LD5yTpQ4/HwzLbpA7CN/XR941Gg/GWJ+mT8woZpNgwOtY+n49liMlKfnQ8yNxFVVW2KOYBKIFvsoOm58ak8yJes+M4ePDgATRNY3QSyjIrioLNzU32DIxGo2MyimQSQoCatk/B65OTuci0Y6JeBBojOm8Kn88HRVFQLBaxsrICYPJ8HKWGhOI+/Px/eRVv/6tdvP9v9zExBBH7a99EI3YBl27/OgJ6deLHwhrwV79n4+ffAr51VcC/fk1EPTx8PqYo4Ed+H37k9+F/ABCybLymDwDyl3UdG73+YIHfU4GjtwdfFC5poHZBIDn3MpC+BLhn67Uv4vMZC1C8iOceZHIwy1CCOuFPCzJzUFWVGXFQRpUauigL4/F42Hb5bnbK2FH5cLR4Qvq1VDrmXej47nkAQ6VT0hrmg14adKwEiIHHYIN4gbquM+4llVSJRmHbNmqFE4Qfvo/VD28iUx137ConX8K9c38NPe+MVbGrhm9G/h9Yc90ZWAdjAIQf+F7EjvcyVFeYWUonNA2dTmeogdDlciEUCrHjJKBOL1bHcWBYApq2NMj0PvrqOD60bAmG8+kBXwEOZMFAWDAQ8/QREXTI0BB1mZAFE6Lw+NoDg7kWkSITq69EbQEey8EBGJoblGmlhVKj0WAgibjtZAJBiijT5AHpHqLtjVYbSIWEOKOnBfHR6XwJRFIZn7K9lBnm5z1db/o/3Ut8hpea+GisfD4f0xLmF6g+n4+dPwFJfmFJAJj4ykQ34O9ZAooEgi3LYk1//PnyKiQ8gKcgOgZdT9M0mSsiz7vlj23WeRFFpl6vM+pQvV4furZEIctms6jVatje3p6odpFMJlGv11mD4yRDIrp2Ho9n5ljT9vksOn1PQZUwAu584oEy1aNGGC6XiDd+YRNLZ6P49j+6Da0zWV6yHV7HO6/9Nzh3/58jW/rRVNa/rwf85bcd/Oy7Fr57RcC/fF1EITH50x2XiO8EA/hOcPC8zfT7eEPT8cajTHKcVyuxDODk/cHXe3Tw3gEwzl9dAOUvWCxA8SKee5BsGj2YR4Oyt6NcuklBVAVd14dKm5SZotJmIpFANBplP6MyfjQaRTgcRqlUAgAGdPkXKHER6WXAN7Lw2TlFUdi+KaM1+kIj8w4elPPB/w29wHjzDlEUIdbLEN/6t3jjw10EjHH2k+mRcf/sL6KcfnXm2K1638dPR/97+MU2CkIG970vYjfwMjpi5DFgeASGCPSQtBfPmWw0Gqi3OvAmlqGIAXxopB9JmnnRtLzQnE+3NBkQTEQEA2FRR0Q0BxxfQUcIBlyCw/jxxKFlTUt4rEENgAHDSXQFGiMeKFHQNSbaiCAITLKMpyIQ/5fA4/HxMask0DZJHpBAH6kF8MfEV0DmbSgiV0dVVYdANqkb8OdOAJJAKS3U6PdUKeDnOYFJMtQhbisva0iLRKIHEJd/1LiGFsvEpabM7ShwprEjBzoeONI2SMFF13UmP0f3HS2eSaGGzovfDt3zwWCQLWpIUYWOh9dZdhwHpVKJqWWMSj9S8yE1uBEw58+NxpboHqVSCYVCYcjcJh6Pw7IsKIoCSZJmHhMB49FnE+2XuOE0H0cTD0TfmmaEsXo5gb/6f7yGf/erN1DYnmwJb7kk3Ln4y6gsX8OF6/8I3l5n6lx128Cfu+7gJ69bePu8gN9/Q8TD3Ox5XnK78fshGb8fGlQxLhgm3tB0fEXTcHWCnjosEyh8OPjigXLmymOgnL+6oF58DmMBihfx3CMWiyGRSAw5kVHYto1Wq4VsNjuQEXsGQc5TlFnhXywkXZVKpbC/v49er8ea9fgSKb1ciPPLZ6IAsEakUCgEv9/PqB+jJWSijVDZclrpl8Tn2+32oIO+10Ng9y7CN36EjYfNqYzacuoq7p39q+h5p8sNCbDwuvw/Yl1+E++IL+CWeBGqPz/IJpleCMLjDD0BxUgkAhsiDls9WIE4ag0XqrqAquFHRQ+j1ROBbROACSD7NJfrY4Vf6CHq6iEiGoi6TYSgQ3ZU5EJueIRBQxOf2RxkCAfZQgJpvAkEf+37/T7TfG232xOvGTmrud3uqWYRpmkyqTzik4/OD2qi83q9qFQqkCQJnU6HHbPX64VhGEgkEkxGLRgMDi2yCKxZloWVlRVcv3791PEjnV1ZlscygaIoYmVlhTVe8Q5yANj9EQ6HB42Qnc7QgpYWCpT9JYnEVCo1xoWtVCrIZDJDEnB0TXhgzNM8Jh0PXW/izJLSBC1WKHsai8WwurqKw8PDgRQZpwRDlILV1VXU63V0u12I4sAFkM8U2/bAOCiZTKJUKk09r2w2y+TaLGvQdEmLFupR6HQ6aDQaOD4+hmEYj1RIHqtdkKpIo9GA1+vFzZs3Ge2LMs5kZnP16lUGwqcd09LSEvx+P4rFIgPPPADv9XrIZrPw+/3smtH1IGBeqVQgy/LEBAdFMCrhF/6LV/Dt3/oAD77fwjQKcTV0Ae/81P8Vlxt/gug7vzdzzooAXr/n4PV7Fq6vC/j9NwTcXBPmaqS7K3lxV/Li16Nh+Gwbr+oGvvIIJJ8hqsVoWObjjDKF2zeQh8tfffT1yqCZT/z0KmCLeLpYgOJFjMW0ruhnFaIo4sKFC2i326hUKkPqE2TNe+HChVOb7IABIJFlmZURR8GB3+9nFq27u7tM7UKWZZimiXK5jEAggNXVVaTTaRwdHaHdbo9xYQVBQCaTgcfjYd3ofPMbAdx4PI5gMAhN01j2mQ96efr9fjx8+JCZbvBNdB6PBxsbG5BlGffefRf+ex9g7fYdxFvT3e10KYYHm/8hyulXZo5XQKxhM/GHuCPH8B3x76L/6CV4dm0N9+/fh6rpMNwy2o7EGtvajg96L4zakQPnOTq2jYYkWIi6eggJOmJuExeW4kj5gPr+Hbjs3lh52OV24dyZLQDA9vY2K+HzmTCPx4PV1VUmc0XyYURRIQAWi8WQzWbxwx/+kOnZ8mYiHo8HX/rSl9DpdPDgwQP0ej34fD52XXVdhyRJWF4e2OJWq1U0Go2h7Fq/34ckSUwKjMwz+GMmW2aS4COKwWhznMfjYTSDUT7taIiiiHQ6jW63y/Sj6dzo+zNnziASieDNN9+EoihD50/ui5cvX8b+/j479tGsI8kHLi0todvtjt379XodgUAAS0tLePDgActi8kHXg7LpPA+fP3/KYKdSKZTLZWZzzmeB/X4/crkcNjc3mTMcaZATpYLOnXR/6brSdujah0IhbGxsoNPpoFwuszE0TXNgkx0M4sKFC2zfgUBgYpWIqGOlUolRRCiDTk21tMjqdrvMTMXn87FFlq7rKJfLKJfLuHLlCjqdztSxvnTpElRVxZtvvglVVcdMLoLBIC5duoR6vc4WTKNzn/6GGimnzjOXiDf+yjn4U3dx79tdGO3JPf6GKeD94E9j4299E2d3fhv9N78946kwiBf3HLy45+BBbiDn9vY5AY443ztLF0X8+4Af/z7gBxBDpt/HVzUdX1E1vK7riNgztAj6OnD0zuCLwhN8xE++Ciy9Mvg3fmahevFjEgtQvIihmNUV/SzFznO5HK5du8Z0iikDl81mx3SKZ4F0Ku/ycm503OFwGCsrKwgEAtA0baawfq/Xw+rqKuNw8uVYAExDtt1uI5VKscYSPqtE8mtut5u9fEbtV8nBjkT8T05OWPe3KIqQZRn5fB4XvF5Uf+tX8bW3PoKnP/2hrEtR7K9+Eye5N+CIs29nn+8Q5fgxjsVzUHtedBwfmpYXHSGAtw9CODKuoGaIsCflSSzgeWj8egQbOdmFTFBEWNARFgz4+x1IZgtepweXa5C9HOiPDhQWStImdnd3x8Z6Y2MDS0tLTOGDXO34LGg+n8eZM2dw48YNhEIhds2IP+v3+5mFNzkR/uhHPxozi/jyl7+M1157Ddvb28yJbfQeSqfTWF1dxYMHD4YUOPgsMNF0COh2u12moUvH7PP5hsAy0Yf47CV9jhaGk1zfgMcNZ7ZtM6tiymS6XC5Eo1GWZXzttdcAYOr5Ly8vo1QqIZ1Oo1AoMJk2Ord0Os2MZ2bd+7ZtsyztqIUxnRsBblKH4MEzAeJwODwwjIlEsL29zZQqaBzPnj2LaDSKRCKBK1eu4P333x/SRCb1jng8jl6vh0QiAU3ThrYTiUQYDzwej+PKlSt47733sLu7y7aTz+dx5coV5HI5tFot9mxwuVzodDqMykXzj647UcBGFXUIDJfLZWbUw2eBybBnb28Pb7zxxtzP2Q8++IBJrNFnrl69ilQqhU6ng6WlJSblRk2e5GJICyFg9vM6FArh5TcuILl0jBt/XEHl3vSF/u7DPoqRv4qv/N//FkJ/9s/Q/jf/BpixuAOArQLwX/2ejWrSi9991cKbLwA995M9t0puN343JON3QzJEZyD99lVNx1c1DVeMKaoWfPQU4OAHgy8KX2TASyaQnH8FiCwvgPJnMBageBEsKMs1Zglaq0FRFGxtbT1zYJzJZGY62p0G0kl2iQw3eD3fbreLnZ0dXLp0iTUDzRLWF0WRuW6NNkCFQiH4fD602wNOHF+K58GzIAxc7QqFAuOnUhiGgUKhAEmS2DGl02nouj546QEI3bwJ/MP/D5yHJ5iluzEAwz+Dk9xX4IizOW2W2MdtuYG7bhda+jm0bWlc0kzrA89J7cEtOEgHRKQDgL/fgb/fRQgakl4LuZiMtbVVuN1ulErNR/JSQLfrQr/vMK45GbFQNuvs2bNjL2JJkqAoCpaWlpBIJNDtdlnTF73QE4kEgsEgy/gRxxV4bGFMXfrdbheJRALpdJo1FrndbqTTaSQSCQjCwFp2d3eX8VgpKFMZi8UYvYDAFoEZMrgg4EIGMXQsgiCg2WzCMAym79tutydyiknflxaCNKdH5c3IwY10ozuPVEWI+kHHWiqVsLy8zGT3+PPPZDJIJBKQJAm9Xg/1ep017PFVm3q9zj5H25l079dqNXZMojgwqOHvMaIekFsdgVRe6UOWZZaVp74CogfQM6LZbLJrXy6XmZ04fz3o58TRpuA54zwP+sMPP8Tu7u5QM97u7i5cLhfS6TRTr9nZ2WELKB7wp9NpZLNZpq5xfHw8ZlwTj8eh6zoURYEsy/B6vWPnT8D65OQE58+fP/U5u7m5ifX1dbaYoSw6ccQpO5/P5yeaiRDVZZ6kSigUwoVL57G2sYqHH1bwo9/dh96drGmstU38yR+a2Hjpl/D6b/9nMH7nn6D1L34XjjneWMxHsmrib/9b4H/5oxBu/rkN/NbFBnaswuwH04SwBQHXfRKu+yT8/2IRRCwLX9F0BpKT1myQzkJvAbtvDr4ogqnHAHnplcG/cuqJj3ERzzYWoHgRAAYP+UKhwHh2fHNQLBZDo9FAoVBgVIVnFaIoTpVdI5A+Tbpoa2sLwWAQ5XKZZTf4EinJP1UqFQSDQWao4fV6WXMLL2JPTTeO47AXDb0cSXaJtjdqTEEZLa/Xi2KxyMDMaHlc0zScnJwgEAgwC9L27i7E73wH4p/9CaTOdMcJS3SjEbuAcuoqSukvnQqGAWDbbeFbgR4UUQbmfH4/ixDgICz2EHObiLp7iLpMhAUDaT+Qj/qRSaewvb0NW7DhCXgejaOIdruFW7du4eLFi2xOAo+bHYlCQBJZtHiJRqMIBAJD/FwykdB1nZkqEEAkikW1WmV8Ycp+8vJW5IQWCoVwfHyMW7duQVVVRKNRtmisVCp4883HL7uDgwM2F3hazMHBARKJBANUlNGm4FUAGo0Ga+Lim/GoeYqapai5jVcuIbpCv9/H1tYWbty4gW63y/Y1CjCJb18sFtn2CNDSvAaASCSCd999F5qmIZFIsIwnnf9P/MRPQFEUdLtdBph4HjDRTsgEZ9q9T2PLA9FRhRfiJpMD3Si4DAQCyGQyODw8xMnJyVBjGj0bTk5OEAwGcXx8jOPjYwaKeWrA8fExADAlEADMTZLue0VRkEql8O677+Lu3btsIUWf6fV6uHv3LiRJws/93M9BVVUUCoUx2odpmigUCqwXoVAowHGGzYZIaYfsvenvR5s/qdGQMuiznrP839BCgg/qb6jVaszZk5+ztFjs9/vY2dmZK6lC8/fKV9Zw5koO3/3Ne9j5oDL12HY/quLobgNf/it/Cxf/zt9F85/+EzR+8zdhP5LUm3pOjQ5e+hfXcTUQgPjzfwU3fmoVb/Zv4+3i2+j2Zv/tpGi5XPgjOYg/koMABg17X9U0/ISm4yXdwBO13CkVYPuPB18UkRWOdvEoq+xbeCA8z1iA4kUAeKz3Own0CoIwUYvykwwCRLx0EU9VoBdILBZDtVplD+Fer8dK37IsM9clkmeTJGkoC+x2u1kJll40VHam/VF5udlsolqtMuA0aodrWRbK5TLTPp2kjUovtlKphHy7A/8//g30v/UtCFMyDn2XD9XEZVSSL6OeuAzLdboiBwBogoNv+3u467E+MdaDAAcRjzMwsfD0cXktjWsX13Hzh38CUWsiFgmzhsQBqBtkNk1DQL1eZxJ3PKABHmfbeQe10UyYIAhMBYT4wKTEQDrRBBApI7exsTGx2ejg4IBZE5NCCJ/hJID04MEDKIrCstTUcElaru+9N2hTN00T+Xx+TJ2kWq3izp07bHE3SSaLzCRI6YAAMY0LAObi5vF4GIWCGtJ4GTFSB8jlciiVSrAsa4wPS/bjd+7cYfxjviGNxqNYLLKyeDKZZMdEBhPVapVZlk9bNNM1azQaSKWmZ8R0XWcUJKJe8GNEi55+vw+/3880humakZScaZpoNBpD40Pzh8a5XC4PPS9GG9+azSZKpRKTdKPxo+1Qw16328Xh4SEDsbzTnCiKMAwDd+/exRtvvIGDgwPGA+YXYKSscXR0xLKvBIj5/RmGwRYGxC0flVKjRRktQJ4mqAKiKAozV+F5xz6fD9lsFsVi8WMlVQJhL/7i376C7XdL+O5v3oehTs4a9wwL//63t3HvRyH85N/8X2Hrb/+v0fjN30L9N34DVq028xxsVYX9m7+Li/+TC1/+2Z9F5H/+9/EgbeMHJz/A90++j5vVm7BnGIhMC2rY+9VoBLJt43VNx0+oGr6q6chyVcK5o3U4+LrzB49+IAwa9/hscvYFwDO9qXERTxcLULwIAI+1TolSMFoio5fIqBblJxWapqFcLrPsHS8DRNJF9EIjOTbeaICaUSzLQrU6EIXndUjppU9SUbwRB2Xd6MVG3EjbtllXO69iwf8ddabTPkZfVqJlYWl3F+p//p9jf/9g8HePfm96ZHTlZXTkZXQffSmBzEwXutGw4eCW18L3fD0oz8j0LSoJWIp4kfELSPls+PodBKwuwoIBn3fAe8xkBh3qubQbBa8NzfKw+URZR2oAsyyLKYFQUxSNj+MMKBLVapUBIR5YAmAOZCSZRdlcvmGRuJuk3kBGMaPNm5FIhDmD0XUFMPZvv99HtVplYGC0iUwURRQKBVYuJjDJg/RwOMycDgGwc+BL46PZPQJ7FPQ9AUTiuZKhAvGgCbhXKhXWUHV4eDjEuY9Go1hZWWG22qM0DDp/mvf1eh3xeHxsDIl2RAuZcDjMFqc8x5toCJVKZSYo7na7cBwHy8vLrDpDgD0UCjFHvG63i9XVVXS73SEt5wHfXEa9XmfjTQsNXhM4EAig2+2i2Wwin89PTQaUy2WmSkMLb/68iGLAVwd4WhVRD3Rdx1tvvYVOp4NQKMQa9/jFt9frZQ3D1NA2Kg9JxxkOh1mz5qhOsWEYWFpaQj6fnzrOTxKhUIhVtnhqRCKRYDSLp0mqCIKAc69lsXQuhj/7p3exd2M6yK0cdPA7/927uPQTeVz7a7+C+C//Elq///uo/dqvo3dwMPtELAvtP/xDtP/wDxF7/XX80q/8Mv6zb/4dtHsd/KjwI/zg5Af4wckPUFCenGrRFUV8OxjAtx9pI581TXxNHWSRX37SLDILB6jeH3xd/63Bj0T3wGxk6UuPwXLqwkLx4hnFAhQvAgAYeCEd1EnOcMTFex5B3ETbtoeki6j5pNPpsJc0vbjpHEb5lXwGiUwn+BcWrzRApWLKUFKmivZNLztq/nks7yWMGX/wLwevrmP9/i7yRw1AlFHybUDb/BJ0XwK6Lw7dl0DP8/GVHWw4uO2x8ENfH03Xkzu3R3wuZIMi0gEBazE/VmISMgFxYGwR8iOTyeDg4IBlnsh1jBztALCXHmXAKPvKBwEaevHTwoW/HsSJpQwYZcvYuT7KhhKYI6DKN38RoKDF3SyjGAKmdL0mKRmQgkQoFBoCJlQlIFUBAjGVSmWs8Y8aoqLRKNrtNivz82Cm1+shHA7j5OSEnTN/PHR+VIGQJIkpqfAAvNfrMV6zZVmsdE4ZdcoQJxKJAY3l0fZmLXp7vd7McaTrSDSkUX3dfr8/xrWeFbIsIxqNsiyty+VitCdaBBBIHl3EO47DlBl6vR46nc7Q9ej1egiFQjNVOSjo2ULqDnzmmrK5/KKaZPYo+IU4SeWRnvFoEK+cztW27SF9a1pYmqaJra0t3L9/H91ul507KXZEIhFcu3Ztoqb2xw1abExqoqPF1rT3w7xJlWBEws/93Rdx70dFfP+3H0BXJjfiOQ5w63snuP9OCa/+7Dpe/I9+EdFf/EV0/viPUfsH/xD67dunno/61ltQ33oL3rU1xH75l/DTv/AL+Jn1n4HjONht7eL7J9/H90++j3cL78KwTzeSGo1trxfbXi9+LRqBDAFvaCa+1m3jq5qO9MfJIlPYfaDw0eALvzb4GSleLHEZ5dj6opHvY8QCFC8CAFiX/f3799nLms+6VatVnDt3Dn6//5nud1qnMr1AZ0kXqaqKUCjEtDxHm0cIBAcCAaiqOtQhTcGX00ljlugRBNCo4Yf0UPmXPR0bgW/bBjQzBKsXhdiTETRcSGh9uEU/qok/j+psWt8Thw0HdzwWfjAHGPaJDpI+G0nJQdLnIClZCFgKkpKN//gX/hIkSRrJBAGRSJJlgo6PjxkoGPWOpyZHylJSNk5RFHZtg8EgPB4ParXaEBDms8C8OxllTwnEUFBpm+gVJNPFc4opg0zXlTLJowCLLKcJuJL2Lw/myM2Qj9HjBx7zXqkywZe1SZfa7/djY2MDtVqNZSDp7wRBwPLyMoLBIB48eMAytzS3aJs0nuFwmM17WthRJpmykalUCrVaDZVKBbZtD3FmSUWAD542wvN4qVGVxnEUhNPP3W43uy/p2OmYSC+YssSWZU1s7JJlmaksEFecPz5qriSAzFMQKEhOjcAxMMzvp+sRDocRCoWYdfIkpQei6BAoJnUSonLR3G6324wCw0s6EhgkXu/h4SEURWFzixbbtNCjMaW+htGxpm1vbm5ieXkZ77//Pmq1GjvuTCaDV155BZubm0Pj9ixkNokLPBp0LtOcSomrPk9SRRAEXHg9h7UrCfzgd3dw9wfTs7Y93cIPf28HN988xhv/4Sa2vvlNhL75Tag//CFq//AfQvnBD0/dn7m/j9J/+39B5b//HxD9xf8Z4n/zb+JM/gzORM/gly79ErSehn/1wb/CDws/xB39Do7141O3ORpdOPiW34Nv+QcvgAvuML5m2vha5RAvqJ2nB2KTFC/88Ucg+UuPs8qLRr5TYwGKFzEUfMMOgDHw9yxjVqcyNc3Ry3aUhkA/p7LeRx99hHa7PeS0pqoqRFHEmTNn8NFHHzHHplHjBQJV9PKmv+eBEQ+WLMuGIUpoWRI6fR88WgT+XgChnhfxvhuxURKvBDxr0klHcLDtsfC+1EeDA8Nu2Ii6e4i5ewjaCtJ+YCsbga/XQUQSIUnD/Mx+v88ssGdlghzHGWq2Gb0e1GyTy+WQSCRwdHTEXuAEEuhz8XiccVwJOPHbInqEKIpDDnKjnFECnSSjNso7liQJ4XAYsiyjWCyOmTMQPzSZTDJVB9o+P+8pO0dGELSQoiAaRDAYhGmarNFslCtNGcKNjQ3kcjmcnJygUCgwRYBcLod8Pg+/348f/OAHqFQqjDZAQdskubDr168zaS4ehHk8HmxtbSEejzPuJ2+8QIu+SqXCxoG4tfy9T/+6XC7E43HU63VW9uf59LZtI5/Pw+Px4MGDB2g0GgCGKUuCIGB1dRWJRAI7OztMAozmWjwex9WrV3HmzBksLy/j/v37bFFLC3SSS1xbW4Pf70ehUGCVBZ4aIYoiMpkMbty4wbL9o1xxyrxvbGzg+vXrODw8HDuvSCSCl19+GY1GY6YJxtraGmva5c+ZxoC4xq+//jru3r3LJCFHF1SGYSAcDiOZTLJrwweday6XY4uItbU17O3tMT3k9fX1IfD5PGQ2R5vxpj0fniSp4pe9+PO/fBEXXs/izX92D42iOvWznbqOP/6Ht/DRnxzi2l/awMobbyD4la9Av3MHtV//dbT/zR8Bp2Sp7XYb9V/9NdR//R8h9Of/PGL/yX+CwLXX4Pf48bMXfxYr1grK5TIangYe9B/gfu8+dvo7MDF/9YPibr+NuyLwDzIxhN3L+GpwBV+zXPhq9Rjx0h3Ani5VN3dodeDBtwdfFJFVDii/MpCJkz497fnPYixA8SIADDInpmkOcfR4LUoq0T6rRrvT5N+WlpYQjUbRbDYnmiUQJ9Lr9eLSpUvQNA0HBwdD2qiSJGF1dRW5XA4ffvghAzg86KEMGN+gBAAejxcGXKj1PKjrLjT7XrQUCY2+G0rfi7WeF+d7LrzcE+F5Dvq9AFAX///s/XmUZOlVHYrvmOc5IiMycs6srHno6uqq7tbQrVlCDJIA82Qw6IENLDACAzY27z1jm4ef/fBaD4N/tnlgYzAgjNADmUEDkrp6VKu7a+qaq7JyniIy5nmO+P2RvU99MWaWqrq6WqqzVq3uyrp5h+9+99797bPPPk3MGRq4ZaijaCgj7DLgqKkJ5GKwtYrwGeuwaxsAbjemmJ6ehtfrQSoFFAqFtvHhx5FNSYD+TNBuim34kR4fH8fi4iKy2SycTqc0o4jH47BYLPD7/bLQ6AUgCEhsNpswd9RqEthqNBrRrrIoSZVfcLFTLpcxOjqKubk5yTyYzWbUajXpCrZv3z5sbm6K24Oq5SVD7Xa74ff7pXGLmkJn4xafz4dkMines9Qdc26RKWOXQgAy7irbqdfr8eijj+KZZ54RllAFq0ajESdOnMDY2BiWl5cFPKsMr8vlwvj4uDSDsdvtUpynyjWoAe0sHGWoxwyHw3IsSmQoWzEYDJiYmIDP58PS0hIymUzXHHK5XDh48CAWFxfbmkVQahGJRMTFY3p6GsViUZxlOtn0mZkZ5PN5XL9+XRrycJ5Fo1FYrVaxV1PHTgXE/P98Po9sNitSnM55ptfrceDAgYFNMPbs2YNMJoMbN27I4lmd03q9HpOTkyiXyxgZGREHlc7GPUajEaOjoxgbGxOva6vVKpIYauX37dsncjcV8HK+EPDeL5vN3b4fvhlyZWSvB//L/34K5/52GWe/uIxGvb/kJbqYxV/9h9cRnHLisY9OYuLwfoz8+q9j6Od/Hsn//odIf/azaL7xnPeNZhO5r3wFua98BabZWXh+6Iegfd975To9Og9O6k7ipOkkGmgga88iYo3g1dirmEvN3fH1ZesFfDFzHV8EoDFrcPjRD+Ddzlm8u2XCwcQqtBvntzXFuHNZXFdkVrb/XP389t812m09MiUXIye29crfxq2rNS316X0YdxTZbBYulwuZTKYrnfx2i2w2i6tXr0qRUC+NXiaTwcGDB+/6WukI0I9VYIvkVqs1kAkKh8OYnZ2FRrPdzW5jYwMbGxvCvIXDYYTDYUSjUfzlX/6lACh1ytegQ0Fr225iAQsyTRMyDSNSdQMqrduMsqYFzNa0OFjVY6quhf5NBsJNtJDWtZDVV1HSl1A0ZWDQ5+DUleHSN2CzWsSSbn19XezmVB10MBjE6OgoPB6PNDbJZrNt6Xen04np6WkZx51iJ9aJ95agWNU72u12SVd//etfF0lCJ8MLbIOwQCAgrZd7MZN+v19cRag775QQWCwWTExMIBqN9mWKx8bGcP36dbH36gytVgu3240DBw7g1q1byOfzXXphu90uHsVMr6uaUQJii8WCD3/4w9Jwoxd4mJmZQSQSwblz56S5C49ltVoRDofx6KOPyvNBa0Gmz2lpFg6HEQwGce3aNTSbTWxsbIimnovdcDiMTCaD69evi7SkUxfPBhGTk5OIxWJthXkcR7PZjOnpaWi1Wpw5c0burQqyCebj8TgikYjoatVFWqlUwvDwMD7xiU+IdVksFhMddCAQwPDwMOx2O+bm5rCxsSHvB9WdhguW1157rU2Pqx6L1xkIBFAsFqUJh2rFqJ7P1taWNMHgM+Tz+bB//37YbDZcvXoV6+vruHXrVpfkYc+ePRgZGcH4+DhWVlaQTqdx69atrvfanj17pCHI6uoqYrFY1zMUCASwb98+hEKhnhZonfNop/fsbp/93cSbzUpn4yW8/BfzuHV2a1fbD0048Nh3TmHyyLaPeCOXQ/qzf4bkH/8R6ht3UExns6H8znfA/D3fg9obPuUsoEyn0zKO0WIUL62/hBfXX8TLmy+jUNsBgO8QXrMX7xp5F54KnsSTsMC5dR1YPwusn98Gt29G6M1A6Gi79OJboCPfbvHa2xIUP//88/h3/+7f4ezZs9jc3MRf/MVf4OMf/7j8e6vVwr/4F/8Cv/u7v4t0Oo13vvOd+M//+T9jdnZWtkkmk/j0pz+Nv/qrv4JWq8X3fd/34Td/8zelaGg38a0EiovFIq5cuSIV0J1RrVZRLpdx6NAh8fL9ZjVq6rFYNNJpBl8ulzExMYH19XVkMhkkk0nRHnq9Xrhcri6Wo1Kp4Pz588hkMnC5XDh+/DhMJhOuzS3gD/78C0hUtEjVDci2zMg0jcg2TSi1Bq+INS3gYE2HJ8p6eJv3yM5BCZ02B482hpqugojegYLDCoO5AL02Cb/f1VYY2FnUFwgEYLPZRD9JSyRaJLEl64EDB3Dp0iUp4GE0Gg3YbDacOnVKOls1m82BJv87baPeW51Oh62tLTknNr2IRCK4fv26FEv1AmFsiZxOp0XnzTlCrbHb7cb6+nrbmKgSAqbH2bVMr9dLAZzJZEIgEBBXiUQiIVZ9XXNAoxFwnUwm+zaLcDqdWF5ebpNLqGl2YPvd9Nhjj4n0Z2VlBcViUVqN53I52O12pNNpkXTQvs5oNMLr9UrRHuUhWq0WGxsb8nyEw2FZAExPT+Pq1avSpa5r/r1xXisrK8Kudzq4kF12uVzw+/3Q6XSIx+Myjn6/H41GA+l0GqlUSgoSyeATPORyOXm3cEw6swSUznz3d383RkdHUa/Xe0oD1Hmm1WqxvLws4zgxMYFms4mFhQVcvXpVnC86AS/fM1arVeYHm5dQTkRv8Y9+9KMYHR3tO/eLxSLOnj2LdDqNarUqjhmszzAajXC73Th48CAWFhbkvDufj2azKQ2CnE4ndDpd1/EajYbc63w+D5fL1aWFphME9z3oPXsn7/R7tc1u3jODYnM+gxf/bA5bS9ldbe8JWXH46VHseyIEk0WPVr2O3Fe/iuTv/wFKFy7s+rgA0Dx8GPUPvB+aU6dgtFq7xpFRa9RwIXYBL6y/gBfWXsCt9K07Ok5n6DQ6HB86jnePvhtPjTyFGb0Tms3zb4Dkc9v/LSXv6hh9w+zu1ic7gm/Osd6k2C1ee1vKJwqFAo4dO4Yf+7Efw/d+7/d2/fuv//qv47d+67fwB3/wB5iamsI//+f/HB/+8Idx9epV0Wj90A/9EDY3N/GVr3wFtVoNP/qjP4qf+ImfwGc+85n7fTkPRNyJJuxu2QCyaLVaTRpxqE4XLpdLPEHj8TjOnz/f1WHu+PHjOH78uOzza6efw7NnLiNSaCDTNCHbNCP/N6so6GxIlVsApu5oPLQt4HBVh8crerjvAgzr6mVYynGYygnE7QlcGUsCjhg+XlnCO2ubuKadxjfwKJIaD4A3CrQ0ZhgMRrlmMo+qxpkfRIPBgGw2i+Xl5bYxSiaTcLlcCIVCyOfz0Gq14qfKj5XdbpcUK31G+zFhBM073XveWzL3KjMZj8cRDoeF8apUKgKcZLzecA+x2+1wuVziR91ZaMe5SL0yF20qU84PP5nUaDTaxnBubW0hGAwKOKcTRef50FmBgIdgW12kUFrE+0VgKfPpDYBpMplQqVSwtbUlne+4n0uXLmFqagqhUEiAOgExI5VKwev1wuPxwOfzYX19HfPz823P0M2bNzEzMyM2hbz+XgVylUoFPp8PDodDJA+ca3Q+4KKA7gmd45hKpRAMBqWxhMlkknmmzlmDwSBSCGpL6fYCQIoFCSoH6Y6p7V1aWhLGlce6ceMG9uzZI1aS7Bip3jMVsHNes9Mlt0kkEuK2QncJFst1BovwOM7MaJChTqfT4qahvmdVy7RWq4VsNgubzSaSHGre1WARKbMVV69e7coADA0NSfMaPnv93rOUwOz0Tt/te7+fBIuxubm543tmpxieceH7f+kE5s5E8fJfzCOfGuwOkYoU8cKf3sTLn5/H3lNBHH5qBIGPfASOD38YN//6r1H47GdhPnsOml24kWgvX4bx8mXUnU5k3/lOGL7jI2g4nV1F3AadASdDJ3EydBK/cOIXEClEBCB/Y/MbKNX7N2rqFY1WA2eiZ3Amega/cfY3ELaFtwHy7FM4+e6fh0VnBtLLCkg+B2xeAGr9tdi7jnIamH9m+w/DOdoBlB8BTPeu4+1bFW9LplgNjUbTxhS3Wi2Ew2H84i/+Iv7xP/7HALatooLBIH7/938fn/zkJ3Ht2jUcPHgQr732Gh577DEAwJe+9CV89KMfxdra2q69Hb+VmGKgv86X6bg9e/YAwI7b8AXZjzEoFos4d+4cUqlUlwdxqVSCVquFx+NBpVLBK6+8IlY/Gq0O6ZoOyZoeeVhgDU2iYnTh+loC8VITrXshaWgBR6s6PFnWw9m6MzBsqOXhyK3AkVuFI78KR24FdSTwt49q8KUTWgSMNfxMKoN3ljW4YHgML1QOoqjp/fGw2bY7JhHo9bIJs1gsIo1Q9dDAbR3jxMQE3G63OB10dtoyGAwYGxvDzMwMLl68KIwc7yv/furUKdjt9h3vvU6nw8svv4yVlRUpYGSwAGx4eBixWAxLS0ui6ea1UQM6OTkJg8GAhYUFcWFQr1+r1WJiYkI6pfF6GRwPOkxQGqF2WWMjCBYXEgx3jiOANossul+ox2IRJ3XbALoWlgAk00FZUC+/49HRUZRKJSTeaEjQuQ0A+P1+eL1eYXg7tak6nQ579+7Fu971Lly4cAErKyuSUVDvrV6vFy3owsKCaNFV3THnSC6XaxtHtWCTgL+g6DU77xnHk0xtp0sDtdcAcOrUKczNzYlDBDW82WwWFosFTzzxBK5evYqbN2+2ucVwsc35QR1wr2dIq9XC5XKhUqm0dQ9U5yuwbUX2Az/wAz27vTGKxaLM+3q93lXwq9frMT4+jieffBKNRmPgczQyMoLl5eWBWbt0Oo1sNive0J3Ho+We2WyWTEu/9+zevXuxvr5+z977g2JzcxOvvvqq6MB5X6mdVrNWu41atYErz6/j3N+uoJTdfdFbcMqJyWMe5LQbyFfS0KZS8J09C/tLX4duh055arQ0GtQOHcLIj/6v8H3oQ9D0cEPpjGqjinNb5/D82vN4cf1FLGYWd328XmHSmXAydBJPjT6Fp0afwoh9ZPsfGnUgfqOdTd66um3rds9D84Y++cRtsPwA6ZO/peUTanSC4oWFBczMzOD8+fN45JFHZLunn34ajzzyCH7zN38Tv/d7v4df/MVflAppYPvDZjab8Wd/9mf4xCc+satjf6uBYmAwG0Ad3240avl8vu9+bDYbnnvuOUQikbaKeACoNxqY30igbvHi7M1VxMoaFDRWZJpGZBpGNN9ELa+/ocGHigaMNHbv7WktRhGIncNQ7ALs+TU5u7QV+OvHtfjb4xoEtXX8dCqDdxR1eE3zGG653olErp2R7Ax1bPV6fRcIo1MAmcfO3+FjbbVa4Xa7pQNWp66STQn8fj8SiURfNnFkZAQjIyNIJpMD7/309DQ+//nPY3V1VYC3qgXWarUYGRlBNBpFJpPpcpYAIP6z/MBTisGgZ7DVapV99dMdB4NBrK+vy36oN6b+mOnlVut2h7nOoHSBWSZ2UGM0m01pOFEsFuXedI5Rq9WS81LdM9RtuHAgAGaBoXrttL9zOBwoFAqw2+1d2zCt/slPfhIvvfSSzJFOLTSwrfUlA0ymm0HWMxQKIZFIIJvNSnFeZ7GaxWJBuVxuuwZ1PnORROkCvc/V82bHQOrJ1e55HOt4PA6fz4eNjQ2RCPWaHzabDQ6HQ7pQ9mLufT6ftG2nUwqDfsMGgwG/8Au/0OUEoUYmk8Hzzz8vFne9xtlsNuOpp56Cy+W66/eszWbDpUuX+lpRstgtEAggmUx2vWebzSZisRhCoRCGh4eRSqX6Hote8HerTW42m33f++r5PP3003ckpWDUqw1ceXED57+8jELmzhwhjO463BM6OMc0MBhqsJw7B+uzz8G4vHxH+9EF/HB/4nvh/jvfD+PY2K5/bzW3ihfWXsAL6y/g1Y1XUW3duaOFGjOuGTw1+hTePfpuPDL0CAxaBZjWSkDkElprZ5C7/iwMscuwFO/cZm5XoTO94Z984jZYfov0yd/S8olBEYlEAADBYLveJRgMyr9FIhEMDQ21/TtTc9ymV1QqlbYPBrVf30oxyJarWCzuqmtRLBbryTyw4jkcDqPQ0CLSsOPiYhHJmh6xsgbRQhNbpRZqTQeAGoDQfblmfQt4sqzHyYoeul2AbnMphlD0NQzFzsNW2Gj7jbgT+J+Pa/HMMQ3cmgZ+KZ3BewpmvG75EP7QeQTVpgbVYlXYXqDdBq/TDku1imPwg05WsNVqtdmeqY4H7MCnajlVZwX6UPN8SqVSW0MTSgLYTIKpY8oMKC/gvY9EIqID7jwvVvOzba7FYhG2UdXn8rgEVwDaAA0A0UiSFWTlvmptZTKZBAzRa7jzg87r5D0g48voBEuUZLBQi+lx2rWpjGRnEPTyeL2C58B7rzon8N9VezKTydTGsHMOsZ358vIyqtUqbDYbjEZjV0EeWUc6L3BBwmujawaLvVh81jmOZJe5OOnMEhBgGo1GOBwOcXvoZJwNBgPMZjPS6TScTqeMr6pxdjqd4m/M+aECdM4P/jvvcS+mWAWvALoWqvz5/Pw8Dh061FcLS/9iar3ZuIZNPyqViiyYgMHvWQA7OjlQWqQ2klHPmc1zWq3t9vVcPKgOPmxrn0gkZKw792O328V3e9A2g7rVMVKplEhSOkEvWftEIiEg+05Db9Th2PvGcOjdYVx7aRPnvry8o6yCUU3rsZUGtl4HzB4DrEOPw/apx2FJ3oT1+dNwX70K7S66uTZicSR+53eQ+J3fgfXxx+H+3k/A8aEPQbuDFd2YYww/eOAH8fGJj+PcpXNYqC3gYv4izqXOIVaN7eoa1JjPzGM+M4//duW/wWFw4B0j78BTo0/hXSPvgtfsBcZOoeQ7jBv6k9uFsq0SzMlrMCeuwJy8BkviMvTle6BPblSAtVe3/zDMbgUkvwGU7UN9d3G/41sOFL+Z8W/+zb/Bv/pX/+qtPo03PfppwtRW0L2CL9yNjY3tj6zViYVMHevZAtYyVaxna1hJZhApbKJUbwHgi7GF23Yzb/4KUoMWnNoq3LoapmrAkbwL5sbOKR5bYRMTy1/CUOwctK12kBZ3Ap97pxbPHdHAihZ+OpPBRwpWXDR9J/7EeRAtzTawsul0wibxY62CGn74abFFQNmZ0CGQUD/26vbq/6t2XaruTfVjJpNKvaLKuBJkUpsZi8W6XCzcbrcwlNSmUmvLIEhiqtpisbQBBZ6T1WoVXS4bNKhaTwLxUqkkjCDlAKojgs1mk/2ohUbqGKptm+mGoJ4PmVoynGTXVWs7o9HYJgcgSOoE+3q9vk0b3eu+qqHKJhjU55Lxt9vtMj7q75nNZuniZjKZEI/HBYiqY+R0OmEwGGQ7NkTp1Gbz9zju6vFU3TXHs9PLmduxONBisXSdj9VqlcJBtgEvFApd50OmudVqwWw2y3OgLiQNBoPomgkguR9eOxlyAGLn1qknN5lMbZrbflpYFtOxG2gvppjNTdR72Q9E7tRWmTZ4fF47Fxe0HQSAkZER8VBWbTbdbjcymUzXQrBzDnKs77ZbHWsjBnVFzGazbcTTNxN6gw5H3jOKg+8KY/78Fi4/t47NW90Wgf2inNr+k7wBQDMLw/QUTEeL8ESvIfT6s3Bsre5qP8VXXkHxlVeg/T9/Dc6PfhTu7/0EzMeODWTT6/U69C09TvpO4vHA42hNtrBeXsfZ1FmcS5/D9dx1NFp31g0vV8vhy0tfxpeXvgwNNDjiP4KnRp/CCc8JmcdNrQvF4SdQHH5i+5daLWgLEbRWX8OEPgFT/DKwcR6o7l5W0jfKaWD+a9t/GI/8PeDj//Hu930P4lsOFIdC2+xiNBpt0yZFo1GRU4RCIelyxKjX60gmk/L7veKXf/mX8Qu/8Avy92w2i7E7SJG83aNX16JirYl1At5UGcvJIiKFDKKFFnLVN6kSdlfRgl1ThVNTgVNThsdQh8/YgFNThkNbhbapgSk5DkNu5w4/9vwaJpe/hEDsAjQdXpFpG/Dn79Diq49ooNUCP5zN4btyFszbPoYvh46hBQ3Mzdtd1iqVivw/AW1nkLVlcVIneFJT83ypkTlmECiTSWXbX/WFzL/Tb5YfTJ4ft+F+G40GFhcXhRFl5HI5KbYqFottbZPZrYvXywWAVqtFPp8XoKMyePw5C+gIcuTOvqEHJpPJ8yaTxf1QM0uA1q/zGceLUoFO1wiOP10hALTZfqkth7lwANB17WSde9m+dQbHX9XcdrKcZIwp/2h2zDOOydbWljRLMZlMAmDz+TzK5TKGh4flmlnkqLLgLFIj8OHiST0ndS5ZLJaeBYsEhXT4ofe5mnEg0MtkMkin0yIdUjMAdFHhWNIysrPwkWNBp5JesiAW/ql6c9XWj/O4Xq/31MLS6vDo0aOw2WwCmFXtNq/D6/V2zeNBLg2D2GQCX76P+cxRR6/X62WhYzAYEA6He7pP8D0+qBMdz3nQNswuDQref557Z9Dbm4uIuw2dXou9J0PYezKExHoel59bx41XIqhV7gBUtjSopQ2opV3I4wmsHnwC+qM12IvrcG9chSO/CksxBks5Bl0fnW4zn0f6s59F+rOfhXF6Gq6PfQyu7/luGHpopzu/sRqNBqOWUYxaRvGx8MeQLqVxLnEOK/oVvBx5GYly4o7GpIUWLsYv4mL8IgDArXfjuOs4TvpO4ojrCCy6NxhtjQZlow/lkfegcegQYLUCzQYQnwPWz9zWJ0cv3xt9snfy7vdxj+JbDhSzevtrX/uagOBsNotXXnkFP/VTPwUAePLJJ5FOp3H27FmcOHECAPDMM8+g2Wzi8ccf77tvk8l0zx7YBzk6X9YavREryRIWYnmcWW5gYSuKWEWDjWwNydJd9HC/BzFkN0JXSsKBEpyaMly6KlzaChyaCrRvrKh1Oh2CwSCSycz2x65sgnlrBrra4CYk5lIcs7f+P/gTF7v467wZ+J9PaPGlExpUjBp8Z76AT2ZtyAz9CF6yTyKZSsHVAnQ6raQKmfKmfpcAuTNdT6Dj8XiQTCbbgBk/+o1GA263WxjbTnDdaDSEudO9wU6r6XVVssEirVKpJJIDBlPMbHUbi8VgsVhgMpnaQGEsFoPdbhfLKDJy3BePq0oVCLA6z71cLgt4KxaLXdZdBGpkCev1el9drZrSB7p11/V6HSaTCS6XS9hfVZvM7V0uF1wulzh5qMVjbMcbDAbFRq0Xw0/mu5dfLu8px81ut0uBlnre3I6d+lKplDDYnfPM5/NhZmYGr776Klqtlthu8RhWq1UcSXj/ydSq9z+bzYoenECrc6zVe1mv18WmjSBMp9Mhm81Cp9MhHA4jHo+jXC63vWfohzw2NoZYLNaVaud4US9dqVSQzWZ7dqmkPMDpdMq8Vt/dfMe53W6RGfH8uS/OM45TsVhs08KSbY7FYlhc3C6U0uv10tmQLZt5rmSogbv386X7CPW5nQu5WCyGYDCIcDgsdQCd188uh61Wq2+tQD6fh9/vH6gp3m23OvWcyWwzms0mMpkMQqEQPB7Pjtd/p+EbsePpH9yHJz8xg5uvRnDztSg25zPfVD+Met2AtHES6cnJ2z9sNWGupGApbsFaisFUScJUyWz/qaZhqmSga5RRXVhA7Dd+A7F//+9hffxxuD7+MTg/+EFo3yis3skFqllq4v1j78fs7CxaaOFa4hqeX3sez689j8uJy3d8Lel6GqcTp3E6cRp6jR6HnIfwqPtRPOp+FKaSqf2+anXA0P7tP8f/3vbPamUgcumNQr43wHJy/s4HNfzonf/OmxRvS1Ccz+dx69Ztz7/FxUVcuHABXq8X4+Pj+Ef/6B/h137t1zA7OyuWbOFwWIrxDhw4gI985CP48R//cfz2b/82arUafuZnfgaf/OQnd+088a0WtUYTq8kirq7GcWkpisV4ARu5OiKFBhLF5r3opfNNhxk1uHQ1ePTbzK+9VYBbX8NH3nkCJ44dxmc+8xkkk9usdJs+943fp6fxmTNngJQL1tQUNK3+xXSaZgPjq1/F5PIXoetot1nVAV84qcHnn9SiaNbg0XIZfz9uQ0r7MZwbOoyn3/MeeItFnD59Wiqqme5lVfijjz6KGzduYH5+XphKVd6g1W43lBgfH8fLL78sjA6ZJ2oJjx49ips3b2J9fV1SoOp+NBoNPB6P+GgyBdoppSBbWyqVUCqVRDKgVqlTo9hLL0tGmOlZVSesXpsKNNmtTtVFEpDq9Xq4XC7Y7XYsLy9LVzbVNUOv1yMYDIr3Ldl1lU3lz61Wa1uamPvhGNH+jSw3C/JYiKe2ix4eHhZ9rQpsqfu0WCwi41DHiosBi8UCs9mMZDLZpi3mggEAfD4fDhw4gDNnzsj58H4wVf7kk0/CYrEMnGePPfaYgE+bzdaTdabWml62BJCqkwXZXxYJ9tMCcxvqx8kMqr9js9kEZNZqNfh8PjkWmWuLxYJgMIh8Pi8FfOp8NJlMGBkZgdPpxMsvv9yz46XBYMCjjz6KSqWClZUVZDKZLucNg8GAUCgEs9mMhYWFvvOMFnmDtLBbW1vweDxwu91oNpuw2WxtzxnZWxYi7qbLHG0N4/G46MD9fj/C4TAcDgf279+PbDbbt8vegQMHZHGVTCa7nFcsFotkU4vFYl/9Mr+Ju+1W148B12q1fc+Zc3j//v3fVJHdbsNo0ePw06M4/PQoCukK5s9v4carm9havEtZgEaLstmHstmHFA703ETbqMBQK8BQL0JfK8CQLcDwexdh+L2zsE6OwHFkH+z7Z9HQmVBO6LAWS8DhtsJkMQDaJoqVvNwzjUYDDTQ45D+EQ/5D+KlHfgrxUhwvrr+I59eex8sbLyNfu7NrqrfqeD3zOl7PvI7/tvzfMGwaxnta78EHnR/sLtZjGMzA2MntP4xicltqQTZ5/SxQ2KHZSvj44H+/j/G2BMVnzpzBe9/7Xvk7JQ2f+tSn8Pu///v4pV/6JRQKBfzET/wE0uk03vWud+FLX/pSW8rmj//4j/EzP/MzeP/73w+tdrt5x2/91m/d92u5n9FotrCRLmExXsBSooCF2PZ/l+IFrKZKaDTfOuhr0WsQdugw7jFjX9iL/SNeTPpt2Lz5Oq5fPIdCoSAfc5vNhhMnHsNTTz2F9fV1WV2rYAWAgBWLxQKDzghf+TBKycFMvzOzgP03/wT2wkbXv710QIPPvEeLmFuDiVoN/2tcDzQ+hNfN+xAMhfD0iROYmZmR7V977TXE43Fhgvx+P06ePClWYiaTSbpfqfrDPXv2IBwOw2KxYGZmRrqasQjO6XQiHA7D7/ejUChIoZSqYaWuNBAIYGNjQ6QB1LpqNBpJu/N3yAgwzapqHinFCIVCwpqpWlCPx9NWpMf0tcqWEmzwd/x+PyKRSFfqNxQKodFowO/3w+FwiDWXep6zs7PCJBNUq/thARpButvtloYKvDaTySRaUKPRKNdPCysye2TGzWYzJicnkUwmxQrLaDSKPdrW1pYsRNhMQb2vBGVutxsGg0FS7QxqVNmxzmaz4dVXX0Uul5PzcblcOHXqFE6dOiW/d+bMGdGMUnf62GOP4ciRI5ibm0Or1YLNZpNOfOoY2e12ZLNZ0WCXy+UuP1uC8lqtBo/Hg1gs1tWJjY1QCIKo91bvt8VikYYSXPSoLbGDwSB0Oh0ymQw8Hg8OHz6MxcXFNm9tl8uFqakpWCwWHDx4EEajUcaICzin04lTp07h5MmTmJubQ61WQy6Xk/b1Ot12i3iHw4FQKASXywWr1Yr5+fk2v2ObzYaZmRmYTCZsbW2JTIPPELM8lCIA7fpdZnCo32VhKH2eVSbQaDTC4/EglUphc3MTrVYLV65cETs5Br2rDx8+jOHhYZw6dUp0zmTNQ6FQm+dvKBTC9evXsbGx0aaFnpqaElZ6kH5Z3WZjYwObm5uSMRgeHhaQDuzMgO/2nO9H2NwmHH3vGI6+dwzRtQQuf30Z0fk8sht1NGo7//6dRlNnQkVnQgXe7n+sAjjbAs7e7PgHRV+tAYzmGi7bL8NsM2z/seththlgdRphd5tw0vM03nPwQzA9qcOl5OvbLPL689+U5dtmZRN/Mvcn+JO5P+ldrNcvrF5gz/u3/wBAqwVk1m4D5I3z7fpk78z27zwg8bYExe95z3sGFqhoNBr86q/+Kn71V3+17zZer/dbslFHq9VCNFsR4LsYv/1nJVFEtbGznvHNCoO2hYAZGHEa8MhMGLPDbkz5rAjZdXAab+sRVSbykbGn8f53P4nLly8jm83C6XTi8OHDom0jw0Ymr9PEXqvVopxp4tpfl1HN9gfEunoJMwv/EyMbL3bphm+GgT94vw5zoxo4Gk38fNmMSeMncMXgR13bgF7Tbr/F6Kxk59+Z7t27dy/279/fsxtXNptFoVDA7Ows9u3b17PzVSKREL0gO/7x2umHS8DIBSHBonq+1KhS69lZIKYGx5spc7VYEIAAIP5MZaYp52Da2e12S6tmAkznGyb42WxWGEuXyyVghMDIarXKPS6Xy6IX5fmTvSQIs9lsCAQCXQwWwb0a/QqBmCpOpVIC1Kh5JrvearWk8xuBEYsHa7UaCoUCHA6HuCx0tvB1OBxy38j0zc3NyfyYnZ1tq2M4cuQIDhw40LPrGwB5nrLZbJvmmAskShq8Xq84JrBRBxdXJpMJbrdbmFuTySTuEgTXlLCQmexVQEZ5BP893+EFy0UYi9/8fj+CwWDPjm50y1DHiI4mHCONRiMuDnwXcAyazaa0zF5eXsbU1BSmp6e7OgO2Wi3E43HpyMdjc3y4YDAYDKKJ7qff5TOzGwefTCaD9fV1mScqC76+vg6r1YqjR49ieHi45xjxuc3lcohEIjCZTJicnGxjiiORiMy5ndwwgO0M7fr6epvnOd+9ZLZ3w4DvdM5vRQRHfRj6O16USiVUqzVkoxXEFotYv5nG5lwa9dpb992UaAHVUgPVUgnZ2M6NPywOAyb978Gj4e+E1l/Dku4GzlS/jq+nn0OtdWeov6tYL3AET48+jadGn8I+z76BxYPQaAD32PafQx/f/lmzAcRvboPkAVjurYi3JSj+do9Wq4VkodrB9haxEC9gOVFAsfrW6Xz1WmDIqkPYqYfXUIcDJXh0NYTsWkwMueHzelGtVnHw4AScTmdbqq1fkP3hR1V9edrtdhiNRqRSKZhMprYPerVaRTVpgGFzEtVG/4fWnlvB4au/B2up3fomaQf+6L1avHhIAy2A78418L2TP4aLUQ+uFkqw2m5bikWjUTz33HMAtlORp0+fllQvt0kmkzh9+rQAp0Qi0aUfJPBSu1r1KsxiVThT37TcUpsjUAbAn3P7Xq4JNptNrNA6WWAypfV6XRqFkAGmNEOv1yMQCMDv98PtdsuHt7O4iYyYz+cTRpDuCRwnNtvRaDS4cuWKNENgCj2TyeDKlSs4duwYfD4f5ufnuxg8gvCxsTFJz1LXSdBLhwACsX4V9q1WSyytbt68KeBNLaRKpVKYnZ0VYNG5H85zq9UKr9eLpaUlABBdNOd0tVoVj2Z2qnO5XMIyJ5NJVCqVtmYJKuvPIjhGKBSCwWBAOp2Wf1PT3IVCAX6/H8PDw7hx44Y4OajgsVqtisxhaWlJABHnZiaTQbFYxOjoKEKhEDY3N1Gv17vS/mTJ0+k0MpmMFOdx4UQ3CnprUzvMtD8Lvqhh5RgVi0VZKGq1WgFbHKNeLKjqCZxOp5FIJOB2uyVLQW1xOp3G8PAwKpUKFhcXxUaQz1A+n0epVMLU1JT4/VJDrI4Tz5mFnyzC7ATPlCHRfUMFz9xXOp3G2toa9uzZI+/DXhZmrVYLm5ubKJfLcLvdbXIlOk9sbm7KMdTMUGf0a7oRjUaRy+Vw8uRJkb8MYsDVY1Fuw/v/Vgev32oF3G5gfB9w4iNAo9FEarOI1ZtbWL0ZQ2K9iFKyhTs0gbjvUcrVUMrVEF2kdawPB/DdOGb5OAyhBrZcSzireQE3DK+jods9SG6hhYuxi7gYu4j/cP4/IGgN4t2j78bTo0/j8eHHYdEP1pa3Wi2UyhXUzSPQ75vYXnzdxXXe63gIih/wSBaqeGEu1iZ1WIgXkCu/GR1pdhcatOAxtuAzNrb/mJoI2bRwasrYO+KHz+tBuVxGNJqSDxGKOqRTLZjeSFPr9fpdFZtcunRJ0sPcxu12S3rYYDDAarUimUwim822yQGM+SGYEpMYZPM2sv4c9sz/eVvlcFMDfPExDT77bi1KJg0eKTXwROYw7MGPYrFkQy6/7dmbyWTkWGSSzp07JyBBo9G0dfjiB+/s2bP4+Mc/jnPnzmFpaakNqF68eBGTk5N43/veh+XlZSwsLAgLqKa0Jycn4XK5UC6XRYcJ3O4YxkYKZrMZdrsd0WhUgKfqG1yv1yWFvLa2Jh60ZD0JFgOBAPL5vHhz9+r8NTw8LNIOFpypdllkhcLhMKanp/G1r31NOtYxtFotgsEg9u7dixdeeEEYSdXajYVzm5ubmJiYEI1opxTBZrPB7XZDo9m2rUokEl2LAp/PB6/Xi7W1NQEXKijUarXI5XLIZrMCEqlx5ThRj10sFiVVzbS3qqN2Op2Ynp6WYrRtVur2dREUAdsgZG1tTezvOPedTicCgQBsNhvsdjsWFhb6tkKemZlBpVKB1WqVudhZuEMWmywkwaq6kGGrY8ohOltqs7irXC4jFAphaWkJ8/PzbfKJZDKJmZkZDA8PY319XeaHKlXh4qvVauHAgQO4du0azp4922VvNjY2Jm3JCcp6Me4co914AicSCZGayHtOo0EgEEA4HEYmk8H8/Lx49qrZFnaWDIfDiEQiOHPmTNc8Gx8fx/DwsIBtVc6hylBosVcqleDz+QayyVw894tSqSRa+itXrnS1eA8Gg7vyF242m7h+/frAQsPLly/D7XbvyIAzm3U3RYb3O3Q6LfyjdphcLVhHaojH66hWqmiW9TDBAX3ThnKmgfRWEZmt0q69kd+qqJYaqC4CNkziKUziad0PoxUoYc1+E2dNzyLiWERLs3v2NlqM4nM3P4fP3fwcjFojTg2fEhY5bG+v0brbAtP7EQ9B8QMeK8kifu5/XHhLjh2w6THiNGDEaUDIpoUdJSC3BaeuBoNW01Fss635TCUTMBr0mJ+f78uoHTt2TFieQam2paUlnD59WoqNLBYLarUa4vE4Tp8+DQCYnp4GcNttodVqAS3AkpmAOT/S99p09RIO3PhjDMXOt/38Zhj43Y/osBzUIFhv4mPxSWjKj6NqtcNitSISiYjvLwEBGZ9Wq4XV1VVUq9U2qy+1iAwAYrEYnn32WSm0UwFmpVLB/Pw8wuEwyuUyrly5gmaz2Vb8lMvlcOXKFRw6dAitVgu5XK7LA7VcLktDC7PZ3NYsRC2AazQa8mGtVCpdrA3BM/enOkrwuoBtoMr2xiyaUovfOn++tbWFeDwuxW4MMoxXr15FOp3u6ozG4xoMBilWI7hXC8kI8pLJJLxeL3K5XBcTzLEjK83zUB0I1KxDPp9HMBjsssCivy7lEmw7re6z2dzuesdWxfRsVYPnTM3m0tKSWKvxXsXjceRyOdH4vvLKKygWi7BYLFJYGIlEJGtht9tFv87FiwrU6U28uroqMhZqZQmAs9ks5ufnpfEHx0gdx1qtho2NDczPz2NlZUWK1tSCvZWVFYyMjKBQKAi45DbA9geT/rvchppwjmO5XEY8HkcikcDq6qqw0p0WaPl8HgaDAaOjo7IoGAT8eB1qESKjXC4jl8t1eQwzuMBPJBKIx+PyHKnuLPF4HPl8HqFQCEajEXNzc5LNoRd3LpdDMpnE0NCQjAnnNwHEoK56nVGv14WhZStoLoCoex4eHt7RX3i3TTcoZ+oVZMjT6TSi0WibFV6zud3NTpVYPGihSkOcTmdboaHJ3MKhp2+fd73WQHIri1yyhEq+gVoJKGYqKOVqKBe2/1QKdZQyRVSKDTTx1slGAGwz3hELRnEMozgGrbWJTGgdZy3PYs52ZyxytVnFi+sv4sX1F/GvX/nX2OPeIwB5yjyFpYWlHeU1b3U8BMUPeEz5+jMB9yL8dhOm/TZM+q2wt0pwaSvYG3Yj7DTCrN9+WFut1hstsW24fLkAvd7Ylqbly7pUKkmF8iBGLZ1OS4OPfqm2tbU1vPbaa8J28uMCQDpGnTlzBuFwWPSSRqMReq0R+s0xaPPuvtfcSy6RNwN//N7tTnR6AB/KDyFc+SD0egtswdsV8yzEoS+w6kBARwN+wPhhZFDHWSqVcO3aNdmOY8z9NBoNvPbaa7DZbOItS7DKD3alUsHq6iq83u0CBTLS6gedBW9MVfZqTkC/VrLN9IVlUNpAjTO1mZ1sYrPZRC6XQzqdxvLychsIV6+/0WgI2CMg79QwVyoVXL58We412UMVzNFjlveDumN1m1KphKWlJQHJlFSoY61W0A9yVgBut59WCxYJIFutFqLRqPifU+7BaDS22zMvLS2JzpnjqzKK5XIZGxsbUjimevcSfJZKJdy6dQupVAq5XK5v1uL8+fN44oknkMvloNFoEAwGxXOYEpt8Pi8gkuCZix5qZWu1GtbX16XYsZcvMD9uly5dQrVaFT2uqmOOx+N4/fXXRVrQam03jWHwecnlcrhx4wZKpZJY5alscqlUwvXr10XbymwAsA2yXS6XvGMIBvsFJQYAsHfvXtH+8trT6TTW19exubkJrXa7RXmn/Vk+n5dj1Wo1TE1N9bRIu379unRSVcdPfW4pgbHZbIjFYqhWq13Xz/ckawD6hVarRSwWQ6lUatPsMiORSqUQi8V21PLupukGn+GdvIwTiQS2traQzWa7mGun0yns/oMgp2CoMpSdpCH5fL6dCdXr4Bp1YfrkcI9ixO1viKYJWJc3oD93GflXz6FWbqCut6ChM6Ou3/7T0Fne+H8ranoragYbagYb6gYb6vrBi707jWZRC8fCGN6DH8b79D+M2kgaF70v4DXjaTS1d6YZuZW+hVvpW/ivl/8r7Ho7DlkO4YmhJ3BcexxGrbGvvOatjIeg+AEPl9UAj9WAVPGbL4d1WQyY8tvkz6Tfhmm/DRM+Kxzm2zYrt1fDRWibWjSb7bY7NptNAGEvwEOwVCwWBzJqqVRKqvb7pdoWFhbEo7cTYNHuKplM4ubNm9KNTdswAitj0Jb7vySGomdw4MYftVmtvbxfg9/7kBYZmwbHmx5859Dfh83va2NnqtWqVG9zDNRxIEhSWdRerAq3J9vMbTtZXIIUviRUEEaglM/nBUio48P/12q1kuamFlIFYSyiok9tIBCQVubqPTOZTOKr7PV627qWEfyT6VpeXpZFEa9LvTayQmRtOZYqQ6fT6cRVQm0UoV4fWSb6DPN8VCCi1+tRKpWwubkpxYEqu88CQ7YUJmOWy+XkY80PGbMU1Wq1p1c59dOlUknumdq9jr7LhUJBZAUEsLw+ZhsoTzAYDG1trrk9G5OoTiK99kOQrmqWe/2XYI4gXi2O5P4LhYJoYVUrOZ4bsM2oskUv7w/vO+sCKPPgvVPvKy3Q8vk8IpGIyF5UXT7HLhKJoFQqyf1R770KnvP5PNxud9f9YlBiYLfbpWhQDbvdjs3NTSli7Fw0AtsyAsqK/H6/nIMaZFPJ2k5MTEj3O9VZhFZuZrMZ8Xhcnl1mATKZDDQaDcbGxnZkvlmAyrnROUbs8qi2fO4lMdlN0w0Wzubz+b5exna7HZFIRN6jncw130Nk9x+UUOfIIGlILBbD+vr6QCYUQM9ixPzkMMz7p7Dvn/1DaM6fR/YLX0Tu9Gm0SjsX1DU1WlSNLpRNHlRMblTMHtT846iGZ5HXeZHLNb4pP2Zguy+HbtmN48vfjcetHwNmc7jiewnPl//2jov18vU8Xsm9gldyr0ALLfY79uNRz6M44T6BIdvQrqQ89yMeguK3QUz5bUitpAduYzPqMKkA3knf7f/32Hqv8Dtjp9aijUZDPja0+GLwhcqimEGMGtPI/TogEczwI6lKI8jw8SVPlljXMEOzPAFNtb/DxOTSFzC19DeiME7bgP/yYS1e3afFUNOIv2f8Luw1PwKn2dl1bSoYUj/26jb8OVnkzqYTBGadoWpqO6UERqOxK71JEFQqlSRN7fV6u7Sw6XS6DdiQwVHBNceW4JeMuLogUJmzYrHYE1w3m7dbKVcqFfm5CngJhHg9nc4V6vWTIedHunMbFjFx4dV5P1Qgzs52lBBwUUWmOJfLYXJyEktLSyIR4Bjlcjm43W4cP34cyWQS6+vrIjlQx5ESHwByXrxmzgHKAlS7u85MAiUYnBM8Fwa3pwuCwWAQ0Kjuh9ZqfFbpQ9u5L4I8VUveuVgjyCYTyHuqMsWq84dWq5VFncpec/yZJejMpLAYtNlsiia8V0aK9oRqMWfnfOT17cQ6cTE16F3E50d9BzG4uOI92amFMTW1LpcLTqezq9COWblSqdS20OGxVavBznPpDL5jG42G+LirQeeMarU6UOu526Ybs7OzWFhY6Otl7PV68frrr6Ner7c1iSFznc1mEYlEdmT373fsZo7U6/Uds58bGxvybPbbJpJIYPb974fjAx9As1hE/tlnkfnCF1B47nm0ar1BqPaNxiHmSur2D1cBvKEM1IyMAe/+TlT3nUTeGEB0KYfoYhaN+p05alSLTeB1G2bxIZwc/hiMh4q44n8Jz22dRrwUv6N9NdHE1dxVXM1dxR+t/BGOOo/i08Of3lHKcz/iISh+G8Sk34ZzK2kY9VpM+qyY9NkwFbBhyneb/Q04TDt+AHYTg4pSisUiwuHwQDN8eueqDQHUIIgj2OuXamNKjgCLAJMpZLJPRqMRupoVmqVxaBq9P0iaZg0HbvwxQtHX5GfPHtHgD96vRdmswTubx/GRoe+F3WQXdqmzYQD1cvzIqGAHuA1sye6poF7VuZLFVIGGKovgH2AblJANJvOp6mVVFodp/85mCQCkoKszFU9AQV1jKpWCTqfrKmxiBzW9Xo9YLCZMFiUjLMYbHh6We6TqclVQo7LpnF+dIIPjxA9FsVjsaiih0213KVxbW5NW1p33g+CX/68y2wQa6r/RoYJAkfMvk8lAp9NhYmICS0tLYg1IcMwuf5OTk9jY2JA2xJ33TNVkq64iKitLVxFeK69BlU+o7HrnwgtA2xzT6bbbKxPsdOquW62WdCvkYqdzPnJ/nfIT9bzJ/HMxRiZSlfsQyKqLJBWkq2PBdHsvZo7zios0ADI/ms2m/Mztdu+oweWzNehdxGJVOq10ymsACAjcqYUx3ys8XiczzWxEoVBAOByWrIBaZMtCPbLy/UJleIHbEi3+lwvPer2+o5XabppusElSP1KFBcjMWqmh1WplwbMTu3+/YzdzhO9BtppXg2wydfSDtlHZUq3VCudHPwrnRz+KRi6H3Fe/htyXvoT8178O9AHIvaK1vgr8j9+GAb8Nv8+Hqfe/H9bvfz8KocPYXMpj81YGG7fSqJV3L41Ib5aATQ2GTU/jXzz+A7A+XsHZ8te/6c56I+YReV++1fHWn8HD2DF+/gN78Qsf3IuwywKt9s3X25DF6gyLxYKxsTExw89kMgJsaIY/NTWF9fV1RKNRGAwGYYXUj3MoFBL7Irfb3aXjy+fzGB4expUrVwRYArdTyHyh6/V6eM1haJfqQKN3hzpDLY8jl38H7sx268mkHfjtj2pxYUaLqWYIHzF/Al54oW/pMTQ0JOldMoa0LyJQmp+fF2DQqc8lSzU0NIRYLCbXpWo4DQYDfD4flpeXAdxOLXPcVZBABwl22eI2BBl2u12sqQhyeSyVYVM9fFVGjWNKKQu7RKoANJ1OAwAmJiaQyWSQSqXaUrGqDIL+vCpTrn6EVckC99GPTaffKwAp9CNws9vtMJvN8Pv9WF9fl99VmeLOqNfrUrTHeUT9sk6nw9ramnzAO7Mb5XIZ169fx5EjRzAyMoJMJoN0Oi3tjwOBAFwuF3w+H0wmE7LZbJvnM4+lAgKC3c451Gw2YTab2zyQ1fPhHCHg7Fx4qeDZaDQiGAzi2rVrACD6bTWbQDs96iEB9NTK22w28elVAbHKyNNBIZvNwuVytR1Lr9cjk8nAYrGIPKfXIp77NJvNbQBUnR8Eb1zAUdrC+cFslsViEXDVTxqwU1tdvouazSbW19dF960+Z9VqFSMj20W9fO91jrP63isWiwOPR3s9ZtscDkdXto0FfYOCTWLK5TL8fn9PjTcLRHfSy87OzuLUqVO4du1am09xMBjEgQMHpOnGIFKFDi6DGPfdsPv3O3YzR1QLzV7Bxa76vuxlx0cv687QORxwf+LjcH/i42hks8g98wxyX7xzgNxIJJD+7GeR/uxnobXbEXr6acx+8AOw/PA7sblexeLrcSy+HkchvTsHjXqlgSvPbwDPA+MHn8S/fd8PwPreJl7c2O6s9/WNr6NYL+64nwPGA3C5XDu2Cr8f8RAUvw1izPtgpJI0Go3YF1UqlbaXNa2bwuEwnE4notGoFJOpWtGhoSHs378fdrsdqVSqrw0SGzRks9k2bSZDq9XC2vTj5c+s9wXE1mIURy/+J1jL2yv0lw5o8F8+rIXGbMZ3NN+Hg9qDsGqt8oEOBoNIJpOSBuO1FYtFjI+PY2hoSJgVFrAxCC4tFgv27duHer0u3bNUZobtV+PxuDCsKjDiWFitVoRCIayvr6NQKHS1aDUajRgZGYHL5RImqbO1LIE4FyadXdYMBgNsNpvoXdlMQu0exwYYTGeHQiHJBHAbtu/lvhwOR5uTgTpGXHAZjUak02lh0DrP2+PxCBvcTy/MdHWnvILjSNCipog7gywQZRLcn8oqU9+5uroKi8Ui1n88DsFVKpXC8PAwSqWSuBWo18V7ViwWpUudyjoS4FInS31ir3P2+/1oNpvC5qkMMAE3G10QmLRarTaNNlv9ms1mufcE0yoTqtfrZV8sbOwsIuSil364ZMV4LACSQuf8AtCWSSKo5cKRVniqg02xWJSFJwAp7lVBerVahc1mg8fjkbm/ubkphWtGoxGBQKCty9qgFsZ8pyWTSSSTybZFHXX2Bw5st/ZNJpNYWlpqc5+o1+vweDzYv38/dDrdjsfz+XxYW1sbmG2jHGhQVCoVeDweGUcudilxslqtcDgcSCQSu2Iv7XY7RkZGZDHJjnadBX/9SBUy7lzk9mPc78Rh434Ev3s7zZHl5eWBbDLvYy6X69KT8/25G7ZU53TC/fGPw/3xbYCcP30a2b/9CgovvICWYvG4UzTzeWT/5m+Q/Zu/gcZohO0d78CxD7wf7/gn70Uqb8DChRjmXosiG+9+B/WKlatJrFxNwhOy4sRHnsDHnvoY6qjjbPQsnl97HqdXTmO9sN71e2atGYfdh9tahb+V8RAUP4xvKsjmqH9Xg8BRZT4bjUYXwFXTqZ3gmAxpZ4oVAExlL/TJCTT6kIOO3AqOXfyPMNbyKJi2tcMvHdTgcPMIPqD/AOyGbT/QTCYjrEgulxMGRk3/01JpZGREimpUmQPPt9FowGw2Y9++fbBarXjttdeQSqUENHg8Hpw8eRJjY2OYm5sTa6JOnScbPASDQZhMpq72syaTCVNTU9IKmmnvXuw2K/FV5w4G5QGt1rYLgNolUHXGMBqNMk70mSXLQcBF6YTP50MwGBRtbOcYEdDQxYLMvHr9Xq8XQ0NDAl4ymUzXB8TlcmF9fV3AOrWaKsPJVLVOp2v7d/V8OM9ULWynXpjMLReD1Wq17cNIeyuv1yv+r1tbW13tu4eGhqSYkeBVtZNjI4ZQKCRuEr2i1douvlJbN3deO/2gCarGxsak6yGvzW63C0il1y4BC8+JIIWd6nQ6Xd/FFe3VyJ6SLSVTTpkO51mnTpeaWovFgvHxcaRSKWSz2a6MlNPpRDAYRDqdlsYranGo0WgUIE03lK2trbb3DG3dDh8+vGMtBRcpPp9PWoWrTLHP5xNgqG7DMTSZTG3bqMfrBdRtNhvm5+d31PB6PJ7eL783glZ11Msnk8k2vfDk5CQAtGXjOqPTSq1cLksTknq9jmQyiWKxuCs7LY/HI5InSo/UzEClUkEwGNzxut6K4D3b2NjoakoTDofbmsD0Y5P9fj9KpZLY8XU2JIrH49i7d+8dsaU6pxOuj30Mro99DI18AYXnn0P2b7+C/HPP7apIT86xWkX+2WeRf/ZZQKuF5dHjmP3AB3D8J9+PRNmOay9HMH92C7XKzhKLVKSIr/7+Nbz614s4/qEJnHzyFJ4MP4lfOvlLuLJ5BV+6+SW8EnsFc8U5NNDAcfdx7N+7/4GwYwMeguKHcQfRat22L5qdne1pX7SxsYHFxUXplNVZaFUoFHDhwgXxF+5ng0QWkUBKTcWHMlUUU3vRQm+G2J2+iaOX/l/oG2VcntDgP36nFjrPEH5E/x2Y1E8KsNFoNHC73cJUzM/Po1Qqwel0dqVIS6WSSCcIYtUPiapPLZfLyOfz8Hq9Aqj0er20tq1WqwiFQqjVaj1T2mRgarWapKI70+PZbBZutxszMzNiG6UW3xQKBUxMTGB4eBgXL15Eq9WSl7AqZaDWM5FISAV9Z4V9JpMBAOlCR3CssqD8GPDf2Pmuky3V6/UYHh4W0ER2W51jlAdQA83t1VQjnTmYjmW7Z/Ujq3oXk43vdLrgzzUajYAG9d9p/QZALKT62VulUilpGnHs2DFpnECbsGw2i2KxiAMHDqBSqYh2UnVWsNvtmJmZwRe/+MU2TbcKeBuNBqLRKI4dO9ZmV8fg71FTq9frYTQaMTEx0dUKnHZ8JpNJWHlVf82UPe8FgbE6jhxfm80mKXIei9dvsVhEduLxeKTwSwUAXDD7fD6Mjo4KM22329vkA06nE8PDw1hcXJQOa51ziBIFi8WCtbW1LhBSLBaxtrYm7ZIHpf353jObzTh58qQsCrgY4HuP867fNp2WU2rtgPp3rVa7Kw1vpy63M5iyJ7tN9yAuUpLJpDyzgxhOrVaLRCKx6251/UK9Lto7qjUpTqdzV9f1Vka/e7cbNplzVpUfcV/82d2Ezm4TDXKzVELhpZeQ+8pXkHvmNJo9smR9o9lE6cxZlM6cxda//b9h2rcPR9//fpz80fdio+zDtZc2sTGX3nE32XgZz33mBl77m0Uc/+A4Dr17BIfDh3Fo+BBKpRJSxRTOJs4i7Ao/MIAYeAiKH8YdxG6sadbX17G2tiZFJCqYoQfq+vq6aEIHFSWQZSKT52vGsT+bwMXU3+0LiP3xizh09ffQQg1/+D4tvvS4AR8b/gSOVY6hUd0uGOv8YDHVT5aIgFhl81qtlthAERSoYIXgr1wu49atW9jc3BQWU10UrK2tiTabDAEZXjJhLpcL4+PjuHLlijCQnfKBZDKJVCqFarUqDR3oJ8qPYTweF8cB1SZMZdRo8UXGVrUBA267HADbbAnBfi6XE4bL4XAgmUyKrZPVasWhQ4ewuroqLJ9Op4PX68XY2Bjsdru0OSaYVRcgbBTi8/mkbS4ZUYI9ts21Wq1i/K8CNRZ0Wq1WcVYgeOwEkAQE6XRavJpVIJ/L5QQMcGxUbbYqU2GjCPWDp/4XAILBICwWC65evYr19XW5rpGRERw8eFAkLFwkqdfFfZVKJdHks/Wzel+p1SeTGYlERC/Nuc/55/F44HA4YDabkUwmEY1G5d4Gg0F4vV6Uy2Vhv3kOarEppTIs1KMUgwCbMqp6vS6tp9mOmfsxmUzweDwYHR2VRVmv9wOPz25wbIutSl7S6TRyuRw2NjYEEPZ67tV2yc1mU9h0Zj+YZdjpvacWUXE8mBGg967a0e3WrVviDEINcSKRENZ1eHgYp06dwvXr12XBqtfrEQqFsH//ftHwDgoWrm1tbcFisbTNzVqthq2tLdhsNoTDYaRSqYFWalyw7SSxsFq35Wi8t7ynnCed10Ud7vDwcNd19dOBqzHoWHcag45Hu9LOe0YZXGdL8V4ZAEpXxsfHpZthuVyWIm673S62jnfrvqG1WOD4wAfg+MAH0KpWUXj1tW2A/NWvopFI3NG+KjduoHLjBvCf/hP04WE8+b73o/bd78Fc3I2bZ2Jo1Aa7WBQzVbz0uVs4/7crOPEdkzj4zjf050YHPjD2gQdCR6zGQ1D8MHYd/PDUajXEYrGuFqXUE7LhBjtzMUwmk7CwhUIBLper737y+fxt/Wg1i3c1XoSjYMczmZ9Dq08HoGDkVRy48YeIuZr49x/ToTk+jV8Z/TGcmj2FmzdvIhaL4datW23peuozTSaTAHG6NahpXdWnlh8Vhspu1mo1rKysoNlsSgMGfvSZOl5fX8djjz0mVlmqpVOxWMTQ0JA4AhgMBkSj0a6CLKvVimq1itdffx3Ly8tSJMUgiCSTSW20CvZrtZrsR9URd7ZUJjCmDvXixYtdTSCGhoag1+vFcsxmswkg5XVYrdYucJJOp9sAZrlchsPhEP318vIyXn/99S7gPDU1hYmJCZhMJjmXTpkNNaq9LNvU/y+Xy5JuJ7jr1NRSi0tGuTOsVitarZaA68XFxa45NDQ0BL/fL/c5mUyKJ3K1WpVUNMcQQNs+VBDearWQTCZlwdh5P7i/RqOB/fv3Y21tDZcuXeqa+8FgEPv27UM8HkckEsHCwkKbVCWVSmF6ehpOp1OK7nrpnFUpFTMxqs6Z8hy73Q6Xy4VarSbgn9dKsDo0NCTSCW5HoKLT6ZDNZrGysiJjEYlEuth0grNsNguj0YibN292PYs+37YXOX2R+7XLDgQCaDQaArJZcEgwEw6HRU4Sj8extLTUtc3k5KS8Q6LRKDKZjIBw9d1XrVaFdR0eHkYwGPymQR9bj7OgsqSk0zlObOzBRVY/jfPKysqOEot6vY7NzU0BvBxHn8/XBnh3c127aQe8m2PtNgYdj17Vu7lnQP8MAL+fg+z4SCLcy9AYjbC/652wv+udCP3KP0fpwgXkvvJV5L76VdTW1u5oX/WNTaT+6I+AP/ojjDidmHz3B7A5+zTm1i0oZAYX/BWzVbzwpzfx2hfnMXzMAM+UBnqD/mGb54fx9g0ykLFYTKrk1RalLByjO0Wr1WqzZSIDyO5h6+vrA/ejQxMnaq/iHbXnsVx8Al/L/Cyg6f1BGF17FrO3Podv7Ad+/ztseNrzHThhPIGgMdim/WQ6UC2CSafT8Pv90rEOQJedFJlHagV7BT+MfLG2Wq02J4dCoSDANB6PizbNbDa3FVvF43F4vV45bmfFP2UPGo0G6+vrbV3B1Mjn85ibm4Pf75dz7vygcjyA2xpjVcvN42i1WsTjcUSj0baCKALHaDQKp9OJvXv3olAoSGqbBUGtVgtbW1tt2joCQlXSwTmg1+sRiUSwsrIinq2qRd7KygoCgYDMxV5BFonAsleoOuOJiQnE4/Eu3a3f75cPWK+iT957ymzotaoyzuVyGZFIBC6XC6urq3jllVdEqsPU+NbWFp577jlMTk4Kq8qxB9B2/7VarQCZZrPZ1uCCDBSBSrFYlGyIOg7UpzYaDaRSKVy+fFl08RzrXC6Hy5cvY3Z2FplMpm3BpAbbQdMtgxX2PG8+g2TlY7GYpOP5XBUKBcRiMUxPT2Nzc7OtyYP6DOfzedE+k9lTGfV6fbtrJHWqsVhM7Mc4Rly8e71erK6u4uLFi133IxqN4rnnnsMTTzyBQqGAlZUV0enyfNhVkBIfFlsx48Bzvnz5Mvbs2YN6vY5oNCqFlp3vPq1Wi2g0Kg0sqNP/ZoKLDvpoq3MIgFhHAhioqdbpdFhfXx8osdDptrvVXbx4USQtHMdIJIJsNotTp04JWB10XWpL5X4Wcfl8Hq+++uqujrVT7HS8kZGRXd0zt9stMja1FTQXuyMjIzva8b3ZtmQanQ7WEydgPXECQ//0l1C5cUMAcuXGjTvaVzObReVv/hxe/DlOGU1IPfEDWLA/hmxx8PmXsw0svtBA9LIOE6cMqNXa2fa3Oh6C4oex66BOM5/PIxAIyMeagCUWi8HtdsuKmIBQDYI8soT99rMHi3hX9r/C24zjSv69eDb3M30B8fjKVzC2/Hn87ke0SL37Mfxy4JPwW/yw2WzIZDJtUoOhoaG21K9Gs21NVCwW5dwIUoHbBX9qW16gG1wCEJBND2GHw9EGYqxWq1SCz8/PD2wJu7y8LF3LXC6X7J/H5X5UQNypOwa2P4xkJfgRUvfDynMCCvrnEmjSbYNAXrU24zjwRb+6ugqj0YitrS2k02kpvlLPix62+XwejUajjV0hs1IoFJDJZDA3N4dqtYrh4eG2gkyNRiMfYOqgO+8Jz7+T1ey1DcfD7XbD7/dL6p/OGwQ/1Gr3sm1LJBKw2+2IxWKo1+tSza/ej3w+j9XVVQFz1NwD28+W0WhEPB4XmznVnUKV4NCqjQssNS1OJp1AuNlsSrt0ajgZnKuvvPKKsLGqfIQa2VKphLW1NSn+46JJPSeCP6fTKVrxzueMoCIWi4mVnWq1FwgEBFhyrvVr8kCZQ61Wg9PpFIkGCysJ0OnFzcp+zgHey2w2i+vXr6NUKsHn88nzStaRrauZaVHvmcFggNPpRDweRyaTkeyMy+VqK1LV6XTIZDKIRCIAgHQ6LbIPznmDwQC9Xi+t0vstvu4kuBgD0LO5DxcXrVZrR031TpZkXq9XFsLqO53zmm2ug8HgQKab+u2dmmCQDLibY+32eOvr67L47HfPuN2g/fD5oJa71zj6fL77JifQaDQw798P8/79CHz6Z1BdW0P+a19D7itfRfHcOaAP2dBzX9UKvM//ITz4I8T8x7Cy/+PI6gMDf6eYauDal7NwBAwYOlqA1bqBvXv33rW2+m7jISh+GLsOeuba7XYUCoWuBhd2u10AINPuZLqoLeRLql6v99yPLjWP923+CYLZ1wEAL6Q+hIvln+wLiCeWvwxz5i/xb37Mhcf3/QDej72opqqIZqOwWq2S+uKHUZUUkAmle0Gr1WrzjlWZOaaOGf3YSQDS/U5NDQOQD2WtVkMqlZIXeueLmx8g/m6/tH8na9dLv8qxJiDvbLhitVrhcrmEXSTI5ceTQIsfUQBt16RGqVTCwsIC8vn8wM5eBPu87k6gSm0qP8KdCysAYvvHINBR/97rHvVjjHU6nRS+qWwFP1Zmsxkmk0mYRzXbQGCq0+mkaK1T6sCsQbFYRCQSgdfr7brvWu12K+R0Oi2aX7VYkeCTBam0XutkgFVm9tatW9ja2pJiN3WMuI/NzU0BRhx79X6pspheTJY6DswgkEHjM0R5kU6nQzweh8Ph6AJ+Wq0WDocD8Xgc+XxezqdzG7WtMhdalAVxzDlnCHB7ZVuY+aJsgK4R3A8lP4lEQhw4+P5Q5TX8XfpQ9zoWPccjkUib9lwNymzo3HK3oWYqALQ56nSy+Pz/XlrW3RSRWSwWJJNJuFyunveM77SdGo7sRr+9ubmJra2tuz7Wbo9H0qSfbR27FTLjOEh3PTExId0l+xXjvVWg0Dg6Cu+nPgXvpz6FejKJ/OnTyH3tGRReegmtHTyxGRq0MBS/gMCLF5D0HsTSxEeQcc0M/J1crIbiCxq4whmUxh62eX4Yb6PgB5oNDFTtodPpbLPJosm9+nLX6XRS0NJqtTA6OopMJoNMJgNNJYND8b/GdPxr0LYaaAL4bPwjSNR+fAAg/hJihr/GmX/4BP6O6zthaBhgMBvaUls8h2aziUAgIB2TeC12ux1WqxWbm5uiNWSaV/04EhjsJpj67nRfIGtDwDMIONbrddHLsqMag2x2v1R2r9i3b5/oIekg4PF4EA6HxWGhVqt1fZDJGLEpharbVD/6/MhS3+fxeISlUwvA+JEhcOmUa6jtmzlGBFUqy8UW1RxvoLtdtgoIGL1AsVa7bQPHseacoZa41WohFApJep2NIng8MoZk/Pn/nTpfk8kkEp5B955ZFrPZ3GV/Rk9o6nR53Z2gmOCQbidOp1PulTqOPAYA0U1zwch71Gq12jyeVVcP3hv1uByzXvOIGm+N5nYDHga1/HxOOK86773qJMJxVh0xCLa5yOVz1FkcabPZUC6XZZ4SzKr6ds5Ro9EoHt1qx0da421sbKDZbLbJntQCQjYuog80F86dbCGLszguuyk26xecl+l0uqcvMBngTh/kXrGTbR2lUDu1ud6p4Qif+0H65UqlIuN4N8fa7fH430H3TN2u33743t6N/d9u427mx6DQe71wf9/3wf1934dmsYjC17+O3Fe/hvzp02i84UY0KDQAfMmr8CavIuk9iPmp70beMd53+4kTNmh0zXuup/5m4iEofhi7Dha6DLLJ4geAxV2dxS38QFGK0WrWMRk/jQObn4Opsc1Eruj0+OvlD6Kl+wcDAPEXcG7iGRz9yX+Nj25sA3C7+/Zqn0A2nU5LypdNE1QwTzBBjReZMH48+F8yfXxoyf4xVBBGGy1VSkHGlQsGfhj7tYRl2hWA7EsF6QaDQZwadgq73Q6Hw4HDhw93SQMIuMhwderAyfBT/6YCJV4/X/jq76oFfSrjTXaTL3CCEupn2UiCWkcCtk4Gj/eY6V/eA4Z6fup97rxnPP+pqSlcu3YNt27d6gKzU1NTmJqawpkzZ6DVarFnzx7R7NKXl4BHp9OhVqt1fZgI1njtg+49WV2Cus6ukGzkUq1W5Vw7r4tzjbpUZi3UecTnmfeTc7Uzu8HzoBsMAa8qZ+FCiduFQqGejhCqvl6v13c1iuH9tNlsosfsDM5d7s9sNndJkGjzSGs/suzqfeUCpFqtig945/io7xNgW4bQKZ1hsRjBcK+FGBtu0Bs6lUr1bSdPnfVuis0GBTvOaTQaWRyp4fF4MDQ0tCtQDAzuVkdguFOb650ajnBODtIvm0wmKSa9m2Pt9nhGoxFerxeFQqHvPaMWfSfdNZ/JfuN4J3G382O3obVabztZ1Osonj2H/DNfQ+5rz+xYqHcbHF9DLPAIFia/C0VbqG0bg66O4dESajrrm6qn3m289WfwMN420dnuUn3pMM08Pj6ORCKB9fV1WCwWsSgCtoFNOp3GyMgI/H4/Mhe/iEej/wPu0goAbLPDFjty598FOP9+X0A8vP43OP3IJczs+Qd4x8g78drGawPPm6wqO02phW35fB7pdBozMzMwm82IRrdlFyobxrT30NAQNjY22uyyVM0oizD4wVa1t8DtNs4WiwWBQADJZLKvOT+rs3O5HILBYJdmMpFIwOfzSRvmQbF//37k83mx3uq8Zz6fT6yryJiq4IEAVf14dOplCbTHxsawsrIibDCvnw4LzWYTHo9H0tJ0AFABTTKZxNDQEBqN7U5kdO1QGfdSqYShoSEpqupcpDDIThaLRQFvanBBwCYPnW2Ta7UalpeXcfPmzbb7qKb4eFy73Q673Y5kMikfbhac0aLL5/PB5XIhHo/3vPfZbBaBQABOpxPz8/MCNlVmtlwuY2xsTGyd1PPg9VGvPT09jfPnzwuA5IKPQK3RaEhRUCaTEV9j9fpZkFapVCRr0TmOdBzx+/3CtnMM6Fih0+mknXQmk2kD+LwudmFjK/TOIkNgW8YVDodFo0wNpqqVJwhxOByIxWKwWCxtc5/volAohGQyKZZn6rVzseZyuWS7QCDQBiL5vIbDYeh0up7zlV7gIyMjmJycbAPo1IVTOqLVamVez8/PDyw22wn4WCwW2ZfL5RI2V6/f7pqo0213GL0TDWs/iQXv2d02HLmTttvRaPSujrXb41ESwWxAr3sWDofRarV2rRfuN467jd0UI74ZRWsavR62x0/B9vgpDP2zf4bKzTkByOXLl/v/HloYip2HP/46okMnsTj5UZQtfgDA5I3PQ/frC7D+h996IOzZHoLih9EVrNrvtMvZjbYsHA4jGo2Kpyk/6mRkDAYDprwGTJ39P+FZf0aOuaHX4ddNPrzz2WOojfx9tLR9GnPEv4ALT5QQNHwAxpZRtMJkQzo9ZtnGttNflSBKZROPHz+OF154AYVCQVhjvgRtNhuOHj2KSqWCzc1NSbkRYBCw0gyfYEhdOKj2XtPT0+L92qnztdlsmJmZEX2uCkTIzrPrXSwWays26xovt1uqp/vdMy4CqJlTX0xk2ygLyGQykk7uLPzyer2w2WxwOBzY2NgQhlGVWWg02w1Tjhw5ghdeeEEK1NRKfavVihMnTmB9fR1bW1uSBuU8YsfBsbExGI1GXL9+ve/1j4yMtHnGds4PAqcLFy4IeOgE/Pl8HufPn8fBgwcHzjOysnRp6Jz7Op0Ok5OTmJ6exvPPP49YLNZ1P6xWKx599FEk3vASJVOuAl6OMTMJzWaz63xUmQlbqqsyF/4etcThcBgXLlwQO0WeE3Xmk5OTqFaruHTpkswJXhsZ8PHxcXi9XhgMBklfq2Pp9/thsVhkLNTFEDMKZH7tdrtkHVQHF95H2vuVSiXxjuV+6HDDttJsBMQFpfou8vv90ia6M43O+Wo0GuV57ddM48CBA9Dr9TJfucBT5yudHPgOpRxHBeEWiwWhUAiRSOSum2Wo7+tSqdQ2t3ksVcN6N6l4rfbeNBzZ7TfG6XQil8vd1bHu5HgABt4zOl3cD71wq7VzceBu5sfdhkajgXnfXpj37YX/p34KtUgEuWeeQf5rz6Dw6qtAj2JRbauJ4egrCG6dwcbwO7AZegLhjRfQ/MTHHrZ5fhgPZuzk/biTtozejXv27MHS0hLy+fxt3bHViPeaLmLPK/8auuYb3rEA/sJuw59V3PjUF/ZhcfbH0dL2npamwmksPmKDxzoiPqTlclkqy6PRaJuHpMPhkHQnu8gxbalKOth61+v14vDhw3jttdeQTCbb2IDDhw9jZGQEN2/ehEajkXbQqraQfscA4Pf7pf2veiy2X+axzp8/j2g0KmPNn/t8PkSjUfj9fty6dautzbPVakU4HIbJZEIoFIJWq+3JGBMQm0ymHfWAGo0GgUCgr2YymUzC7/e36fXU62KXMUopKKcBIGl3Ms+NRgPhcBgnT57EmTNn2lphu91uPPbYY9JQYO/evVhcXGy7ZzabDVNTU7LA8Pv9sg8GFygES+Pj42IDpo4j732hUBArss6g7rZYLMLtdmNra6trnrEg0OPx4NChQ1haWpKx5ByanJwUHfdO1768vNyWvlc/FjqdThwKzGazSAU65TX1eh2RSESeSd4zBvWuBJ5HjhyRbpQ8b6fTiampKXEJOXDgABYXFwU8aTQauR9DQ0MAIKAlm83KvHY6nVJYq9frZd6p8iqCfV7L/v37sbW1JY1iOL7MIqitrjv3wy54drsdBw8exOrqalstgcvlwtjYmCySCY5VX2jun/8/qJmGy+WSpjVra2ttPsVut1ss1kqlUtc7lIszv98v79Cdir/UZhmDYqdjkU28F6n4e9FwpNc599LdOhyOe3Ks3R4PwK7G8V7qhfvFbooDdzs/7mUYQiF4f/AH4f3BH0Qjl0PhhReQ+9ozyD//fFdHPW2rgdGNFzCy8QI0AIY/8YkHwo4NeAiKv+2iHwsMbAPi3Xg/DtKWsYUnV+CtVgvNRgOj+Qs4uvmnsNXici4xnRb/yu9DZdmEn3p+GlcO/SSa2t76NkPrAsonh+DR3S6moa7WaDSKNMLr9cpHrdlsIhKJIBAIiO0Tm0qoVlFkSNbX17G5uSnm6nypaTQabG5uCgvs9XoxOjqKZDIp+l+v1ytFaixY63WscrksWtpsNotQKCQ+wkzfZrNZOBwOpFIpLC0tSUGMWiSztLSEsbExSUmrRVlsgkAmm+lSi8UiYM1ms2FyclJ0gGTIBmkmw+GwsGmdmlqTyYTR0VEpcKSNXKemHNj+ALMN9N69e7u0p2SMyZKGw+GuYisA0g6c18bxp26PxZG0GRseHkYqlZIx8ng8cn1kW9UMAuUWvF42XiBYVNP10WhUQNb09DQmJiZEamOxWCS9zg6G9Xods7OzXZ0Baf/GtsGjo6NdmuJsNoutrS3Rt1PDqJ4PGWPahLGdeWcTFBa+lctlTE1NYXJyEuvr63LeIyMj0Gg0iEQi0Gg02L9/P2ZnZ7GwsIBisQir1Yrp6WkB6haLBaVSCcFgUHS27ELI1uSUPYyPj3fNIzbtabVaCAQCCAQCXe8rANLNL5VKYXR0VApFVccILopCoRBGRka69qPRaLC8vCyLAo/H03U+nFN8nw0NDWFzc7Or6102m0Wj0cDo6GjfY6mNGXZ6h+5U/EV9OO/1IIZ30LH4PN6rVPzdNhzZ7Tnfy2Pt9nj3apu7jd0UB6rz460IncMhLadbtRqKZ84g97VnkHvma6hvbMp2GgD6cBjeE4++ZefaGQ9B8bdRDGKBg8Egrl+/vmvvx36aKJ1OJ8b4brcbttIa9t76r/CmX2/b7ks2K37N68b7XtHioxcmcOHYT6Op611NrNfPozijBZqN7T/Y/hAQjAFo05aqKVvqOglWacOk/jvZq9XVVWGIO8FDrVbD4uIi3G63rMK9Xm9XwQVbHrP7HdCuh+TLMZFISIekzo5+9Xq9rSOg6nsKQCrZ4/E4XC6XdBCjPIAfSUoc9Ho95ufn+3bsmpqaEj1gP81kKBTC1NQUNjc3kc/n29LVtVoNdrsdQ0NDUsnPBi2qkwH/lEolbG5uCovSqb9LpVJiy0VA17kNbbjIajMbQOZPLZRyu92IRCJtDgpcuDgcDvHCVdP66nymlSDbIJNxvD0/twuoeI9yuVxbYRMbc9jtdmi1WiQSCWkGQL9mWsI5HA7pRMbiUHW+8mPIBjdcLPQrtOM9YpGrw+Foux+s5NdqtVKESrlKpVJBNBqFzWYT4M0GLmRC6RgRDAYl1Xz16lWcO3euLeNgMpkwNjaGmZkZ0fGTnafjS6FQkKIlk8kk58PCWJ4b/a/37duHs2fPCqvOa43FYvB4PNKpjzp4v9/f9l6hmwC9qDsdGljEarPZYDabe7KpxWJRMiSq5r7XsTrt7Pq9Q3dT/MV97Zbh7XesNyMVr9V+8w1H1NiN7vZeHWu3x7tX29xN3Mn8eBBCYzDA9uSTsD35JKw/+2lsfv3rKD33PPTnzkG3vIzGo8fl3fcgxIMxag/jTY+dWOB9+/YhkUjcE+9HANA3Stiz8BcY2/gCtK3bICOj1eLXfB581WzFT36xieNLIzj/yD9EQ9enUti8juL4tiuFBrfBJXWNAKSVq2rFxKA1HAD5CHd2qyNDEo1GhZ3s7BAGAJFIBAcPHhQg0qvgwufztXmoMtT/bza3G2HwY9+rQxJTy4N8T2mlpkoZGNQyEsy/+uqrfTt2AdhRD7hv3z4pOvF4PD2LdnK5nIxDNpuVNL461rRSqlQqGBoaGpj+6+eHrAbZUOpnVea/2dw22y+Xy+Kx3Gklls/nEQwGpdGLaocFQEA+2xzTrk3dDwFVOp2Gy+XCysqKZDHU+5pMJjE+Po5oNIrNzU3U6+3d0dRubZxfdHtRx1CdD2Qx1eA5UVPLzETnB5TFbVarFQ6HA8vLy8K0cjHAxdfs7CzK5TKuXr0qIJvuDXwvHD16FPV6HfF4XArruJ9yuSwAlYWjvEZuy7kzPDyMUqmEubk5cWvhYi+dTiMej2Pv3r3w+Xzw+/0oFottDiYmkwl+vx8+nw+1Wm3Hoi0AWFtbk3eFuhim7Eqv1w9kU2dmZnYs2NptY4bdFH+xgczdFuM9qKn4h9E/djs/HoSiNTVyudz2fHW5YP/Bvwvdj/ww6pHItlb71q2HHe0exv2LZrPZxgKrxTFslXvz5s078n6sVqu4fPkystksnE4nDh8+vP2RrNcwk3sFU/O/D1Ot3c/wRYsZv+L3olzV4f/4Hw2MJ8M498jPoKHv/fCaA3kU/CloWhqRQzC1Sgu1SqUilfXhcLhnOjaVSsnqulKptLF8qqNDNpsVjSQBJhk1dlkjOKJRv1ooZLVa4fP5cPPmTQFonc4KBCzJZFJSa4lEQtJ/Pp8PpVJJupF5vV5paat+rB0OBxKJhIA92gGpFnkEY6+//row29wXmeJkMonz58/jE5/4BE6dOoWrV69Km2J6sx48eFA8qH0+n4ytmrIkgGLKmqyfanHFYiqy96ozg3reZPMJKOmzqt5XvvAjkQharVabfy3vG1OIXGhwbqtFa9VqFel0WtwcyO6qUhWdTofR0VHEYjEAkJbgqoQnn88jEomIhzDvM/Wx/NNoNLCxsYFarQaXyyWAmgwpOzCqc7Mz1DGkjRqLQnndHBNKNxYWFrC1tdVV+KhKOzgmqqcvcHtBx4I1FhOq1mWUfVDCMzExgWw2K2PkdDqRSCRw48YNPProo8IAa7XaNumQzWbD8PAwFhcXJfuj6qW5SAC2F/omkwmPPfZYl3SIHeToLUw9Oec1i/lGRkbaFvyqdKZarQrjHI1GJftFRw7qhdPpdNux6CozqKgN2F7Q9ZIz7ab4Sy3G63dOKsPbT2LxVqXi76ao70E+1v2I3cyPB6VojdE3IxEOw/BGdvB+FAfuJh6C4m+DSKVS0pWJ+ivVWJ6pQQC78n58/vnncebMmbbir2effRbv2efGsfU/xv6NM22/W9Ro8P943fhTpwOhZAu/9tkGnJUgzj3yadQN9p7nPH7EhfJQEq2MFbFYrEvyEAgEBBSwupvgiduxcIofcjaOUE3dySrxA0EHATVYIMbio1Ao1FOGMj09Lc0m9Hq9HEsda7/fL/Zq6XQaW1tbbZ29lpaWMDQ0JGlcsmDqdQG3i9cIXPhBZ2g0222mi8WiWMitrq522a3RQoxyhl5sEXBbx5bL5aQJiFpoFg6HBZCxyIpd0FRAw6I8Mstqepyab6brjUYj1tfXsbS01JWKn5ycFFmF0WgUZpjbEBA2m02USiUpyFILqejMUCwWEQqFMDs7i8XFxa5jTU1Nwel0YmtrS/TlnfuxWq3I5/MoFAoIhULi9qGOUSgUamue0lmsSYsyNjRpNpuyuFGPxZ8zy8B2x+p8pY6aRWQbGxuo1+tdsg+TyST676GhIWxtbXWd99DQkEhatFqtyEdUUMxMC2UMc3NzXXpyr9crzwwt53rJeair12g0XcVvTOsnEgmYTCaRvtAbnfZ1ZDhHR0dRq9Vw8eJF0TIzs3Py5ElhpthJr9fik3Z1Op0OkTeYrc75ymPx3cDx5rthamqqjQW7dOkSzpw5Iy2f1ULLI0eO7Kqgmee0ubnZJcFSGV4uxOLxuCwc/H4/wuHwW5KKv1/+uvf7WPczdlsc+KDE2ykj8RAUfxsEJQJkLlXdHLWwzWYTLpdLUsj9vB8vX76MF198UXSWWq0W5mYBT2e/jOOvXUTnGu91kxH/W8CHFYMB+9Za+Cefa0Df8uPc8Z9FzehEr/BOGXD8YyF89auXsLW1hWazKVXyLLLb2toS9wGNRiPMqyp7YDthWjfFYrEuVqReryMWi8Hn80nFfqcUgb6iBBmRSESAmcoGRSIRDA0NiRaToEVlHekowfNhqPrc9fV1hEIhWK1WRKNRKbDisdiEhFINssXAbf1yq9VCNpuV8eFCQJVhsNmAzWbD+vo6IpEIisWiFBTyfHO5HI4ePYpCoYCVlRVZPKgMYS6Xw/j4uCxaCFDV1D9Zc1p+7ZSuX11dxdzcnGQG1LGem5vDnj17hNHrZDW5HcEjFwNkiTiP8vk8dDqdAGc25lBZYDZJqVarUkTaS4LDDmn8XcpDyBpTcsLjAmjr5qfai6lOIPRaps7VbrfLPaP1mzqnee3q4tZisYimuZO5pza4VCpBr9e3nTfPlYw9Pat7jSNtzgjEOp9Fsprr6+tYXFwULbJqR3j58mU0m01xnQC6u4Tlcjm0WttdBmu1WpsUiUDV5XKh0Wjg8uXL+MY3vtGmRaae/Bvf+IYsnOgH3pmKzuVyWFhYkPFhBoPzNZvNyoc8nU4jGo32fTewic6lS5dw+vRpVKtVWK1WOadEIoHTp08DgADjQcV4xWJRGPJOqQ7nI/23Y7FY26I5kUggmUzi0KFD9zUVfz/9dd8qL9/7FfejqO9exduhOFDO5a0+gYfx5gf1ttRY8qFhKprp5dnZWdy8ebOvrnRmZgaf/exn5QE06LR4pHEB7248BwtKbcesAfhttwv/xe1EU6PBk9ea+Id/1URT78G54z+Lqsnd81x9UyaMvVODeDwmtlcE6UwJs5sRZREEsP1YTo1Gg1wuJwVF6oPJlr2FQgEOh0O8KDt9T1mwxZSwy+VCOp3uqq6nbzA1naoMg5pFeg8zqE/l8YDtgqbp6WkBL6pMgyyx2WxGJBJp2w+vl/tRO971YoIbjQaKxSI2NjZQLpf7FlkuLi4ikUigWCzC7/fLNpQ7xONxpFIp6e7GNHYnM12tVpHNZgV4qWy36vjQaDSwuLgorLsqIyDI3NjYAADRkqoFgmRFKVlhcwl1sUNZjLoYcrvd0oqcc4VFfbwWLo7UeUZJEn19XS6X6I91Op2wibx/6rzmvWdWg7IZMreqJRl/XqvVsLS0hGazKcCawXNllqJarWJ8fFyafagssN1ul3tCBrdTisA5T+aXTA6fR8qJWPDJsVa3sVgsIkFaXl6WZjjqQslmsyEWi+HGjRtIJpMoFovSIVNdXFDL7/V6sb6+3iYnabVayGQyUrhz69YtVKtVuN1uOR8udDKZDL7xjW8IGKYdnCp3ogyDz4pa9EmGnPMjkUhIepgMN11OKGcwm804c+YMqtVqWwEtZSGZTAZnzpwR3+N+RVsszCyXy7IAYEZMnWurq6tYX1+XBR6BYblcxvr6OqxWK6ampu5LKv5++uvez2O9lfFmF/Xdq1AzEvwOdEr9HpTiwLf+DB7Gmx4EN2RYOtmAWq0Gh8OBsbExuN3uvt6PtJjS6XSY0EbxwcqXEGxFu463YNDjlwM+XDWZgFYL3/ONJv7e6SbKJjfOPfJzKJt7F+p5x004+l0+NJo1rKysyIeZL3wGPyC1Wg2xWEzS4GQN1fSnyWQSho0fGTX1y3RrrVaD1+sVQKmmUSmpYIFQPp/HtWvX2nxP7XY7xsbGkMvlBCgDaEuPGwwG0duqxVK8D8DtlsT1eh2ZTAZjY2NIJpNt52Oz2eD1ekV3zFD/v1cQTKv3HoAUS1ECoQYBXiQSQa1Wg81mk+YdKgNOAByLxaSqn04HqtsBCwiLxWIbUFOLrex2O9bW1qSJCueoes9MJpPMRf6sU6rBv6tabs4B/p3gB4CwSJ1pf9XlgRIR9Xx4bdxns9kUKY86F/kcEmxzbjPIsLN4sV6v4+DBgz31srxP/Iioc5r7b7VaMtdoM9j5MWq1WpKNYaq587ztdrsw7gDamHng9uJTndO9tuGzx4LeXnInOnxQmtW56FXt8dhhz2QyIZ/Pt0ksWNSZzWZhMpmkEI/BrBIlCPQV7zwfvjc7s2edwee11WrhypUrXfKiYDCITCaDmzdvSoOaTr04pQ/pdBpLS0vYs2fPwOeZx02lUm1Fv7SBpNyDdRK9gPPa2hr27NnzLeev+3ZK1387BIsDe3UF5Pc1HA4/EMWBD0Hxt0FQV1iv15HNZttS3ywY8/v9aDabA70fr1y5Aksjhw9pX8LRendLxxaA/+Gw4//xulHWaqFttvBjf9vEh863UDE6cf7Yz6JsCfQ8R/eIEce+xwudXgNNUy8fZqbh1QIh1YKqUCi0AWKCKG5rNBrlg0kQp65GKQPhGFHHSEaI6XGbzYbp6Wlcv34dS0tLkkalO0QmkxHpgQo21bQ/QZia6u/1oeX10aFhaGgI+XxegBEbKbDr2W5DBSnq3zUazY5FlgQ5Q0NDXY4X7OZGjTCBTLFY7GIwaW/Hqv5+QG1+fr7tPhPkqfIQ/g6BamcRmch7zGYBBCowIutJ7TlT0up++LywiQPnpDqGakFcs9kUL2RqhNU0O9vqkjVRWVAWZFmtVrjdbskosH00gRczEEw58lpVcKp6PvNY9GTmPCJDzkUh3SHU8yYbOTQ0JAwPNc8MAi2OAa9FXYAwS0MpDxlrdXHFsadEgwxS50KG58aCQDpo8HoymYyAZ15f5/ygBrter0uxLo+png+dZyhhIXuqMq68n6lUSrzLVVcRFiAODw/L/VLHTw3a8NEFp19wH2rBIs87m82KLp8ZsH6Av1AoiAPLt5K/7jdzrG+1grwHKZiJ4XfS5XKJu0s0GhWHowdhvB+C4m+DYBGLxWKRdCxZNnaIYmEO0Mf7sVHHdOzLeBd+H+ZmtesYWzodfsXvxUvW7ZWeqdrCP/p8EyfmW6gaHDh/7OdQsgZ7np8zZMAjH/NBZ9gGUJQeEBCRQQMgjCuZMXaz02g0iEajXewtm1EQWJM1ZBgMBgE2fr8fMzMzwpTT13h4eBj79++H3W7HM888g0qlIsViANrcK+hzTDaTL12NZrsrFsEBf6bKB9SfAbfBqNlshtPZrr/utNjqtR/179SNd26jMo6DiiwJVjUaTd8GH3QPYCtfSlFU8OzxeOSDTICjtsIGIAsOzoVOFlh1miB7SJCk3lfOC4Jagl6VoWCTlmq1KvdbHR86YTAD0XnfOkE6wYzBYGgDIbxXdBpwuVxdHd2oOeUYh0KhvuxdOp0WINR57VwoaLVauN1ulEolLC4uiv5cfT6cTidCoRCuXbsGACId4SKSmYFsNiv75QKUQdDKZwm4LXFRr1+VxpTL5b5SrlarJa2ddTpdGytPQMyf2+12ATME52xnzIU13yeqnIr/Dmy/70qlUluaneeTSqWkEyUX2L0yG/y4d6brDQYDXC4XUqkUYrGYFMnR/acz+HPKT/qFTqdrk2ioP6cMh9c/CPDvxjf5XsX9LOq703T9t2pB3oMS1Oe7XC4pAKdDEXsf5HI5hEKhtxwYPwTF3wbB1MWgdKzL5eqfulh6CfjCP8b01tWe//xVqwX/yu9F+o2XvKvQwj/9bAN7IkDVYMf5R34WRVuo5+86AgYc/4QfetPtTlz5fB6BQEA+HvzgqrpagtxAIIBoNCpFW51FQltbWxgaGhL7NlXryIePaXrqNfsx5WtrayiXy10aTuC2QwWZNh6L8g0yxCz6UG3xCNTV4iej0YjJyUmk0+mBhY9MfVMyoO5HBZGqhEHdDzWpoVBoxyJLptoDgXa2nzrOUCiEyclJXLt2DSsrK9DpdG2WUJVKBZFIBLOzs9Jdrl9xz8TEBG7evCmgUQ2ybUajUT54BoOh7UNLXa3FYoHP50M+n8fY2FjXx5EFRvH4dqdFMv2qvpktlQn4CLDU+6o2ESGLR0YfgBTYVatVDA0NIZvNYmxsrKtbXTweRygUkjk3iL3T6XTiiMJFJFn/SqUijHO1WpUudU6nE1arVRqK8EPFRRblIur181kymUyimVYL7ajDZdqezV3UMeLCyW63i29yPykX2fJ0Oi0yB/XesygYgLCxanAxw3tMuY8KQtX54XK5pFV0Zy0B5zC11sPDw9JWmzKudDotC+BBDHC1WkUgEIDb7UYikZCFm3ptxWIRPp8Pk5OTPfejjhVZbM5ZjrVGo0EqlZKFSydQVwE/izbvR9xPf907Sdd/qxfkPQhBOQsL2jvfw7Va7YGRszwExW+T2E1qp982Gk27r6Gqf0ylUrBarb2LKbKbwFf+OXDpz3qeU0Gjwb/1efB5x+2P/3CihV/+bAOhNFDTW3Hh2KdRsIV7/r7Vq8Pke7Uo1wqo1NtTyA6HQz4e/BCr8gm9Xi8FJrx2VdPJa6SEwu/3i7sCgR8ZQoJrfnzJ6rJ4h+NSKpWErSKrqKZ1CRroE5rP59tS5fzojo+Po1gs4ubNmwLoCCDIJO/btw/Hjh3Da6+9hmg02uZiwZf31NRUWxc+nrtajOd2uwW0qh9NjiMAuFwu7N+/H1evXpXOa2Rn8/k8bDYbDhw4AGDb+m1hYaFrP16vF/v3728D1KretlPPzAYN/Yp7hoaGhOVUpRCqfIGNZsjikslUt2Fa7tq1a6JlZdoukUjAarWKbttmswkrTmadqXAuhnivCWj4d1Xn3s/ajgBobGwMi4uLiMVibWl2/r1zHHtFrxS8Ks0BIOfDZiJkNOlYEQgEUKlUsLa2JvOa7w71HcF5TVafIFSV35hMJlitVknJs4CR94N/93q9sFqtSCaTovflXKPTRigUgsvlEnmEqrvmPZ2YmMD6+rq4zqjPIlsz22w2jIyMYHV1VYr21GMZDAYMDw9L9kL1Yed1sVkNrRTT6bS0VacjB63byIZ3dsZTZV3NZhOPPfYYTp8+LW2xef/5fnjssceEwaQcp3OBzoyEqvFnASgX5FzwskiyF+C3Wq33jZnr/A7tVNR3N3KG3abrAXzLF+Q9CLIQVc7C94UaD90nHsYdxW5SOzttw3a2/bxB21bCjRrwym8Dz/5boJrveU4XTEb8csCHNeXDPLvWwj/9XAPOElDT23Dh2M8gbx/t+fvOIRPe9SPjWFq9haWlzS5PT6PRCKfTiXK5jGw229WYwWazwel0CpD2eDxYXV3tKnAZGxtDo9HA6OgoGo0GYrFYm7+y0WhEIBDA5OQkDAaD+PD28vRkq1w+2Kp/qvrRI9u8urralq52uVxSzDgxMYFWq4WlpaWu/UxOTuKDH/wgHA4HpqencebMGWxtbbX5mU5PTyMcDiMUCsFgMIjdHINAn1pUrVYrDgNqMZrT6YTH44HX68Xhw4dx/vx5RCKRtvlx+PBhDA8PS8c6jiFBNovq7Ha7fMDZDKOz65/dbpcGKoOKewhanE6nvNBVlwmCCdXPudM71+l0wmazwefz4dSpU30LSMlk8tw69dImk0mYaYfD0eV3zFbBtD1kpqBzjDhHvF4vbDYbzp8/j2g02nOsd3qmaemmdnRT9aJkWzY3N5FIJODz+QTQdrK39B8myO6UKwCQlPfQ0BCSyWRPD2J13FOplGiDAcjzTGBnNpuxtrbW5tNLv2v1+eD4qPc1GAxienpa5ql6virDTemTw+HA4uKiWNxxLk5NTQnj7Ha7xV+b98PpdAoYcrvdcDgcfecrpUeU26gFtlxgc4Fx5MgRlEolvPbaaz29k48cOSL3rpcfOucsnwN6tavHoxSFmYFBgL9z0fFmxm79de9WzrDbdD0b5nyrFuQ9KLKQt8IP+5uNt/4MHsbA2E1qB8CO2+TzeVy+fFkKnFQm8PLly8IWY/F54Av/BIhd73k+NQD/r9uF333Dao1x8mYTP/c/mzDWtyUTF459ui8g1lkbeMcPjSKdi/f19JyamhL/VDIcjGazKQyizWaT1XytVoPb7ZZro+0QmSdq9cioEKzwg1yv13HlypW+np4HDhyA1+sVr2G1o51Go0E8HkcwGMTY2BjS6TQef/zxnrZtLpcLgUAATz/9NPx+PxYWFmSb6elpHDp0SD7ACwsLwi6rMoSFhQX4fD4BqUzril3eGykqh8MhVlksqOI5U3LAFHM2m8Xw8DBGRkbapCrZbBbZbBaLi4s9dV/8AC0sLEhBp9/vh9fr7SoQbLVa0rTC5/P1lQfQd5Y6207JD7MAtPJyOp1dellKEggm+sliqPMtl8s99dJs30zwPTIy0iV7SKVSMoey2WybbhyAACWfzweNRiNjHQ6H2zIb2WxW/HkHPdO0l+O86jwfFtRxTpDF7GSXyTASEPr9/q5UfDweh8fjgcFgQDablQW2CkRLpZIsKkqlUhswV59ZMvGbm5twuVzwer2yHzKxQ0NDCAQCIn2howkXsMPDwzK+nAf8f/W/BJparRYTExPS1c5sNiMQCCCbzcLn86HVaiGZTGJ4eFgYYVUawZQ+QVKv+dpsNuHz+RCJRKRoWZUsqbKYXC4Hg8GAY8eOde2LC/N8Po9XX31VWE7WF0QiEWSzWRw5ckTmLIFtp5Uc5TNckPQD/PcbiOzkr3sv5Ay7Tdcz2/h28M+903iQZCFvp9bUD0HxAxyt1s5eixsbG5KyG7TN+vq6tHnmC5svo3g8joXXX0Loxb+B5sqf9z2fZf221dolc3vq48Nnm/jRv21Cizc0xMd+FgX7SM99aEw1+E+WUKhk5JxVjR4N51dWViTNq9Vqu5o38L/ValU+dp0PG3Vz8XhcGjAQnKlpVLY+XlhY2NHT85FHHsHzzz+PeDwOp9MpH6tsNguLxYLjx49jaGhIGG6n0yn7YTMNMn2RSAQWiwWHDx9uA0aRSARWq1Vac6t+riwkop8r999sNqX7Fl1F9Hq9SFAAyIuRQZaNwEf1WKU8hJrzpaUlrK6uQqPRwOl0dr3U0uk01tbW4PV6odfrpVGFypbSkUDVifYr7iGAI5NGvSsXStQGulwulEolSScTLJXLZTidTni9XgGC/WQxVqsVo6OjuHnzJnK5nMgAGo2GHH9mZgblchmXLl0SRwoyryxCm5mZwerqKhKJhMg5VN0tFyKUBHm93p761M3NTdGf93umuSDkM6Cmxqkltdlscs93KqKcnp7GhQsXRMLBDo6FQgFWqxVHjx6VRio8FzUVT3DKLIzJZGprcMJFARvqMFPR+Zyp+nGCp9HR0S7wRPsztWW4Oq8JYghyyQY6HA6RE7H1MgAUi0WkUqm29uyUP+3Gp1er1WL//v3iVc7FNwtOKYvRaDTyTqfbjfoc9Xpfc06rvuHLy8sIh8OYn59HoVBoK14tFApotVqYnJyE1WpFIpHYEfDf7+j33O/mm7cbOcNu0/UA3hIG882WNNyrcbxXcafSmbcyHoLiBzhUr0UAbWwQvXlZINQJVIDb6Z/NzU1sbW3BYrEglUohn8/fTlnazDiSexb75j4PTbPSeQoAtq3W/txuw//t86KkvX0MTauFv/tsEx//xvaHkrZrRdtw7/0YqrAcjMFg3WYtjEajtAwmC0ipB5tJ+P3+vulBMifU5ZLhUlkuWhxtbW3B4XBIcRK3cbvdaDQaAkQ4ZhxLfnQJ+t7znvfg6aefxvnz55FMJpHL5SSle/z4cczMzADAwBSh3W7H3Nyc6Hw7W7TWajXMzc0JmO/XmjsajSIYDGLfvn3ivKGCYaYJqa/jtfM6rVarHJeex1evXu2SoAwNDSEejwuzNijNqNdve+zOzc2JLpfB65ydnYXH4xk49+kqwt8vlUpy3moLZBbJkYVkUBfscDikmGZQGnF6elrmSTabbTvW6Oio3NdyuYzV1dW28zGbzRgbG8PevXuxuLgo9nFqwRoBouqt269lMDsdDnqmWZDFDmqd8olKpSKFj6urq4hEIgOLKB955BEYDIa+rYenpqawvLwsjhGd8gkW+tVqNXFKUWUNlI+wec0gj+pqtbpjuppsvs/n65IrqPZsJpNpV62X6cCh+qMPDQ21bbPTHBoeHsbhw4dx7tw5yVwZDAb4fD6RxRSLxR3T9XxfMzuhBhfFiURCFmokBdT9jIyMyJwtFAp9tdD3SsN7r+Je+QvvNl1vt9vvO4N5PyQND6JP89ulNfVDUPwAB1e7g1qZ8sMzKP1DNrRQKAjgabVaGG8s4X1rX4KvGev5uwCQ0mrxL/1ePGNrf3B0jRZ+6m+aeOoKAbEL5x/5ORT72K7BWEU1PAfUNHA3bCgWiwK0+DErFovCrhLcWq1WOByOtqYcZPHS6bQA4kajgWQy2XVYVsQXi0WEQiFZnarp8Xq9jmg0Co1GI+4VaqgvkXw+j5mZGUxOTmJzcxOlUknYJLWSfFCKsFgsSvtkeveqLVqZpifA7fRzpZ0eWcLx8XEEAoGeXsYs0qNEQk2ft1otYbRUj9XOFs6FQqHLEq5fEJTRE7iTLdXrb7fm3mk/BL8EOp2ZAqZFyWyrxX/sUlgul5HL5TA/P79jGnFychKlUkkWWkajEV6vF5OTk/LCPnHihFilcRtKTqhZpSSH2laCY2Ym2HyjX8tgVfbB61UXw/z59PS0sOS0CCTD6XQ6sX//fuj1+jYGs1enyv379wuDOzs7K0wrGdh6fdv7lw1auJjjIrRUKgmAS6fTwgB3yjCouWZWo59HdSaTkWehH3ignrzZbPaUD+TzeZnzO7VeBoClpSVZ4PLe5XI5LC0tyTa7kbFtbW1JFzuVlaULjvoc9Aq+r3fyDacu/vDhw31rIDhn74eG917FvfIy3m26nrLB+8Vg3i9Jw70ax3sdb4fW1A9B8QMcalU6q835QSRAY7epQX6MRqMRxWIR+Xx+mx3VlfBU6cvYW7008Phft5jxvw8NId5RCG+ptPCLf97E0aVtQFw2uXH+kX+EUp/GHE1DBc3xRTjd2/61TK2zHXDnx5PpR7PZjFKp1GUZREbDbDaLhphFJCpj1llNz+j86Kv73W0wvV0sFuVD2xn9UoS1Wk3YONqzqZ2maA/FgjSbzYZSqSQfPbbMpbMGr6UzOCe4aMjn8126QjK/kUhEwA0XUATXmUxGZBP5fL6t1S3Hgi4VOp0OyWRS2EqCFYJck8kkHcsGWUERUNPbubNojYCZLXjJCqkFWQCwsbEhxUi9WnNnMhnJEiwtLUkGg8xuNpsVYMQsxszMjEgr+HeDwSAuIGwXzGdR1a4Xi0XpkOZyuXraI1K2ksvl5E9nJkWn0yEcDsNms+HatWvY2tqSbUKhEA4cOCDSgOHhYZw6dQrXrl3rAvMHDhxAKBTC3NzcwJR+IpEQ0A2gDYTSycVisch8prsBt+Xv2e12kZ2oAFBtJqLT6USyVCqV5H5Qj14oFDAyMgKv14tUKiULSd57SsNU9xoWAnZeF7t0rq2tSWEnr7NYLGJtbU2a9NBZhu28yaRT8lIsFkV+5Xa7e8qv9uzZ0+af27mQJbvNd3mv4kjKkFjgunfvXoyNjfUFGQ6HQ2ovOvX0wG2g1m+s7xSo3Q3jfK8Ksu4kXX+/GMz7KWlQx/FBa6vc77v4oMRDUPwAB7t/0QezVytTm80Gv9+Pzc3Nvn6M1O226lU8rruIxwunYWx1N+BgVDTAb/gC+GNHd8rIk9u2XJvc2v57wRrEhaOfRsXcOx3e0JeQ9V1Gq1RFuWEUj1R+dPgBZRA8WK1WDA0NYXl5uY2ZosZYr9djdnYWk5OTeP3118WhQk2h8+M1PDwsaWmmZtXULwEKwd0g0Ge323Hp0qW+aWZWjg+Ker0uoKFXpynKBqj7Y6cx9cNIJwCv14uNjY2+jRkmJydRLpdx9epVsQtTmbv19XVx5qhWq1hZWek6FseI84yFQior22w2RfuZSqUEAHe2DKbFVC6X2xEUc0zS6XQbo8HOcGSDqaPtHEeTyYRYLCZFYdevX+/pTqLX66XlbT9gRF3t5cuXu+79q6++isceewyBQEA+OL0+arw3ZDJ7tVR2OBzw+/0wGAyYn59vkyAwk2IwGHDw4EHRWI+MjECr1QrgoUSnX3Se225SraVSCaVSSWQ26jPExYPH40E4HMby8jKazWabswItBcfHxzE8PIzl5eW+c3ZqagqpVEqe+2Qy2SYvYjZjaGgIqVSqSxJmt9vFD5gFgP2uKxaLIZFISFEet6MMKJ1OY3l5GT6fD0ajsa/kZWtrC4lEAhrN4JbKMzMzcLlcWFhYkII69bzpitFsNrG+vt4ljeF7f3R0VCRIO4GMQSww5RrqWKvfj2q1ekdA7W4Z53tZkHUnYPd+MJj3U9LQy6dZfYZUn+aH0R4PQfEDHOVyWVIc/VqZUls5yI/R4XBgrLGEp+p/BX+1v1QCAG4aDPil0SnMo9z1byPxFv63P20gkN3+e8YxideP/hTqht4f4Lq+iJz/Mlq6mlxPtVptq+Tn+RNgsYiqWCxiYmJCnBnI9rKwh7o5dvLiR5r+sXSfYDrVYrFgfn5eCpTIwKRSKeh0OoyNjcHlcmFubk6KrVRgRNC3sLCA06dPy4eZPqCJRAKnT58GgB2BMYs/6LGqFkkxjU13AX6AdbrbTT7IrgHbGsNBjRmoO8zlcvLxVIvxyHazMLDVagmDwDSzykDWajWsrq52uViMjY1henpa7LooHehsY8z/qguhfmOUTqcRiUREHsQxajQaiEQi8Hq9AobVcSWLT6cBAhbKFXj96XRaWEeOZT9gtLa2hmq1ipdffhmVSkUkL5Q2nT59Gk8++aSADAJzMtpk9unuEIvFhBXsbKnscDh2vGedshCfzyfbkIknw7e5uSlOBh6PR+QTlPAcOnRIUq395BqcJwSxnbIPbjMxMYGlpSUBoyo7Z7VaMT4+DgAD5+zo6Kjo9fvJi3Q6Hbxer9gD8nkhAIjFYpicnBTWuN8co/xkkFaeLPkgyUuzud20ZKeWyrQFI0Pd6/pnZ2dlHCkn4z2LxWKwWCwYHx/f0csa2DldPzIyIsdtNptt18ax3trawujo6I5A7V5IA+51QdadgN03m8G8n5IGMuBvh7bKD1o8BMUPcNB03W63o9lsSsEZ/WUJHmkZ1cuP0VxLYezVf4kT2ecHHqsF4L/6xvGfXDrUWt2AeN/qtgex/Y1/SngO4NLhH0dTZ+raFgDq+gJy/iuAvg4Nth88frTy+bzo9sgeMsVOdjyXy0mzBb7UCHopmYjH4wiHw4jH49IogKlaFuQ1m00kEgno9XphL/ni4UuQzNcjjzyCUqmEaDTapnXW6/UYHR3F+Pg4Pv/5zwuwZ3qYusFMJoMzZ87gwIEDA9NSqpyj14eYY5VKpYQdoi6VgICey0zX9mvMMD8/L+cLoE2rTJ1wJpNpq+ZXz4VAgB9ItpzutACjswHPlS141etkuphM906RSqUEEHOsyUxyYajRbDsFcGHBMWJqvlKpIBaLoVartRUuEexlMhlsbGyITd4gYLS+vo5yuSwLKn68jEajOFNQt67OReB2q3Kn04n19XXRczMDQkaxXC6Lvp3nS0s9jWbbL7fZbAo42ikVq7qY9HMyWFhYkLbTvSQ2tNKj3MlqtcoHnrIaLgxpM8d9UWYQDAbFso5zltdbLBah1Wrh9/slW8EFlLpIoXwsl8shmUyKVzPHQc3+ABDbt51S8bsJLtbVLBLZ7XQ6Le+wnVoqN5vNHZupLC8vY2RkBCMjI8Kml8tl6PV6DA0NSWtyvp/6xW7dixKJRNdY89o41mob8W/2WLtlnO+1nOFBSdffK2nIboI2mSou4HP2oLVVftDiISh+gINpdq6UgXbQpNfrBRQz5Sosj6aF4NLn4b/8O9DVS4MOgzWdBf84NIMrxizQ6jZyf/x6E5/+yyaMb/xTZOgEru3/FFra3h+Uuj6PrO8yNPr29rxkD/lCZwq+cxvadyUSCYRCIQEdfDmqHcmWl5dFd0ywooLZSqUibWfZXleVmBDkkQGj3CAajbZVj09OTiKRSCCdTguLrAbTUul0GktLS9izZ89AbR3dQzQaTVsRIdP+lH6QSVSdDPg75XIZm5ubCAaDfRsz0HPZarUik8m0pX5brRZcLhdSqVTbC5lsK//bbDZRr9exsrICAPJRUnWulUoFm5ub0phB1cqqMguVQR0Uq6urbRZuqosFPxr1el00rdw3g0CTmYVexX1qOnqnjz5TnxqNpq3lszqvk8mkfLjZ4EMtDuX9JoPdq6WyeiwWlPUqDE0kEojFYhgaGgLQ25mGemkuLvs5GWSzWdhsNnFeUK+Nco1QKIRyuSzPmQrKqOkvFAqIxWIYGRnpqZet1WrY2NiQromdjJhWu93WmgWznelz9Z5xzvL+8/6ykI2LYXpl90vF+/1+lEol5PN58aztLNgjoOICp1fQ5Wanlsq8d4OaqWxtbUGj0SAcDotLR6fueDdp9t2k6+nc0s/phPUoO7GX91oa8HYoyLrTuJ9evW+ntsoPWjwExQ9wEKCkUqmuNDtfFpzQ3NZkMsGydQ7BM78OU2Zhx2P8gf0A/n/eGsq6bM9//47XmvjUV7c9iAFgdeQ9mJv9O333VzNmkPVcAXRNoKXpsoACIB9TfoDIrKkpdq1WKytqsoyqHpYV2J1p+E53BYbaCtXr9crP6/W6HJNaV5PJhD179rSl/elIwILHXsEPI10+Njc3ezYeaLVa8vGo1Wqw2WxyLBZGZLNZAfZk0fkS5Zjx3Ac1ZlAdD+gfq0o1isUigNvstcqeqZXyzWZTbP2YZiXzxwLOaDQqHfLInKn3hwsHNoIYFGQBWfSjsifMGLCIrfNe8+9cYBGUsqU3g64elPAM0pPr9XoB3r0WDlyM1Wo1hMNhrK2ttX3QzWYzQqGQ6E77tVTmPSMA63Vf1XszyJmGns79mCnOkWw2i0KhIDULnSlrLhrIfnO+dJ6LRnO7yI37puyHv0MnHF5j5ztNp9OhVquJlpbyG/VY1Oqy2LZzwdNqteQ5dLlcyOfzfVPxk5OTaLVauHr1KjY2NtruKe/TyMiIMNp0qOA5U0rCAridWiqzeHbQM0uLOR6n0/llt2n23aTrVReXfmNNJ5TdHqufBOdOpQEPCsN7r+JeS0MGhXo/iAvUeKvcJ94O8RAUP8ChvnxVBoTsErWm1Bmb6xn4z/17eFa/suO+l7UB/B++aVywb/b8d02rhR863cT3vLINNpoaLeb2fD/WR57uu8+qOYmSfw6aVgs63fbUUpkwtRpdfcl2VrIDkLQ8WRLVh5TV2UzvUoLBhhOdQIXtdSuVSheTQUYLANLpNGq1Wt9KdbUrXK+0q1rIdvnyZWxtbbWldePxuPiLWq1WWCwW8WolGCVDQiBIoKH6FDM1SxnCoMYMvL5KpSLFhATZFotFUuJkiWu1WluxoupeQQDeL6XN1HEwGBS9Nl/OBEdOpxPBYFDAQD82nQ4LnDcqM0mQTjZvUPCjzLSl2uKbjLXFYkE4HEYikZCPk+oc0Gq14PP5sLq62vY8MlRGHYBY9fHcNZptX9/NzU3JBBDsd7LJvN90IekH0tmAZn19vUsLypS7x+NpA2wsfFSBOu81W95SksV2yFzg5HI50fWWSqW255cfVvrg5nI5FAoFsVbT67dbnKvZnEajAYfDIcCSbG8ul0Or1RL9Ohck6jPN54T2b72YSVoWWiwWsdDrl4ofGhrC3NycHEsFvFarFcFgUFphR6NRmdeUGPB9QRCdyWTkOvgztlTm87LTM8txups0+27S9XSi4KKoc+6ziHinRSyPRdlMZ2EX5yvP+W4cKt7OcSfSkAfBxePbMR6OyAMe7ORFGYCqm7NYLLBarXDarTC//vuYWvwM9I3iwP1VYMJnzCfxX/xpZA29AbG+3sI//Osm3nlt+8Nf09tw+dDfR8qzr+9+q7YtVAPLcNodIkVQi6z4X3rlWiwWASgEPAT5BEVutxubm5uwWCxtzEuxWESpVMLk5CT27t0rVlOdaV2CvEAgIK4TvYLFM7Tt6pf+ox9sOp0WIMlgQR67lN26dUsAPc+HDLDFYoHT6ZTWsr26mk1NTWFzcxPRaFRAAVlyegAHAgFMTEwgGo32bcxAaQit8HpJDPghZFOJTrad48KW0IPSrCxOI4hXgSHB/W4aauzdu1eAD+UjPB8CdNp7saNcZyqalnc2mw3RaFTOg+fPOTEyMoLjx4/j2rVrfRsh+Hw+aZMOQPbF81Ft8dgkQnVMKRQKyGaz2LNnj7QK70yTkuEMhUKYmZnB/Px836LP8fFxpNNp5PP5Nq2wXq8XpxW73Y5QKASfz4fFxUW5l6pUR6PZ9uamTKBXAaTFYkGpVILb7RZ7SPVdRGeJ4eFhuFwuXL9+XcaJY5TJZKRzHhdjQLvLCJlkzgE6najuE3RooPNEuVzuOfdZmMzt+6Xiqb0kaO20vyNzbTAYsLy8LE09VEnQ1tYWxsfHhY1LJpMoFApti0GbzQar1QqfzyftoPs9s8FgEOFwGMlk8q7S7LtJ1wcCAdED892qNlOhBnU3xzIajZibm5OOoGpxZDKZxOzs7K4a6Xyrx26kIQ+Si8e3WzwExQ9wkFEls6I2MGChj794C/ue/RWY0rd23N8V41H8lmsc37BdRlPT7LmNtdzCP/v/NNi/sg1C8tZhXDryk309iAGg5o6gaF+EUbfNiLDin5IFAiIyrdyGdkSdulMyim63W9hWssj84Ol0Ong8Huj1ehw/fly0wp0pKZvNhkOHDmFzc1NeMmxYUa/XpUUqbesGpRoB4OjRo/jGN74hjQ/oPlEsFmE0GnH06FHcunUL8XhcQCHTztQJ37x5E08//bTowXt1mhodHUUoFMLW1pawfLRqowwgHA5j//79yOVyfRszjI+PIx6Pi+ND50eYmmW/349cLif6W44Pf2dsbEyO3S/NqjoV7NRSezcNNY4cOYIXX3yxDfio6eiDBw8KM0XgwmPxXg4NDcFgMEiHMVre0Z2ELI3L5RrYCIHtjpkWVq+ff7dareJGwAyFeu21Wg2pVAp79+5FJBIRfTpT5lwA7N+/H5OTk6IvVb2wNRoNRkdHMTIyIowx57DK8Kntnf1+Py5duiSOKSxmZVvj2dlZZLPZnhKbUqnUViy3tbUlGn7OfY7N+Pg4EolEX9cM3n+Hw4FUKiULRJXhpiWezWaTMWPrcD6vlUoFHo8Hw8PDWF1dlbbb6rH0er3UIwD9U/G70V4yA8KFWec8a7VaUvewtLQEAPJuosRpaWkJR48elXbPg5qpHDhwQLoW3k2afbfpegBCNFBbzGd6t22u1XeBWgDIfXHsdttI51s9BklDHkQXj2+neAiKH+Ag+0XWSy0Q8xjreDz+OczkvrHjfoqOKfyp9h34jG0OEdPFvtv5Mi38yz/TIxjbZovivsO4cuB/RUPffzVZ8a6i6Y3BbXHDZrPBYrEgEAhAr9eLnpZhMpkQCAQwOjqKjY0NaTOsslNkxQj8pqamsLKygkQiIR8rp9MplkSlUknambL1svpRPH78OIaHh1Eul2GxWMSGqlgsCrBmNS5wu+FFP7PzQ4cOwWKxiFct9+Pz+fDYY4/B7/fj5ZdfbgOJ6kKGljjNZnNgGo2scDAYRCaTadN1UjOq0Ww7FJw6dQrXr19HIpEQe6xQKIT9+/fDYDDg7Nmz21rzN9h51YeUaWK3240jR45gcXFRgAf1jFNTU3C5XKhWq8jn88jn821ghlpQj8eDer2OeDwuqXi2X2ZRF50TrFbrjlXxoVAI09PTWFtb63IXGB0dRTAYRLVaRSAQQC6XE8s/FmyR6Wo0GggGg8jlcm3nw0YYXNQMaoRAhpvZDtU7li2MTSaTaE4paaIOme27OY+npqYQi8VE2kKLsUAgAJPJBLvdLiB9c3OzzYM4HA6LFnRkZETmocrwud1ucTaIx+MiVSgWizKP2MCBc6JQKLQ1daCEiR9Vk8k00FmCVmiD7n0qlQKwDRorlYo0peE2BOS0keJ++fyFQiGRMI2NjbXJRdQW53xH7MSE7UZ7yU58ExMTYqvGuUhZCAt/qTHmddBGsVQqIZ1Oo9VqSTOVfs8sgeq9cGDYbbpe3YZj7ff7d30s3utBY0RnjfvRvOLtGg+yi8e3SzwExQ94MMXLl72m1cBs5gUc2/grGFuDXSXqOgvW934Kn7cG8Yfxz6Ci7e8NOx3R4lc+p4U1V0YLGiyPfwgLU98FaHp7YWp0LTgPFqD1GGA0TsLpdAogCAQC2xpns1laB7NlrsfjgcvlwurqqqS2O+29CMqy2aw4EFBnyzQr9X/UMw5qvUyXhXq9jkOHDnV1EUun06IL3I3Z+ZEjR3DgwAEsLS2hUCjAZrNhcnISer0e169fF5CoumtQPkEQSXeBfmm0aDSKQqEgVnCZTEbGkQCDXQ2Hh4cRDAZ7dqxiu1vqplUNK4MgZmpqCpOTk+Iha7FYMDIyAo1mu4EGX9IcH+6H4HNoaEjYPNpttc0ZzXZjg2w2K21v+0lV4vE4ACAcDkOj0fQsWKR1mN1uRzgcRiaTkftKV4VarYZarSauGOymSJkHuwMyq9CPwSGDScDAAj1KNViMRO2tej38L50u6KG7Z8+ernumOguox1b/C9zWDBoMBoTD4Z4MJ2UOiURCOrGxwJVSBIPBIIW8dJFQWXm1e1o2mx3oLLG1tYVsNis+y73uPRu/mM1mBIPBLlcZgrJsNrtj5fzExIRkhzgvWbTI9r07AYfdaC+5qB3UmpoNdEKhkGQh+L6y2+1ieZdKpQSU9HtmGffKgWE3+7nbY3Fx4XK5+o5RLBZDpVLp0skD9755xds1Hrp4vPXxEBQ/wKHq7bRaLYaqS3gs+ifwlFd3/N0N/7twJvgJ/FXzFXwj+QVggM/7O+cd+Om/yMFQq6JidOLq/h9Bynug7/Y6cxOmvTE0bFU06xph2uhpShaQDJ0qe6DNGAEj0/HqR1jt5FcsFuXDq6bP4/E4Wq1Wm65Xp9NhdHS063zVVBLlCjabrU2uEA6Hkc/nxbZMlVhEIhHYbLY2s3O9Xo89e/Z0HWsn/1BVh8q/7/RyI/BUixFVizJu4/P5un6XRVi8H5RLELySfWORDBckwLZjRzQalbEgQzkozUomkoWRvVLxqqtFr6AWPJvNSoGj2mUum81K6162AmerZs4pzkeTySQfGjorqPOo05GiXxBoJ5NJYWnVAjt+sJLJpDC/nVkCzg0WUfa6Z8wKpdNpRKNRlMtlOJ1OmYvMhLAzGjWDKsOpagZZ5EfGnAs8Po9cNPh8PpjNZnF0UeUafr9fmO5BzhKUE5AR7XXv6dXMBRsLOXnOzECoGu1e86PRaMBsNvdkwu619tLtdkuBotFo7GKTKaFoNptShNqpb2+1ttsmq1mxfs+sGrt5P+wmdrOfuzlW5+Ki1xhxTu10X7+dHRF24xjy7e7i8WbHQ1D8AAdfrMMuAw6s/Smm0i/u+Dt56xhu7vkJXLa68d8T/x3RerT/xi3gRy5N4KNfXIS22UTCsx9XD3wKNaOz769YfIDjcA6lWg3AbRYMgHwEaElUrVal/TKZKdVqSdWtqqlxVZdar9eFiQRu+36WSiUp1NtN7JRKYicyk8kkbB4/zARXuzE7t9lsAjr7uWEYjcaBLY4BCHBPp9MC9FSXAgK8Qe18AbRZV3E/bBZA9p3sIAuJ1MKuTCaDeDyOvXv3IhAIwGKxDEyzcmwGpeLpvLFTZTQXRUajUXS6zBRQZzsyMtKl8WUxHR0Odjofh8Ox4/2gFtlsNsPlcnW1CqcW22AwCAhVg/ef0oidrj2RSAxMoUYiEYRCoR01g7QAUwEbr191e+B8czgcXcVmlJgAkIVTr1bHZKA41rxmrVbbdu/pgrK1tYVUKiXPosPhgNfrFaA96FhcdLCY7ptlwnajvZyYmEAkEtkROHMB18uHmxZrnYuXbxUG75tZXHTGQ0eEh64RD0I8HNkHOPR6PWazL+LA+mdhbBQGblvXmrA48UmshD+KZ4sv4a+iv4cG+pvNO1p2/OLLEzj43OtoarS4Nf0xrIx/aOAxhg9a4DpYwlasjOHhYfGiJXtJI3gWzBmNxrZKbQJF6gUTiQQCgUCXYX4sFhNmlBXpnU4PBDGFQkGAYaPR6CmfkGsekEoqFovY2tpCoVAQrSR1vfz7btqd+v1+eL1eSf93Gv5rNBp4vV74/f6BY221WuH3+7GxsQHgtg815RcAMDk5uSMDQJ0twURne+ZCodAGlHmvgNtev3eSZiUrwe6Lnal4Ai232z2wqYLZbBYmF0AbM8vz1el0cn2hUKhLhhOPx0Vewe52vaQBLGDdaRzZTZHzTy0OpRMMpQRAe+qd99DtdsPn8wlI6HQeYfEcO1kC/RtzjI6O7qgZpGyD4Fd9ZjWa7SwPwWixWEQ4HO7phsImF3QX6LVwmpiYgMPhQKlUkm6EKuOs0Wy7ypjNZiwvL0shpDqOW1tbmJ2dRavVwtzcnNgOkm1Np9OySKNM5W6ZsN1oLwn2+wHn6elp5HK5ga4SoVAIHo8HwN27Czxoca8WF9/ujggPXSPe+ngIih/gMBgMCNbXdgTEEf87cGX0h7DZAv4i/ru4Wb05cPv9zVn81Je0GH79dRQtAVzd/yPIuqb7/4IGmHzCgsc+Mo2bN29KZTSBGp0wqIOip6fdbhfAS6CSz+eRTqdx8OBBaancqQe02+2YnJzE1atXodPppDKe29jtdml1ypifn5dCO4I1r9eL48ePSyEecJudoY6PDGqtVkM0GkUmkxFArP4OLaR26nzG6vLz588L0FT3Y7FYsH//fgH1g9gispPUzqrpWPozd15X534IRFks1uniQD/USqUi3bM6i7bYjpY6tkEgpNFoyMs8l8u1FVpqNBr4fD4BhvQy7vUBZWvzQT60vP9Go1HkE+o2ZBSZaegnDfB4PAO7lQGQcR0ZGRGJDZuHuFwu0TDT4qpQKHQBI6fTiXA4DL/fj0KhgJs3b3YBx6GhIfh8PukuF4vFuvTtBOb1el0kS/3mULPZRCAQaGsfrc4Zm82GQCCAQCCAaDTa1w1leHgYi4uLAxdOzWZTHFl4/Zxr9F5mce0gJwf+Gz2RO8doJ8/cXrETK7vTYm83wHknV4n9+/dDq9XeE3eBBzHuxeLi290RYTeLi2/3MXqz4yEofoDDYrFg84l/gtpfvgxDPd/17wXrKKIn/jESzsOYz57H7238HgrN/gDapDHhezQfwkc+dxn2uVtYHX0v5qe+B01d725XAGC0aXD0u/w4+NikaCl9Ph/W19fbAKjdbsfIyIgATovFIkUkapU+C2c8Hg88Hg8WFhZQKBRkG5vNhrGxMUxNTeHWrVuIRCJdjT14jFAoBLvdjvn5eTz33HOideXHKBqN4rnnngOwXYi3ubkpFd88b5/PJy4NBE30wVXN9wlOdwLFGo1GAP/KykqXL+z4+DgOHjwooLEfW6TT6UQ3zfQzgQH/kJknQ95rPwT+1Omq98NisUi6nyxevyIZyiV2CvrkWq1W5PN5AfQE8lw4ud1uOByOvh9QtlQm+Fb9numXrDKo6vgTVLHIkpKHftIASjJ2ui4yp8PDwz2LvwwGA7xeL9xuNyKRSNf9CIVCci94rup5q3p1AuJms9nl+VooFOB2u9v202+RQnBHqUSnbzb3vdP9oCPMTgsntoImwFQlJvRJttvtA10KqGvnuALtHQvZhW+3xUa7ZWV3Ypx3As67cZW4l+4CD2Lci8XFt3s8HKO3Nh6C4gc4NBoNhqYOYvPgj2P84m/IzxtaE1anfxDpAz+I8NQk/tOrv44vR788cF+HfIfwK1Ofhuaf/F8obOZx7vjPI+OaGfg7owfdePffnYHH7xSJAQtpDAaDNA2grVcikRAj/0QiIb66/AiziMDj8WB+fh4XLlyQ9K2qL75w4QI8Hg90Oh2KxWJbowG1AQEByfnz51EqleD3+wU4m81mGI1GxONxnD9/HmazGWfOnEGhUJBiM36gstkspqenUS6XUShsLyror8zjMXYDDB0OB06cOCGdtOiaMDw8jJGREQFnt27dQqlUgtFolGIjtuwdGhrCxsYGGo2GuDqoTHE2m8XGxgZSqZR4x/baz/T0tDQL8Pv9PSUGPp9PWjaz4E6tiOf2BGGDWDc1/Tc8PAyn09nl9MH0H3XNvfbForBSqSTnpl5/LpeTgkwAwq6ox+IiJxgMolAo9DwfetQyHdnv2tTrcjgcovk1m80IBALI5/Pw+/3QaDRIJBI4dOgQ0um0jKPb7UYmk4HL5RJbsj179vTcJpVKSTc3tTEHWVU25mCmgFZnvVwMzGaztB13u91tzyNZS/ocU7Pba1+sDRjkLrC1tSWSJ5fLJXpmammZ7anX6/B6veJXrB4LACKRCFKplHhxd8qrmG3ivR90/feblR0eHsbQ0FBfGde9dhcAHjxt8t0uLh7GwzF6K+MhKH7Aw+FwIP/4P0B28a/gzN3CquNRXBz+JKzDe2F01PATz/8E5jPzfX9fAw1+eO8P46f934O1n/hpLOkPYOGx7xrIDmv1Grzz+/bgyHtG2x5CplQ7P9YAxH+WwIxm8Kobg06nE3Dx+uuvC2OsMn1WqxWZTAavvPIK3G43TCaTNAhQ98NzWV5eRjKZlIr2tut4w2c3kUjgzJkzSCaT8gFV2Vt68rKYjcBSZRxZpX8nhX379u3D+Ph410uNYJwgQe3YxQYRLCTkWHYyfCw2W15elvPutZ9oNIp9+/YhkUhgaWlJHAMajYYsUA4fPox8Po8bN26Id65aHBkIBLBv375ddaNi+i+RSPSVB6jpv34fUKPRiFAohKWlJcRisa5UPMEGOz0SSKjMJFnyYDCIhYUF3Lp1q0tiQXu3nZh7h8OB4eFh3LhxA6+++mpXBmB6ehqHDx8GACQSia5jxeNxBAIBuN1uLC8vo16v4+rVq8jlcm2FZmwpTIBUKBS6PKFppVYul5HJZPpmP4aHh8X1Adhu0ADcljzwGSmXyzJ2/a5/N+4ClDYVCgVsbGx03Xs25WD9AT3DO6+fciaLxdKWIVLnBr2LB2V/uCi9V6zsbhjnXtsUi0XZ5l67C7xdtcl3qwP/doiHY/TWxENQ/IBHLpdDZCsG7aGfg6VZQC54CkPNJr609SX8+Tf+HPVW/5enrWXD91u+H/9L+jCu/NN/ihujP4Ssc3Lg8VxDFnz4HxxGYLz7hap2KRv0sS4UCm06SLXQrlj8/7f35uGNXFX6/6t9tRbLtiTb3bZ77/QSmu500yRMGJIAIcMvQzKQMCEkQJ6ZgWTINoHMAoGHL9NsM0wCISwTErZMnjBDBhoGyELogWy9JCHpTi9u9+L2LstaLcmSrPr90XMvJVkqlVpluUo6n+fxk7RUqrp1696qU+ee854UZmZmeFnZUjksZtBFo1Eeo8gylsXqA1arle+rUqYucPYByB5+zLNUWjIaAE8uYgk94jADo9HIH2LVYk/FVLqpsVjqRCJRdnlcbACy4iDiRDOxt5cZ+MxDxwx6tp/JyUl4PB6eKMV+q9PpYLVa0dHRwT2fJ0+e5FXPbDYbcrkcv1b9/f1IJpM1ed0qhQfIgRWBYdeNGRPsejDPaigU4ln/drudx0iz6nDi2HP2oiMOsWDI8SieOnUKJ06c4LrhJpOJy42dOHEC/f396O/vlzwWi6NmKwhi6b9IJILZ2VnuRRXHLzMD3OVy8UIqY2NjOHToEE9cZWFDExMTiMfj2L59Oy+EwTSIxcYWSzJMpVI8nr7S+ZdKwJVLAPJ4PIhGo0gkEkXXXxzfzLzbR44c4fkDYrk5VvHPZrNVrZ4YiUQkz3/z5s2KeWXljA8AVbdRUl1AaS+42jzOBLEUkFGsYoriz/q3Q9DpkM9G8fWhr+OV2CuSv91k2YT3ed8H4zMvY//hvRhbf0vFQhyMjX/Sg51XrYTZWn5YlEs2Kn1YRyKRIp1ScVIM87ayAgqVbtisGhgr78z2IVZNYAL5TNqNSSGVwpLdWGIg02kFwI3PVCqFZDLJPcIsdlocGsI81Eo8JFgICpMMY/tkHmBWeY2dczkdXfb57OwsN7zYiwTzlOp0OkQiEYyOjsJisWDr1q0LQgxisRhGRkZw9OhRHuvLvMgGgwEejwfpdBpHjhzh3kkpr5vD4cD4+DgAYPXq1WWVDGrxzrGkO5aEyRK62DWJxWI8HpUlfOp0Or58z5QRAGDNmjUV28PCZCqd25kzZ7B//36u6VuaHBqLxbBv3z5uzFQ61vT0NC9dLj4W00GORCKIRCLweDyS8cu5XA4nTpxAKpUqWrVhYUOhUAhHjhzB2rVrefU5s9nMY3tZiApLZGPxy/VIwLGS4nNzcxXVabLZLO8X5gkGwOPm0+k0EokEvF4vL05Trnqi2+2uev5Hjx7lcezlkOuVlRsHXG0MjY+PY9WqVYqoCygdm6xVjzNBKA0ZxSqmNP7spchLuP/E/YjlYhV/Y9aZcZXnKuyw7UDmfw5jLPZG5ILSNzWjvYDlbzbhgnctg9lqrOgxECcblavGND8/z2W02DKp+IHDHlgssYstuTKpKLFMFFO2YDHAYk8piz222+0IBAJob2/H5ORkWSkkVmErHA4XyZqJ4yqZUc3Ojz1ExOEKTF+4dNlYikr9KG5/OQ8W6yOr1cqPK/aUsxhOsWwZMzTEY8dkMsFisXBJLYPhbIlsMU6nE2fOnEEoFOLhKqWayBaLBeFwGMPDw7wEbSWZsEgkwscsM3RKjyfHOycuG5tMJjEzM1OUzMZWK1hcr8PhWDCOdLqzcnrhcJjHm5drTygUAgBekKT0ejidTpw8eRIzMzNFqxviscZWN06cOMFLkJc71vT0NF9xKQd74bNarVyyrlxhDpYY6na7y4YNMcOLvQSJC3eIt2PSebOzs+js7JT0qFaTgGMeTqZOw+Y7WyFi+szRaBSBQGBB4ifTME6n0wgEAtyoLlc90W63Y3JyUvL8o9EoXzmQKt1ezSsrJw5Yzhhinngl1AWUjE1uVjUMgjgXyChWMWzJuKAv4Lunvov/mfgfye17Tb34oO+DcEU7MPKLGczqNgCVQ4cBCPCuBqx9SeQtBu7FreQxYOVKT548iXg8jmQyWaQ+4XK5uOHHHjwWi6WorDDTl2XybMzYYzBDxuPx8KSv0qVutjTNjL4tW7Zgz549mJ6eLlKfiMfjsNlsWL9+Pfbu3csNOXE1OGa4M+1VtmRbahgyI7acN7ocUv3ItFellodZmAqrkCauRJZOp+FwOOD1ejE1NcVl5Fj/Mc3X2dlZXihBKoaRLavrdDpevpcZKmypmhkwuVyOJ/GJCyq43W7+UlRLzKTUiwO7VuFwmKtfsPAWq9XKxxK7PkzPViw3l8vl+AtPpfaw1QSpbVhFQCljlnlkqx2LhV4w761YN9lisUAQBK7jLCVZVy1siFX+Y2Ee7KVCPEZYaBDr22rXTEoCLh6Pw2QyoaenhxdeEStUsHCXXC7HkxJLEyhZkqjb7YbBYKhYPZHJPUqdP3D2RTwcDlct3S6FnDhgOWNI3If1qgsoFZvc7GoYBFErZBSrGKPRiIncBL598Ns4U6W088W2i3G59f/DzF4dTo0aAJ10cQizQ0Bwuw72TiCTOZu4EovFeFZ9JY+BwWDA6Ogof1gxQ3JqagqJRAIbNmyAzWbjS9zM+8o8vIIgwG63Y8WKFXj11Vf5Q54diz3EV69ejcnJs9X4WEwpQ6f7YxGA+fl5rkPMdIoTiQSMRiP8fj+2bNkCj8eDw4cP8wey2BBh1cfa2trQ2dnJ1QDE5YCZEdrZ2ckNaqn4u2qel56eHrS3tyMSiUguD5vNZng8ngUJSe3t7fD7/cjn8xgZGeFtKjV4mKHFjI9KMYwsxjoajXKNZubBn5ub4woger0eo6OjPH6ZGTHsBYl5NeXGTFZ7cZidncXw8DDy+XxR7G00GuVqD0xurJLcHAuxkWoP+1xqG6YZncvlyoazMAOTJYBK9bXVapVsM1NnYIli5QwnZlxLhQ2xly82BtmKgngeORwOrmohN861Uqy8eCWpu7u7bNgHS8StVvmt9PxZG1j1RLnn7/F4cOzYMR53zF6mJicnYbfbi0q3V0JOHLCcMSTuw3rVBWqNTa50v1oMNQyC0DJkFKsYq9WKhyYfkjSIvSYvrrVfB8vBbpweNUAQpG+qOszDd54OvvV66A3FnslqpWXHxsYwOjrKvZjiSmidnZ18Gdbn8/FwBnEFNaa/2tXVxXVKx8bGkEqlkM1meWxyd3c3PB4PEokEN6hLlQVYohi76a9cuRL9/f1lpZDm5+f5zV2v1/PlZGaIFAoFOJ3OolCMckYoO141b3o1z0s0GkVnZyf3kpVbHmZLqEzeqzQWmMniMW8R85AyrzpbHmZKDVLV43p7ezE6OopQKMRDNvh4+T/PfUdHBywWCy+FzZIExZ50p9MJj8cjK2Yyn89jaGio4ovDwMAAIpEIUqlUkdSeyWSCy+XiYQgdHR0IhULo6OgoG8Pq9/sRDAYxMzNTsT2s4IbUNgMDA5iamkI4HOZGJGN+fh6pVArt7e1YsWIFotFoxf2wWNvJycmKbWaVz5iaQzljplAocKk9qQpq3d3dOH78OFcSKacdzBRGpMaInDjX0mpc5cI+WPiNnMpv9Z6/3+/noRRtbW1IpVL8BY99J6d0u5wqY+IxVKlSYWkf1qMuUEvlM6n7lSAIiqphEITWIaNYxej1enx6x6dx0zM3lVWZ2OrchhtNt+PYb2aQyRTK7KGYNuMUHBfaYXUZkM3pIGQF7lFgcYBSMXFjY2OYmpqCz+fj3jexwTs3N4eZmRmsWrUKsVgM8Xh8gZSWy+XCeeedh0QigdWrV2Pt2rU88chqtaKrq4vHAjPvRbllUpYEJvY0GQwG9Pb2Ljjvubk5eL1err8rlkZj3ruOjg7Y7XYUCgWcd955FTVmmTFXSReYJSFW87z09fXxhzQzzFk/2e12dHd3AwCPPWQFKwRBQCQS4R5ScWGPUsOAGcputxuZTKbiUry4NHUqleLeXhYOwR6iLJGNeV/ZNrFYjD88WZKVVMxkIBDAxMSE5IvD8ePHkcvleInm0jADFj7Q09OD2dlZvuTOwlJY/O/69ev52JaK4QQguc2yZcswPz+PZ555hlcoM5lMvGy52WzGBRdcgN7eXsm+7u7uhsvlQiKRqNhmVvmMjZlyhpNer+cV1Kampoo86bOzs7yyotPpRG9vL44dO8ZfqMSrCblcDr29vejv78fQ0JCsONdKXkedrno1rp6eHl79b2pqCk6nk4+jZDLJ213L+VeqIDcwMMB1uMvFFOdyOVleUDnnxcbQzMwMBgcHJaX/lEBum6opxvT09CimhkEQzQCNdJWzddlWfGzjx3Dfa/fxzyyCFTcUPg7nvgEcik5X3Yc1E8baNwDJdWe9r1NTxTq0bW1t8Hq9XGqqHMzoZUugLAFNDItjbGtrQyAQ4DGFYj1X5gViIQ4srk8M2769vR25XK5s/LLNZoPf75dVA54tv/f39+PUqVMLtFH7+/ths9ng8/n4Q6Scxiwz5qT0hVmoQjXPC+uLchqrK1as4HGFbJuxsbGibQYGBri3nPVXaTIe+9zr9cLhcGBsbAzT09Pc49zR0YHu7m6+nd/v53GoYt1Zl8uFQqGAZDLJY8TFnmKr1cqVA3K5HNxud9WErGovDpOTk7xEMatwJg4zYMU53G531SpiAGTFcFbbZtOmTQCA/fv385hZts22bdv499X209bWJqvN1QgGg9i4cSNefvllTE5O8vHR3t6OjRs38v2sWLECqVQKU1NTyOVy3CjW6XTo7e3l401OH1VTKZCzn7a2Nt7uiYmJiu2Wc/5S/ehwODA5OckTbEsTH+XGtwPyqowxKTop6T8lqdYmp9OJwcHBqqtWLpdLcpVEzioBQTQLZBRrgI9s+Qj2h/fj+dHncWHqclww8i6kZ+aRQlbyd4b5OfSFfo+LPvVeJLv92Lt3L3K5HNrb24u8SnNzc3C73Tx2VyoeUhwPWAqL42Oe32XLlvHlOabqkMlkMDIywo24SscyGs9WxWLFKdxuN3/IsIIEYjkzKVjYRjKZRHt7O9rb24sKGCSTSR6CAFR+qGUymSJ9YZYwJdYFLhQKsjLeM5kMJiYmYLFY0N/fX5RINDExwWWkpLbp7u5Ge3s7rx4oviYsWY5p3hYKhSJPH/BH/VimhsGMZJZsJw6TiUQi3OhmsZri65XNZvmLCyAdM8mqo0m9OIg93+3t7WVXJYzGs3rFPp+vrBqK2HMuJ4ZTzjabNm3C+vXrcerUKe6R7e/vLzoXOfsJBoNV21yNRCKBeDyOYDBY9HLD4rwTiUSRETo+Po5QKMSTJ5n3khm81dotV6VAzn5Yu3t6eopWScTtloNUP7KXlnrj2+X0D0tYA6Sl/5ROWJNqE8sTkbtqVY8aBkE0C2QUawAddPibtruw8chR6KM2pFGlgIRQQPf4c1g7/ypWffOrMPX2YnxwEG63G06nE5FIhHuF/X4/DAYDNzylYtS6u7shCAKPFy5XxrW9vR0zMzM8Pri0RGssFsPU1BSX2qoUf8e8xCwxRvywYiVu5cQDAvIq8bHytkDlh9ro6Ch/+ALgyhhiL5QgCPB6vTzjPR6P8z5iVfe6u7sRiUSQyWTQ3t6+oK9Z/DZ7iai0TSwWw8DAADKZDLLZLFeQYG1yOBxYsWIF5ufnefyuy+VaYMz4/X6+9Fzq/WdGOFPkyOVy8Hg8Re1h/cOq5fFxWyUhS8pYYeoC4XAYnZ2dRe0qjT0Fzi6n+3w+yXEgJ4ZTzjZGo5EXa6gHOW2uhFg1gOkmi78rVQ1gxlNvb69kYlel8xcfj81ZpizBinaIjydnP3LaLYdK/Sg37rZafLtYkqzSeS1WwpqcghqV2iRXocJqtdathrEYUDERYikgo1jlDB8K47nHBxEeSUGP6ktYvvBrWDn0U3Su70bv178Do9fLPQYGgwGhUKjoxicIAgKBAOLxOE9+qxYPOTExgcOHDy+Im+vo6EBPTw9ef/112Gw2bkCKDUdWic7hcCCZTFYsBez1enH06FHu1erq6iqKA2YZ5L29vVUfMnIq8QHgkm5SGrPMyGUJZmKFikKhwIsSsPhMcVnlUCgEr9eLvr4+TE9PSz5AWcGJarqnfX19SKfTfGlcrK3c1dWFgYGBqvG7zGg4ffo0Tp06tWA/HR0dPJGSGeqlMb4svlpOxT+5xsqyZcuwb9++ijGj4thTNdGIQgjnYoTVk9jFjmcwGDA+Pr5A3qz0eGpQO5ATdysnvl2Okc4M0GqShbUkrNU7jmpRqGDXUC1GKBUTIZYKMopVzuvPjyI8kqq6XVviNFYO/Tfao8fgetflCO7aBf3/GXb5fOXSsiw2MhgMYu3atVU9BslksmysnHhJXiw1VWo4sip1DPFNl3mYgMpV39ixEokEotFokcRUJdhNXqoSXywW47Ja5WDyYyw5SewtZaEL0WgUer2elx0WG9YsPIMZ89W0g5lhKsfLI7U0Lid+l12Xqakp3lfiUI2pqSl0dnaira2Ne8lLY3wBFKmBSCE3SUip2NtG0qhCCErp1NZyvFQqxVVQSkuTZzIZOBwO/m+1qB1Ui7uVMz/kGOnsOrPKgOIXb7FkodyENSXGUS0KFex81SC7RsVEiKWEjGIVIwgCgucbceIlQKggLuFIjmLFqZ+jY/pV6AD4bvoIOu+4A7qSylWhUEiytGwoFOKJVVIySEeOHAEArFu3bkG8bDgcxpkzZ+XjZmdni2IkWcGOSCRSlJQiVQq4WtW3VCol6+Ep1k+tVDZXjs4o85xKeUuZlq/FYkFXV1fZ8BEmxyV1LBYuUIuXp9zSuJz43Vwuxw11plvLYMvk09PT6O3tRTwe5woZYs+9Xq9foAZSb+ISoEzsbaNoZCGEWnVqazmHctfMYDDw4iTiF0JWmpyVdk+n0xgbG1OV2kG98e1yjHSmO10aomU0GuFwOBAKhXjYVzWUGkdyXz7VFJJAxUSIpYaMYhWTTqeRxSwC51kxfjBT9J0tNYWBU7+Af+oAdBAAvR6BT/0TvO9//4L9sJhTqWpc2WyWP8gqeQzYUrvb7S5KTGMwA5sZoOWktEwmE8xmM5LJpGSoQiQSgV6vl6z6xiqYVUOOfqocrVqmncy8pOKqd1arlb84sKQ1JkdXem5MY1hKF5ZV/JLr5amEHONpdnYWiUSCx+eKwydsNhvXOTaZTDxpj72QMAUPpl7B2sO8hfUkdjHqib1tJI0MDajVCygHKQ+vnJcQlm+gRrWDeuLb5RjprJIhC9EqLUjEDNJMJlP12is5juS+fKoFKiZCLDVkFKsYtkS6wjGMyYIXBb0JlswMBk79DwKTL0L/f+5jnc2Gnq/+K9re+tay+2HGaCUPJystm81Kq1mwxDqp0qr5fB4ej4cnwon3qdPp+AOvWqgCcDaeNp1Oc3UIsWfSYDDA6/XK8kzK9ZgAZ7VqZ2ZmuOycuLRsT08PkskkotEoCoVCkeYr85YajUaesFcJFn8di8Uk47eBsx73Su1hXh45pbnD4XDFpEamoMHOp7SghCAICIfDcDgcsNlsSKVSRVJwrEqhuD0HDx7E1NRUUbz49PQ0wuEwNm7cWDVxaamoJ7mnkSENSnsBqy1Z+/1+Pj7KVWFk1fhisdiCREzWXjWqHYhfLuQW3SgHW92RKnHNkmGrUes4qjZm662e10gaHRZEEKWQUaxijEYjLL/7HcwPPYz+3kthmJ9Dz9iz0IsKeRTcbnTffz/atm2tuB+W4MaWyiuVli312JbbD4uLrSTJxryJdrsdyWRygfqC0+nkccBS3hmr1Qqv14vR0VEkEokiTWCmrdzV1bXAM1mPfiogrQvMykCzkIhy3tL29nZeiKDUOGCeMIfDAb/fj87Ozrraw8JQqsXfBYNBhMPhikUFenp6MDg4yK9rqYoEkz/z+XwwGAyS2sqCIODEiRMYGRmByWSC3W7nxlMqlcLIyAjsdjs2b96suodyIxOblEApL6CcJetwOAy73Q673Y7Z2dkFcfkOh4PfV7SkdsBeLqTmhxwjXRyiVanEtZSxV25fSknJsXNR08tnJRo9hwiiFBpZKsZqscC6bz9QKKB/+IkF3+e7gzB/7nPwbH2j5H68Xi8viVqttCxQ2fMg3o9UaVVWVjcYDKKjo2NBvLCcUAWfzwen04kTJ05U1FZmCXhK6qdW0w5m3rl0Os2VIcTe2/7+fgiCgMHBQe75Ei+jCoLAFTPEiW7n0h6HwyErcz4QCPC+Lae/3NXVxctbl7uu8Xgcfr8fTqcTJ0+elOwfvV7PdajFCZJGoxFtbW2IRqMYGRnBqlWr4HA45E+GRWYpEpuUQAkvoJwl60wmw8NoAoFA2TwAj8eDTCajSbUDoL6iG3JCtORee7ne61qk5OSe/1Jfj6WYQwQhhoxiFaM3GOD6/P9D6CM3wTI1VfRdYqAfkzfcgG0rVlS9cYlLolYrLVvN81CttKq4rG40GuXJJUzpQm5ZXSaV5Ha70dbWVuSVDQQCvK1+v7+mxIxKHhOxt6ySLvD4+DhWr15d5OVibero6OB9xCqIseRGhk6nQ09PD1aIrlk97Tl9+nSRpNzc3FyRd4rFLzPFiEr6y1NTU3jDG96A//3f/+WydOy6xuNx2Gw2vOENb+BFWaT6x+VyYXZ2lhdbESNeQmceczWg9cSmer2AcpesmSFWaV739fVhYmJCU2oHShXdUPLai73XlSQrlZKSY6hFAk2LyYFEc0FGsYoRBAGzgoD4rR9H+xe/BEM8DgBIbN2K5A0fRJvZXFTAolAoVMzSr1YSNRgMcm9ZOp3murMsFlS8FL99+3YcPnyYa+OaTCb4/X6sX7++qKzu2NgYV5GwWCy88pacsrpMqYEt2TODzGq1clUHFpNbS2KGHP1UJvkmruom3k817xyrIFaprLKch4y4PUBlg5ctV1fSRs1kMpibm4PH46mY1BiLxbBhwwZcfPHFePnllzEzM8PLcPv9fmzZsgXBYBCHDh2SbE8sFlvgIcvniyvRqZFak3uUUNWQixzvXb0ePrlL1h6PB21tbZLzWqfTKWrQKOW9lDPvpeZHLUlt9cz7UsTnyl7qgbOJfUolpKlNAk1ryYFEc6HOpxQB4I8Pa++aNTB8+tMQ/vEfMf9nV8D6vvfBZbEgl8vxG18sFisb6ynWc5WSt2Iek1gshkKhUBTDa7fbkc1muefB6XSiu7sb+Xye76e7u5sbTMDZ0smjo6NFhnOhUIDL5ZKlPsCkkqanp3Hq1CkkEgnenra2NvT398Nms3HjTE5ihhz91EQigbGxsQXH6+7u5qWpgeperra2NqxZswbLli2rK2lLqhgAC10YHR2tqI1qs9kkVTrE/bNy5Ur09/djfHycJ0+xFxR2PaoVJ7BYLHA4HFyzubR4S6FQ4GNILdSS3FNvOeBakHMsJTx8tSxZT0xMSM5rJQ0apbyXjdZNZv1XWk69lEqGuth7XUmyUpxjUE+71SqBpqXkQKK5IKNYxYgf1vrVqyA88AAMvnb+PbvxjY2N4dChQ0ilUkUhDRMTE4jH49i+fTs3jCvJW6XTaUxOTnJj0Gg08gdePB6HwWDA5OQkPB4PhoaGipQFUqkUjh49ikgkgo0bNyKZTGLv3r0L2sP2L26PlFRSJBLB8ePHkc/nYbPZuAcjGo3i4MGDWLVqFVavXi3Ly5XJZDA6Oiqpnzo7O4vh4WHk83nY7Xa+TSQSQSKRwPLly2vydtazPCynGIDb7UahUJDURmVJlnITVwwGA3p7e8+pPV6vF21tbejo6MDo6Cg/f5bIyTzb/f39S75sLkaup7TaGJJTDlgucrx3ABTx8Mldsp6YmJA1r5UwaJTyXlbbj5K6yeJjlSunLm6zlKEuLihSyXsd/79Vw3rbrWYJNDWE16gdNcSBNxtkFKuY0oe1TmQQA+AyYCdOnEAqlSoyjKxWK8xmM0KhEI4cOQK/3y+pNcoqyLEY2GQyWeThy+VyiEQiVZUFrFYrIpFI3e0xm82YmJjA3Nwc3G43V0Mwm838oTExMcEr0lXzckUiEUlvyMzMDGZmZpBKpbjGMNuGhSdEIpGqCh1i6rlhySkGYLFYYLFYJLVRWeUxKU1kOYkrtRQnsFqtaGtrQy6X4y92giDAYrHAZDLJKmDQSOR6SquNIaU8anK9d4IgKNaeah5eh8OB/fv3y57X9Rg0Snkv5ewnEokokthVS5uTyWRV+btq3mu9Xq/IvCYJNO2iljjwZoOMYhUj52HNtEHdbvcCI1Ov1/PfRyIRyQIIzCuUzWah0+kWlGdmD+BsNiupLDA0NIRcLie7PZUMR5ZEYrPZMD8/z+Pp2HKnzWZDJpPB5ORkVS+Xx+PB6dOnJb0hzABn0lKlWs4Oh4O/OMgpJFHvDUtOMQBmfFTTRmUJUvXEecotThCNRpHNZrFy5UokEomi2Gy2vJ7NZlUlvi/HUypnDCnlUZPjvQuFQgDAFVCUaI+UhzccDvPCPfXcZ+SglPdSzn7i8Tj6+vrqjoOW2+ZUKiVL/o6FalXyAhuNRnR3d2N0dLSudpMEmjZRWxx4M0EjXcXIeVg7nU7Mz8/DbDaXTWwym82Ix+NcgaAS7MaYzWZ5pTn2ucViQTwe54ZpZ2enpHEJQLLAB2uPlOHIDHGPx4O5ubmiKmtmsxkWi4U/GHt7eyW9XHLiBlnsa2dnJ1KpFObm5ng/2mw22O12Xs2uGkrcsPL56sUAkskkdDpdVW1UliBVT5ynnPZks1ke4+12u+FyuRa0SRAErtqhJqp5Shcj9rQScrx37OW1UR4+OYV75NxnxFR6IVbKeyl3P0roJss9VjKZrEn+TsoL3NnZCZvNVle7SQJNe6g1DrxZIKNY5VR7WGezWRiNRiSTyQWFOdhytdForLrsPz8/D5PJhPn5+YrlmYE/Jo9UgnkvpQp8MCNRSunC4XDAaDTyxKxSbWVWUILdrKW8XKlUqqo3hPWVTqdDe3v7gpcLdrxq/ajUDYt5cKQMXpvNBqvVyh+eUtqo7IF7ruEcctrDEu3EfV3aX7lcTrWep3rHkFLnJcd7xz5nLyul1+Nc2iP1kiqncI+c+SHnWEp5L2vZT726yXKPBaAm+btqXuB647dJAk17qDkOvBlQ35OJWIDUjY8ZjadOnYLNZoPFYuHGbCqVQjqdRn9/Py/MUQn2YGA37tKqd8AfDV6pam2s9DIrTVyuwEcgEEAqlSpSumAGKFO6sFqtvKBER0dHUZU1cUEJlrAHVE7MkOMNCQaDKBQKmJycRGdnZ1H5aHG7q/WjUjcsucUAAoEAhoaGZD3Q6onzlNser9erac9TPWNIqfOScyxWAGd8fJzPdbFajF6vR3d3t+z2VFvdWLFiRdXCPXLmh5xjrVy5UpG+rvWaKTk/Kh3L6XTWJH8nxwtcb0IaSaBpC4oDX1zIKNYIlW58Ot3ZSnNnzpzB3NwcTCYTN4rn5uZgMBgW3KTLYTKZ0N7ejkgkgkKhwCuusZAMvV4Pj8cDi8WC4eHhitXali1bhs7OTuzbt69igY/+/n5evrlUySCRSHDv7Pr163nBkXIFJbZs2VJUjliq76p5Q7q7u+FyuZBIJCq2mxU4kUKpG5ZcD06jHmhy26PX65fE87TYWdiN9KjJPVYymcSRI0e4GoTVauVqEHa7HWvXrpXVHjmrG5OTk1i7di3i8Timpqa4ccdCAhwOh6z5IedYExMTCAQCdfe1Gq+Z3W6XbajXu7pTCySBph0lB4oDX1x0Qq31LDXAZz7zGXz2s58t+mzt2rU4cuQIgLNJQ3feeSceffRRzM3N4R3veAe+8Y1vwO/313SceDwOt9uNWCwGl8ulWPvLUWnCplIpHDp0iCedlerr+v1+WK1WbNiwQdKbwEoTj42NceOUHautrQ0GgwHd3d0IBAI4dOgQQqFQUSgFizXeuHEj93CUFvjo6urC+vXrYbfbsWfPHhQKhaKEPdYOZhhffPHFmJ6exksvvYRwOMz34/P58MY3vhErV66sqQ/lJL+Nj49X1XuWgl0PlpVfSjabRSaTqXo9xG2WUwygUTd0uQmEjcyMVvJY1fqRHSsUCiGbzcJsNqOzs3PRzqvStXc6nXy+Mk+xeLWFeYpXr17N21/tHiJnzI6Pj/MCL2w/7e3t2LJli6z5WMux5ufnF12neCnGYiVPOTOe1ZwkpRXDsVa0pOTAntWVXqxYsqt47hPy7bWmfZXYsGEDnnrqKf5v8VvT7bffjl/84hf48Y9/DLfbjVtuuQVXXXUVnn322aVoalXkiM93dHSgs7OTxxazSmxyE5uYpyMcDmNqagr5fJ57iZPJJLq6uvgNQk61NqfTiZ6eHuh0ugUFPrLZLLLZLPdGl7bDbDbzss5dXV3YtGkThoeH+X6WL1+Orq6umvtRjjdEqsCJHBZjmV1s1Ij/XbpNI+LH5HqUGuV5UjILW+6DsfQ6LKZfodK1Z2E6Pp+vbEyxuLAPSxKtt4BFNBpFPB5HMBhET08PN8JZOFMikZCVQCp3JcXlcikyhhrpBZVzLK2GK2jJcKwFrSk5UBz44tK0RrHReLaEcSmxWAwPPvggHnnkEbztbW8DADz00ENYv349XnjhBbzpTW9qdFMlqVV8vnTynmtik9Fo5OETpVSr1iZuc0dHB28z0wH2+/2wWCzIZrOwWCwLDEf2+dzcHIaHh5HJZHgCDks+SafT53SzkmM8VipwInf/St2waikG0EjkGuCLbagrmYVda7EM8fVg41rJ61Ht2ou1bFlSrRixcalUAYtwOIxMJgOfz1fWO1VLAqncpV+lxlCjXhrlHktr4QpaMxzlolUlB62+WGmBpjWKBwcH0d3dDavVip07d2LXrl1Yvnw5Dhw4gFwuh0svvZRvu27dOixfvhzPP/+8qozipRCfB4A1a9aULS0qvjlUuvHLaTPTO43FYmVjk5kHgj2EtXSzApS5YYn70ePxIJfL8b7xeDwLrodWqWc5VqmkxqUoliGF3DlUTcuWJaeyKnyVxpH4HsK2Ec/9ZDLJDTilE0i1loipJI001OtBq4ajHLSs5KC1Fyut0JRG8Y4dO/Dwww9j7dq1GB8fx2c/+1m85S1vwcGDB3n2tMfjKfqN3+/nGruVmJubK9LhZKU2F4ulEp+vVFpUKcF8dnNlMmulmrd6vR5er5d7JbR2swLqv2GxfjQYDJiYmMDs7CxfrnY4HHA4HKo+fznUuxyrVFLjUhXLqKc9crVsAfBxND4+vkClQnwPCYfDOHbsGFedYf/t6uqCz+fD8PCwogmkTKGGHSubzcJms9HSr8rQsuFYDa0rOWjlxUpLNKVRfPnll/P/37x5M3bs2IG+vj489thjdXkgdu3atSCBbzFRo/i8UoL5TIcznU5zQ0P8YFTqIbyU1HPDyufzmJ2dRSqV4prELNs/Ho/zB5Caz1+KWpZjK3mTlcrCVluxjFrnkNTLMCvBPjs7i1wux8t+s7CKTCYDh8PB2y02etiKEDumUhnvbW1tCAQCOHLkCMbGxooSWgcGBmjpV2Vo3XCUgpQciFJa4kp7PB6sWbMGx48fx2WXXYZsNotoNFrkLZ6cnCwbgyzm7//+73HHHXfwf8fjcSxbtmyxmr0o4vOLbWDI3U+pDif7vKOjA8FgkC/7turNymAwYHZ2li97i40Tp9OJaDQKQRBkSdKpjVqWY5PJZEVvstPpVGQpXs6YFRfLUEPxDrlatrOzs0gmkwvGESvqw8bR9PQ0AGD16tVlQ6fkhFjIDXtIJBJ8xa40YW9iYgJOp1Nxw7hZVRMaQTMbjhTOQ5SivVF8DiSTSQwNDeH666/H1q1bYTKZ8PTTT+Pqq68GABw9ehTDw8PYuXOn5H4sFovsik1KoLT4vNRytVIGRi1tltLhFASBblZNitzl2FAoxONhK3mTlUhqrKVYxszMjCqKdyipZZvP57lcUaXQKXGIxeDgYFk5Rjl9zV6IWOGeUim5XC6neHxqs6omNIpmNhxJyYEopSmN4r/7u7/Du9/9bvT19WFsbAz33HMPDAYD3v/+98PtduMjH/kI7rjjDrS3t8PlcuFv//ZvsXPnTlUl2QGLp2SwmAZGrW2uZMi3+s1qfn6eGwblkhFtNhscDgfm5+eXuqk1I2c5Np/PY2xsrKo3efXq1XWHDskda8BZnd3FjoVVag4BZ8eRw+Hg40gchpNOp/m/mUFdjtLlcUEQiuKOayGdTmNqakqycM/U1BR6e3sViZVsVtWERtLs92JSciDENKVRPDIygve///0Ih8Po7OzERRddhBdeeAGdnZ0AgK9+9avQ6/W4+uqri4p3qBGllQyUMjCkliOVusm08s2KhcSwZKrSZERmMGtxyVLOcmyhUMDs7KysxDYlsrDljrVGxcIqNfaNRiMcDgfsdjtmZ2eLEjZdLhccDgdyuRwA6dAQvV6PcDgMQJ46TSVyuRwv6y4u3MPCghKJBGZmZnib6qGZVRMaTbPfi0nJgWBo74kqg0cffVTye6vVivvvvx/3339/g1pUH0opGcjJHpZzLDnLkUrdZFr1ZiVesgwGg2WNEK0uWcpZjnU4HEin07K9l0pkYVcbaywW1mKxoL+/v8hTvBixsEqMfXFfBwKBsuNITmgIU7yoV50mn8/LLtxTL82smrAUNPu9mJQcCKBJjeJmRM6EreS9FS9Xs4e4uPpVLQYGW45Mp9Mwm80wmUw8Uad0ObKeNtd67lql0vmLlyyj0SicTiesVivy+Tyi0aimlyzlLMd2d3fj9OnTspN7lEqkqjTWxF7H9vb2BYZjqddRLYldcscRcDY0pNL1UEoNxmg0yirco8QKSDOrJiwVzXwvJgiAjOKmQcp7y5arE4nEgiVUpnkrJ3u4NEmGLYOyJJlsNlvTcrqWD00AAB+1SURBVGSrJ8BUO/9mXrKsdm5MYUNOck8jxlEtXsf5+XlF2qPUeckdR1LbKKUGYzKZeNGQSoV7PB4PTCaT7POrRDOrJhAEsTjQ3aAJqJZMsnLlSpjNZhw7dgwmkwl2u50nt8RiMUxPT2PNmjVVl+KVTJJp9QQYueffzEuW1c5NTnJPMplsyDiS63WMRqOYnJysuz1Kzw8540hqG6XUYGw2Gy9RXalwj9/vb7iKB0EQBEBGseaRm0wC/FGMn2WMswxyuQaWUkkyrZ4AU+v5N/OSpdS5yfEmDw4ONmQcyfE6smS0etuzWPNDzjhabDUY8X4qFe5ZKhUPgiAIMoo1QqUYRTnLukyYf/ny5RWVDLLZbMOSZFo9AabVz78WpLyXqVSqYf0ox+soTkarpz1qHR+LpSpTWrhHjSoeBEG0BmQUawCp2EJBEKom0eVyOQiCwHWZS7cRBIE/nKRQKkmm1RNgWv38a6WS97KR/SjH61hrMpqcxNjFPq9a0aKqTDOHIBEEoSxkFKucarGFPT09PImOeYGZwctKP7OkFbb0WyqnlMvlGpok0+oJMK1+/krR6H6s5nWsJRlNTmKsWseHUuE8jQwLauYQJIIglIOeuipGTmxhJBKB2WzG4OAgTCYTrFYrbDYb8vk8j/FdtWoV7HZ7w5NkKnnCWj0BRnz+Ho9ngXZss5+/UizFOFIiGS2fz2NoaEgyMbaV58dSoRYZPUJZ1Hhd1dgm4ixkFKsYubGFQHHpVfa9+LNAINDQJJlqclKtnADD+jEcDmNwcLCoVK5Op0NnZ2dTn79SLFUiVT3JaIFAABMTE5IvuhMTE4rMV0I+rS4P2ayo8bqqsU3EHyGjWMXIiS3MZDLQ6XTo6+tboEHsdrvhcDiQzWZhNBobliQjV06KEmCKX2bExjEhD7WNIzkhFnJedHt7e1V1Xs1Mq8tDNitqvK5qbBNRDBnFKkZObCEzptra2qom0blcrkVPkqlFTqpVE2BYHwHAmjVrypbebWZJOqWRO44atWQp1Z54PC47iU6p+UpUptXlIZsVNV5XNbaJWAgZxSpGTsykx+NBJpORnUS32EkytcpJKZkAo5U4LXEf6fX6BdeMJNlqp9o4avSSZaX21JpERwlii4ta5e+I+lDjdVVjm4iFkFGsYuTEKPb19WFiYkI1STlLJSelpTgtNUtuNSNqWrJs9SRTtUFzsTlR43VVY5uIheiXugGENCxG0efzIZPJIBaLIZPJwOfzYdWqVXC5XAgGg7BarYhEIshmsygUCshms4hEIg1PyhF7wsqxGHJSzOgJh8OwWq1wu92wWq0Ih8M4fvw4EomEYsdSgqXoo1aldMnSbDZDr9fzJctMJoPx8fGGxXOzF121zNdWh+Zic6LG66rGNhELod7XANViJtWUbNRouTEtxmmRt7BxLMaSZb1hOmy+jo2NYXp6GrlcDiaTCR0dHeju7lbdykYzQ3OxOVHjdVVjm4iFkFGsEarFFqolaU0sN3bs2LEiZQWdToeuri5FPWFajNNaKimxVkTpJUslw3TY9WVearrejYfmYnOixuuqxjYRCyGjuIlQW1KOeHLrdLpFmexajdNSk3e/mVGyOpxSscni/bhcLpJlWmJoLjYnaryuamwTUQwZxcQ5USgUEIlEMDc3B4vFAq/XC71eXyQ3tmrVKszOzvLlYYfDgVgspmg4w2KUxFWDdFczoAY1EKWWLJUK09FiuE8roNa5qIY5pGXUeF3V2Cbij5BRTNTM+Pg4jhw5gnA4zCe1z+fDunXr4Ha7EYvFYDAYMDk5WVRMxOFwcMNYqXAGpeO01CLdpXXUogai1JKlUmE6Wgz3aRXUNhfVMoe0jtquK6DONhFnIaOYqInx8XHs3bsXqVQKbrcbZrMZ2WwWExMTiMfj2LBhA2ZnZ5FKpZDL5WCz2WAwGDA/P494PM4f9kqFMygZp6Um6S4to7Z+VGLJUqkwHa2G+xCNRW1ziCBaBTKKCdkUCgUcOXIEqVQKnZ2d0OvPKvpZrVaYzWaEQiGcOHECc3NzyGaz8Hg83Bg1Go1wOp2IRqMQBAEGg0Gxdilh9NCytjKotR/rXbJUKkxnMcJ9iOZCrXOIIFoBuvMSsolEIgiHw3C73dwgZuj1erjdbszMzMBoNDb8Zl2v0UPL2sqg5n6sZ8lSqTAdkmUiqqHmOUQQzQ4ZxYRs5ubmuIdLEATk83keL2w0GmE2m5HL5eBwOGAwGHj4Alv6y2QysNlscDgcmJ+fV7x99Rg9tKytDFrvx0qJTUqF6ZAsU/NTKQlZLlqfQwShZcgobiIWO1PZYrHAaDQikUggn89jbm6OG8XsO5PJBKfTCYfDgWQyiVQqhUwmA4PBwL25Op1OdcvDtKytDFrux2qJTUrJKZEsU/MilYQcDAZl7UPLc4ggtA7NqiaBPdBDoRCy2SzMZjM6OzsVfch6vV60tbXh5MmTsNlssFgs0Ov1KBQKSKVSSKfTGBgYQDAYRCQSQTAYXFDRLhqNqnJ5mJa1lUGr/Sg3sUkpOSWSZWo+qiUhb9++XZZhrNU5RBDNABnFTUAikcDBgwcxNTVVVEFuenoa4XAYGzduVMQw1ul08Hg80Ov1yGazPHZ4fn4e2WwWer0eHo8H3d3dSKfTiEajcDqdsFqtyOfziEajql0epmVtZdBiP9aa2KSUnBLJMjUPcpKQjxw5Ar/fXzWUQotziCCaBTKKNY4gCDhx4gRGRkZgMplgt9u5BFoqlcLIyAjsdjs2b95c9000nU5Dr9dj3bp1mJqaQiKRQDqdhsFggNfrRVdXF48v1uLyMC1rK4PW+pESm4h6kZOEHA6HEYlE4PP5qu5Pa3OIIJoFMoo1DjN89Xo92traiiTQ2traEI1GMTIyglWrVsHhcMjaZ6XYZJYA0tnZic7OTiSTSV6tzul0AgBisRjy+TxcLpcml4dpWVsZtNSPlNhE1Is4CbkcZrMZ8Xgcc3NzsveppTlEEM0CGcUaJ5lMYnZ2Fm63W9LLlUwmZRnFUslGpQkgLper6LfZbLYoAUSry8Nabbfa0Eo/UmITUS8s0TibzcJqtS74noWbWSyWmvarlTlEEM2CfJ0YoulhyUbhcBhWqxVutxtWqxXhcBjHjx9HPp+H2+1GMpmEIAhFv2UJIG63mxJACE3BEptoXBPnitfrhc/nQywWQ6FQKPquUCggFovB5/PB6/UuUQsJgpADGcUaRyx/VumB7nA4eHhDJUqTjcxmM/R6PU82ymQymJiYQCAQgNVqRSQSQTabRaFQQDabRSQSoQQQQpOwxCYa18S5wnIt7HY7QqEQMpkMCoUCMpkMQqEQ7HY71q1bV5NeMUEQjYfWAzWO3W5Hb28vBgcHkUgkYDKZuPpELpeDIAjo7e2tugQnN9mot7dXdgLIYusmEwSj3rGm5cQmmmfqIBgMYvv27VynOB6Pw2g0IhAI1KRTTBDE0kFGscbR6XRYsWIFIpEIhoeHkc1muSyb2WzG8uXLsWLFiqoPyVqSjeQk0VUrhEAQSqHUWNNiYhPNM3URDAbh9/vrqmhHEMTSQUZxk2CxWOB2uxeUXpab2FFrspFUAojcQggEUS9KjzUtJTbRPFMner1eluwaQRDqg4xijcNigQFgzZo1ZSvIiQsPVEKpKkq1FkIgiHOllcdaK587QRDEYkFrOhpHHAus1+thsVhgt9t5CWZx4QEplEo2qqUQAkHUQyuPtVY+d6J2BEFAKpVCPB5HKpVakJRNEMRZyFOscZQsPKBEshEVQiAaRSuPtVY+d6I2KO6cIORDRrHGUbrwQL3JRlQIgaiFepQTWnmsncu5k0pF60Fx5wRRG833tGgxlIoFFlNPstFitIdoTur1YLXyWKv13Mlb2HpQ3DlB1A4ZxRqHxQLPzs4iEokUeQOSyWTDCw+orT2lkLdMHSjhwVL7WFtMajl3tXoLaS4uLrXEnWtFcYUgFhsyipsAtRUeUFt7GOQtUwdKerDUOtYagZxzV6u3kObi4kNx5wRRO2QUNwlqKzygtvao1VvWiijtwVLbWGsk1c5djd5CmouNoZVj7gniXKHZ0ESorfCAWtqjVm9Zq7IYHiy1jLWlQOrc1eYtpLnYOFo55p4gzhXSKSaanlbQdNWSDqnYg1UO8mAph9r6uhXmolpQSnueIFoJeuoQTY/avGVKo7X4TPJgNQ619XWzz0W10cox9wRxLpBRTDQ9zRxbp8X4TK2rRmhJNUFtfd3Mc1GttHLMPUHUCt15iKZHbd4ypdByfKZWPVha88oD6urrZp2LaqeVY+4JohbIKCaaHrV5y5RCjcoCtaA1D5YWvfIMtfR1s85FgiCaAzKKiZZATd4ypWiG+EyteLC07JVnqKWvm3EuEgTRHJBRTLQMavGWKQXFZzYOrXvl1UazzUWCIJoDeloSLYVavGVKQPGZjaMZvPJqo5nmIkEQzQHpFBOERiEd0sahNr1fgiAIQnnIKCYIDcPiM30+HzKZDGKxGDKZDHw+n6oTv7QG88onk8kFhVGYV97tdpNXniAIQsOQW4MgNA7FZy4+pJpAEATR/JBRTBBNAMVnLj6kmkAQBNHckFFMEAQhE/LKEwRBNC9kFBMEQdQAeeUJgiCaE0q0IwiCIAiCIFoeMooJgiAIgiCIloeMYoIgCIIgCKLlIaOYIAiCIAiCaHnIKCYIgiAIgiBaHjKKCYIgCIIgiJaHjGKCIAiCIAii5SGjmCAIgiAIgmh5yCgmCIIgCIIgWh4yigmCIAiCIIiWh4xigiAIgiAIouUho5ggCIIgCIJoecgoJgiCIAiCIFoeMooJgiAIgiCIloeMYoIgCIIgCKLlIaOYIAiCIAiCaHnIKCYIgiAIgiBaHjKKCYIgCIIgiJaHjGKCIAiCIAii5SGjmCAIgiAIgmh5jEvdAC0jCAIAIB6PL3FLCIIgCIIgiHIwO43ZbZUgo7gOEokEAGDZsmVL3BKCIAiCIAhCikQiAbfbXfF7nVDNbCYqUigUMDY2hra2Nuh0uqVujmzi8TiWLVuGM2fOwOVyLXVzmhrq68ZBfd04qK8bB/V146C+bgxL0c+CICCRSKC7uxt6feXIYfIU14Fer0dvb+9SN+OccblcNPEbBPV146C+bhzU142D+rpxUF83hkb3s5SHmEGJdgRBEARBEETLQ0YxQRAEQRAE0fKQUdyCWCwW3HPPPbBYLEvdlKaH+rpxUF83DurrxkF93TiorxuDmvuZEu0IgiAIgiCIloc8xQRBEARBEETLQ0YxQRAEQRAE0fKQUUwQBEEQBEG0PGQUEwRBEARBEC0PGcVNyq5du3DBBRegra0NXV1d+PM//3McPXq0aJtMJoObb74ZPp8PTqcTV199NSYnJ5eoxdrlgQcewObNm7kQ+c6dO/HLX/6Sf0/9vHh84QtfgE6nw2233cY/o/5Whs985jPQ6XRFf+vWrePfUz8ry+joKD7wgQ/A5/PBZrNh06ZN2L9/P/9eEAR8+tOfRjAYhM1mw6WXXorBwcElbLE26e/vXzCudTodbr75ZgA0rpVkfn4en/rUpzAwMACbzYaVK1fic5/7HMT6Dmob12QUNyl79uzBzTffjBdeeAFPPvkkcrkc3v72t2N2dpZvc/vtt2P37t348Y9/jD179mBsbAxXXXXVErZam/T29uILX/gCDhw4gP379+Ntb3sbrrzyShw6dAgA9fNisW/fPnzrW9/C5s2biz6n/laODRs2YHx8nP/9/ve/599RPytHJBLBhRdeCJPJhF/+8pd4/fXX8S//8i/wer18my996Uu477778M1vfhMvvvgiHA4H3vGOdyCTySxhy7XHvn37isb0k08+CQB473vfC4DGtZJ88YtfxAMPPICvf/3rOHz4ML74xS/iS1/6Er72ta/xbVQ3rgWiJZiamhIACHv27BEEQRCi0ahgMpmEH//4x3ybw4cPCwCE559/fqma2TR4vV7h3//936mfF4lEIiGsXr1aePLJJ4WLL75YuPXWWwVBoHGtJPfcc49w/vnnl/2O+llZPvnJTwoXXXRRxe8LhYIQCASEL3/5y/yzaDQqWCwW4T/+4z8a0cSm5dZbbxVWrlwpFAoFGtcKc8UVVwgf/vCHiz676qqrhOuuu04QBHWOa/IUtwixWAwA0N7eDgA4cOAAcrkcLr30Ur7NunXrsHz5cjz//PNL0sZmYH5+Ho8++ihmZ2exc+dO6udF4uabb8YVV1xR1K8AjWulGRwcRHd3N1asWIHrrrsOw8PDAKifleZnP/sZtm3bhve+973o6urCli1b8J3vfId/f/LkSUxMTBT1t9vtxo4dO6i/6yCbzeKHP/whPvzhD0On09G4Vpg3v/nNePrpp3Hs2DEAwB/+8Af8/ve/x+WXXw5AnePauCRHJRpKoVDAbbfdhgsvvBAbN24EAExMTMBsNsPj8RRt6/f7MTExsQSt1DavvfYadu7ciUwmA6fTiccffxznnXceXnnlFepnhXn00Ufx0ksvYd++fQu+o3GtHDt27MDDDz+MtWvXYnx8HJ/97Gfxlre8BQcPHqR+VpgTJ07ggQcewB133IF/+Id/wL59+/Dxj38cZrMZN9xwA+9Tv99f9Dvq7/r47//+b0SjUdx4440A6P6hNHfffTfi8TjWrVsHg8GA+fl5fP7zn8d1110HAKoc12QUtwA333wzDh48WBQPSCjL2rVr8corryAWi+E///M/ccMNN2DPnj1L3aym48yZM7j11lvx5JNPwmq1LnVzmhrmzQGAzZs3Y8eOHejr68Njjz0Gm822hC1rPgqFArZt24Z//ud/BgBs2bIFBw8exDe/+U3ccMMNS9y65uXBBx/E5Zdfju7u7qVuSlPy2GOP4Uc/+hEeeeQRbNiwAa+88gpuu+02dHd3q3ZcU/hEk3PLLbfg5z//OZ555hn09vbyzwOBALLZLKLRaNH2k5OTCAQCDW6l9jGbzVi1ahW2bt2KXbt24fzzz8e9995L/awwBw4cwNTUFN74xjfCaDTCaDRiz549uO+++2A0GuH3+6m/FwmPx4M1a9bg+PHjNK4VJhgM4rzzziv6bP369TxchfVpqQoC9fe5c/r0aTz11FO46aab+Gc0rpXlrrvuwt13341rr70WmzZtwvXXX4/bb78du3btAqDOcU1GcZMiCAJuueUWPP744/jNb36DgYGBou+3bt0Kk8mEp59+mn929OhRDA8PY+fOnY1ubtNRKBQwNzdH/awwl1xyCV577TW88sor/G/btm247rrr+P9Tfy8OyWQSQ0NDCAaDNK4V5sILL1wgmXns2DH09fUBAAYGBhAIBIr6Ox6P48UXX6T+PkceeughdHV14YorruCf0bhWllQqBb2+2Mw0GAwoFAoAVDqulyS9j1h0PvrRjwput1v47W9/K4yPj/O/VCrFt/mbv/kbYfny5cJvfvMbYf/+/cLOnTuFnTt3LmGrtcndd98t7NmzRzh58qTw6quvCnfffbeg0+mEJ554QhAE6ufFRqw+IQjU30px5513Cr/97W+FkydPCs8++6xw6aWXCh0dHcLU1JQgCNTPSrJ3717BaDQKn//854XBwUHhRz/6kWC324Uf/vCHfJsvfOELgsfjEX76058Kr776qnDllVcKAwMDQjqdXsKWa5P5+Xlh+fLlwic/+ckF39G4Vo4bbrhB6OnpEX7+858LJ0+eFH7yk58IHR0dwic+8Qm+jdrGNRnFTQqAsn8PPfQQ3yadTgsf+9jHBK/XK9jtduE973mPMD4+vnSN1igf/vCHhb6+PsFsNgudnZ3CJZdcwg1iQaB+XmxKjWLqb2W45pprhGAwKJjNZqGnp0e45pprhOPHj/PvqZ+VZffu3cLGjRsFi8UirFu3Tvj2t79d9H2hUBA+9alPCX6/X7BYLMIll1wiHD16dIlaq21+/etfCwDK9h+Na+WIx+PCrbfeKixfvlywWq3CihUrhH/8x38U5ubm+DZqG9c6QRCVFiEIgiAIgiCIFoRiigmCIAiCIIiWh4xigiAIgiAIouUho5ggCIIgCIJoecgoJgiCIAiCIFoeMooJgiAIgiCIloeMYoIgCIIgCKLlIaOYIAiCIAiCaHnIKCYIgiAIgiBaHjKKCYIgNM6ePXug0+n433PPPbfUTSIIgtAcZBQTBEFonO9973tF//7+97+/RC0hCILQLlTmmSAIQsOk02n4/X4kEgk4nU4kk0l4vV6Mj4/DYrEsdfMIgiA0A3mKCYIgNMzjjz+ORCIBALjvvvsAAJFIBLt3717KZhEEQWgOMooJgiA0DAuV2Lx5Mz70oQ9h7dq1RZ8TBEEQ8iCjmCAIQqOMj4/jqaeeAgB84AMfKPrvr371K4RCoar7CIfD+MQnPoG1a9fCZrPB7/fjsssuw+OPPw4AePjhh3kC36lTpyruJ5PJ4Otf/zouueQSBAIBmM1mdHV14dJLL8WDDz6IfD5f59kSBEEsLhRTTBAEoVG+8pWv4K677oJer8fw8DB6enpw8uRJrFy5EoIg4N5778XHP/7xir9/7bXXcNlll2FycrLs93/1V3+FnTt34kMf+hAA4OTJk+jv71+w3R/+8AdceeWVOH36dMVjXXDBBdi9ezf8fn9tJ0kQBNEgyCgmCILQKOeffz5effVVvO1tb8PTTz/NP7/ooovw7LPPYuvWrdi/f3/Z30ajUWzYsAFjY2MAgOuvvx5/+Zd/ic7OThw/fhz33nsvnn/+eezYsQMvvvgigPJG8fHjx7Ft2zbEYjG4XC7cfPPN2L59O5YtW4ZwOIyf/exn+Na3voV8Po8dO3bgd7/7HUwm0+J0CEEQRD0IBEEQhOZ4+eWXBQACAOG73/1u0XcPPPAA/+7QoUNlf3/bbbfxbf7t3/5twff5fF648sor+TYAhJMnTy7Y7s1vfrMAQNiyZYsQCoXKHuuXv/yloNfrBQDCt7/97dpPliAIogFQTDFBEIQGYYl0NpsNV199ddF373vf+2A2m4u2EzM3N4eHH34YwNmwhltvvXXBNgaDAd/61rdgtVortuF3v/sdLxTyve99Dx0dHWW3e+c734m/+Iu/AAB+XIIgCLVBRjFBEITGyOfzeOSRRwAA7373u+FyuYq+b29vx7ve9S4AwI9+9CMUCoWi7/fv349oNArgj4l55fD7/XjHO95R8fuf/exnAIC1a9di06ZNkm3+kz/5EwDAvn37KOmOIAhVQkYxQRCExvj1r3/Nk+MqGbXs85GRETzzzDNF3x08eJD//9atWyWPtW3btorfsXjlo0ePFpWZLvd3yy23AAByuRxmZmaqnCFBEETjIaOYIAhCY7CQCJ/Ph3e+851lt/mzP/szeDyeou0ZkUiE/39nZ6fksaS+n5qaktPcBaRSqXP6HUEQxGJiXOoGEARBEPKJxWI8bCEcDvPYYSl+8pOf4Bvf+AYcDoeibZmfnwdwVgXjhz/8oezf9fT0KNoOgiAIJSCjmCAIQkM89thjyGQyNf0mmUziJz/5Ca6//noAgNfr5d+FQiGsWbOm4m+lCoD4fD6+/40bN9bUJoIgCLVBRjFBEISGYKEQwWAQ//qv/1p1+7vuugsjIyP4/ve/z43iDRs28O8PHDiACy+8sOLvK+kcA8CWLVvw3HPP4cSJE5iYmEAgEJB7GgRBEKqDincQBEFoBHG1ultuuQVf+9rXqv7mtttuw7333ltU9S6TySAQCCAWi+GCCy7A3r17y/52cnIS/f393DNdWrzjiSee4OoUd999N3bt2lX/SRIEQSwRlGhHEAShEb7//e+D+TGY7m812HaFQoHH/VqtVnzwgx8EcFYi7d57713wu0KhgL/+67+WDNV4+9vfju3btwMAvvzlL+Oxxx6TbMtrr72G3bt3y2o3QRBEoyFPMUEQhEZYtWoVhoaG0NXVhfHxcej11f0ahUIBvb29GB8fx4YNG7gc28zMDDZs2ICJiQkAZ8s8X3fddUVlnp977jls376de5JPnTqFvr6+ov0PDQ1h+/btXGbt3e9+N6655hqsXr0aBoMBU1NTePnll7F792688MILuPPOO/GVr3xFyW4hCIJQBIopJgiC0ADPPvsshoaGAADvec97ZBnEAKDX6/Ge97wH3/jGN3Do0CEcOHAAW7duRXt7O371q1/hsssuQygUwg9+8AP84Ac/KPrtjTfeiLe85S3cKC5X3W7lypV4/vnncfXVV+PgwYPYvXu3pDe4tNAIQRCEWqDwCYIgCA0g1houLetcDfH24v2cf/75eP3113HnnXdi9erVsFgs6OjowJ/+6Z/ikUcewUMPPYR4PM63d7vdZfe/Zs0avPLKK3jkkUdw9dVXY/ny5bDZbDCbzQgGg3jrW9+Kf/qnf8KBAwfw6U9/uqa2EwRBNAoKnyAIgiAqctNNN+HBBx9Eb28vzpw5s9TNIQiCWDTIU0wQBEGUJZ1O46c//SkA4E1vetMSt4YgCGJxIaOYIAiiRRkaGkKlxcL5+Xl89KMfxfT0NADghhtuaGTTCIIgGg6FTxAEQbQoN954I/bu3Ytrr70WO3bsQFdXF9LpNF599VV85zvfwUsvvQQAuPTSS/HEE09Ap9MtcYsJgiAWD1KfIAiCaGEOHz6Me+65p+L3F154IR599FEyiAmCaHrIU0wQBNGiHD16FP/1X/+Fp556CqdOnUIoFEIul4PP58O2bdtwzTXX4Nprr5Ut/0YQBKFlyCgmCIIgCIIgWh56/ScIgiAIgiBaHjKKCYIgCIIgiJaHjGKCIAiCIAii5SGjmCAIgiAIgmh5yCgmCIIgCIIgWh4yigmCIAiCIIiWh4xigiAIgiAIouUho5ggCIIgCIJoecgoJgiCIAiCIFqe/x9sKwgOfg7XagAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(X_age,\n", @@ -2029,7 +834,7 @@ " linewidth=4)\n", "ax.set_xlabel('Age', fontsize=20)\n", "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='Degrees of freedom');\n" + "ax.legend(title='Degrees of freedom');" ] }, { @@ -2053,17 +858,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "703d2570", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.194776Z", - "iopub.status.busy": "2023-07-25T23:59:49.194595Z", - "iopub.status.idle": "2023-07-25T23:59:49.246216Z", - "shell.execute_reply": "2023-07-25T23:59:49.244766Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "ns_age = NaturalSpline(df=4).fit(age)\n", @@ -2072,7 +869,7 @@ " ns_year.transform(Wage['year']),\n", " pd.get_dummies(Wage['education']).values]\n", "X_bh = np.hstack(Xs)\n", - "gam_bh = sm.OLS(y, X_bh).fit()\n" + "gam_bh = sm.OLS(y, X_bh).fit()" ] }, { @@ -2092,29 +889,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "766a7b1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.261161Z", - "iopub.status.busy": "2023-07-25T23:59:49.260747Z", - "iopub.status.idle": "2023-07-25T23:59:49.446433Z", - "shell.execute_reply": "2023-07-25T23:59:49.445528Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "age_grid = np.linspace(age.min(),\n", " age.max(),\n", @@ -2134,7 +912,7 @@ "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n", "ax.set_xlabel('Age')\n", "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of age on wage', fontsize=20);\n" + "ax.set_title('Partial dependence of age on wage', fontsize=20);" ] }, { @@ -2155,28 +933,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "2a9ed841", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.451802Z", - "iopub.status.busy": "2023-07-25T23:59:49.451326Z", - "iopub.status.idle": "2023-07-25T23:59:49.615361Z", - "shell.execute_reply": "2023-07-25T23:59:49.614953Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "year_grid = np.linspace(2003, 2009, 100)\n", "year_grid = np.linspace(Wage['year'].min(),\n", @@ -2197,7 +957,7 @@ "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n", "ax.set_xlabel('Year')\n", "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of year on wage', fontsize=20);\n" + "ax.set_title('Partial dependence of year on wage', fontsize=20);" ] }, { @@ -2216,17 +976,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "ccf068b1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.617403Z", - "iopub.status.busy": "2023-07-25T23:59:49.617260Z", - "iopub.status.idle": "2023-07-25T23:59:49.637989Z", - "shell.execute_reply": "2023-07-25T23:59:49.637017Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "gam_full = LinearGAM(s_gam(0) +\n", @@ -2235,7 +987,7 @@ "Xgam = np.column_stack([age,\n", " Wage['year'],\n", " Wage['education'].cat.codes])\n", - "gam_full = gam_full.fit(Xgam, y)\n" + "gam_full = gam_full.fit(Xgam, y)" ] }, { @@ -2253,34 +1005,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "38b719f1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.642419Z", - "iopub.status.busy": "2023-07-25T23:59:49.642108Z", - "iopub.status.idle": "2023-07-25T23:59:49.817915Z", - "shell.execute_reply": "2023-07-25T23:59:49.816775Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_gam(gam_full, 0, ax=ax)\n", "ax.set_xlabel('Age')\n", "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n" + "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);" ] }, { @@ -2296,24 +1030,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "02142f6e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.823446Z", - "iopub.status.busy": "2023-07-25T23:59:49.822838Z", - "iopub.status.idle": "2023-07-25T23:59:49.864597Z", - "shell.execute_reply": "2023-07-25T23:59:49.863709Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "age_term = gam_full.terms[0]\n", "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n", "year_term = gam_full.terms[1]\n", "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n", - "gam_full = gam_full.fit(Xgam, y)\n" + "gam_full = gam_full.fit(Xgam, y)" ] }, { @@ -2329,39 +1055,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "94587b05", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:49.871560Z", - "iopub.status.busy": "2023-07-25T23:59:49.871033Z", - "iopub.status.idle": "2023-07-25T23:59:50.059620Z", - "shell.execute_reply": "2023-07-25T23:59:50.058456Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Partial dependence of year on wage')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_gam(gam_full,\n", @@ -2369,7 +1066,7 @@ " ax=ax)\n", "ax.set_xlabel('Year')\n", "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of year on wage', fontsize=20)\n" + "ax.set_title('Partial dependence of year on wage', fontsize=20)" ] }, { @@ -2377,33 +1074,15 @@ "id": "5b373d5c", "metadata": {}, "source": [ - "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable. " + "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "bba4c757", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.065747Z", - "iopub.status.busy": "2023-07-25T23:59:50.064570Z", - "iopub.status.idle": "2023-07-25T23:59:50.193175Z", - "shell.execute_reply": "2023-07-25T23:59:50.192814Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " deviance df deviance_diff df_diff F \\\n", - "0 3.975443e+06 2991.000589 NaN NaN NaN \n", - "1 3.850247e+06 2990.000704 125196.137317 0.999884 101.270106 \n", - "2 3.693143e+06 2987.007254 157103.978302 2.993450 42.447812 \n", - "\n", - " pvalue \n", - "0 NaN \n", - "1 1.681120e-07 \n", - "2 5.669414e-07 " - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "gam_0 = LinearGAM(year_term +\n", " f_gam(2, lam=0))\n", @@ -2694,65 +1187,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "08026a6b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.332475Z", - "iopub.status.busy": "2023-07-25T23:59:50.332022Z", - "iopub.status.idle": "2023-07-25T23:59:50.342074Z", - "shell.execute_reply": "2023-07-25T23:59:50.339526Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LinearGAM \n", - "=============================================== ==========================================================\n", - "Distribution: NormalDist Effective DoF: 12.9927\n", - "Link Function: IdentityLink Log Likelihood: -24117.907\n", - "Number of Samples: 3000 AIC: 48263.7995\n", - " AICc: 48263.94\n", - " GCV: 1246.1129\n", - " Scale: 1236.4024\n", - " Pseudo R-Squared: 0.2928\n", - "==========================================================================================================\n", - "Feature Function Lambda Rank EDoF P > x Sig. Code \n", - "================================= ==================== ============ ============ ============ ============\n", - "s(0) [465.0491] 20 5.1 1.11e-16 *** \n", - "s(1) [2.1564] 7 4.0 8.10e-03 ** \n", - "f(2) [0] 5 4.0 1.11e-16 *** \n", - "intercept 1 0.0 1.11e-16 *** \n", - "==========================================================================================================\n", - "Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", - "\n", - "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n", - " which can cause p-values to appear significant when they are not.\n", - "\n", - "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n", - " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", - " are typically lower than they should be, meaning that the tests reject the null too readily.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_33473/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n", - " \n", - "Please do not make inferences based on these values! \n", - "\n", - "Collaborate on a solution, and stay up to date at: \n", - "github.com/dswah/pyGAM/issues/163 \n", - "\n", - " gam_full.summary()\n" - ] - } - ], - "source": [ - "gam_full.summary()\n" + "metadata": {}, + "outputs": [], + "source": [ + "gam_full.summary()" ] }, { @@ -2767,19 +1207,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "9191d615", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.348263Z", - "iopub.status.busy": "2023-07-25T23:59:50.347814Z", - "iopub.status.idle": "2023-07-25T23:59:50.369772Z", - "shell.execute_reply": "2023-07-25T23:59:50.368479Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "Yhat = gam_full.predict(Xgam)\n" + "Yhat = gam_full.predict(Xgam)" ] }, { @@ -2793,63 +1226,23 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "92007a5f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.375102Z", - "iopub.status.busy": "2023-07-25T23:59:50.374536Z", - "iopub.status.idle": "2023-07-25T23:59:50.481702Z", - "shell.execute_reply": "2023-07-25T23:59:50.479739Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n", - " fit_intercept=True, max_iter=100, \n", - " terms=s(0) + l(1) + f(2) + intercept, tol=0.0001, verbose=False)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "gam_logit = LogisticGAM(age_term + \n", " l_gam(1, lam=0) +\n", " f_gam(2, lam=0))\n", - "gam_logit.fit(Xgam, high_earn)\n" + "gam_logit.fit(Xgam, high_earn)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "4dd6aa2f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.491174Z", - "iopub.status.busy": "2023-07-25T23:59:50.490770Z", - "iopub.status.idle": "2023-07-25T23:59:50.634371Z", - "shell.execute_reply": "2023-07-25T23:59:50.633430Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ], - "text/plain": [ - "education 1. < HS Grad 2. HS Grad 3. Some College 4. College Grad \\\n", - "high_earn \n", - "False 268 966 643 663 \n", - "True 0 5 7 22 \n", - "\n", - "education 5. Advanced Degree \n", - "high_earn \n", - "False 381 \n", - "True 45 " - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.crosstab(Wage['high_earn'], Wage['education'])\n" + "metadata": {}, + "outputs": [], + "source": [ + "pd.crosstab(Wage['high_earn'], Wage['education'])" ] }, { @@ -2975,22 +1286,14 @@ "we could subset the model matrix, though this will not remove the\n", "column from `Xgam`. While we can deduce which column corresponds\n", "to this feature, for reproducibility’s sake we reform the model matrix\n", - "on this smaller subset.\n" + "on this smaller subset." ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "c92b60be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.664968Z", - "iopub.status.busy": "2023-07-25T23:59:50.664462Z", - "iopub.status.idle": "2023-07-25T23:59:50.672960Z", - "shell.execute_reply": "2023-07-25T23:59:50.672194Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "only_hs = Wage['education'] == '1. < HS Grad'\n", @@ -2998,7 +1301,7 @@ "Xgam_ = np.column_stack([Wage_['age'],\n", " Wage_['year'],\n", " Wage_['education'].cat.codes-1])\n", - "high_earn_ = Wage_['high_earn']\n" + "high_earn_ = Wage_['high_earn']" ] }, { @@ -3014,36 +1317,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "e525e8d0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.678086Z", - "iopub.status.busy": "2023-07-25T23:59:50.677710Z", - "iopub.status.idle": "2023-07-25T23:59:50.738388Z", - "shell.execute_reply": "2023-07-25T23:59:50.737375Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n", - " fit_intercept=True, max_iter=100, \n", - " terms=s(0) + s(1) + f(2) + intercept, tol=0.0001, verbose=False)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "gam_logit_ = LogisticGAM(age_term +\n", " year_term +\n", " f_gam(2, lam=0))\n", - "gam_logit_.fit(Xgam_, high_earn_)\n" + "gam_logit_.fit(Xgam_, high_earn_)" ] }, { @@ -3057,28 +1339,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "c3e66cf9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.744284Z", - "iopub.status.busy": "2023-07-25T23:59:50.743537Z", - "iopub.status.idle": "2023-07-25T23:59:50.893111Z", - "shell.execute_reply": "2023-07-25T23:59:50.892009Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 2)\n", @@ -3086,73 +1350,36 @@ "ax.set_ylabel('Effect on wage')\n", "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n", "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n", - " fontsize=8);\n" + " fontsize=8);" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "fd924348", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:50.897946Z", - "iopub.status.busy": "2023-07-25T23:59:50.897534Z", - "iopub.status.idle": "2023-07-25T23:59:51.054702Z", - "shell.execute_reply": "2023-07-25T23:59:51.054277Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 1)\n", "ax.set_xlabel('Year')\n", "ax.set_ylabel('Effect on wage')\n", "ax.set_title('Partial dependence of high earner status on year',\n", - " fontsize=20);\n" + " fontsize=20);" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "2d3ec90a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:51.056652Z", - "iopub.status.busy": "2023-07-25T23:59:51.056495Z", - "iopub.status.idle": "2023-07-25T23:59:51.177538Z", - "shell.execute_reply": "2023-07-25T23:59:51.177183Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", "ax = plot_gam(gam_logit_, 0)\n", "ax.set_xlabel('Age')\n", "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n" + "ax.set_title('Partial dependence of high earner status on age', fontsize=20);" ] }, { @@ -3172,28 +1399,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "4f2bc0eb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:51.179519Z", - "iopub.status.busy": "2023-07-25T23:59:51.179374Z", - "iopub.status.idle": "2023-07-25T23:59:51.330925Z", - "shell.execute_reply": "2023-07-25T23:59:51.330552Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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RaBRnzpzBmjVrYDAYFrs5pIRyzpfSeK0ug+J8mpub8eCDD+I973kPnE4nnnjiCbznPe8BAAwODmLjxo04cOAAdu7ciV/96ld4+9vfjvHxcbS2tgI4X1HlM5/5DNxuN/R6vaJtUlBMyOJzu90YHByEx+NBMpmEVquFw+FAd3c3nE7nYjePEFInKCiuLwsRFNfl8IlMqVQKP/7xjxEKhbBr1y689tprSCQSuO666/gy3d3d6OzsxIEDBwAABw4cwNatW3lADAA33HADAoEA3nzzzYLbisViCAQCWX8IIYvH7Xajv78fExMTMBqNsNvtMBqNmJiYQH9/P9xu92I3kRBCSJ2o26D4jTfegNlshiRJ+NjHPoannnoKmzZtwuTkJPR6/bza862trZicnAQATE5OZgXE7HP2WSEPPPAArFYr/7Ny5Up1d4oQopgsyxgcHEQ4HIbT6YQkSRBFEZIkwel0IhwOY3BwEEvkZRghhJAFVrdBcVdXFw4dOoT+/n7ceeeduO2223D06NEF3ea9994Lv9/P/5w7d25Bt0cIKczv98Pj8eQdPywIAiwWCzweD/x+/yK1kBBCSD2p25Rser0eF110EQDgsssuw6uvvoqHHnoIf/qnf4p4PA6fz5fVWzw1NYW2tjYAQFtbGwYGBrLWx7JTsGXykSQJkiSpvCeEkAsRi8WQTCah0+nyfq7X6zE3N4dYLFbllhFCCKlHddtTnCudTiMWi+Gyyy6DTqfDs88+yz8bGhrCyMgIdu3aBQDYtWsX3njjDUxPT/NlnnnmGVgsFmzatKnqbSeEFCbLMnw+H6ampuDz+fhwCEmSoNVqkUgk8v67eDwOrVZLD7KEEEIUqcue4nvvvRc33ngjOjs7MTc3hyeeeALPP/88fvOb38BqteLDH/4w7rnnHjQ3N8NiseDjH/84du3ahZ07dwIArr/+emzatAl/8Rd/ga985SuYnJzEfffdh7vuuot+gRJSQ4pllnA4HHA4HJiYmIDT6cwaQiHLMgKBAFwuF6xW6yLuASGEkHpRl0Hx9PQ0PvjBD2JiYgJWqxXbtm3Db37zG/zRH/0RAODrX/86RFHELbfcglgshhtuuAHf+ta3+L/XaDT4xS9+gTvvvBO7du2CyWTCbbfdhi984QuLtUuEkBwss0Q4HIbVaoVOp0MikcDExAT8fj96e3vR3d0Nv98Pt9sNi8UCvV6PeDyOQCAAo9GI7u5uyldMCCFEkSWTp3gxUJ5iQhaGLMvYv39/wV5gt9sNl8uF3bt3w+PxUJ5iQkjFKE9xfVmIPMV12VNMCFnaysks4XQ64XA4qKIdIYSQilBQTAipOeVmlhAEYV5uckIIIaQcSyb7BCFk6aDMEoQQQqqNgmJCSM2xWq18SETutAeWWcLhcFBmCUIIIaqhoJgQUnMEQUB3dzeMRiPcbjei0SjS6TSi0SjcbjdlliCEEKI6CooJITXJ6XSit7cXLpcLkUgEXq8XkUgELpcLvb29lFmCEEKqJBKJ4HOf+xw2bNgAg8GA9vZ23HHHHRgbGytrPatXr4YgCAX/DA4OLtAeKEMT7QghNYsySxBCyOKKRqO45ppr0NfXB5fLhXe+850YHh7GY489hl/84hfo6+vD2rVry1rnbbfdlvfniz0kjoJiQkhNo8wShBCyeL70pS+hr68Pu3btwm9/+1uYzWYAwNe+9jX83d/9He644w48//zzZa3z8ccfV7+hKqDhE4QQQgghZJ54PI5vfvObAIBHHnmEB8QAcM8992Dbtm144YUX8Nprry1WE1VFQTEhhBBCCJnn5Zdfht/vx7p163DJJZfM+/w973kPAODpp5+udtMWBA2fIIQQQggp5P9+HJg+ttituDAtG4E/+cYF//PXX38dAHDppZfm/Zz9/PDhw2Wt98EHH8SpU6cgSRI2b96Md7/73TUxeZqCYkIIIYSQQqaPAaOvLnYrFsXIyAgAoKOjI+/n7Odnz54ta72f/vSns/7+t3/7t/jGN76BO+644wJaqR4aPkEIIYQQQuYJBoMAAKPRmPdzk8kEAJibm1O0vj/5kz/BT3/6U5w9exbhcBhHjhzBPffcg1gshr/8y7/Ez3/+c3UafoGop5gQQgghhCy4hx9+OOvvmzdvxle/+lV0d3fjox/9KD7zmc/gne985yK1jnqKCSGEEEJIHizbRDgczvt5KBQCADQ2Nla0nQ9/+MNoaWnB0NAQhoeHK1pXJainmBBCCCGkkJaNi92CC1dh2zs7OwEAo6OjeT9nP1+1alVF2xFFEevWrcP09DQmJiawevXqitZ3oSgoJoQQQggppILsDfXu4osvBgAcPHgw7+fs59u2bat4W7OzswD+ME55MdDwCULIsiDLMnw+H6ampuDz+SDL8mI3iRBCatoVV1wBq9WKU6dO4dChQ/M+f/LJJwEA73jHOyrazptvvomhoSEYjUZ0d3dXtK5KUFBMCFny3G439u/fj3379uHFF1/Evn37sH//frjd7sVuGiGE1Cy9Xo+7774bAHDXXXfxMcTA+TLPhw8fxpVXXonLLruM//yb3/wmuru7ce+992at65e//CWee+65eds4fPgw3vve90KWZfzlX/4l9Hr9Au1NaTR8ghCypLndbvT39yMcDsNqtUKn0yGRSGBiYgJ+vx+9vb01kTSeEEJq0X333Ye9e/filVdewfr16/HWt74VZ8+eRX9/P5xOJx599NGs5T0eD4aGhjAxMZH184GBAdx///1YtWoVLr74YhiNRpw+fRoHDx5EMpnEVVddhX/913+t5q7NQz3FhJAlS5ZlDA4OIhwOw+l0QpIkiKIISZLgdDoRDocxODhIQykIIaQAg8GAffv24bOf/SyMRiN+9rOf4ezZs7j99ttx8OBBrF27VtF6brjhBtxxxx2wWCx4+eWX8eSTT+LkyZPYvXs3vve972Hv3r1oaGhY4L0pTpDpt8EFCwQCsFqt8Pv9sFgsi90cQkgOn8+Hffv2wWg0QpKkeZ9Ho1FEIhFcffXVsNls1W8gIaRmRKNRnDlzBmvWrIHBYFjs5pASyjlfSuM1Gj5BCFmyYrEYkskkdDpd3s/1ej3m5uYQi8UUr1OWZfj9fsRiMUiSBKvVCkEQ1GpylnQ6jdHRUQSDQZjNZnR0dEAUa/8Fn1rtruaxrld0jAhRDwXFhJAlS5IkaLVaJBKJvD3F8XgcWq0272f5uN1uDA4OwuPxIJlMQqvVwuFwoLu7W/VxyUNDQxgYGMDMzAxSqRQ0Gg2am5vR09ODrq4uVbelJrXaXc1jXa/oGBGiLgqKCSFLltVqhcPhwMTEBJxOZ1YPmizLCAQCcLlcsFqtJddVzQl7Q0ND2Lt3L2KxGEwmE9+W2+3G3r17AaAmA2O12k2TI0ujY0SI+mr/PRwhhFwgQRDQ3d0No9EIt9uNaDSKdDqNaDQKt9vNc2KWet1czQl76XQaAwMDiMVisNlsWduy2WyIxWIYGBhAOp2ueFtqUqvdNDmyNDpGhCwMCooJIUua0+lEb28vXC4XIpEIvF4vIpEIXC6X4t40v98Pj8eTd7ymIAiwWCzweDzw+/0Vt3d0dBQzMzMwmUzzxuGKogij0YiZmZmCZVcXi1rtruaxrld0jAhZGDR8ghCy5DmdTjgcjguekLQQE/YKCQaDSKVSRbcVDocRDAYr3lauSiZtqdXuah7relXuMaLJeIQoQ0ExIWRZEAThgtOuqT1hrxiz2QyNRlN0WxqNBmazuaz1lgqMKp20pVa7q3ms61U5x4gm45WPhp3Uh4U4TxQUE0JICWpO2Culo6MDzc3NcLvd0Ol0WUMR0uk0H0fa0dGheJ2lAiM1Jm2p1e5qHut6pfQYxeNxDAwM0GQ8hdg1m0qlFrklRAl2ntRMU0lBMSGkLNV+Fask562SNlXSbjZhz+/3Y3p6GgaDAaIo8kl7JpMpa8JeJdsSRRE9PT3Yu3cvZmdnYTAYoNFokEqlEI1GYTAY0NPTo/gXAQt4Q6EQDAYDDAYD0uk0xsfH4ff70dPTg6GhIYTDYTgcDsTjcYTDYR44ezweDA4OwuFwQBCEgvumVrszj7Xb7YbFYoFer0c8HkcgEJg3OXKhz3251NxWoXUpOUZdXV38vGYGzmwyHntQYueVADqdDhqNBpFIpOw3MaT6IpEINBpNwWFEF4KCYkKIYtV+Fask562SNqnRbqfTifXr12NgYACTk5NZ7dm+fbuq2+rq6oLf70dfXx/8fj/S6TREUYTZbMaOHTsUp2NjWQpmZ2eRTqcxMzPD12U0GhGPx/H6669jbm4OOp0O586dQzgczlomc9JWIpEoum9qtZtNjmTbmpubg1arhcvlKvu8VvOaVXNbpdZV6hjpdDrFk/GomuN5giDAaDTC7/ejubkZGo1msZtECkilUvD7/TAajao+1FFQTEiNq5WesIXIi1qs3Zk5byVJgiRJSKVSWTlvm5ubS7YJAF+GrUeW5bLb7Xa7ceLECUiShPb2dsiyzHtOT5w4gebm5qxtVXKM3G43pqen0draCrvdzgNwrVaL6elpuN1uvp5ix9Dv9/Ne9nQ6DUmSeO9tMBiEKIoYGxuDLMuIx+NIJpPzlmE94ZOTkzh58mTJYz09PY22tjZ+bNh/c9tdSqnJkUqux3LOR6X3kJr3h9J1FTtGU1NTNTthsZYn/rW0tGB4eBhnz55Fc3MzJEmqmbaR89dOLBbjD/gtLS2qrp+CYkJqWK30hOXmRVXjVWyxdtvtdgwMDPDXY+FwmAdYWq0WkUgE/f39WLVqVdE2HTt2DAAwOzuLVCoFr9fLey8bGhoQi8UUtTuzxzWVSiESieRdjyzLFR+jUtuKx+N8PWxoQ6FzH41GeS+xyWTi29VqtdBoNAiFQpibm+MTVhobG+ctwz4/e/asomOduwzbpwu5RgpNjlRyPRZqT77zUeo4lqLm/VHuugodo1qdsFjrE//0ej06Ojrg8XgwMTGx2M0hBZhMJrS1tUGv16u6XgqKCalRaveEVaKcvKhKXsWW2reVK1fC7XYjlUohnU5Dq9XyHsdEIsF7HgVBQEtLS8E2TUxMIBaLIRQKzespDYVCEEURo6OjJdtdrMeVrefMmTO8UEUlx0jJtkZHR3H69GkcO3as6LmPx+NIJBIwGAx528QeMDQaTdFgLZVKYXZ2Fk1NTUWPNYCK918JJdej0vacOXMGR48eregeUvP+UGtdtThhsV6q8BmNRnR2diKZTCKZTC52c0gOrVYLrXZhwlcKigmpQWr3hFX6+k/N3LFK9u3EiROIxWIQRRE6nY4vIwgCdDod4vE44vE4YrFY0TbFYjH4fD6Ioliwp3R2dhbRaLRom5X0uPp8PthstoqPkZJtzc7OKppEddFFF/HAQ6/XzwuMkskkNBoNTCYTkskkH2LCgnD2epv1OBbbt3g8DgBVeV2v5HpU0p5AIKDKZDQ17w+11lXuhMWFthBvmxbaQgZfpDZRRTtCapDSnrCJiYmqVLXKDIzyKedVrJJ9CwQCSKfT/PVw7jKs11gUxaJtEgSBF5Qo1FOaSCR4AFUI63Ettp5UKgVBECo+Rkq2FYvF4PV6S577RCKBpqYmPgQlmUzyYDgcDkOj0cBms8FsNqO1tRVmsxmJRALhcBiJRAJmsxktLS0wm83Q6/VF902v15dcRq3X9UquRyXtYT2mld5Dat4faq5LjWqOaqEqfKQe0CMQITVIrZ4wtXrm1HwVq2Tf2KQyNuYwN+dtMpmEXq9Ha2sr/H5/wTbZ7XbMzc0V7SllwVMxer2+ZI+rwWCA3W4v2h4lx0jJtjQaDWRZLnnu2fjIZDLJxyezHniTyQSNRoPVq1dDlmVMTk5i5cqVfMKdVquFXq+Hx+OBy+UCAExOThbdNyXLqPG6Xun1WKo9FouFZ94odhxL3UNq3h9qD3uotJqjWqhSIakHFBQTUoOUTJJhgVw1JtKo+SpWyb6xANPr9fIhEiwvcCKRgEajgdPpRHd3N44dO1Y0V6vf74fP58s7NID1lBoMhqJtNhgMaGpqKrmerq6uou1RcoyUbMtisUCSpJLn3mAw8PMWDof5mGA2g5u1CQACgQA8Hg8sFgtP1+bxeGA0GrFx40a+TKF9U7KMWq/rlVyPStqzYcMGHD58uOJ7SM37YyGGPRSajFdNtTrxj5BMFBQTUoPU6glTcyKN0tyxau2by+XCiRMnMDc3h2g0yrNPNDQ0oLGxEevWrcPatWthsVgKtsnhcGB8fBzJZJJXVWM9pWazGaIooqOjo+QxslqtvMe12HpKtUfJMVKyrcze3VLnXhCErPPGeoFz26Tk3Kq1jBqUXo/FlmHXhxq9smrdH2qvq1bU4sQ/QnJRUExIDVKrJ0ztiTRqvIrN3LdC1eEy981sNs/LeZtZQa5Um9i2QqEQbDbbBVWiy2xzMBjkwxhYz5bZbFbcHqZQpb7MbRVrMzs+breb51LN7QFm21TSpmouo1SpfLZOpxN2u71oxUOl14ca1fOUtEepWhn2oJZam/hHSD6CzBJUkrKxCRp+vx8Wi2Wxm0OWoFrJU7wQ1KpWp4Rax/G5557Dq6++ilgsxoN0SZKwY8cOXHPNNVXfdyXrqVfVvPbV2la93ovVRMeILAal8RoFxRWgoJhUQ61UtFNTZr7SfL2calYaY4qtp1D+VFZGtLe3F2fOnMG+ffsQj8eh0+n4OF82Ie7qq69GT09PyXZkVuozmUx8W6FQCJIk4brrruMBrZI2h0KhvL3ttZLz9UIoOR9A/hzdmcuUs/+VXh9qt2cpq7fvK1L/lMZrNHyCkBqnZJJMLUykUapQvlL2mdKKXeUqtB4l+VOPHj2KQ4cOIZFIwGQy8dfhOp0Oer0e4XAYfX19uOyyy6DRaAq2IZ1OY2BgALFYjA+LYNvS6XTw+XwYGBjA+vXr+VCKUm3OLV5isVhqMuerUkrz2apRPTBTJdfHQrRnKaun7yuyvFCeYkJIVdVavlIl7Tl9+jSCwSD0ev288aGiKEKv1yMYDGJwcLDotkZHRzEzM5MVWGeux2g0YmZmBqOjoxW3uV5zvirZNzY5rhr7X2vtIYQsHAqKCSFVpSRfaTKZrFq+UiXticVivNx0PlqtFul0GoFAoOi2gsEgLyZSaFupVArBYLDiNlfzGKpJaY5uNoyl0DJq7X+ttYcQsnAoKCaEVJWaFbuq1R5JkiCKIpLJZN5lkskkRFEsObfAbDZDo9EU3ZZGo4HZbK64zfWa81WtanW1Vj2vXs8HIcsJBcWEkKpi+Ur9fj9y5/myfKUOh6Nq+UqVtGft2rUwm82Ix+NIp9NZy6TTacTjcZ6WrZiOjg40NzcjFArlXU84HEZzczM6OjoqbnM1j6GalOxbe3s7XC5XVfa/1tpDCFk4NNGOLBnLeUZzoZy3C7EepcdZSc7fQnmKy81XWsm5V5I/ddOmTWhoaMC+ffsQDoeh1+t5GWr22nznzp18kl2h9oiiiJ6eHuzduxc+nw9Go5Fvi2Xi6OnpKXnuFirnazUyfZSi9PoAlOforiSDS+6xLpQTupz2VNqmhTgfy/n7kxCGUrJVgFKy1Y7lnPtSrVy1auYNrmaO3Wrmqh0YGEBfXx+CwSDS6TSvMrdz506ejq0e913Ndam1HrWuR7WWqfb9UWs5mAmpZ5SnuAooKK4NSnKILtUv9nJy3la6nubmZkXHudycrqXyFBej9rlX0luWSqUwODiIQCAAi8WC7u5u3kNcTnvU6t1Xo4dPreO4EOupJI+1WvmF2TJKckKXOh/VzHlM+ZUJOY/yFJNlQWkO0aWYH7TcnLeVrKe/vx+rVq0qeZztdvsF53QF8ucpLmQhzr2S/KkajQabN2+uuD2iKKKzs1NRuyptczFqHceFXg/7TGkeazXzC7NllOSELnY+qpnzWMm2jh07BgDL8vuTkHxooh2pa0s5X2spauW8VbIer9eLkZGRksd5dHS0ajlda+3c11p7lFKr3fW4HiXXopo5iKuZ81jJtiYmJii/MiEZKCgmdW0p52stRa2ct0rWkzmhrNgywWCwajlda+3c11p7lFKr3fW4HiXXopo5iKuZ85jyKxNSPgqKSV1byvlaS1Er562S9Wi1WkV5WM1mc9Vyutbaua+19iilVrvrcT1KrkU1cxBXM+cx5VcmpHwUFJO6tpTztZaiVs5bJeux2+3o7OwseZw7OjqqltO11s59rbVHKbXaXY/rUXItqpmDuJo5j5Vsy+VyUX5lQjLQRDtS1xYqX2s9UCvnrZL19Pb2orm5GXNzc0WPsyiKis4HUF5O13xq7dzXWnuUUqvd9boeoPS1qGQZJedVzTapsf8bN25Ubd8IWQooJVsFKCVb7VjOeTbrNU9xreXFVUuttUepWjsftZant9o5oWtt/wmpZ5SnuAooKK4ty7kiUz1VtLuQdZVSa+e+1tqjVK2dj1qr6Kbmea21+6Ner1lClKCguAooKCaEEEIIqW1K4zWaaEcIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLKnXewGEEJIPZFlGX6/H7FYDJIkwWq1QhCExW4WqTN0HRFSeygoJoQQhdxuNwYHB+HxeJBMJqHVauFwONDd3Q2n07nYzSN1gq4jQmoTBcWEEKKA2+1Gf38/wuEwrFYrdDodEokEJiYm4Pf70dvbSwENKYmuI0JqF40pJoSQEmRZxuDgIMLhMJxOJyRJgiiKkCQJTqcT4XAYg4ODkGV5sZtKahhdR4TUNgqKCSGkBL/fD4/Hk3fcpyAIsFgs8Hg88Pv9i9RCUg/oOiKktlFQTAghJcRiMSSTSeh0uryf6/V6JJNJxGKxKreM1BO6jgipbRQUE0JICZIkQavVIpFI5P08Ho9Dq9VCkqQqt4zUE7qOCKltFBQTQkgJVqsVDocDfr9/3nhPWZYRCATgcDhgtVoXqYWkHtB1REhto6CYEEJKEAQB3d3dMBqNcLvdiEajSKfTiEajcLvdMBqN6O7upjyzpCi6jgipbYJM01wvWCAQgNVqhd/vh8ViWezmEEIWGOWXJWqg64iQ6lIar1GeYkIIUcjpdPLX31SJjFwouo4IqU0UFBNCSBkEQYDNZlvsZpA6R9cRIbWHxhQTQgghhJBlry6D4gceeAA7duxAY2MjWlpa8K53vQtDQ0NZy1x11VUQBCHrz8c+9rGsZUZGRnDTTTfBaDSipaUFn/rUp5BMJqu5K4QQQgghpAbU5fCJF154AXfddRd27NiBZDKJf/zHf8T111+Po0ePwmQy8eU+8pGP4Atf+AL/u9Fo5P+fSqVw0003oa2tDa+88gomJibwwQ9+EDqdDl/+8peruj+EEEIIIWRxLYnsE263Gy0tLXjhhRewZ88eAOd7irdv345///d/z/tvfvWrX+Htb387xsfH0draCgD4zne+g8985jNwu93Q6/Ult0vZJwghhBBCapvSeK0uh0/kYnXim5ubs37+ox/9CA6HA1u2bMG9996LcDjMPztw4AC2bt3KA2IAuOGGGxAIBPDmm2/m3U4sFkMgEMj6QwghhBBC6l9dDp/IlE6n8Td/8ze44oorsGXLFv7zP//zP8eqVavQ3t6Ow4cP4zOf+QyGhobw05/+FAAwOTmZFRAD4H+fnJzMu60HHngA999//wLtCSGEEEIIWSx1HxTfddddOHLkCPbv35/1849+9KP8/7du3QqXy4Vrr70Wp06dwrp16y5oW/feey/uuece/vdAIICVK1deWMMJIYQQQkjNqOvhE3fffTd+8YtfYN++fejo6Ci6bG9vLwDg5MmTAIC2tjZMTU1lLcP+3tbWlncdkiTBYrFk/SGEEEIIIfWvLoNiWZZx991346mnnsJzzz2HNWvWlPw3hw4dAgC4XC4AwK5du/DGG29genqaL/PMM8/AYrFg06ZNC9JuQgghhBBSm+py+MRdd92FJ554Aj//+c/R2NjIxwBbrVY0NDTg1KlTeOKJJ/C2t70Ndrsdhw8fxt/+7d9iz5492LZtGwDg+uuvx6ZNm/AXf/EX+MpXvoLJyUncd999uOuuuyBJ0mLuHiGEEEIIqbK6TMlWqD78Y489httvvx3nzp3DBz7wARw5cgShUAgrV67Eu9/9btx3331ZQx7Onj2LO++8E88//zxMJhNuu+02/Ou//iu0WmXPCpSSjRBCCCGktimN1+oyKK4VFBQTQgghhNS2ZZWnmBBCCCGEkEpQUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLJHQTEhhBBCCFn2KCgmhBBCCCHLHgXFhBBCCCFk2aOgmBBCCCGELHsUFBNCCCGEkGWPgmJCCCGEELLsUVBMCCGEEEKWPQqKCSGEEELIskdBMSGEEEIIWfYoKCaEEEIIIcseBcWEEEIIIWTZo6CYEEIIIYQsexQUE0IIIYSQZY+CYkIIIYQQsuxRUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNnTLnYDCCGEkKVClmX4/X7EYjFIkgSr1QpBEBa7WYQQBSgoJoQQQlTgdrsxODgIj8eDZDIJrVYLh8OB7u5uOJ3OxW4eIaQECooJIYSQCrndbvT39yMcDsNqtUKn0yGRSGBiYgJ+vx+9vb0UGBNS42hMMSGEEFIBWZYxODiIcDgMp9MJSZIgiiIkSYLT6UQ4HMbg4CBkWV7sphJCiqCgmBBCCKmA3++Hx+PJO35YEARYLBZ4PB74/f5FaiEhRAkKigkhhJAKxGIxJJNJ6HS6vJ/r9Xokk0nEYrEqt4wQUg4KigkhhJAKSJIErVaLRCKR9/N4PA6tVgtJkqrcMkJIOSgoJoQQQipgtVrhcDjg9/vnjRuWZRmBQAAOhwNWq3WRWkgIUYKCYkIIIaQCgiCgu7sbRqMRbrcb0WgU6XQa0WgUbrcbRqMR3d3dlK+YkBpHQTEhhBBSIafTid7eXrhcLkQiEXi9XkQiEbhcLkrHRkidoDzFhBBCiAqcTicfRkEV7QipPxQUE0IIISoRBAE2m22xm0EIuQA0fIIQQgghhCx7FBQTQgghhJBlj4JiQgghhBCy7FFQTAghhBBClj0KigkhhBBCyLJHQTEhhBBCCFn2KCgmhBBCCCHLHgXFhBBCCCFk2aOgmBBCCCGELHsUFBNCCCGEkGWPgmJCCCGEELLsUVBMCCGEEEKWPQqKCSGEEELIskdBMSGEEEIIWfYoKCaEEEIIIcseBcWEEEIIIWTZ0y52AwghhNQPWZbh9/sRi8UgSRKsVisEQVjsZhFCSMUoKCaEEKKI2+3G4OAgPB4PkskktFotHA4Huru74XQ6F7t5hBBSEQqKCSGElOR2u9Hf349wOAyr1QqdTodEIoGJiQn4/X709vZSYEwIqWs0ppgQQkhRsixjcHAQ4XAYTqcTkiRBFEVIkgSn04lwOIzBwUHIsrzYTSWEkAtGQTEhhJCi/H4/PB5P3vHDgiDAYrHA4/HA7/cvUgsJIaRyFBQTQggpKhaLIZlMQqfT5f1cr9cjmUwiFotVuWWEEKIeCooJIYQUJUkStFotEolE3s/j8Ti0Wi0kSapyywghRD0UFBNCCCnKarXC4XDA7/fPGzcsyzICgQAcDgesVusitZAQQipHQTEhhJCiBEFAd3c3jEYj3G43otEo0uk0otEo3G43jEYjuru7KV8xIaSuUVBMCCGkJKfTid7eXrhcLkQiEXi9XkQiEbhcLkrHRghZEihPMSGEEEWcTicfRkEV7QghSw0FxYQQQhQTBAE2m22xm0EIIaqj4ROEEEIIIWTZo6CYEEIIIYQsexQUE0IIIYSQZY+CYkIIIYQQsuxRUEwIIYQQQpY9CooJIYQQQsiyR0ExIYQQQghZ9igoJoQQQgghyx4FxYQQQgghZNmjoJgQQgghhCx7FBQTQgghhJBlT7vYDSCkmmRZht/vRywWgyRJsFqtEASh7relFiVtVrpfSpZLp9MYHR1FMBiE2WxGR0cHRLG2n9XVanMqlcLg4CACgQAsFgu6u7uh0WgWoMXn1eP1qES93tO1du0v1euDkHJQUEyWDbfbjcHBQXg8HiSTSWi1WjgcDnR3d8PpdNbtttSipM1K90vJckNDQxgYGMDMzAxSqRQ0Gg2am5vR09ODrq6uRTkGpajV5oGBAfT19SEYDCKdTkMURTz77LPYuXMnenp6VG93PV6PStTrPV1r1/5SvT4IKVdtd8kU8MADD2DHjh1obGxES0sL3vWud2FoaChrmWg0irvuugt2ux1msxm33HILpqamspYZGRnBTTfdBKPRiJaWFnzqU59CMpms5q6QKnG73ejv78fExASMRiPsdjuMRiMmJibQ398Pt9tdl9tSi5I2K90vJcsNDQ1h7969cLvdMBgMsNlsMBgMcLvd2Lt377z7uRao1eaBgQHs27cPgUAAWq0WRqMRWq0WgUAA+/btw8DAgKrtrsfrUYl6vadr7dpfqtcHIReiLoPiF154AXfddRf6+vrwzDPPIJFI4Prrr0coFOLL/O3f/i2efvpp/Pd//zdeeOEFjI+P4+abb+afp1Ip3HTTTYjH43jllVfwgx/8AI8//jg+97nPLcYukQUkyzIGBwcRDofhdDohSRJEUYQkSXA6nQiHwxgcHIQsy3W1LbUoafOxY8dw7NixkvuVTqcVrau/vx+xWAw2my1rGZvNhlgshoGBAaTT6cU+NFw6ncbAwEDFbU6lUujr60MikYDRaIRer4coitDr9TAajUgkEujr60MqlVKl3fV4PSpRr/e0WteRWpbq9UHIharLoPjXv/41br/9dmzevBkXX3wxHn/8cYyMjOC1114DAPj9fnz/+9/H1772NVxzzTW47LLL8Nhjj+GVV15BX18fAOC3v/0tjh49ih/+8IfYvn07brzxRnzxi1/EI488gng8vpi7R1Tm9/vh8XjyjpETBAEWiwUejwd+v7+utqUWJW2emJjAxMREyf0aHR0tua6zZ8/C6/XCZDLNG0MpiiKMRiNmZmYwOjoK4Pwvbp/Ph6mpKfh8vry/oJUso0Sh9YyOjmJmZkZxmwsZHBxEMBjkwXDuevR6PYLBIAYHBy+o/bnq8XpUol7vabWuI7Us1euDkAu1JMYUsxu2ubkZAPDaa68hkUjguuuu48t0d3ejs7MTBw4cwM6dO3HgwAFs3boVra2tfJkbbrgBd955J958801ccskl87YTi8UQi8X43wOBwELtElFRLBZDMpmETqfL+7ler8fc3FzWua2HbalFSZvZg2Kp/QoGg4rWVWqZcDiMYDCo6jjnUoqtJxgMIpVKKWpzMYFAAOl0Glpt/q9erVaLeDyu2ndLPV6PStTrPa3WdaSWpXp9EHKh6rKnOFM6ncbf/M3f4IorrsCWLVsAAJOTk9Dr9bDZbFnLtra2YnJyki+TGRCzz9ln+TzwwAOwWq38z8qVK1XeG7IQJEmCVqtFIpHI+3k8HodWq4UkSXW1LbUoabNer4dery+5X2azWdG6Si2j0WiQTCZVG+dcSqn1JJNJaDSakm02m81Ft2OxWCCKYsG5C8lkEqIowmKxKGp3KfV4PSpRr/e02WxW5TpSy1K9Pgi5UHUfFN911104cuQIfvzjHy/4tu699174/X7+59y5cwu+TVI5q9UKh8MBv98/77W6LMsIBAJwOBywWq11tS21KGmzy+WCy+UquV8dHR0l17Vq1SrY7XaEQqF5YyfT6TTC4TCampowOzuryjjnUkMplIyr9Pl8aGpqKtrm5uZmdHR0FN1Wd3c3zGYz4vF43vXE43GYzWZ0d3cXXY9S9Xg9KlGv93RHRweam5srvo7UslSvD0IuVF0HxXfffTd+8YtfYN++fVlfIm1tbYjH4/D5fFnLT01Noa2tjS+Tm42C/Z0tk0uSJFgslqw/pPYJgoDu7m4YjUa43W5Eo1Gk02lEo1G43W4YjUZ0d3erkpOzmtvKVMmYWiVt3rhxIzZu3Fhyv0RR5Ouanp6G3+/H3Nwc/H4/pqen+bp6e3shSRJ8Pl/Wunw+HyRJwpYtWzAzM6PKOOdS4yGVjKv0er3YsmULb3MoFEI0GkUoFOJt7unpKZlnVqPRYOfOndDpdHwdiUSCr0un02Hnzp2q5SterOtxodXrPS2KInp6eope+0quo0wLfe/X4/VByIWqyzHFsizj4x//OJ566ik8//zzWLNmTdbnl112GXQ6HZ599lnccsstAM6nwRkZGcGuXbsAALt27cK//Mu/YHp6Gi0tLQCAZ555BhaLBZs2baruDpEF53Q60dvby8eMzs3NQavVwuVyqZ6Ls5rbAtQZU6u0zUqWcTqdWL9+PQYGBjA5OZmVh3X79u1wOp18WZarNRwOQ6PRwOl0oqenBzabDadOnVJlnHOp8ZBKx1WuWLECO3bsQF9fH/x+P88vbDabsWPHDsX5ZXt6ehAMBvHqq68iFotBlmUIggBJkrBjxw7V8xRX+3qslnL2q9LCFGoeQ3adFLr2y8lTXM17n5DlQJDrMNfKX//1X+OJJ57Az3/+86wvEKvVioaGBgDAnXfeiV/+8pd4/PHHYbFY8PGPfxwA8MorrwA4nxpp+/btaG9vx1e+8hVMTk7iL/7iL/CXf/mX+PKXv6yoHYFAAFarFX6/n3qN60S9Vr8qhI2FDYfDsFqt0Ol0SCQS8Pv9MBqN6O3tLeuXmhoV7TLbJEkSBEGALMuIxWLz2lSoqpfP58O+fftgNBrzjmeMRqO8B5iltsq3TCQSwdVXXz1vfkEmJduKRCK4+OKLcfToUYRCIb5P7L8mk0nxsWbHJxQKIZlM8ocGrVZb1nrKtVQrlim5HtUqTFFLFe0W494npF4pjdfqsqf429/+NgDgqquuyvr5Y489httvvx0A8PWvfx2iKOKWW25BLBbDDTfcgG9961t8WY1Gg1/84he48847sWvXLphMJtx22234whe+UK3dIItAEISiAVI9bSt3LCz7BcbGwrJgwOFwKP7lpqTNxZYp1Cb2WW6bRFFEZ2fnvPWwsY4TExN518PGOQPnJ8YWW6bUeEgl22pra8Po6CjC4TBaWlpK7lchmcenkvVciGpe+9VUbL8KBY4TExPw+/1lB45qHsNC174Si3XvE7LU1WVQrKRz22Aw4JFHHsEjjzxScJlVq1bhl7/8pZpNI6RqyskxWq1fdmq1iY119Pv9cLvdsFgsfMhEIBDgY5OB8z0Abrc7b6+0kvGQSra1YsUKHD58uOL9qsVztlQtROBYK+g6ImRh1PVEO0KWMyVjYZPJZFk5RistgqFmm9hYR5fLhUgkAq/Xi0gkApfLxXv42PjlWCyG4eFhnDx5EsPDw4jFYli/fn3Z4yoLbctsNquyXwtxzpaySq7HpVyYgq4jQhZGXfYUE0Kyc4zmGwtbbo5RNcZeqt0mp9PJU0blG+vodrtx4sQJ6PV6rF69GqIo8tnzJ06cQHNzc1mBcaFt+Xw+VfZL7eOzlFV6PS7lwhR0HRGyMKinmJA6pWaOUbWKYCxE3lM21rG1tRU2m40HxLnjc61WKxobG2G1WtHS0qI4T7GSbam1X5QXVhk1rselXJiCriNCFgYFxYTUKbVyjCopXqE0uKxm3tNqvh5Xa78oL2xpal2PSzlwpOuIkIVBwycIqWNq5BhVe9JOtfKeVvv1ONuvY8eOYWJigpesbm9vXzJ5YdVKy1VJar9qTtas58Cxlq8jQuoVBcWE1LlS425LWYjgstI2KVEr4yovJNV7NY5PudTK56tkPcWWSafTql2PSz1wrMXriJB6RkExIUtAJTlGFyq4XOi8p0pzGav1ejwz563NZuM5bycnJxEIBBYk5221Ciqolc9XyXoAFF1m06ZNVZ2sWe8ovzAh6qGgmJBlrtrBpVqq+Xp8MXLeqlmJrRi19k3Jeo4dOwYARZcZGxuD3W6vuChLJgocCSFKUFBMyDK3UMFlNXo5q/V6vNrFEtSuxFaMWvuWuR7gfHlsFsxLkgSLxYKJiQkAyMrskW9b27Zt40VZltpYYIbKKi9NdF7rGwXFhBDVg8tq9XKyti/06/FqTuqrdq+0WvvG1sOGlITDYaTTaYiiyNOqxeNxACi5LbPZvKTHAlfz/iDVQ+e1/lFQTAgBoF5wWc1eTmahX49Xc1JftXul1do3SZKQTCZx7tw5pNNpSJIEjUaDVCqFYDCIcDgMk8kESZIUbctmsy3JscCLcX+QhUfndWmgPMWEEK5Q8Qql1Mx5XEuqmfO22iV81do3i8WCZDKJSCSChoYGaLVaCIIArVaLhoYGRCIRAEBbW5vibVV6PdaapXp/LHd0XpcOCooJIaqpZkGNaqpmsYRqV2JTa98CgUBWAJxMJiHLclagrNPp0NHRsWyLTizV+2O5o/O6dFBQTMgCkWUZPp8PU1NT8Pl8y6KXoNxezmoeIyXbKrYMG3ftcrkQiUTg9XoRiUTgcrlUfTW6GJXYytm3QscoFotBq9Wio6MDZrMZiUQC4XAYiUQCZrMZHR0d0Gq1fLzwQh/HWlTttwCkOui8Lh00ppiQBVDtCRe1MuO5nPGp1TxGlRaUYMtUY1LfYlViU7JvxY4RO/d6vR6dnZ08UGDnm/19KY8XLqVWCs4QddF5XTooKCZEZdWecFFLM56V5jyOx+MYGBioyjFSo6BEZnuqkfN2sSqxFdu3Usexp6cn69wbDAb+b/PlF16OuYPrNSc4KY7O69KhalB84sQJ/K//9b9w4MABTE5OIhKJ4De/+Q0uuugivsyRI0cwMjICk8mEK6+8Us3NE7Loqp1Oq9ZmPCvp5ezq6sLQ0FBVjpHS8yHLclULcyjhdDpht9sxOjqKYDDIhyCIYvVHvSk5jkNDQ+jq6qp6D3c9Way3AGRh0XldOlQJitPpND796U/joYceQjqd5mPMBEHgeSmZkZERvP3tb4dWq8WZM2ewYsUKNZpASE2oZjqtxaiypkSpXk6dTle1Y6TkfIyPjwMoXVBCrRRoSuV7A3D27Nl5PcXVGDqj9LreunXrks4vrIbFegtAFhad16VBlaD4r/7qr/Doo49ClmWsWLECu3btwpNPPpl32be97W1Ys2YNhoeH8eSTT+KTn/ykGk0gpCZUs8hDtfPZlqPY+NSpqamqHSMl50NpQYnMyYELHYQqfQNQraEz5VzXra2ty3K8cDmqMTadVB+d1/pXcVD87LPP4vvf/z4EQcA//uM/4v7774dGoyn6iu+9730vvvKVr+C5556joJgsKdWccFHNAPxCFBozWs1jpGRber0eAGpmcmA5Qz6qNS673HO2HMcLl4uO0dJE57W+VTw47bvf/S6A8z3AX/rSl6DRaEr+m56eHgDAm2++WenmCakp1UynVe18tmqp5jFSsq329na4XK6S7YnH4+jv78fExAQvW2w0GjExMYH+/n643e6K2wsoewPgdrtx6NChqhULWIw0cYQQUm0VB8UHDhyAIAj48Ic/rPjfdHR0AAAmJycr3TwhNaWaRR7qNVCp5jFSuq2NGzcWXSZ3cuBCBqFK3gBEIpGqFguo5jkjhJDFUvHwienpaQDA6tWrFf8b9mWfTCYr3Twhi6LYuFI24eLYsWOYmJjgr+hdLhc2btyo2ivtWp/xrOQYqTUpRY1tFTtn5U4OrGTcsZKhCoIgQJZl6HQ6yLI8LyfwQgydYcfx6NGjGBkZ4ceos7MTmzZtoolEZaqV3OKEkD+oOCg2mUzw+XxlvTocHR0FADQ3N1e6eUKqrpbyAtfqjOdqFsKoxrbKGb9d6fWhJOep0+nE3NwcAoEA/H4/wuEw0uk0RFGE0WiExWJZkKEzMzMzOHv2LLxeL983WZbR1tZGQXEZauk7hBDyBxUHxWvXrsXBgwdx9OhR/NEf/ZGif/OrX/0KALB58+ZKN09IVZVbCMJms/FlJicnEQgEVM8dXGsznjOPkSRJkCQJsiwvWmEOJUU3MteT75xt3LhR0USzUCiEo0ePVjT5TckbgIsvvhgHDx7E0NAQD341Gg1SqRSCwSB8Ph+6urpUHTozNDSEvXv3IhaLwWQy8X3zeDzYu3cvAKCrq0u17S1VtZZbnBDyBxUHxddffz1ee+01PPLII/j4xz9eMrH80aNH8fjjj0MQBLztbW+rdPOEVI2SrADHjh0DgKrnDq6VGc/sGM3OziKVSsHr9fIezIaGBsRiMb7/Ho+not6ycvM0F3pdrWQ9Y2NjsNvtmJycLNh729bWhtHRUVXOfak3AA6HI2v7ucdFbel0GgMDA4jFYrDZbPx7XpIk6HQ6+Hw+DAwMYP369YtSXKRe1GpucULIeRUHxZ/4xCfw8MMP49SpU/jYxz6Gb33rW9Bq86/2mWeewYc+9CFEo1HY7XZ85CMfqXTzhFSNkqwAExMTAGqvEES1+P1+XoEtnU5n9WCGQiGIoojR0VGcPn0ax44dQygUgsFggMFgQDqdxvj4uOLesnLyNCcSiYIBuJLxwl6vFxdffDECgUDB3tsVK1bg8OHDquWNLvYGwOfzIRKJoLOzE4FAAOFwGLFYDKIoorGxERaLBZFIRLXrbHR0FDMzMzCZTPOCXjZkY2ZmBqOjo+js7Kx4e8xSG3dby7nFCSEqBMWtra34zne+gw9+8IP4/ve/j9/85je46aab+OcPPfQQZFnGyy+/zGdni6KIxx9/HGazudLNE1I1C1EIoh4VC1Si0ShmZ2eRTqdhMpn4z7VaLTQaDUKhEGZnZzE0NMSXm5mZyRoPG4/HFfWWKR3nOzk5iZMnTxZ8XX3RRRcpWo/JZCrae5tOp1XPG13oDQDbd7vdDpvNNm+inSzL8Hq9ql1nwWAQqVSq6L6Fw2EEg0FVtgcszXG3tZ5bnJDlTpWKdrfeeit0Oh3+6q/+CufOncN//Md/8F9m//N//k8Af3ilZzab8YMf/CArcCaklhQK+tQuBFGPSgUq8XgciUQCBoMhb0+YVqtFJBLhGR5ye5ODwSDvTS7VW6bkfGg0GoyMjBR9XX3u3DloNBpF58xmsxXtvV2soiQGgyHr81gspup1ZjabSx4jjUbDOzoq7eFdquNuq1m4hhBSPlWCYgB43/veh2uvvRbf+ta38PTTT+PQoUNZKdc2b96MP/mTP8EnP/lJtLS0qLVZQlRVLOhzOBwlswK4XC4AKDr21OVy1VzuYCWUBCp6vZ7/XK/Xz9v/ZDIJURQRDAah0WgK9ib7fD5Eo9Gi7VGSpaGpqQlzc3NFX1fPzc3BbDbD5/MpOmeFem+VtEetc1/NbQHnc8s3NzfD7XZDp9NlDaFIp9P8oaOjo6PiHt6lPO622ueNEFIeVWdE2O12fPazn8XAwACi0Simp6cxMTGBWCyGN954A//yL/9CATGpWSzoK1SxzOPxlCxgsHHjxpKFIOqxyEFuoFKoeIUkSWhqaoJGo0E4HEYymeTBcDgc5r2J6XQaOp2uYG9yPB7nQ1EKUVJQYuXKlSVf+6dSKXR2dlZ8zmqxKIla15koiujp6YEkSfyBhW3P5/NBkiT09PTA6/VWXPWvnHG39YaKoBBS21TrKc4limLWDGlCapnS3qndu3crLgShJHdwOp3mE9PMZjM6OjpqcvZ+ZqACnB87nDmGlQUqwPlexWQyiWQyiUAggFQqBY1Gw3Pn2u12xGIxJBIJ6HQ6RCIRvq6GhgY+5pINRSkms1DKyMgIf13f2dnJi24cO3aM91znjr1lr6vb2tpgt9sVFaYods7KKdxS6bkvVExj1apVeYvEKNlesWVYujUW3Pr9fmi1Wt6ODRs2YP/+/QiHw7wsdjgc5j3FLNsI6+EttK3McbfpdBpzc3N83xobG+eNu63mZDw17tdqFq4hhK6P8ixYUExIPSmnd0pJXmAlywwNDWFgYAAzMzM8cGxubkZPT0/N5XtlgUo8Hsfk5OS8YhHNzc388+7ubpw9exYejweJRAKyLEMQBMTjcbhcLnR1dcHv92N8fJxPtmPY+trb2+eNky1kZmYGw8PD8Hg8/Dim02m0trZiw4YNcDgcGB4e5q/5M9stiiJWr14Nq9WK48ePlyxModY5U2s9hYpptLa2ZgVXSrantE2yLPM5Ipn/z+4hnU6Hc+fO5S0owu6hqampgttqbW2FVqvF1NQUpqenEYlE+DXU0NCAlpYWGAwGSJJU1cl4at6v1SxcQ5Yvuj7KR0ExISh/VriSvMDFlilUCMHtdtdkIQRJkpBMJjE6Opp3chwreiFJEqampjAzM4NkMgmNRsPXkUwmMTMzg0QiAQAIhUI8oGL5gtPpNEKhEAAoGleppKBES0sLDh8+zJeRJAmJRAJerxeSJKGlpQXHjx8vuR4AJc9Zc3NzycItMzMzqpx7pcU0lFxrbN+i0SgPOFOpFKanp/Puf0NDAz//Xq8Xe/fuxY4dOxAKhRAKhZBMJuddI9FoFCaTCcePH8err75asD3XXXcdAGB4eBiyLEOv10MURX5tDA8Po7u7G/F4HAMDA1WZjLcQ92ulucWX6mREog66Pi6MKhXtyiUIAgwGA6xWK9avX4+dO3fiT//0T6nsM1k01ZwVXo+FECwWC5LJJCKRSFabtVotRFGEz+eD2WyG2WzG008/jWQyidbWVqTTad5byJYbGBjgY0s1Gg1EUeQ9gWz5yclJpNNpaDSagq//lBzH/v5+rF69GhaLBalUCpFIBJFIBKIoorm5GRqNBlNTUxgeHi66nr6+PgiCUPKcdXZ2lizuUmpbSs690mto3bp1JZdj+xYOh6HT6RAOh/n5YH9ny0QiET5enC3DMoocOXKED6tpbGycN4Fybm4O6XQaR44cKXnO4vE4ZFnm14cgCBBFMSsQr9ZkvFq8X5fyZERSObo+LlzFQfHw8HDW31mPTz75Puvv78cPf/hD/N3f/R0++9nP4t577620SYSUrZqzwherEEIlAoEAH/MbiUSyegFZz6FWq8XQ0FDWvuXun9FoxNTUFGKxGB8znDl8ghX+iUQiGBwcREtLS8HXf5FIpORx9Hg8EEURLS0teccUx2IxjIyMwOPxFF2P2+2GIAgwm81FtwWc75kuNARHybaUnHul19DBgwcVHaN0Og1ZlvnDH/uuZpMdJycneW9tOp3OWoYNkZmZmZmXcSRXIpFAJBIpeayB8/dkIpHIGoLDHl4DgQBGRkbm3auZx1qtIhi1eL9SERBSDF0fF67ioPi2224DABw+fBi///3vIcsy7HY7tm/fzrvm3W43Dh06BK/XC0EQsH37dmzZsgWBQABHjhzBqVOnEI1Gcd9992FiYgIPP/xwpc0ipCxsVrjf7y9YsUytWeGLUQihUizv7cqVK+H1erMqqJnNZtjtdkQiET6xrlS2h3Q6zQMoNi5VEAT+93A4jPHxcZw5c6bg6z+Hw1FyW2zIC8t0kTtOmQXKStoMlC7KEo/Hiy6jZFtKzn3mNSTLMj+mrDeVrcfn85Xcns/nQzKZ5On02DXOeooTiQTPIqLT6fIuw7KFmM1m3uuc++DEioqw81GoPSyrRENDA4xG47x9k2UZkUik5HrUKoJRi/crFQEhxdD1ceEqftfz2GOP4eqrr8abb76JtWvX4uc//zmmpqbwzDPP4IknnsATTzyBZ555BlNTU/jZz36G1atX480338SVV16Jp556CidOnEB/fz8uvvhiyLKMRx55BH19fWrsGyFlYbPCXS4XIpEIvF4vIpEIXC6XquOvMgsh5JNbCKEWsB46nU6Hzs5OrFmzBqtWrcKaNWvQ2dkJrVYLrVYLi8WiaN9EUUQymcx6Lc5ek7Of+/3+oing3G63om2xMcSFlmHBW6n1sOE1hZbRarXQ6/WqbKvUuWfXUDgcht/vx+zsLHw+H2ZnZ/lx02g0sNlsirbH5OtVAv7Qm88eXHKXYQ8zer0era2tMJvNSCQSCIfDSCQSMJvNaGlpgclkUnQc2TJseIZer+e900rPa+ZwJ1mW4fP5MDU1BZ/PV/BtZj61eL9mDvcq1CYqArJ80fVx4SruKf7973+Pj3zkI2hra0NfX1/BNGyiKOJP/uRPsGvXLlx22WW48847sW3bNlx++eXYsWMH9u7di23btmFychLf/e53sXPnzkqbRkjZ1JoVXkw5hRBqRe7wkswe18zhJd3d3Th48GDRfWttbcXMzAyCwSAfk5y5TDwe56+qM8emMuz1XygUQmNjI3w+X8FtORwOdHZ2Fi2m0tnZiXQ6zTMnFDofgiCUXKazsxNTU1MVb6vUue/o6IDZbMbU1BQ0Gg3vvWU9sew4X3rppRgaGip6Pmw2G3w+H0+Rl6/gCutxYkNPctfDJta1trYiFAph5cqViMfjfHm9Xg+Px4POzk7Islxy/wEUXUbJeWXDnSqdgV+L9ysVASHF0PVx4SruKf7a176GZDKJe++9V1FeYqfTiXvvvReJRAJf+9rX+M/tdjvuvPNOyLKM/fv3V9osQi4YmxXe2toKm82m+kQEpYUQamWSHaC86IBGoym5b729vXjLW97CJ3Gxcs8sr61Op8O2bdtKvrJOp9PYsmVLyW2VKqayceNG9Pb2Fl3Pzp07Sy7T09ODTZs2VbwtJedeEAQ0NzdnTU5k2TvS6TT/XMn5uPTSS+F0OqHRaPhEORYMR6NRiKLIHxY1Gg1/NcuC4VgsBo1GwwPNzPHVRqMRwPkA12g0YtOmTSX3v7e3V9EySorkeDyeiouJ1OL9SkVASDF0fVy4inuKX3zxRQDA5Zdfrvjf7NixAwDmBb+7d+8GcH5SB1kelmticZa+ieU9Za+7nU5nTeYpBpQXHShn3/r6+hAMBhGPxyGKIiwWC3bu3IkNGzZg3759JbOBbNiwAVarteS2SrWbtV1Jm6u5rULYuNs1a9ZgamoqK5ev0WjklUP9fn/J87FhwwaEw2GkUinMzc0hGo3yoQsNDQ1obGzEhg0bIMsyTpw4gWAwOG97ZrMZ69atw9q1a2GxWGriWDscDl5MpNIZ+LV4v6pdBIQsLXR9XBhBLmdwVR4NDQ2Ix+N48cUXccUVVyj6Ny+//DLe+ta3QpIkRCIR/vPXX38dl1xyCQwGA8LhcCXNqopAIACr1Qq/3w+LxbLYzak7lFgcSKVSGBwcRCAQgMVi4b2ttUzpg4yS6l/JZBIHDx6Ez+eDzWbDpZdeygtQ7N+/v+DrP7fbDZfLhd27d0MQBEXHUUm7lbS5mtsqZGpqCi+++CLsdjtkWYbX6+XbstvtEAQBXq8Xe/bsQWtra8ntZeY0Zb3PLFWe0WhEb28vgPPZgoLB4LwhBGazOWvcvVr7r2SZQtvy+XzYt28fjEZjwcwjkUgEV199teIZ+GpWoFSrQ6DW1kNqC53X85TGaxX3FDscDoyPj+OZZ55RHBT/5je/4f82E+v9WC4B0XJGicXzPxTMzMzU/EOB0qIDoigWTVGVu/+hUAixWIzvv9JsIEqPo5J2l9vmhdxWMWwijd/vRyAQyKoeF4lEeEntzF72YtvL7VUq9JCqZBk191/JMoW2xYJgVjwlt8Ke3W7nwz+UquScZVKzQ6DSIiBqt4fUFjWuj+Wk4qD46quvxg9/+EN89atfxdvf/vaSwyheffVVfO1rX4MgCLj66quzPjt8+DAAwOVyVdosUsMosTg9FCjd/1Kv/9Q+jsV6VcrZ1kL3zlitVjQ0NGBoaAgajQYGg4GnP5ubm4PP50NXV1dZE2nUKl9eK1gVxnPnzpWswlhNi3Hvq3VdE7LUVRwUf+Yzn8FPfvIThMNh7NmzB5/4xCfwgQ98AJs3b+Y3nSzLePPNN/Gf//mf+MY3voFoNAq9Xo9Pf/rTWet66qmnIAgC9uzZU2mzSA1b7onFl/tDQTn7XywIU/s4Fustczgcirfl8Xiq2utWKI3aha6rkvLli6FQwKe0CmM1h74txr2v1nW9FL+LCMlVcVC8efNmfP/738eHPvQhRKNRPPjgg3jwwQchSRIv2zwzM8NfUcmyDFEU8b3vfQ9btmzh6zl16hROnz6Nzs5OvOMd76i0WaSGLffE4sv9oaDc/S8UhKl5HEv1lm3cuFHRts6cOYOjR4/y4hWsYIXavW5+vx+RSASrVq3ieYkzi6lYLBZEIpElew0xxQI+nU6nqApjIBCo2jGq9r3PrutQKASDwQCDwYB0Oo3x8fGyruulfh0RwlQcFAPABz7wAaxduxZ33XUXXn/9dQBANBrF+Pj4vGW3bduGRx55ZN7443Xr1uHMmTNqNIfUuMzE4sUyCyzVxOKL9VBQ7QkXhban1v6rtR4lvXfHjx/neXwLbSsQCGBoaAizs7NIpVLwer18DGtDQwNisVjZvW6ljqHdbofVap03iSxz8t1SVepB5qKLLoJWq0VHRwfPGJH54NDc3IxoNFrVY1TNe59d17Ozs0in05iZmckaUx2PxxVd10u5g4KQXKoExQDwlre8Bb///e8xMDCAvXv34siRI5idnQUANDU1YfPmzbj22mupKAdZ9onFF+OhoNoTaYptT639V2s9Snrv2CTgYttiWTFCodC8MayhUAiiKGJ0dFRxr1s5xzC3fDUry71UHyyVPMiMjIzwktednZ15s0+woiPVUs173+/380wZ+cZUs+wirDLgcuygICSXakEx09PTg56eHrVXS5YQllhcSWaBpWghHgpqaSJNqe319PSosv9qHUclvXeCIKCxsRF+v7/gtiwWC86cOYN0Og2TycSX0Wq10Gg0CIVCmJ2dRTQarZljWK+UPMiwtGk+n69oFcZqHqNqdghEo1HeS1zoegwGg3zM/nK8jgjJpXpQTIgSyzmxuNoPBbU0kUZJD97Q0BC6uroq3n+1jqOS3judToeuri4cPXoU09PTMBgMEEWRV4kymUxYsWIFjh8/DoPBkDdQ02q1iMViiMfjNXMM65XSYQidnZ2Ix+M1c4yq2SEQj8eRSCRKXo8dHR0YGRmpmWNEyGKioJgsmnpK76Q2tR4KSvUobtq0qaoTaZROJNq6dasq+8+O47FjxzAxMYF4PA69Xg+Xy4WNGzcqWo/S3rs1a9YgkUhgYGAAk5OTSKVS0Gg0aG5uxvbt27OOP+tdzlxPMpmEXq+HXq+vqWNYj5QOQ2hra4Pdbq+pY1StDgG9Xl/yetTpdGhpaYHL5aqpY0TIYlmQoHh4eBgej4eXAi2G0q8tb7WW3qmanE4n7Hb7BVfIUtqjyCbSyLI8b1yl2hNpyplI1Nraqmj/lUwQlGWZT6RKJpMlv3cyZfbeFeoF7u7uhsfjwYkTJyBJElavXs3TwsViMZw4cQKbNm1CU1MTfD5f1phNViFOq9XCZrPx1/hKJiKm02nMzc3xYL+xsXHeMVTzwbJeqqNlPsg4HA7E43F+XbNJj+y1vyAIFd1nC6EaHQIGg4FfjywbSmb2DY1Gw69Hm822bDsoCMmkWlA8NDSEL3/5y/i///f/IhAIKPo3giAgmUyq1QRC6kq+YQ9nz55V3DtTzgSxfJXPjEZj3spnlShnIpGS/S81QdDtduO3v/0txsfHeTAsCAI8Hg9GR0dx/fXXKzqWTqcT69evL9gL7HA4sH///nkPIMAfyk6PjY1hxYoV8Pv9CIVCSCQSvD06nQ42mw0dHR2wWq2KJtFNTU1henqady4IgoCGhga0tLTAYDDw46vWg6VakzGrMamTPchMTU1hcHAQ6XSafyaKIlpaWopWPCznPlsoC90hYLVa0dHRgWQyiVQqhUgkwrNvmEwmaDQafj1Woz2E1ANVguKf/exnuPXWWxGNRsvqoSFkuVJj8puSXllRFKHRaPhMfDUqnxWjdChCPB7HwMBA0f0HUHKy2csvv4yRkRHIssz3N51OIx6PY2RkBPv378e73vWukj1ebre7aC+wVqtVNKShubkZfr+fD5Vg7UkkEvD7/ZAkCR6Pp+R+AeffuOXuVygUwvDwMLq7u1Wd/JR5PVaSX7nakzrZ75t8bw4Woz21JPMNSDgcRlNTU9Z1TeOFCZmv4qD43Llz+MAHPoBIJIIVK1bgU5/6FIxGIz760Y9CEATs3bsXMzMz+N3vfof//M//xPj4OHbv3o1//ud/hkajUWMfCKkralW1UtIrq9Fo+H2mZuWzQpRMJOrq6sLQ0BDC4TB/9R0Oh3mPosfjwbFjxwCg6DE6dOgQTp48CVmW+ZAH4HxPoSiKiEajOHXqFGZnZ3khoXwKnQ/2mdI8xX6/HydPnuRpwFjPNeuZS6VSGBoaQiQSKXnuZ2ZmIMsyNBoNRFGEIAj8ASeVSvHP1ZwcWWl+5WpWa2PbkmUZ3d3d84ZPKL2Gar1aW6XDUHLHL7NjROOFCcmv4qD44YcfRjgcRmNjI/r7+9He3o4333yTf3711VcDAG655RZ87nOfw4c//GH85Cc/wfe//3386Ec/qnTzhNQdtapaKemVbWpqwtzcXFUrn5WaSKTT6eDxeKDT6XDu3Lm8QzomJiYAgFe0y3eMzp07x4OF3PGhoihCp9PxHuNiQbFaeYpZ4M/G/aZSKb5fGo0GsVgMHo8HANDS0lJwW2fPnkUgEIDVakUikcgahsEehILBIEZHR9HZ2anwrBRWLJ9tOfmVq1mtLXNboijOy9Os9Bqq5Wptag1DWc4TmgkpV8VB8d69eyEIAv76r/8a7e3tRZdtaGjAD3/4Qxw/fhw//vGPcfPNN+OWW26ptAmE1BW1qlop6ZVduXIl3njjDdhsNlgslnmTtgCUXfksnU6XnLRU7Bfx1NQUQqEQQqEQL56QWVQgGo3yHr9iEwTZfAQ2kY39YT3F7FVxIpEouj/l5ikuNLHLaDRCluWS55WleCu0X2zdVqsVRqNxXnAtyzKfzAco700sdN6U5LNVkl+5mtXaMrdV7DgCUNyeald8LEbtYR80XpgQZSoOioeHhwGcr2jHZH6RsC8qRhRFfOITn8Dtt9+ORx99lIJisuyoWdVKSa/ssWPHEAgEeE9xJRPthoaGMDAwgJmZmazJaD09Pejq6spattAvYr1ej2AwiHg8jsbGRqRSKSQSCd6mubk5JJNJmM3mohMEjUYj74GVZRnpdJr3qLKgWKPRoKWlpeg+lZOn+LXXXss7scvpdOKiiy7C6OgowuEwksnkvIl2Wq2WB9HF9kuv12e1J/P7EwDPHGA2mxX3JhY7bw0NDYry2ZbKr1zNam1sW8Wua5b67kInfS5kxcdiqjkMhRCSreKgOBQKAQBWrlzJf2Y0Gvn/+/1+2O32rH+zefNmAMDrr79e6eYJqTtqV7Uq1isryzIaGhowNDTEA4DMXtlyJtoNDQ1h7969iMViMJlMvPfK7XZj7969ADAvMC4mlUrB5/NlZY1gAaBOp4MkSTh79mzBCYIbNmyAz+fDzMwMgPMBHDuWqVSKH+vM76Z8lJ4Ps9lcMAgRBAFtbW0wm82YmpqCRqOBTqfLmtgUDod5GroTJ04U3S9ZlvkQk8xe+HQ6zYMlSZIU9SaWOm+XXXaZKvmVq1mtzWq1lryuN2zYAJPJhMnJyYonfVYzMK7mMBRCSLaKEzWyL7jMV2uZQfCpU6fm/Rs2Po+NryNkOWHDHoxGI9xuN6LRKM+J63a7L2hWOOuVbW1tzTuGEvjDjPxCfy8mnU5jYGAAsVgMNpuNj+OVJAk2mw2xWAwDAwNZPajsNf/U1BR8Ph/fHhu+kUgkEI1GeTAsCAKi0SgPzti6Ck0QZEMVigWqub2shZYrdT7Y5MB0Oo3u7m5cdNFFWL16NS666CJ0d3cjnU7j+PHjfIY/G8rBerDT6TQEQUBzczNvb6H9EgQBvb29kCQJPp8vqz0+nw+SJKGnpwfHjx/PCpDZ+XA6nQiHwxgcHEQqlSp53o4ePQqbzQaNRsN7uVkwHA6Hs/LZVnocFyLbQaHrupzzWuo4VjOrkpJhKMlkUrXc4oSQP6g4KGY9Q6dPn+Y/a2xsxKpVqwAAv/3tb+f9m2eeeQYA6CmXLFts2IPL5UIkEoHX60UkEoHL5VK1Z8rv9yMSiaCzsxONjY1IJBIIh8NIJBJobGxEZ2cnn2hXzOjoKGZmZmAymfJOajMajZiZmcHo6CiA82Mi9+/fj3379uHFF1/Evn37sH//frjdbj7eU6fToaGhgQdgrFdbp9Px4HjVqlUwm81Z7Tabzejs7OTHzGq1QpKkrKBSkiRYLBbEYjHepmJKnQ+W0SBzYpfZbOZZLywWC8bHx5FIJLBmzRoYjUYeuCSTSRiNRqxevZqP9y62X5FIBK2trbjuuuvgdDoRi8Xg8/kQi8XgdDpx3XXXobW1VVFvIstkUey8scmBZrM5b5vY+GMlPby1dl3r9XrF57VUr2y1ZA5DyUfNYSiEkGwVD5/YtWsXDhw4gL6+Pvz5n/85//nb3/52PPLII3jwwQdxxRVX8CwU//t//2889NBDEAQBV1xxRaWbJ6RuVWNWOAvK7HY77xnMnJAky7KiiXbBYBCpVKpo71U4HEYwGCw5SWjjxo0Azk/kMplM8ybIsSwIyWQSTqcTVqs1b7tnZmb4vrEAmI2XZctkTkgrpdTkwFK9d2zM7YoVK9DS0pJ3UuPY2BjfVqH9Yuejq6sL69evzzs5LrM9xSoVBgIBReeNDS8IhUKw2Wx5K/opvS5r7bouVvVPyXlVs+KjEtUchkIIyVZxUPy2t70NX/3qV/HTn/4UX//613lO1E996lN47LHHEAwGcd1116G5uRnRaBThcJjn3/zUpz5V8Q4QwlRz9rha21roWeGZvU75xoTm9joV2i+z2QyNRlMyJ7LJZCo5Sej48eMwmUw8iAbAg2K2LBvbWqzdmRPScoMaWZZ5m8xms+LjlU6nMTY2hkAgAIvFgsbGRh5kZx7HQCDA22CxWPj/A+cndmm1Wh5MSZLEe2Azl9HpdHwCGxuzm3s+WG8lK6iReTzZRLPZ2Vn+gKDVatHc3AybzQatVguLxcLPG0tRxx4c2PY0Gg3a29uxbt06HDt2DBMTE7xNLpcLGzduXJAxtUrLd+dbJndSX+7Qjlgspqg3tdzJgWp+xxRal5KsMpkPKbWUNUNNS3W/SG2rOCi+6qqr8PnPfx7JZBJjY2M8b2ZnZyf++7//G7feeit8Ph+8Xi//N5Ik4dvf/jZ27txZ6eYJAVCd0rKLsa1KsV6n4eFhXuo1szCDRqPB6tWrS5Ye7ujoQHNzM9xud9HJX42NjYpy/ur1ekSjUd6TyWg0GrS2tsJqtfJJa4Xa3dnZCVmWMTk5yXuWMyfsiaIIl8uFjo4ORcdqYGAAfX19vKdaFEU8++yz2LlzJ3bs2AGHw4Hjx49jbm6OV+8UBAEGgwGNjY18gtzhw4fh8/my9mt4eBg2mw0XX3wxZFnGiRMnEAwG55VwNpvN2LBhQ8nz4XA40NDQgEOHDs3rxQwGg5iamsL27dvR3d2NgwcPYmJiouQxyvyOroSS+6PSZRwOh+Le1Gqsp9z7vtS6SmWVKec41qOlul+k9pUVFP/Hf/wH9uzZw19/Aud/0X3+85/Pu/yNN96IEydO4Mknn8Sbb76JZDKJ9evX433vex9WrFhRWcsJ+X+qWcq13srGCoKAlpYWHD58mJd2bWhoQDwex8zMDCRJQktLS8nSw729vejp6cHevXvh8/lgNBp57xUrDdzT04NEIqGo9HQoFMLk5CR/a8Sk02lMTk7CbDZj8+bNOHHiRMF2t7W1IZVKYWRkhPd+ajQapNNpnrZsxYoV88bS5jMwMIB9+/bx3lytVotkMolAIIB9+/YBAC/RnLtMMBhELBbD1q1bMT09zavNseMvyzKvQuf3+/nxzl0PC7a3bt2qqBQ0K16STywWw7lz5yCKIlasWIGzZ88WPUZer5dvz2az8e1NTk4iEAioWuYZKF6+W+kySnpTlVzXaq1H6X2v9Duk1DCUevsuUmqp7hepD2UFxXfeeScEQYDD4cDu3buxZ88e7NmzB9u3by/4WsNut+Ov/uqvVGksIbkWo7RsPeUPlWUZ09PTsFgsPF0VC4aam5uh0WgwPT2Nqampkvu1e/duAOD5bllmAqfTyfMU+3y+kq+jBUGA1+vlPZaZQbEsy7z3d2JiAhaLhfdERyIRiKIIu93Ox9WOjo7y6nHsjyAI0Ov10Gg0GBsbyxqakU8qlUJfXx8SiQSMRiNflgWs4XAYBw4c4JPs2FCIZDLJh5YkEgkcPXqUB/qZ5z/zNfcbb7yB9vb2rFLQbD3llII+ePAgJicni577yclJuN1ujI2NFT1Go6OjfD8rua6V3B9KSi8rLc+8e/fuor2pDocD+/fvr9p6lNz35X6HFBpeVY/fRUos1f0i9aPs4ROyLMPtduNnP/sZfvaznwE4X1LzLW95Cw+Sd+zYoSgVEiGVWqzSsvWSP5S1ubGxET6fb97nZrMZ4+PjAJSVwy02+QtQNklIEATe88V6URnWwxwOh3H69GmsWLECer1+3kSqWCyGs2fPwuv1wmq18mUyJ9rFYjGeEYMNtcjX6zY4OIhgMMjHMWcWAWHBI5s0x3oS85Vwnpqa4g8cLNDPDJBTqRTi8TimpqZ4my+0FPSpU6ey0t/lk06nsX//fszMzBQ9Rh6PhxcgqeS6VnJ/KCm9XE55ZqfTCbvdnvd69Pl8iu/XYr2y5ayn1H2v1ndIPX4XKbFU94vUj7Ii18cffxwvvfQSXnrpJRw/fpz/3O/349e//jV+/etfAwAMBgN6e3t5kLxr1y40NDSo23JCsHilZRd6W2qJxWJZJZUzi0WEQiE+IalUSeXM/RJFkc8dyKVkkpAkSUin07yCGusdZkGoLMsIhUI8bRsbt5spsxxysWUyM2IUmkQWCAR4HmEWNDIswGVp49i2ch/6M8tOs2p67HgwoigilUopuoYyS0HnTupjOZ5zjzuTmVN3bm6OZ58odIzYua20HLKS+0NJ6eVyyjPnG3t69uxZnju6nPu1UK+smve9Wuuqx+8iJZbqfpH6UVZQ/MEPfhAf/OAHAZwf98MC5Jdeegmvv/46/2USiUTwwgsv4IUXXgBw/svt0ksv5UHy7t27YbFYVN4VshwtRmnZamxLLaykMss5y4InrVYLjUbDSyqbTKaipYfL2a9Sk4Smp6chiiKvlJbbI8RKPrPgr9Cxzi2HnG8ZjUaDZDKJF154AdPT01m9q16vF9PT01ixYgUE4XzhEJYWjmElqNkxK3Xuk8lkweEabNtarRaRSASJRKJoKeipqSlMT0/Pm4zX0tJSMGjIZbVa4fP5Sh4jSZIqLoes5P5QUnpZaXnmUCiEo0ePFhx7umnTJlXu19zMI7kPjeXc92p9h9Tjd5ESS3W/SP244DEOTqcTN998M26++WYA52c8v/LKKzxIHhgY4FXu4vE4+vv70d/fjwcffBCiKGLr1q248sor8fWvf12dPSHLUrVLy9Zr/tBiFblYueFiJZWVloJmir2Obm5uxrPPPotAIMAzIDDpdJoPU1i7di2mp6cLHutVq1aVLIfscDgwNjaGsbGxefsWjUYxNjbGs1kkk0loNJp544HT6TQf0zgzM1NwW62trZicnEQsFpsXXKfTaaRSKej1ejQ1NcHtdhctBW00GnH8+HHIsszH/rLe/eHhYZ4tgp3XfOdXEARcfvnlmJubK5o1xOFwoLOzs+JyyD09PYruDwAlt1Vqmba2NoyOjhYdezo2Nga73V5yW6Wu68wMLuyYZT40iqLIM7iUotZ3SD1/FxWzVPeL1I+KK9oxZrMZ119/Pb74xS/i+eefh9/vx/79+/HAAw/gxhtvhMVi4a9JU6kUDh06hIcfflitzZNlir2ur0Zp2WpuSy3xeBxmsxmSJOUt4StJEi+gARQuPXwh2Ovo3NLTGo0GO3fuhE6nQzgcRjwe58FwOByGTqfDzp07sXnzZn6sWS82G5ZhNBqxcePGkuWQN2/ejLNnz/KJbKycNCscIggChoeHeT7fZDLJx/myoQ4ajQZ2ux0XX3wx31YoFEI0GkUoFOLb2rVrF3p6enheYNZrnEwmkUgkoNFoeOAoCALfDisFzSbANTU1YXZ2FrIsZz00iKIIrVYLWZZ5MFuMw+GA2WxGT09P0WPU29uLjRs3VlwOeWhoCF1dXUXXs3HjxpLbUrIMy5hRauzpihUrKr5fWQaXQCAAr9fL0wKKogiv14tAIJB3/HehdanxHVKP30VKLNX9IvVDkKtU1D0ej+Pxxx/HV77yFZw5c4a/Dswcv1dvAoEArFYr/H4/DQdZZJSnOD+fz8dTihUaGsHGcEqSBL/fn3cZALj66qtVndySLy+w2WzGzp070dPTAwAYGhri2S7YBLHm5mae7aLUMhqNBk8//TTMZnPeIQeJRAJ+vx/Nzc3Q6/UYHR2dN6Sho6MDkiRhz549OHv2bMk2P/fcc3j11VcRi8X4eiRJwo4dO3DppZdi3759iEajmJ6ennesW1pakEqlMD09zYc05BtikUwmsW7dOpw9exazs7NZw0JEUURTUxMuvvhi7N69G4IgKDqOxa5rnU6Hffv28THhuaLRKCKRCK6++mokEgnV8hTnjgNvb2/n44VffPFFnokkVzqdhtfrxZ49eyCKYkX3qyzL2L9/f8lc3+xYK6HWd0g9fReVY6nuF1k8SuO1BUsREYvF0NfXhxdffBEvvfQS+vr6EAqFABR/lUvIhahGadnF2FalMl9Hrly5kk9OY+NWPR4PLBYL5ubmYLFY+P/nlidWUgq6XD09PbjsssswODjIK8h1d3fzzA1utxsnTpyAXq/H6tWrs0oPnzhxAs3NzXA6nUUzYpw4caJkO1haOKfTCZfLxfdVkiTY7XYkEglEIhGEQiFMT0+jpaUFbW1t/N+n02lMT0/D7XbD6XTimmuuwZ49e3Dw4EH4fD7YbDZceuml0Gq1vKyw2Wzmx5MFqaxgyfT0NFKpFBoaGmA0GudlqGDlq9nrZYfDgWg0yqvWGQwGmM3mrB61UllDgMrLXLMJUMXKKivZVjHsd0c5Y09tNltF9yvLiNDS0lIwE0q5GRHU+g6pp++icizV/SK1T7Wg2O/34+WXX8ZLL72EF198Ea+99hqfoMK+yDQaDbZu3Yrdu3dj9+7deOtb36rW5gkpOHu8lre10KVMM7NBsADYaDQiHo/D4/HwV+Ovv/46AoEAfD4fgsEgD9TMZjOsVuuCTW5hxSUcDgd/JQ9k5yvNfTVtsVjm5SuVZRlzc3MIBAJ8mBYAXvktGo3yohUswBRFEZFIBCaTCW1tbTw1V0tLC99WvjGsDocDMzMz/Jyxv2e2R6PRYMOGDXwZFuhLkoRkMonR0VGkUik+4VEQBF7hji3PAr7cTBcs7Rsrzay0R61Y1pDc/WWBs8Vi4T3d5UyAqvT+yCzgkK+YSOb4ZYfDMe9hL3fsaSXtycyIUCqDB6D8nlbSJiXrqub3XjUt1f0ite2Cg+LJyUk+qe7FF1/EkSNH5k36MBqN6Onp4UHwrl27eM8TIUtdqV9o1XpFWCobBCtffPToUaRSqaw3OZFIBDMzM9i0aZPqk1tKva5Xmq/0+PHjRcszr169GkePHoXX6503zIAFr5dccgkGBgYKppFbsWIFDh8+zPMn5w5paGtr4+0pNnzAbrfz6nWskEZmERONRgOTyQS73Q6v11u0pDbr6VWrR63YEIsNGzaoOgGqVOnlUgUc2PjlqakpDA4OzjuvLS0tZY89LXS/lvNAUM1S0IQQ9V1wnuJTp07xn2f2ylxxxRW8F5i9MiRkuSn1C63apUyLvY6UZRmRSISPgWWlmNnkt2QyiUgkolpbgNKlXC+66CJFr+sPHjyIV199tWh55jVr1mBoaIhnhWD7zFLCrVmzBi0tLUUfHDKHSKTTad67K8sy4vE4RkdHEYvFMDk5iZMnTxZNExaNRvnkQo1GA1EUIcsyEokEUqkUYrEYLr30UvT19RUtqc2CZTV61IaGhrB3717EYjGYTCbebrfbjb179wIAf+MwPT0Ng8GQNZzFZDJlBaHFHghLnfuNGzcqeiBasWJFVjntTOUO0SsVpCt5ICiVneNCS0FLksQL3VCpY0IWVlkR6x133MF/EQDAunXreC/w7t27+YQNQpazUr/0e3p6smby5+sJq2YpU5/Ph6mpKf5aOLP0MCu6MzU1BZ/Ph6ampoq3p6SU67lz57KGEORi5aIPHz5csjzz9u3b0dzcDIvFglAoxHtBWTaK6elpbNiwoeiDg9fr5T3NLIOFLMs8yE4mk/B4PDh79mzJ8zo7O8t7mDMLl+h0OqTTafh8PrS3t+O6664rWlI783hW0lOcTqcxMDCAWCwGm83Gj6MkSdDpdPD5fBgYGMCtt96K9evXY2BgAJOTk1m9ydu3b1c0iU5JL/Dx48f5+Oh82PCIoaEhyLKM7u7uvGPlld5DSh5QSxWkyc3OUck9ze6P2dlZpFIpft2xiX2xWIxKHROyQC6oG1er1eK9730v3vOe92D37t30xErI/6Mk4Hv99dcxNzdX1VKmxQIVn8+HSCQCs9kMrVY7b2JXMplEMBiEx+NRJShWUsp1bm4OZrM5a0IZk1kuOhwO857tTKz4RzAYxOnTp9He3q5oklShXteJiQmeKSd3iAlrWyqVwvj4ONrb2wvu18TEBA/4coNiQRB4tomZmRls3ry55OQ4NV6xj46OYmZmhqeoyyxAotFoYDQaMTMzgyNHjmBkZASSJGH16tVZ+ZXZxEcARQPMTZs2lTz3fr8fQPHiHewaYEF87jhfpfeQkvt1cHAQu3fvLvomoZzhPkpKQbNzznJkZ+apFkURo6OjVOqYkAVQVlDM8mcmk0n8+Mc/xo9//GMAwPr16/mQid27d2PdunUL0lhCap2SgM/tdkOW5YK/0NQuZVqqJ4wVS2BtzDfkiQVtalBayrWzsxPxeLxkuehCQ7S0Wi1isRgvYaxkklQhc3NzAMCD2NxxvsD5YxSNRovuFwusWVCduZ7MAJkpNjmu3CE46XQ6b4DNJlYC4GOiM3uvGxoakEwmceLECaRSqbwPKSx9GoCSY4FL9QILgoDGxkb4/f6Ck+jYg1Ol5YCV3K8smFWanUNJqfRiotEoT7PHHlSAP1ShDIVCmJ2d5cWxCCHqKSso9nq9ePPNN/nkupdeegljY2M4fvw4jh8/jsceewwA0NramjWs4pJLLqHXPGRZUBLwsYBDzVKmhV6hZ/aEsQAjHA7zHkWPxwOv1wuDwYBIJMKHBmSuNxKJwGAwwG63l3UsKp241NbWBrvdrrhcdK5kMskLTFR6rNkEYUEQ+FhahmW1AACDwVB0WwaDAZIk8fLRmUMxkskkgPNBZKkJyUrOa+Yr9mKT6MxmMwDwHlo2JIS1mQ1ViUQi8wJidkxYLziArEItucso6QXW6XTo6urCa6+9lncSndPpxIYNG/jQmUrOq9IHNBbMFnqTwK7pckqlF7o/4vE4EokEDAZD3uPIHvZYfvGlaqEz8xCST9nDJzZv3ozNmzfjYx/7GABgeHiYB8gvvfQSjh8/jsnJSTz55JP4P//n/wAAT27PepN37tw5r8eGkKVAScDX0NBQcmiAWjP52WtdnU6Hc+fO5f1lHQwG0d7ejuHhYYRCoXmlkGVZxurVq3kwoOSXlRoTl9h6KykX3djYiLVr12JqaqqiY+1yufhQksx8wSygZZPm2tvbea9ivm11dnbySXtsfZm9sqwntqOjo2h7WA9nsfPKejinpqaKTqK75ppr+BhsNo6bEUURqVSK/7tiwSML0or1lrJiJ8WOkcvlgtlsLhgAsZ5kNbJhlJtqrhCr1YqGhgYMDQ0pKpVe7P7Q6/X8HLGe88x9Yw+B+R4ElwrKvEEWS8WpIVavXo3Vq1fjgx/8IIDzF3NmkMzGTz7zzDN8FrNWq8Ull1yCt771rXjwwQcrbQIhNSOzWEaxX9ZdXV1FU4ApTSelJItDKBRCKBRCMpnMGp8YDAZ55oDLL78ciUQC09PTWa9lRVFEW1sbLrvsMgiCoLgSWaUTlzL3v1DvHCsXvW/fPj62mGWfYD2Ou3btwpo1azA3N1fRsU4mk7Db7XC73bz0syAIvDwzS422atUqnDx5suC2Nm7ciNbWVh6kGo1Gfj5isRgaGhqyMksUegCJxWKKzmskEik5ie7AgQNoaGjgQydYRox0Oo1EIpE17KRY8MiCtFK9pRs2bMCxY8dKTlpLp9MFJ9GxlGxKr6FClN6v5aQjzNe7m6nU/bFp0yY0NTXB5/Px7BOZ14hGo4HNZluyHUvVzsxDSCbV86U5nU7ccsstuOWWWwCcH4uXWdTjd7/7HWKxGAYGBvDqq69SUEyWFEEQFAV8pXIHKy0/W2qS0MjICObm5pBIJNDY2DhvfOLc3BxkWUZrayvsdnvBsrpK08gpyS6gZOKS0l96rLQyy1Mcj8chiiIsFktW6eVKtyVJElpaWiBJEiYnJ/lkL0EQoNfr0dbWBqvVWnLIh9Pp5Nvr7++H1+vlAV9uZolSvYlsfxsbG/lwDBaAsvPq9Xr5JLp8kxGNRiNmZ2eh1WphNBqRSCSyso+wXktZlvk432LBYzgcLtlbunbtWlgslnnXmsvlwsaNG7MmrRWbRLd169aKz6vS+7VUcO33+xGJRLBq1Sr4/X6eh1qj0aCxsREWiwWRSAQ+n6/k/TE2NoYVK1bwCY/hcJinEzSbzRBFER0dHarnDa8FSic+UuYNslAWPIkwe305NjaGc+fOYXh4GOPj4xWVen7xxRfx4IMP4rXXXsPExASeeuopvOtd7+Kf33777fjBD36Q9W9uuOEG/PrXv+Z/n5mZwcc//nE8/fTTEEURt9xyCx566CE+vo6QC6U04K20lKmSSUJswo6SdZbKZazkl9WWLVtUmbhUjlLlokvtmxKsRzEYDKK1tRU+n48HqjabDRqNBg6Ho+SQD6a5uRmrVq3iY0j1ej06Ozt5BgcluXyB8xkvWFtYkJ458TASiSCVSpVMb5ZOp3lAGIvFeEDHMnTEYjGsWLEC586dKxo8Hjx4kJ/rTOWc03LG+SopKV2KGg+orM0NDQ3zfrfJsgydTodIJAKPx6Po/ti2bRsCgQBCoRDv4S+UE3opKWfiI2XeIAtB9aBYlmUcOnSID5946aWX4Ha75y1TiVAohIsvvhh33HEHbr755rzL/PEf/zGf+Adg3iu/W2+9FRMTE3jmmWeQSCTwoQ99CB/96EfxxBNPVNQ2QoDKgzAllAQPbHJXMpnM+ypWkiSYTCY+HrTQUAWlv6xYr2alE5fKpdFosHnz5orXU4ggCGhpacHhw4fnjc2dnZ3lPcmlhnwA2QFvS0sLX8/U1BTm5uYU5bE+fvw4dDodAoEAD3rZhD9W0pqV5y6V71mj0fBxtXq9PqtnNnMMa2trK9rb24umJcvsLc3t4WS9pWfOnMHRo0f5+HWj0Yh0Oo2JiQkEAgFs3LixqiWlgcrvV1a++9y5c0in01m95KFQCJFIhE9AVHJ/mM3mrECd7XPmm5ulqNyJj4SoreKgOJFIoL+/nwfAr7zyCk9fBMwPgNetW4e3vvWt2LNnD/bs2XNB27zxxhtx4403Fl1GkiS0tbXl/ezYsWP49a9/jVdffRWXX345AOAb3/gG3va2t+Hf/u3f0N7efkHtIuWrxRnG1WoTS2OV7xWyklRaSiYJsQk5kiTB5/PxlIparRZNTU38FSz798XGsLJfVul0Gh6Phy/jcDj4LyvWU8nSbs3NzfF2NDY2zgtokskkDh48CJ/PB5vNlrcKZiKRwMsvv4zZ2Vk0NTXhiiuumPdLM5VKFe0pZsd6ZGSEt7uzs3PesS7UHlmWMT09DYvFgmQyiVAoxIPPpqamrCIggiAUbE9mj7vFYsHp06cRjUZhMBiwZs0aBAIBHDp0CMFgkJ+baDSaNWGNZXGIRqPQ6/VIp9OIxWJ8/K4kSbwHmvU+u91u6HQ6xONx3gus1+sRDofR1NQEg8EAv9+PUCjE3wyw/2aOYbXZbGhubs67bywtmd1uh8lk4lX+JElCe3s7NBoNHw/MClO43W7eHrPZjHg8jrGxMdjtdkxOTsJut/NhInq9Hmazed44XyXXh1r3dKF7kV0XkUgEjY2NWZMMMwPd5ubmrAeQ3MmImfeHzWaD3W4vmqdaybWv5v6rRY2S2pVuq9bVa7vrXdlBcTAYxCuvvMIn07366qtZT225Se23bNmCPXv28EA4MyfqQnr++efR0tKCpqYmXHPNNfjSl77EU0odOHAANpuNB8QAcN1110EURfT39+Pd73533nWy14hMIBBY2J1Y4mpxhrFabVJS5vmFF17A9PR0Vsopr9eL6elpXHnllXA6nUVTaW3YsKHkJKH29nbIsoyjR49idnY2KwdtNBpFNBrF5s2bYbVai7aZ/bIaGRnB5ORk1npOnz7Nx9Ta7XY4HA4cP34cwWAQkUiEL8eybmzYsAFWqxXPPfcc//5gy+zbtw87duzANddcAwD4xS9+gUOHDvE8ugCwf/9+bN++HW9/+9sBAAMDA3xMMQsMn332WT6m2O124ze/+c28dk9OTmJkZAQ33HADnE4nnnvuOT4pjXnuuefQ09ODSy+9FB6Ph4+rzcR6Qtlr3ePHjxdsz4YNG+DxeDA2Nsbz+gLnv0syg25WbntycnLehDW73c7Hm8bjcR4QM2ziXUNDA0RRRE9PD375y19ifHx8Xnozk8mEt7zlLXC73QgEAggGg/PyFNtsNj6GNd/1ePDgQfT09KC1tRVarRanTp3C1NRU1jkbGxtDa2srz9MdCAQQiUSy2sNS/2m1WvT29uLcuXM4ePDgvKEhrLdUEARF14fSyaGllil2L7J9l2UZk5OT846z0Wjk6fccDgeGh4f5eOHMcyuKIlavXl3wfjx79mxWm0pd+0r3rZrUykxT6bZqube9Xtu9FJQVFF9++eV4/fXXs274zCCYZZVgQfBb3/pWVSpgleuP//iPcfPNN2PNmjU4deoU/vEf/xE33ngjDhw4AI1Gg8nJSbS0tGT9G61Wi+bmZkxOThZc7wMPPID7779/oZu/LNTiDGO12qSkzPPBgwcxNjY2b0JSNBrF2NgYXnvtNaxZs6ZoKi0AiiYJvfHGG/B4PDxLgiiKkGUZ8XgcHo8HiUQCHo+nZJuj0SjOnTvHew9ZT2I8Hse5c+d475YkSXy9mRkh5ubmEI1GsXXrVuzbtw+vvPIKDy4yx0y+8sorAM4XgTh48CD/jmHbS6VSfOxqS0sL9u3bN29bgUAA+/btgyzLGB8f5+3W6/X8WLN279+/HxaLBa+88kpWIQ6WVuzll19GOBzOyvaQec4ysz289tpr+N3vflewPcFgECdPnkQ4HM577QQCAcTjcbS2tvJX8bmZJcLhMBoaGvjxyj0+mWNP2bAY9vNM6XSaB5uSJPHsE2woBpu85/f7IUkSjh8/XvR6vO666+D3+zE+Pj5vv1i1P1mWEY1GEQ6HedDN2p1IJBAKhfg9xCYhZvaMJpNJPnmwv7+/5PXR29vLr2tJkiBJEmRZzrqngeJV+Hp7ezEzM1N033fs2JE30GfHORKJ8HObOwyH5dH2er18GA67H9kQE4PBgHQ6jfHxcd6mM2fOFL32AWDNmjU19T2rdmaaSrdViwFmvbZ7qSgrKGZfNIzBYEBPTw8fCrFr1y6YTCZVG3gh/uzP/oz//9atW7Ft2zasW7cOzz//PK699toLXu+9996Le+65h/89EAhg5cqVFbV1OarFGcZqtUnJeg4dOoQzZ85AEIR5FatMJhPm5uZw+vRpjI+PF02lNTAwgFtvvbXoJKHm5mYcOXKEBw65v7BlWcYbb7wBjUZTtM3Hjh3D5OQkr+bG2pMZiE1OTiKZTPLsA3q9PiuTgclkQiqVwrFjxzA+Ps7HwrJ1sfUmEgn09fVlBW25OYjZ3AWWMYH1sgHgAUI4HMbLL7/Ml29oaJiXfSMSieDkyZNIJBJ8QmLmuGC2b4cPH4bNZkMqlcrKoZubxYMVlCjUnoMHDxYMiJloNIp4PM7HobL1sFzMPp+PZ59g7WQy/9/v90MURQwMDCCVSvHX+pmBcDKZRF9fHwRB4EMYWM8sm2jHXs+zh4RC12NfXx8v4FGI2+3mxUEyc/Cyv8diMQSDQRw6dAjJZBKtra1Ip9O8F5Tt/4EDB/iDTrHrw2Qy8aEaXq+Xr6ehoQGxWExRFb5jx45heHi46L4fPnyYrz8z0Gf/ZcG8RqPhbwRSqRQikQgikQhEUURzczP/fGpqik+SnZmZyepNjsfjOHr0KA4dOlT0Wjtw4AAikUjNfM+qVVJbrcw8tZjFol7bvZSUFRSbzWZcccUVPAjesWNHXSQQX7t2LRwOB06ePIlrr70WbW1tmJ6ezlommUxiZmam4DhkALyngVSmFmcYq9UmJethr8QtFkveZVjO2HA4jMbGxoKptGZmZjA6OorOzs6Ck4TefPNNHrDl+xKVZZkH4e3t7QXbfPr0ad7bxnrjGDYGOBKJ4OWXX8bMzAwaGxt5WWP2C12j0SAWi/GUZqyHOHff8hWQyF0mlUrxnlODwZB3Gb1ez3seC1UH0+l0WUFqZlCceYwSiQTPhVwI6wFlRSqKtaeUYDCIhoYGRCKReZMjWdlldg5yK+yxv6dSKRw5coSXFc8sBsH+DgCTk5PQarVFzxmbLM1SguXum9Fo5Oe1GJbjuVBpbuAP1yS79nO3ZzQaMTExwfe52PVx5MgRaLXaeT3uoVAIoihieHiYv+EodO2PjIzA4/EUTWs3MzPDH/4y/2SuK5VK4dSpU/B4PGhpack7pjgWi/GH4VAolPdNgSiKGBoaQjAYLHqtBYPBkvd1Nb9n1Sqprfa2aimLRb22eykpKyj2+XzzbsB6MDo6Cq/Xy8cz79q1Cz6fD6+99houu+wyAOfHDqbTaf46jSycxZxhrGQiWSVtUrIe1hNXDPuFX2w94XA4q7ewUNYIVj449xe1LMs8wI1Go0W3xcatZvaCsUCb/T0cDvNeObZcbvDDgi5gfjDDsKCGtTmfzDawsZy5wZxWq0UkEpnXk5i7rcx15vtFlNnrJ0lSwSwerLe5UMBXLBDMlUwm0dHRgZmZmXlZHJqbmzE2NgYAfL8z9yPz2M3OzvKCD+ycsM/Ya1l2fRQ7Z2wcdanrmsl3vDOHprA3Arllrtm/Y9daoW1lrisfts5QKASz2TzvjYxGo0EoFILP50NTU1PJa79UWrvMY86uwcz9Zttm92Op7bHftYXaHQqFkEql0NDQkHc9rBR0LBarmUwOapXUXoht1Yp6bfdSUlZQXCsBMRuXx5w5cwaHDh1Cc3Mzmpubcf/99+OWW25BW1sbTp06hU9/+tO46KKLcMMNNwAANm7ciD/+4z/GRz7yEXznO99BIpHA3XffjT/7sz+jzBNVsBAzjJVQMpGs0jYpWQ+rNBaNRnlQwMiyzCccsTGdxVJpsbzahWbF585CZ9vIDSZKpe2SJAmiKBb8wmYBTVNTE0ZGRkq2O3O4Qm6bcucs5A75YD8HwB9oWECc+Tqd9USz7bHX9pnrYBXcUqkUD7ILbauhoQEtLS0IBAJ5izP4fD4e9OTrUc4MGkthZZE7Ozv5WFS9Xg+LxcKDcLb/LBBj2Jhx1ubMfco8RpnHngWp+XqK2bUPFK9ox8pWs3YV2zd2XeYO5wDA11NsW5kV//JhP2c5gvM97LBhBoIglLz2laS1Y4Eze1Bh+8X2NZ1Ow2azIRQKIRAI8LdBuVX/WK8yyyKSr93snLH7Mfchld2P7GGtmt+zhVTze3+xfsdUql7bvZQsePGOhfC73/0OV199Nf87G+d722234dvf/jYOHz6MH/zgB/D5fGhvb8f111+PL37xi1kX0o9+9CPcfffduPbaa3nxjocffrjq+7IcLURp1VKUTH5jbXI4HPNKyyptk5J9a29vh8ViwfHjx/P2OsqyjPXr1/O8v5njbgHwWetOpxMdHR1FZ8Wz18Lsl2ZmsJCJvaostO9r167lwWBuTzcLyiwWC6644gpe4KFQu9va2jA+Po54PF4w4GWp31hwkQ/r/WRlqTODXvb6vKGhAc3NzZienuaBcWZ70uk0mpub+XjQfO1h6167di0fipDb3mAwiHXr1vFjlDkRkf2XVZ/LTFlZyNatWzE2NsbTv7HzajKZeAaG8fHxvG8d2D5otVqsWbMGR44c4cc698EhlUpBr9fzssIsIM3M9pBKpdDS0gJZPl8hr9B5tdvtmJiY4A8yucsA56+VVatW4ezZs/xhhm2LBYLd3d1wu91Fr32Xy8UnIhbaFntozBw2knnO2ITJ5ubmopX6Ojs7eRrCQu1paWnhlexyO49YGy0WCy699FLMzMxgaGiIBzeZQyN8Ph9WrlzJq1AWarfJZOLzANhDDMPOKyucNTU1VbXv2WKq+b2/GL9j1FCv7V5KaqPrt0xXXXVV1i959ufxxx9HQ0MDfvOb32B6ehrxeBzDw8P47ne/i9bW1qx1NDc344knnsDc3Bz8fj8effRRqmZXJYJwvrSq0WiE2+1GNBrlM+bdbndZM4yVyJ28wHo92eQFVpq2q6sLgiBgcHAQJ0+exPDwME6ePInBwUHe5lJtUrpvl19+OR/rx8aiRqNRCIKA9vZ27NixA729vTy/cOZ6fD4fJElCT08PTpw4gb1798LtdvM8sgaDgc+KHx8fL/naXqfT8epqhfZ906ZN2Lp1KwBkpezKHJu6ZcsW6HQ69PT0FG33zp07sXXr1qxgnZ0ntt5t27Zh9erVRdu9atWqrIm9uYE/cH786e7du/nEKjaxKRKJ8PG511xzTdG5BADQ1taG1atXIxAIYGZmho/B1Wq1mJmZQSAQQGtrK3bt2pX1ijsSifD/12g02L17d8nJuStXrsSKFSswMzODyclJBINBPgFtcnISMzMzaGpqKvl9xQInm83Gg/LcID2dTqOpqQkXXXQRH2ubSqV4kMr+3t3djZ07d0KSJMzOzs77I0kSLr/8cj4BKHM4S+aDjcPhgNPpRCKR4NthDxDszYjdbi957e/atQuXXHJJwW2x+9DhcPDgkQX7rJANyzFd6n7duHGjovbk3h+sxz7z/sh8c5NOp5FMJvlDaGYw39TUpKjdAPh5ZA8jbFz31q1bsWnTpqp9z5ZSze/9av+OUUu9tnspqcueYlL/1CitqpTSyQsrVqwoOkZRKaX7dtVVVxUt3pGZi5SNLdVoNHA6nejp6cH69evxox/9qOis+FOnTilqc75Jf5n7znpvHQ4HfD4fH4vKXnvbbDb+Grerq6touzds2IDp6Wk0NzfD5/NlBU0siLNYLJiamuLbztcm1mvd2NjIe8wYjUaDhoYG3vvOArHcXlCn04nm5masXLkSXq8371g9SZKwcuVKnjWA9Q6yrAF2ux2iKGJ6ehpdXV1obW3lmThY27VaLVpbW7F69WrEYjF4vd68WSiMRiMuuuginD59mvcOZ44bZr2AbDJWMbFYjBfmYMMiWPDIxhSzlICCIMBqtfJzG4/H+TJWqxWxWAwbNmzAiRMn5uUF1mg06Orqwvbt2xEMBvH6668XPK9btmzB8PAwtFotDAZDVqDOxiUPDQ3h1ltvLXoNdXV18euMtSdzW9u3b8dNN92E/fv384Azd2y2KIro6OjAmjVr0NjYWPR+LXUvsmu61P3h8/kQiUTQ0tKC6elp+Hw+vv9seE4qleK5qFmGCtZu9qDT0dEBWZZhs9n4sc48H2x7Doejat+zSlTze7+a21JTvbZ7qRDkUjN+SEGBQABWqxV+vx8Wi2Wxm1OXCk18U9PU1BRefPFFHrzkYq9G2avtfEMIPB4PXC4Xdu/erbh9SvZNyTKFKlaNjIzgqaeegsFgyDvGLBqN8tew+SauZQZsmzdvRjQaLbjvTU1NmJub46/vWQApSRLsdjsSiQQikQiuvvpqPjmm0Dhnn8+Hffv28YIGExMTfF0ul4vnfx0dHeWBWW7FtsxJbQ6HA8D57Bes3WwCks/nw6pVq3igkVsdzev1wmaz8Vn9s7OzPJOAVquF3W6H1WrlvW82m61g1oBwOAyz2Qyfz8dznrP9amtr4z287DjKsoyTJ0/yinYXXXQRb7PX6+XbZPsqiiIfzy2KIgKBQN7hMOwci6KId77znThy5AgPnHLTm7EhGQB4DubM4Q/s4cFms8HlcqG/vz/vMBSdToerr76a58VluZtZ3mODwQCz2YzOzk7s27cvK1DPzXQRi8Xw7ne/G52dnYqqtRWraMeGTbF8v5n5sE0mU1bOVyX3otJrutD9sXXrVvT19fFjnfkWh/3dZDLh8ssv5/ms2dhi+f+lxDMajdi0aRP6+/vh8/n4OcscU8zO2Y033sjfFNRSdbRqtqfW9l2pem13rVIar1FPMVlUlcwwVkrJ5AU2Xov1uBoMhqxlLiQVjpJ9K7VMvsmBMzMz6O7uRjAYLDkrnvUKskAyM3jKDLK8Xi/a29sL7jsbS8uOT27xG9Zzm9l7KYoiOjs757Urc4Y1663LXRd7ba/X6/MO18jMXsDOa26OdDYchY351Gg088bisfMqyzIfq+dyubICXlmWebYHNmkr9xjp9Xp4vV5Eo1EYjUaMjY3xSVSstHLmcWQPG2ydwPnUaA6HgxcKYTIzNLBX8bkTA/ONqZVlGW63mwf3ExMT8yoMtrS0IBaL8Zy4QPbwGJ1Oh1QqxYdysGOfex2x3NKXXXZZVi9X7oRWVta5WKYLllWl2LWf2Vum0+lw1VVXzbvOgD/0uikpp66E0mu60P2RTqd51cDMfNds31n6xNbWVtjt9nn7z3oLU6kUP2e562FZN2ZnZ/l4+2p8z5ajmu2ptX1Xql7bXe8oKCbzLLUnVCWTFywWC+bm5vgEL4/Hw/ff4XBcUCqcQr1KSpWaHNjZ2aloVjx7Vc1ei7OeOZbon83mZ8MfcntB9Xo9D5LUmBWd+ZCi0+nmHet4PM4DRfYaPzflWGZBEDZeN7fd7Ljp9fqiDw6Z+5YvawQLpADwNs/NzfGfNzY28nbGYjH+WWaPayAQQDQa5ROnzpw5k1WNDjjf0z03N8czj7CgNLN3l53vzJ7/3CEmLGiVZZkX6PD5fHwcNNtfALyKGpvgCSCr3ay3OhaLZVXHy8S2NTc3h8HBQWzevLlgjtlIJKLomk0mkyUr0ZXTw8vOHTtPudxuN44ePYqRkRF+Xjs7O7Fp06aswLnQtsrNGsCOIRvakS9TTLFcvSzDC3s4y10PS8lWKm80ISQbBcUky1Ksuc4mLxQrHbphwwYcPnwYIyMjvEeMBQ+nT59GW1sbrFar4lQ4xTJCsLGQxeRODmS/yDMrG7Ecq6UyVAiCwMdU574aZz3fjY2N8Pv9CAQCedNENTQ08KEBlc6KZg8pb775Jh9/mXmsbTYbVq9ejampKX4NZm6PTcrSarXo6enBCy+8wHtyM5lMJlx66aUYHh4uGqywfZuamuJjODMrn2k0GqxevRqyLOPEiRO8XDVrs8FgQGNjIzo6OnDmzBn+eW5GALYcAD5OMDdPL8tewPaRBTuZGSPYfzPPZb5sIBqNBi6XC6dPn+YPDvkyS7CHIdajnSuzDLcgCHlTy7H2snzGhXq5Ojo60NzcXDQ7CRuXW6wSHavq5fF4in5fud1uvPDCC5ienubng5Vcnp6expVXXgkA+O1vf5uVzUMQBExOTmJ0dBTXX399VjWxfNtyOByKsgZIksTvI/aGIvMYsmuRBbOFjiN70GOTRnN794E/pPUjhChXl9knyMJgPZMTExMwGo2w2+28elR/fz+valWP2GtUl8uFSCQCr9eLSCQCl8uF3t5erF27FtFoFOfOneM9p6ysbjwex7lz5xCNRhUFfUNDQ0UzQgwNDZVch5LJgV6vF1u2bCmZ6YHNnGc5dLVaLZLJJPx+PyRJwlve8hYYjUaMjIzw3nKj0ch7REdGRmA0GrF9+3ZVZkWzXlnWQ8wCNNbT6vF4kEqlYDQaebDFektZhgFBEGA0GhEKhQr2hsXjcSQSCd7blq+HMxAIwOl0YtWqVTyzBAtOBEHgmSVaWlpgMBjg8Xj4+GOWxSQYDMLj8fDeafZQwXpxWZAaDof524bcQJa1h11vLIDNHQvOAmVWRpv9LHcZAFmV71iBFhY0CcL5rCesDDdbJp/MlF+FpqCw3uJSmU5EUSyZnWTz5s0YGxtDMBhEKBTKuh5DoRCCwSBGR0dx+vTpot9X09PTeO211zA2NoZ0Og2DwQCTyQSDwYB0Oo2xsTH87ne/w/79+3nPKxv/zN7KjIyMYP/+/Zieni66LY/HoyhrgMFg4JMnCx1HVl67GIPBAKPRyDOqZN5DmZlVcof5EEKKo55iAmB51Fwv9joylUphcnKSByasB4v14qXTaUxOTvIeuELS6TQGBgaKZoQYGBjA+vXriw6lUFrZaMWKFbjuuuuKztJnis2cHx4e5vubKfPv7MHizTffxOnTp/lr5rVr12Lz5s2K3ySw8rtsPzJ7Qtnkq+PHj8Nut2eNL2bY5DCbzYbf//73fFxl7qStSCSCvr4+vO997yv6lqCrqwtDQ0NFM0tMTU1heHiYB6PsVTzLCMDanFsymmFBEOthtlgsiMfjWcNZWM8em4RVLPNG5tCI3PHFgiBkBVZsCAJwfghBIpHggT9w/lrLLKudT6H8zblWrFhRcplS2UmsViv6+/uRTqcLVnSbnZ3F0NBQ0e+rQ4cO4cyZM3yYTeZ6TCYT5ubmcOrUKT7Omg3RyCx8EYvFcOrUKTQ0NJT8bty9e3fJrAEsDRtLPZc78ZFNnCs1cdtisUCj0fDrkT3csDcX7O0UTQAnpDwUFBMAy6fmeqHXkYODg4hEIvwXY2a+U9b7FYlE+JjJQkZHRzEzMwOTyTQv6GXDEWZmZjA6OorOzk5Vxih2dXXhoosuKjpLv6urC+vXry84cz4SiWDVqlW8ylZm6iqLxYJIJAK/348zZ87g9ddfRzAY5L/M5+bmYDQaFQfFg4ODCAaD/BVw7sz5RCKBcDgMm82Gzs5O+P1++P1+/ovearXCarVidnYW4XCY9+qxwJkFx1qtFsFgEF6vt2iwwsY1t7S0FMwscfbsWXi9XjQ0NMwLIFkgwl73syEOmYEk6x1kPd2lsF5XNlwh86GBPURkZo7IHc7B/j3rLW9oaCiY7SGzF52dg8x2ZAbcxQiCAJ/PpzgwLnTNZo6XzfddxMp3e71etLS0FPy+mpycRDgczptukD0UeL1e3vMeiUTmPXxpNBpEo1GMjo5i5cqVJb8biz14A+dnwLPsKOz7hk1mjEQiaGhogFar5ZN+C2HrYQ9k7M0GC+oNBoOi9RBCslFQTABQzfVAIMBTS2Xml818LZxMJhEIBIquR0lGiGKz68sdo2i1WhXP0i81c95ut/MJh5mTyIDzE7IOHjyIV199lU9IY8dkbm4O+/btAwD09PQoPtaFXrWzYI71yHZ2duZNEydJEtLpNO/5zJ2Mx85hIBAoOvmLjV0ullmC9eqyayNzLDALPDNLHLOAKnNSW2axCr/fzydEZa4nEonwY8uKWWRei2wSIpsoyR4iMisXsn1hvaPsoYGtN/M6Yr2iwB8C99xxriw3cimZPdulJr8Vu2bZw1Kxim6srcUmh+ar9pevzZmVDDO3lflztb4bY7EYtFotz4udOaTJarXCbrfz4Q9K1tPR0cF729kDWWNjI5qbmxGNRpfs9zUhC4WCYgKAaq6z3iQ2xjW38hT7eanXkWazuezZ9fkyS/T29pacHNjd3Q2Px1NyPaV6cNm5LzbRThAEHD58GIlEAkajkQdSLNAKh8M8JVex4SXsWLM0XqxXnmH/lpVV9nq98Hg8sFgsMBqNiMfj8Hg8MBqNaG5uxunTp3mvKYCsCWksSGWBfaG3BEqu/cweWpY3lq2T5VHObH/mZEb2d7ZfBoMBwWCwYBYHg8GQVcksX1DIUtCxCZK5QRG7ThsbG3kJ53wlxVnPO2t/oV5hNgaa/ZcFwJn/ZX9KTdYtlVVl48aNJdtssVggSRICgQDf/9xr1mQyIRKJIBqNzpusKcsyfwDJzLyRD+tVZkF6bgCe+d1Yat/ZtTY3N4fZ2dmsSZ3pdBp6vb5g3vF81yzLkpHv7Qa7TgghylFQTABQzfWuri5otVpEo1HeY8iwV9gGg4GPhyzUE5Y5u54FWuy1v06ny5pdr3SMYqEcqw6HA/v37+frjMfj+P/Z+9MYObI0OxQ8Zr7vu3vsEVxi4ZZkkskgMytbVZWdvVZhXi+voW7UjFZ0DwS1Bj2SIIwwI2gEaCBA+tFQ/5A00GCmNY1SA9NPT6VXvWR1MTNrYXFPJrdkRDAYu0eE7/u+mM0P53d53dzM3CMZZG5+AAcZHhbmtrnZud893zmVSoU9iKkzv58O3OPxwGazYWVlhVmzGY1Gdt5zuRwCgQAqlQqr3PE6YJrWL5VKfeUlALCwsIC//uu/Zk4LPIkmtwWXy4XFxUVkMhlN2YMoivjRj37ENJpqoOX1wF/7agEf1GgXj8eZBlhJCmmWgZrBiOQQqNrqdDrh9/shiiKTqRAMBgMjtORpS9ci/1k0mJiYmEAymcTk5KRm2MzExAQmJiaY9phmMgwGAxvATU1NoVwuo1Ao9MgngA6ht9vt7LP5ijLfSEiDBT0rtcXFRaYF1rpmd3d3MT4+rrvNMzMzKJVKePLkCSOCRJxLpRJyuRzm5+fhdruZ1ltJrmVZxvj4OFZWVvqSYq/XyxwslARcFEXMzMyg0Wjg1q1bujZyFDSzubkJWe74VdM2VSoVbG5uYmFhgd1ntcJLlPdrfnbji3C/HtRGb4ghXjWGpHgIAIPZln2ZM9dLpRL8fj+bSjcYDIyEUAXO7/cz4329MIDFxUX85V/+Jfb393uIkcPhwKlTp7C9vT2QflsPpAM3mUzY2dlRrZbxOvB+DyJyKFBagFElj8hgvV5X1V5KktRXXkLHYWRkhOmSiVgRuRQEASMjIxBFUVejGY/H4fF4kE6nuzSx/P89Ho+qhZjymC8sLGB7ext3797tsS0bGxvD9PQ0dnd3kUgkUKlUegaNFKk7MzPDJCbUGEaOESaTCa+99hrS6TQikQj29va6zhkl35XLZTZbQWSQPod+puuxXC5rVtJp4LCwsIB4PM4s52g95XIZkUgEJ06cwN7eHjsfaucrEomg3W4jkUgwOzxerkEBIdFoVNdK7f79+ygWizCbzdje3u4hvHQ+z549i3g83mWlJkkSyuUywuEw5ufncffuXXZcePA/X7hwAaVSCYlEoicQZWxsDBMTE1hdXdVtJKTvbTQaRb1eh8PhYGE45PMcDoexsrLS10burbfeQiaTYRIcmnEht5tWq8V+f/v2bdy4caNLv//+++/j8uXLWFxc/MLer7+Mtp9DfHkwJMVDMHyVM9fJLcJqtSIajfb4fk5MTMBqtSIWi+Hhw4csmYyWSafTiMfj+AaXrKU29Q2AdYr30yjGYjEW9er1ehk5jcViKBQKOH78OMrlMsrlMiNhVOElr1yHw4F6va77IDKZTMhms0ybqdSUksYX6DR08e4cAJj2VRTFgbrd8/k8rFYrJicnEYvFWNAFVZxHRkZgtVoZmdeSPVCVzWq1skYznhCT/dUgXq2ZTIY1XfH71mq1mH0fWVxRsxxtM22DzWbD+fPn4XQ6GZmh9bndbly+fBlzc3P4q7/6Kxam4ff7uz4rlUqxQYbT6USxWOwi6SaTCS6XC/V6HSaTaeDvq7JplI6RLMsolUoAwCzulOef7NBGRkZYIhufjOdwOOB0OjE+Po50Os1IHF+Zper57u4uZFlmqX08GaX3KFabvjvK8yoIAtuGqakpJvmh5lCqtpM84rXXXsPNmzfZ+aUQk9dee61LB67l9AEA2WwWbrebNcSRO4nf72fNgclkUnffo9EoazL1eDzMCYS/9u12O0qlEn74wx/i3r17Pfr9QqHQpd8/zPv1q6je9pPODCL3GmKIl4khKR6iC/26p7+sII2ey+XC6Ogostkse4D6fD44nU5mW7e3t9czZVuv17G3t4c7d+4wwjw2NtYjn8jn8/jkk0/g9Xr76o63t7d1JRbkK0z+szxRoYcNkZ6lpSXNad1jx44hHo8zOzGeONG0LpE/2jalvIT0kIMEk5De0W63w2KxdDkiWCwW2O12tFqtgZuEqOlMSa4HTQ8kG71Wq4VIJKJqk/Xw4UO2bCQSUT2vZKW1uLiICxcuqE57kyVXtVrtsuwDwCz7nE4nk7CEw2EWqkEEn86B0+ns+32la1aWZSwsLKjKLFZWVpDJZGCxWOD1epl/MSUhVqtVZLNZnDt3Do1Go8dZhSzxJicn8fTpU10rtWKxyK4pJQml8A9JkrC9vY1qtQqHw9FVTbbb7ahWq1hZWWEe1F6vt0dTK8sy0uk0G1harVYcPXqUkd96vY7V1VUcOXKkazuVUhWaEanVarruJMlkEul0mg0S1PY9m80yFxWXywW73d7jBkLb/fDhw4H0+4d1v34V1Vu6Fr/Mtp9DfPExJMVD9ECrMvdlBq+rJXLMV3ry+Tymp6cRi8WYPEH54CsWi3j69CkajQbTPiqbzux2OwuMIAsnNf22z+dDsVjUlVhks1k0Gg1WIeMbu4hIGY1GrK2t6U7rPnnyhDV+KfXU1BBH1lZ8eARPHCixr1Qq9b12LBYLyuUy4vE4q6qRBrdWq2FjYwORSKRvkxBVE0nywethifxRBLMelDZ6SjJtt9uRSqXg9Xphs9lQq9XY4IIkJ0orLVEUMT4+jmAwyPYPULfk4gdWNpsNDocDRqMRuVyOzV4Q+KTCiYkJ3f0Cuq0WqcmPB0lsSBbA27wRKDLY4XB0VSaV5ImS1fSs1KiiyztxEIgglstl7O/vM2kJVfz5qis1PNLAUrlf1JCnNrAEOt+zZDKJ9fX1rihtfsBH3xM++U/LnYSqvjwhVu47OUbwjbhKBxaaqanX66oDOxoADqrfHwRUvS2Xy7BarWw2ZG9v71Crt18V288hvtgYkuIhhlBAS6NYq9VQqVTgcrlUb+o2m43JEPpZsoVCIaRSKU094OTkJB4+fKi7Hmr6IqsuJYhI7O3tsS53tWldmsbWqs7wjVQzMzM98hKz2YyJiQlWNesHl8uFQqHQUwkjQlqpVFAoFJhrhBYocU/LS5dkJFrHkDCIjR59Dm+BxXs58xZYelU3sqLrt57Z2Vncvn0buVwOdrudnW+q9i8uLkIUxb4VvkGsFknfqmeBZjabYTabdSuTsVisr5Ua0D8IpN1uI5PJdNnKAd1V11KpxLbjRQaWmUwGVquVDf6UUhWTycT2XW9mh5alVDzlbAMdw0Firm02G3O0UAPJmQqFwgtXeKl6m81mIUkSMplMV19Co9E4tOrtV932c4gvBoakeIgh0Kli9NMoEjHSA1Wx+kkjxsbGcOzYMd1AiaWlJd31CILAKm5qDyxBENBoNJgnrhbBILs5Ir/KBzX9DTW+jY6OIp1OM1IUCATQbDZZ5bMfdnd32cORHuR80xa9v7u7q+qrTCA/ZTWPWWraazQaKBaL8Pv9mprJQW30yP9WzwKLl6po2Y0NYqU1NzcHj8ejm1Q4iD5zULs5r9eLcrmsaYHGV6y1ZpKsVmtfKzW73c5cTJR2fHQdkO8zPxtD4KuuExMTTMv7aQeWsizD5XKhWq0y/TeRYhrAeL1eBAIB5HI5TQJOBDSdTnc1ftI2U9y73W7H4uIirly5ojngmZ2dxZ07dxiRVoLXvb+oPjefz7NAH+WgmeLMo9HooVRvv+q2n0N8MTAkxUMMge4ACy2NIj3Mq9Wqpu8p6UHz+bxmJYimvilGWC1lTpZlZrlE1lW8FrRQKMDv92Nvb49NMaullRHxIEJDEcJGoxF2u53Z0PFVKaVjBvnThsNh5PN5BAIBZr9FD201CygtEkqNXV6vlw0+lJpiqgYC2rZUVOEFepPY6PPp4a5XUeOrd3Tc+HNP9mFTU1OIxWLw+XzY399n+zUxMcH2f3d3t6/dWCAQQCwWY/Zc/Pbyx9Hr9eLYsWO4e/cuk1KcP3+eNVOSPjMQCPQMUtLpNJaXl/G1r32tr9Xi2NgYZFnG1tYWa+biY4Kpsj2ITRjZv9G1zlfB6d9cLsfsyMjVhFxOKDqbr7oqNdVEFsPhMEZHR7G0tITt7W22/1NTUzhx4kTXwFLLX9hmsyEUCjHXGYpZJjJM+z4/P49bt24hkUiwyjLJfRwOB86ePYu7d++yqHilO0uz2YTP52PnFdCOueaT/niHCvpuNhoNJgt6UX1urVZjVWLyYqYBDOnJc7kcarWa5joGxVfd9nOILwaGpHiIIdBbxVDTKNpsNkxNTWFzc5Pp76iqQu4HMzMzmJmZwfvvv/+ppr63trbY1CdZaS0vL/cQ1XA4jImJCSwvL6PVaqm6BlA1mPTOJKHg12Oz2VhFLpvNwmg0dlXWqDEsFArh8uXL+OEPf6hqW0YVbnrQ6ZFQqsyS3ICXYtC2UyPZrVu3NG2pADDCTpU9ft9IUhGPx7GxsaFbUVtcXMT3v/997O3tqbovXLp0CX6/Hw8fPsTS0lLXtbG9vY1gMIg33ngD9+/fh9ls1rTII7uxRCKheV7pOK6srDDiRKRwZWUFi4uLiEQiSKVSqFaruHPnTk+TIf2+UCgMZN0FADs7O0ilUl3no9FoYGxsjG2T3vngbcLK5TLTVvPkcWFhAbu7u8jlcj3T5LVajRFxivGOxWI9Th92ux2BQABWqxXxeBybm5tIpVLsGFEz5NzcHILBIDY3N3X9hefn5/Hnf/7niMViXdd1tVrFyMgI+z7Ozs7i1q1bbJtIR3/u3DlVpw/+ZyX0YtcB4PLly/jwww/ZQJzcJ0iqcfbsWWQymRfW51JVnjTWyu81DbZJnvUiDhVkffhFtJEb4quDISke4isFrZv6oFWM+fl5NJtNJBKJruoJee9SRzgRiH5T3+RJS5ILvrmFPpukAPSwouWp8YePNOa3ud1us4ou3+1Pf09NTUT6fvrTn3YRB/pMh8OBt99+G7lcDplMRtW2LJPJIJPJDJRYdvHiRZb+BTxPgQPAGpZCoRBisRg+/PBDTVuq+fn5HiLNExLeLo8q71oBJyRdUDuO9Xod+Xweq6urSKVSqtdVKpVi5JSq8cqpaCKGRGAB9HhC0/srKyu4cuUKarUaSzgjn+ArV67gjTfeQDweZ04n5F1M6Ys7Ozuo1+uo1+uIRCKqITBEdkOhEFZWVphlmTJMJZ1OI5PJYGNjg50P0tCSN7XSJkzrs4LBIJxOJ9LptOpxbLfbcLvdcLlc2N3d7ZoJoN9XKhVGiK9cucK+y3SsU6kUrly5AgAIh8N48OCBrr/w5uYmqxTz56HVarEBFQCsrq4yXb3SxYJsDU0mE5ux4e8N9HuepGrFrtNxBMAGIFRBJ2u/6elp/OQnP3lhfS4lNdKsjNp30eVywWw2H4pDxVfZ9nOILwaGpHiIrwz63dQHqWKEQiF8/etfx+PHj7G9vc0e+tPT0yy8A9CvBNHUdzweZ5ZqRIysVitcLhez0SIiRVIBChKo1WrY3t5mVR4iRQRq9KHqElVUAXT9X5IkFItFvPbaa3A4HLh58yarupnNZgSDQVy6dAmzs7P47ne/q2tbduvWLTb1qzetSwliyoos/54sy7h+/bquLdX6+jqb4lUL6KDjSYRIq3qbSCSwubmp6YjQbrdx9erVrvAHpfYU6BDZQCDAkuvU3ElIpkCOFeVymZ1XcrZYWlrC5uYmKpUKS0Hkm78qlQoePnzImqL4pja6DhqNBtLptGazFh1jugb6WdLduHEDuVyO+VHzx5ua1MgmrN9naQ0sCFSx1NOKZzIZ3LhxA9VqFQaDoesYGY1GVKtV3Lx5EzMzM7r+wrFYDPfu3UO73Waae96SrVqt4vr166hWq7ouFmRrJwgCAoFAzzGsVCoHliHoWfuRz/WL6nNJwsLPuBDo2iZdPiURvqi/8FfV9nOILwaGpHiIrwQGNY0ftIrB62m1wiG0KkH5fB5ra2tIpVIsypeqfNVqlVUszWYzc4ZQs6UiayvSHSqblogUk9UUbRNP5gwGA1qtFlZWVnDq1ClNIr+9vc1sy9Sma+12e1cks960LnnQejweRmj5qX+r1YpSqdTXlop8bKvVquZ5p3jiRCLBqqCkyaXqLVXMiVApLelarRaKxWLXfhBh4wkUTdHzTWQ8MQI6JDsWi6najVUqFYiiiNXVVeZiQuSGPoOmsWlgp+XFTNcDpSrSta8WAjM1NdXXko6fGaF94q8jcvq4efMmmx1R+yy/38/8sLVAx4as0JTXkMFgYJVmOu48oSOSl0gkmCRFy194fX0dpVKJXWf8QI23P1tfX8fY2JiuVIEGX2rHkJoD+9kDKmEwGFRt1w5Ln0tx4nSN8RVufvbh8ePHqNfrh+YvrNWsOcQQnzWGpHiILz0OYhrfr4rBk2u1hz5fMdGSalSrVSSTSdYAp7Qkq9VqSKfTjDBpuUbw6XJkq0UgwksEgbSBRDJoeWos6xfPzDe15fN55vpAxMFms6HdbqNQKDAHCZpe5okIkZN2uw2v19vV+U/+vQAQj8d79okHkQyeNCkr5QBYYx8FnCirrtTASNV2NdJDxIC/npTXFw+DwcAGPPy2ORwOltRHP6udVzq+pO/mJSJ0vdF5pcGPct+JaBcKBWSzWd1rf3V1ta8lHQ2ueBmDUqrSbrexvr7OrNvUPovkHv1AMzA0qOGlQ6SDpeMKgP3Mv9dqtVCr1XT9hanJUxD048tpPVrHZxBbO7J3Owwclj63XC6zQSJJsgh0XGRZRiaT6TsoGPoLD/FlwJAUD/Glx0FN47WqGAch16RVVZNqZDIZFsusVgUlH1LSEOrZUhHZbLVaPd7BRCipkkcuGjzB4OOZ1Rq7/H4/FhcX4XQ6AYCFgPDEptlsolarwWw2M7eCfD7PrO2UcgXSf1LYA203kR2Kq6agEC1bKqpsOZ1OVCqVHkJjt9uZppIs7IggEWFXykrUoPa+ViMVTUe3Wi1VBwKbzYZWq6Ub8sA3RKotQ/8qK9B8MxrBaDQiHo/rXvvJZJJdC2azucfFhK5Ffn/p+uH3X5ZlVYmB8rMGAf8ZauuiCrGyEg88lw7RsdCTGJDmXi++nAj1YdraHQYOQ5/LpyeqxU7TvYi+O2oY+gsP8WXCkBQP8bnHoB3PWssd1DReaz08uQbQFYdrsVgYuV5fX8fS0pJmQlQgEOjS0CoJFk9u9CpP1IBFKWpqWtBIJIJMJoNSqdRTCSW9oNvthiiKuo1d77zzDqvSEXkjEAG0WCyYn5/Hzs4OVlZWmK0TkYNisYhcLoe5uTnUajUkEglWjaPtqtfrjFhVKhUUi0X290qiZrVaWYWSBhh80xr9DZFE2n5lYxtVX6mSrpQG8I2H/DlQAyXRkUyDX54axIg40zniLcCoQY+kL1Rx50ki38RHUgS1bXO73ZiYmMDW1pZu5Z6u3Ww2y+QifJqbJEnw+XzMboyOpfLziDj1+yweWoML3sOafkfL0vs8gVNqYWmwQzaCWraGR48eRTweZ8SV/yw6/xaLhS2ntZ6D2todFkKhEHw+n6ptHw8tGz3ejlDrHuLz+eByuYb+wkN8JTAkxUN8rjFox7Pecgcxje+XRkaNa7wulKqgfr8frVYLT5480U2IoqotVWaU4RUAGBnnrdzUKk/z8/O4fv26pv3bpUuXkM/n8f777zPdLE/ITSYTLl26hDt37ug2dl2/fr2HwNB6CKIodskwtKqcAOD3+5FIJBjJ5rfJYDAgGAxifHwcP/zhD1lnPA+j0YjTp0/j3r17jMQpG/fowU7Eu1qt9ixjs9mYcwiFitDveLI3OTmJXC6HfD6vea06nU6mGaUKI3+8SAfu9Xqxu7vbNeVP58xkMmFiYoJFS5dKpZ71mEwmhEIhhMNhfPTRRz0JcUTsT58+zbyoC4UC8vm8auXeZDLh2LFjrLGRPx8kG5iamkIul2P6bbVUOqosFgoF5HI5Jrkhez2Px9N1HdP+KGEwGLpCQJTnjAYQhUKBeRir7b/ZbMbk5CSePHnC/Ippe0wmEyKRCPt9LBbrOh8EUex4iU9OTrJ7g5aNHjCYrd1hQs0i78aNG8wiT2sZ3kavX5jIW2+9hUQiMfQXHuIrgSEpHuJzi0Gb4/ott7i4OFBTSqPRwK1bt3TTyFqtFqLRqGr6U6VSYY1f1CCnlhAliiK8Xi8ymQxrdONJaLvdRjgcxvT0NLa3t1nnPAUhOBwOVuV5/fXX4XA4+tq/RSKRLh9WoEMsI5EIbDYb03rqNXYJggCfz8csx/htttvtkCSJNdFNT08zEsYHOLjdbhQKBdTrdZZupvRGtdvtLFJbD1RRVZIiOq9EjshHWm0Zam47ffo0097yLhiC0GkifO211/DgwYMubTUPkmtQRZ7WwVcdqfKfTCZVCRjQIdPZbBbz8/OsiqxGCkdGRlCpVDRJFs1suN1u2Gw2rKyssIEffz3mcjnMz8+zQBo6bvw1YjQa2ayHGgml/Xc4HLDb7VhfX++R2FSrVWQyGUxPT7NrQGs9brcboVAIxWKxZz10HEOhENP68rpwuh5J524ymRhJ5x0zSL9OAR4UGKP8LLvdzpZTq5IDz4l9JpPpa2t3UMsxvVmyW7du6VoWEvotw5NnrXuI3+9/5f7Cg84QDjHEYWJIiof4XGJQ/W4gEBjIAmx+fl73pj4/P88sh7TWQ9W9arXKggkAsKn7XC4Hk8nEtKpajVSFQgHnz5/HnTt3GDnkq8A2m42FRRSLRVQqFfh8vi4tLP8gGsT+zel04tKlS6rJZ0+ePGHVZ63GLgoNsFqtcDqdrCmJ4o8p4pj0tIFAAB6PRzUZMBqNIpfLsUqtsmlPEDpBIuScQAMFXlbSbrfx0UcfqRIrHqQLJag141FqVyQSYalmtG+UalYqlViVmJqveBkG8DwtkN7jdcH0M0lW9EAEjo43nXf6l0j+2toaI8lq27O2toZsNtv1nVJ+x4CODCgWi8FqtcLv9/dUVMvlMvb391kVn2RIvKSBNLyUiCbLMnN0oH1utVpotVoIBoPMpkwp1bDZbAgEAiy9jhpRlXIWOjf5fJ5dM/wxooS9+/fvs7ANcqigY0h2bMVikQ3ayI1EFEV27EulEra2tiDLMhYWFnrkE6lUCo8fP2bSCT3LwtnZWU3HECX0Zq38fj9u3Liha1n4s5/9jNnlaS1DNnr9wkRetb/wYXgiDzHEp8GQFA/xucSgzXHRaHSg5c6cOaN7UzeZTH3Xs7+/DwCw2WzMH5V/yNpsNjZdSilxyvVQg9zo6Cjeffdd3Lx5k1WX6MZ/6dIlzM/PA0DXNtMyag8iWe5YYhUKha5KJ38cqaGO1kM/U1S0lvsC/Ws2m1Gr1WC321kTHABmJWez2RAOhxGNRnWTAekY8b7AVFmjaWeqABIR4kEEjHyDlQ1f/Hs8MVWui8gW6b3HxsaYRpSap9xuN+r1Ora3t5mfNICuajoRcyJ4RCiVBLwfgeextrbWVW1XSh7S6TRz1KBjSKDzX6vVsLy8jGq1iqmpKWSz2R7C7/P5WAIdacvp2qCXzWZjseV0PpXVfTrGqVSKab35wQ4RzGw2iyNHjjAvZ3IrMRqNcLvdrEJZLBYxPT3dI8NwuVzweDyQJAnBYJDpxtXS6gKBAO7fv697Pra2tmCxWJi0hs4vXdM0yMhms2xgyoPuDdvb20in07q2dplMBtFoVDOwgwcf7qPWl+D3+5mVHF3X/KwEWcnR//WWWV5exqlTpzQtJAmvyl940BnCIYZ4GRiS4iE+lxi0Oa5UKg3cRBeJRDRv6pRopbceImnBYBB7e3ssjIEqnmNjY4wsD2LNZLPZMD09zUii2WzG1NQU/H4/+5tQKIRAIKBZwQH0NYPT09PMmUJNBx0IBNgUOW2b2jaTPnN9fZ156BIEodOdPzc3h8nJSWxvb+tKVfx+P5NyUCWLQJVFNTJM61CCiJlaQxo/5a1WKaXtIxKuPEa5XA5+v7+rqVIQulMEqbGMSL7advIODYOgXq+zuGO1ansikeiySFMbENB+tVot1Ot1puHmta7kNkHnuVwud+lh6Vqla5yWowEg3+gIdHxvzWYzKpVKT3y33W5HtVpFMBhEsVjskvPQTMPIyAgmJyfx8OFD5jrCWwnS8SaiTwmCapHSFIZCJJU/LjQgoqo2HQNlcAtVv8kZRes7RNVjvftHpVJR1ccrQbM7en0J+Xx+ICs5+i7oLUN9AIPIFQTh5foLDzpDeBBP5IN89lCuMcSQFA/xucSgzXFOp/NAyU5aN/VBPo98TVOpFKu0ETFotVpIpVJwOBwDWTOVy2UsLS2hUqkgHA6zagil3PF6aWWleGtri1WK++kKL1++jFarhZ2dHV0dNEkpqLGKliGXhFAoxLS+aoSPLNlEUezrnzoxMYHHjx93TTETqPrMkxM90N8qAxyIUPOBDFqkmCqtelpx2h6SDygJOC85IKLJkxCqzPEVdj1QI6ZWtZ0IoxrJ5gcCTqcT6+vriMfjXbMBVMnd3NxEIBCAyWTqcs3gl6lWq7Db7axRjqrXJEkgrTi5i9BshTIyuFAoMFJfq9V6kghJFkLOExsbG13XW6vVQjKZRLFYRDAYxMjICJNPpVIp9l2nprbHjx93nSMe9B59d+k64WU6dD1RQ6zed4i+e3r3D2o47Id8Ps8GwXp9CXQt8JIZOo4k4xEEfbs5mi36vMgVDmqfeVj4vOz/EJ89BhM3DTHEp4Asy8jlcojH48jlcpoPcLVlKLGJKiK1Wq0rgaxQKCAYDGJiYoItp0Z6aDnqjB7k87TWMzo6CgCMJJB1mdVqZVUwAJiZmYHT6YTdbkexWEQ6nUaxWITD4WCV3t3dXVQqFdbAsru7y6ZFK5UKlpeXkUgkcPPmTezv77OKlN1ux/7+Pm7evIlYLMZ0hTabjVVLSZvZbDZx79491Ot1Jm8gwkPL0DbPzc0hGAyyv6NKn81mQygUwtGjR1n0tNa5pqlq0h9GIhHs7+/jyZMn2N/fx8jICC5duoRwOAzgeUWTHuJEItVIjBZIpkKVZZpOp/doSl6t6gw8J9M0hU7hIVS9pmMkiiIja+RCQvpp+tloNMJqtbLGLRpcUCOizWYbiBQBwPHjx3Wv/Ugk0vcYiaKI0dHRLt9p2mcAjGiR6wBJSfjjSFPuDoeDNaOOj4/D5XKxSPLx8XE0m01EIpEuGzxeekPnoNVqMamB1WqF2+2Gy+WC2+2G1WpFOp3GysoKyuUym4kBuivtpLOnhrw333wT4+Pj8Hg8GB8fx5tvvolQKMSIFX0uf874MBJ+P3kyzJNk6iWgAQpdHxQzbjQaEQgEWIOt8hqj7/rExETfc1+r1ZgfuNr1SISWzh9d6/yLqvcmk4ntG51f/v9GoxF+v1/3PjOot/RBoHUfHmSGkGY+Dgsk13iV+z/E5xfDSvEQLwWDjLz7LbOwsIBYLKZqg0S/H6QySQ1pg3werYdM/fnGNiKzRJSUVWDqdp+YmMDTp097bJ6q1SpGRkYwPj6O+/fvo1KpYH19vWe6emRkBMlkEtVqVXca8erVqyiVSl0+uwSq+JVKJUbU+m0zRfnSA5OIoMfjYQ4EeiiVSlhaWsLp06exsbGBe/fudUk6CoUCrFYrRkZGuv6OryjSedYisUqMjIxge3ubWYnxzVYmkwlnz57F3bt3u2KalaCqq8lkQjqd7rnWyFvY7/cjHo9rWsQFg0GYzWY2BcsTORpguFwu1Go1VTcMgt1ux7Fjx/Dw4UMsLy/3NKOFQiGMj4/j3r17mi4WtO1ra2usGqtVoSYiS24gpK8lMmu32+FwONh1/fTp0y5N8fb2NkZGRjA/P8+uU63PkmUZOzs7XQ2D/LUvy3KXe4VahDcFnOTzeayurmpakh0/fpyRVi2YzWZGmvVABDOTyahKh8xmc197xMXFxYGa7PgQm0Kh0HN/oIo0eUuT2wVP5EVRhM/nAwCWnqkWme33+/Hw4cNXKlc4LPvMw8BnKdcY4vOJISke4tAxSKMEgIGW0boR8e8P0hk9aPPG7Owsbt26hf39/a5Ut7Nnz8LhcDATfrIu4u3GSHu6ubnJNMp81anVaiEej2NzcxOJRALJZFJ1SjsajcLn88Hn87GgD+W+u91ubG1tdWlbldOjwPM0u37bzGtN+QQ52i7SNepBkiTE43FUKhVVSUexWMSHH36ICxcusHXz9mVUwR6UEAOAw+HAyMgI9vf3GbGhavnIyAgjoXqo1+tsipm2iwedQ9oPNbRaLeZVnUql2N/xaDab8Pl8aDab2Nvb09wer9fLpDpKbS65mzSbzYHOh5b1GY92u83cRai6zVdNyf1jY2OjZ6BHhD8Wi2F7e5tZDKptGx1H0t3TeeKvfdK/0sAM6A34oCa4a9eu4dGjR5rSIVmW4XQ6dUmxzWbrq/Ol7xidTyUpJtu/8fFxvPvuu7rWZoOANNzkiqE8RrVaDRaLhVXYyRWH//6Oj48DeB7jrDZ4opmlVCqle585TLnCYdlnHpYn8mcl1xji84shKR7iUDHIyHtpaQkA+o7O6QGoZYPEj+D1OqMHrQbIsozV1VWYzWbMzMx0Ne6srq7i5MmT7PMnJydRLBaZ1tjlcqHRaKBer+PBgwdot9uw2+0Ank/RA52p0fv377NpVmXUMzWI5XI59tCgarUyHYyIBRFrOv78FCoA1iw1NTXVsx4iWNvb25BlGfPz8yiVSmy/nE4n81gdBEajsa9V1IMHD9h2qTU38eEOdEz4faP/A53qu8PhwOLiIjKZDDv3fr8fmUwGW1tbutVUoENoSSJBTVO8PrhUKrGXHvb29uDxeNjfKUNZaJ3pdBqAurVbu91GOp3G0tIS0uk0LBYLnE4nW0+z2UQ6ncbq6upAZJc0vP3QbDZhMpkQCAR67MRKpVJXYxwFovCf02q1sLy8zPaRv/5oX0mqQNcn34hKhJ+OPf0tr52mZcn27fHjx30tyfoNiMrlct/jyDtR6F0fVC3WszYbBBaLRZWA8z+TfaHP58P4+DhSqRS79il1L5vNshkSp9PZkwpZr9eRy+XYbJHWfeagEc5aDWuD3IcHsc88TE/kg6adDvHlx5AUD3GoGGTkTdZm1KimtgxV0qirXNlspDaC12qiG2SbeLlCOBzuWo5+v7u7i0AggK2tLU0bKJvNhkqlwqpWStJDkgZlYxi/PUQoqMu8UCio2nJR5VpZueObzUwmE8bGxpDP5xEKhbqOI1VefD4fcw3Y2dlRTSIjItcP6XSaEQQlEaAHOWl36fd8lYumRenY0MOIJ09EDMxmMyPuRqORaZX58/b06dOBtpua1/L5vKrdGAWoaFULZbkTBBKLxVStxOg4JhKJrhkEZRS0KIosFZH+jr9GLBYLisWibqWZRz/JCw/lPvP7SWEq9Ht+m2ggR9pa3mpPeT3ysc1a28Cvl/5VDoyIqJLVm5bdWL8BwSBNj7ScIAiqcgblZ/SzNhsEREoFQej5fshyJxGT7PQo3ZBA32uHw8GkFMqBDF1rNCDQSzw8iFxBTxoxiO3lIPaZh9n49qrlGkN8/jEkxUMcKgYZeVN4wYsuM+gIfpBtIueFftOI09PTLJHN4XAwlwCq6rnd7q4IW574Elngq2VK9wXeZov0mgaDAVarlVVQqct/cnISbre7S9pA66T/ezweHDlyBE+fPtWsvExOTuLOnTvsocg/5Pl0vkFAzWA8seJhNBrRaDRYNazdbncFVJA+0uv1wuv1soAOGlSQ1MJgMOD06dPMrUDrvParEhOIfLTbbSYhoFkC0mfz0/1Ab+W62WyiVqv1kEZaP1VCZVlm26Z0O6BzU6/XNa/FQab8CeRV248c0myCmmOKxWJh2l8tL2si8/RZatcLHQNRFNkMhbKaTseGvtd8gyD9rDz+WnZj/D6rVWoPItOhbaaBJpF/GihRJPthoNFosMFQq9Xq+n5QA53D4cD09LTu9zoYDOLJkyfs2lQea95RZWtrSzfxcBC5Qj9pxPHjxw/FPvMwQQ3WwwjrIQhDUjzEoWJQazMAL7zMoCP4QbaJHhZ6N+xCoYB4PM6Ib7VaZQ9Fv9/P/In5hxd/kyXiADwnCLQsgXcIoG1RIyH0Lzke0AObn16lB1wkEumyrlJWXoxGI6tsKjWMpNEk/2I9ciUInRho2k9em0ygKmk4HEatVmPHkbf3ogjrt99+GxaLBbdv32b7JwidxqaLFy/i/PnzTLusdV6tVivzYdUD7TeAruYv8ublNdpqdnFEsKiRjM4fnUOy7qJBUqPR6PpM+lsiVv0e/oNOx/PSCy0IggCHw8Eqj1oVbtpGNfBNhXqQJAk+nw+VSoV5KPPb4XQ64XA4WMMj70pCyxAxJ32tlt2Ych9fFCT3IIs2/vrg71kvCovFAofDAYfDwWaJlPZ3AHos6ZTf62q1ygZ45LzB646J2GtV7weR3fDL9pNG7OzssHvki9hnHiYEQRi4UXuIrwaGpHiIQ8WgI28AiMVimsuMjY1BlmXdZZQjeC0t2yDbFAwGWUyx1g2blg2Hw6yapdTnktcr3/SlNoVst9tRLpeZTRovp6hWq3A6nSzcI5/Ps2l9o9EIj8fDktao0krbT8s4nc6uaqJeCEgmk2EVXurG590OeD0sXw0k8AOKCxcuYHV1FYVCoUvzzJNBt9uNn/u5n8OdO3f6Rli/8847+NrXvob33nsPmUwGfr8fv/zLv8ymkem8UiAEr/EuFAo4cuQII3R6oKoWEXUCVeroQanV4Eb7b7Va2TVAFXa+WS0YDLJUQ74Jk6+wUhOVWtVZlmV2fZCWl/dipvNBpPzs2bNYWVnRrYqSty9ZT5FtGZGlUqmEmZkZlrxI1VrldvMaaS0v52aziZGREWYTpyTgBoMB09PTMBqNiMfjsNvtrPGO5AOtVguhUAixWIwdRzo2yu8ZbzGnnJEBnofF6IH/XIfDoaop5iFJ0gtpivn71cTEhGo0O937BEHQrKhKkgS/349kMgmv19ujFc/lcvD5fDAYDJiamtIcEJHThx5BHUSiViwW4XQ6kcvlBrqna93PDxuvOsJ6iM83hqR4iEPFICPvEydOAOjo2PRG54MsQzfJg9itqa3r3LlzWFlZ0SXOdGOnqqlS50xT4mSVpDadSg+x48eP49atWyiXy4w0EIkwmUw4c+YMIw7ZbBbVapU90CRJYn7CVqsVtVoN6XSaESPy8bTb7XA6nWg0GrohILlcjm2DUodJFW2q4hUKhR4SQTIRv98PWZZx+fJlXLlyRXWK32Qy4fLly8yzuF+EtTKtb39/H1tbW7h8+TIWFxexsLCAra0t3L17V9XaLhgMMsKrBar8qulv2+02yuUynE4nQqEQ9vb2NCto4XCYVcKoMYumwOv1OhtgeDwets9KULNfKBTC1taWqpxBlmUcO3YMuVwOT58+7XEwIQJ45MgRVnnN5XKa++9wOOD3+7G0tNQjlSmXy12NjKurq6rbLQidZtdUKtU1I6LcNwDsOtEaEJ04cQKRSAQ/+MEPmPwD6B4QnDlzhrle8LMv/EwJ9Qbs7u4CUJdLhEIhFAoF5lCh/N4Dz5v3JElStezjv2crKyvMfYJ3rzmI+wTdQ7e2tvDxxx93Xddkf8ff+7QqqqIoYnFxEVeuXNG0iTt16hSLutaS/FSr1b4ytUEb1iiF8EXtMw8bryrCeojPP4akeIhDx6Aj78NaZlC7tUuXLmFpaQn7+/usojg6OooTJ04wIqxHnOfm5vDgwYO+sg/SF1LTCoFvXpmZmcHW1lZX1C3QqdpFIhEcOXIEOzs7LImMT5mrVqtYX19nvrjlcpkRar5aRg9l8g/WOj4jIyOs6sj/PfA85pmm2Futlu5+0cOEKmtKgk2/p+tE70HUL60PAGsCpHQuIkak8y6VSrBYLKzzXgnS8fbT6GazWczMzPQ4YxAMBgNsNhsbnPD6Z/ocWe4ET1CTpJKAiqLI4okXFhbQbDaRTCZRq9XYfomiiLGxMbzxxhsAOlIP3o4O6Aw8RkdH8e6777KKtR5KpRLW1ta6Bl4Eamh7/Pgxjh49ivX1dVXCazAYMDIywhoy1a4jGiS43W4cP36857tISXT0vQ4EAoxs8YOdQCDAjpPD4WADOTrWdK3abDbMzc0hm82iUqn0bLPdbsfx48fx4MEDeDweViXl98npdLLPV7OIo2NkMBiwu7uL69evs34D+p4lk0lcuXIFAAYmxplMRve6zmQy7DjpVVTp87Rs4iKRCNbW1pjkh+9dIMmP1+vtK1MbtGGtn+TjIPaZh41XIdcY4vOPISke4qVgkJH3YSwziJaNrNsG2WY9Eh4MBrG3t9dXGrK5uQlBEDA6OopKpcIqHXa7nXWvR6NROJ1OXLp0SXV6NBqNIp/P91hOkYVbpVJBuVxmzVv04ODJA033R6NR3eND1RiST9D+0LI0XUykZmxsjOlBSdNM8g6n04nvf//7EAQB4+PjKJfLbDmHw4F8Po9bt25hdnaWPezVHkTtdruvtdv169fh9XrRbrcRDod7pBr5fB7r6+uMtNhsNqZNJs11o9EYKLyh0WggHo/D6XRClmUmN+GdUdLpNMrlMlu3Uj5AfsiUcjc6OtojwalUKiiVSkwHrjeIA4Bvf/vbePToEVZXV9k1NDc3h1OnTiEUCmFtbW0gS7qtrS0AgMvl6tnuarWKtbU1ZDIZluBIx40GFbIsIx6Ps3XyunReYgFAM9WP1yUvLy/DZDLh/PnzqhaBW1tbLLhmfHy853qkRLxcLoeRkRE0m02WEmcwGODz+VgVlFxj3G43i56mxEfSLQMdWQlVuZXXWbPZxKNHj1Cv15ljDn3PKEKbv+71IEkSbt26pXtd07rS6XTfiqqeTRxd+9VqtWu76T6Ty+W6dMxaOEjDmp7k4yD382EVd4iXgSEpHuKlYZCR94suM6j5+sbGBh4/fswqH0QiY7EYCoUCqz70I+H9ZBiUemcymZDNZnskDUSgYrEYvF4vDAaDqpUYpbRRrKmy+Y2kGtSkRj6zNM1LDUiNRoN9ltbxIS0puUHwDgM0FU+fbbPZWHiA3W5nqWSkjV5ZWUEmk2HTpXzjIZHuTCaDaDSKqakpzSrX8vJyX2s30hC73e4ueylaxm63M30vVe7pfJDOVK1pTgv0oKYGJd7ztdVqsfNN28tvt5Ic0t85HI6uZXgMOqVrMBjY7ITyeH3yyScD7RtJF4gI8zCZTEyi4/F4mJ6ejh/p6UulEtORK3XO/KCm2WyySqDad/HkyZPsO037xoPkSXyohtpxlCQJ2WyW9QCMjY319ABQXHQul2MVUXLIoHNO79O1Tsvw136z2WQ2aGrXq91u77ru9RCNRpHJZNi61K7rTCaDR48eYXt7e6CKqpZNXKFQ6Ip8V0u8pGQ9vfv0QRvWtO7pwzCNIT5rDEnxEF9oDKJlKxQKWFlZQaVSYcb25CUcDAZ7gkD0SHi/arKyqUcpHxBFkXWu620zaUep0UXZOW6z2ZgWkqYrKeCEKl8A2O/72d+R7ZgyLpo+jz57cnIS6XS6JxkvEAigWq2ySjg1JirTuGjfS6WSrm6QtMt61m781Ho+n++pXlqtVmaxxk+vE+i9gzRB6YHOL51jpQyBfkfnhqayeS0wOQ8MYu9F08zlchlWqxV2u53prmmgd5DQAa3jQASXSGgul+tJQLTZbCywhq+28gM5k8kEn8+H1dXVvgEONMgjrbEyUIL09CTnUbORI/s/vR6AYrGIU6dO4ebNm4jH4z36fofDgdOnT2Nra0v32qcES95Rhh/8kN3dIFZ6vIyDqtD8DJDNZkOr1WIBLi9SUSWte7/Ey0Guo8NoWBuGaQzxWWNIiof4QmMQLRtN31ksFuzs7Kga1B+k+qBXwctms6wpTCsdrFarweFw6G4zPeSBztSkWhoVTX+SVzI//UwNe2Q31++zTCYTW4+SXNLxNZvNMJlMmsl4RqMRLpeLSRH45h16oNPDjK8WqlW5AoFAX2s3IjC5XK6LyFPlnKQK9XqdkTdlmAqdn0FA/sB0PHmiIssySzCk6j1PHI1GI6vIkRNAIpFAsVhky9hsNvj9flitVlgsFt1BQzAYxPLyMrLZLCRJYl7VdE03Gg0sLy9jYmICjx8/7rtvPJFXTn2Tx60oiuzz+AFGq9ViftFerxehUAjxeBzVapXtm8PhQDgchiiKrOqoVQnM5/MA9AMlKLiFBr1KMud2u9nAop/ONRgMdumXyfrPaDQiEAggEAiw2R+ta5++JzSAVV4fRqORaZT7gZah46AcWNJ3ulqt9kgV+OM4yD2N7p96iZetVmvg8IoXbVgbhmkM8VljSIqH+EJjEC0bPSByuRy7wfPNJERSD1J96Cf74MkQ/x4ApmnM5/Oscs1HWBcKBUxNTUGSJKRSKfZQJVCantfrZRG8pFPlG3Jov/x+P0u+Ujs+/Gd5PB5VDWMwGMTU1BTi8bhmMt7o6ChGR0dZhU6t2YokFLFYTLda6HK54HA4WLVJWXVtNBpwuVzsHCqPNVmH2Ww2pinW0vkCzy3ZtECk4cmTJ2x9NLVNnsMzMzOQZZlZoJGUQZIkRpKOHj0Ku92OlZUViKLIyCGds0Qigfn5eTQaDdy6dUtz0HDixAmmEaWBEX9Ni6KIaDSKX/iFX2DNilowGo3MOUIt5EGSJIRCIRSLRVbpVM6AUJV7cnISiUQCp0+fVtUCu1wu5uCiVQWmgZ9eoMTc3BwcDgdisRgmJydVY+AHsX4cHR1lhFdLv0xJlrQetWt/enqa6c6V30WSaUQiEUxMTGieB8L4+DjTzfN6errGK5UKa+R70Yqq8v6p9b0+SHjFizSsDcM0hvisMSTFQ7w06HVFEwbx9NRbhteyJRKJnuYeh8OB2dlZbG1todlsdsXmUvWGqnVUkWy321heXmaEemFhoUd7qrVvlEZVLpfZA5avutL0+PT0NB49eoTl5eUeB4JwOMxsqa5cuYJkMtlTKXY6nTh37hyuX7+uew5EUcT8/DyWlpYQj8d7GpKcTmfXZ2WzWTZdLggCs3+7dOkS/H4/isUi9vf3ezSldJxarRb8fj9SqRTTNfPrMhqNcLvdSKfTuumBmUwG586dw40bN7pCL4hYkwfvvXv32LqVcdd0jZBEgQ84oUoxaWD7kWKXywWbzcYs15ShEzStLcsyc94gQkk+xVQp5gllsVhkx5EICd9sRI2XykbMlZUVVrW1WCxswEc+1uSZ3Wq1cPnyZVy9erVHPkLb/uabb2J8fBzvvfceyuVyV5WTqrznz5/Hj3/8Y9V18O9NTk6iVCohHo+zdZlMJpTLZbjdbubgQt7b+Xye7b/H42E6Yvq+tVqtru+Q8ntfKBSQSCR6Bk10XQNgoTsk+zCbzfB6vXA6nRgfH8f9+/fZYJCIJH1P6Vo9e/Yss4ekGRAivKSXTafTSCQSXSmFdL0KggC/3991vWvdZ8ihhMKBaABG0iyakQEGDzfSul8Ncv/ktcCD3NNfZJmDapNfNQbZt1e5niEOH0NSPMRLwSA+k4N4eg6yTCgUQjgc7vKzpWnUy5cvIxAIABgsoUnpiyuKIt5//33mi9tv3+hhmslkkM1me9bv8/kQDodZpQzotXii9+fn5/Hee++x7nfguTTAarXi6NGjuH37NiNTSusqImJ+vx8A8PTp0x7LqXPnzrEGwwcPHmBpaamnCjg9Pd1lJbW+vq65HgoEqNVqSKVSPesiMkJEVKtaWCwWsbCwgEQigaWlpR6d89GjRzExMYGPP/6YyTKU55eqdSR3UNPp0hQ8AOb5rITVakWz2cT29raqHRudl2g0CpfLBZfLhXg8zpalAY3P52PpepIk9XgHU9NXoVBgevH19XVV/+V6vY56vc6m6wn1ep25kpA2+Z133sHm5iZ2dnZ6tntiYgLvvPMOAODBgweqUouxsTEmR9AKu6CmTrJwe/r0ac+5P3HiBI4ePYrV1VU8fPiQLU8gnfnc3BzTgqtdQ8FgENVqFWazGeFwGGtra13fV5fLhaNHj7L7TC6Xw9raWtf2xuNxHDt2jNkM7u7uYm9vr+v8bm5uYmxsjM1azM7O4tatW9jf3++6F509e5ZdQ8FgEPF4vOucUKokACZn0LvPTE9Pw+FwMF9svtpLjbkulwtOp1N3Bogqqv3uxf3un7z1Zb97+mEs83kN0zgs7+RX7cE8xMEwJMVDHDoG8ZnMZDK4cuWKrqcnALaMxWJhHeBK38+VlRXcvn2bBSPwDTe3b98GAFa91WrKcTgcuHv3Lu7cuaPri3vkyBHdfVtcXEQqlVIlxEDH79Zms2F3dxeyLGNhYUF16nd5eRn/7b/9N83QhVwuh+9+97ts+r3VasFms3U1bZEN2tWrV3H//v0eJ4l2u4179+4xLezKykpXhZCkBisrK/jggw8AAPfu3dNdzze/+c2uMBHlwzqdTsPhcLDEuXw+31UtdblccLvdMBqNWF5exvr6epdul/Z1fX2dNXVpVXipmk3aajWQPpPWq9ZsRlIMaiDjK5YAWJU6lUqhUqmgUCj0kOd2u414PM6IrJpvLtCpatK5zOVyPcEcjUYD0WgUXq+X2f2podlsolwuw2w244MPPsDe3p7qcnt7e+zcrqysAIDq+aeYYDWvXqqGNptN/OxnP8P29rZqU+PS0hL+4i/+AsVikUVpKxsNa7UastksyuVyVyANvx4iyk+ePOmaSaBzUi6XcePGDXg8Hjx48KCHEBPW1taYPIgn3/z2RKNRlha5uroKs9mMmZkZNjio1WpYXV2FLMvIZrPIZDIQhI4fN1/dzWQyTN/ez3/78uXLKJfLSCaTTIpBkCQJyWQSoiji/PnzWFpa0q2oplKpge7Ft2/f7ppJIznP7du34fF44Pf7+64HwKEsM4gLEH89vIqK62F5J39WHsxDDA5BHqR8NoQqCoUCPB4P8vl8Xx/HrwpkWcbVq1c1NWHJZBIjIyPY3NxEKpXq8sYEnlfQgsEgW54sr3idbrvdRiQSwe/8zu/gT//0T3WjTL1eLzweD2vyUWvckSQJOzs7KJVKjCTypI6qeK+99hoSiYSqFjiVSiEUCuHq1au6zVskaaCQCyVqtRpyuRwjKXqYm5tjBEotorXdbuPp06eo1+tdVlN0rOnBTPICNX9ZvtmNuvnV1mO1WvEHf/AH+A//4T+wiqjy/ANg0+grKys9oRokaVhYWMDq6iqKxWKXrpI+r1KpsMa3wwDJPNT0y3xktyiKqtHLdH0qq5rKfT/o9vCkkSdrg67zd3/3d/Enf/InLDJaKWchjT0Rbl4Ly5MjXl6iNnAYNDaZfJ3Vqs70MzmL8AM0Aq8JD4VCyGazXXILoDMAIY9fSrPTA78dynMPdKqzb731Fvt+q93TPB4Pq37T4JRfhlxg/tbf+lv40z/9U0Zc6ff8fYYa7ehepHbtu91u/MN/+A+RyWR0mzH73YsjkQi2trYQj8dVvc4plpvvJ9C6pwPa+u1BlxkdHcXbb789ELF9VRXXQZ5pg2z3Ya1niE+HQfnasFI8xKFiEJ/Jra0tVjHU8vQkHS1VoXiyRjfuZDKJu3fvMl9csgPjb+o0FR8MBlGr1TSbckSxE4hBVUWlPMBoNKJYLGJ9fR1+v1/TxWJpaamvmwFVe7QCRcxms+pUtxqSySR8Pp/q78g3lizX1I41/R4Aq5YqQVppALrrqdfreO+991jlTknaiOCUy2VWNSUtN1Xu6dyQJlXPp7hfUttBQLpPvkKqJGT9oFbVfJHtAcC+AwTavkHx53/+58yVgb47BBo40vS8mhsHbzGn3Da97db7fbVaZf63ymNNQRK0j8qBBoFmHWi/lBIDANjf39fdFrVtVtt+GlhOT08DQJcun/T0ZMmmZ2tHAR8UOqJ1n6Hfm0wmTWs7knxMTU1pVlRzuVzfe/H29jYSiQQbFCmdLgCwUBaKzVZbDx1rPVeRQZYZ1AXoVVZcD8s7eejB/MXAkBQPcagYxGeSiI/eMpSQZjQaWfUKeN7U1Gg0UK/Xkc1mu3xxeUsp3hc3HA4jlUohlUqxRpl6vc4qNhaLhRECasIiEDGRZZk1D2m5WGhNiytBtk3kScw/ZOn4DAKadiVLLCI26XQaVqsVgUCgq+lMCT4cQIts8e/3Ww9pv3nJg5L0UKiC1Wplel86T3a7HbIss/VYrVZWHeUbDYlQHRZov9RkFvSZtF9qUcg8oT5MvCjRpiZSNZKvtFbj90NrmcMAvy1q5GDQz+OvW7Xv66BWe4OgXq+zgBGl3CcQCLDrlxoxlUSWrleSxOjdZ+iY+3w+TY9yauSlY6ZGoga5F9frdUaG1e6zdG+lmSat9dD35kWXGcQxQ5ZfberdYXknDz2YvxgYkuIhDhWD+Ezy3rday/DTvGoPTnrgUOIbERVlgwu9Pzo6Cp/Pp9kow/v88oluAJhfLj3gAGi6WCgfxFpT6B6PB4lEgk2H8hVnURQHDnAwmUzsQUyJbfRAczgc7DOVRI5wUOKgtx5BELqSxfjBBH8eqWIZCAR00+Fo4EK/p/XQcocJcqHQaiKj40zVQK1p/36Ryq8aLperizypaTJ56C2jVv3XW5cWaHCk/J7R94sGILSsXqWcl5gQ6Pt6UPSThWxsbLB4bwIF1tjtdta0qfQppvtdu92G1+sd6D7Tz6N8EM/jQe7F/DlVu88Czwcd/e7pgL4bxiDLDOJB/KorroflnTz0YP5i4HCinIYY4hnIZzKfz6tWucjTMxAIoFwu95AQIok+n48RXnoo8P/SiHt0dJQ9TPgmKOV7zWazq1FmdnYWMzMzMJvN7H0iRmrbTXpTfhmyi6IHGYCe1CxaVrnOU6dOoVAosClgm80GURSRTqdRKBSY00U/UFWdrLlsNhuretOUqtls7tHu0rE+SNQxkRUiqPy/5FN7+fLlrgYj4PnDVflw1wOFXdAUM5FhQRCYzEKr4vJpQOslzTC9qAJuNBpx/PhxRiJImkPVcFEUMTk5eWjbw28X7Tc/CzIofu7nfo6RPTon9FIbAGhVpmmgpSefGfR8+Hw+5jVN31Heeo236uNnf5SknAgyf09QDqAGAf9ZfAWbHwwAYINe/txLksSq8RQA4nK54PP54PV64fP54HK50Gg04Pf7cfr06YHuM36/H+Vymf1MxFqWZVQqFfj9/r6ex4PciyORCLvPqi1D/QSRSER3PeRR/qLLBIPBvh7Eg1RcW63WoVVcBzmOg2z3Ya1niJeLISke4lOBpgLJ/5N/eC4sLDBdMFVWarUakskk7HY7Tpw4gUuXLjF/VX6ZXC4Hi8WC8+fPMw1bpVJBpVJhXfuVSoU12lAVj39A0ouvmj558oRNt5F8gqbbKpUK1tbW4Pf7GZlWEnBRFOF2u+FwOGAwGJBOp5HJZJDL5ZDJZJBOp2EwGJizgh68Xi/S6TTcbjf8fj/TWUqSBL/fD7fbjWw225dkGI1GluZG1QUiorxTx6lTp1g1mRKxSFpiMBiYVrIfqOue1sP/K4oiLl68yPx8Cfz5IFitVjYFTLrHdDrNAlZKpRJsNlvXetQGF4dJink3DbVrSBAEnDt3DlNTU0zTycuApqamcOHChb7bpCU/UYIGDcrBB+0/ETM9kBSFLPm0QP7AtP88cab9d7vdrCmWnwGg5UVRhMfj0YzlJphMJpw5cwY2m43Zx9GrXq/DZrPhwoULXcSY33+gc4/xeDysUY1INX1XqUpst9vZMnrHyO12d+07fe/5fSd5BG0PP6gQBAG1Wg2nTp2CxWJhg1FJklCv15HP52GxWLC4uMhIv959JhgM4syZM+z+WC6XUavV2PeF1kXX0ovci6kpzWAwoFarsWu60Wgwn/FQKMTWE4/HkUgk2Csej7N7+okTJ/re9/stM4gHMV9xVYOaR7Pa8RkUgx7Hftt9WOsZ4uViKJ8Y4sA4DJ9JaoIgD+JKpQKDwYBQKITFxUXMzc0hkUgglUr1EBQiwiMjI8z6yOFwoFwud02bkhwBAEvT0mqQKxQK8Pl8sNls2N/f75n+HBkZYQ9y0g0rq1ZURQoGg6pVcNom8qsNh8OqmmJ6kPbrnvd6vcjlcqx5Tbk9RISPHTuGQqGAtbW1ngfCzMwMjh49io2NDd2HBQ1CaOpSCb/fjzNnzgAAi7AmnTe/DrPZDJfLBa/Xi7W1tZ5GMmoKDAaDyGQycLlcqFQqPQ1JFIJBdlZaIJLWbxlRFGGxWLqavAika5YkCW+88QZu3LjRde2HQiG88cYbsNlssFqtmlVYum6JDGmBqq56y1D1jhoSlXA4HDh27BicTmdf8myxWOD3+1nTqfKcUQyymjyIQDMVXq8X2WxWddtJrjQxMYG7d+9qVkqDwSD8fj/T6Wttbz6f12y2lOVOiEo4HGbBP0pQEBDQOV7kLc3LGyKRCIvcJltHspMTBIHZHpId5MWLF3Hjxg02eyOKHb/fixcvYn5+HrlcDuFwGBaLBbFYTPU+4/F4MDc3BwC66wJe/F4cDAaxt7fHChK86wfFdh87dgxHjx5FOp3G+vp6j5cx7wk9iL/wi3oQHyT17rAcKg7LO/nz6sE8xHMMSfEQB8KgXb+D+EzOz89jdnZWNa2Or4gR8aWbNV95JDsxengqR9lUfaIIVq0GObvdzhozLly4wNwRLBYLIpEIstkssy8iPaya/RvQIeB65CGVSmF8fFx3+k8QBGQyGd1zQQ0/amSW12eura1hd3dXVcO5u7vL0rz0IMudbn96+BNJpAprq9XCRx99hHPnzrHtV061k+7YZDKhVqt1bTtV6qhaRx3+NA1NHsGUCgeA2XHpEV6a4tZbhrZfq/JEJKhQKGB/fx82mw3Hjx9n21yv17G6uopjx471rQQbjcYuaYkaeE2tFlqtFgKBADKZDEuwo/NBMyE2mw2jo6NIJpO668pms5iamkIsFoPX6+2RuTSbTTagVCb50fkjHfj4+Di7bpXXmizL8Hg8+OCDD1AsFlW3pVgs4v3332dyFrKL4681uj601sGva2xsjGlnlSAJDn3vjxw5glgsxr73IyMjzNmG0iDJUo5A1WBBEFgqXCQSwcjICLtfybKMRCLBHGeCwSCazSbeeOMNZDIZ9nl+vx+ZTIbZPfZbF9Dx/C2Xy7BarbBarZAkCXt7ewe6F4fDYTx48ABms7ln38ja7smTJ7pe8B6PB/Pz8wPd9wf1INYCVVz7pd4N4tF8UGL8Itt92OsZ4uVgSIqHGBiyfLCuX62uaB6iKGJqaqrn/Xw+j2q1iqmpqR5vYafTyWJQSYfIT+nyoPdpqkov5vmtt97C3bt3sbq6ykgtNdKEQiFMTExgb28PNpuN+Y5SRY9spog86YEqbhReoVa5ppAHPQyimWu323jy5Amazaam5+mTJ0/6rgcAi9Ol6WaCLHficTc3N1kYCUVaKy3y6FyUy+Wu+Fp+FkCSJGQyGfYQo3AVHrVarYt4a1VmqTFJDzQ1zm8Df4xkWWYkRXnt0/4nk0lsb2+z6rhWg1ir1er78JNluW/DXqPRYNeHyWSC0+nsIipEXpeXl/uuq9lsMiJL1yCtiwYvpVKp53pUftcoeIMGicr9B4BMJtM106DWiLq3tweXy8VIMA2y6LiIooh8Pj/Qfu3s7Og2h2azWXzta19DoVBghJQIViaTYQTr6dOnaDQasFqtPddHrVaD2WxGMplEpVLpsS6j62N5eRlvv/02I3SZTKaL0NHnUSBRv3XJssyivjOZTNc9pNFoDHQvJpJNqY18FZyIdjwex+bmJur1eo+vPHmk37p1C7Ozs+yc9bvvD7KMHgapgF+9evXQHSpedLsPez1DHD6GpHiIgfEqu37pwR4IBOD1enskBlS5JEshIsRqD1mqCA1SDdW6QQqCwKyWJiYmkE6nkc/nWaOax+NBIBDA1tZW19+oESOg4yGcSCTY/vCV61wu11cLeRBUKpWehzlwcL/fcrms6VVKGmFK8qNpZWU1vVQqMU0zaQP5c0dV20ajwaar1cJCKpUKk8x4vV6USqUeAk5/TxVqtfPPV6gJWpX3/f19jI+Pa177iUSCa1gS0JRFNGFAUxbRghENWURLMqEpG1CXBDSe/b4ti5AgoA0BEkS0GyIkGZAgQH72kgDIEGCABKfQgFOoI75WxPHRGcj1PKrVAiSpOxWQ4paV+6r2cy6Xw9TUFPL5vGoITDwe7+tUQk1nXq9XlWAZjUbk8/muz1c7F0CHYHu9XqZZp/VQDDifGKm3X+VymUls1ORFVK3TI1hGo5GRRjXfYLreKOK8372xH6GjeGu9+yzFP9P1rbyHiKKIaDQ6kHduNBplOlzyEKZ9azQaWF1dRaFQ0PWVz2QyiEajqgWOlwW9iusgHs1DT+Ah1DAkxUMMjFfps6i0r1G6OlD6WqlUYg8mIl4EqvJR1C9pb5UPNYvFArvdzirEWtHL29vbMBgMKJVKPRVe2maeOBDJ40EPZqpyqekqAfSdPj8ISHurhsP0+6XjSWRUrZpusVjQarVQLpdZVUl5jOh4zszMYHNzkzUXKaO55+fn8fHHHzONMV/ttVqtrMsf6La/4gc/tM30uWq+vLyHMX/ty7KMbF3GTlHCdkHA8r4Ne5U5lCUTmjBAxkueDs08eyEAs+BD2FTDiLmJaUnAKbsJDrkKk/G5y4fe9SjLcpdPLf83Sn2znnUZOXVQfDatg9Y/qAUg6fNtNpuqZZ9yG7X2i777JLnhzz05FBSLRd0QjHg8zogTyXj4z6bmUkr+U4Py3qhH6Ejm1c9fOJfLsX4K5ewXNeWpabJ5UHVfkqSu9dCxKpfLLHpcb3sqlcqhpUseBFoV16En8BCfFkNSPMTAeJU+i4M2U9A0rdlsZgSYQOSXbo52ux2JRILJF6jKQd3ghUJBN22Jqpybm5vsM4lIlctlbG5uIhgMdlWxtEB2ciQNodQxqvL10xMrt4+Oi9p7/DFQIxhaVVQlzGYziwtWng+Sj1Bjk9PpRD6f7wk6cLvdKBaLKBaLXR39PFkhV4zTp08jFArh+vXrLPSAbzaanZ3Fo0ePkM/nmYyC1kPyApJgSJLURYxpn3kfWv595XEUBAGC0YxPEg1slEU8yUrYyLdR7prBt+GzQkMWEW3YEW0Ad0rAf9sDrKINx4NWSM1J+IUKAoYqvEINBqH3XNtsNuzs7DD7OzpWxWKRNcES+Mq78niRtAFAlytMs9lEPp9n7hWAvq8xNT26XK6uAR1da06nk2n4+d+pfXdpu9SgPNdqBIvkOw6HA4VCQfWaJk9xvUAe5b1R7/PoPqu1Ll5zDaDLq53CbWgWjfZTjYBTJZ6CdJTHjtbTz6d4EN/kV4mhJ/AQnxZDUjzEwDhI1++LYtBmilwuxx7YJKvgp+LJ6N5ut2N/f78rilUUOxG3+/v7mJmZYc1WsVisR+cbCATQarWYpQ+RMKpQ84lntJ1aVTGTyQS32w2Px6MpDRlU0qBGiOlnqvJZLBZUKhVWNeLJA1W6qKOcjguB3jMYDFhYWMD6+joqlUpP5VaWZczMzGBychLb29vY3NxU9UUulUqYmZlhjY/k9sCfMwAYGRmB1WrF1tYWO3+0jMFgwNbWFqanp5mmUS3oxGAwMLkHv39KOJ1ONJvNrqpRUxaRl63ISVZkZTuSsgulNSN8iCGAPMJCAfMowWBoQ4QMwzOxgwgJBkgwog0TWjChDaPQhgltAPIzKURHJiEDkCHCgDaM3Mv0TEjRhgEtGNCAES0Y0IIRLVmEBHoJ7CVDRPvZezIEtJ/93EiZUEfnlW6bsC9bYRYkuIQGvGIdXrGOiM8Fh6GGWCaNpiSg2pTRlgUIz9wyRFGE3+9nAwz+euOvO5PJBKvVinK5zCqzRKANBgOq1SocDoemOwd/3k6cOIG1tTXWRMZrnGVZxuzsLNbX13uaWvnt8Xg8XbZ5ajIdk8nELOskSVJt+uXvexMTE2xwbDab4XQ6kU6nMTY2BlmWsbW1xeQ9/P1DFEXMzMwMdG+kz9vc3GQzLLQum83G3FmKxSKq1WpP6h1VRylMRM+BgfyWqcqtlDu1Wi1YrVZYLBYUCgVNKRP1XHxe8CqfVUN8uTAkxUMMjEGJ6kEaF/QwiH2N0WiE0+lkDyq6aVNzFPA8fY7IGz0sKASi3W6j2Wyi3W6zxhylRq9SqcBsNqNarcLj8fSkVlFlolwuY3p6Gqurq5r7NT8/31V5UZOGEPHWk1EYjUbY7fauLnxl5c7r9SIcDuPx48fsfeUys7OzAMBsspQaXEHoePReunSJeWpSo5ssd8IVxsbGcOHCBYiiyLrZ6/U684ptNBrIZrPMzcNqteLKlSuqzVLkZfvxxx9jb28PRqMRLperi4Tv7e3hxo0bPR7IPOh3J06cwJ07d3oGDpIM1GDEaCCM2M5TiM08rKjCgQo8KCMo5BEScggKeQSEAhzCl3SqNffspUCrbUC73SHmcs3cIeiywAh6EyY0YXz2MkGAA4LkQF5uoVYyoCmY0RRMaMid5SSDBV77KASvHWtbe2jCyIh/Gwbg2X1jfn6eXWuJRKJLAiCKIiKREcydfh3plhmfJB+gJhtRkw2oy0bUYURdNkIAcNQUhEEuol3LwJKvwiG2YBVaMAgyk3OEQiHYbDasrKwwe0g+7XJxcRHz8/NYWFhALBbDyspK1/XG+/hmMhk8fPgQ9XodDoeDNZqm02lmDzfIvVEQhJ7vEDnoZDIZWCwWnDx5EvF4vCtQhFLzqtUqq0KXy2UsLS1pOjCcPHkSPp8P6XS6Z4BBThuBQAALCwu4du0a63eg+z4NkHnf5M8DXvWzaogvD4akeIgD4bB9FrWqM/zn6dnXeL1eHD9+HI8fP2aNIbyLgMlkwsTEBPb399kUIVWPqIojyzJ72PCkl6rMVqsV+XwegtCxg6NgCdIzEzEHwPSvgUCAafUI5FFMxygWiyEQCCAWi6FWq8FqtWJkZITdtCmAQ8vvl7S0ZF/G62EpmY1vVtNaJpvN4jvf+Q6A58SYJ8Tnz5/Ht7/9bQDAN77xDTx+/Jg5LZjNZkxNTeHkyZMIhUKsm52mlMmz1mAwsJTCeDyOSqXCtkm5byaTCXt7e4jFYuzBTPtITU/lchnb29uoVqtsqp/W1Zn2NaHZllBKbiNd28NIexsOqQQPCvAJBQSQR1DIw48ixM1nnz+8G3aBKtcWAJD0nVAAAI1nL4JSHSEB2Nf+85bcqZlLK1bIG068AzNKTRHFlgFl2Yy6bEJdMKEWM+D+g/8PzLIRX4MBTRhRhwkNGNGUjWg8+39tz4yabEYNZhRhRhJm1GQHGjBBggEwmOCIu3D1//cxUokYhLYMi8EPkyjDJEiQig18uHsTM8eyMJqtWN01oVDyodGW0JY7jY8QDTAVLfjz3BNUKyXUKqMQZQntUgOiLMEommG3eGGBiN0bO1hIW2A1G+CyGBF0WRB0WhByWeAwd2ve6TtEldhqtQpRFJnUi5rpDAYDi5amexp5ZRsMBkSjUVQqFQQCAZRKJRQKBZjNZgQCAaTTaezu7sLr9WJ/f78ntEQQOs3FPp8P586dg91ux82bN5FOp9l9PxgM4tKlS8w3eVBoyTkOCr31DD2Bh/g0GD4GhjgwDstnsV91hqClvaPfXbhwAel0Gnt7e132XmS+Pzk5iY2NDabHU4LIMFWZY7FYj02Yw+Fgv8/lcj2BEkRkBaGTwEdeo2oyjHQ6jbNnz2Jpaamn8rS+vo5wOIw333wT0WiUVbHViCORfpvNpurZarPZmOSDSCpZ09HD02Qysc5xWreaplZ5zCkQguyyCORQUqvVWBgCoVarIRKJYG9vD+l0WlWTSj+vbmwjWTegAisyySZKkgVl2YmKbEYDRhhELwRZgiBLMKOOKSGOaSGNCSQwI8RwtLWHGSEGS7sFpAEIAAZLsx7iMwKRcLTqQCsPO4CuLD66zA6zIFntvMqyBWXYUGpbUYEVZVhRlG0o1u0o3rejCBvmZTsKcCAvO5CDAznZhZzsQLZiRCWbfbaBpFHl9OWkhErUgaVPVDfDahIRdFow5rVhxGlEM5vBqCuIMbcJAV8TNqENk+l5uE8sFuv8ndXK7jE0y9NsNpk9ZCwWgyzLePToEZMqCYLA9P/JZJLJJJRNebQegt/vx/T0NCss0IC4X2KiEocVqDHIeoaewEMcFENSPMSngh5RHQQrKyu4cuUKm2qkab1kMokrV64AwIGqD2azGXa7vavSQVUUelhQdzTvPkH2YABYWh3fXU5/W6lU4HK5IAgC8yGmago1+BWLRRaksL+/j3a7zazQyOR/Z2cHwWAQjx8/RiKRUNXdkjm/xWJBIpHonfZ/1tjn9/shyzILD+Ar7CQXqVQqrNGMGttIg80HaPz4xz/uSrXjG9Hu3r0LoJNERWb4Xq+XnbNYLIZCoYBLly6x8AAivTza7Tb29/dZ13e53upMfcP0TL9rQ1ayIlexoST3NsBYUce8sIM5MYpj2MNxcRfHhD1MCQnV5rEhhhgUDqEOB+oIf0qu1JANyMCNtPzsxf7vQRIeJGQv4rIPCdmLLFyAwpmk1pQQzVYRzVJF3ohOab0j2bEaBITsMsL2OkI2Aea6ALcowCEZYUDvd5/kWZVKBYVCgblw0L2oUqlgZ2cHXq+XuXCoge5Z6+vrTIYRDofZd58kHIMGYQwa/nSY63nRZ9UQXy0MSfEQrxySJOHWrVs9ZvBk36U0g9eDLMvMyH5hYUG1CYaM7alphHcaMBqNqNVqLMSDd0TgQdpALesnfrlMJtPzWdS4QppcnhArO/klScLt27fhcDi6SCr/mUTESZ5AvsZ8Ex2FZFC1WWk3Rh3soigyRw0luaa/uXfvHhwOR18zfNJWdrYRqMKEjGRDRrIjI9tRlMyoVU2oyZ3mMS0EkMcZcQMnhS2cELdwUtjCjBD70pNfCQIkwQhBEAG5DYOsncQ3xOcHZqGNEWQxIvR3nmnIBiTgQ0z2Iyb7sc/9uy8HEJWDSMEDmSuJ19rATlHCDpsQcjx7+WEQZPhMLfiMLQTMbQQtElzVAkLtGlq1su69iO5VvCUffy9qtVrsu/2iQRh0r/68rGeIIdQwJMVDvHJEo1FkMplDMYOn6XqTycT0c8p0uGw2yxrrtMz3jUYjKpUKALDmMQKRV2ou09LCkn0RX43lZRi0f9TUR3/Huy7Q5/OOFEqrOdLQkkMFH3/L2zLxvrtqDXv0vpo1GX8+2u022u02lpeXcfToUda9TYMPsqXai6fwIH4fd+ojSEoOZCQ7alD3CeVhQw2nhU2cFddwTlzDWWENk6J+NPHLRksWUYATJdhREeyowYoy7CjDjqroQBkOVAVbpwkNAlptmQVtPHd/MHTCOJ65RZBvsc1qgc/rhtlkgtRuoSUBMJggGkyoP7OSCwaDCIfDMJtMaNSraDfrMIuAxWxEo15DJpXC3l4UUrsNSO2umA8BgFEEjKKAibEw9rc3njlctJhEoSkLKMsmVGQjqrIRbVmACS2YhSbMaMOM5rMXvdeChd4TWrChDisasKEBq1CHHXXY0ID4JR+0HAbMQhsTSGFCSGkuU5eN2JMD2JOD2JWD2JFD2JHD7N8kR5rbsoBUw4RUw4TVCq3BDQBwCXUEDHWE2k1ETHWMmGpwGtps8NtqtZh0jS8W0L90v9rb22P3Yl7fb7FYBg7COKzwp1cZIjXEVw9DUjzEKwclZh2GGTylOpXLZRbEwbtGkK+u0+lkxFdpvk8NLfR5ajdaqs7y1VQlkSW3AyKtajHNyrAMJSGmdQEduQENHJRWSZIksSYzWZZ7LMeoiY7Wowe+Yq0G2v9arYZ8Po/9/f1OiIEE7LedSMKDODyIVkxoy3UAY5qfJULCcWEX58SnOCc8xeviGuaEnVdWAZZkAUl4kJS9yMKNAlyowIaaYENTsKAGO0pwoCpYmRsCD2Ulna1X7A390EIn6c6EYrHWY6VltVpZw6iePWC+bUZRtkOCBFnl2AkQYBSNaFTdSAnjvRuh2LWabEBCciIuuZCQnEhJdkgHFu/KsKAJB2qwC3XYUYMDNdgEjkCjAavQgBV1WNCERWg9J99oPnuv+Zx0PyPcdtRhFpowPbOs4wm+STi8oJvPCyxCC0eEOI4grvr7umxCVA5iWw6z144cxrYcwbYcRgUdR5uibEGxZcEmN+HgFJsIGyqImGoIogivnIcBvT7P/CwVXZNq16Pf79eVYLBtPqRAjWEwxxAvE0NSPMQrh9PphMFgOBQzeLPZzCQTpPkFnic7FYtFFhdNjhCksTMYDHC73fB6vYjHOw8fIrtKAsxXU2X5eUIc/yAhss0nqak1kRkMBk0iqkaQeRLO65h5v1U1bTJ1ow9K1njXCeX7hI2NDSSbZqy2xrFSd6Mma99CDGjjmLCH08IGTolbOCVu4rSwAaegn7L1oqjLJuwgjKgcxr4cQkbwoQo76oIVLcEMCL2yDWXDnxa0lhn0GBPIzYQfXDUaDeZ4AoDZAxqNRkbEyR6QrjG97aEB2iCwG2QcM1cw3Sp0ZlBkEUnJgZxsQ0WwoiyZUJRMz6rLZo20PgF1mFGHGRl+szQOjUmQYBeasIstOMQWnEYJLqMMU7sCY7MCm9CETWjCghZEvVlwmfyh2wrP5xZMHXfnThVcbMMkd4zkTHIDpmdk3GpowSjVYZYbMAutzvtyAxbUYUUdVtRg0NqJzwgWoYljwj6Oadh5JGU3tuUItp6R5E1pBFtyBJtyBFnJhZLkwXrTAyACERKCYgUjxjLGjBVEDBVYxXbX7BE5WWjZVXq93r5BGIcVqDEM5hjiZWJIiod45ZiYmIDf70cymTw0M3g97RjZgSUSiS5pA1V0S6USwuEwUqlUVzVWqfMlEkNNa1qfRZVu6gCndVL1mFwh+N/x+0/7Q24Zau4TsiyzyGTaNiUGjdQd5G9aohn3Kx48LLuQluw9vzehhTkhitPiBs4I6zgtbmBB2IFVUD9Oh4EqrEjCjzR8SMGPFPxIIIC84IYsdDce6ZFWkr5onVMAXTIWrXNG51jvuIuiyMJUtJw3SFufzWZhMBhQqVQ4q7mOF63H4+mq5KlpzmVZxtGjR9mATw8ej4e5D7RaLRghYcxYwpSxM9MSCoWwu9sJk5BkoAozSpIZlWf/lmUTKrBABp5VfdswC89eaMEstGFBC1axjWOTI6jk0gj6PD0zILlcDi6XC3t7ha7jwu9bzzE1GAAY0JKNaAsCGugOnaFQnc7KwP6lYz06OsqcUpSNugaDAZFwGMVsAq1iChbUYZGqsKEGO6qwC3XYUIPH1ILH2IShnoVTqMHaLsIiv9zBnx5CQgEhoYAL6PVLL8h2bD4jzJvyCDalEWzII9hsjOBBIwxAgF+sYsxYxqhYwHFPh4im0+muHhAarOVyOTidTrjdbt1tOqxAjWEwxxAvE0NSPEQPDstDUguiKGJxcRFXrlx5YTP4RqPBvHjL5XJPowhFtHq9Xjx9+pS5XdjtdtYUR2b429vbLMJVTeLgcDhgMBiQz+d7XBro/y6Xixntq6XSmc1meDwemM1m1pBGD23+M4PBIJxOJ7a2tno0zrS83+9HKtXRJepNfQ6CYDDI1vV8HUBSdmC5FcKmFEBbfqaLhoRjwt4zCcTaMwK8DYvwcprC2hCRQAAJIYwYQojJQSQQhD04CYPRqEv6IpEI2u22qhsGwefzod1us3hiNbhcLkxMTODRo0eq50wQBJw4cQLZbBbxeLxrIMQPqgKBAACwBkj6W947mkIfqEGSzj1JWMgSSynrUYLXnfdDJBJhCWq8S0Gr1YLBYMCpU6cQCoVw9+5diJDhQANOYxOyXEInd0PA7OwsdnZ2VGVDBJvNhnPzR3H/flHzuz8zM8NsEWm/9cDPmvDLC4LAgn2ooZQ/H+T7e+bMGZRKJRZnTOujquiZ117DRx99hFy5gXy7DYPR0OU6YzAYEAlF8MYbb+DatWvsvmkSZViaeZjqGXgNNZyfG4etlcfO49twCWW4pDwc7TwsUkVv9w4dbqGC14QNvIaNnt/lZXuHIMsj2GiPYqM1iquJEdTyVvjMo5jMVHHE1YbDLLIgHZvNBqPRiEKhoKvhFYTDCdQ4rPV8GrzsZ+MQnz2GpHiILhyWh2Q/kN0a+RTTlHAoFOrxKda7ERHpJScGPrzDbDZjZGQEdrsd+XwebrcbzWYTxWIR5XIZBoOBWYtREh2FgPAgycPY2Biy2SwLk+DJiCAILByEpBtqD3ODwQCLxYLx8XGsra0hl8t1kXDaJnpQ7+/vM+s0HhaLBTabjaVZaTX+KfdFC6FQCM1mE/l8Hk1ZxHrbj+VWCBnZAT8KeEf8CK8bnuKssIbXxHW4hAHCHD4FJAjImceRskxjWwoh2g5hr+lCkyu+GoydYyjJMnxcSIgSPp8P4XAYTqcTsixrhqkcPXoUjx49YkRQCYoDf/PNN2E2m/Hxxx/3hKC8/vrr+Na3voWrV692XYsEs9kMn8/H0g6JfCmvIVEUWWyvKIo9ceHkL00EjhoslaAq56BoNpvweDyq2002g9/61rcAQHP/jx49iv39fdjtdta0ysNutzMv8nfffVfzu0/3HTVZEH0e7bvyugfQ9f33+XzPKs97PcdxbGwMbrcb4+PjuHjxIq5fv860+aIowul04uLFixgbG8ONGzfYYJfXgVM6ZrPZxOjoKC5evIhr1651+XC73ZM4/tZb5KlOIwAA6T5JREFUGFlcRCwWw0/2/ldGuKvVKtCswCtWEDDWYK0n4Gxl4ZJycMsFuOU8HK0sDDj4rM+ngUeo4JywjnNY7/H1TtY9WK+NYiM3gqQYQsUUhNEZwsTkUYjyc02x3v36sAI1Potgjlf1bBzis8WQFA/BcFgekoNifn4es7Ozuol2/W5ENEW2u7vLqnd0A6auaUqfo6AKvlEEAJNOiKLY5WvM63nNZnNXHLPa1DfQedhms1lVUgAAlUoF6XQab775ZlczIJ/W5nQ6MT4+jps3b+o2v1HEK9BbTVPKNvTIsclkQrVWh2FkAZsGGfv72zgjPMWvGX6AC+ITHBN1YsheEDnBg6gcwR5GEMUoMpZJBEcnYTAYOufELEKQKxA4ImIwGNi1WS6XWYIgRXaThMVkMqFSqeDkyZNIJpMol8vMmo6CT3w+H3w+H6uIqpEw0r9nMhmEw2G4XC4UCgW2PS6Xi0X4LiwsYHV1VVXjbTabMTY2hvv377M0QqV8gAJWiIgrQfIii8Wie16p4uvz+foOjgwGA5tpUUpI6BhHo1Hk83nd/Xc6nWi32z0hEARKbXQ6nZiamtL87m9vb+vKWcg+jJxltMJtyJKrWCx2DULoO10sFmG321EqlbC1tQWDwcASLuk629raYpV7fpDBfy/p+kgkErh9+3bXjAMRxNu3b+PIkSOwWq3w+XyIRqNIp9NsuSwM2IADdvsp+EN+dnwrlQokNOFCCT7k4RdLCBkrcLXTcLWz8CEPN/o3JB8GQkIeISGPS+Jy5402gDzQzgmICUEUt4+gGjyKhtGLRNuJvCGEhi2EYCj8UgI1XmUwx6t+Ng7x2WFIiocA8Nl5P4qiqGm7xt+ILBYLLBYLZFnuuhEFAgHE43H20FfarZHPptVqZZ6cRqORaUTL5TIzsTebzSzBSWlvZrVakcvlGGlRklBJklCr1WAymfq6ZhSLRfh8vq5Kh5Lwt9tt7O7uak5FUzqVlt0a7QNVFtVIkSQDOckCu1xBfvXHOIWn+I64Cr/55TxkS7AjLoQRN4yj6J7HJzkLSrBD4Lqo5KaMnZ0dpicncsAPlCjS1m63o16vQ5IkuFwuWK3WrnNWLBbZOU6n06yqzvZfkpBOpxEOh1nErRqIED19+hTLy8uswYcqy6VSCR9++CFbnvSpSslLPB7vSg6kaj7bd645c5AOfK3ue359b775Jj7++GN2LJSgGY5CoaA6kCNnk729PXz00Ue4c+cOms0mrFYrI9u0/1//+tfZoEMNpMUfG+u4k2h998fHx9lx0Rp8Go1GTE1NdcmdeBmF0WjE+Pg4tra2WNojf6wpbMdqtWJlZQV7e3swGo1wuVxMh1yv17G3t9d1npXrabVaKBaLcDqd+OlPf9ojQSKkUil873vfw9//+3+fheqogZrW2u02k74IogElwYui7MGWLAPNzkC2KTVhMplgFtpwywV45Sx8cg5eOQuvlIVXzsIvFGGQX56uHwAMgoxxJIFiEijeAgDMPftdSzChZA6jencMlSMXYJ86CwSOQwgch9d7sCQ8NQjCyw/m+KyejUN8NhiS4iEAfP68H+lGROEURGpEUYTNZkO9Xsfy8jImJyeRy+UYGeandU0mE2u84HWFfAWKfs5ms6wxjrdNI2szSZJQKBQ0yQVtc6lU6quBlCQJm5ubuHDhgmalY2NjQ/PBSdCqyCk/i28QhNyGS8piUt7BSWEdC8J2p7P+kCOQc3AhLowgLo4gJoQRF0dQlB1otdswCAZYGhaU5TKAXj2sLMvIZDKsAqhF/Imctdtt5HI5NpWuJJv37t1Dq9VCJBJRbexaX1/va1vXbrfx9OlTRpD5QQZVCq9du8aWJecUXutdqVSwvLzMrqdms9kziNOScGhtE0FNTy5JEvb39zEyMsIInbJBUBAEeDwe7O3t6X5WqVTCxx9/jGazCbvdztZjNpuZ1eHPfvazvnKddruNaDSKmZkZzWUKhUKXy4qSzNL1TD7ZFEzBa4FpkMoPqvhjRMvRdUP2YjzppoEVSZj01kOfxb9PoPOxt7eHeDzOIpqB3vMBdIoBSh9zfp3896HdbqNpNCIjBpBBgP2ezsOv/9r/hNNTASC7AaTXgMz6s9cGkFkDmi9Xz2yUm/DWd+Gt7wLZ28Bd7pc2PxA4/ux1DAjOdv7vPwqYbJrrfNX4vD0bh3i5GJLiIQB0ez9SpYo3aH/V3o/5fJ5NrSptgMrlMkRRRDQaZQ8I+r2yYtRut1GtVnua5/gHKD1g+AowPXgBMI0l/zBSqwiQZGEQrK+v48KFC12knTq4BUHA7u5uX3I9EGQZ5so+Rtv7OCat4azwFFbxcCtHBdGDvP0oCs6jcBz/Gr53axOFlqkrEKCzKc+lHnrNWAC6glTUQA9+QRBYUxq5iVBDGum38/k883tWNm/a7fYuMtNvm+iaUSMzVJEkaYNytsFsNqNWqzFtLW07r089yDVE3wvS3vLT/uRSsb29DavViqmpKezv76tq7gcZXNH+88loBJIXlctlRhyVzX/0fZQkCVtbW7qkOJVKQZZleDwe5ivObzM1yebzeUxPTyOfz3dJouh7lE6n0Wg0YLFYWHCPUoJDDbF+v1+V8NhsNuRyOSbZUGq4aYCj1oeghr/6q79Co9FQdSqh+xcN2vkGS36b6D1yp6Frn0D3OpfLhRMnTwEGA+AZB2be7t4YWQaKsQ45Tq89/ze9BjmzDqH9ku/11QwQvdV5dUEAPJMdohw4/owsHwMCs4BnAhAPeQTfB0Nf5K8WhqR4CADPvR/z+TybSlUmw71K78darcYaoxwOR08Fp1wuI5vNsqlYZQc6QflwUqtk8Z3kyoQ34LnVVjfBezHCKssyVlZWWLMRPdj8fj8WFxc//Q1WluFDFkEpgUns4qSwjkD7WTbsQbMYVNAy2FFwzSJlmULCPI2SZx5mf0fqkE6nMeGYQMuchcXQVm2UIhI3aKAIQa3y1m63GcGQJKmL8JFdFDWq6T3Q9KzYlNtERJ/fHkr94/Xd5XK5R5duMpkgSRK8Xi8KhQKTttD1R9IDh8Mx8Pkn4q8k4DxRJp9uqqjTteb1ehEMBvH06dOBPktZteWhnF1Ranj5Cm4/0LG12WxwuVyo1+tsm+n6ITLodrtZIyA/iJdlmbmS8ISY9uMg318i+kRa+WNA10S/65lAM0lazjpKrTIdP+W/7XYbR44cwfr6etfAivbNZDLha1/7mn6zpSAA7tHOS0GYBUkCCtFnJPkppPQaynvLkFNP4ajuvuTGPxnIb3de6x92/8pg6VSSg1Rhnn1OnO0vLsdQw9AX+auFISkeAkDH+9Fms2FlZYVpaOlmXyx27JPm5+cP3ftRq1OZuryJNPAg0lOv1+H3+1nFRFmZ5Keo1brU+W0gIqz2cOuX+PZpIAgCrly5gnq9DqPRCLPZDEmSkEwmceXKlb7x1mzbZAFl2YApOYrXsIzTwlMEhcKhEGAAqNpGIUxdhjSxiGtRCW3fMVisz6c2qfWQkgPdbndXMhtV30m7ajQaUSqVBnbF4KtlPMEEnp9fmv7myQpVYemaaDabMJvNPQSLSPVBwW+PElR55QdpNOAyGAxYWFjA7u4uEolEjwxjdHQUPp8PV69e7bsNROh5os4TbLPZjOnpaezt7bEgELfbzb7X1WoVOzs7B7quqWKmRsKJCCsbXvlBp8FgwPT0NDsmy8vLKBQKcLvdWFhYgMFgQCAQgM1mQ7VaZVpx/riT1zdVjM1mc892NhoNOBwOpFIp1UEPHSNqeKxWqxBFsafptVqtsmuZGu6U1X26v/CuHVrw+/1MEqYcxPPSL/46V7rTEE6fPo0jR46ouma8+eabWFxc7NrfAzWkiSLgneq8jn0TIgAX/a7VgJzdQGzjE+yvPUB57wmshQ1MYQ8RIdf3GLwQ2nUgudR5KWHzcSSZI8z+o4DJ2rv8gOB9kYPBIBqNBrtGzGbz0Bf5S4YhKR6iB2ok9GVAz1mC3APooaesFLZaLZjNZgSDQRw7dgzLy8uo1Wowm81dlUNBEDA5OYn19XXdbRmkgvVpwjC0kMvlmFctH85ABCMej/foS+uyAWnJjpTkQEEyYUHYwDfFu3hX/Ahu8cUt0pqCGZXAaZgmz6MRPgth6jLcY7OM1FiuXu0Y5lusPeeDHgwLCwu4e/cu4vE4I2v0sCZZA1X/BgFPPNWqxVSBVHMgIFsyu93OqvFq4QwejweJRKLvttC+8BVHfjqbJAv879Xwxhtv4Pjx41haWsL29jYjKlNTUzhx4gR8Ph9u3rzZN0xkbm4OKysrzJ+bPzYGgwHHjh3D5OQkfvrTn6JarWoGL6iRSjVYLBbU63XU6/WeKjAANiDSIqKCICAYDGJqagq3bt3CjRs3usjc+++/j8uXL+PixYuYmZlh+8YP0GmQdezYMdjtdmxtbTFHDn5mSxRFTExMYGtrS3efaF2ffPIJk0nw22u1WnHmzBkkk0mkUil4vV5VXfrIyAi2t7d1P0sQBPz6r/86/vAP/1BVhsUTX7K14xsNaWAhy3LXIOL8+fO4e/cucrkcvF4vzp8/3zVAPHQrMaMZQmgeo6F5jC7+BgCg0Wrj//sXP8HdtX00KznYKrs4IuzjiLiPo8I+jgixl2bjyFDNassxvFOcFIP+nQXcY6px7l1/LXRcZeLxOJaXl3tmEcPh8EvzRR7i1WNIiocA0NHwVqvVLo0eeaGSRq9arR5aM0E/i5uTJ0/C5/Mhl8sx9wm+K5ymf202G95++21UKhXs7e11VXFMJhPGxsZw5MgRrK2t9d0mrcof//tB0G861WAwIJPJsMoSr4Ulx4xYOoeCJYStooyU5OgQYdmC14Wn+JuGv8Svmm7BLbxYk0xeduCRMIeSexaLv/Tb8M2/DY+xMwWozKyjB0M/w3yDwYD5+Xns7e2xQYrZbEar1UK5XIbJZGIWeP1AFmnKxD76mSqWPNElEIGo1+uYnJxELBZjlUpaL80ujIyMMGcSvW1xOp0oFApdD0X+mnA4HCiXy7rrEUUR+/v7zOWENPy8vMNoNOK1Z4ERWjh79ixmZ2exvb2t2txps9lw8uRJ5t9qs9lQqVR6GvtsNtvAlfLR0VEWJgM8rwDTsT927BjC4TB+8IMfqH5XBEHA+fPncefOHXz44YdoNjvuCSQrKRQKzMXjwoULKJVKSCQSXZpnURQxMjKCCxcuIJPJ4OHDhyyQhyq56XS6S2utB1mWWXCHcpupt8JiseDSpUu6YUNnz55FsVhU9csmeL1epNNpFrrDV9X5Y+r3+zE9PY2PP/6YWQUqw1ROnz4Ng8HQQ3jL5TLq9TojvK/KSsxsNOB/eus0RgxlVCpGSJYjeJwz4P14E8tZGS0ZCCHfIcjiPo4I+zgqxHBU2MOUkIBJGEx+8ukgA7mtzmvt/e5fmRysyU8OHEfFNo6acwqGyDw8wVHNgXjX2g+j92OIzw2GpHgIAM+bCQKBgKZGL51OH0ozgSz3t7jZ3d3F+Pg4e7ApSTpVgmga8Bd/8Rfx+PFjbG9vs6706elpnDhxAhsbvclNeuAfUvzPg9z8eKcHLWJgMBiY/tVkMqEFEYmWHcm2rfNqWVEoW0CZtD4U8FuGD/A3DR9iTtw90L7waMsCHslH8RjHETdOAs5xuDwezM3NwXvy7b4Vk0EM84lIBINBFItF1Go11Ot1CEInXczlcg1cUSG5g5J8EYmjyiUA1gTFaytbrRaazSZSqRSTp5CeWRAEZquWz+cHIk8+nw+VSkW1CkqSEb1UPADMai+dTjP7LSIqsVgMhUIBi4uL8Hg88Hg8zD2BQN3uHo8H8XgcwWCQaZSJ9JP+P5FIwOPxwGg0IhAIYH9/nxFoaiILh8PM7UHv+hZFEZVKhUk2lOEd5AGu3F7lMVxdXWWVZC0Xjxs3buDChQv4+te/jqWlJdYgSD7PCwsLCAaDWF5ehtvtZlIQkkD4/X4YDAbE4/G+31lZlrG5ucmkEEodeLvdxqNHj/D1r38dgHbYkNfrxejoKERR7PIfJgQCAUQiEZRKJYRCIVitVuzt7fVII8bGxuByuSCKIoLBIHK5HJMI0TVL10wikcCtW7c0Ce/i4iJWVlZemZWY8v5wxt7C67NGOL0BZMwR3IxW8cFKCDcrJ7r+zoA2JoQkjgr77HVE2MdRcR8jgvYg41DQLAOxB0DsAQQAjmcvAKhZQhBDczCNnkQ+b0CoZsPYkZMowoVWu83kE6lUamjJ9iXCF5IU/+QnP8G/+3f/Dh999BH29/fx3//7f8ev/dqvsd/Lsox/+S//Jf7zf/7PyOVy+NrXvob/+B//I2ZnZ9kymUwG/+gf/SN8//vfhyiK+M3f/E38+3//75mV0lcNymYCXscHgGlfqZngwBo1DrzFDYAuHZ/FYmEWN6+99hoKhQLT4FFliZLI+CmrUCiEy5cvI5fLIZPJwO1249KlS7BYLMwbth/UpumVPysrB2oNfnw1Uqm9FEUR5ZaAaNOOpOxCvO5Esm2HDOWxk3FZfIz/veEKflG8DfOnrKQsS1NYs53GhjSGgjEMO9cMQtPRFDwBdKqwemEqoVAIgUBAcxk6txMTEzAajdjf32fXyOjoKHto97MeE0URDoeDkWz+GMuyzKy4iAjxEg3aDyI1uVyOkUOy4DIajfB6vWg2m0gmkwNXFEm7qySq1NRHP2sNiCRJwvr6OgvW2NraYhrZ6elpZLNZ3Lt3D6lUihEgktkYDAY4HA6YTCZsbGzAYrEgHA7DYDBgZ2eHHefJyUm0Wi2kUilMT0+j1Wohl8vBYDDA5XJ1DRzS6XRXY5xWghzQaRLz+XwQRRGFQoEdRyKmfFWXGu/470e73cbGxgY7N2ozAEDHxWN5eRmnTp2Cz+dTlQbkcjmkUim2/5ubm6hWq7BYLJiYmECr1eoaDGs1/gGdextJNKhiTCSZAnZoe7QCR+i6ohksZZAMDQCcTifzUJ6amkIsFmPnbWRkBK1WizU7HzlyBEajkRUjKKSGrtlqtYpKpYJAIIBSqYRCoQCz2YxAIIB0Oo179+6hVCr1vc/S7N8g9/R+y+gFavxNAK22hNubWfz14xjee7iL/UITbRiwJY9gSx7Bh3i96/PsqOHIs4ryESHG5BhHhf2XLsew1pNANAlEf4bjAI4DwDbQEi0oWUc7VWXXFHymCDI7MeTTs/AGRw7t81/kGft5/qzPO76QpLhcLuPs2bP4e3/v7+E3fuM3en7/b//tv8Uf/dEf4b/8l/+CI0eO4F/8i3+BX/qlX8Ljx48Z2fvOd76D/f19/PCHP0Sz2cTf/bt/F7/3e7+H//pf/+ur3p3PBfhmAr6qAHRrRj0ezwtr1KgK3Wg0EIvFevSAfr+fPUDIR5ZHOp3GsWPHuj7rz/7sz7C0tMTIyPb2Nu7fv48TJ07gjTfe6FsJA8AsmtSIAVVpaPv5dfEPW4pfrlQqnJUbkGhbsVPzICr5kJa0PTgtaODXDD/D3zG8hxPiTp8j2Ys92Y+r0mtYwzSK5lGMR3w4fvQIjBsbEHM55rNM8gOHw4FEIoG5uTk8efJE0w2DYrfVzv3W1hY793Ru8/k89vf3WciIIAhIp9MYHR3tsifTgtlshsvl6mrW42Gz2VilFwCrAvNVPiJeZOOnlBmUy2U4nc6Bmv5kWWbSCDVS3Gp1x9xqrQPoBK9kMhksLXU3C+3u7sLv97P0PJoKJ5DbBskEvF4votEo9vf3u+Q6u7u7GB0dZRpfmlYnYkbHyGg0olarwePxdFXdlaBlyY+XAjMI1BBH1ng0GFEeIyLGdCyUsy/8wCyfz6vqjm/cuIHLly8zsr+5udlTEd7b20MkEukJauHPg/IcEYnljzWfYke2fVqBI263mwWZ0OfyTaJEoilQhO6zFFJDy9GAvlgssgJAOBzuOR/pdBq1Wg2CIODRo0dd3zOaASCJhslk0r3P1uv1ge7pg973BUE7UMNoEOFvpXA0dxd/055BwmjERsOF7bYHu9VeOlKBFZ/IM/hEnnl2YtjRQgh5VlE+Kuxj3hjHgjmBUHMPojxYI++ngVGqw1vZhLeyCaR/xt6XV/8l4J0GgnMdzXJo/tn/5w7sjPEqI6WH8dXdEOQvuCBGEISuSrEsyxgbG8M/+Sf/BP/0n/5TAJ3qVSQSwR//8R/jt3/7t7G0tISTJ0/i9u3beOONNwAA7733Hn71V38V0WiU2Xz1Q6FQgMfjQT6fh9vtfin79yrB68/UNKOXLl0CAFWNWj6fZ8vQF0lr9JnL5fBXf/VXyOVyPR7EJJGghhY9u6iTJ0/it37rt/Bnf/ZnePz4seZyU1NT2NnZ6UuKnU6nbhodVVz0pshpmWSuhH3JhZ22B9G2B1XoNzONIo3/g/GH+B3DB/AJB0uUS8sefISTWMJxZIXuKTyXywWn08mq7Xylk+ykIpEIzp49i2vXrrFzxZ8Pi8WCd999F36/v++5N5lM+N73vteV6kYPa/q8QCCAfD6ve6ydTicMBoPusSYixuvICTzxo0Y8LQxqqUUkh5dwAOiach9kPVarVXdAYLPZ2JS5FkwmE/NYVmp8CaFQCN/+9rfxwQcfIB6PM2s62t9mswlBEBAKhVCpVJDNZjUr3G63G7Varcv2jqCUGwwK5QwLT1ZJ9kSNtrzDg8lkwqVLl/DgwQNdDa/L5UK1Wu2rFe+3zYIg4Dd/8zdx6tQpzWVyuRy77uk406Cs2WxCFEVEIhH82q/9GprNpu599uTJk7h//z7sdruqzVetVkMmk0GpVGIWe8rPo4q12WxGtVrVvc8uLi5iaWlJ93sNDH7f18PKygpz3aFZD4oSbxrtMM+cx8OMgKtPU2i0Pl1jsxEtTItJvBsu4m/4czhhjsNX3YKQegqU+zfUvhTYg89I8iwQnAdCc51/PRM9sjUtHfhBj/UgeJWf9VljUL72hawU62FjYwOxWAzvvvsue8/j8eDSpUu4fv06fvu3fxvXr1+H1+tlhBgA3n33XYiiiJs3b+LXf/3XP4tN/8zRTzMaDAZx9erVgTRqpLNSG30GAgG0Wi3djnibzdbVOa7W8LC0tIRSqdRVcVNbrl9XOKFfiEG1WtWcUqrLBsQlJ5IZL3ZbDmQkm4okohenhA38nvEv8C3xBozC4A+BKix4iAU8xAlEhVF2Y1V+orKJSJmCRtPed+7cQbVahcFg6HLDMBqNqFaruHnzJmZmZvqe+zfffBOFQkG1cQkAq6ZRhK0WlIRZ7bwWi0W43W5WkefJKRHEQXW+g0BZhVbu16DuJINcZ/3QbDa7qv5qJD2TyTAtMJErGkRQRRHozHwo3Tt4UEMYX2VWm1b/NO4sWo1MOzs7uul5d+/e7bpGtK6PflHYgxB5We44VOihVquxcBOataBZBQo8qVarqNVqGBkZ6Xuf3d3d1Z21CwQC2NvbQ6vV6gpUoWuzVqshn8/D7/fr3medTiei0aju95rury+qTZYkCbdu3UK9Xu/aHovFApPJhFwuh2B+Bf+vv/Ud1FoSfrqawvtLcXywnESqNHgvSwtGrEmjWIuN4v/5LDww5LLg63MhvHvUire9OThLm0B6FdXoQ1S27sHTTsGIl1ddRiUFbKWArZ91v29yPKsqLwChOcjBOWxtl1EtywiFR16qDnyQ3p6volb6S0eKKUIzEol0vR+JRNjvYrFYz5SU0WiE3+/viuBUguyICIMmYX2RoKcJIx1fv7jLjY0NPH78WNdZgjriSQfIVzAoRYpft/KzqKr0J3/yJz1Shk8L0gTSOviKmCAIjEyQW0ReMmO96cVmy4uUZEMvJdWCjG+I9/G7hr/A1wyfHGgbNzCJuziNZRxHS9B/4NM+kBevMnSCiEy73UYmkwHwXItLyxFZSiQSEEWRVQ209InU2KNHsPhmL3qPoPae2s8EImJ8nDXtF68xPizw6+e1ucqmsVcBLRcMAjWJNZtN2Gw2mEwm5hFtNBrhdDpZohtVF6m6z38Gr7Wlz1L7vEGPNX2OkkjT39NAjKwYlYMds9ncI4XRuz76abwHwY0bN/CNb3xDU3PP+6qr+WHTs4MGcHr3WQB9nV5GRkbw8ccf93ynab9EUWTXo959VpZlxGIxeL1ezXv6/v4+AOguM0jMcTQaRSaTYemSPEjSkclkEI1GMTU1hV86NYJfOjUCSZLxYDeP95fiuLKUwNL+wZ+7yWId/8tHUfwvHwEGUcDrk5P4xvx5zJ/6LXySeQ82qxkBQwXuVgKeZhyeZgLuZgKeRhwO6SU+55tlYP9e54XOE+QNAOcFIyrb46jYJ1B2TKFin0DFPomG039okdLD+Gp1fOlI8cvEv/k3/wb/6l/9q896M146tDRhg8RdFgqFvh3PFBAyOTnJuvB5Z4lAIIDl5eWBtpWidQ8L5AxBoIcpdabn2yZExTCe1t1ItbW1wWpwoIpvGW7g7xnew8IB9MJl2PAxTuMuTiMr+A70mQSePBL4ZjciRUT+CTRIIG0v6cBJV0rNW6RPzOVyfUMM+MqcGvk9SFWCD+XgyRqRp0HT6uhvtMgT/UtESumaoNaA9ypAmlfleQXAXBlMJhOKxSKbRifk83nYbLYuMqwkqjSY4m3v9I5Rv/0XBAF2u501eSo/i8J6yKOXyCW/v/zAldap9XO73WaWgMpGO2osHgTZbFY3gZIG/+SCwUt6aGaA7An57dQiG/1m7ZLJJGtmpIAi2m8a3NOx1bvPZrNZJklRA5FxAC8cc1wqlZjUQ2s9lUqlZ5ZIFAWcm/Ti3KQX/+QX57GXq+LDlQQ+XE7gZ0/TqDYP1oTclmTc2crizlZHemMXj2DGWsGCx4NjjiActo47BvURiK0KfO0UfO0UvO0kfK0k/FIKPjkDI16OlZwot+Asb8FZ3gKSz6vLEkSUzGGYC2eAibPPKszPtMsHDCgZxler40tHikdGOt2f8Xgco6Oj7P14PI5z586xZZRm/a1WC5lMhv29Gv75P//n+Mf/+B+znwuFAiYnJw9x6z/fGCTukqb36GavVlGk6WyTyYSpqake+zfqBu83xQ481w0eFtQInSwDG1Ur7rWOIiEdzJ1EgIRL4jJ+y/Bj/Ip4C3Zh8BvMHsK4ifP4BHNoCy/+VVVWxZRVOrWULb5hitwDlNII8q8OhUJsSn8Q9GtIGwQ8WeOrT0QWDpJWp5aexx8Li8XCiBwfjkDSCfr9qwZPFGl76Vz6fD7s7+93Ve95TXm5XIbb7Wb7zMcZ88spBxxKaOmalSBXEUmSWFIcVfWNRiOb2q/VaqjVaj1SFdJC89CSc9DvKKFT6QRDUpJB0G63VbWwlED51ltvwW63o1gsMk0xhbmQ7Mrr9fak831aJwca7ND9WC1ljwaMevdZIul69/RBlhkk5pj6BPTWQ37gehjz2vCdS9P4zqVp1Jpt3FhP48PlBD5cSWI7c3Dv9opkwOOKC48rACBj3NrCUVsNk6YSxq0GWFwBxAomFJzHsP/MkUSSJBgEwCPnYa9E4azv4/ykE87aHpBc7gSJvASIkOBuxICNGLDxw+e/EETANwOETnRIcvhEhzAHZwGTevFmGF+tji8dKT5y5AhGRkbw/vvvMxJcKBRw8+ZN/IN/8A8AAG+++SZyuRw++ugjXLhwAQDwwQcfQJIk1lSgBovF8pW4QLRu1oM4VFDntJ6zBFUqiEgpHxSFQgEnT57E1atXuwgKvwzQeeB95zvfwR/+4R/23Sd6iGmBqorKB3pCcuCj5jhi0sEaKcPI4ncMH+A3DT/BlJgc+O8kCHiMOdzE64ibptDUmZKn5qMXBV8B1QJVE0ulEgwGQ5cvcLPZRKlUgsPhGLhJVfn5BK2Kn9YyRNiITPHvH+TYUIAEXyklkigIHVcRh8PBpCj8NhBB9nq9A3njHhaI2CilMSSJsVqtuHz5Mm7evMn2hd8vWpaqsu1n3qvK46gMmADQswzw/Dqi80GfRdtFUdDj4+OIx+NoNBpdFmiiKMJsNuPo0aPIZrMspIc/93Sd0vmiQZHa9tD6qBrGD2Sookq2fvz6+WUI5ASipYV9+PAhI+D0mSS3IkJOHtLAi3f8T0xMwO/3I5lMaqbsUXpgPB7XvM+OjY0xCYWe6xCAvsv0iznmt5kaA/lzRrOLvCNHP1hNBnxjPoxvzIfxf5dlrKfK+NFKEj9aSeDmegaN9kF17gJ2aybs1kwAXLAZJJz0iwi3jZiuVRAxo+s6KspBxIw2iOZ5zL3zG3BSUa2cBlIrQOoJkHzy/P+5wfpbDgxZAjLrndfKX3C7IwK+I89JMkeWD+I49VXCF5IUl0qlLleCjY0N3Lt3D36/H1NTU/iDP/gD/Ot//a8xOzvLLNnGxsaYQ8WJEyfwy7/8y/jd3/1d/Kf/9J/QbDbx+7//+/jt3/7tT/VQ/zKh3826n9Ztbm4Ot27dQjQa7el4LpVKLLBgbm4OS0tLmus5c+YM0uk0c5VQIxonTpxg/qv9EuSOHDmC1dVVzanfqamprtS7jGTD3eYYdqSDyRUiyOD/ZP4+/mfhfViEwUlZHWZ8hDO4ifMoCC54PB7MT07ik08+0dzm+fl5rK2t6VYnKfVNj/DyZIZ+JtB7pCvV0jBS9apSGaxSw0s31PaPwjBIHqO2jN1u7wn34LebyGs/T2Sj0QiXy8XcUJQOHTSIczgcbNpfOaVvtVqZs0M/twNAv7nPZDL1vV7Pnz8Pu92Oa9euMekLb0FnMBhw8eJFrK2tsel0+lv+GNE0O08yaTl+9oB+TzIE5SwDyRFEUWThHrRNtB4K+fB6vdja2kKr1YLL5eqKcCaLuEAggHg8zrad3x4KzfD7/ez7oZYMR4l+8XgctVqtx3mDgj6KxSLrD1E73k6ns68WNp1Ow+12syq4zWbr2maqhBcKhS73Cb2UOWpy48NLRkdHceLECRYaopeyd+nSJbZ/yWQSFouFbVO9XmcplECncKR1Lz5x4kTfZXjPeK2iiiiKfbd5cXHxQLM7yu/FsZATx0JO/P23j6Bcb+HaWho/WkngRytJ7OYOPqNYbYv4KAkAQQBAKNXArKOBWVcTY+YaWo3n6apd3v6OAOB4C5h+q3uFjQqQXn1OlJPPyHL6KSC9hJ4EWQIya53X8p8/f18wQAgcw6LnCLYqDqQKIbQDc2i5plBvSarn9auCLyQpvnPnDr75zW+yn0nS8Lf/9t/GH//xH+Of/bN/hnK5jN/7vd9DLpfD22+/jffee6/rov3ud7+L3//938fP//zPs/COP/qjP3rl+/J5wqCRoJcuXdJMjwsEArh27VrfjucjR47A7XbrpqOR3RrvPwx0bn4nTpzAb/3Wb+GTTz5hD3YtAkGVTb/fj2w226Nj9Pl87GFfhxG3GhN42g5g0MY5ATLOm3fxz/w/wcXiDyG2B5dIFODEDZzHXZxBXejMQgSDQYTDYVZZJ9slAt2EQ6EQ8vk8dnd3Nffd7XYz2yo97SU/ba48PnzTHbk9kJ8zVeNI18tXUfRA03LKhilB6CTftdtteDweOBwO1ebXkZERGI1GxONxRsp5KYXavmgFU9D1Qde7cv9NJhPMZjMsFguOHDmCZDLJLO4MBgN8Ph9CoRBisRj7bDXSS+TOZrOhVqtpJuN5vV5885vfhMvlwr1793rO/blz5/Dtb3+bvXf79m2mvaXK5MWLF/HOO+/g+vXrkGWZeXDz66JzR+eN9l8ZlU5kkhr2KEyE3yaHw8EqsnyjGU+YKRSIZpTsdjurwJI+l86px+OBzWZDNBrtkQZMTEzAYrHg7bffhsViYceIH8CdO3cO3/rWt3D16lW0222Wrsg7b7hcLoyNjaFYLMJmsyGRSPRci+FwmKX1USS3UoZB2kugW79LZJj0u+Q+sbq62rfjX5Zl/PjHP0YymeyaLUun04jH4/jGN77BvMO1Uvbo97Ozs7h16xbzs6ZjffbsWVaV7pdUScssLS1he3ubEd6pqSlG0oH+RZVBt/kw4LAY8QsnI/iFkxHIsoy1ZOlZFTmJWxufpooMJJtmJHNmXMsBZkHCEUcTc64WvumPDFZRNduB0bOdF492E8hsMKJci95Hc/8T2Eo7MMr6PRqfCnIbSD2BJfUEcwDmACAKtAUjSpZR1D1HYXe/AWfGABhPAJ4p4FMOVr5o+ML7FH+W+DL5FMuyjKtXr2pOpSSTSYyOjuLtt9/GkydPcPPmTaTTaXbjCwQCuHTpEiKRyEAexL/yK78ycIpSo9HABx98gEwmA7/fj3feeYfp3D755BP8j//xPxgxVj7QiSyNjo4yUpzJZNh2+/1+eL1e7O/HcHO/hZvNSdTQ39VBgIxxQxHnDJv4juGHuCjfP5ClT9Y2jej4t5EI/xw2tndZqtnMzAwLvwA6U/IGg0Ez+aperzPPUiVRcTqdsFqtzMQf0J4epmOlfF/ZZOZ0OlGtVrumvYnsNBoNnDlzBteuXetbmbZarfB4PMwCjs6H3W5Hq9VCpVJBOBxGq9XC3t5eF4E0mUwYGxtjUb4U3azcbqpU8tVOIr1Edmm99D1WSmjo74LBIFwuFyRJQjweZ01roijCZrMhEomgVqthb2+vi0Tyn0UVylAohFqtxhqy+GVsNhtCoRB+5Vd+Bc1mEw8fPsTy8jI79wsLCzhz5kzXFHur1VJNfQM634/vfe97upVpCoio1+uoVqs9gwubzQaPx8Oa9UibSiDCa7PZ4HQ6GSGkQRf9S+SZZGj5fL6nYZO32PN6vZqJbtVqFd/85jfRbDbx6NEjLC8vo1arwWq1YmFhAadPn2Ykkwb6JL2g6rXSE1gURUSjUfZZExMTaLfbSCaTSKVSLAhFeY8hXSYNZmlQoNTvVqtVXLhwAR999JGuBzERxc3NTfb3/D201Wphfn4ev/RLv8T2SSthkva/XC4z6zYKYXE4HAP5yhNWVlZw8+ZNpFIpds6CwSAuXbqE+fn5A3ne9kvOfNmoNFq4vpbGj590SPKn0SIrcSzkwNfnwvj6fAiXjvhhNRleaH2yLCOfy6KZWoettAVHeQdC6klHs5xcARoH87N/IZidz+UX4ZPP/3WGe3yWP6/4yvoUD/HpMKg9y927d3Hjxg32ALLZbOzBceXKFVy8eBFGoxGTk5NIpVI9cbDBYJA9WAcFTXMSoeZvnn6/v2tqn28SoooOxbUSgSIy1m63UalUkKq08YNUCE+b/d0kXCbgjHEfZ4RN/LxwA6+3P4ZpwPSkMmx4KJyE8Pp30PDNYX9/Hz5FA51Sm03T1krQtCNFXxMR4/WZymStdrvdUymkaXUaZNBDV1nhI0KgrLQDHf/SWq0Gt9uNiYkJFk6hVZm1Wq3wer0oFousKkwkirSFwWAQoiiy0BV+Cr3VamFnZwfHjh1jjWRqnyXLMoLBYBfZo8o3XR+075Sap9W0ValUYLPZsL29DVnuxEwTwahUKtjc3MTU1BTTpisf8LTdJpMJgUAAW1tbbL/4anyr1YLP50Oj0cCtW7eYFzORuXg8jkql0kUwBEFg0g6Hw9G1/fPz88yeix/08P8XRRETExNYXl7uaaqjgWY4HIbRaEQmk+mJZ6ZmufHxcZbYRk4k9N33+XyMRKVSKeRyuR6LQ6rmOhwOFvASCATYtD9do6R1pGNE1wvtF5EzfmZLq3rJewIHg0GMjo6yZQwGA7LZLKamptBoNBCPx7sa/2RZZtd5JBJh+t1AIMDs16jRjbaZ1ziTjIEnz2azGel0mnlM2+12dqxpm4rFIjY3N5HL5VjstlrKniw/96ENBoMolUqo1Wowm80IBoNIp9NdPrTUDKgGrdCNVCqFK1eusMLJoJ639EyhAdKrnqK3m434+RMR/PyJjnXrZqqMH60k8OMnSVxfT6PWPHgVeS1ZxlpyA//vn23AYhRx+WgA35gP4etzIRwJOg68j4IgwOvzAz4/OkZtHGQZyEc75Di51CHKCSLLh+vIBKBDwHfvdF487IFnJPkkEDn5nDBbXJqrGqQQ9lliSIqHADC43dq9e/dQqVRgMpm6Qh7o50ePHrGqUi6X66qoUeXYarXCYrEM1GzywQcfsOlh+qwPP/yQTQ/TupTWTQR6mNLfk+av80AW8UndjytJJxqyfpXChBbO2zJ42xnH+dL7uCjdhXmAyrAM4AmO4mOcxiqOQIIB1qUU/vbf/hbu3r2rGvVLwTL379/H2toaS4cjbG5uIhKJwOVyMY2ixWLpkTSQ1ZLNZlN1hSACRKl3VPmn5YjICYIAv9+PcrncFRjB9vGZTMFoNCIUCiEUCiEajaoeD0HoJKidOXMGP/zhD5kHKqFQKMBiseCNN97AD37wA0bQ1Pxs4/E4Jicnsbe3pyofkWWZNSGRA4NSGmG32+F2u5FMJhm55KerBaFjC0bVOyWRpYEFaeaJZCmdU0giEAqF2PI0aOMHMkS4lpeXEY1GmcUdLx/wer2sSeb27ds9Ucjvv/8+Ll++jMXFRRSLRXZtKDXXBKryU2KcslJOlVWKElb+Pf1cqVSYzp2CJQiVSgVjY2OYnZ3F1tYW80tWDsDIZuzy5cu4ceMG7t6927PM6Ogo5ufnsbKygng8zsg0LWO1WuFyuRgJG8QTOBaLsUEBf32EQiGcOHECmUwGsVhM9fiQHOHEiROIRqOq2zw2NoaFhQVWWS4UCsjn8z2NyBQX3Wg04Ha7VQsUNpsNpVIJqVQKPp92zwMVOiRJ0o2C7udDO0joxrVr19h1qVdUodmYz1us8EzQgb8TPIK/87UjqDXbuL2ZwY9XkvjRkySeJg5eka23JPz4SRI/ftJpsp702/CNuTC+PhfCW8cDsJtfkHoJAuCd7LxmnweVQZaBwu4zgrz0/N+XVVmupIHNn3ZePLxTQPjUc6IcOQUEjiOZyX3uzr0SQ1I8BIDB7FkajQaLgiW7Fqre0ZRnLpeDyWRCNBrVrKgtLCx0VXm09MsPHz7EtWvXWIWPHs61Wg3Xrl0DALz++us9WlkePLlpNpuMROZaRnxYHsdey6F7XAyQcMIYx6JxG79kuY9zxWuwoL/GSwbwEAv4KS4hJQS6fler1fC9732vK6CEBzmjlMtl7O3tsff5xp29vT3WOU5JdDypIv0oaR75v+fXB4Bp7alaq7Q2oyYoquwpm9poXXzDn9Z0Pb2/t7enOVtQr9dx584d1rSn9pAFOo4A29vbmlINSZKQyWRY5U7Nkq5er3dJRwD07D8tS9eqUndL3x0iZnr73m63mUac18ET8W+320xHS4SG35Z6vd6lO71+/TrzqaXtLhQK+PDDDwEAPp9P1T2CP5bNZpPpRNXIbr1ex9raWo/+W7lcKpXCvXv3EIvFekJA2u02YrEYotEok45QlZqOPc18EGkkmRNtJ/DcOnN3dxdra2vsGPFNdKTdNRgMOHPmDAud0CN+WpUqQRBQKpWQz+dVjyFdA/l8HtFolA0s+X1vtVqs+js3NwebzYaVlZUeaQTFNkciEbY9kiRpfpf7Vdfq9Tqy2SyTO/BR0JVKBTs7O2wmRQ+DhG5Qw7PWMSbddSwWw9OnT1Eqldi6ms0mdnd3u/pWPktYTQb83GwIPzcbwv8NwG6uij//aB1/eXcDy1mgLh28qrmTqeJPbmzhT25swWwQcfGID5enXLg44cD8qEc1GOVTQRA60dGeiV6ynN/hyPKzV3IFaB2epSlDbrvzevJXzzdBNMFsGcWkeQxhz3FU3UeQM45jf2/vc3PugSEpHuIZBrFnsdlsjAzzRILXa9ZqNeRyOd2KWiaTwdLSku5U2+PHj3Hr1q2uBzqvi22327h9+zbm5ub6+hSTXrfzcJGw1A7jejmMFvSrw1NiFn/DvIafN3yEy+2bsA3gQatHhnnE43Hd9Tx+/LiraY0eICQxADp6QbvdDqBDKJTnjI43OQFoNX+RNRaRapJUAGB/Xy6XWeWZCBxf4SRSHI/HdVMhgQ4h3tnRDy/Z2NhgD34tey9qoCIoNawAmJ5aj6iWSqUuza8yCIG3L7PZbCx8gm+2kmWZabv57eHPB9A57yRFURtctFotpFKproqk0rqKKt9kfQigq5osCAIajQZu3LiBX/iFX2Ckh84dvxyFsvSLeVaTzShRq9Xw4MEDtNtt5jXLnzOKZ+YHKMrzQoPeu3fvotFowGq19uiuG40GPvroI6TTabTbbeb0ADyXGFSrVSSTSdbwqwWSGEiSxAbrVMEym80srp587R0OBzsP/AA0mUzio48+QqvVQiQSUbVIu3XrFo4fP9712cptAToJdDabDfl8XrOaTs17eqAGQb0oaGog1MMgoRs0U9HPg3h7exvxeBylUqmncl0ulz+XscJjHitOmjPwTVfgOx/Eel7Gw1QbD5JtbBcPLrNotCX87GkaP3uaBgD4zDJeHzHjV1+fwa+cPwqH5SXQMkHoVG+9U8DcLz5/X5KA3OZzkkyv1BNAGjz4aKBNkJrwVLfhqW4D+Rvs/abRhRXfN7H8jIN81ud+SIqHANB5gPezWwsEAlhZWWHLK/8eAGvi0KuoFQoFbG9vIxwOa061PXnyhD3M1abrgc5D+M6dO6yCqVYJJWJTrVZRFh24UhrBXh/tsMPQxq/4EvjfyR/gjeqPYW/3n3aSATzCPH6Cy7pk+CDgCRm/X3yFhYIB1I4jkSN6mCpJGE391mo1Vkmr1+s954ykM+12m1W2lA99IpRLS0t9K0+DBiZQJVENajdOtal9WZY1K/KEUqkEm83GZiKItJH+GQCzWqNABCWomqck0QR6T9kUqOYJzFeQ1c4rnUe+cqv0KQY6FePNzc2u74/av/ygQXkt0WdpEWYlqtUqS6VTXmuUWKbX9Ad0jmU6nWb7wg9S6NpMp9Nd54IfpNH13mw2kclkukKclOB7KfjzTaCYY9Luq7mr0IxDLpeD2+3umrEhUITx8vIyqtUqpqamUCgUulLmqNGQtOE0oOOt7aiaPj4+3jd6t1gssuuRBtP8MSKtebFYhN/v19R6DhK6QdPg5D2vVlTx+XyIRqNd1X2+cq2s7n9ewF8jJoOIeT8w7zfgf54DcnUJ9/ZruJ9sYa1kRqF+8IS7bEPAB9tNfLC9iv/L959i8Ygf35wP4xvzIRwPO18uSRRFwH+081r41vP3200gvQYkPulUlxOPO6/MBjpPu8ODqVWE2Wr/3ERKD0nxEAz9okVJc0wPCOWNj/dD1auopdPpvtGiSu9drYdyPp9n5EltGVEU0Wy1cafkxY1yEO0+1eHXbDn8nz0/xmLxB3C0c7rLEj7BLH6MN5EUggMtfxDQMVaTPQBgTU92u72nCkiEmf6Wt66jhyL9TA980p/y07V8g1qz2VTVwtI5PuxEN55A8uSO9nsQska6aOU1wkt/xsfHsbe3xyQQhGazyRwNkskkEokEI8h89Y7ihCmFTdmMBqDreNPPStmG0lpQj5D2Ww9JCXhCrwR//uhn5XYPSoj5ddLAgr9G+OhhPdA5oSY15YwUVXOB54NwNRvBQcjEIL0UROL57xe/rfQeET2t9VQqFdZ4HAgE2CCUb7QjGQq5ZSi//0S2+X4OLZTLZXZtqvmHU1W9XC7r9ncMGrqxuLiI27dvaxZVxsfHmX2eVuU6lUr1re6/auhdI16LiL8xZcVJewoOpxGP41VEW248TLWxkZcOTB9bkoxra2lcW0vj//GXSxj32vCN+RC+MR/GW8cCL6eKrAaDCQgvdF48GpVnTX1Lz4ly/DFQ0p8d7Ieq+yhardbnIlJ6SIqH6IJeU0oul0MoFNI1w/f5fKhUKqyqoKyskCcqdenrVR4GAXUua7kP1CUBP2rMIirpe0j6jA38X30f4hcq34cnlxnos1dwFB/iLcSF8EDLfxqoRS8riZUoil26UqqoUVc/TT9TZZhfN1WiLBYLaw5TWk7Jcse+qtFoMI9aPmmMyJbdbh/4vA0CvnkQ6JaO0JTroGEhgPaMAx1PXlPLV2R5O7T9/X3WwU+JguVymUXpUkWd1sF/lppUQO/nQaE1awM8j4DWqs7T+VcGqShlKAcBBarwZJ2q6AeB3gwIDYRpcMJf1zQIslgscLm0u+CBwXopqJlXS14DoMuGT08+4Ha7uz5PWZkm1woKOaKmO76B1mg0olQqIRqNqrpOEJxOJztGgHpaJP2+nz/9IKEbkUhEt6iSSCR69O8EUeyEmzQajb7V/VeNQa4RWZZRKhbx2pgXFy1m/PosUGrIeJRqP5NatFD8FGqE3VwV3725je/e3IbZIGLxiJ+R5GOhgztavDDMdmD8fOfFo5IB4p88I8ufdIhyYmlgJ4yseQxG6fMRKT0kxUP0gKbSlfB4PDh27BjTcpK1Gnm1ulwuzM7OYnNzE6lUit1IiESRQ0UoFGL2RcFgsEfHVygUMDExwXR8ejh16hQ2NjZY/C7w/IFekMz469ox5GVtuYQACf9H64/wu4b/DYFi/88DgKeYxod4C3uC+o3bbrd3dep3fd6nIHP0dwSeEDudTiZl4KuXVNEhMsZbnvHkkAhFMBhkdmP8Q58qVlNTU8jlcmy71SrXLpcLIyMjPY4anxZ+vx+ZTIaRen6/TSYTG4ANAuU+Kytw+/v77Lgpq46SJOHhw4c4d+4cgsEgisUiKpUKu+acTidcLhfC4TCePHmiSYqV76lJDJTLDwJlVZr/rOnpaSwvL/esX3lcqKLIV6b59ZBEph+oksvLdXhiPGg0OVW3teK7LRYLRFHscgWhz6KBusViwfj4ONuXTxtdPzU1BUmSkEgk2ACU/55JkoRwuDMwpvueLD9PQxQEgd33FhYWkMlk+vZu0EBUEATV2bZcLtelX1fD+Pg4jEYjKpUK6z3gzwfZDFKIhp6V2ttvvw0AzKe4WCz2+BQD+kWVRCLRNdjUqrh/3jDINcJbaBKcZgGXx4y4PGZEq23EnbU4VksmbDWcWMsdvIrcaEu4+jSFq09T+Nd/sYRJvw3fnA/jm/Ph/z97fx4fV1be+eOf2vdFVSWV9sW2LHnvttuy3SuYhg4hfBmWBAhhSDJ5zUymmQBhkjBLQnjNLwNZZoEsZBkGQgIhMIFAM6EB06bTmy233e32IsnyIstaa1Ptu+r+/hDP8albdauuWmWpJJ3368WLtlSqe+7+nOd8ns+D4zu8sBjX5ou8JqweYOCRlf8RkrRSbBe4CmnxCkKXfwxj7DocuUVocfcZW9RZEcga0dHpa4qW0iIoFqhGo1nRHVOmmH/wGwwGuFwu7NmzB36/H//0T//ErLL4JX273Y6RkRF4PB4EAgFFGyQS3NcKDuhlY7Va2YuKMkbzRRt+mBlATvESl/DTupfxn4x/j25pDlAhBYu37Mc/xvfjVkm5FbhOp8Pu3bsxPj5e5tZA/28wGLB79268+uqrdbfncDiYbrRaQEIWSKlUihWB8RpRyixRsFwtW0iFO+R1nM1my+zvNJoViytqrOFyuZjNHq8pJrlMNT2p0nGq1+pYr9fD5XKVFaXxmlG1mUf+RSwP+Oj39ALX6/UV+1YsFpFMJnHz5k1Yrday4j7CarWyTmyRyMpKQ7XVC5fLVVbYxmc4+WwggJoFpBaLhWlZlSQWlGnnNa7yAJOOJU1GlbBarTXdJ4C7fsmFQqHquaHfU2OaWjgcDqa35bWntAJCVmSkseUnOXSfUaBCWneldsn1ainomfb000+XOaLQ8afGFADw9NNPs+CPl8NYrVaMjIxAp9PV3V5vby9zIFFabaPmPLUgj2ty45BnuA0GA2w2GxYXF6u6H2g05VZqHo8H/f390Gq1ZR3tPB5Pxd9VS6o4HA6YTCZWa1It464mu7/e0Huv1jnbvXs3XnvtNcVscrFQwIBLhwFXCUZjFotLCVyNSLiZNuFWzorU8uoD2juRDL780m18+aXbMOm1OLHTy4LkXq+1Ebu+NjQaoKUPaOmDZuitwJ4P4bmzZ5FNxtBhSKAlPwdz/CYKuQysNlvTtJQWQbFg1VBwwhccyQMhym7wfyMP7pRuAPo5ZYuUoMCKqrBpaX+i4MWL+R6UFPTDx7VX8DvGv8Ne3FRVMzCn6cCo/S1oH3k/Zn/8YyCvbMmm1+sxMjICh8PBvD0Jk8mEo0ePYs+ePbh27VrNLKfZbGaNC0KhUMXvqXuW3W5n2SR5oxSdTgev18sym9WgDHI6nWYvKXlWVq/XY2lpCQaDAX6/n3n+0jk1mUzMRqqnp4f5Jyths9lYdb1Sq2OHwwGDwYDW1laWyeL9XD0eD+bm5lg2XCkrT/tQr6MbBYikp+azjiQvoJci6Yfp7zKZDO7cucNaF1NXMjk0biokU8ru0zmrFRQbjUaYzWYEg0HFJik+nw82m40FkeQNzG/Pbrcjl8uxhitKba6pNbX8vuY/R1lRpfuWVovqnTOtdqUZBWUk5d0DqT1zKBRiQRY/JpJO6PV6LCws4PLlywgEAmWfCYfDCAQCeOyxx+rWUpBNFHXT4+VI1IyFAkP+MyTjkH+GticP1MnL2Ov14uLFi3U1vN3d3YrXBwDWaKOzsxOzs7NlXs60PQB16zt4KzXaNkksFhYWEI/HVdlpdXd3M/mdXJdP7i9q9msjUDpnNLny+XyYm5urmU3u6OhAOp3GxMQEdDodDrrNuN9bQrGYxJ1ECTcyZixqvbiV0GC5tLo8cq5YYi2sP4kr2NlqWwmQh9twtN8Do37jWzTz91kgFMKc3gt92/0rqw3Cp1iwGZEkifmjKtkXjY2N4dq1a0yLJ3845PN5PPfcc9i9e3dNGyQKPmpRKpXgcDhgs9lgs9kQjcXx3Tt6nM+7q37+kOY6fsvw93hQe0XV/gY0rXje8Bhum/dhuVRC+OrVuhlQSZKQSqUQiURgt9tZppb2LRKJIJPJsJeDUhaUXq5kFyYnmUzCZrPh8OHDbDmWf8lks1l0dXVhcHAQV65cYcEGBSR88JvP55k0gnSG/P5QgOd2uxGPx1mQxWd5wuEw3G43zGYz2tvbmUWTPFOu1d5tKcwv+/NLp+Q4YDQaYTQa0dvbW7VtLi0J16KaLrUaVOgj19TSpAEAK+hSKhKKx+OsjbHX62VBKFloJZNJpFIpHD16lHWFpCVyyuSbzWYcPHgQTz/9dM3xJhIJtLW1sQ5zcqihBDkH0ISGmtyQjpzvvFYrK8/76SpBLbs1Gg0cDkeFlVo2m2XthsnxpJrsw263Y/fu3cxdhQrQaCwulwu7d+/GjRs3kM/nKzqi0TW9tLSE8fFxzM7OQqfTwWw2s33IZrOYnZ3F+fPn8cQTT9Rc9qfnntFoxOHDh5kVntFohMPhYM89ADU/U89ujJ+IqtHwKjmzECaTiXUWpHMgt4lzuVxsYlHPSk1ttzol+P2i+5dvX22xWFTtVzOiJps8PDyMCxcusM/f/VvAbyrAq8tgaKgNJx49iRduhHF6PIAfXwsimFh98Rl11/vfz9+CzajDw4O+nzhatKHdZa7/BfeIeo10mgERFAtUo6YVNOmJ+WpzuR4uGAzCZDLVtGQLBoOqAlCbzQafz4cbd+bxf4NtuJKqDBCGNNP4uP4beIvuvKr9jGg8eF7/CK5q90Cj1ULzk0CBjPlrUSgUcP78eSwsLLDAg142kiRhdnYWdrsdO3bsYNpseRbU4XBg586duHjxIgqFAtMY8hrFTCaDeDyOXC6HcDhckVUsFAoIh8OsGx4VxvFZYDofpVKprCNaNQ0n6Q8zmQycTifS6TQL+KxWK+LxOOx2OyRJgtlsRk9PD+sARphMJrS3tzN7KgBMywrctYijhgXt7e2sbS7tKwWc8Xgcfr8fMzMzZdvgIX10PQs40rrSOOT2ZrQcz08Y5Npsklnw2m0e/t87duyA3W7HSy+9hGAwyI5jW1sbjh8/jmg0WteloVQqYWlpicka5E4PlLF2OBzMOYACELqGyZXA5XLBbrejtbUVoVAIkUiEXTMej4e1ZlcKwGk8fMadd5+gZwB9jloO08oOodPpYLPZ4Pf7YTQaK3TY/ASKpAs0SZFLXqhByMzMDDQaDdP589dsMpksa5dcKpUwOzvLNKIOhwM6nU7Vc486M5JsIJfLMWkLWa3xHd3Onj3LJghWqxWl0or3NGVdSaM7OjrKVkqoI+LIyAj7fS3oPk0kEiwoJsiKzWq1oq+vD4uLi4oZTrfbjWQyqapbHXWQnJmZQTKZhN1uR3d3N7tv5PvFd3qU75eSDlx+3Slta7XU2h61Dq91zuqtABgMBmQyGfT19bFuhlSTY7fbmdRFyqfx0wc68NMHOiBJEq7MxfHstSBOjwdwYXoJq0wiI5VfxvevLOL7V1a88fd2OPHG4VacHG7DfT0t0GmbJyBtBkRQLFANZeto2UwezHm9XvbioayRXLtJy2TkUKH0PZRxqqdhDIfD0Ht78bkfRhHJl7+w+zXz+Jj+H/B27UvQauo/SWJw4jndg7iwPAypqAW4znWkNVUTqM/OzrJl9WrG+1NTU3j88cdx584d5kVKf5vL5dDa2so6kdFxlC9pk6b2+eefx9LSUtWxLC0t4fLlyyxQ4INHkrJQJpt3H+DlAwRplJeXlzE7O1uxHXKeoGYBlN2i7yatLAUjpAPlA1YKdihAbWlpwY0bN3D9+vWK49je3o4DBw7g0qVLikHxapBnx+XXHhVPAdW1vmRvRcvNFPTLjxHJJ2KxGHsB0zGPRqOIxWJ1fZUJ6gom10pT1nlpaQn5fB4jIyN46qmnKtphU7B43333YXp6GgsLCxUtxekaJv/dWvAFdfJ7hYJ1WmWgiZb8M2azGd3d3Sw4peYi/IQwHo/j8uXLZcdB/j10vjKZDPR6Pebm5qoG4NlsFuFwGJOTk4rtsvv6+lAsFhGLxTA/P1/RdKKjo4Ndg4FAALOzs2XFeDdv3kRXVxfLkE9OTrJmKDTRoGdfPp9nWdehoSEMDg6+7qAvFouxa1Xu/kFZ+kwmg66uLiQSiZoa50uXLtWVWORyOUxMTLCAl59Y8QGvmv2qZRFHy+xqtqWWWtvz+XwYHx9Xdc6qQdcmvT+9Xi9cLldVO75wOFwmudNoNNjf5cL+LheefOMuRNN5PDcZwumJAJ6dCCKcWv2z7+p8HFfn4/jT0zfgthrw2O6VAPnRwVa02Co92BuJmvO60YigWKAaWo67c+cOSqVSRYtSCmTJI1UOLWvSf9f6HqBSp1yNs3MF/Mm5q8gW7z5QOxHCr+m/iffo/hl6TX1f1CRsuGA/iefTO1EoaYEqE+fV2EmRBhIAy9BSoEJLynfu3GG6Ur6QplQqIRwOs4cGBYM8fGFKtaIvnsXFRZbVkB9POle8pIL/vfy/efcJOel0GpFIhMlHSM5B2V/6+fXr19kyvdIxJRuqqakpJjGhoIr2fXFxEdPT08xGTgm1jUKAlcwaZS/5bLrNZmMOHtWODR03avVc6xjF43GMj4/jzJkzrPkETXASiQROnz7NHBPqQc4uNAYaB+lO6X7jg28emoQtLy8jFovVbClODXBqQVZsvG0eDwUuNpsN169fr7j3KfBcXl5mK07k/cvrt0nywU/m5FDGnvfo5Y8RafDNZjMmJiZw+fLlivNB7bKPHTuGVCqFxcVFNh4aczqdxs2bN+Hz+djqTKlUKrvv6Vpub29HPp9nwWC1Z59Wq8XMzAzLupK++vVAARZNZuXngwrmANTUVBsMBoyNjdW10JyZmcGZM2eYlpl0x8FgEKdOnQJwN1Nca78oK1vLIi4SiTAZRr1t1aPe9vbs2aPqnN28eZN1anW73RW6671799a146MAWQm31Yi3H+rE2w91olSScGk2htMTAZyeCOK1mSjqvC4riKYL+Parc/j2q3PQaoD7e1twcnilWG9Ph6OhsgY157UZAmMRFAtU43Q62cuRHtgAmG0SdXSSBwwE/3OdTodEIlHze3jk31OSgPP5Trz20t0saSui+Hf6b+PndT+CSVM/iM1oLDhnfAgvFvfDqHWhIJW3DK42bjVQoEKZFT7DWSgUWGahVkvYyclJ5n8p/x4A7HvUQN/Du1PQ/5Nmkw+M5dui7GM9+7NIJAKn04mlpSXmWy33Rabgj4LValkvWhqfnJxk3b2qjfvVV19d9blRQqNZcUZxu90V0hByn6DMmpKchdrdEnIZCh2jCxcuoFAowGq1ss9QMJZOp+u2ySaqdaHjzxkFPC+88ALL9MvHtLy8jOeff76s6Qp9hg9wKUCtNx6+SFZ+zmiycefOHWi1WthsNlbYqNGsOFMUi0VMTk4iGo3WbPJALZApAKV95rPV/ISSJlX8flHAevXqVSZT4ic41C76lVdeYVZs/Dmj5xVJFGiCIS+OoxWRSCQCrVbLMo50XdN3USONaDTakCY4NBGnFQG5VpzvXllPU13Pkqy9vR1XrlxBLpcre6abTCYYDAbW5npwcLBmppv027X0y2NjY5iamlrzttRub2JiQtU5m5iYqPk9s7Oz8Hq9WFhYqFmMp9aWTKvV4FCPG4d63Pjo47sRSubwz9eCOD0RxD9fCyKWWZ0xckkCzt9ewvnbS/jD70+g3WnGG4db8cahNjy0y7emxiFqjnOztPgWQbFANfF4HHq9nmlL+RkzFUrUa1BAkIWY0vfQS4xf1ieykg7/nN+B2Z805HAiiX+j/y5+Sfd9WDX1ixJyMOKC6UGcNx5DelkPjVaqCDDXEmyR9lSe5eV1qolEgrWWlT+4yfZLXvT1esdGATlVmfMNV/R6PSuKoswtH2TxxT9qdK7nz59nRVRKY6Egnb6/WvAIgOn3+MCaMBqNdTOXq4GCLbvdDpvNxn5OATgV/RUKBbYiwn+GLOSqeRTTvykwTCQSZcEeP4bXs1+8fIKnVCrhwoULLKAndxF+ezRJoX/TOOma43XnaqB94rPttP90T8diMWajyAfOJEcg2Qc5fMi/n65l2nelFQf5Nat0jOj88n7YNFa9Xs+as/BFszTuYrHI5AZkFyc/H5IkMbeF69evs4LKateIXq9nmfC1Qu5A/D1c7VjQ5zSa6lZqGk39IjK3242lpSXYbLaq54zqCOo1HFGj356enkYoFFrzttRuLxgMssJApXOWyWQQDodr1smEQiEcPHgQ8Xi8ZjHe6w0KfXYT3nW4G+863I3icgmv3Ini9PhKFnlsXtkNSImFeBZ/N3oHfzd6B0adFsd2eFgWud9nq/8FHGqOs2jzLNh00PJOd3c3K/7gCwU8Hg9mZ2frWi7RC4W+J5FIsKVVKgyidrryl2uoZMXp/E4kJROsyOKXdE/j3+i/C6emfhOHAgy4aBrB83gAGVigKWhgNq9YW5EuVyn4UxMU8vCBBUESBspU1dLoUQaMt+3ij2E9mzE5AwMDTA/JZ486OjqQy+WQyWTY8mC1fTcYDKpacJJFGHUAk+s4dTody4LRdSLfJn+s+SYQfPDUyM55wMoqSC6XQzKZrOggJkkSOjo6kEql2MRPrvHmJ4QUxMs/A9wNFpXGTy9Y+o5q57jW73g0Gg1isViZRIcP+uQTT37yUe3aVYvSvULfT6tN8sJOkkSQXIY/7/yY5WOrdwz475KPk35OqzW8hKhYLLL7jPSqdP/QGIxGI2uAAaDsuucnFjqdjhXf0bKx0WismFyRfzAf0L7eSn2TyQSr1crqN6r5AlutVlVdxOrZ1pFbUL021/Uajqhpu81n5NeyLbXbo0lNrXNGk6F6umu73a7K/k8tSteHXqfF0X4PjvZ78Js/NYz5WAY/ngjimfEAXrgeQjqv/t0BrDQOeW4yhOcmQ/jUU1exw2fDG4fbcFKl5Zua40yrLRuNCIoFqiHvz1o2WbQUTy+TakFRqVRiWZdqUPaFsoj0HdeKPpwp9EKPIn5J9z08qf82fJr6M+Bl6HDJcBjnrG9AOKf/yUutUPZwIx0hvcTkS7+rgV62QPXsFGWg6tkg0ZKtUqCqNoNHdlutra2Ix+MsC0eBYCwWg9lsVtQnUxCn5oHFSz34v6+WeebdSfjsHL/MTQGI/DpqNIODg7h8+XJFgRyw4ihw8OBBnDp1ChqNhhVWUbBEsgl+VaNa4MIv5dM1LodesHQcSRcrD7Do5/WgBg98oEZU0/zKM8Wrvf7p3PGdFnnZA00I8/k8O7/83/Jtk0kbLYeOD12P/P1G+8VfU3RvVytWpeOipD2nY0yTCpfLVdFhjpIFfIEhf//T9avVatHS0oKWlhamz5evkul0OmZtuNaiJLPZzBxFstlsRaEdOffIta1K1JJY0ESu3jOtXsMRNS2V6ZitdVur2Z7b7UYqlVI8Z06ns661Hb0n3W53Q2zJVnN9dLgseP9IL94/0otccRmjtyI4PR7E6YkAboVq12VU42YohZvP38IXnr8Fu0mPh3f5cHK4DW8YbkWbo/J6UnOc6+mp1wsRFAtUI293yT9MSRO1c+dOLC0tsSVbfmZILx9qBzw5OVnhH5pIJBCNRtHX18de/EVJg7OFXtxY9uDdumfxEf030aUJ1x1vCRqMGe/HK87HEdO4EQ6HAeTLMib5fB75fB5tbW0wGAxs5i7XIhaLReY1W49aWVXKJpDnr5I5P//QtNlsVTWsNputbqEdsFJwEovF0NraWqZX4/WACwsLLHiRB0WryRLu3r0bs7OzZRIA+i76Hmo8kUwm2TVE+waAyRgkSSpb+icoULbZbHUL7dSg0WgQiUQUXR+i0SjrQEjHRH7tAytBCOk0gfLlagquaN9TqVTVFsb5fJ5JOKjNOS9noe/yer0so62EVqvFoUOHMDY2plj4Sp+ja5y2xUt9aFIkD6rlaDQrumzKTJJUh7StWq0WZrOZZVurac5plcHlciEYDFbdTrFYREtLCyvsqzaJpXGaTCYWjFd7FtE1VGu/tFotPB4PYrEYk8kQdL+2tbUhHA5XvV5ppcfhcODw4cMsmbC8vMwKB0ljrdPp0N3djXw+j9HR0TUVJblcLnR3d7PsOxWRUrEjrfqtprWuksSiu7ubWf+tpeHIatpuh0KhNW1L7fY6OjoAALdv31Y8Z/39/ZAkSbVeWOk4qmUtRWsmvQ6PDLbikcFW/M7b9+JWKIVnxgP48UQAZ29GkF9W/7wHgGSuiKevLODpKyu1EAe6XHjjcBveONSKQ91uaLXq2qmvRk99LxFBsaACJe9HNdqy4eFhzM7O4vr16xWZTHpptbe3s5uimr4IuPsiSZaMOJ0fwHHNZfy58RvYoVVXhHTdfBBnLSeRNHeuFEbF42XZSj7oJU6cOIEf//jHbNZKv6es3n333Ydz587VzJgajUaW5eWLgIC7tmZGoxEHDhzAyy+/jGg0WpF5MJlMzG6M2i7TOPnld74NtBJarRa7du3CjRs3FM8Z2b9R5rKaxpe8fGu5cFD22WKxVGT4+DHa7XYcPXoUP/7xj5HJZFjwRHZ/RqMRDz74IG7evMmuI7nzg0ajYV3NlCzpgBVZBDWBqDXu8fHxsmMm3//x8XF0d3dDo9Ewz1heJ0xL1Uajkbk4VHP86OjowN69e3H69GmmVeU13kajESdOnMDc3BwCgUDF8SYpBGnwakFBaD2dstFoRFtbG6anp8vuC/4+6erqQj6fZ3681fD5fMzblwJjfizUBpyKRPnJF78tanhBbibyZX/KclJzFAr0gLuWgpRNdrvdWFxcrLotjUaDlpYWNjFWQq/XY9++fTh//rxiM43jx4/jwoULZc89+bba29vL2jyn0+myJjikWx0aGqpbtKWmKIl/XqfTaXg8nopt8RrWtUg1tNrGNBxR846htttr3dZqtges1NQonbPh4WH2mXuhF+aRpMYWrQ34bPhXDw/gXz08gFSuiOevh36iRQ5gMb56OcOl2RguzcbwuR9Nwmsz4rGhFcu3vf276sYOjTg+a0UExYIy6nk/1tOWGQwG1tVM7nmq0+ng9/uh1WoRj8drmpgvLS3hTs6CYjGGLxv+G/Zqb6sa/3XtLjyrewRBtMMCC9p+4phBWSFyFuDHZLFYkMvl0NHRgX379uHVV18te0nqdDrs27ePLbFrNJqq1eFms5kF09QsQL7/VETY0dHBOpuR7pP2/+jRo+ju7sbNmzfLXuqERqNhDQ7sdjtSqZSixMLpdMJms9XVAwIrWRMlzSR1a6MgXQ4tC5IsRmnZnSYZu3fvRiqVYpMM2h61wh4aGsLc3Fzd64jkBEraW8pUkrZSDrlLVNPV0r9piZ1cVwKBACuE1GhWisM8Hg+b8ABgY6ZjQGOmbmzJZLLmvo+NjSm+IDQaDauGr8Xy8jImJydVvWgMBgM6OzsVx22xWJhuWt5YR6PRoLW1lbUx7urqQiwWq6gVcDqdSCaT0Ol07FqTt/o1m80ssO3v70cgECjzBbbZbGhra2MTVco8K32PxWJBb28v5ufnK67r9vZ2JvEwGAxVtfu0auRwOPD4448rNtPw+/1lTWuqbctsNrMVG/5+pKVv/hnaqKKketuibGIj/GMb0XCk2pir6W5pTGvdltrtAVB1HBupF1biXhat2Ux6PLGvHU/sa4ckrTQO+fFEAM+MB/DKndVbvoVTeXzzwiy+eWEWOq0GhzqdGLSb0VdIwmdIwGBo/PFZKyIo3mbU6gA0MTGhyvuxlrZscXGRBWlkq8YvjZdKK93TjEYjW86Xa5NLpRImJi7hl0pP4ajhmqr9uqPvx0uWNyNoHlh5if1EehEIBNDa2soCYWqgIT8mhUIB165dw+3bt6su6d++fRutra0ss+fz+RCPx9lLn7oRkc1VLpdjumhe9kDZwFQqhUAggLa2Nng8HvY9er0egUAAXq8X0WiUBazyynH6DAUBcu0xFcdRoEYNQS5cuIBoNAq3243Dhw9XFHYpaSYNBgO6urpw584dAHcz1rR8bTQa0d/fj3w+j2w2y5aq+QkIHfdsNosbN24gnU6jv7+/LNvH25LVu45isRhSqRQrTOQDEdp3GovBYIDD4ahoFMNr1msF8hSokaSBvJ9JbhIIBNDT04NisYidO3eiv78fMzMz7P7o7u6GTqdDOBxmDWv6+vpYYR9NcNLpNObm5th5dzgcZQ1QyAs5FovV1frSsiSv0ZefN5qkZLNZ7Ny5Ezt27MCdO3eY9VxPTw80Gg1r2EKNF65fv84+s2vXLmi1WoTDYdjtdiQSCXR1dbGOZSaTCR6PB5FIBF6vF4lEAqVSCW1tbRXabDo/kiTB7/fD7/cjFAqx76EGCaFQCE6nE7FYDD6fr+IZEo/H4XK52LXf3d3NfHupK6JGs9JYg46FxWKpaE3NS1SGhoawa9cujI+Ps653w8PD0Ol0zE+7t7cXPT09VbfFN2ao9wxdTVFSvQxvvda6jfSPXWvDEbVjbuS21G6vUZ9ZK+tVtKbR3G0c8uGTg4ik8nj2WgDPjAfx7EQA8ax6734AWC5JuDCTwEqjawPabBY8OODE24cH4HB71jTWRiKC4m1ErSzw4OAgRkdHVXs/KmmiqOgon8/D4Sg3/5YkCYlEAsViETabraqJuSF6A5ZLf41PLF8EVDzbcr59eM33L/DcrA7LpRIkTmNKLzu+CEbu2kAFG1qtFjdu3GCBmLyDWiqVwpUrV+B2uxGLxVgLXcokZrNZlhGjNr18QEjbpGM3PT3Nsn3yQK1QKLDmHtV8TykAjMfjLHsnD5AoAKIM5OjoaEXHrjNnzuD48eN44IEHyvSA1TSTra2tOHDgAObn51lxCb88rNfrMTAwgHQ6zaQWVCjJ65NpYnDjxg1oNJoKCyNJWmkDPj09zfyMla4jmoDwxU10HPiGH3a7nbXXpd+T8T5lFfnmFvJsIa9PjUajZU0z6BqiyRKwsoRK10ipVEI2m8Xc3BycTid0Oh2mp6exuLiIRCKBbDbLjk8ymYTD4UA2m2X2XhQ00pjp3KltSkKZcr1ez1pV8+eDgkCdbqWdcTweZ5M7apNMS53ASgacz97mcjlcuXIFbW1tMJvN2L17N1566SW88sorZffQ7du3WUaIuvaRHSM5NtDkjIqWaDz8/UFtxg0GA+6//348//zzCAaDbH9oImCz2XD48GFMTU2x50xbW1vZsclmszCZTDAajWUWhfQ9dIxNJhMcDkfVbGokEsHw8HBFIVG1bckLiZSeoaspSlKb4VXaVqOX4oHajTlWgxrdbaO2pXZ7jfrMWtioojWPzYh33t+Nd96/Yvl2YTqKZ8YDOD0ewMRi/doWOYFUEf94OYJ/vBzBgQ4bnvrIGxo63teLCIq3CfWywMFgEJFIpCHej0ClVpiHWr3SUqJGo4ElPYuOya+gd+kFVfsTgBfPG9+Iw2/7L7h+5gyMpjB7UfNjoH7zwF2LJX6JnLJwRqMRS0tL7EXOf4YszEKhEPbs2VO2nC4vuPD7/az4TekYlEqlsvbW1TokTUxMlFn98PtF46POb0rL6BSYv/baa1U7qFHHLgCq9ICBQAAtLS1sGZwmVna7HTqdDoFAAH6/HxqNhgXO8mIzCpiz2WyZtpw/ZySfIV2mEhqNpmzyQRMO2hYAdp3zATF/HAuFAtxuN5NDVPsMfTdNqOTHm4LzpaUluN1u3L59m72U+PNKBaRzc3Nl3droM5lMBtlslgXKFJTJr9fV0NHRwbqRyVdISKNrMBiY/6tS4evQ0BDS6TQmJiZYkE26+VQqhampKQwPD7MmFXSv8eOORCIoFAqs+IsmXLx0SqvVoq+vj22LMrY0yYrH42w83d3d8Hq9LHNG14ter4fX60VXVxeSyWTdoi3K9NMEmc8ULy8vo7W1FSaTqWY2dWRkpGGFRGqLkhpRjLeZ/GMFKzRD0Zpep8XIgAcjAx584q3DmFlK4/REEKfHA3jxRgjZwuqK9Tr1SQSDwaaQUIigeBtQKpXKssAUSJD9TzQaxeXLl+suyfDej/l8Hs888wwikQg8Hg9OnjzJAinSuaZSqYqlcZPJBJvNhr6+Ply/fh2J2QkcXPoeesP/DC3q30gRuPBjnMBV7V7YrS7sTqVYoRVV5MuXY6PRKMtsUlaR4LNmfDW7fGJAkghaCk2n07DZbGVL6FarFT09PRgfH2dZSnlmjjK4sViM+dtSFtNgMDCtJVXEm81mdgz5ZW+SPfCNMOj7+cKZbDaL8+fPs45dxWKRBSHUPOXMmTN48sknAQBnzpxhL0HKOh0/fhx+vx9Xr15FW1sb9Hp9xfJwoVBAKBRCT09PVQssug6Bu3ZYlLmUL33TdUT7ThIA/jqi5gfx+F1LPv5Y8+eNtMTVrNxKpRLi8Th8Pp+i2wEAtvxfKpXYceQlDblcDsFgkNlA0T1G540vWAwEAqxbG2X9tVotc0qg4knKtPPQNUzHsZ77hN1uR2trK+bn5xXtzVpbWysCb/nqgyRJbP8BlGmh6ToPh8M4e/YsisUi2tra2P2v1+ths9kQi8UwOjqKt7zlLUxzTLZ8NFl1OBwYHh7GhQsX2D3HO4zQ8QZWih8NBgMOHz5cIUMJh8OYmJhgziu0wkHXkMlkgtPpxN69e9He3o7vf//77NlG54scZ0ZGRnDt2jXmCkOTRbo/QqFQ2bYCgQBrzkIrBTabraKQqFgsVpUzqSn+4ovxlMbEZ3iVJBYb5R+7lqK+Zt7WeqDm+ljvorXuFis+eLwPHzzeh2xhGS/dDOP0+IoWeWapfjOiXZas6GgnWD9mZmYQiUSY1k6uvSSNKwBV3o/f+MY3MDY2VvbSHB0dxZ49e/DmN78ZNpsN8XicVY8TGo0GPp8PNpsNHS4T2pLfg/PG30Mv1V8KjkgOvKgZwSvYj5JGB5PRxIJTWmqutm8UoJG2lrJwPLS/FIRUy5RTQY5er2dSE7kM5dChQ6wFMAWt8v23WCzsuwqFwk9s4lbIZrNIJBIsw0h6T7nfM/991Zb8+SCZsqS0PC9Hp9MhmUxifHy8ouMZfQdwV8dGAQZf/DQ/P4+Ojg62PavVyiYn8qCNtJt6vb7q8rjVamUPeaPRiMXFxapWaS0tLbDb7RVZkmrHggIzpUIquuZbWlqqOlm0tLTAarUiGo1Co9FUWHjR9+TzeSQSCbS1tSEQCCAajZYV47W1tbFrVKvVMqkJf2zIGo0PFvlgn65NuQyoGnQ+2tvbWVOVal69pGmncVNQzo87FAqx/eelGxRAGgwGRCIRdt3Oz8+XjY9e1qQzbmtrw82bN8vkPHa7HTt37mSrNiQTqaZfDwQCrLiTJhQ0YaIC01AohAMHDgAAbt68WXGs77vvPpaZIrtFuQbebrdDr9cz+687d+5UvV5pW/RsWFhYKHs28NsCgGeeeaai0PL06dM4evQoTp48qaqgORQKwWg01hwTTbjHxsYwPz/PJg4dHR3Ys2fPhizFN6Korxm3tZ6oLQ7cCMwGHd44tNL57lP/n4TrgST+36u38dTLt3ArqUVJVgrhMAD7OmxNsyIhguJtQDKZLLNBkvv00kvO4XAwmygl78czZ85gbGysYhuSJOHq1auQJAnpdLpq1k2SJEQDcziefx6OsQ9Dm6/v+RuVbPiRdALj2j0oau5mM3jZgkajYVkn+b5lMhlmA1XNMQK4qy3kM8nyYIuWZqminwrL+GzQ5OQkdu7ciWKxyLJy8u9Jp9OwWCxlWlE5VNhmMBjKur8RFCRTwxQlqskB5FDgev36dVYYZrfb2VJsKBTCqVOn8OCDDyKVSmFxcZEFQbTv6XQat27dgt/vBwCWAVYaE+nU6y3Xk+66GktLSxXnqdZxoGucz9jTzzUaDbsGPB4PC8RIK0xdtORBpfz7tVota9Gr1+vhdrvLVgfi8XjZdqtltek6I21xtbbCBoMBFotF0VeZHxd9Pzk18Bpv2p/l5WXWmIBWj/jtxeNxdo3zRYny/SfLQXm7cNp/mvBdu3aNyYNcLleZHeG5c+dQKpUQDAbZxEteZEotdWlliJciUVDo8XhQLBbx3HPP4eLFi2xs/HheffVVNoFLJBJwOBwVxyeRSODixYvs+BSLxQpZDGWCFxYWcP36dZhMJvT397Nt5XI5TE5OwuPxoLW1Fc888wxefPFFdo3xz5AXX3wRAFhgXK+gmbLxtcZ06dIl5hhC+xYOh7G4uIjHHntsXZfiG1nU10zb2gjWo6hvrWg0Ggz6HXj/fa3oio/B4vTg6pKEi4FlvBYqIpEH9rfqYDYZkUomRUc7wfpgs9nYi4qCP+Cu5pYuxP379+PixYuKutLDhw/jH/7hH2pua2xsrGwplrallZZxv/QaHsMZ2KP1WzKnJBP+bvlxTOkGYdBVr7jL5XLsRV8LWiasBeltC4UCy9TxL1CNRsO6TGWzWXi93rLqevr31NQUC2SrZV0pWK+X5aOMK31XtSBTbevlekiSxAJipSLLS5cusSIzcm0A7mpt0+k04vE40+fScrg8MylJKw05KIBWykzzTg9KUOay3r7x303j5idAFPwtLy/D4XBUuG+Q3rieBVqpVGISHLvdztoWU3acMrC8hEcehFDQ4na7YTAYFC3J8vl81e57PLQfmUymzP6Qz8xSV0NyUiHbPQrGLRYLa/rAF4vy8AE2HSMKiPl9pGt/cnKyTMpFKzAU6FMQSis81YpM6btmZmaYTp10x4lEAul0Gg6HAzdu3GC/lwe8xWIRo6OjcDqdbIJN46HtJpNJzM3NsQkxX/Sp1+vLro/bt28zOQNJDoxGY5mcwe1249y5cxUFtLS9QqGAc+fO4dFHH2WToVoFzWQ1yctw+GttbGwMc3NzFRr3XC6Hubk5XLhwAYcPH16Xpfh7UdTXDNvaSO51UV+joBUJA4o46jfioLuI/ICE2YwONrNBdLQTrC8Oh4Mt7yplQY1GI/bu3YvW1lZF78ebN2/WzTwCd9vFAoBUKmEfJnASL8CDWN2/zUs6fGX5cTxjegv6NXMwaJS3VyqVWFETOVjI/UqpXayagIYy5RQA0PfQy8/n87FGEC+//HKFVKO9vZ0VoPHHl5AXgdUjlUoxG7FqfseNnFXHYjF4vV7FIstgMMhe5ErZy2KxiJs3b7JA0GQyVWiqqeNbMpms6VNN3aPqoeZ6rPd5/rxQ4M8XW60GyrxS4w3+GiIdNR03Ota8NIIyxWRVuH///qp6WbWteaPRKIrFIrxeb1X7Q0mSMDc3xzKa5JlNaLValsnn97EeSqsUlH0kiVU1KVcsFmPHji+epPFQsE/XERXT8t9DHuH5fJ4Fw/z5oONNEwKn06k4nnQ6XbF6JocKLSVJwqVLl8rGw0tQXnjhhapFqLRvZIF44cIFjIyM1D3OlMmngluSidFkfm5ujgXK1QLnqakpHDt2bNP7627ktgT1oeLAqakpVlRM12vJYkHwJ10BRUc7wbpQKBTg8XiY3ye/9E1V6bRsXMv78ezZs6q3qdFoMFC6hTfhOXSgdsYPAEqSBt8qPYzPFt+DR4Y6cTQ+gaWl+gFPLBZj2lLK9tLLSJKkMglCPUhTmcvlKoI+k8mEffv24ZVXXmH6TD6bTFkri8XCvq/aBGQ1lEoluN1uuN1u1qCAMmoAWOe0RkDHqhoUqABgY6nW4COVSrECPTom8v2n7HE+n4fT6VQM1KamphqyX6RDpSBBvs/U4EOn0zErNJ58Pg+DwaA6g0HL4Lw3NX0P7SPfKEKeBSXNLPnzhsNhOJ1OOBwO5PN5hMNhWK1W1eOhDAwVcpK8gzTbFDTSi4rPYNOEmfyIeQ37WigWi2xb1aRcfEaa96amY0T/5jPh5OJB/9ZqtWVFqHwwTN9DkOQAQNUW8ABYrQCtmsk7UOr1ekSjUTap4F1F0uk0pqenWXApP+88dC7qSWPoHNIkjr/P6BlPnQytVqtiwJ/JZBAKhTA4OLhl/HVf77YkaWsV5DUTGs2K/eZrr73GugCSjJDqneT2nBuFCIq3AVREZLFYKpZjrVYr8xilF62S9yNV1tejQ1rE48vPYQemVX3+B8tH8EfFn8OisRcfHjGhx1LAdF7dS59m+Xq9HouLixUZGq/Xq/pF3tnZiYMHD7JMOWU8KVPu8/lw6tQpttzLv9g0Gk3Zy57P/BH00lY7HlpOJccOnmw2q6rYSi38tuRQYEXjVmrwQXpUsrKian+CL7IjqYrcpxoAW3ZuFLSSQJpQPjCgYIeCsWoPZfKwVQMFUXJ9On0v7Rs5f1TrxKbRrLSw3rlzp2L2bnFxUZX7RFtbG4xGI65du4ZkMllxf9jtdnR1dbFJCK8DpgwrAFUTS/q7WsEzL0lSknKR1rZaAEv3HE3oeQcPkluQpIrOWbWMM/+9dO9S4M+PhwJup9PJOtJVW9nI5XK4c+cOlpeXy76HVpkymQyWlpYwMDDAro1qgTGtFNTLXvKNTpTkLFTEVyvg54/zvV6KX8+iPn5bVBPAT7zl29qqBXnNgiSt2B46nU5Wh0KTV1qhDAQC2L1794YHxiIo3gbwvoZKy7E+n6/u0sXDDz+MS5cuKf6+RYriJF7AfkyoGtfZ0jB+v/A+XJB2Y9CtxafuM8Fl0iAYjMPv92N+fr7ud/T29iKXy7GirZaWFva7QqHAvHMDgYBihoZeRJ2dnRgYGFDMlF++fJkFTtWW5PiXHW2LspR85o2cEOrh9/sRj8cVCx89Hg8WFxfrfo8aWlpa6hZZStKK7ZY8+0IZtNbWVhw+fBgXLlxghZZKjUB6e3uxuLioWNwzMDCAUChUd9x8AFYtK09SBNqu3NqNOp+RPpeCUv6cyYP7WvCaVLnkgKQVLS0tSCaTaG1trSjqI+9uuuaUsnfVgrxqx6azs5NlA+U+1dQ8xOfzseuazhe/ysF7d9e6bmniRI1T5N/DZ+ZrFbTS5IDkW/z1RseQJjN0f8n3m8ZK51Bp1YZqBShI5OUtJDUwm83weDyIRqPo6ekpy/objUaEQiFYrVZWzFcNCtJ7e3uZ9V41vTRZ9R0+fLjmuZUkiU1UaOWI30fShQOoGfCTO8Z6sJ7+urWW6y0WC3Tccv1WL8hrBkjOQpN0+SQll8s1jZxl9f0QBRuCJEmIRqPMokpJr1ftMxrNiq+h1WpFOBxm+jN+OVZNMYXX6626HGWV0nir9AyexJdUBcRXS334xfxv4r3538YFaTceaS/hV4cL0OQSCAaDsFqt8Pv9dfWccmkEvZDpf7T/RqMRVqsVAMqKq/jiKZvNxl4upDnzer1wOp3suCQSibLgV/499DsqROO9eqmwh2bGfPBejba2Njz88MMwmUxYWlpCPB5HIpFAPB7H0tISTCYTBgcHq3rv8lSzmJOj1+tx3333sW2lUilks1mkfuIBbTKZcOzYMRw/fhx6vR4LCwsIBoMIh8MIBoNYWFiAXq/HyMhImU6Wz5jzwQkAdj1S4SLJDuj8DwwM1M0W813kan2G9o08jek8x+NxmEwm7Ny5k9mTyYvJ+O58alCjtx0YGGAd2yggJu9qapRS75yR7KEWpJmdmJiATqdjTi0ULFLDmenplRUduq7pv2l/6Lqud6z5FQ3+XPP/bbfb4Xa7mbczSSby+TyTLfl8PvT09LAsnryIj9wd9Ho9WwKn80f7TMWe1CKcstMUePKrHi6XizlP0FgoIHY4HGhpaUFvby+sViubqNHzhALitrY2NtEiTTo9F0iiRisqR48eZY1CaFJE/63T6XD06FE2OSmVSpiensbVq1cxPT3NjgFN4kiKQ+cbQJlsgI4JPfNpTHT98Nn6ew3/HlK67/n3kJp3Xq1tkQViJBJhKyMajQaRSATxeJx1HeQL8kwmEzsura2tSKfTGB8fb4hsaKNYy3FsFLychSaidrudJSGMRiPz0N9oRKZ4E6BmaafeZ1pbW9HW1lbR6tdut+P48eOqZsKzs7OsoYIkSTBIBZzAeTyIczCh/vLydKkV/734s/hO6UFI0MKo0+Dt7Qn0lhYwdbPS79dsNlf1ugXuFgABQCaTgdvtxsLCQlkxIWkzC4UC2trasLCwUOEdDKwESZ2dncxdQsnTk6rUq2lUKWCgTCBlROTa2/b2drhcLhw6dAjf/e53FX1x3/Oe96C1tRVzc3M4d+4c88bVaFbaNu/Zswe7du3C5cuXkUqlqi7vkxaWpAxKx5H2X6tdaf1Muki6Po4ePYqhoSEEg0E4HA5W9U7o9Xo4HA54PB7mYkFNSHh5ANmC5XI55HK5msU96XS6bClfDr88TcEM/znKehqNRnR2dqKlpUWxgFSn0+GVV16B1WplhYD8mEl/SOdeye+Y76JXLBbLjhEV7UmShM7OTrhcrprHGqh9T8uDIvl46Hq8du0aIpEIHA4Ha2csl73QZI/GV+26JikRFaPKJQ0UJFJAUe3lRkvabrcbZrMZCwsLZT69BoMBbW1t7P4olUpYXFysKFj0+/04ePAgKxCkSbD8+tDpdGhra4PNZsPi4mJZMEzdJylo9nq9rKEITVQcDgf7fXt7O7xer+L1mslkWBae76BI1xBdH3a7HSdPnkQ6ncarr75a9nwj7+STJ08CWOlCWs0PnSZNVMxKQbz8ms1kMrDZbDCZTMzPXX7OXC4Xk/ysB2r9ddcqZ1C7XE8FkFu1IK9ZZCEb1Zr69SCC4iZHzdIOgLqfiUQiOHfuHAqFQpk3aDabxblz5+ByudjLWAnSpnlbXNgRewEPF5+HA6mafwMAIcmJPy6+E19dfhMKP7nk/HY9fnFXAR4NYDZX+v0+8MADNV0j6HcejwdLS0us+I1m+pQRWlxchM/nQ0tLS5lNGJ/F1Gq1aGlpQT6fx7PPPqvo6fnII4/AbrczqyKgfMkynU7D6XRix44dCAQCeOCBB8ps26grms/nw8DAAJ544gmcOXMGc3Nz7KXX2dnJJikTExMYGxtj2le+8G9sbAydnZ2seyAFNHINq81mY8v/FFzwn6HCr1QqhUAgwFov8/KBQCCAQCCACxcusEp9+Wfi8TjOnz+Pnp4eLC8vw+12w+l0Mh2vXq+H1WplWYtkMone3l5FecDk5CQAsIyXvEiIXwWgiQIF/hSYUSW+0WhEb2+voixmaWmJFX1U00snEomyLKvdbq/oaJfNZmE0GlkWTv6CJR0nnUd6Ibe3t5dd04FAgElPat3T1FmRAiP5mMlBhc49ZWjkrZ6NRiObYGazWcXrmvaf/Jzln6GXHbUep3uLPkPHpVgswmw2Y3FxEVartUxeRNny9vZ2DAwMwOFwVExQOzs7MTw8XLYfSpMmuq+1Wi0GBgZYEG4ymdDe3o5IJIKOjg5IkoSFhQVFaQQt6Ws0GsXrlZ5FwWAQLpeLZWRplYSXxdD5HRwcZMWdJBkBVp75kUgEp06dQi6Xg81mY+c/GAzi1KlTOHHiBCwWCwqFAgv85FZyZrMZLpeLaaGVAv71DkTq+es2Qs6gdrk+HA5vSEe/9aCZZCHN0JpaLSIobmIkqb7XIjXSqPeZqakp5g3Ka1vpgTk6OorBwcGaS7J2mw07C+N4OHEKXims+DkiJZnwV8tvw18V34YU7roy9JvS+LX7HCim0/D5WtmLyGQyweFwIBQK4eLFi2XZH7n2Drhbxb60tFRR4ELHL5PJMN9lWhKlQIoCTLJZO3/+fE1Pz1dffRXHjx/H6dOnkU6ny/SZpEc8fvw4BgYGkEwmEYlEynw/I5EIWyIMhUKYnJxky/f8vk1OTsLtdle05uZfetFoFOfOnYPb7WZ+vhTg0JItsFKISEu+FCjy26KsIHXF8nq9VTXnr776KqampqDV3m2Ywh/nRCKBqakpdHd3Q6fTsWCYt4ril5GpaLNWcQ9lMkk7StDyv8FggMPhQCaTYS8tPkizWq1oaWlhwQZlfkwmU9mysdvtRn9/PyYmJpBKpWA2m1mxIGXod+3ahXQ6jYmJCVbdr9frWUGTRqNBX18fZmdny1wP+GNE+zQzM4N0Os38huVBGC3X1rqnaQJIGmm5Wwp9vqWlhS3V18rQDA8P49VXX2Wtgunap4Dh/vvvx+XLl9m+yLW3/PfRfsoLUWkSTuOjgJ6/z/h7vlbwtLCwUDYpkV/XNPHt7e3FjRs3WLbc6XRCkqSyexFYkdIEg0FWpEf2dNXaM1dDq9ViZGQEp06dYjIYOu409pGREWg0GvZMl1fbS5JU9XlN+8b7hl+5cgW9vb2YnJxEJpNhv+NdRHbs2MEaeNQL+NcbpftezTtPjb9wteV6Hgp2JUnakAymJN1bp4tGHcdGQdKZZmpNrYQIipsY3msRQJmpvslkgtPpZMVo1IWKh4KA6elphEIhpquUZ9SMRiMikQhmZmbQ29tb/YadeRk9p/4LejNn6o67IOnwd8sn8bniuxBC+QP3iDmIN/hSyCdXCkqmp6eZty8FSy6Xi5ny80u+lFGiYp3l5WXcvHmTBbu8xpeOBenpAoEA05LyWUfKXtJSLC0r8svzlC0kT08ATIZC2jyn04njx48zb9FaS4Q+nw/PP/88lpaW2NIenQ8K3mnZtFZr7lAoxLLOcucN0jnSdVBt38m+jrKlpVIJly9fruqxOj8/j1QqVaax5q8z8j2lQHp+fr6iuImCx46ODnR3d9e8higTCtzVg/LfR9eBx+PBjRs3WOaWXwHIZrNoaWlhxTS1lhGPHDmCZDLJOoXxy/VdXV04cuQIgJXJJ8liCDq3R44cwfT0dE3JR6FQYOdVqT0vWe3VuqdTqRTcbjcrfJSf10KhwAofJyYmEAwGaxZRvvnNb4bBYKhoPWwymXD06FEcOnQI4+PjbDLBT3bIRg8Ac5WoJsMg6Uk8Hq/pUZ3JZOouV9PEw+Fw1JQr2Gw21nqZWk/zMi3KlA0ODuLs2bOYm5tj14fX6y1rz1zvGhoaGkIsFsNLL72EaDRaVRYTjUbrLtfT89pms1UkKeg6iUQiOHjwIFKpFNPn0nWn1WrR2dmJBx54AMBKwB8KheB0OlkHP9JCyzW8G21J1ih/YbXL9T6fb90zmOshaWhGn+Zmbk3NI4LiJoZmu7VamVJmptbyD+kkqcKaf2FTRlCn0yGZTFbcsK7lMO6PfAetgReg5vH43eVj+KPiz2FK6ij7uQHLeMR4CwO6OLQaD1KpFMLhMNLpdNl4MpkMUqkVSYYkSSz7KV8eJqkF6UMpKyzXVvL2YC0tLexlyX9XsVhkvqBKgQgFfaFQCCMjIzhy5AjGx8eZnGB4eLgsm1kryxWNRtkyPmX8+BatWq2WZT9rteamJemuri60tbUhkUiw8+lwOADc1YFT8xaSHtCSLwUQVMzHd9qioOnOnTuw2Wx1CzRIkkETDSUNMz/pqPVdVOBGQS5BwS+v36xWIEYrAMFgEKOjo3WXEQ8cOMBalNPLqqWlBQcOHGAP7CeeeIIVPdGx7u3txd69e1mjDABlVld0LVNmnjyfldrzUiaPzpV86ZeyXPv378e5c+eY7IGXRFmtVoyMjLACyFOnTil2qhwZGWH3Ym9vb8X9kU6nsbi4CLvdzrLnfAFpPp9nAVwmk2H3He03HQeauJRKpZoe1eFwGLlcrmbwQMeHvku+kkINN1KpFG7cuKHYlp2cFy5dusR045RdTCQSuHTpEvuMGhkbNROie4Cu49u3b2NgYIAV3NZ7XtN9qPQZ2r83vOENijUQdM2uh4a3UTTKy1jtcr3b7V7XDOZ6SRrW0xN6NWyG1tQiKG5iaHm0VitTKqSo5cdIlZ30wpY/IGg5uFAosBvWZ9VgMPgtdM0/Da1U347qTGkPPl14Py5Kuyp+59Zk8CbzTfiMy1he1rCiDz67wetcyRWAlh/lLgS0ZEvFMtevX2fBozxTXCqVKgIU3paLD2TVBn302VQqhUQiUdUWij5fbRaezWZZZtZqtbIAjoreKHtGbhWU+aOXvsFgYPtLv6v28OObNJBuki+ytFgsTFc4MzPDgjSSlNC2c7kcUqkUrFYrMpkMC9D545bJZJjsIBwOs+BDjlarRTgcRjQarenAQdlDmgDxmWIKfCRJKrPtk0+IgJVJwSuvvMKkIRRwUWvucDjM5AoUGNHqgDwwooLVBx98EJIkYWlpCS0tLXjwwQdhMBgwOzsLSZJgMpnKpCkAmLVYoVBgEzm73V7RlCWZTKJYLLKOb6S/lq+k6PV67N69Gy6XC2fPnkUoFGKfoYwM1QjQ/585c4Zlh/R6Pfvc7t278fzzzyOdTjNNOX9Mg8EgszykfZJf7yRfokCerl36LF0LRqORPa+Umono9Xokk0mMjY0xOQv5Ec/NzSEWi2HPnj1oaWlBNBpFKpWq0C9TQR9l4+VSBafTyaQK6XQas7OzzKGDn1zMzs7i/PnzsFqtrIUzTSgocCTJC7WEpsJTufzq/PnzOHbsWJl/LnWjMxqNcDqdyOfzFc8++SSF/IXtdruqIKO1tRVer7eqnh4oD9RIWkT31usJ1NaScW5UQdZqluvXK4O5npKG1fo0rye1JHPNgAiKmxin04lisVizlanNZkNHRwdri1vNj7G1tRW3bt0CcLcAhaCsW7FYxPz8PLLJGI7kzqBv4v9Cv5yuO8ZrpS58uvjzOF26D6iSS+7TRvCIcQoGlJDPg2VmyYZIKXAyGAxoaWlBKBSqyG7TPnR0dODw4cM4e/Yskslk2Wfov0ullfbNpJ3m7dooi0OZHQB1gz6v14tnnnmmYpn59OnTOHr0KKscrwVlenU6XVVpBGlVSVMrn83TZMJqtaK1tZW97Ko1ZhgcHEQqlcLY2BgLVCi4z+fzmJ+fR1dXFyvMomJK+fnQaDRMRkHXI68plSQJ/f39yOfzLNtNL3Re9iJJEsu41wqKeRcRedts3saMt5qSB2qlUgmJRAILCwuQJIlJbfhj3d7ezgqb6gVGTzzxBE6fPl1x7s+ePYujR4+ip6eHBfF0jPnJHl94lc1mq7ZUJlmUyWTCzZs3yxwT6PqMRCLYu3cvXC4XCoUCy4BSENLb21vVe1bpRatmqTWRSCCTySCbzZYVbdLns9ks3G43c3mhFQ3+PtNoNPD7/ejt7cX169cVr9ndu3djdnaWTRwjkUjZClk+n8fs7Cy6uroQi8WY+wp/Xt1uN3w+HxKJRM39mpubQzgchkajKdPK6/V62Gw2JBIJ3LhxA16vt67kJRQKseeufKJLLZWpCdC1a9eYTzSN22w2w+FwYHBwkBVd8m4ndByXl5fh9/uZBKlekFEtC3z79m0m5RofH2d1GeFwuOz9kcvlVhWorTXj3MiCrNUEu+uRwVxPSQPv01xNoqfVapumrXKzIYLiJoaWs0sl5VamFPSRVrha+0R6uFDgwL+I6YWrAWC+9h08lvw+LPn6TRPCkhOfKb4P/7D8KEpV7K41kHBEP4v9+gXw9z8fJNVyligUCvD5fMwJAkDZPkiShIGBgTKpRTV4nXAoFGJZJDqOJC0h265r166x7BQfGFHQ98orr+DFF19kgRm/HPviiy8CQN3AmAr9KMssl0ZQAK6kTeX3i7TFSo0ZDhw4wLKjvByFlr1pIqVkfUfno1Qqobe3l0l5aBLCB5dHjhzB7OxsWfMGeQMLYCVTT+et1jHKZDKKdlEUDCs1k6DgmYIqWvKnDDAd65mZGbS0tLBAVikwmpqawve+9z1cuHCBNWmg7CSd+8OHD8NsNpcV8NBnyJGBfHzpmgLKWyqT9IHkM9XOGRWY8rKQ1tZWloleWFhAPB5nGb6JiQnmZGC329nnQqEQTp06haNHj7KlViW5RjgcRjabZcedP47ktZvL5TA0NISFhQWWCaZ7iK5PylrXumYHBwcxOztbU17EB8XyZeJisYhYLMb+pp4MIZPJKAYqFosFsVisruRFo9GoaqkcDodhMpmq7n8ymUQul8OBAwcwPDy8kqT4iasJfYacZoaGhmoWRRP1luv37NmjKOWiDPzMzIyqQK0R0oDVZHjVsJpg915nMNdT0kBJDGqrzK8o0zXYLG2Vmw0RFDcxpJmt1cqUCn9q+TEuLS2xh7PczxUAdurmcbL4DDoj9bujZWHEl4s/hf9RfCeyqL70YkIBbzDeRKcuUfX3SsEXD3WjI7spypZQtblOp8Ps7CyGhoaqZjd5UqkUC1AoSKHAjaydMpkM3vSmNyGVSikWWx06dAhf/vKXy3S3wN0GGYVCAefOncOjjz5aYX1VdnxkLhDVoGxiLRKJBG7dugWdTlcmkaHga3l5GZcvX0Y+n4fZbK7I3lHgTYFFLYrFIlv6phUIyjzQz4G7TQ2qyVY0Gg27fulzSlCWvN6Y6kGZaT5IlY9naWkJRqMRLS0tNQOjixcvsqy1/BpeXl7Ga6+9Bq/XywJ+/jM0ObDZbAiFQuxapjHSf5dKJRaAUcU8f16pYHJhYYG5RdRaivV4PGUuJkpOBk6nE/F4vKZ1l9zPmA/6SqUSk3pQhpYKgzUaDRwOBxwOB7LZLKamppichGom6JoolUq4du0amxDIJyk6nQ6pVIpNdGgFgs+m0nV3+/ZtOJ3Omkvx8mu0GhTwarUrzTzk46FJEF1r9Dt+8kXbkiQJExMTrACPnukajYb9e3x8HH19ffD5fCybTkWqDocDdru9bKVCCTXL9deuXWMZeaVjvbS0VPdZ1EhpQKPlDM2yXL+eXr2SdNenmRIfdA17PB7odLqmaavcbIiguImhZXa6kXjo4Z/L5RAOh2v6MQaDQWi12rK2pwDglSJ4M57DUPFG3bGUoMM54zH8VuLnMCW1KX7Oo0nhpPEGHNq1GcKTLs3j8TANLb2oeYuzS5cuscYC9NLhl6wpS5VIJJiGk58YkC0dn6VJp9PMv1Kv18PpdOLAgQO4c+cOcrlchd0UAKb3y+VyuHDhAkZGRmpq62i7AMpe6AaDgRUH8pnEascHAMLhMFwul2JjhkgkwnSupOXmX6aklVTD2NgYe3nyMgabzYZsNovx8XEmIaCiKn7s9G+y4qoF2X81Al62wMPLGupN1KhQtRa06kAOCnJNMQW0lGnms638fUkZQY/Ho1gYGo/HMT8/z3yOqznTkNY1EonUdDKIx+Ow2WxMgsUfC8putrW1sclgNU9sOj6Li4vo7u6uqpfN5XK4ffs2wuEw89iVj8doNLJJgd1ur3rO9Ho9UqkUm7hSgE2QnGZpaYk15VBaim9vb2eyEJro8QV7tGpTTZ7DQ8+eYrF2S2V6blEGtNo9S3KO7u5uGAyGigJaco+ol71Vs1wfDAbZCqPSsSbddy0aLQ3YDAVZq2U9vXo3U1vlZkMExU0MLbPH4/Gay+ySdNc3tJofI99pCQCsyOAx6SUcwWvQoX7W9oZxH/4Wb8ffJvajIClnOHfqQnjQcBt6TWPaSPL7xWd6NRpN2dInD//i519iFBzz2Tb6OQWlpAs0m83YsWMHO9a5XA6Tk5Nl2epqkEtGNBqt2RmvVCqxFz7/EqVAU6/Xl+2XPCPE7xfvxVmtMQMVfFGmR77sHYvFVL9oQqEQ+05eXsIvs7a3t8Nms7HsLC+X0Wg0LENWLygmN5BGQedGrqcnWQI14FDSk/MykFqQ3nNubq5Mx0fdFakTHV8YRtuja4sPwKqdV378tZxpKHhWIyFIpVJlcg1es02Fl3zgLr/P6Of8tmgpnsZPk1n6Tsre8880qjOgAlJ6BvLbotUGmqDS9c9/htpGt7a2snbk1Zbi77//fkiShKtXr7IMPkHHvru7G6lUih0nuSc0Zd1pIkguL/x9RnrxdDpdt5kKBcG0GiUPktQus6tZrqdJRa1jTe8QtdtSkuCsVhrQLBneRtFoaUgt1Po0b8amJPcaERQ3Mfwyu5IWmDKntapM6eWjxzJGpAt4BKMwo/7NsGjoxQv2t+FryQMYTbgVP6dBCSOGGezRBdCA+5lBwaHch5SK0XQ6HWtOQMdHHvQA5a1q+aYN9DsKBgKBALLZbNVZfDAYLFs6rxYYUyZUr9fj2WefxeLiYpmfaygUQiAQwOHDh2Gz2WC325lXKwVs5NUaDAbLAlb5y5p+pmY5jsbAv6gpY0svbDVQcaDSMisFsq2trSiVSmXWXfTytVqt8Pl87CGtlE2vVYS3WigYp4kRQRl/jUaD9vZ2ZksmbyghSRLcbnfFBEwJajdO2wbAgleaTFAxpTybzC871yv6pEYg1XS36XSadRXkgx6y+6JsPWmdE4kEk9NQ0EoBtiStuN3QCgZJAQjaB5PJxApa4/F4VRkGTapo9YLuW7qv6XiTE0O1IJSeeXwrcPn5ps87nU7s2rWr5lL8wMAAa8pC1wpljE0mEwYGBjA1NQWDwYDFxcUyPb3FYoHX6y17NtGqE59xdjgcTGerppkKPdPXssyuZrneZDLB7XazoF9+7et0OtaWW822SIJTrRiRH3OtVbStzGqkIWs5Rusp1dhqiKC4yaEHpLy/Pb0YSAsZCAQUq0ztNhsG85fxiHQKbsTqbjOqbcFp3WN4VdqLHwX7MFN0KH7WggLeYLyBdt2Klk7JiksOn3WqhsFggN1uRzQaZQ4RfEYpnU7D7/fjoYcewmuvvYZ4PF6xbQqE7HY7e9FXgwLGdDqtqCulhgm09EQvO/47qHEB6SapmIrPCGYyGdjtdni93pqdpgYHB1kxjhw6blTgFgqFFBszkP6QNOnyAIuCeHKf4ANuefCt1+srsnJ0fGiZla5HsieTywOKxaKqhhoPPfQQnn/++bKgUQ5tt9oxIqjISe6HDYAtXTscDjz++ON47rnnFBsh9Pb2ltm/1YKyszwkL7DZbKx4j7+m6ZiS/GJwcBCTk5OKRZ8DAwMIhULIZDJlWmFaUYlGo7Db7RgaGsKFCxdY4wq5G4hOp4PL5UIymWQFunLomqYW51QQyF8/Wq0WbW1t8Hq9zOmEhyRJAwMDLPjNZrMV9ysFot3d3ZiZmUEikSgLQs1mMywWCzo6OhCLxZi8TH7tU4aTrPSUluJpMuzxeNhx4O3vyL+dGg3RRJw/r4FAAP39/azOI5FIVDQTofPe19fH2kEr3bM+nw+9vb1YXFxc0zK7muX6zs5OSJKE27dvs+3zzVS0Wi26u7tVbctisWBiYoIFW/wkLRqNYmhoSFUjna2OGmlIM7l4bDfUpYgEGwItu9OLjIIm8iqmjm9utxvxeJz5w1osFuYHaw5fwcmp38fbs/9QNyDOaix40fEz+GrLR3HJ8Si+nR2uGRC3apJ4u/kqC4gBMDupWphMJlaFr4TVamWNNKgYhaQOFNyR5vL48eMsi0WTBb7y/ODBg/D5fNDpdEx7SQEaaQmpM1atpcZSqYR9+/axTA8tl9KYdDodDhw4gMnJSZb9pawYBc3UKrizs5M5YtD+AmCdpvbt21f34dfa2oqRkRGYTCZEo1EWZGSzWUSjUZhMKy2kedkJBen0wqbf0YtevjxO/+bdDeTBJR1LvsqeXzK2WCxs1YNvqHH27FnMz8/DarXC6/XCarVifn4eZ8+eZS/RWgwMDLAiMCVcLhdr7UvIXxAtLS3w+/14wxvegP3796O1tRVOpxOtra3Yv38/3vCGN8Dj8agqjuS1xDQRou2RtGZwcJBlZHnJBGVtDxw4gKNHj6Krqwta7UpnvlQqhWw2C61Wi66uLgwNDcFgMMBisTCvYzoPmUwGFouF+f12dXUx2QJwd+WEfub1egGAdWikoIZWGSgo7enpKTvX5EtN2x4eHkY2my3z2KaAtVgsIpfLsUkhyQr480HXlsPhQEtLC7ue6d8Oh4Nd3x0dHWhtbWUrPfy9SBlOn8/HpFK0FO/3+8sa9PDay76+PuzatQs7d+7Erl270NfXx4rE+GVm/hlD0DNvbm6OyYusVisrkqYJVUtLS9179tixY9i7dy+sViubpNFngsGg6mV2Wq6v9z3k+Ww2m9kEsLOzE2azGS0tLate0q/2fCDq3ffBYFD1djYzStcj0JhjpPbcb4fs/GoRmeImhjLEFLjI/Sq12pUWxtFolHka08vThQQeLz6DnenzdbezDC3Oae7HK47HUdDbMVXy4TsLThQl5RtmWB/CiP42dJpyGQctBQUCgaoFXNR+OBAIKGaVab8oS0fWRHzmhQqMYrEYa61MrZfphUytl3fv3o1YLAaLxYJAIMCCNo3mbjtkCnjqmZ0/8sgjsNlszKuWsqtmsxlHjx5Fd3c3Lly4UFV7SQEyZdtqLaNR9tZsNrMlZYKOAelXH3/8cdYWmhpDUMBsNptx7ty5siIt/hqiANnhcMBoNGJxcbGsyI8apFChVL1lViq2ouVWkm7wWu7FxUXWUEOpUn1sbAx+vx+zs7Os8IrH5XKhra2NNRWplnWkQCWfz7MsqHzfjEYjC0hqZXCog1gtb20KYOnf/Dmjaz2dTiOfz8Pn8yEajZa5D9BStsFggM/nw2OPPYaxsTFMT0+XeRCTLl2v16Onp4fJPvgMn9frZRXns7OzTHIk3x7VLFBASdlOus5Ia03/Xc1Zwm63w+FwIBqNMokIHXe5a0YkEmH3C7lq0PXBT/iXlpbKHHVIXkSOOslkEjt27GCZSHmLc7vdjp07d9bNhKnRXlLhW29vL+LxeNmxJllIKpVimmR5/QclMKjolSZ7Svcs/b4RDgxql+v5z9CzrrOzU/W2YrEYMplMzWOUTqdVOaY0onnFZkWSmtfFY7sgguImh+9Exz+w6ee5XA5LS0twOByIxWIwSDmMZH6Mw9kXoUd9y6oxDOKHeBgxrRdWWPBi3IvzSeUXiQ4STrpDON66jFTKzQJHm82GVCoFnU6H9vZ2pv2TyzlcLhdaWlowPz9f5rTAV2DTi5sKTqhQidfokdaQMji1Wi9LksSWkg4cOFBRzR0KhViQzS8jKpmdnzx5Eo8++iguXLiAaDQKt9uNw4cPQ6/X44UXXmBBk9zxgNcqBoNB7N69WzEIu379OjKZDLPPoWBfr9eXtdsNh8MYGhrC4OBg1Y5VkUiEVb9TUA7c7QpI2TyNRoOdO3dix44duHPnDss29vT0QKPRIBwOs4c0WfzQS4+aXXR3d7MCUF6vystHjEYjUqkUFhYWKrqnAXelKpRZo/PCd+Kz2+3w+/2s4QB1p5MfI/p3oVCA3W5nWUz6HtLAZrNZhMNhJp2pVtxjMplgtVpZowg5pK3mO7nRcaZ9pPMfCASYjCAUCrFzT93SqCqcoOIlfhWDMrkGgwG9vb1Vq8v1ej0ikQhzn+C7PtK5ILcHmuDQd/ByF/psNBqt6SxBBX80Aal27kknTdIGuasMyReWlpbqVs4fPHiQOWfIJ592u11VJkyN9pL2w+Vywe12V21NPT09jVQqxZITvHyCtpFMJjEzM4Pe3t6a9yzRKAcGtV3v1rItOiZer1fxGFFyw+v1Kt73290RQbh4bDwiKG5i6AUO3M1E8VpHCnJSqRRi0SXsSp3D8dQPYSvF6373LNrxQ81jmNZ0Q6PRICvp8MNYD6ZzFsW/ceiW8Z6OKHyaFCKRNBtboVBgtj52ux0tLS24fv06K3ShMVNrZ5vNViYBqVZIQ8U/lIWkymj6TDweZ5XyhE6nw759+yrGTUtJ9MBxOp1l1kZWqxV79uxhFm/08KBAoZrZuV6vZxlqHjXeufznlIIweWaYPFz5QJuv+tdqtejt7a34HnrZk38qX4REx580q1QgRQF0JpPBzMwMK5LZvXs3k4BQAEmV5rQcF41GWcBOkh9eC04BZT1HBCqyIn0uf/4zmQzu3LkDp9NZFtTSsaDJotVqZRMn2n/KjtJ1lEwm2cSpFmazmWW75FlgOqZUHEeZT146wZ8rCnBJhyvfd+rCd/36dVYwJ2/MQZ3RSDPIT5h5zSBdAxQQy/efJqQul4u5I/DXML2IzWYzc0Sg4yh3lqDvq3XuJUmC3W4HANYqmZ4FtAJBQWq9Jgd2u70sE3avtJfkF0z3kjybTCs5pVKJWZvJJ/qSJDEfZ0LpnuVRej6sFjXfs5ZtyScX1Y4RXQvr0bxis3IvGnw06hraLoiguImhFxnpXeXFG/TCd0Wv4NH099C2PFf3O+Nw4Bnto7is2QNoNDDodAguW/CDdB+SkrLtzoCjhA/0ZWEsAbHYSjAsn23SSyAajcJsNqNQKLACJ1qaNJvNbCmaHqJK+0U/5wtpaBmX9MD1ghmi3lIStTulcdOSMmm0zWazKrNzfhKgpK2jQKMWpIekl6hc9gAAFosFPp+v5vdQURPpHylTTYVWwMp1ZrVacfv2beh0urLCrkQiwfS9O3bsgNPprAhC+OU4+s5CoaC4FE9a2HqV0VSwRLpygv4dj8fh9/sVi/GoyJKakyiNh5bla0EuDnR8eFcR3imAn6zKAyzars1mq9tQYnp6uuYS6sTEBIaGhuraO/FBLu+8otGUuz3YbDYmgZAXm7lcLqZHpolTNXcBCgjp3NP1Jj/3LpeLFbhVc3Kgc1RrW5SBpHbOrzcTxk+YlY7jfffdh4mJibqBM3WyowCRh+5BmhDQ326VDN7rmVzIEY4IwjWiGRBBcRNjNBpZtyjS1/ESAl1iBk9kn8Zg4Urd78rDiDP64zinPwZJbwbN46+mHXg23YnlGjWXJ3t0eNybQleHH7dv30ahUIDH42HZEcpexmIxZLPZMl0aFRFQZiqfzyORSLAXsNvtrtivaDTKKvRJSy1fHiUNYCQSgcfjAbASBFWTTxC1lpKi0ShmZmaQSqWYZIMyfZS1VNPutK+vD2azmWkceejfZrMZfX19Nc8XFWGMj4+zlQGNRlPmItHf3183A0CBJS1Zy4816T4p81BtyY6n3nIcTX5IAlNtKd5iscDj8dRsqmC1WlnmUd4shYoWNRoN07Z6PB7Wbpi8a2OxGJaXl2GxWJBKpRTHQ1ZktYjH42XSFb4NN1n9WSwWttxP45QfR7PZjLa2NsRiMSaX4J1H4vE4WlpakEgkmB5WqTHHgQMH6moGI5EIK5iTF1nSGI1GI1pbWxGPx1krb7kbSkdHBysSVZo49ff3s8lONfs6Ovdms5lNwKo5OQwNDUGSJFy7dg1arZZNoOna4J0M6HvXkglTo72k55tS4Hz06FHmT67kKtHa2oru7m4Aa3cXaDYaNbnY7o4IwjVi4xFBcRND1b/RaJTpNA0GA7SFFPaHf4D7Ms9DB2XLKgCQoMF1x3FMdv8cxmejKC0vo7S8jGUJeCnTjks55UyjXgv8/KAG+20rD7Xu7m7Mzc3BYrEw1waNRsOCF2pCQYUptORfKpXKvEWj0SiOHDmCl19+mRXesPFKEiwWC4aHh/HKK68wXTLf1tRgMMBqtZYVTY2OjrJCOwr6fvSjH+H48eNlMgd6sJCOjzK7VJVLumh+6RsAaylbr91pS0sLBgcHMTY2VjY+4G7Xu8HBwTK3B6UAk9opyzPiFMjxTUiUvoffH3r58t23yC83nU6jr6+PeYzyRVvUoY0mBLWCkHw+z35Hrgj8uB0OB9xuN/r6+nD9+nXFF6jX68WVK7Une/zqQiQSqSi0o8wwWUspSQP4TKgSpNHt7u5mxVFU/OVwOODxeBCNRuFyubC0tMQCbgqcAbDAp6+vD5cvX8b4+HjFmNva2tDT04NLly4xuYQ8U+r1etlExu/315yk0ASWru1qBZsejwf9/f24fv06kxdZrdYyedHw8DAuXLjA/o6H/r28vAyr1cqygfL7mlYI+H2m7LvcQ5q+jzTq/LbUNlLhqZeVrTfZUxM4j4yM4NSpU4hGo7BareyaJlnIyMgItFotcxdIp9PMu7hQKGB+fh6xWAzHjh3blIFxIyYX290RQc3kYrsfo3uNCIqbGJfLhe7ubhZ0ZtIpDGTO43jqB7CVEnX/ft48iLHeX0DM0oeDBw8ipb2AQCCAeB74f0t+zOaV9cNuo4R/uSOHAZcWPt/KQ40q3n0+H+bm5pBIJMqWPjs7O1khkslkYsuJfDBLS0MdHR1oa2vD2NhYxYu6r68Phw4dwuXLl5mmlA9SC4UCkskknE4nfD4fRkdHcfr0aeYcQfKKeDyO06dPA1gpxJuYmGAV35RR9Hg8zKWBgg+SKvBFQlTcV0/LpdFo8PDDDyOVSmF+fr5i/zs6OvDwww9Do9HUzBYZDAYsLCwwaYDcUm15eRkLCwvMq1Xpe+x2e1lHw2pSFSomogCxWpFMOBxWpWMjuz2bzabYwAFYKaDzer2KL9BAIMC+j/aX4AMp3ltZ/qIg7a/JZGKTy2rSANpOvf2izKlSYRsVrrlcLiwuLjJ9MbV49vv9zMaMl1PwkE6+WCzizp07NRtz0JhrTVIog81niwmSNVgslrrng4o1a02cyHKNNKVyiYkkSchms8jn8zVdCigwpcI8+ZgNBgOWlpZUFxupzcrWyzjXC5zVuEo00l2gGWnE5GK7I47RxiKC4iaGnzVagq/i/vjfw5Odrvt3cb0Pr3W8F7m+kwhHIujw+Zge9KkXL+EvzqUQLyrLJY7v8OC//cwu2HSlColBsVhENBplFlz8EjK1SCYDez6Io6V/KrS7ePEiJiYmygJP+uzExAS8Xi+zJSP5AEHL57ScfebMGRQKBeYSAYAFfOl0GmfOnIHD4cAzzzzDdI0kJQgEAjh16hT27t3LAmKg3DmC1+GqCQxbW1vxxBNP4OrVq5ienmZV+r29vdi7dy97+Z09e5Y1ZyA/07m5OcRiMezYsQPBYJAVJsllD5TZnpubw82bNxW/5+jRoyxT6HK5qkoM3G43063r9StdqeiF5vV62c/VdKPil/96enqqOn3Q8p9Go1F8gVKxCa/R5KU6lLnlPat5mYHZbGaTmM7OTsRiMfT09DAvXqPRCLvdjnA4XLYcqbRv/H61tLSUuUb4/f6ywraFhQUcOHCATSToOIbDYfh8PtbYZGhoqOpnZmZmmAShVmMOmmCUSiVFFwMqDqQgkz5Pv8/lckin03A6nUxbXe27FhcXmbuA0sRpbm6O1QCQ24XcWYKeFW1tbXC5XBXuGwAwOzuLaDTKxiO/9mm1iVZtau3/emdlh4aGWAe9ajKuRrsLAM2nTV7r5EIgjtFGIoLiJqfVkMGjgS/COX2q7mezMOEF3YN4WXMYpYABhqVX0d7ezpZbvnMpiE+fzaBYUg6If/mhAfynnx6GXlf5GbIbkr+sgZUXUzQaZab2lN2WQ0v3V65cYQ4E8u8pFAoYHR1lATZZRRGUuS0Wi7hw4QKSySSMRmNFcwXKUCUSCZw+fRqpVIrZiVEwThm4iYmJMkcIeXZKnq2sR2trKx599FHFLlrj4+NYWlpCqVRiS/+0PJ7P53H16lXWEISCC/4YUiOMq1evsoxkte+5du0aRkZG8P3vfx+BQKBiAmKz2fDQQw8hEAjgypUrzDuXz2673W7s27dPVTcqmsgtLi4qygP45T+lF6jFYkFraysWFhaq6lP1ej2z9MpkMkwzzBcjUmZ3586deOWVVzAxMVExntbWVjaeevs2PDyMq1evVnzPrVu30NbWhje+8Y0AVnyY5Z8JhUJobW1FV1cXLl68iOXlZVy+fLnsWpyfn0dbWxuzo6PGHHJPaMr8xuNxLC4uKq5+UCEeHT9aEQDAJps6nY5JY4rFYsX+3759G8PDw6rcBciej7rXyY+11WplE9fFxUXmGS7ff5oUkdWa/L6mQtx8Pl9z9Wf37t0NzcqqyThX+0wkEmGfabS7wGbVJq9VB74dEMdoYxBBcZOT/8a/hnP2pZqfkTRaXDIcwTM4gZzOvhI0/sQCLRwOYzEYxmdOTeEfLgYUv8Ok1+IP3nMQ77ivS/EzVGxU62VNFe21HAGomE1eQAXc1d2SHR1VcfMV/eStm8vlsLi4WPY5OfQ5sgoDwDS2lLUqFotYWloqs8xSop72lEfpoRaLxVhmq9ryOGXDJOlu1y9eR8kH7xR80DGg40nfMzMzg87OTng8HvZC5jWuHo8HHo8Hs7OzCIVCLLDQ6XQolUrME7ZQKCAUCqnOutWSB6jB5XKhvb0dwWCQrQwQND6v11sWWPHjJgswh8PB9L1K5whQl1G8dOkSa6fOQysOly5dwoEDB2puK5/PY2lpie2XwWBg5z6dTmN6ehput5tp+GkZnpcqUFb82rVrrImMzWZjYw4Ggzh1amUSrdVqy+ywqhX/5XI53Lx5E/Pz84r7L7eAq1YA5PP5EA6Hy+z/5MeJHDpu3brFpCK0/6lUClNTU+jr64PJZGJyKPm2KKicn5/Hyy+/rLj/6XS6YVlZNdcHgLqfaaS7QKOz4M2WcRYINgIRFDcxkiThSsd7cH+NoHip5RCes/00rsdXtIx2mSPEbCSJX/3GOO5kqmcmAMBtWMZ/OGbD/3eos+Z4qhUbyV/WkUikrBhNngUG7gaW8oCY/xteL8r/DwCTYhQKBdbSulgslnkWExQQUSBJrhX0PRqNhmlt6efVAjcKxhvxkshmsyxLbLPZ2HdSERwt+9N+yo8T/VyjWWkrTP9OpVLs85SBj0ajmJiYgNFoxOHDh6vKB65cuYJLly6x48NLXsj95NKlS6zosVbWjXSpkrTS9reak8FqsnNGo5FNGmhMvKSGgn++MQrtR6lUQiKRYA1Zao2HfHKV9u3y5csYHR0tcz6h8dDkZXR0lI1DaVvT09Msq0mFqfy5z2QybNm9ln45n8/j8uXLyOVyZas2VIwbjUYxOjqKkZERNknlJ4P86kcul8P09DQKhcKaLOAOHTqEH/zgBzXdaXhdOz+Jo4kuNe1paWlh2uVq3RNdLheuXLlSc/8vXLhQphuvdm2pycqq1QHXu4bGx8fx0EMPNcRdoNHa5M2acRYIGo0IipuYWCyGqYIXbb5H0RX657LfZcztGOv9BVxd7kcgGGRdxXim03r8fbQVqWXlau0+UwbvaA1Dl0ixjIlSxoAvNuru7q7QQ1KTAKrKp2CNoMCGfkaBHVDe+Yv/G7Jj4/eNtL8GgwE7d+7E2NgYy2LLg3AKADOZDPsOuS0VBeF8Uwb5y4o+x/uM1kPpOFIWs5oVGC39UyadMqB8ppz2g4IIkqrwwTxJL2i1oK2tjQUTPE6nEzdv3mQSFKUOg8lkEjdv3kRn58rESckmbGZmhmXnyAFCvj012Tm+bWwsFmP2aqRldzqdzG6ONKt0zOmY0nEOBAKslXe18czNrfh7k0Zefj6cTicmJydZQMZ7HdP/0+rG5OQkent7Fbe1uLjIOglWg65Fq9XKLOuqNeYwm81IJBIV3dzoO6xWKyKRCJt88assBP2cbNX8fn/NjGo9CzgqpCV3GgpQl5eXkc1mYbFYWNBL+nZ54Se17O7o6GAT42rdEx0OB4I/ee4p7X88Hmce0rVat9fLyqrRAau5hkKhEOLxeEPcBRqpTd6qbhgCwetBBMVNDD3Eb+38l2iPnIWulENRZ8Xtvp/DTPfPYBk65G7dqugOJknAy1EzvrdoRwnKD9eHPGmc9CWRSRdY4UqtjIHP54PP58O1a9eQTCYr9IB2u51lvyhQ4DN8AMokDBR8ySH7JvLRVfL7pcYax48fx+nTp5FOp8vcJ6gD1+7du3Hp0qWKNsfygJwK+/gCP9oPykQ7HA5V567WcaTgs9bysF6vZ4ERjYmykPR7l8uFaDTKsspyKDtO2c1q0AuZLySsJlWgIsl8Pl/VJszj8bAOcavRTCpNHHipB2UM+aw5tRKmwJ2OCU1s6N9k61Vv/wHU/AyNt14wS4WctbbFTzbkvsnkx+v3+xEKhRQDJ5/Ph+vXr9fcVjqdRiqVYseEn4QCdyelpE9Xc85qWcAtLi5Cr9ejp6cH4XC4YiXJ6/Wygj2Xy8VsFat1fqNJjFL3RJ/Ph8nJybr7b7VameSlVuv2WqjRAau5hvhjuFZ3gUZpk7e6G4ZAsFpEUNzEUGY2pV0JhM3ZRdwa+AAKRjcAIP+TbAy9YE0mEwol4J8W7Xglpmy3ZtBIeEdHAvudOQAaprsNBAKsk1YtTRxpTPkANJFIIJvNskpr+dIpcDcDrNfr0dvby3SFQHlxm0az0phiYWEBACqCZ8oq0fjIh5h8iinwcDqdOH78ONrb2zExMcF0ynwwzMsEXC4XsxCjoJzPSre0tDCNby39Xb3My969e5lFmNLysNPpZN9LulmSk9hsNrS1taFYLCIcDrPxVJOqUAa8loaRAvNsNsuCJDouxWKRSUtIo0xBOgVwiUSC2YTZ7XbVmslaEweTyYRUKsU043Lt7c2bN9HS0gKDwcD0p0p2c/T7WvsPoOZn6OfV5Cz88abGJbW+p96Yl5eX0dnZiZ07dyoGTrTyUa8znsVigclkKltRIGgiSO2a1epclbTy9LwyGAw1bev4bSl1fpPvv7x7otr99/v9uHjxItMd03Gv1rpdCTU6YDXXEH8M1+ousFptstLz6l64YQgEmxkRFDcxvA3U7d53V9Wf9fb2olQqIRQKIQMTvj7vxlxWWT/cYijifV1x+M3L7HvohTMzM1MzYzA2NoapqSm2XE0vWgrUlpeXEYvFYLPZkEwmmXxCbrtmt9vR09ODWCzGCuAoIKblcbfbzazfSD8s18uSLyyw4kN85MiRqlZIpN+k6nt5VTywEsy0t7ejpaVFMQg1m811gzlqF13rOM7OzqKrq+uu/3SV5WHqeEf2XtWszUwmEwvy5VlvCmzJYaJW97iBgQEEAgE2Bj5LDtwtkDKbzQgGg0z7ymfYs9ks7HY7urq6cPv27bqayXw+j9HRUcWJwwMPPMB8rnmrPcoGp9NpZLNZpmOn1sFyDavP50Nvby8WFhYUx9PZ2QlJkmp+ZnBwEJFIhB2jahaBJpMJg4ODCAQCit/D369KY6bOZ1qtVjFwKpVKzGqvVge1oaEhvPbaa6wwtpp3MPkU17pG1Ohc5d24qsk+ent7IUkSQqFQ3c5va91/n8+H5eVlOJ1Odp9lMhlotVp4PB7odDpVrdvVdBnjryGlToXyY6g0uVDDajqf1Xpe8ZP+aqzWDUMg2OyIoLiJ0Wjqd7fZs2cP/H4/vvjd5/DtqRZkJOVTusOcwU97ArBDi1zu7pKtTqdjbZVraeJu376NcDgMh8MBo9FYsfSZy+WwtLTEutHJO1QBK8uL9913HyKRCAYHB6HT6bCwsMBeeu3t7SgWi4jFYrDb7cxGjV7gFFhT8My/eHU6Hfbt21exzUQiwbqyyVv9UnMFynIWi0Xs379f0WOWgjklX+A9e/aoyrwcPHiQNS+w2WzsOEqSxM4rsOL4EQwGy7ZFWkraBq9x5Y8R4fP52PdUu4Z6e3sxNjaGUCjEJgz85IEmBuRxTDpe2k42m2XBdCKRqHvNDg0NYWJioubEYXR0lElWqrVnputv165duHjxImKxWNUuYseOHYPH46m5/8PDw2XHutpn9u/fD0mS8OKLL7L9p5UE0jqPjIxg3759SCaTde/XU6dOKY6ZOp/Rsa8WOGm12rIOavLVBvoe6lY3MTHBVk/44sBSqYT+/n7cf//9GB0dVaVzVco6qnle7d27F+3t7Th16hSWlpbK2kVns1mYzebXtf/VjuP+/ftx+/ZttLW1VdUUk7NKvSyomv2ia0jJipC3/msEasdUzzFm7969DXPDEAi2AiIobnLqdbfx+Xz47rUkvhHqwHINt6t37DJiPxaQSuYRjWZZ8ETtgltbW5FIJOrq5iirQBlC+WdoGd3j8VTYV1GGxu/3IxAIsAxPd3d32fdQ1qy1tZUVefGZSYvFAovFgu7ublU94GnptLOzE7OzsxU+vF1dXTCbzayoS8ljloK5Wv7C165dU6XPtNvtGBwcrOqxeujQIaYrpM9Qdzv6zH333ccq9mkM8qJG+nlHRwf27NmDsbExzM/Ps4wz/ZyW1K1WK/M8luu2yd3CYDAgn88zrTddB9TxLJvNor29vW5BVr2Jw/z8PEqlElwuFzKZTIXMwGKxIJVKwePx4PHHH6/ZRQyAKg1nvc+cPHkSAJgNGq0kmM1mHD16lP2+3vfQ9uqNuR7kBnHmzBnEYjF2vu12O44ePcq+58iRI0gmkwgEAmV+3zqdDh0dHThy5IjqLlr1XArUfE9ra6uqcavZ/1rH0e1248aNG+x5JS98VKtvB9R1GQsGg4pB773Q46p5Nzz//PN1V628Xm/NVRI1qwQCwVZBBMWbACX9Wa5Ywse/cRHfvDCr+Lc2ow7/4733oV8fw6lT11AqleBwOMqyStlsFm1tbchms3V1c/WyCjqdDtPT08hms2Vdwuhhm81mMTExUVcPaDAY0N7ejps3b7IldMpw5fN5NmY1LxvSVCaTSVYox48pmUyyZc5aL7VEIlHmL1zNF1iSpDKPVaWK92QyicnJSRiNRvT397OgNpvNYnJyEh6PBwAwOTkJk8mE/v7+smKjyclJ7NmzB3a7nXUP5C3pKGCz2+3w+XxlwVC1402evh6PpyyjTrITytjzkyl+W7QqQAVHtTSTVGxVa+LAZ8BdLlfVVQlq1dzb24vBwUHFrmb1xrOaz5w8eRKPPvooLly4gGg0CrfbjcOHD5dNENV8z9DQUN0x1yMYDDJnjfb2dvZz8k0OBoMsCH3ssccqJkWdnZ1lAW+9cat1KVDzPYFAAO3t7RWrG/y41VDrOEaj0Ybo29UcHypYq2f91+iCtVpjikajq1q1WosbhkCwVRBB8SZlZimDX/3KeVyejSt+ZkerDX/5wSPY2WrH889PMm0dtfGl5g3kjVsvY9DX11emB5S3caUscSgUYgGZvEVrMpnE/Pw8BgYGmOazmv6uvb0diUSCFS5RMwsKyMxmsyo9IKCuE5/NZsOdO3dqvtQoS0zLzvyYeOcDn8+HQCDAmnHQMbLb7dDpdOjv78fs7CzS6XRFYO90Opl+G0BFlofOB7V4Jku6YrFYUYxoMpmwa9cu5qFL54eCmYWFBcTjcezcuZP9TJ7955slkAaa99cFwDTGpPfmx1CrIKtWsOJwONi5Iasv/pzx2lNgJaPd29tb8zpQo+FU8xm9Xs+KO9eCmjErwbsGyK3U6PrggzC1hV1K+89vj+7ZdDrNgkd50Kfme5Su69UGj0rHUa3utp6+nbckU9qve1WwVit7zX9/te9U61Bht9vX7IZxL1Cz7wJBoxFB8SZAnsW4mTLgb24YkSwo6yUe3+PH/3zvITjMBpYx0Ov1CIfDbImcspN+vx/hcBiHDh2qmTEgPeQ//dM/YW5urkIaYbPZsGPHDrzyyiswmUysUIqXKhiNRmSzWfh8PiQSCcVWwF1dXRgdHWVZY4vlrpsG2a3NzMyoesmo6cQHrBS1UdBczWM2GAwinU6zLCyvc6X9pMKe69evI5VKlRUYkq/s/v37MTU1VVc+ANT3PT148CBzaeDbHBsMBrS1teHw4cN19bt37txBS0sL5ubmmDSEPx9WqxUtLS0sC1zNSoys26rpyOWoDVb2799fUzPKa0+bifVohPB6grC1FHbR9oxGI+7cuVNhbybfXjO4HajR3arRt6sJ0ikArWdZuJqCtbVeR6txqHC73Wtyw2g0opmIYKMQQXGTwy9ZOp1OPDOnwTeuFSGhekCs0QAfe3w3PvzGXdBq77ZxVWotm8lkMD09jdbWVthstroZg0gkUtH8ArhrbwbcbeUsSVJZ8ETL9HzBzsqYK1sBUztcedc3YCXDkUqlmLdyPdR04otGo3X9bCnolWdLKQil7nIUoPINR+izxWIRt27dapjvqd1ur7k0rka/SzZ29CLnoays1+tlzReoYp0PwOVuILVQWyTUKO3terJejRAa5VO7mu2lUimkUikUi8WK1uTZbBY2mw25XK6p3A7q6W7V3B9qgnSSaM3MzFRt3U6rNGoL1hpxHa3GoYL2txls10QzEcFGIoLiJoZfanS2+PB/ruQxuqCsDXWY9Phf77sPb9rjL/u5wWDA0tJSzdayS0tLMBgM8Hg8NW2QRkdHAQAdHR1sOZ18TuPxOK5fvw4ArGMbX0VO1l1GoxHBYBCSpNwKeHJysm7XN2p3Ww++E18t/1Qat1JWRaO5201OKVuq1WoxOzsLvV4Pt9utKB+hDoCN8D2tleVRo9+l4JS8jvlgnnTE4XAYfr8fiUQCpVKJXUc0UdBqtRVuIGstXAIao71dL9azEcJqfWpXsw/VzpnRaEQymUShUIDdbq94hiQSCUiShGQyibGxsaZyO1irvl1NkK4k0SJZVTQahd1uh9PprDveRl1HaiefzSRJEM1EBBuNCIqbGFpqzBmc+P+dzWImqSyXGGyz4y8+eAQ7WivbEFM73FrduAqFAhKJBDwej2LGYGZmBpFIBDabjTVv4KHWqmTXVc1KizKL0WgULS0tilKFSCTCxqXU9Y3kGPVQ45+qxqvW6/WyY6mULaXW0tTmWH7MLRYLkskkTCZTXV9YAGuuClcTPKVSKRYsUJBLULDA/56yxxQM2+125iJC4yFddDW3C7WFXcRatLfryXpKA1abBVRDrQwv3eu81Z8cSZJw48aNpnQ7WIu+XU2QrkaiRYmDeue+kdeR2slnsyCaiQg2GhEUNzG5XA6XwyX83dQy0soJYpwcbMHnfmEEdlP105lKpVigWqu1bCqVqjkeKhqrl1VxOBxMTsC7Hmg0GjgcDlgslrq2ZZIkweFwIJPJIJVKVc3Kqs1Mqs2YAHe9ak0mE9setZal74hGo4rZUoPBgFgsVvM4AkBvby/m5+dr6rfVjEej0ahqzT0/P69Y1EjBLllX8fIY+jct/+ZyOSSTyTKNd6lUgt1uLxvPs88+W2HJFw6HEQgE8Nhjj9UtXNoo1lLcs56ShkZnAestWe/atYv5hlfrwmgymdgqj9frrauDbxa3A35yobbpRjVIolWrxTU16anHaq+jetfsWrvnrSfrLQsSCOSIoLiJMRiNODVvVAyINQDe1lPEf33XHsWAGADsdnuZnZpSa1m7vTLLLP8eNa1V7XY7bDYb4vE4a5us0+ngcDjgdDrLCraUvoe8k+/cuYNEIoFkMlnhrSzPTK7VPxW46ws8Pz9f4R08MDCA2dlZpqutli31+/24efMmstksC+QJSZKQyWRgsViwY8cO9Pb2rmk8lH2rp78bHh7GwsKCYlOBHTt24Pbt26wphvxlSasMnZ2d0Ov1Vb2VBwcH0draCkmScP78eczOzkKn01U0Z5idncX58+fxxBNPNN1LeT0LmxpBo7KAapas79y5A6vVyu5redBHzXFoclWNZnQ7oMlFrftDTZCupsW12nO/mutI7TXbbJNPJdb7HhII5IiguIlpcbvxsWNO/Odn40gtly/Dm7UlvLcnicf3d9d92HV3d7OWqPVaywLKmQf+e2q1VqW2uj09PVX1wmqkCh0dHWhra8Pk5GRNb2XKTDbKP7WedzBl51KpFNMO0mdsNhvuu+8+lEolVtEuz6hJkoT+/n7mKrGW8bS0tKiqnB8aGlJ8qWs0GgwMDODcuXNsCVh+XvP5PJxOJ7xeL15++WVF32SPxwO9Xo+pqSloNJqyAkm9Xs+6Jk5NTTH5TLOwEYVNjaARWUA1S9YUzEajUcX72ufzMd3xZnM7qHV/qEGNREvtuVebvV6NlZwamsECbSPuIYGARwTFTYxGo8Gevna83TOJvw+2Q8LKA6LNkMdbnTNoK2nR1na47oOLb4lar7VsvcxDvdaqfFvdUCgEp9PJur2FQiHVbXXJKsnpdFZkZb1eL7RaLQKBAAYHB1dVmKGUMeGzZUrewePj43j44YfLslz0oucbIRw5cgSJRALBYBDZbJYFj5RtPXLkSJlzxesdz8WLF5FIJNgLIpvNlmWn6HNkwVerqPH48eM4ffo00uk0Wz0giymDwYDjx49jcnKyrr9se3s7MplMWTEWfz1TJ7pwONw0QfFmL2xaaxZQ7ZJ1b28vu4+r3df33XcfJiYmNpXbQaOabjTy3NN3KbWMbmtra5iVHNEsFmibsThQsLUQQXETQx2e9rWZ8IQ2hacX7dhjTeKt3iW47E4WGFIDi1KppFilX68l6tDQEMuWpVIp1iCjVCphbm6OZR7oe86ePcuWPnU6HXw+X9nvjx07hrGxMUxPT7PMQ29vb1mhlRqrpLa2Nuj1eszPz7Pv6ejoQKFQQCgUwszMzKoKM9T4p9ILnArESPZB31MvO9fa2oo3vOENdQvNasGPB1AOeGm5WskblVov00SiWlFjKBTCG9/4RgDAmTNnmEWbVquF0+nE8ePHsXv3bpw+fbrmeEKhUJkEh7TWfCc6+nmtgq31ZrXFPY1w1VCLmuzdWjN8apes29vb4fV6a97XdC81KqBpVPZSzX1f6/5YTVHbWu57+ZiB6paViUSiYQVpzWaBttmKAwVbCxEUNzH0wG5ra8PPdRmwcz6HYacNBoOL6dTowbe4uFhV68n7udayt6KMCXkDRyKRsgArn8+zzIPH40FfXx/TiZrNZvT19bHWxAAQiUQwNTWFUCjExkONQtS4D5BVUiAQwOzsbJkOemZmBl1dXTCbzUgmk6oLM9T4p8ZiMczPzzN9JGU3Ozo6oNFoWIFHvSzXWpe11TQDoKYhtbxRLRYLDAaDquMzMjKCI0eOYHx8HPF4HE6nE8PDw9DpdOx81GtOYLfbmcMGgDKHDnIwsFgs8Pl8qo7DerCa4p61tgNeDWq21YgM32qWrK9du1bzvm5kQNOo7GUz+SbzKAXq9CyuZVl57dq1usXKasbdrBZom6k4ULC1EEFxE8O/rLVaLY52Wcp+Tw++a9eu4dy5c8jlcrDZbGymHwwGcerUKQB3M8VK9laxWIwFy8vLy8xyjbISOp0OMzMzuHXrFi5cuFDmLJBKpXDlyhUEg0E89thjiEQiOHXqVMV4QqFQxXhqWSVFo1EsLi6iVCox9wxq7DE1NYX29nbY7XZVWS41/qnUGY4anJCGN51O49atW/D7/asq8FjL8rCaZgAulwulUqmmN6rJZGJuH2oKV3Q6Hfbt2/e6xkM6Ub/fz17qRqORBU7U3IQ01c2C2kxpvWtITTtgtajJ3gFoSIZP7ZL1tWvXVN3XjQhoGpW9rPc9e/bsaVhhF7+tau3U+THXCtT5hiJK2WtyuFnruJvZAq0Z5DXNTjPowLcaIihuYtS8rLVaLS5fvoxcLlcWGJlMJuYHPDo6isHBwZoND7LZLJaWlpgzBJ8pNRgMKBaLWFpawiuvvFLTWeDll19GMBhc83jsdjvLesuL+shaLhKJoKOjQ1WWa3Z2tmY2hFpGFwoFWK1Wtj3yGk6n04jH43A4HGpP35oeWGqaAZjNZhb0Knmjms1mViC1lsKV1TQnsFgsMJlMTDpBkyfqpMjbuTUDajOl9a6hRmXU1GbvyCqvEeOpl+H1er14+umnVd/XawloGpW9VPM9jfJNXs2YQ6FQXfu7etlrjWbF3rKe17kaKzlhgbY5aRYd+FZDBMVNjJqXtcViQSKRgM1mqwgyaWk7EolgZmamZgOEfD7PdKIAKtozAytZidnZ2ZrOApOTkygWi6rHoxQ4TkxMMD9iuQZVkiTodDoUi0Vcu3atbparq6sLFy9erJkNmZ6eZsuR1ZqO0M9nZ2dVNZJY6wNLTTMA2pd63qhUILUWnafa5gSzs7PIZDLYsWMHotEoW3kgqz6Xy4VMJtNU5vtqMqVqrqFGZdTUZO/m5uYAgLmYNGI8tTK809PTrHHPWp4zamhU9lLN94TDYRw6dGjNvslqxxyNRlXZ39WzvjQYDBgaGsLVq1fXNG5hgbY5aTYd+FZCBMVNjJqXtc/nw+TkJAwGQ9XCJqPRiHQ6zTSeShgMBiwvL6NYLCq2Zyb3BqUHv8ViwdLSEvu+avDjqRU4xuNxSJLEMo58lzWdTgedTodcLod4PI59+/bVzHKp0Q2SXZrb7UYmk6nwcibXhHrHEWjMA0tNM4BYLMYy+bW8UalAai06T7XNCUjj7fV64XK5KsZELaObLfNUL1O6ntpTNdk7mqiuV4ZPTeMeNc8ZHqUJcaOyl2q/x2azrVkHrXZboVBoVfZ3tbLAAwMDcDgcaxq3sEDbfDSrDnyrIILiJqfeyzqTyUCn07FAjg/mDAYD9Ho9y9LVolAoMO1ntUwpOQfUgwLneg0+CoUCCxxNJhMLmChw9Hg8rACQfsd3WaOGEk6nkx0npSxXNBqtmw2hzCew8qKQTy5yuZyq49ioB5aaZgAWi6Xs5VnLG1Wjqe2JXA+1zQnkGm+5HnI1TQzWm7VeQ43aLzXZO2pvTm3Q5efj9Yyn1iR1NY171rqtRmUvV/M9a/VNVrstjUazKvu7elngteq3hQXa5qOZdeBbAREUbwJqPfioxe7i4iIrjuNlD+l0Gn6/nzXmUMJoNJZliOWZUuCuvrZWtzZq8RyLxRQbfLS2tiIajWJpaQnLy8sIh8MsALVYLMjlcqwrHk0ClBpKkOcxoFyYoSYb0tvbi1KphFAoxCYT1cZd7zg26oGlthnA0NAQRkdHVb3Q1qLzVDue7u5u3L59e9NmntZyDTVqv9Rsixrg3L59m12fvBuIVqtFf3+/6vHUW904evRo3cY9au4PNdsaGRlpyLFe7Tlr5P2htC2v17sq+zs1WeC1FqQJC7TNhdCB31tEULxJUHrwaTQaeDweBAIBFItFltmkjC/9vt5M32w2o6WlBdFoFKVSCWazmQXX1DTD5XLBarXi9u3bNbu1DQwM1GzwsX//foyPjyOZTFY4GaRSKWi1WszPz+P+++/HSy+9VLOhhJoMtppsyJ49e+D3+2uOmxqc1KJRDyy1GZz1eqGpHY9Wq92QzNO9rsJez4ya2m1FIhFcunSJuUGYTCYUCgWEw2GYTKaKpi9KqFnduHbtGo4ePYof/ehHWFpaqiiyNZvNqu4PNduamJjA0NDQmo91M54zykirCdTXurqzGoQF2uZxchA68HvLlgyKf/d3fxef+tSnyn42NDSE8fFxACtOCx//+Mfxta99DblcDk888QT+7M/+DH6/fyOGq4pa5vMA0N/fj0AgwGQUVAzX1tYGAKoyk93d3SgWiygWi0ilUqxAymazMT3p0NAQs3tT6tZGgdjZs2cRDodZoEYNPlwuF1566SWUSqWKgj2dTodUKoWlpSW88Y1vhNlsxksvvYRkMsk0rA6HAydOnMDIyIjq46cmeKRx12pwUo9GPrDUNgNYrxea2gB8vTNPjazCVtOYQ34++G6GjaLeuff5fBgfH4fT6cTy8jIymQwymQy0Wi08Hg90Ol1ZY59a+6Z2dePAgQM4evQozpw5g1gsxjLTdrsdR48eVXV/rGZbjbiG1vNaVLut1QTqa80CrwY129osgeNq2UxODkIHfm/ZkkExAOzbt495ZwIoWw7/2Mc+hv/3//4fvvGNb8DlcuHDH/4w3vWud+GFF17YiKHWRY35vN/vR3t7e1knNqfTqbqwiTIdCwsLCAaDKJVKkCSJ+cu2trayB8QbVHRrowYfGo2Gfaa3t5d1WSsUCiwbLR+HXq9HLpdDPp/HwMAAMpkMbt68yTJSO3bswMDAwKqPo5rgsVaDEzVs1ANrvV6eagPw9QrUG1mF/XpfjBvRnY9v7FNNU8w39nG73Q1pYLGwsIBAIID29nY2Gab/DwQCCAaDqgpI1a6k+P3+hlxD65kFVbOtzSpX2EyB42rYbE4OQgd+b9myQTHpsuTEYjF84QtfwFe/+lWcPHkSAPDFL34Re/bswZkzZ3D8+PH1HmpNVms+Lw+2VlvYRDcS6YflPwfqP/j5Mbe1tbExLy4uIpFIYOfOnexn5LlJUGGf0WhEKpXCa6+9hnQ6jc7OzorveT0PKzXBo1KDE7Xf36gH1mqaAawnagPwex2oN7IKe7XNMu71+ah37nkvW41GU1HQKO/Ct9YGFjqdDtPT0xXHGlg5D6stIFW7ktKoa6jZMq6bTa6w2QJHtWxWJ4fNOrHaDGzZoHhychKdnZ0wm804ceIEPv3pT6O3txfnz59HoVDA448/zj47PDyM3t5evPTSS00VFG+E+XypVFJsLco/HJQe/GrGPDMzA7fbjVgsVlWbrNPp4HK5cOfOnU33sAIa88Dij6PP52PaZsrOyM/HZmUty7GNKmrciGYZtVAzHjVetnTvXr58ueZ1xD9D6DP8vR+Px9HS0oJEItHwAtLtvPS7noH6WtisgaMaNrOTw2abWG0WtmRQfOzYMXzpS1/C0NAQ5ufn8alPfQqPPPIILl++jIWFBRiNxooL3O/3Y2Fhoeb35nK5MhlCPB6/F8NnbJT5vFJr0UYZ5icSCfh8PmZ7Jve81Wq18Pl8SCaTm/JhBaz9gUXH0WAwsMkB7y7Q7PuvhrUuxzaqqHGjmmWsZTxqvWwBIBQKwWg0Kl5H9AwJBAJsYkxotVq0tbWhp6cHly5damgBaSAQYI43pVIJ2WwWNptNLP02GZs5cKzHZndy2CwTq83ElgyK3/rWt7L/PnjwII4dO4a+vj58/etfX1OL2U9/+tMVBXz3kmY0n2+UYT75cKZSKdY2ln8x9vb2NuQlvJGs5YGVy+WQSqWQSqVQLBbLsunJZJIdp2be/1qsZjlWKZvcqKLGZmuWsdp7qNZkmO6xetdRPp9n2mh54CNJErMpbFQB6eDgIEZHR7GwsMAKej0eD+677z6x9NtkbPbAsRbCyUEgZ0sGxXLcbjd2796N69ev481vfjPy+Tyi0WhZwLK4uFhVg8zzH//jf8Sv//qvs3/H43H09PTcq2HfE/P5ex1gqP0euQ8n/Zwq+Q0GA8bGxrbtw8poNCKZTCKfz8PhcFQ4dCQSCUiSxDykNxOrWY4lmUi1bLLP52vIUvxqm2U0Q/MOtV62S0tLSCaTKBQKsNvtitfR7du3IUmSonRKjcRCrewhGAxicnISJpOJ+S1Twd7k5CQ8Hk/DA+Ot6pqwHmzlwFHIeQRytkVQnEwmcePGDXzwgx/EkSNHYDAY8KMf/Qjvfve7AQATExOYnp7GiRMnan4PdV5bLxptPl9rubpRAcZqxlzLh1OSJPGwQmXWbiugdjn25s2bGBsbq5lNbkRR42qaZaxVu6+GRt1DPLUcMpaXl7G0tISWlhZF6RRJLBYXF6tKLMidpt6xpgkRNe7JZDIVjXsarU/dqq4J68VWDhyFk4NAzpYMiv/Df/gPePvb346+vj7Mzc3hk5/8JHQ6Hd7//vfD5XLhX/2rf4Vf//Vfh8fjgdPpxL//9/8eJ06caKoiO+DeORncywBjtWNWCuS3+8Mqn8/DbrcjlUpVLUY0mUxs2XuzoXY59tq1a3WzyQ8//PCapUNqrzUATLtvMpnY5C2Xy21II4h69xCg7jqiLGC985HP5xX3bzVaebI7VGrcMzMz0zB96lZ1TVhPtvqzWDg5CHi2ZFA8MzOD97///QiHw2htbcXDDz+MM2fOsIv7f/7P/wmtVot3v/vdZc07mpFGOxk0KsBQ0+RgMxnvNxsU9NrtdubSwRcjOp1O9rnNhprl2FKphHg8rqqwrRFV2GqvNdLCzs/Pl2lhDx06tCGNIOpB15HNZkM8Hq96HdHESo0lm1p3GiWy2SyWlpbqNu7JZrOv46iVs5VdE9abrf4sFk4OAmJLBsVf+9rXav7ebDbjT//0T/Gnf/qn6zSitdEoJwM11cNqtqVmObJRD5nt+rDilyx7enqqBiGbdclSzXIsOSyoLe5pRBW2Gv/tyclJGI1G9Pf3lxWH3gstbCOufTXXkRppiNvtZm4wa3Gnyefzqhv3rJWt7JqwEWz1Z7FwchAAWzQo3oqouWGVsrf8cjUt9/Ldr1YTYPDLkaSxliSp6nLkWsa82n3frCjtP79kGQqF4HQ6YbVakc/nEQqFNvWSpZrl2KGhIVy8eFF1cU+jCqmUrjU+69jW1lb23U6nsyLr2CyFXWqvIwA1bR0b5QZjNBpVNe5pRAHpVnZN2Ci28rNYIABEULxlqJW9peXqWCzGllDlXqVqqoflRTLhcHhNRTLbvQCm3v5v5SXLevvm8/kwOzurqrhnPa6j1WQdC4VCQ8bTqP1Sex3V+kyj3GDMZjNaWloQjUYVG/e43e6KTPTrYSu7JggEgnuDCIq3APWKSUZGRmCxWDAxMQGdTgez2cxeRIlEAtFoFENDQ3WX4htZJLPdC2DU7v9WXrKst29qintCodC6XEdqs44LCwu4fv36msfT6PtDzXVU6zONcoNxuVzo7u5GsVhUbNzT3d297i4eAoFAAIigeNOzmha1QGWV+GqCq0YVyWz3ApjV7v9WXrKstW9qssnPP//8ulxHarKOVIy21vHcq/tDzXV0r91g+O9RatyzUS4eAoFAIILiTYKSRlHNsu78/DwAoK+vT9HJIJPJrFuRzHYvgNnu+78aamUvo9Houh1HNVlHvhhtLeNp1uvjXrnKyBv3NKOLh0Ag2B6IoHgTUEtbWCqV6hbRUZDa2toKl8tV8RlJkhAOh9etSGa7F8Bs9/1fLUrZy/U8jmqyjqstRlNTGHuv92u1bEZXma0sQRIIBI1FBMVNTj1t4Z49e6DX6xGPx1kWWF5EJ29RKy9iyeVy61oks90LYLb7/jeK9T6O9bKOqylGU1MY26zXR6PkPOspC9rKEiSBQNA4RFDcxKjRFs7OzsJsNuPatWvsRUmBajKZRDQaxe7du2Gz2dbcona1RTJKmbDtXgDD77/P56vwjt3q+98oNuI6akQxWj6fx+joaM3C2O18f2wUzWKjJ2gszXhem3FMghVEUNzEqNUW6nQ6AGDFdARfXDc8PFzTh7TRRTL17KS2cwEMHceFhQWMj4+jVCqx32m1WnZ8tur+N4qNKqRaSzHa0NAQJiYmak50JyYmMDQ0tG3vj41gu9tDblWa8bw245gEdxFBcROjRlsYDoeh0WjQ29tb0cbV4XCwIjqj0bhuRTJq7aS2ewGMUlAjgh31NNt1pEZioWaie+DAgabar63MdreH3Ko043ltxjEJyhFBcROjRltIy7Yulwtut7tmEZ3f77/nRTKrsZPargUwdIxKpRKGh4ertt7dypZ0jUbtdbReS5a1xrO4uKi6iK5R96tAme1uD7lVacbz2oxjElQiguImRo1m0ufzIZlMqi6iu9dFMqu1k2pkAcxm0Wnxx0ir1VacM2HJtnrqXUfrvWSpNJ7VFtGJArF7S7Pa3wnWRjOe12Yck6ASERQ3MWo0ivfddx8mJiaapihno+ykNpNOq5ktt7YizbRkud2LTJsNcS9uTZrxvDbjmASVaDd6AILakEaxo6MDmUwG4XAYmUwGHR0dOHbsGNra2jA8PAyr1YpgMIhsNssK34LB4LoX5fCZsGrcCzspCnrm5+dhtVrh9XphtVoxPz+Ps2fPIhgMNmxbjWAjjtF2Rb5kaTKZoNVq2ZJlOp0u6/h4r6GJbrPcr9sdcS9uTZrxvDbjmASViEzxJqCeZrKZio3W225sM+q0RLZw/bgXS5ZrlenQ/To2Nob5+Xnk83kYjUZ0dHRgz549TbeysZUR9+LWpBnPazOOSVCJCIo3CfW0hc1StEaZsMXFxap2Y5TZbtS4NqNOa6OsxLYjjV6y3EwyHUF9xL24NWnG89qMYxJUIoLiLUQzFeXwHsnVft4oNqtOq5my+1uZRnaHa5Q2mf8et9vNvmdhYQHxeFzYMq0z4l7cmjTjeW3GMQnKEUGx4HVRKpUwMzODZDIJu92O7u5uaLVaJmeQJAlDQ0NIJpNsedhutyMcDjdUznAvWuI2g3XXVqAZ3EAatWTZKJnOZpT7bAea9V5shntoM9OM57UZxyS4iwiKBatmYmICo6OjiEQiWF5ehk6ng8fjwcjICPx+P0KhEAwGA2ZmZpBOp1EqlaDVamG1WhsuZ2i0TqtZrLs2O80iM2jUkmWjZDqbUe6zXWi2e7FZ7qHNTrOdV6A5xyRYQQTFglUxMTGBU6dOIZfLwWazsaXfYDCIU6dO4ejRo0ilUkilUigWizCZTNDpdFheXkYymWTtoBslZ2ikTquZrLs2M812HBuxZNkomc5mlfsI1pdmu4cEgu2CCIoFqimVShgdHUUul4Pb7YZWu+LoZzKZYDAYEI1GcfnyZeRyORQKBTgcDhaM6vV66HQ6JBIJSJIEo9HYsHE1IugRy9qNoVmP41qXLBsl07kXch/B1qJZ7yGBYDsggmKBamZmZhCJRGCz2VhATJA8IhqNQq/Xr/vDeq1Bj1jWbgzNfBzXsmTZKJmOsGUS1KOZ7yGBYKsjgmKBapLJJJaXl2EwGCBJEpaXl5leWKfTsaVfq9UKjUaDdDpdJp+gYNVmsyGfzzd8fGsJesSydmPY7MdRqbCpUTIdYcu09VEqQlbLZr+HBILNjAiKtxD3ulLZbrdDp9Mhk8mgUCigUChAkiRoNBoYDAbo9Xro9XrYbDY4HA7EYjGk02nkcjlotVrY7XY4nU4AaLrlYbGs3Rg283GsV9jUKDslYcu0dalVhDw0NKTqOzbzPSQQbHZEULxFCAaD97xDVnd3N+x2OxYXF6HT6WAwGKDRaCBJEnK5HNLpNPx+P3p7e7G4uIienp6KjnahUKgpl4fFsnZj2KzHUW1hU6PslIQt09ajXhEyAFWB8Wa9hwSCrYAIircAwWAQzz77LAKBQFkHuXA4jEAggMcee6whgbFGo4HH40EgEGBZEK1Wi1KphOXlZfb7PXv2IJFIIBQKwel0wmq1Ip/PIxQKNe3ysFjWbgyb8TiutrCpUXZKwpZp66CmCHl0dBSDg4N1pRSb8R4SCLYK6oVOgqZEkiScP38es7OzKJVKMJvNsNlsMJvNKJVKmJ2dxfnz5xvSSS4WiwEA+vv7YbPZUCwWmf7NZrOhv78fwIrm7dixY+jo6EAmk0E4HEYmk0FHR0dTWwnRsvZmG3ezsdmO42oKmwSCaqgpQo5EIpiZmVH1fZvtHhIItgoiU7zJiUajmJqagkajgc1mK7NAs9lsSCQSmJqaQjQaRUtLi6rvVNImUwDs9/vh9/uRSCSYVMPhcABYyU7ncjn4/f5NuTwslrUbw2Y6jqKwSbBW+CLkahiNRqTTaSSTSdXfuZnuIYFgqyCC4k0OZRDsdnvVLJfFYkEqlUI4HFYVFNcqNpIXgMg1bdlstqwAZLMuD2/WcTcbm+U4isImwVqhIuRa15BOp4Pdbl/V926We0gg2CoI+cQmR40sQpIkVZ+jYqP5+XlYrVZ4vV5YrVbMz8/j7NmzyOfzLHMh/z4qAPH5fKIARLCpoMImcV0LXi/d3d3weDxIpVJldR3Ait44nU7D4/Ggu7t7g0YoEAjUIILiTY7P54PFYkE2m636Qs9kMrBYLPD5fDW/R15sZDKZoNVqWbFROp3GxMQEhoaGYLVaEQwGkc1mUSqVkM1mEQwGRQGIYFNChU3iuha8XrRaLUZGRmAymRCNRsuuoWg0CpPJhJGRkVX5FQsEgvVHyCc2OW63G/39/ZiYmEAqlWLd5CRJQrFYhCRJ6O/vr7sEp7bY6MCBA6o9Vu+1b7JAQKz1WtvM3sHiPmsOyG6NfIrT6TR0Oh1aW1tX5VMsEAg2DhEUb3I0Gg2OHDmCcDiMubk5FghrNBro9Xp0dnbiyJEjdV+Sqyk2UlNEV68RgkDQKBp1rW3GwiZxnzUXQ0NDGBwcXFNHO4FAsHGIoHiLYDQaYbVasby8zIJiar2shtUWG9UqAFHbCEEgWCuNvtY2U2GTuM+aE61Wi97e3o0ehkAgeB2I6esmh7TAkiRhz5492L17N3bu3Indu3djz549Zb+vRaOKjdRok9WMRyCox3a+1rbzvgsEAsG9QgTFmxxeC6zVamE2m2G322E2m6HValU3HmhUsZFohCBYL7bztbad912weiRJQjQaxeLiIqLRqJgsCQQKCPnEJqeRjQcaUWwkGiEI1ovtfK1t530XrA6hOxcI1COC4k1OoxsPrLXYSDRCEKyGtTgnbOdr7fXsu3Cp2H4I3blAsDpEULzJIS3w/Pw8Wltby15ypAXu6OhYVeOBtRQb3YvxCLYma81gbedrbbX7LrKF2w+57pyuEdKd0zXh8/nE5Egg+AkiKN7kkBY4FoshGAzC6XTCaDQin88jHo+ve+OBZhuPHJEtaw4akcFq9mvtXrKafW/WbKG4F+8tq9GdbxbHFYHgXiOC4i1AszUeaLbxECJb1hw0MoPVrNfaeqBm35s1WyjuxXuP0J0LBKtHBMVbhGZrPNBs42nWbNl2pNEZrGa71taTevvejNlCcS+uD9tZcy8QvF5EULyFaLbGA80ynmbNlm1X7kUGq1mutY2g1r43W7ZQ3Ivrx3bW3AsErxfhUyzY8mwHT9fN5EPKZ7CqITJYjaPZjvV2uBebhUZ5zwsE2wmRKRZseZotW9ZoNps+U2Sw1o9mO9Zb/V5sNraz5l4geD2IoFiw5dnK2rrNqM/c7K4Rm8k1odmO9Va+F5uV7ay5FwhWiwiKBVueZsuWNYrNrM/crBmszZaVB5rrWG/Ve7HZ2c6ae4FgNYigWLDlabZsWaNoRmeB1bDZMlibMStPNMux3qr3okAg2BqIoFiwLWimbFmj2Ar6zM2SwdrMWXmiWY71VrwXBQLB1kAExYJtQ7NkyxqF0GeuH5s9K99sbLV7USAQbA1EUCzYVjRLtqwRCH3m+rEVsvLNxla6FwUCwdZA+BQLBJsU4UO6fjSb369AIBAIGo8IigWCTQzpMzs6OpDJZBAOh5HJZNDR0dHUhV+bDcrKx2KxisYolJX3+XwiKy8QCASbGCGfEAg2OUKfee8RrgkCgUCw9RFBsUCwBRD6zHuPcE0QCASCrY0IigUCgUAlIisvEAgEWxcRFAsEAsEqEFl5gUAg2JqIQjuBQCAQCAQCwbZHBMUCgUAgEAgEgm2PCIoFAoFAIBAIBNseERQLBAKBQCAQCLY9IigWCAQCgUAgEGx7RFAsEAgEAoFAINj2iKBYIBAIBAKBQLDtEUGxQCAQCAQCgWDbI4JigUAgEAgEAsG2RwTFAoFAIBAIBIJtjwiKBQKBQCAQCATbHhEUCwQCgUAgEAi2PSIoFggEAoFAIBBse0RQLBAIBAKBQCDY9oigWCAQCAQCgUCw7RFBsUAgEAgEAoFg2yOCYoFAIBAIBALBtkcExQKBQCAQCASCbY8IigUCgUAgEAgE2x4RFAsEAoFAIBAItj36jR7AZkaSJABAPB7f4JEIBAKBQCAQCKpBcRrFbUqIoHgNJBIJAEBPT88Gj0QgEAgEAoFAUItEIgGXy6X4e41UL2wWKFIqlTA3NweHwwGNRrPRw1FNPB5HT08P7ty5A6fTudHD2dKIY71+iGO9fohjvX6IY71+iGO9PmzEcZYkCYlEAp2dndBqlZXDIlO8BrRaLbq7uzd6GK8bp9Mpbvx1Qhzr9UMc6/VDHOv1Qxzr9UMc6/VhvY9zrQwxIQrtBAKBQCAQCATbHhEUCwQCgUAgEAi2PSIo3oaYTCZ88pOfhMlk2uihbHnEsV4/xLFeP8SxXj/EsV4/xLFeH5r5OItCO4FAIBAIBALBtkdkigUCgUAgEAgE2x4RFAsEAoFAIBAItj0iKBYIBAKBQCAQbHtEUCwQCAQCgUAg2PaIoHiL8ulPfxpHjx6Fw+FAW1sb/sW/+BeYmJgo+0w2m8WTTz4Jr9cLu92Od7/73VhcXNygEW9ePv/5z+PgwYPMiPzEiRP43ve+x34vjvO94zOf+Qw0Gg0++tGPsp+J490Yfvd3fxcajabsf8PDw+z34jg3ltnZWfzCL/wCvF4vLBYLDhw4gJdffpn9XpIk/M7v/A46OjpgsVjw+OOPY3JycgNHvDnp7++vuK41Gg2efPJJAOK6biTLy8v47d/+bQwMDMBisWDnzp34r//1v4L3d2i261oExVuUZ599Fk8++STOnDmDH/7whygUCnjLW96CVCrFPvOxj30MTz31FL7xjW/g2WefxdzcHN71rndt4Kg3J93d3fjMZz6D8+fP4+WXX8bJkyfxjne8A1euXAEgjvO94ty5c/iLv/gLHDx4sOzn4ng3jn379mF+fp797/nnn2e/E8e5cSwtLeGhhx6CwWDA9773PVy9ehX//b//d7S0tLDP/MEf/AE+97nP4c///M9x9uxZ2Gw2PPHEE8hmsxs48s3HuXPnyq7pH/7whwCAn/3ZnwUgrutG8vu///v4/Oc/jz/5kz/B2NgYfv/3fx9/8Ad/gD/+4z9mn2m661oSbAsCgYAEQHr22WclSZKkaDQqGQwG6Rvf+Ab7zNjYmARAeumllzZqmFuGlpYW6X//7/8tjvM9IpFISIODg9IPf/hD6bHHHpM+8pGPSJIkrutG8slPflI6dOhQ1d+J49xYfuu3fkt6+OGHFX9fKpWk9vZ26Q//8A/Zz6LRqGQymaS/+7u/W48hblk+8pGPSDt37pRKpZK4rhvM2972NumXf/mXy372rne9S/rABz4gSVJzXtciU7xNiMViAACPxwMAOH/+PAqFAh5//HH2meHhYfT29uKll17akDFuBZaXl/G1r30NqVQKJ06cEMf5HvHkk0/ibW97W9lxBcR13WgmJyfR2dmJHTt24AMf+ACmp6cBiOPcaL7zne/ggQcewM/+7M+ira0N999/P/7qr/6K/f7WrVtYWFgoO94ulwvHjh0Tx3sN5PN5/O3f/i1++Zd/GRqNRlzXDebBBx/Ej370I1y7dg0AcPHiRTz//PN461vfCqA5r2v9hmxVsK6USiV89KMfxUMPPYT9+/cDABYWFmA0GuF2u8s+6/f7sbCwsAGj3NxcunQJJ06cQDabhd1ux7e+9S3s3bsXr776qjjODeZrX/saLly4gHPnzlX8TlzXjePYsWP40pe+hKGhIczPz+NTn/oUHnnkEVy+fFkc5wZz8+ZNfP7zn8ev//qv4z/9p/+Ec+fO4dd+7ddgNBrxoQ99iB1Tv99f9nfieK+Nf/zHf0Q0GsUv/uIvAhDPj0bziU98AvF4HMPDw9DpdFheXsbv/d7v4QMf+AAANOV1LYLibcCTTz6Jy5cvl+kBBY1laGgIr776KmKxGP7v//2/+NCHPoRnn312o4e15bhz5w4+8pGP4Ic//CHMZvNGD2dLQ9kcADh48CCOHTuGvr4+fP3rX4fFYtnAkW09SqUSHnjgAfy3//bfAAD3338/Ll++jD//8z/Hhz70oQ0e3dblC1/4At761reis7Nzo4eyJfn617+Or3zlK/jqV7+Kffv24dVXX8VHP/pRdHZ2Nu11LeQTW5wPf/jD+O53v4vTp0+ju7ub/by9vR35fB7RaLTs84uLi2hvb1/nUW5+jEYjdu3ahSNHjuDTn/40Dh06hM9+9rPiODeY8+fPIxAI4PDhw9Dr9dDr9Xj22Wfxuc99Dnq9Hn6/Xxzve4Tb7cbu3btx/fp1cV03mI6ODuzdu7fsZ3v27GFyFTqmchcEcbxfP7dv38apU6fwK7/yK+xn4rpuLL/xG7+BT3ziE3jf+96HAwcO4IMf/CA+9rGP4dOf/jSA5ryuRVC8RZEkCR/+8IfxrW99C8888wwGBgbKfn/kyBEYDAb86Ec/Yj+bmJjA9PQ0Tpw4sd7D3XKUSiXkcjlxnBvMm970Jly6dAmvvvoq+98DDzyAD3zgA+y/xfG+NySTSdy4cQMdHR3ium4wDz30UIVl5rVr19DX1wcAGBgYQHt7e9nxjsfjOHv2rDjer5MvfvGLaGtrw9ve9jb2M3FdN5Z0Og2ttjzM1Ol0KJVKAJr0ut6Q8j7BPedXf/VXJZfLJf34xz+W5ufn2f/S6TT7zL/9t/9W6u3tlZ555hnp5Zdflk6cOCGdOHFiA0e9OfnEJz4hPfvss9KtW7ek1157TfrEJz4haTQa6Qc/+IEkSeI432t49wlJEse7UXz84x+XfvzjH0u3bt2SXnjhBenxxx+XfD6fFAgEJEkSx7mRjI6OSnq9Xvq93/s9aXJyUvrKV74iWa1W6W//9m/ZZz7zmc9Ibrdb+va3vy299tpr0jve8Q5pYGBAymQyGzjyzcny8rLU29sr/dZv/VbF78R13Tg+9KEPSV1dXdJ3v/td6datW9I3v/lNyefzSb/5m7/JPtNs17UIircoAKr+74tf/CL7TCaTkf7dv/t3UktLi2S1WqV3vvOd0vz8/MYNepPyy7/8y1JfX59kNBql1tZW6U1vehMLiCVJHOd7jTwoFse7Mbz3ve+VOjo6JKPRKHV1dUnvfe97pevXr7Pfi+PcWJ566ilp//79kslkkoaHh6W//Mu/LPt9qVSSfvu3f1vy+/2SyWSS3vSmN0kTExMbNNrNzfe//30JQNXjJ67rxhGPx6WPfOQjUm9vr2Q2m6UdO3ZI//k//2cpl8uxzzTbda2RJK61iEAgEAgEAoFAsA0RmmKBQCAQCAQCwbZHBMUCgUAgEAgEgm2PCIoFAoFAIBAIBNseERQLBAKBQCAQCLY9IigWCAQCgUAgEGx7RFAsEAgEAoFAINj2iKBYIBAIBAKBQLDtEUGxQCAQCAQCgWDbI4JigUAg2OQ8++yz0Gg07H8vvvjiRg9JIBAINh0iKBYIBIJNzl//9V+X/fvLX/7yBo1EIBAINi+izbNAIBBsYjKZDPx+PxKJBOx2O5LJJFpaWjA/Pw+TybTRwxMIBIJNg8gUCwQCwSbmW9/6FhKJBADgc5/7HABgaWkJTz311EYOSyAQCDYdIigWCASCTQxJJQ4ePIhf+qVfwtDQUNnPBQKBQKAOERQLBALBJmV+fh6nTp0CAPzCL/xC2f8//fTTCAaDdb8jHA7jN3/zNzE0NASLxQK/3483v/nN+Na3vgUA+NKXvsQK+KamphS/J5vN4k/+5E/wpje9Ce3t7TAajWhra8Pjjz+OL3zhCygWi2vcW4FAILi3CE2xQCAQbFL+6I/+CL/xG78BrVaL6elpdHV14datW9i5cyckScJnP/tZ/Nqv/Zri31+6dAlvfvObsbi4WPX3//pf/2ucOHECv/RLvwQAuHXrFvr7+ys+d/HiRbzjHe/A7du3Fbd19OhRPPXUU/D7/avbSYFAIFgnRFAsEAgEm5RDhw7htddew8mTJ/GjH/2I/fzhhx/GCy+8gCNHjuDll1+u+rfRaBT79u3D3NwcAOCDH/wgfv7nfx6tra24fv06PvvZz+Kll17CsWPHcPbsWQDVg+Lr16/jgQceQCwWg9PpxJNPPomRkRH09PQgHA7jO9/5Dv7iL/4CxWIRx44dw3PPPQeDwXBvDohAIBCsBUkgEAgEm45XXnlFAiABkP7P//k/Zb/7/Oc/z3535cqVqn//0Y9+lH3mf/2v/1Xx+2KxKL3jHe9gnwEg3bp1q+JzDz74oARAuv/++6VgMFh1W9/73vckrVYrAZD+8i//cvU7KxAIBOuA0BQLBALBJoQK6SwWC9797neX/e7nfu7nYDQayz7Hk8vl8KUvfQnAiqzhIx/5SMVndDod/uIv/gJms1lxDM899xxrFPLXf/3X8Pl8VT/3Uz/1U3jPe94DAGy7AoFA0GyIoFggEAg2GcViEV/96lcBAG9/+9vhdDrLfu/xePDTP/3TAICvfOUrKJVKZb9/+eWXEY1GAdwtzKuG3+/HE088ofj773znOwCAoaEhHDhwoOaYH330UQDAuXPnRNGdQCBoSkRQLBAIBJuM73//+6w4TimopZ/PzMzg9OnTZb+7fPky++8jR47U3NYDDzyg+DvSK09MTJS1ma72vw9/+MMAgEKhgEgkUmcPBQKBYP0RQbFAIBBsMkgS4fV68VM/9VNVP/MzP/MzcLvdZZ8nlpaW2H+3trbW3Fat3wcCATXDrSCdTr+uvxMIBIJ7iX6jByAQCAQC9cRiMSZbCIfDTDtci29+85v4sz/7M9hstoaOZXl5GcCKC8bf/u3fqv67rq6uho5DIBAIGoEIigUCgWAT8fWvfx3ZbHZVf5NMJvHNb34TH/zgBwEALS0t7HfBYBC7d+9W/NtaDUC8Xi/7/v37969qTAKBQNBsiKBYIBAINhEkhejo6MD/+B//o+7nf+M3fgMzMzP48pe/zILiffv2sd+fP38eDz30kOLfK/kcA8D999+PF198ETdv3sTCwgLa29vV7oZAIBA0HaJ5h0AgEGwS+G51H/7wh/HHf/zHdf/mox/9KD772c+Wdb3LZrNob29HLBbD0aNHMTo6WvVvFxcX0d/fzzLT8uYdP/jBD5g7xSc+8Ql8+tOfXvtOCgQCwQYhCu0EAoFgk/DlL38ZlMcg39960OdKpRLT/ZrNZvzLf/kvAaxYpH32s5+t+LtSqYR/82/+TU2pxlve8haMjIwAAP7wD/8QX//612uO5dKlS3jqqadUjVsgEAjWG5EpFggEgk3Crl27cOPGDbS1tWF+fh5abf28RqlUQnd3N+bn57Fv3z5mxxaJRLBv3z4sLCwAWGnz/IEPfKCszfOLL76IkZERlkmemppCX19f2fffuHEDIyMjzGbt7W9/O9773vdicHAQOp0OgUAAr7zyCp566imcOXMGH//4x/FHf/RHjTwsAoFA0BCEplggEAg2AS+88AJu3LgBAHjnO9+pKiAGAK1Wi3e+8534sz/7M1y5cgXnz5/HkSNH4PF48PTTT+PNb34zgsEg/uZv/gZ/8zd/U/a3v/iLv4hHHnmEBcXVutvt3LkTL730Et797nfj8uXLeOqpp2pmg+WNRgQCgaBZEPIJgUAg2ATwXsPyts714D/Pf8+hQ4dw9epVfPzjH8fg4CBMJhN8Ph/e+MY34qtf/Sq++MUvIh6Ps8+7XK6q37979268+uqr+OpXv4p3v/vd6O3thcVigdFoREdHB97whjfgv/yX/4Lz58/jd37nd1Y1doFAIFgvhHxCIBAIBIr8yq/8Cr7whS+gu7sbd+7c2ejhCAQCwT1DZIoFAoFAUJVMJoNvf/vbAIDjx49v8GgEAoHg3iKCYoFAINim3LhxA0qLhcvLy/jVX/1VhEIhAMCHPvSh9RyaQCAQrDtCPiEQCATblF/8xV/E6Ogo3ve+9+HYsWNoa2tDJpPBa6+9hr/6q7/ChQsXAACPP/44fvCDH0Cj0WzwiAUCgeDeIdwnBAKBYBszNjaGT37yk4q/f+ihh/C1r31NBMQCgWDLIzLFAoFAsE2ZmJjAP/zDP+DUqVOYmppCMBhEoVCA1+vFAw88gPe+97143/vep9r+TSAQCDYzIigWCAQCgUAgEGx7xPRfIBAIBAKBQPD/b7cOBAAAAAAE+VsPclG0J8UAAOxJMQAAe1IMAMCeFAMAsCfFAADsSTEAAHtSDADAnhQDALAXbmxtNHKGXO4AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "lowess = sm.nonparametric.lowess\n", "fig, ax = subplots(figsize=(8,8))\n", @@ -3209,27 +1418,15 @@ " linewidth=4)\n", "ax.set_xlabel('Age', fontsize=20)\n", "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='span', fontsize=15);\n" + "ax.legend(title='span', fontsize=15);" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch8-baggboost-lab.ipynb b/docs/source/labs/Ch8-baggboost-lab.ipynb index 2fb8cfa..04147cd 100644 --- a/docs/source/labs/Ch8-baggboost-lab.ipynb +++ b/docs/source/labs/Ch8-baggboost-lab.ipynb @@ -2,36 +2,25 @@ "cells": [ { "cell_type": "markdown", - "id": "257f1382", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2ade0554", + "id": "7b0242c6", "metadata": {}, "source": [ "# Tree-Based Methods\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", "We import some of our usual libraries at this top\n", "level." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "4cc7120f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:52.562040Z", - "iopub.status.busy": "2023-07-25T23:59:52.561717Z", - "iopub.status.idle": "2023-07-25T23:59:53.673192Z", - "shell.execute_reply": "2023-07-25T23:59:53.672771Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -40,7 +29,7 @@ "from statsmodels.datasets import get_rdataset\n", "import sklearn.model_selection as skm\n", "from ISLP import load_data, confusion_table\n", - "from ISLP.models import ModelSpec as MS\n" + "from ISLP.models import ModelSpec as MS" ] }, { @@ -54,17 +43,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "864fa15e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.675446Z", - "iopub.status.busy": "2023-07-25T23:59:53.675242Z", - "iopub.status.idle": "2023-07-25T23:59:53.720701Z", - "shell.execute_reply": "2023-07-25T23:59:53.720361Z" - }, - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import (DecisionTreeClassifier as DTC,\n", @@ -76,7 +57,7 @@ "from sklearn.ensemble import \\\n", " (RandomForestRegressor as RF,\n", " GradientBoostingRegressor as GBR)\n", - "from ISLP.bart import BART\n" + "from ISLP.bart import BART" ] }, { @@ -102,22 +83,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bfb4c83b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.722793Z", - "iopub.status.busy": "2023-07-25T23:59:53.722661Z", - "iopub.status.idle": "2023-07-25T23:59:53.727612Z", - "shell.execute_reply": "2023-07-25T23:59:53.727266Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "Carseats = load_data('Carseats')\n", "High = np.where(Carseats.Sales > 8,\n", " \"Yes\",\n", - " \"No\")\n" + " \"No\")" ] }, { @@ -128,28 +102,20 @@ "We now use `DecisionTreeClassifier()` to fit a classification tree in\n", "order to predict `High` using all variables but `Sales`.\n", "To do so, we must form a model matrix as we did when fitting regression\n", - "models. " + "models." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5d9e7a14", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.729444Z", - "iopub.status.busy": "2023-07-25T23:59:53.729340Z", - "iopub.status.idle": "2023-07-25T23:59:53.738990Z", - "shell.execute_reply": "2023-07-25T23:59:53.738679Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n", "D = model.fit_transform(Carseats)\n", "feature_names = list(D.columns)\n", - "X = np.asarray(D)\n" + "X = np.asarray(D)" ] }, { @@ -168,37 +134,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "970c4515", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.740700Z", - "iopub.status.busy": "2023-07-25T23:59:53.740605Z", - "iopub.status.idle": "2023-07-25T23:59:53.745991Z", - "shell.execute_reply": "2023-07-25T23:59:53.745690Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "clf = DTC(criterion='entropy',\n", " max_depth=3,\n", " random_state=0) \n", - "clf.fit(X, High)\n" + "clf.fit(X, High)" ] }, { @@ -220,31 +164,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2d9a51dc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.747622Z", - "iopub.status.busy": "2023-07-25T23:59:53.747497Z", - "iopub.status.idle": "2023-07-25T23:59:53.750600Z", - "shell.execute_reply": "2023-07-25T23:59:53.750316Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.79" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(High, clf.predict(X))\n" + "metadata": {}, + "outputs": [], + "source": [ + "accuracy_score(High, clf.predict(X))" ] }, { @@ -267,31 +192,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "cbc04b67", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.752228Z", - "iopub.status.busy": "2023-07-25T23:59:53.752135Z", - "iopub.status.idle": "2023-07-25T23:59:53.755654Z", - "shell.execute_reply": "2023-07-25T23:59:53.755371Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4710647062649358" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n", - "resid_dev\n" + "resid_dev" ] }, { @@ -310,34 +217,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "809830c7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:53.757328Z", - "iopub.status.busy": "2023-07-25T23:59:53.757207Z", - "iopub.status.idle": "2023-07-25T23:59:54.066251Z", - "shell.execute_reply": "2023-07-25T23:59:54.065834Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = subplots(figsize=(12,12))[1]\n", "plot_tree(clf,\n", " feature_names=feature_names,\n", - " ax=ax);\n" + " ax=ax);" ] }, { @@ -358,51 +246,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "abe8c7fc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.068192Z", - "iopub.status.busy": "2023-07-25T23:59:54.068071Z", - "iopub.status.idle": "2023-07-25T23:59:54.071170Z", - "shell.execute_reply": "2023-07-25T23:59:54.070769Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "|--- ShelveLoc[Good] <= 0.50\n", - "| |--- Price <= 92.50\n", - "| | |--- Income <= 57.00\n", - "| | | |--- weights: [7.00, 3.00] class: No\n", - "| | |--- Income > 57.00\n", - "| | | |--- weights: [7.00, 29.00] class: Yes\n", - "| |--- Price > 92.50\n", - "| | |--- Advertising <= 13.50\n", - "| | | |--- weights: [183.00, 41.00] class: No\n", - "| | |--- Advertising > 13.50\n", - "| | | |--- weights: [20.00, 25.00] class: Yes\n", - "|--- ShelveLoc[Good] > 0.50\n", - "| |--- Price <= 135.00\n", - "| | |--- US[Yes] <= 0.50\n", - "| | | |--- weights: [6.00, 11.00] class: Yes\n", - "| | |--- US[Yes] > 0.50\n", - "| | | |--- weights: [2.00, 49.00] class: Yes\n", - "| |--- Price > 135.00\n", - "| | |--- Income <= 46.00\n", - "| | | |--- weights: [6.00, 0.00] class: No\n", - "| | |--- Income > 46.00\n", - "| | | |--- weights: [5.00, 6.00] class: Yes\n", - "\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(export_text(clf,\n", " feature_names=feature_names,\n", - " show_weights=True))\n" + " show_weights=True))" ] }, { @@ -423,29 +274,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "f7a1f736", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.072938Z", - "iopub.status.busy": "2023-07-25T23:59:54.072835Z", - "iopub.status.idle": "2023-07-25T23:59:54.079362Z", - "shell.execute_reply": "2023-07-25T23:59:54.079048Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.685])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", " test_size=200,\n", @@ -454,15 +286,7 @@ " D,\n", " High,\n", " cv=validation)\n", - "results['test_score']\n" - ] - }, - { - "cell_type": "markdown", - "id": "45f99d7e", - "metadata": {}, - "source": [ - " " + "results['test_score']" ] }, { @@ -479,17 +303,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "c663a623", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.081283Z", - "iopub.status.busy": "2023-07-25T23:59:54.081145Z", - "iopub.status.idle": "2023-07-25T23:59:54.083681Z", - "shell.execute_reply": "2023-07-25T23:59:54.083365Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "(X_train,\n", @@ -507,38 +323,19 @@ "id": "9e618049", "metadata": {}, "source": [ - "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.\n" + "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "c2f2a403", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.085505Z", - "iopub.status.busy": "2023-07-25T23:59:54.085354Z", - "iopub.status.idle": "2023-07-25T23:59:54.089710Z", - "shell.execute_reply": "2023-07-25T23:59:54.089437Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.735" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "clf = DTC(criterion='entropy', random_state=0)\n", "clf.fit(X_train, High_train)\n", - "accuracy_score(High_test, clf.predict(X_test))\n" + "accuracy_score(High_test, clf.predict(X_test))" ] }, { @@ -547,28 +344,20 @@ "metadata": {}, "source": [ "Next we use the `cost_complexity_pruning_path()` method of\n", - "`clf` to extract cost-complexity values. " + "`clf` to extract cost-complexity values." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "b2505658", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.091391Z", - "iopub.status.busy": "2023-07-25T23:59:54.091299Z", - "iopub.status.idle": "2023-07-25T23:59:54.094646Z", - "shell.execute_reply": "2023-07-25T23:59:54.094353Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n", "kfold = skm.KFold(10,\n", " random_state=1,\n", - " shuffle=True)\n" + " shuffle=True)" ] }, { @@ -582,29 +371,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "09a7bd33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.096582Z", - "iopub.status.busy": "2023-07-25T23:59:54.096441Z", - "iopub.status.idle": "2023-07-25T23:59:54.355162Z", - "shell.execute_reply": "2023-07-25T23:59:54.354839Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.685" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "grid = skm.GridSearchCV(clf,\n", " {'ccp_alpha': ccp_path.ccp_alphas},\n", @@ -612,7 +382,7 @@ " cv=kfold,\n", " scoring='accuracy')\n", "grid.fit(X_train, High_train)\n", - "grid.best_score_\n" + "grid.best_score_" ] }, { @@ -625,35 +395,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "a75dea32", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:54.357064Z", - "iopub.status.busy": "2023-07-25T23:59:54.356931Z", - "iopub.status.idle": "2023-07-25T23:59:55.201547Z", - "shell.execute_reply": "2023-07-25T23:59:55.201208Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - "Truth No Yes\n", - "Predicted \n", - "No 94 32\n", - "Yes 24 50" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(accuracy_score(High_test,\n", " best_.predict(X_test)))\n", "confusion = confusion_table(best_.predict(X_test),\n", " High_test)\n", - "confusion\n" + "confusion" ] }, { @@ -796,9 +456,7 @@ "id": "adbe5709", "metadata": {}, "source": [ - "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.\n", - "\n", - " " + "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance." ] }, { @@ -813,23 +471,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "1ca6bc7a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.216363Z", - "iopub.status.busy": "2023-07-25T23:59:55.216227Z", - "iopub.status.idle": "2023-07-25T23:59:55.224376Z", - "shell.execute_reply": "2023-07-25T23:59:55.224038Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "Boston = load_data(\"Boston\")\n", "model = MS(Boston.columns.drop('medv'), intercept=False)\n", "D = model.fit_transform(Boston)\n", "feature_names = list(D.columns)\n", - "X = np.asarray(D)\n" + "X = np.asarray(D)" ] }, { @@ -838,21 +489,14 @@ "metadata": {}, "source": [ "First, we split the data into training and test sets, and fit the tree\n", - "to the training data. Here we use 30% of the data for the test set.\n" + "to the training data. Here we use 30% of the data for the test set." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "15cfb553", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.226102Z", - "iopub.status.busy": "2023-07-25T23:59:55.226001Z", - "iopub.status.idle": "2023-07-25T23:59:55.228520Z", - "shell.execute_reply": "2023-07-25T23:59:55.228200Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "(X_train,\n", @@ -861,7 +505,7 @@ " y_test) = skm.train_test_split(X,\n", " Boston['medv'],\n", " test_size=0.3,\n", - " random_state=0)\n" + " random_state=0)" ] }, { @@ -874,35 +518,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d18820a3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.230261Z", - "iopub.status.busy": "2023-07-25T23:59:55.230165Z", - "iopub.status.idle": "2023-07-25T23:59:55.529885Z", - "shell.execute_reply": "2023-07-25T23:59:55.529499Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "reg = DTR(max_depth=3)\n", "reg.fit(X_train, y_train)\n", "ax = subplots(figsize=(12,12))[1]\n", "plot_tree(reg,\n", " feature_names=feature_names,\n", - " ax=ax);\n" + " ax=ax);" ] }, { @@ -928,16 +554,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "619f8d94", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.531947Z", - "iopub.status.busy": "2023-07-25T23:59:55.531809Z", - "iopub.status.idle": "2023-07-25T23:59:55.575222Z", - "shell.execute_reply": "2023-07-25T23:59:55.574850Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n", @@ -949,7 +568,7 @@ " refit=True,\n", " cv=kfold,\n", " scoring='neg_mean_squared_error')\n", - "G = grid.fit(X_train, y_train)\n" + "G = grid.fit(X_train, y_train)" ] }, { @@ -963,32 +582,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "779a14b6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.577111Z", - "iopub.status.busy": "2023-07-25T23:59:55.576968Z", - "iopub.status.idle": "2023-07-25T23:59:55.580045Z", - "shell.execute_reply": "2023-07-25T23:59:55.579754Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "28.06985754975404" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "best_ = grid.best_estimator_\n", - "np.mean((y_test - best_.predict(X_test))**2)\n" + "np.mean((y_test - best_.predict(X_test))**2)" ] }, { @@ -1009,43 +609,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "2b04030b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.581772Z", - "iopub.status.busy": "2023-07-25T23:59:55.581667Z", - "iopub.status.idle": "2023-07-25T23:59:55.879963Z", - "shell.execute_reply": "2023-07-25T23:59:55.879563Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "ax = subplots(figsize=(12,12))[1]\n", "plot_tree(G.best_estimator_,\n", " feature_names=feature_names,\n", - " ax=ax);\n" - ] - }, - { - "cell_type": "markdown", - "id": "cf40b6ac", - "metadata": {}, - "source": [ - " \n", - " " + " ax=ax);" ] }, { @@ -1070,35 +642,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "7dedce31", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:55.882069Z", - "iopub.status.busy": "2023-07-25T23:59:55.881917Z", - "iopub.status.idle": "2023-07-25T23:59:56.036284Z", - "shell.execute_reply": "2023-07-25T23:59:56.035918Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestRegressor(max_features=12, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestRegressor(max_features=12, random_state=0)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n", - "bag_boston.fit(X_train, y_train)\n" + "bag_boston.fit(X_train, y_train)" ] }, { @@ -1114,43 +664,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "1f2c7336", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:56.038138Z", - "iopub.status.busy": "2023-07-25T23:59:56.037994Z", - "iopub.status.idle": "2023-07-25T23:59:56.137043Z", - "shell.execute_reply": "2023-07-25T23:59:56.136608Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "14.634700151315787" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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importance
lstat0.356203
rm0.332163
ptratio0.067270
crim0.055404
indus0.053851
dis0.041582
nox0.035225
tax0.025355
age0.021506
rad0.004784
chas0.004203
zn0.002454
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" - ], - "text/plain": [ - " importance\n", - "lstat 0.356203\n", - "rm 0.332163\n", - "ptratio 0.067270\n", - "crim 0.055404\n", - "indus 0.053851\n", - "dis 0.041582\n", - "nox 0.035225\n", - "tax 0.025355\n", - "age 0.021506\n", - "rad 0.004784\n", - "chas 0.004203\n", - "zn 0.002454" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "feature_imp = pd.DataFrame(\n", " {'importance':RF_boston.feature_importances_},\n", @@ -1387,9 +765,7 @@ "\n", "The results indicate that across all of the trees considered in the\n", "random forest, the wealth level of the community (`lstat`) and the\n", - "house size (`rm`) are by far the two most important variables.\n", - "\n", - " " + "house size (`rm`) are by far the two most important variables." ] }, { @@ -1417,40 +793,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "887b4fa9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-25T23:59:57.024067Z", - "iopub.status.busy": "2023-07-25T23:59:57.023921Z", - "iopub.status.idle": "2023-07-26T00:00:00.413857Z", - "shell.execute_reply": "2023-07-26T00:00:00.413519Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
-       "                          random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n", - " random_state=0)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "boost_boston = GBR(n_estimators=5000,\n", " learning_rate=0.001,\n", " max_depth=3,\n", " random_state=0)\n", - "boost_boston.fit(X_train, y_train)\n" + "boost_boston.fit(X_train, y_train)" ] }, { @@ -1465,28 +817,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "6c56696c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:00.415790Z", - "iopub.status.busy": "2023-07-26T00:00:00.415651Z", - "iopub.status.idle": "2023-07-26T00:00:00.945897Z", - "shell.execute_reply": "2023-07-26T00:00:00.945457Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "test_error = np.zeros_like(boost_boston.train_score_)\n", "for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):\n", @@ -1502,7 +836,7 @@ " test_error,\n", " 'r',\n", " label='Test')\n", - "ax.legend();\n" + "ax.legend();" ] }, { @@ -1515,31 +849,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "8a636f63", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:00.947856Z", - "iopub.status.busy": "2023-07-26T00:00:00.947708Z", - "iopub.status.idle": "2023-07-26T00:00:00.959031Z", - "shell.execute_reply": "2023-07-26T00:00:00.958695Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "14.481405918831591" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "y_hat_boost = boost_boston.predict(X_test);\n", - "np.mean((y_test - y_hat_boost)**2)\n" + "np.mean((y_test - y_hat_boost)**2)" ] }, { @@ -1556,29 +872,10 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "4e2ab3c7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:00.960939Z", - "iopub.status.busy": "2023-07-26T00:00:00.960794Z", - "iopub.status.idle": "2023-07-26T00:00:03.124886Z", - "shell.execute_reply": "2023-07-26T00:00:03.124529Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "14.501514553719565" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "boost_boston = GBR(n_estimators=5000,\n", " learning_rate=0.2,\n", @@ -1587,7 +884,7 @@ "boost_boston.fit(X_train,\n", " y_train)\n", "y_hat_boost = boost_boston.predict(X_test);\n", - "np.mean((y_test - y_hat_boost)**2)\n" + "np.mean((y_test - y_hat_boost)**2)" ] }, { @@ -1596,9 +893,7 @@ "metadata": {}, "source": [ "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n", - "as $\\lambda=0.001$.\n", - "\n", - " " + "as $\\lambda=0.001$." ] }, { @@ -1623,35 +918,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "b0b41c04", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:03.126938Z", - "iopub.status.busy": "2023-07-26T00:00:03.126803Z", - "iopub.status.idle": "2023-07-26T00:00:04.882575Z", - "shell.execute_reply": "2023-07-26T00:00:04.882239Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
BART(burnin=5, ndraw=15, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "BART(burnin=5, ndraw=15, random_state=0)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n", - "bart_boston.fit(X_train, y_train)\n" + "bart_boston.fit(X_train, y_train)" ] }, { @@ -1664,32 +937,13 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "fda8fffc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:04.884481Z", - "iopub.status.busy": "2023-07-26T00:00:04.884344Z", - "iopub.status.idle": "2023-07-26T00:00:05.801195Z", - "shell.execute_reply": "2023-07-26T00:00:05.800892Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20.739185417498764" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n", - "np.mean((y_test - yhat_test)**2)\n" + "np.mean((y_test - yhat_test)**2)" ] }, { @@ -1703,64 +957,22 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "be77f0e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:05.803030Z", - "iopub.status.busy": "2023-07-26T00:00:05.802900Z", - "iopub.status.idle": "2023-07-26T00:00:05.806091Z", - "shell.execute_reply": "2023-07-26T00:00:05.805780Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "crim 25.466667\n", - "zn 30.600000\n", - "indus 24.933333\n", - "chas 21.133333\n", - "nox 27.333333\n", - "rm 28.800000\n", - "age 23.466667\n", - "dis 26.000000\n", - "rad 25.000000\n", - "tax 21.733333\n", - "ptratio 26.800000\n", - "lstat 31.866667\n", - "dtype: float64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n", " index=D.columns)\n", - "var_inclusion\n" + "var_inclusion" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch9-svm-lab.ipynb b/docs/source/labs/Ch9-svm-lab.ipynb index 94aa676..4a7215e 100644 --- a/docs/source/labs/Ch9-svm-lab.ipynb +++ b/docs/source/labs/Ch9-svm-lab.ipynb @@ -1,20 +1,17 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "67b2823a", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, { "cell_type": "markdown", "id": "d45c6d2b", "metadata": {}, "source": [ "# Support Vector Machines\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", "In this lab, we use the `sklearn.svm` library to demonstrate the support\n", "vector classifier and the support vector machine.\n", "\n", @@ -23,23 +20,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "eeaa5be0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.016487Z", - "iopub.status.busy": "2023-07-26T00:00:07.016196Z", - "iopub.status.idle": "2023-07-26T00:00:07.822651Z", - "shell.execute_reply": "2023-07-26T00:00:07.822165Z" - }, - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib.pyplot import subplots, cm\n", "import sklearn.model_selection as skm\n", - "from ISLP import load_data, confusion_table\n" + "from ISLP import load_data, confusion_table" ] }, { @@ -53,21 +42,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "41a59634", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.824899Z", - "iopub.status.busy": "2023-07-26T00:00:07.824653Z", - "iopub.status.idle": "2023-07-26T00:00:07.850806Z", - "shell.execute_reply": "2023-07-26T00:00:07.850496Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "from ISLP.svm import plot as plot_svm\n", - "from sklearn.metrics import RocCurveDisplay\n" + "from sklearn.metrics import RocCurveDisplay" ] }, { @@ -81,19 +63,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c9a175d7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.852684Z", - "iopub.status.busy": "2023-07-26T00:00:07.852555Z", - "iopub.status.idle": "2023-07-26T00:00:07.854324Z", - "shell.execute_reply": "2023-07-26T00:00:07.854058Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "roc_curve = RocCurveDisplay.from_estimator # shorthand\n" + "roc_curve = RocCurveDisplay.from_estimator # shorthand" ] }, { @@ -121,29 +96,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a7216b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.855974Z", - "iopub.status.busy": "2023-07-26T00:00:07.855853Z", - "iopub.status.idle": "2023-07-26T00:00:07.959154Z", - "shell.execute_reply": "2023-07-26T00:00:07.958758Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", "X = rng.standard_normal((50, 2))\n", @@ -153,7 +109,7 @@ "ax.scatter(X[:,0],\n", " X[:,1],\n", " c=y,\n", - " cmap=cm.coolwarm);\n" + " cmap=cm.coolwarm);" ] }, { @@ -166,35 +122,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ed329198", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.961084Z", - "iopub.status.busy": "2023-07-26T00:00:07.960943Z", - "iopub.status.idle": "2023-07-26T00:00:07.965329Z", - "shell.execute_reply": "2023-07-26T00:00:07.964984Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
SVC(C=10, kernel='linear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "SVC(C=10, kernel='linear')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "svm_linear = SVC(C=10, kernel='linear')\n", - "svm_linear.fit(X, y)\n" + "svm_linear.fit(X, y)" ] }, { @@ -210,34 +144,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "95494b8b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:07.967158Z", - "iopub.status.busy": "2023-07-26T00:00:07.967019Z", - "iopub.status.idle": "2023-07-26T00:00:08.140288Z", - "shell.execute_reply": "2023-07-26T00:00:08.139902Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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wmny9Q1aXAAAJgeAJAKfBm1FErycAzBDBEwBOQ83aOrV6VhI+AWAGCJ4AcJoInwAwMwRPAIiA8eGTVe4AEB7BEwAipGZtnboH6qwuAwDiFsETACKMVe4AEB7BEwAiyJtRpPY9ffR6AkAYBE8AiKCatXVqaH2f2vf00esJACcgeAJAhAXDJwBgIoInAAAAYoLgCQBR0ryhjel2JBXTNOULBGSaptWlIEGlWV0AACSjmrV1avi5pA3PqFovyLFsjdUlAaes3evTL9p79X9HB+QJmMqx23RNSZ5uKi9SYTpRAjPHiCcAREmw15PtlZDIDg179ddvN+v33f3yBMZGOgf9AT3d0ac7dzeryzdicYVIJARPAAAQlmmaerjxiIb9AflPmF33S+odGdV3mjotqQ2JieAJAFHG1kpIVPuGvDo45JN/iuf9prSp361ORj0xQwRPAIii4HQ7C42QiBqGvCe9x5R0aNgX/WKQFAieABBl9HoiUWXYjIjeBxA8AQBAWOfnZcl+kkyZbTN0drYzNgUh4RE8ASBG6PVEoilMT9O7S/KnDQs3zS1Sho04gZnhbwoAxAC9nkhUn66eo0sKciRJacZYcAiOgr53Tr4+Ul5kXXFIOOz6CgAxEtxUvrx3vRxWFwPMULph05cWztXeIY/+76hLfSOjmpORpqtL8jU/k7/JmB2CJwDEmDlcbnUJwKwYhqGzsjN1Vnam1aUgwTHVDgAx1vxWN9PtAFISwRMApjE07NY5y0t1zvJSDQ27T/vr1aytU6tnJb2eAFISwRMAYiy40MjV2CfT1Wh1OQAQMwRPALCAWZgvv6vU6jIAIKYIngBgka7mQfka9lldBgDEDMETACxQu6ZC3QN19HoCSCkETwCYQt/IqH5xpCf0+OsH27VtwC2ZZkS+fvnNK9XaeBnhE0DKYB9PAAjjgNuje/Yd1uDQYOjan/pd+tO+Ud1YWqC/qiqVjJMcYj0D5TevVOuTkvSqqvWCHMvWnPbXBIB4xYgnAJzA5w/oH/a3asjv1/ixzcCx/326s0//d3QgYu8XHPlklTuAZMeIJ4CUFm5vzpePDqjH7ZIkmV5P6Lrp9SggyZD0VFObLsme/E9oVmb2KdXhKZsvv6vllF4LAImC4AkgpdVfOn/G9x75/PWhz1sl1Ye5Z9eWzlOuZajPK1/DPjmWzbwmAEgkTLUDSHiRPl3ICrVrKtR0eJG6tney0AhA0mLEE0BKe+O1yT2VLx0d0KNNHZLGpteDI51zv/4bGQ6nDEnVToe+fXZ1RGspv3mlDjwpOXO3KL1wo2zVF0f06wOA1QieAFJauJ7Md83N1I+73RoY9cs/7rrhcMrmyJQkfaim/JT7Oadj1C+Vp29EUn/EvzbiW5dvRF2+UeWm2VXpSJcRgV0TgHhD8ASAE2TYbfqnRZW6b1+Lxk/cB3uT3jenQFcX50Xt/V1dnpPfhKTROOzV91s69dbAcOjagswM3VE5R/X5kf/lBrASPZ4AEMaZOU7955L5+nB5Uejahfk5+toZlfrr6sjs4RlOWV2eOrpz1bZ+vwLNG6PyHogfjcNefXZ3s7aNC52SdGjYpy/ub9VrvS6LKksePSOjah72ye0PnPxmRB0jngAS2g7XkH7VdCT0+LGmTn2wpkI1mY7T/tpFGWn6SEWxvnns8f0L50Zlev1EwV5PaYMqrhC9nknsseZO+QLmhJYOSaFtux5t6tBFBdlKNxgnmq2driE90dat7a6xGQS7IV1emKPb5pWo3JFhcXWpi7/JABLWT9uO6u/3tmhj//HThf5wtF9/teuQXutJ7JEio36pXO1LrC4DUdTu9Wmra3hS6AwyJQ2MBrS5LzF3arDS5n63Pr/vsHa6jret+E3pld5BfWZ3s454fRZWl9oIngAS0lv9bj3Z1i1JE35wB459PNRwRF3eEStKA2akwzd60ntsktr5ezwro6apRxrbFTCPnzYW5DelQX9AjzV3WVIbmGoHkADC7c35i6Y2yTusgMKfLjQi6dctR/TRecWTXhuL6fLTVVaXp/Y3cpW7fifT7UkqN81+0nsCM7wPx23uc6tvdKpx5LHwuanfrZ6RURWlE4NijT9xAHHvVE8XevTYx4lO53ShWKLXM7nNd2ao0pGuVu+IzCnuSTekFQU5Ma0r0R32+GSXpmxhkMbaGI54fARPCzDVDgDTyMrM1q4tndq1pdOSkVJ6PZOXYRj6y8qSKUOnJH14bhEjnrOUnWabNMUeThZ/rpYg6gOIe+FOF/p/B49oQ/9gaKo93OlCH6so1gfHbYcExJtLCnN133xT32nqkDtgyq6x6XW7IX24vEgfnzu5VQTTW1GQo39r7pR/ikRvSKpwpKnWycp2KxA8AcS9cCONH6qdpw17mmXTxAUEhsMpuyNTGYZN11fOVVaCT6XR65n83lmcp5WFOdrQN6gO76jy0uxaWZijPEbkpuQJBNTmHVGaDFU5J57yVJSepuvmFOiZzr6wo8mmpFvmlXAylEUS+19kACnr7JxM/U11mb7d3DGhZ8iQlGEY+uoZFSpI8NAZRK9n8nPYbFpVFL3TsJLFkD+gH7d26/fd/fIExmJlWUaaPjy3SO8pyQ+FyU9VzdGoaep3Xf0yJNmMsUVF6Tbpr6pK+bO2UHL8qwwgJb2ntEBLcjP1dHObfnjs2tryYr2vaq6KM5LrnzejfqlcjS5JXqtLASzhCQR0z94WHRzyTlg41OEb1bebOtXhHdEdlXMkSXbD0N/UlOlD5YV6pWdQA36/yh3pWlWYqxxGki2VXP8yA0g5NZkOfbK69HjwrChSVpKFTgDSbzr7dGDIO+XCoafae/XO4jzNH3dqWbkjQx+aS593PGFVOwAkCM5wRyr7bWfftKvV7Yb0fFd/zOrBqWFYAAASQFldnjq0VG1vdUvaT68nUoppmic96clvSm2c8hT3GPEEgARRVpen4fNX68DOFRrpZWQHqcMwDGXapl+FbpeUYyfWxDu+QwCQQMrqWI2L1PTO4jxNtyzIL+mKotxYlYNTRPAEkPCsPl3ICq7GPno9kVI+WF6oDJsRNrjYJZ2Z5dCF+anx//9ERvAEgARj1C9Vw+YaFhohpVQ4MvTwmZUqOrZrRZqh0AjouXmZenBxpWxsCh/3WFwEAAlmbKHRarW9JbHQCKnkrOxM/WTpfG3ud2vfkEfphk0X5mdpYZbT6tIwQwRPAEhAhE+kKpth6OKCHF1ckGN1KTgFTLUDQIIKrnJv230Gq9wBJASCJwAksLK6PA35S6wuAwBmhOAJAEnA1dgn09VodRkAMC16PAEgwY2tcu+WtFlF53fQ6wkgbjHiCQAJLtjr2bC5Ru6GI1aXkxLc7iHZnUtldy6V2z1kdTlAwiB4AkASKKvLU699sdVlAMC0CJ4AkES8fT56PQHELYInACQJ57J5athco6PrNhM+ERYtArAawRMAksT4Xs/BLVutLmcCAg8AieAJAEmFXs/oGzEDernHFXr85f1terlnQKOmaWFVQGJgOyUASELePp9yXI0ycudbXUpScfsDun/vYe062he6tt01pB0N7VqS3acHF1cq086YDjAV/t8BAEmGXs/o+demdu0f8ky4Fjj2v2+7Pfpuc0fsiwISCCOeAJBkyury1KHVatj8khwFW5W7ilHP2QrXh9o9MqqXWrsVkOT3eEPXg5/7Jf2h1aO1hTkqTLdPeG12dlY0y50R32hAr+7vCj3ucY8oO9vCgpCSCJ4AkITK6vLUtG2xpPjYUL5x2Bf6/I0+ty7PypTNMCysaHp5xRfN+N5Xr7t9wuOaMPf4PTtOs6LT88q+Ln335QMacB0P1J/6rzd1Y/183XHZ/Lj+XiC5RHWq/ZVXXtF1112niooKGYahX//619F8OwDACaze13Ng1K/79rXob3c3h679U8MRfXxHo/a6PdO8EpHy5qEePfLCXg15/ROum5Ke3damx1+lHQOxE9URT7fbrWXLlun222/X+9///mi+FQDgBGZhvho210jarOIrFfOFRgHT1Bf3tU7qiZSkHt+o7tnbosfOqVGFIyOmdc3EwNFNk651eEf0yV1NMjU2vR4c6bzs2cdldzokjY3mPL50vopOmGqPlRNbBEzT1H++tFejPu9Y3b5xLQLHPv/fNxq1pq5YRVnpk75ePLQIRJonENCwP6DcNLvSGOmNuagGz3e/+91697vfHc23AABMoXZNhTp2H+/1zFkevfAZridyc587tPr7xJ5In6RRQ/pZ4xF9umrOpNdaHXjCvf+CbGnl3GJt7nPLN+663emQPdMpu6TLinJUVZAbszpPNJsWgXVfuTf0ee0/hL/H6haBSDo45NFP23r0p75BBSQ5bYauKcnXR+YWqTCdzsNYias/aa/XK6/3+D9OAwMDFlYDAIkv2OtpDkd3j8lT7Yn8o6R7w9wTr4Hn7ppy/b2nRY3Dx0dxg2NmtVkZ+kx1mTWFYVrbXEP6wr7DCpjHdyHwBEw929mnP/UN6tt11SoifMZEXP0pP/TQQ/rKV75idRkAkHSGuz2ybhwueeSn2/Xts6v125YOvXTs2qIsh66vLtWVxXly2KzdpfDEFoHeoRF98sk3Ffy1w+/zhkY6r3zgYdkzxloE/vbKRbrsjMkjz8nAb5p6uOGI/ONCZ+g5SUdHRvX9lk7dv6DCivJSTlwFz/vvv19333136PHAwICqqqosrAgAEp9ZmK+u5kHZ10Wv1zNcT+QPW7r0u+5+BcypeyLLMtL0wyW1Ea8nmjJtNl1bkh96/I2zqixvDQg6sY7sbOmSs+bqjUO9CgQmjnrbMxxKz3Ao02HXO5dUKSMtObf2fqPfre4R/5TP+03pld5B/fWIX/kW9eamkrgKng6HQw6Hw+oyACCp1K6p0KEXrpV2PadoLTQKF7zeV1Ou5wa9OvFHebAn0ibphnklcRPaktXtK+drV2u/hkYmjvcFWwTuXLUoaUOnJDUN+2Q3xgLmVPymdMTrU356ZuwKS1HJ+zcNABBSu6ZChzOvld9VGrP3nJ/p0IfKC8M+Z5e0MMuh68sKYlZPqqooyNQ3PnSe6msLNX4Nd01Jtr583dm6fHFyTrEHOeyGAjNocXZw1GlMRHXEc3BwUAcOHAg9bmxs1NatW1VUVKTq6upovjUAIA7cMa9E5Y50/aShLXTNYZPeW1qgW+eVyBnBnki3eyi0yGng6CZGUsepKMjUF99ztg5fOE81x1awP/LBc1Piz2hFQY4ea+6a9p7yjDTVOuNvW69kFNXg+eabb2r16tWhx8H+zVtuuUVPPPFENN8aABBGV/OgsrZEd2ul8QzD0HvnFGhVZrp+eezak+cuUEluTtTfG5MVhtmrM9mVZaTryqJc/bHHNWlxUdDHKoplsKdnTEQ1eK5atUqmGd0tPAAAMxPq9Xz9OdUoduFT0oQf6pkWr/yOhOzsrLjd8gmT/W1tmTymqdd6B5V27K9icPr9tnklunrcYjFEV1wtLgIARNf48FneZ82JRkCsOWw2fXlhhQ4OefRSj0uuUb/mOjJ0VUmuStJTbxTYSgRPAEgxsVjlDsSjhVlOLcxyWl1GSkv8+Q4AwKxZsco9WvyBgF7d36WvPvt26Nq63R3yjU7V0Ze6gi0Cfs+OlFhYhPjDiCcAIOqi1RPpGw3oH3+zSzta++X3HT9y+bH1DXphX68eunGp8jKZSgXiBSOeAJDCmnZnanDLVqvLOGWPv96onW39kqQTl7Ie7h3WN1/cF/uiAEyJ4AkAKap2TYV6SurV9HqaXC8/bXU5J+V2D0346Ohx6bm3mjXi9WrU550w4un3eeXzeLRpf7sOtPZMeq3bPWThfwmQugwzjvc7GhgYUH5+vjatP6icnFyrywGApHTohTadmf6iat5XIFv1xRH/+pHa2N3uXBrJstgOCYiQgYFBFZauUH9/v/Ly8qa9lxFPAEhxmdU58njKrC4DQApgcREAQK5er9wNR5Qbx6cZDxzdNOHxoMevv3zyDY0e2wnc7/Nq3VfulSRd+cDDsmc4JEmPrj1PlQWZsS0WQFgETwBIcWV1eTrUXK+m159TjZ5W7qobrS4prBOn6LOzpWuWVemFtzt0YtOYPcOhDKdTy+bl68x5xTGsEsB0mGoHAIT29ezekalA80ary5mxOy5boLPKx3rKxp+0bUgqz3Pq765ebEldAMIjeAIAJEW+19Mz4tcv3mzRp/5rS+jav798QC29kVtR7ky368Ebl+jv3rVYZ5YdX4R6x6Xz9eja81SYlRGx9wJw+gieAICQYK/n6RryjeqeX23XTzY26ajbF7r+8t5uffbnW/X2kYHTfo+gdLtN7zyrVP9845LQtWuWlCsz3R6x9wAQGQRPAICksV7PSO3r+ZONzWrqHprUexkwTY34A/ra7/fIH+BISyDVEDwBACGn0ut54sbsPX2Deu7PTfJ5PWE3dh/xetXV69LLu9rY2B1IMaxqBwBMkFmdI09jmSTvSe+VFNocfiaC2x1J0h/+Ifw9bOwOJC9GPAEAk0Sq1zOVuN1DsjuXyu5cysgtMAVGPAEAE5TV5alp22I1vd41o309T9zY3TRN3fXfW9U54JGpqTd2//oHl2l+yakdnxlOdnYWo6VAnGPEEwAwSc3aOrV6Vqp7R6a8216Y9t7s7KwJHzk52froykWyZziUluEIBU3p2MbuDqcuWFimJTUlk157que4A0gMBE8AQFjOZfM04F1y8hvDuKquVGsvHDt/02YYE547ozRH915z1mnXByDxMNUOAJhSb5dHvt4hOU5+6wSGYeijF1Vr1eISPbulSc8fu/7F99TpkjPnyjghjAJIDYx4AgDCKqvLkzejSO17+k55X895hVn6+Iqa0OPzqgqSMnR2+kb0ZGt36PEPWrrUPOyb5hVAaiJ4AgCmVLO2Tg2t71P7nr6T9nqmqs39bt22o1H/29EXuvZcd78+ueuQXujut64wIA4RPAEgRoaG3TpneanOWV6qoWG31eXMWM3aOrU2XmZ1GXGpyzeirxxo1agpjT+HKXDs8TcPdWj/kMeq8oC4Q48nAAAzEG5vzqfbjso35FFAkt8z7oSmY5/bDOkXDW367PzySa9lBT9SEcETAHBS3owiNW94VdV6QY5la6wuxxKzOaHp1etuD33+R0lfDnMPe44iFRE8AQAnVbO2Tg0/l7ThmVmHTys3dne7h0KBceDoJkYZYRnTNPVq36Ce7ezVoWGfnDabrijK1fWlBSrNSLe6vJgheAIAZuR0wmcyOPGEJkl6/HCXftPZH5pqD450Xvbs47I7HbIZ0iX5ObpnweSpdqSOgGnqa41H9HLPoGwa6//tV0C/au/Vbzv79LUzK3VWdqbVZcYEi4sAIMpM09TGvkH984HjZ5//rrNPw6N+C6s6NcFV7s0b2lJulXu4U5Y+UDtXaVlO2TOdsjvHndDkdMie6ZThdOpDteWc0JTinuns0/qeQUkTF6H5JXkCpr68v00jZiDsa5MNwRMAosgfMPXgwTY9cKBVWwYGQ9d/cLhLn9x1SJ3eEQurOzWscj+uwpGhe+aXy66xhURBwc8/VVmic3IJmanMNE39b0evzCmeD0jqG/Xrtd7BKe5ILgRPAIiin7f36NW+ySMdktQ9MqqvHmiTzKl+JCERrC7K07+fU6OrivJC1y4tyNGjZ1XpA+VFFlaGeNA76leHb3Tae9IMaddgamy7RfAEgAgZGnZP+Bhwu/Sr5jYFvMMKeIdleo//YDG9Ho16h7W3r1d/7u6Z9Np43+dzbJV7fE+3Hzrq1r+vPxh6/PDze7T9cF9U3mt+pkN31ZSGHv/9/HKdnZMaPXuYnk0zO6krVQIZi4sAIELqL50/43uPfP760Ocfm+KeXVs6T7Oi6In3hUav7OvSI3/YK7/v+LGVW5r69Oe2nfqLi6r1kQurLawOqSQ/zaZqZ7paPCNTTrePmtI78lKjJSNVAjYAIMKCC418vZM3VrdSp8urb7y4T6Y5tpo4KPj5zzY1a2tzn0XVIdUYhqEPlRdNGTrtkuZmpOmi/OxYlmUZRjwBQGPT5MERyzdea1RW5ux/CLzxWuOEx6OBgG7ZcUiD/rHV66bXExrpnPv138hwOCVJ/3JGpc7JZVr2VIQ7TeiZN5o14vHIlOT3jTtNyBc8TcjQ/2xq0BnFZ016LavNEQ3vKs7ToWGv/qejT3ZD8psKTcAXptv14OJK2YyZTcknOoInAERIuLB6Q+Vc/Xf7UZmauLjIcDiV5shUjdOh+jnFUgL/0Gnf06eMQmum22dzmtC6r9wb+vx5SQ+FuYfThBANhmHok1WluqwwV7/t7FOjx6dMm6HLi3L1ruJ8ZdlTZwKa4Akg5URidHOmPlpRpAPDHm3ud09YYmBIKkxL0wOLKhI6dMZ7r2csWXlCExJDXU6m6lJ80RnBEwCiKM1m01cWzdOrvS79prldrceu3zKvRNdXzlVumt3S+iIhGD7Le9fLcfLbIyrcaUK/eqtVP9/cHJpqD450XvnAw7JnOGQzDK1YUKS/e9fiGFcLgOAJAFFmMwxdUZSn+ky7nj927cayQmUlQei0WriezOsvqNFvdnbJ55+4c6o9w6G0DIcMSTddvIB+TsACqdNUAABT8I369dLu41sXPfbSAe1rd1lYUWJq39MXF/t6FmRm6MvXna0M+8QdFG2GIcOQ7rpykc4sz5vy9QCihxFPACntqMunf/r9brUe7Qtde3lvt14+6NIN51XojkvnJ3QPZqzEW6/nuZUF+sHNF+g3bx5SMAq/+5xy3XBhrSoLIzvS6XYPhRY5DRzdxEgqMA1GPAGkFLd3VP/3dkfo8YO/e1sdLu+Ee4L7Pf56a5te2NWhRDY07NY5y0t1zvLSqJ+GFG/7ehZlZ+imCypDj2+7NPKhE8DsMOIJICUMDbv1ws52PfmnQ/J6h0PXm7p6ZUtzyhwdd5zlqEcBja08/8XGA7psQc6kUc9oroQHZotRVyQKgieAlDDVcZbNT90+6VrTuGuNkuofm/y6UznOMiszO66PwYyUptfTdIZF+3oCiG9MtQNIOkPD7gkfbveg1SWljJq1deoeqFPzhra4WGgUa75A4OQ3ASmMEU8ASWeq0c1TtXH9QdkT9GSRIa8/5u9ZfvNKtT4pFZZvUcaCRhm5kf1+xJPDHp8eb2wPPV67rUFXz5ujv6go1lxHhoWVAfGJ4AkAJ5Gbk2t1CbN2pG9Y/7WxSa/ubg1de/DZt3Xr5WfpjPLo//d4yubL72qJ+vucTDRPE2oY9uru3c1yu4/3DI+a0v/1uPSnPrceratWlZPwCYxH8ASQdN54rXHC444+r+7677dCj81RT6iPs+bDj8tIcyow6gn1e9asfVyG3al3VBXonmvOjF3hEXK4Z0h//8ttGh4JhFboS9L21n7d86vt+qf3LdGSyvyo1zHU55WvYZ8cy5JvxNM0TX298Yi8AVMnTq77TWnIH9Cjh9r1jbOqY1LLtoHj4bd7ZFQsfUO8IngCSDonrjifn5mt82rLtLOtXwFTE4KCkeaULd0pZ/rxqfRFZcW6/oIFWn3mnIScYv/e+oOTQqeksf9209S3/m+f/uPmC2TYorc/ae2aCjU9uUjp2zdoThzs63k63O7J20Ptd3u17+iAJMnvOb4dV/Bzv6Stwx7tnZOvSmf6hNdGcsX5nsFh/UvDEbX2H+9j/ssdh7SmslR/U1OqDFvi/f1FciN4AkgJn7pigT7/y+3yjAYmBE+bMXaizeevPlN3PDF27Ws3nZsw2yWduDdnZ79Xf248vvfoidtEjUpq7/Fo04EjOrdq8qhnJP+7y29eqQNPSs7cxO71DG5TNBOvXjdxl4Szw9wTqan/pmGf7tnXIt8JQ64BSf93dEBD/oC+vKgiIu8FRArBE0BKqC7O1jc+tEw/3nBIG/cdn5Y8qzxXt19xlmoKE/Ofw9kspBq/TdQdPw1/T6S3e4qXXs9EF27U9cnGdg0PeRQww4+6rh/2aFt+phZlOSa9ln0+YZXE/JcWAE5BZVGW/uE9Z6tjxTy98ydj175ywxJlZWZH/VSfVDbU55XZ25GwI54DRzdNuub2B3TLjgb5AmNBLzjSedmzj8vuHAt6WTZDT547PyLT3ac66nr+FPdEa8FVtARMUz7TlMMwZHCEbUIjeAJIObmZyfNP34kLqQIBU5/+ry3qGRqRFH4hVbrN0A9vqVe20x71+kK9nus3qOIKyVZ9cdTfM9LCjQ5mS7qpZq5+3t474brd6ZA90ylJ+ti8EhXm5sSixKR1YMijp4706LW+QflNqSjdrvfOKdAHygqVmYD91yB4AkBCC9eTefNlZ+rbfzwgKfxCqhuXV2pOYV6MKjze6yklbvgM59Z5JXL7A/p18/E+2pff9VFJ0qPbfqsPlRdG7L1OHHUNmKY+sq1Bw4GxBWThRl1tkj40t1B/Mbc4YnXE0pYBt760v1WmxnYKkKSeEb9+0nZUf+ob1CNnVimL8JlwCJ4AoOQ6zvJd55TL7fXrxxsOaWTcrKRhSNedO1cfv7gm5jUlY/i0GYY+U1Oma3KcOuuE526vnBPRKeFwo67vrSrTM519OvGIgOCoqyHpuqoyZSfgRva+QEAPHjwyaRcKaexx45BXP2nr1ierSq0oD6eBXxUAIAndcP48/fj2C3X7JbWha//+0eX65BULZYviNkrTKb95pQ7sXKGet5oUaN5oSQ3RUHHCdkmx8uG5RSrKSNNUDRN/MbdI5QkYOiXp1d5BDfoDk0JnkF/Sc139HFGagAieAFJOcHRz15bOhNk26VTkZabrmnPnhh6X5FofQjxl8+XpO9fqMpJCYXqavl1XrZWFORN+mBel23VX9RzdXJGYU+ySdHDIo7ST/H40FDDV7RuNTUGIGKbaAQAx5erynPwmzEhxepq+uLBCtxbnKthA8Z9LapWXk9i/UDlsNpknv03pFo3e49Qx4gkAiJnaNRXq6M5V2/r9STXdfqKG7thuz1WYfnzC3Z4E2w2tKMgJLSgKxyZpQWaGStIZP0s0BE8AQEwFez2TJXwOek5c3iPd8z/b9blfbFPHAKO7p2JxtlPn5WZO2b8akPQXFcXs6ZmACJ4AgJgz6pfK1b7E6jJO22ggoIfXHdR7H/mern7w0QnPHega1D3/s10DwyPWFJfgvriwQmdmj+2JmmZIdo2FFpukT1WW6PLCXCvLwylijBoAklgybRNltXDHVr5+oFu7W45Kkvy+ccdW+rzySeryePT05kZ98ILKSa/l2Mrp5aXZ9a2zqrTVNaxXe11y+/2qdDq0piRPpRnW7CSA02eYpjmT/l1LDAwMKD8/X5vWH1RODr/ZAEAyaX/ydS1askEVV5yREPt62p1LI/r1Eu3YSmAqAwODKixdof7+fuXlTX84BVPtAABLJFuvJ4CTY6odAGAZo36pXI0uSd6T3mu1E4+tlKRvvbhPGxp6FDBN+X1erfvKvZKkKx94WPYMhwxJVUVZ+uaHlsW4WiA+ETwBAJiBcD2Z7zm/RptaBidNH9ozHEo7FjyvX15DPydwDFPtAABLJfK+nudVFeiShcUKt6uPzZDmz8nWVXXlsS8MiFMETwCAZcrq8mTUL03YXk/DMPT5a87UB8+vVGb68R+paTZDV9aV6qH3L5UjnR+1QBBT7QAAS5XV5alDidPreaI0m003X1Kr95xdomfuGbv2w5svUHnx9Kt7gVTEr2EAAESAc9zIZq6TcR0gHIInACAuJHKvJ4CZ4VcyAIDlgtPtbW91S9qviiuUEJvKA5gdRjwBAHGhrC5Pw+evVtvuMzTS2291OQCigOAJAIiJoWG3zlleqnOWl2po2B32nrK6PA35S2JcWWRkZ2fJ79khv2cH+3YCUyB4AgDijquxj15PIAnR4wkAiCtG/VI1bO6W1KQi0esJJBOCJwAgrowtNFqths0vifAJJBeCJwAgJkZGAzO+l/AJJCd6PAEAUdXr9urfXzqgWx/fHLr24LNva8+RgWlfF1zl3rC5hlXuQJIgeAIAoqZ30Ku7f7FNL+zqkM9vhq5vb+3Xvb/aoTcP9Uz7+kRe5Q5gMoInACBqHn/9kHrcIwqY5oTrAVMyTVPfenGfRmcwBe9q7JPpaoxWmQBihB5PAEBEnLg3p9vj1/q3D8t/LHSao57Qc+aoR35JfSPS+t2HtWJR8aSvl5WZLWn8KvfNKr5SMnLnR+2/AUB0ETwBADMyNOxW/aVjoe+N1xpDwTAo+NxMND11e+jzv/lp+Ht2bemUNHGhkaNgq3JXETyBRMVUOwAg7pXV5anXvtjqMgCcJkY8AQAR8cZrJ/RgmqY++9Q2tfUOy9TY9HpwpLPmw4/LSHPKkPRvf3G+SvMdM3oPb59POa5GptuBBEXwBABExIlT75J086WL9fDze2VIGr+EyEhzKi3DqSsWz1FtedGMvr5z2Tw1bK4RvZ5A4mKqHQAQNZeeMUefvmKB0myGDGPicysXleiudy6a8dcav6/n4JatkS0UQEwQPAEAM9LWMxT6vG/QN+PXXXtuhX58+4W69ZLa0LVvfWiZ7rnmLGWk2WdVA72ekeF2D8nuXCq7c6nc7qGTvwCIEIInAGBa3S6vvvC/2/W3T20LXfvUT7bo0Rf3yjPin9HXyMtM17Xnzg09rizOOq2avH0+9vUEEhA9ngCAKbmGR/T5/9mmHvfIhOsBU3ppb5e6XD790w1LZLMZU3yFyKPXE0hcBE8AgKTJG8BL0tNvHFZ338DYSUMnbAA/KmnrIY827CvUO2oKJ7023GKjSGBfTyBxETwBAJJOfQP4T55kA/hoKKvLU9O2xZKORO09AEQePZ4AgIRFr+fsDfkDerarL/T4nr2H9Yfufo2YgalfBEQII54AAElhNoCXdNdP/6yOgbEp9nAbwNsMQ5csKtbfXnXGSb9+VmZ2REdBzcJ8ej1nqWdkVJ/b06yWvsHQtX1ujx451KHfd/XrXxZXKtPOmBSih79dAABJY8HwxI9rz6tVWoZTtnSnjDRn6F4jbeya0hy69rzasK+Ntto1FeopqWdfz1n4WsMRtXtHZY67Fhzn3O326IeHu6woCymEEU8AwJSuXVqu/9vdoSP9Hp04EWsY0oW1RVpWmW9JbdJY+Gz6+WKZw6ZMjtIMCbc3Z/PwiLZ09kqS/B5v6Hrwc7+k37V49KGCbOWmTRyXys4+ve2vgCCCJwBgkqFhd2ix0bo/7NOPNh3Rn/a2hp7PsBt6z7IK3bqiVpOOJLLAcLdHuVYXEUfyii+a8b2vXnf7hMfzwtzj9+w4zYqAMQRPAMC08rLSdf+1dWqpL9c1Pxm79sNb6lVSkGdtYceYhfnqah6UfR29nkC8I3gCQAIYPwL5xmuNMemhPFFxTkbo8yzH7I66jKbaNRU69MK10q7nxEKjMQNHN0261uoZ0affbpI0Nr0eHOm87NnHZXc6JEkZNunJpQuUxQIjRAnBEwCQ8ILhc47rFatLiQvhejIXZ0sXlxXprYEh+cZdtzsdsmc6ZZN0XWmB5uTlxKxOpB5+pQEAIEXcM3+uqjIzJlwLBoFleZn6y8qS2BeFlELwBABMMDLq12v7jm+r84ed7RryjlpY0cx1NQ+ytdI08tPt+re6av1NTWno2rLcLH1p4Vw9dEalMmzEAkQXU+0AgJDmo259+Zld6uobCF37j1cb9bMtHfrCtWdH9RjM0xXq9Xz9OdXoaeUsPy/lez3DybDZdFXx8YVhXzmjgu2SEDP8agMAca530KufbWwOPb7nF9v1/M52jY5G9ojDIe+ovvD0TvUOjUy4bkryjAb0T7/dpdbeyftDxpPaNRU6nHmtml5P0+CWrRynCcQZgicAxLHmo27d+d9/1jNbj++heeioW9996YC++MxO+Ub9p/R1h4bdkz5e2N6kvgGXRn3DMkc9oXvNUY/8Po98Xo+e3twQ9rXxhPAJxC+m2gEgTpkBU//yu91ye/0KjDvj0JRkSNp9ZEA/3dis2y6d/XRy/SxeEzyfXZIeO/Zxonibgg+tcm95RTnLra4GQBDBEwDiQLhRw+0t/Wrp7pOkSSOQAY2dsf27Pzfq/ctKlH7CEYdW7PMJACdD8ASAOHCqI5CSdMkTk+852QjkG69Nnn7+z1ca9Ye3OxQwTZmjntD71Hz4cRlpTtkM6czyXH31hiUzrtVqXc2DytqyVbmrWGQ0XnZ2FsdgwhL0eAJACsrKzJ70cd3y+VKaQ7Z0p4w0Z+heI80pW7pTSnPqxvqFYV8bj8b3erpeftrqcgCIEU8AiAvhRiAbOt2691fbJSnsCKQkFWSm63sfP1/2CBxxWFuSrVtW1OjHG5pkM45fD3565VlztHJh8Wm/TywFez2zd7yo7AUbZau+2OqSgJRG8ASAOBBu1HBJTbaWVM/RnvZBjd++PTQCKemDF9UqNyc3YnV88IIqVRVl6Rcb96vh2LXKwky9v36h3nV2mWQY074+HmVW58jTWCbJa3UpQMpjqh0A4th97z5LlQXOCddsx8LfmrPLdMN58yL+nhctKNY/3bg09Piba8/T1UvKZdgSL3QGuXq9cjccsboMIOURPAEgjhVmO/To2vP0N+9cFLp2+eJiPfyBpbrrnYsSOgzGSlldnnpK6un1BOIAwRMA4lx6ml2XnTkn9PjOd56hsyvyE3La2yrBhUbdOzIVaN5odTlAyiJ4AgBSQmZ1jjyeMqvLAFIawRMAkDLo9QSsFZPg+d3vfle1tbVyOp266KKLtHnz5li8LQDgFGVlZmvXlk7t2tIZt/t0zha9noD1oh48n3rqKd1999164IEH9NZbb2nZsmVas2aNOjvj61xfAEDyo9cTsFbUg+c3v/lNfeITn9Btt92ms88+W9/73veUlZWlxx9/PNpvDQBJIxlHIK1CrydgnagGT5/Ppy1btuiqq646/oY2m6666ipt2LBh0v1er1cDAwMTPgAAiDR6PQFrRDV4dnd3y+/3q6xs4m+WZWVlam9vn3T/Qw89pPz8/NBHVVVVNMsDAKSgsro89doX0+sJWCCuVrXff//96u/vD320tLRYXRIAIAnVrK1Tq2elundkyrvtBavLAVJGVM9qLykpkd1uV0dHx4TrHR0dKi8vn3S/w+GQw+GIZkkAAEiSnMvmaeDQElVol9WlACkjqiOeGRkZWr58udatWxe6FggEtG7dOq1YsSKabw0AwEn1dnmsLgFIKVEd8ZSku+++W7fccosuuOACXXjhhXr00Ufldrt12223RfutASBlDQ27VX/pfEnSG681shI+jLK6PDVtK1LzhldV3vu0clfdaHVJiLDGYa9+39Wvwx6fstNsurwwVysKcpTGcbOWiXrw/PCHP6yuri59+ctfVnt7u8477zw9//zzkxYcAQAQazVr69Twc0l6RhmFL8ixbI3VJSECTNPUj9uO6mdHemQ3JL85NsW7vmdQtZkZenhxpQrTox6BEEZMFhfdddddampqktfr1aZNm3TRRRfF4m0BADipmrV1am28zOoyEEF/ODqgnx3pkTQWOiUpcOy5Fo9P/3igTaZpWlNciourVe0AAACnwzRN/fzIUU01me43pd1uj/a46e+1AsETAJJcj89vdQlxz5tRpOYNbWytlAQ6fCNq9Y5quvFMuyFt7nfHrCYcR/AEgCTz9uCw7tt7OPT4tp0Num9viw4ywjOlmrV1amh9H+EzCYzOYAbdkDTKVLslCJ4AkES2Drj193uatcc9POH6NteQPrunWXsHCZ9TIXwmhzJHmnLs08ebUVNanO2MUUUYj+AJAEnCNE1961CHAtKkacaApBHT1HeaO8K8EkGEz8SXbtj03jn5UwYcm6TCNLtWFOTEsiwcw14CAJCAhoYn96ftHBhWm2tAkmR6j49sml5PaEXvXu+w9hzNU3VWxoTXss/ncTVr69T6ZI+qVxy0uhScoo9WFGvn4LB2HRvhD/4iZjekdMPQA4sq2MvTIgRPAEhAwc3hZ+LI56+f8PgDYe7ZtaXzNCsC4ofDZtPXFlfque5+PdvZpyPeETltNq0uztP7Sws0z5lx8i+CqCB4AgBwgrFV7q+qWmwqn6gybDbdUFqoG0oLrS4F4xA8AcQ1z4hf3S6vnOl2leRkSNNMj6XSMZFvvNY46ZrXH9DNOxrlCQRkej2hkc65X/+NDMfYQoqitDT9x5Ja2W1MM04ndKLRhmcIn0AEETwBxKVBz4h+srFZL+5ul+/Y/ijzS7L0kfoarVhUbHF11gsXqrMk3Ty/Uv9xuCvU0ylJhsMpmyNTknRbbblys1lUMRPB8Fneu14Oq4sBkgSr2gHEnUHPqP7+f7br9zuPh05JOnR0SP/y+916bnubhdXFt5vKCnVLRYnSTji3JcMw9OmqUl1Tkm9RZQBA8AQQh375ZouO9HkUOGGD5+DDH7zSqL4hnwWVJQDD0F9UFOuJcxeELt1VU6anli3UDWX0up2K9j19bK0ERAjBE0Bc8fsDen5n+6TQOZ4pUy/tmbwK2+QkkpDctOP/vL+rOE9ZaXYLq0lc7OsJRBbBE0BccXlGNTQy/dnihgy19h0/mcfrD+hnbUd1245DoWsP7GvTtgHOYsbpC4ZPX++Q1aUACY/gCSCuODPsmsl666yMsRE8rz+g+/a16Mm2bvWOjoae3z7o1j37DuuP3f1RqhQAMFsETwBxxZlu1/LaQtmm2TbJb5q6dNEcSdL/dvRqt9sT9ohISfrGoQ4NjE4/ggrMBL2ewOkjeAKIOx+ur5KkSSOfgRGPNOrRsnKnKvMMuYcG9XTLEfm9wwp4hycfE+kdls87pN8dPqKhYfekD2Cm6PUEIsMw47gbf2BgQPn5+dq0/qBycnKtLgdADL3ReFTf+MM+uX1+pdkMBUzpwI9ujOh7cEwkZqvp57t1yZXrlbsqsn8XgUQ2MDCowtIV6u/vV15e3rT3soE8gLhUP79YT95xof50oFtNPcNypNn0Tz+yuioAwOkgeAKIWxlpdq06qyz0+Powx0Q+sK9N2wfdCkhTHhP51YXztCw/KyY1I/m17+lTRiHHaAKnguAJIG6MjgbU3Dsk05SqizKVfsLek+GOifz4/Hn6/L4W2aRJx0SmOTK1MNOpi8tKZEyzWCnZpdIZ9tFWs7ZOrU/2SHqVM9yBU0DwBGC5UX9Av3yzRc9uOyKXd2xLpOwMu957boXW1lcpLW3qdZDn5mXp3tpyfeNQh048y2hhplP/vHheSodORF75zSvV+qRUWL5FGQsaZeTOt7okIGEQPAFYygyY+n/P79HGhp4JWyK5fX79YkuLDnYN6kvvPVs229Th8Z0l+bqgIEe/O3xE/3zs2lcXzkv5kU5Ej6dsvvyuFqvLABIOwROApTYf6tGGhp6wz5mm9GZTr/508KguPaNk2q+Tl2bX+8oKQ8FzWX5WSoZOj8+vdXs69OLbHeob8qkk16nVC6dfZYpTM9TnldnbwYgnMAsETwCWen5Xu2yGMeXZ7DZDen7nkZMGT0h9Qz7d97871NY7dpyoKal3aES7W7qsLSwJ1a6pUNOTi5S+foMqrpBs1RdbXRKQENhAHoCl2nqHpwydkhQwpbZx57Jjao++uF9H+sZOcQr+iQZO+KNdv6dTPk5yiojym1fqwM4V6nmrSaZr8o4LACYjeAKwVK4z/aRns+c4mZw5mba+YW1p7p02xEvSv710UH/9k7fU0e+Z9j7MzFivZ6nVZQAJg+AJwFKrzpwz7fOGIb3zzLJp74H0dtvAjO/tGvTpH5/dJfPE4VCckmCvJ4CTI3gCsNSVdWUqzXXIFmYhkM0wVJydoavOnlnwzMrM1q4tndq1pTOl9qocGnbL5x1SYMQT9sMcHXeG/ahHo75hNXf26k/7WjnD/jTVrqlQ0+FFalu/X4HmjVaXA8Q9zmoHYLkul1f/8rvdOtA1GAqgAdPU/JIs/cO1Z6ss32lxhfHtnOWRnerlDPvZa3/ydS1askEVV5zBQiOkHM5qB5BQ5uQ69K0PL9OeDpd2Hu6XKWlJRZ7q5uaNzbUDca785pU68KQkscodmA7BE0B8MAydVZ6ns8rZc3K23jh2hr3bM6qv/GaXGo8OTXjeHPWo6anbJUk1H35cRtrYCPJfr1qo1XUsjImUYPh05m5RcWEZ+3sCYRA8ASDBBftZszKlb370Ir26v1s/39ys9gGvpBPOsE9zKi3Dqcx0u65aWi1Hut2CipOXp2y+PH0jbCwPTIHFRQCQRDLS7LqyrkyPfWy56msLJU3sVrAZhtJshr7wnjpCZ5S4utiqCpgKwRMAklCa3aZ/uLZOf/PORZpffHyF/5V1pfrXte/QuZUF1hWXxGrXVKijO5dV7sAUCJ4AkKTsdpvedU65Hr7p3NC1T16xQJVFWRZWlfyCJxoRPoHJ6PEEACDCjPqlcjW6JHmtLgWIK4x4AgAAICYIngAARFhZXR69nkAYBE8AAKKAXk9gMno8ASDJBc+wR+zR6wlMRPAEgDhgBkzt63Rp0DOq8nyn5hWy8hxA8iF4AoDF1u/t1I83HFKXyxe6Vjc3V5+6fKEWluZYWBkiwdXrVdv6/ZzhDogeTwCw1B92tuuRP+ybEDolaW+7S/f+arsOdbstqgyRUFaXp+HzV6tt9xn0egIieAKAZTw+v374WkPY5wKmNOI39fhrjTGuKvkMDbt1zvJSnbO8VEPDsQ/ywfDpal8S8/cG4g3BEwAs8qeD3fKMBKZ8PmCa+nNLn44OsjAFQHIgeAKARboHvbIbxozuQ+Jz9XrV81YT0+1IaSwuAgCL5GVmKGCaJ70v35kRg2oQTWV1eerQajVsfklSk4rEQiOkJkY8AcAiKxcWy26besTTZkhnluWqvMAZw6oQLcFez4bNNRrp7be6HMASBE8AsEhuZro+dEFl2OeCcfSWS2pjVk8y6vWN6hdHekKPf9vZp8FRv2X1lNXlachfYtn7A1YjeAKAhT5yYbVuXlGjjLSJI59F2en60nvP1tLKfIsqS3yv97r0se0N+tmRo6FrPzzcpZt3NGrP4LCFlUmuxj6ZLnYsQOqhxxMArGQYuumCKr333Aq9cahHg54RleU59Y7qQtmmmYbH9A4NefXPB9sUkHRiF+2Q36/79x3WE0sXKD/dHvPajPqlatjcLWmziq+UjNz5Ma8BsArBEwDiQGaGXZcvnhPZL2qa2tPu0mv7u+X2jWpeQaaurCtVYbYjsu9jsXB7c/6yuVMB7/BY8PR6QtdNr0d+SW5Jzx5u0/vLiya9NiszO3rFauJCo9z5++RYRvBE6iB4AkASGvb59S/P7dbWlr7Qlk2mTP3XxmZ94tJavfe8eRZXGDn1l848uB35/PWhzx869nGiXVs6T7+okyiry1P7GyWSWGSE1EKPJwAkoUde2KPth/skSX7TlN80FTDHNqX//quNev1At7UFQhK9nkg9jHgCQJI51O3W5kO9Uz5vGNJ/b2rWyoXFYw8S3BthjhX96v42/dnlDk21B0c65379NzIcTtkkvXdOvu6oKo1tsePQ64lURPAEgCSzqeGobIYx5eb0pik19Qyp0+VVaV7i7xEarifzA9UV+vOBw7JJGn8oqeFwyubIlCTdUFWhrEzr+l0nbipP+ERqYKodAJKMdzQwo3/cfRbuZxlt9flZ+mBZoaSJP+iCn3+mukxVFobOoPGbyh9dt5lpdyQ9RjwBIMnUFGdp9CRHcWakGSrJTfzRzikZhj5ROUfn5GTqF01tOnzscn1ejj5cW6GluVmWljceq9yRShjxBIAks2JhsXIcdk3VvWkzDL2rrlxOC/awjCnD0CWFufqXM4+fDvWFRXPjKnQGldXlqde+WL7eIatLAaKK4AkASSYjza7PXX2mbIYh2wmLh2yGVFHg1McurraoOkzH2+djuh1JjeAJAEnogtoiff2D56q+tjC0cD07w64b3zFPX//gMuU4060tEJM4l82j1xNJjx5PAEhSZ5Tn6ovvPVu+Ub+8IwFlO9I4hjOOje/1dBRsVe4qej2RfBjxBIAkl5FmV25mOqEzAQR7PYFkxYgnACCpZWVmx+QYzEjy9vmU42pkX08kHUY8AQCII2ZhPr2eSFoETwAA4kjtmorQpvKDW7ZaXQ4QUQRPAADiDL2eSFYETwAA4hT7eiLZEDwBAIhDZmG+2ndl0euJpELwBAAgDtWuqdDhzGvVviuLXk8kDYInAABxqnZNhVo9K2UOlzPqiaRA8AQAAEBMEDwBAIhjZmG+mt/q1uCWrYx6IuERPIEk1Dfk06Fut3rdXqtLAXCagr2eTa+nET6R8DgyE0gih7rdenLDIb15qFfmsWvLqvJ1y8W1OqM819LaAJy62jUVOvTCtZrT8opylltdDXDqGPEEksTBzkH9/S+3aUtTXyh0StKOw/2651fbtau137LaAACQCJ5A0vj3lw5oxB9QwDQnXA+YUsA09e11+6UTngOQWLqaB9laCQmN4Akkgeajbu3rHFRgilwZMKW2fo92tQ3EtjAAETO+19P18tNWlwOcEoInkATa+jwzuu9I/8zuAxCfguFzqKWARUZISARPIAlkOewzui87Y2b3AQAQDQRPIAmcPTdP+Znp097jTLfpHTWFMaoIQDTR64lERfAEkkCa3aaPXlQ97T0fuqBKznRGPIFER68nEhnBE0gS7146V7evrFWazZAhyW4YMgzJZhhaW1+lm5ZXWl0igAghfCJRsYE8kERuPL9S7zq7TK/s71bPoE8FmWm6dPEcFWRlWF0agAgLbiqv159TjZ5W7qobrS4JOCmCJ5BkcpzpunbpXKvLABAD4080ym7eKFv1xVaXBEyLqXYAABJYZnWORvzlVpcBzAjBEwCABNe2r1/uhiNWlwGcFMETAIAEVlaXp56SehYaISEQPAEASHDBVe7dOzIVaN5odTnAlAieAAAkgczqHHk8ZVaXAUyL4AkAQJJw9Xrp9URcI3gCAJAEyury1GtfTK8n4hrBEwCAJFGztk6tnpX0eiJuETwBAEgizmXz6PVE3CJ4AgCQZOj1RLwieAIAkESCvZ7te/ro9UTcIXgCAJBkatbWqaH1fepvGaTXE3GF4AkAQBJyLpunox2rrS4DmIDgCQBAkurt8mikt9/qMoAQgicAAEmorC5P3owiNW9ok3fbC1aXA0gieAIAkLSCvZ6ET8QLgicAAEmsZm2dWhsvs7oMQBLBEwAAADFC8AQAIMnR64l4QfAEACDJ0euJeEHwBAAgBdDriXhA8ASABGQGTAUCptVlAMCspFldAABg5rY29+p/32rVtsN9CpjSwjnZuuG8Cq06s1QyDKvLQ5wb6/V8VdV6QY5la6wuBymIEU8ASBDPbm3Vl57ZpW2H+xUc7GzsdusbL+7Xv/3xgGQyAorp0esJqxE8ASABHO4Z0g9fbZQkBcYFzGAAfeHtDm04eNSK0pBgCJ+wEsETABLA8zvbZUwzlW4zpGe3t8WwIiQywiesQvAEgASwv9M1YaTzRAFTOtg5GMOKkOiC4dPXO2R1KUghBE8ASADpaTadbOlQepo9JrUAwKkieAJAAriotmja522GoYsXTH8PEE77nj6m2xEzBE8ASADvrCtTrjNNtjDDnobGdlK6fllFzOtCYqPXE7EWteD54IMP6pJLLlFWVpYKCgqi9TYAkBKyHWn65xuWKteZLmlsMZFx7CPdbugfrj1L1cXZFleJRESvJ2IpahvI+3w+3XTTTVqxYoX+8z//M1pvAwApY/6cbP3nLRfo1f3d+nNzn/xmQGeV5enKulLlZqZbXR4AnFTUgudXvvIVSdITTzwRrbcAgJTjSLfrqrPLdNXZZVaXgiTTvqdPGYWcaIToiqsjM71er7xeb+jxwMCAhdUAAJAaatbWqfXJHkkcp4noiqvFRQ899JDy8/NDH1VVVVaXBABASii/eaVaGy+j1xNRNavged9998kwjGk/9uzZc8rF3H///erv7w99tLS0nPLXAgAAs+PNYEsuRNespto/97nP6dZbb532ngULFpxyMQ6HQw6H45RfDwAATk9/y6AytjHdjuiYVfCcM2eO5syZE61aAACAhWrW1unAkz2SNmgOvZ6Igqj1eDY3N2vr1q1qbm6W3+/X1q1btXXrVg0OcpYwAADxqvzmlTqwc4V8vUMyXY1Wl5MQ3O4h2Z1LZXculdtNj+x0oraq/ctf/rJ+/OMfhx6/4x3vkCS99NJLWrVqVbTeFgAAnCZvRpHM4XKry0ASitqI5xNPPCHTNCd9EDoBAIhvZmG+jh7ol9nbYXUpSDJxtZ0SAACwXu2aCjUdXqS29fsVaN5odTlIIgRPAAAwSbDXs+etJno9ETEETwAAEJanbL78rlKry4hbpmnqjUM9evj543uYv7S3S77RgIVVxbe4OjITAADEl6E+r8zeDhm5860uJa6M+AN6+Pk92tTQo4Dv+HHf333pgJ7fc1T/fOMSFWRmWFhhfGLEEwAAhEWv59R+tqlZmxt7JEknjm+29A7rGy/sjX1RCYARTwAAMKXym1fqwJOSM3eLigvLUnLk88S9Ob0jAT3zZqNGRsYip3/ciKff55VP0psHPdpz+KiqCjMnfb3s7Kyo1hvPCJ4AAGBaY72eLVaXYZm84otmfO+6r9wb+vycfwh/j9+z43RLSlhMtQMAgJMK9noCp4MRTwAAMK3aNRVqenKR0tdvUMUVkq36YqtLiqmBo5smPB7y+XXHE29qJHB8qj040nnlAw/LnuGQJD38gXO1cE52bIuNcwRPAABwUsFeTyn1wueJPZnZ2dK151fruR1HZJoT77VnOJThdGrRnBydWzsnhlUmBqbaAQDAjAQ3lWeVu3TrJbU6qzxPkmSMu25IKspK173vPtOSuuIdI54AAGDGUnnkczxnul0P3rhEL+/t0rNbDumFY9c/dnGNrl9eqxwnESscRjwBAMCsGPVL5WpfYnUZlku32/Sus8v00PuXhq6977wKQuc0CJ4AAACICYInAACYtY7uXHo9MWsETwAAMCtldXky6pem1EIjt3tIdudS2Z1LJ51khJmjCQEAAMxaWV2eOrRUrkaXJO9J7wckgicAAMBpyc7OSuljMGeDqXYAAHDKXL3elJlux+ljxBMAAJySsen21Wp7S5L2J/W+noFxRxR5AgFxEOapYcQTAACcsrK6PA2fvzpp9/U0TVO/7uzVHTubQtc+vr1R32vu1PCxs9oxcwRPAACAKTzW0qV/b+7S0ZHR0DVvwNSvO/t0797D8hI+Z4XgCQAATpur16uet5qSqtdz35BHv+7sC/tcQNJet0fPdfXHtKZER48nAAA4LcFez4bNL0lqUpESr9cz3N6czzR3yvR4FDAlv+f4llHBzw1JTze36+ocx6TXZmdnRa3WREbwBAAApy0YPrv2/0m58zvlqLa6otnJK75oxve+et3tEx7/NMw9bK8UHlPtAAAgYnzGHKtLQBxjxBMAEHFmwNS2w/3a2twrv2nqzPJcXTy/WGlpjHcks7K6PLW/4ZGrsU8ZCxpl5M63uqQZGzi6adK11/sG9XBDu6Sx6fXgSOdlzz4uu9Mhm6Tr5uTrjirC9kwRPAEAEdU54NFXn92lpp5h2Q1DkvTrrW0qyEzXF99bpzPL8yyuENFk1C9Vw+ZuSZtVfKUSJnyG68m8MitTv+4fVuOQV75x1+1OhzIynXLaDH14QYWyM9JjV2iC41dPAEDEeEf8+sL/7lBLr0eS5DdN+Y9tvD3gGdEXf71THf0eK0tElAX39WzYXCNfwz6ryzktdsPQQ2dUamlu5qTn5mSk6etnVamM0DkrBE8AQMS8uq9bHS7vhFNeggKm5Bs19dvtbRZUhlgqq8vTkL/E6jIiIj/drv93ZpUePasqdO2BhRX68dL5WpTltLCyxETwBABEzKsHunVsdj2sgGnqlX1dsSsIlnI19sl0NVpdRkQsyDq+ZdLy/CwZ0/1Fx5To8QQARMyQb1RhBjsn8Iz4Y1MMLJWovZ6ILkY8AQARU1OUJds0I0E2Q6osZGPtVJBMvZ6IHIInACBirllSHra/MyhgSteeOzeGFcFKydTricggeAIAImZRWa4+8I55ksaOExzPMKQLagu1ajF7HqaaZOr1xOmhxxMAEFG3rqxVZVGW/mfLYbX2DUuSCjLTdf15FbrxHfNktzPmkUqSpdczOzuLYzAjgOAJAIgsw9BVZ5fpqrpSHXX75A+YKs7OIHCmqOAZ7g2bX1Kih0+cPoInACA6DEPFOY6T34ekR/hEEL9+AgCAqBu/yn1wy1ary4FFCJ4AACAmyury1GtfbHUZsBDBEwAAxJS3z8cq9xRF8AQAADFjFuarYXONjq7bTPhMQQRPAAAQM7VrKtRTUk+vZ4oieAIAgJiqXVNBr2eKIngCAABL0OuZegieAAAg5szCfLXvyqLXM8UQPAEAQMzVrqnQ4cxr1b4ri17PFELwBAAAlqhdU6FWz0qry0AMETwBAAAQEwRPAABgGbMwX02vp8n18tP0eqYAgicAALBMsNez6fU0DW7ZSvhMcgRPAABgqWD4HGopsLoURBnBEwAAADFB8AQAAHGhq3mQrZWSHMETAABYbnyvp+vlp60uB1FC8AQAAHFhfK8ni4ySE8ETAAAAMUHwBAAAcYVez+RF8AQAAHGDXs/kRvAEAABxhfCZvAieAAAg7hA+kxPBEwAAxKXxq9wDzRutLgcRQPAEAABxbcRfbnUJiBCCJwAAiGtt+/rlbjhidRmIAIInAACIW7VrKtRTUk+vZ5IgeAIAgLgW7PXs3pFJr2eCI3gCAIC4l1mdI4+nzOoycJoIngAAICG4er30eiY4gicAAIh7ZXV56rUvptczwRE8AQBAQqhZW6dWz0p6PRNYmtUFAIAVDvcM6Xfbj2hHa58kQ++oLtB7ls5VeUGm1aUBmIZz2Tx5Gsskea0uBaeA4Akg5fxxd4f+dd1+SYYCpilJaukd1rPbjujea87UikUl1hYIYFquXq9GevvlqLa6EswWU+0AUsqhbrf+dd1+BUyFQqc09nnANPXw83vV3uexsEIA0wn2ejZvaKPXMwERPAGklGe3tUkywj5nHvv4/U5WzQLxrGZtnRpa36f+lkF6PRMMwRNASvlzS++Ekc4TBUxTbzX3xrAiAKfCuWyeXO1LrC4Ds0TwBJBSpsmcABJMsNcTiYPgCSClLKvMl80IP9UuSTbD0LmV+TGsCMCpGN/r6d32gtXlYIYIngBSynvPnSfzJMOe1y6dG6NqAJyOYK8n4TNxEDwBpJRFZTn69KqFMqQJI582w5DNkP7uqjM0rzDLugIBzErN2jq1Nl5mdRmYIfbxBJBy3r10rhaX5eq329u0/XC/DEnnVRfqunPnqqYk2+ryACBpETwBpKSFpTn626sWW10GgAjwZhSpecOrqtYLcixbY3U5mAZT7QAAIKHR65k4CJ4AACDh0euZGAieAAAAiAmCJwAASApjvZ5Mt8czgicAAEgK9HrGP4InAABIGoTP+EbwBAAASYXwGb8IngAAIOkEw6evd8jqUjAOwRMAAAAxQfAEAABJq31PH9PtcYTgCQAAkhK9nvGH4AkAAJIWvZ7xheAJAACAmCB4AgCApEevZ3wgeAIAgKRWs7ZOrY2X0esZBwieAAAg6ZXfvFKtjZfR62kxgicAAEgJ3owiq0tIeQRPAACQMvpbBhVo3mh1GSmL4AkAAFJCzdo6Hdi5Qm3r9xM+LULwjKHOAY+2Nvdpb/uAAgHT6nIAAEg55Tev1IGdK+RuOCLT1Wh1OSknzeoCUkFr75C+t75BW1v6QteKs9P1sYtqddU5ZdYVBgBACvJmFMkcLre6jJTEiGeUtfd59Pe/3K7th/snXD/qHtG//nG/fv1Wq0WVAQCQuo4e6JfZ22F1GSmH4BllP9nYpCGfXwEz/NT6jzcc0sDwSIyrAgAgddWsrVPT4UX0elqA4BlFQ95RvXqge8rQKUl+09T6vZ0xrAoAAAR7PXveaqLXM4YInlHUNzwybeiUJLthqNPljVFFAAAgyFM2X35XqdVlpBSCZxTlOtJknOSegGkqPzM9JvUAAICJhvq89HrGEMEzinIz03V+daFsxtTx05R0+eI5sSsKAABIkmrXVNDrGWMEzyj72MXVshmSLUz2NCRdu2SuSvOcMa8LAADQ6xlrBM8oW1SWq6++b4mKsjMkScHBzzSboRvfMU+fvHyBhdUBAAB6PWOHDeRjYGllvh6/pV7bDvfpcO+wsjLsqq8tUi69nQAAxIVgr6eRO9/qUpIawTNGDJuh86oLdV51odWlAACAcTKrc9T0xiKlr9+giiskW/XFVpeUtAieAAAgpZXV5alDS3XgDUkifEYTwRMAAKS8sro8qW6lDjwpET6jh+AJAABwTPnNY+HTmbtFRSJ8Rhqr2gEAAMYx6peqa/9iq8tISlELnocOHdIdd9yh+fPnKzMzUwsXLtQDDzwgn88XrbcEAABAHIvaVPuePXsUCAT0/e9/X4sWLdLOnTv1iU98Qm63W4888ki03hYAAOC0uXq9alu/n17PCDNM0zRj9WZf//rX9dhjj6mhoWFG9w8MDCg/P1+b1h9UTk5ulKsDAAAY07F7QOYbO7RoyQZVXHEG4XMaAwODKixdof7+fuXl5U17b0wXF/X396uoqGjK571er7xeb+jxwMBALMoCAACYILjFkqvRJcl70vsxMzFbXHTgwAF95zvf0ac+9akp73nooYeUn58f+qiqqopVeQAAAIiyWQfP++67T4ZhTPuxZ8+eCa9pbW3VNddco5tuukmf+MQnpvza999/v/r7+0MfLS0ts/8vAgAAiBBXr1c9bzUp0LzR6lKSwqx7PLu6unT06NFp71mwYIEyMjIkSW1tbVq1apUuvvhiPfHEE7LZZp516fEEAABW6tg9oMy3XlJF3X56PacQ1R7POXPmaM6cOTO6t7W1VatXr9by5cv1ox/9aFahEwAAwGpjvZ6r5Wp0iF7P0xe1xUWtra1atWqVampq9Mgjj6irqyv0XHl5ebTeFgAAAHEqasHzxRdf1IEDB3TgwAFVVlZOeC6GOzgBAACctmCvZ3FhmYzc+VaXk7CiNvd96623yjTNsB8AAACJoqwuT8Pnr1bD5hodXbdZpqvR6pISFk2XAAAAJxEMn137F8vXsM/qchIWwRMAAGCGfMbMFlgjPIInAADADPV2eeRq7GO6/RTF9MhMAACARBU8RrNhc7ekzSq+Uiw0miVGPAEAAGZo/EIjej1nj+AJAAAwC2V1eRryl1hdRkIieAIAAJwCej1njx5PAACAWTLq6fU8FYx4AgAAzBK9nqeG4AkAAHAK6PWcPYInAADAaaDXc+bo8QQAADhFnrL59HrOAiOeAAAAp6h2TYV6SurVsLlGR9dtZuTzJAieAAAAp4HwOXMETwAAgNNUu6YitMp9cMtWq8uJWwRPAACACCiry1OvfbHVZcQ1gicAAEAEeft8TLdPgeAJAAAQIWZhvtp3ZdHrOQWCJwAAQITUrqnQ4cxr1b4ri17PMAieAAAAEVS7pkKtnpVWlxGXCJ4AAABRwnT7RARPAACACDML89X0epoGt2wlfI5D8AQAAIiwYK9nMHxiDMETAAAgCuj1nIzgCQAAgJggeAIAAERJsNfT9fLTVpcSFwieAAAAUTK+15PwSfAEAACIqmD4HGopSPkV7gRPAAAAxATBEwAAIAa6mgdTfmslgicAAECU0es5huAJAAAQA/R6EjwBAAAQIwRPAACAGErlXk+CJwAAQIykeq8nwRMAACCGUrnXM83qAqZjmqYkadDtsrgSAACAyBnyDMo1PCyna0iGOWh1OadlwOWWdDy3TccwZ3KXRQ4fPqyqqiqrywAAAMBJtLS0qLKyctp74jp4BgIBtbW1KTc3V4ZhRPRrDwwMqKqqSi0tLcrLy4vo18bp4/sT3/j+xD++R/GN70984/szO6ZpyuVyqaKiQjbb9F2ccT3VbrPZTpqcT1deXh5/qeIY35/4xvcn/vE9im98f+Ib35+Zy8/Pn9F9LC4CAABATBA8AQAAEBMpGzwdDoceeOABORwOq0tBGHx/4hvfn/jH9yi+8f2Jb3x/oieuFxcBAAAgeaTsiCcAAABii+AJAACAmCB4AgAAICYIngAAAIgJgicAAABiguAp6frrr1d1dbWcTqfmzp2rj3/842pra7O6LEg6dOiQ7rjjDs2fP1+ZmZlauHChHnjgAfl8PqtLwzEPPvigLrnkEmVlZamgoMDqciDpu9/9rmpra+V0OnXRRRdp8+bNVpeEY1555RVdd911qqiokGEY+vWvf211SRjnoYceUn19vXJzc1VaWqobbrhBe/futbqspELwlLR69Wr94he/0N69e/WrX/1KBw8e1Ac/+EGry4KkPXv2KBAI6Pvf/7527dqlb33rW/re976nL3zhC1aXhmN8Pp9uuukmffrTn7a6FEh66qmndPfdd+uBBx7QW2+9pWXLlmnNmjXq7Oy0ujRIcrvdWrZsmb773e9aXQrCWL9+ve68805t3LhRL774okZGRnT11VfL7XZbXVrSYB/PMH7zm9/ohhtukNfrVXp6utXl4ARf//rX9dhjj6mhocHqUjDOE088oc9+9rPq6+uzupSUdtFFF6m+vl7/9m//JkkKBAKqqqrSZz7zGd13330WV4fxDMPQ008/rRtuuMHqUjCFrq4ulZaWav369br88sutLicpMOJ5gp6eHv30pz/VJZdcQuiMU/39/SoqKrK6DCDu+Hw+bdmyRVdddVXoms1m01VXXaUNGzZYWBmQmPr7+yWJnzkRRPA85t5771V2draKi4vV3NysZ555xuqSEMaBAwf0ne98R5/61KesLgWIO93d3fL7/SorK5twvaysTO3t7RZVBSSmQCCgz372s1q5cqWWLFlidTlJI2mD53333SfDMKb92LNnT+j+z3/+8/rzn/+sP/zhD7Lb7br55ptFF0L0zPb7I0mtra265pprdNNNN+kTn/iERZWnhlP5/gBAMrnzzju1c+dO/fznP7e6lKSSZnUB0fK5z31Ot95667T3LFiwIPR5SUmJSkpKtHjxYtXV1amqqkobN27UihUrolxpaprt96etrU2rV6/WJZdcoh/84AdRrg6z/f4gPpSUlMhut6ujo2PC9Y6ODpWXl1tUFZB47rrrLv32t7/VK6+8osrKSqvLSSpJGzznzJmjOXPmnNJrA4GAJMnr9UayJIwzm+9Pa2urVq9ereXLl+tHP/qRbLakHaiPG6fz/x9YJyMjQ8uXL9e6detCC1YCgYDWrVunu+66y9rigARgmqY+85nP6Omnn9bLL7+s+fPnW11S0kna4DlTmzZt0htvvKFLL71UhYWFOnjwoL70pS9p4cKFjHbGgdbWVq1atUo1NTV65JFH1NXVFXqOEZz40NzcrJ6eHjU3N8vv92vr1q2SpEWLFiknJ8fa4lLQ3XffrVtuuUUXXHCBLrzwQj366KNyu9267bbbrC4NkgYHB3XgwIHQ48bGRm3dulVFRUWqrq62sDJIY9PrP/vZz/TMM88oNzc31Budn5+vzMxMi6tLEmaK2759u7l69WqzqKjIdDgcZm1trflXf/VX5uHDh60uDaZp/uhHPzIlhf1AfLjlllvCfn9eeuklq0tLWd/5znfM6upqMyMjw7zwwgvNjRs3Wl0SjnnppZfC/v/llltusbo0mOaUP29+9KMfWV1a0mAfTwAAAMQEzXIAAACICYInAAAAYoLgCQAAgJggeAIAACAmCJ4AAACICYInAAAAYoLgCQAAgJggeAIAACAmCJ4AAACICYInAAAAYoLgCQAAgJj4/w6lcdW+qeaGAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_svm(X,\n", " y,\n", " svm_linear,\n", - " ax=ax)\n" + " ax=ax)" ] }, { @@ -255,29 +171,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "98c2236f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.142381Z", - "iopub.status.busy": "2023-07-26T00:00:08.142228Z", - "iopub.status.idle": "2023-07-26T00:00:08.288040Z", - "shell.execute_reply": "2023-07-26T00:00:08.287728Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Truth-11
Predicted
-1250
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" - ], - "text/plain": [ - "Truth -1 1\n", - "Predicted \n", - "-1 25 0\n", - " 1 0 25" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n", "y_hat = svm_.predict(X)\n", - "confusion_table(y_hat, y)\n" + "confusion_table(y_hat, y)" ] }, { @@ -755,40 +378,21 @@ "We fit the\n", "support vector classifier and plot the resulting hyperplane, using a\n", "very large value of `C` so that no observations are\n", - "misclassified. " + "misclassified." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "2e4ed2f5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.448970Z", - "iopub.status.busy": "2023-07-26T00:00:08.448846Z", - "iopub.status.idle": "2023-07-26T00:00:08.571150Z", - "shell.execute_reply": "2023-07-26T00:00:08.570802Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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1025
\n", - "
" - ], - "text/plain": [ - "Truth -1 1\n", - "Predicted \n", - "-1 25 0\n", - " 1 0 25" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n", "y_hat = svm_.predict(X)\n", - "confusion_table(y_hat, y)\n" + "confusion_table(y_hat, y)" ] }, { @@ -892,35 +432,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "c67591a1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.579932Z", - "iopub.status.busy": "2023-07-26T00:00:08.579791Z", - "iopub.status.idle": "2023-07-26T00:00:08.710958Z", - "shell.execute_reply": "2023-07-26T00:00:08.710610Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_svm(X,\n", " y,\n", " svm_,\n", - " ax=ax)\n" + " ax=ax)" ] }, { @@ -945,22 +466,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "322be574", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.712863Z", - "iopub.status.busy": "2023-07-26T00:00:08.712722Z", - "iopub.status.idle": "2023-07-26T00:00:08.714989Z", - "shell.execute_reply": "2023-07-26T00:00:08.714695Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "X = rng.standard_normal((200, 2))\n", "X[:100] += 2\n", "X[100:150] -= 2\n", - "y = np.array([1]*150+[2]*50)\n" + "y = np.array([1]*150+[2]*50)" ] }, { @@ -973,45 +487,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "04fda182", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.716729Z", - "iopub.status.busy": "2023-07-26T00:00:08.716589Z", - "iopub.status.idle": "2023-07-26T00:00:08.810747Z", - "shell.execute_reply": "2023-07-26T00:00:08.810365Z" - }, - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MACCda/Fd2fu8e2cdHr+/g1PiagwfbwkTxkRa7OEuCkBwkAeGDAhxbGBOoqoqSstMHOElrvEkImrubK3W7KyqzhqNiJce74y9B6OxdGU6MrIqEBzsgXGXRmBQ32C3W99Z6c6bWmPfoUIkni3FhYPNkghIGhEvP94FGjf93GxVUSHjp0Up+GNpGnLzDRAEYFDfYNw0Ix49uuicHR45AQvIExE1c9t25+Hh5w9YPSdIp8XCeYObfSLkaGVlJvy0OAWLlqUhr8AIrUbA6OHhuGF6HFrHN+9NRXq9jP8+ux+HjxXVaAwAFXjhsS4YNTTMafGR/bCAPBERVenfKwix0d5Izyiv2lB0IQHAjCtimHQ2AR8fDW69rhVuva4VDEYFGklwSvUAZ5j/RzKOXJR0AuZSUgKAV987iv69guDvx1SkJeEaTyKiZk4UBbzxTDcE+GurbeKpXH84bHAIrp8e75zgWhAPrdhikk5ZVrFwWRos7WdTARiMCv5Zl+nQuMj5+DaDiKgFSIjzwfdz+uOvf9KxYl0mSktNiI/1wdTLozFicGiLSYjIMYqKjSgoNFo9RxQFnEosdVBE5CqYeBIRtRCBOi1umhGPm2ZwdNMe8gsNOJNUCo1GRKf2/vDQchKxkkcdpaQqedZRw5WaHyaeRERE9ZBfaMCHX57Cmg3ZVbVR/f00uP7KONwwPY6jxwB8fTTo1VWH/UcKoViooCTLKoYNbBnlpOg8vtUgIiKyUXGJCfc8therN2RVK8hfXGLC59+dwTufnnBidK7lP9fEW0w6JRHo3N4fvbsHOjQmcj4mnkRERDb69a8UpGWUW0yoFi9Px/FTxY4NykX17x2MJ//bEZIkQBDO1S89VzmhXRs/vPlcNwiWWlZRs8WpdiIiIhv9uTzdYtIJmBOrpasy0KGtv+OCcmETx0Tikn7BWLY6A2fOlsHLU8SIS8LQt0cglyS0UEw8iYiIbKCqKnLyDFbPkWUVGVkVDorIPQQFeuAGluuiczjVTkREZANBEODnK1k9RxIBXYDWQRERuR8mnkRERDYaPyoSkpVXTlkBxl0a7riAiNwME08iIiIbXTs1Fj4+mqquTxcSRaBfz0D06RHo8LiI3AUTTyIiIhtFhnthzuu90CrOBwCqWpAKAjBqaBhee4Y7tYms4eYiIiKiemiT4It5H/XDgSNFOH6qBFqtgIF9ghEZ7uXs0IhcHhNPIiKiehIEAT266NCji87ZoRC5FU61ExEREZFDMPEkIiIiIodg4klEREREDsE1nkRE5Nby8g34fWkq/l6diaJiI8LDPDFlXDSuGB8Fby/rBd+JyLEEVVVVZwdhSVFREXQ6HQoLCxEQEODscIiIyMWkpJXj3sf3oKDIWNVDXTj3n7atfPHxa73g58sxFqKmVJ98jVPtRETkllRVxXNvHkbhBUknAKgAVBU4nVSKj74+5bT4iKgmvg0kIiK3dOREMY6fKrF4XFGAFWszcd/MNgjwZ//0upTrZaxYm4mV67NQXGJCQpwPpo6PQp8egSyKT3bDxJOIiNzSkePFEATz6KYlJpOKk4ml6NM9sMH3KdfL+GNpKhb9nY7MLD18fDQYPzIC10yNRVRE8ygan5GlxwNP7kN6lr7qOU1KKcPajdmYMCYCTzzQEaLI5JMaj1PtRETkliRJsJp0Vp3XiISppNSEex/fg8/mnUF6ph6Kan5s4bJU3PLgTpw4bXnE1V2oqoonXjmIrJyKc/82Py7L5v9ZtioTv/6Z6qzwqJlh4klERG6pX8+gOs/x9ZHQqZ1fg+/x2bwzOJVYWiPBlRXzSOjTrx2CorjsHl2b7DtUiJNnSiFb+Tx+XJhclYgSNQYTTyIickux0d4YOjAEooVXMgHA1VfEwtOzYSWVSstMWLY6o9rGpQspCpCWoceuffkNur6r2LW/AJJkfVQ4J8+A1IxyB0VEzRkTTyIicltPz+qETu38AaAqAa1MosaMCMd/rk1o8LXPppTBYLCQdZ4jicDRk+493a7aOGKrWn8qiGzCzUVEROS2/P00+OTN3ti8PRcr1mYiv9CA6AgvTLosCj276hq1G1ujrXtsRlUBjca9N91066yDLJ+1eo7OX4PoyOaxkYqci4knEVELo6oqVBXNZpeyRhIwfHAohg8Otet1W8f7IiTIA7n5BovnKCowqG+wXe/raAN6ByEm0gvpWfpalxUIAnDV5BhobUjEierC7yIiohZi5758PPz8foyY+i9GTPkXtz20C/+sy4QLN7BzKo0k4Iar4iweF0VgUN8gtI73dWBU9ieKAl5/thv8/bTV1stW/v+gvsG4aUa8c4KjZoctM4mIWoDfl6Tivc9PQhRxvrXkuXqNU8ZH4ZF727NIeC1UVcWHX53Cr3+mQpIEyLJa9Rx27eiPt17ojgC/5lGcPi/fgEXL0/DP2kyUlsmIi/HG1MujMXpYeJ2bj6hlq0++xsSTiKiZO5tShhvu2QFrv+xffaorRth5qro5OXG6BH/9k47U9HLoArQYMzwcA/sEMyEjQv3yNa7xJCJq5hb9nQZRNNeerI0oAr//lVqvxDO/wICU9HJ4eUlom+DbbNaLWtK+jR9m393e2WHUy859+fhpUQp278uHqgLdOgfgmqmxGDqAbzDIeZh4EhE1c0dOFFtMOgHztPGxU8U2XSsrpwIffXUK67dkV03ZR4Z74rbrW+Hy0ZF2iLZ5UlUVOXkGGIwKwkI84dHEG3V+XpSCj74+BemCNxz7DhViz4FC3DQjHnfd3LpJ709kCRNPIqJmzsOj7iRHq6n7nJzcCtz58G7kFxiq7X7OyKrAq+8fQ36hEddfaXkzTku1ZmM25v2UhFNJpQAAP18JV4yPxi3XJMDHu2HF7a05caYEH319CkD1Ue7Kr9n3v55Fv56B6GtD5ycie+OudiKiZm7ogBBY2zckSQKGDQqp8zpzf05CfoHB4ujpZ/NOWy091BIt+CMZz71xGKfPllY9VlIq46eFyXjw6X0o18t2v+eiZWmQrLy6S6KA3/5i73VyDiaeRETN3OWjIxHgr6m1tWRlQjrjilir16gwKFi2OtPqlD1UYMXazIYH2sykZ+rx6dzTAFCj17uiAMdOFuPXP1Psft+DR4usfp1kRcWh40V2vy+RLZh4EhE1c36+Grz/ck8E+JvL/oiCOeEUBPMU+6tPdUGbBOu1KAuLjHW2jxRFAWkZervF7e6Wrky3OtKsqsAfy9Lsfl9b1o/asrSCqClwjScRUQvQvo0ffv1qIFb/m4Xte/Ihyyq6dgrAhNGRCNTVXYfSz0eqqvtpiQpAF8CXlUpnU8utPl8AkJNr3nBkz81GQwaE4NjJYlhqwS6JwLCB3NlOzsHfEERELYS3l4RJl0Vh0mVR9f5YHx8NhvQPweadubW2VQQAWVYxZnh4I6O0r8TkUvy5PB2JyWXw8ZFw6SVhGDE41CHtH328JXMZKyvLOCVJgMbOtUAnj4vC/N+TUVEh10g+BcE8Mn3lpGi73rO+TieVYs+BAqgq0LObDu1b+zk1HnIcJp5ERGSTW65LwNbdeVW93i8kCMDo4eEu1T5y7k9J+Gp+YlVJIVEA1m3KQXyMNz54tSfCQjyb9P6XDgnDkpUZFo9LEjBySKjda6CGBHng7Re647GXDqCsXK76Wl24tCI+xseu97RVbr4BL7x1BHsOFFQtQ1BVoEeXALzwaBeEhzbt14Scj52LiIiaGaNRQWGRET7eEnx87Du+sHt/Pl565yhy8gwQRXPSIAC4fEwkHr6nfZPXp7TVyvVZePHtI7Uek0QBbVr54Jv3+zZpm1BFUXHnI3tw4lTNOqqCYB7t/PLdPk022ldcYsLfazKwe18BFFVF9846TBobiaBAjya5X130ehm3ztqF1HQ95IuGYiURCA/zwtwP+8LXzt+z1PTYMpOIqAUqLDJi3s9J+OufDJTrZQgABvYNxi3XJqBbJ9t+h8qyii07c7FrfwFUxdzt5uKpaZOsYtuuPCQml8LbS8LQgaFOH6lSVRW79hdg3aZslJaZsHNvAQqKjFbXWH74v57o0z2wSeMqLDLiqf8dwr5DhZAkAQLMz5+/nwYvPdYZ/XsHN+n9Xcni5Wl4a84Ji8cFAXjg9ra4uo4KC+R6mHgSEbUw+YUG3P3IHqRn6autwRRFQICA157pikv6W6/VeTalDI+8eABpGfpqSVJwoBZvPtcdndr7N+0n0UBFxUY8+tJBHDpaBEkSoCg1lwJcTJIEXH1FDO67tW2Tx6eqKg4fL8am7bkwGBS0b+OHS4eEwdOGwv7Nyb2P78GBI0VWvzbt2/jh2w/6Oi4osgv2aiciamE+/+5MjaQTMNeLFAQVL797FIvmDbaY7JSUmvDAU/tQUGguAC/L57ODgiIj/vvMPnw/p7/TRzZr8/Rrh3DkXF3KC+O2RgBgNDlm3EUQBHTtGICuHVv2AEp+gfURaMA8QkzNW8t6u0VE1AyVlZmwYm2mxd3mqmpe77d+c7bFa/y9OgN5FroSKQpQrpexsAlqTjbWkeNF2HOg0OLnbolJVtGlg2uO4DZX0ZFetTYxqCQKQFSEl+MCIqdg4klE5OYysitgNFofStJIAhKTyyweX7Mx2+polKIAqzdkNTTEJrNhWy6kepYjEgUgwF+DEZeENVFUVJsrxkVZfYOgqOZzqHlj4klE5Oa8POv+Va6oKry9JIvHy8rr7hlebsM5jlZhUFCftFMSBWi0Il59smuLW2PpbEMHhmJgn6BauzmJItC7mw6jh/HNQHPHnzoiIjcXFeGFNgk+VtszKgowYrDlbjXtWvlaHTkURaB1HW01a1NSasKR40U4cabE5vWX9dGutS9MdVy3snSRr4+EiWMjMfeDvujdxLvZqSZJEvDaM91w7dTYam+WPD1EXDkxBm+/0B0atvJs9ri5iIjIzQmCgFuva4VnXj9c63FRBIYNCkV8rOWi4VMuj8aKdZan0hUFuHKC7d1uiktM+HTuKfy9JrNqGUBosAdumhGPKydG261+5qghYfjgi5MoLZNrXSogisA1U2Idsnud6uahFXHfrW0x87pWOHG6BICK9q397F5vllwX31oQETUDlw4Jw+y725nLIAnmNZ2VI5iD+4XgmYc6Wf34Hl10uGZKDADUOnI6ZngYhlsZMb1QWZkJ9z2xF0tWZlRbe5qTZ8B7n5/Ep3NP2/hZ1c3TU8Lzj3SGKAqQLnpFE0WgXSs/zLw2wW73awxFUZGZrUd2bgVcuJKhQ/h4S+jZVYeeXQOZdLYwrONJRNSM5BcasHxNJlLTy+Hro8GooWHo2M623duqqmLZ6kws+P0sklLKAQCR4Z64ekospk+MsXkTz9yfk/DNgkSrG0m++7gf2jRg6t6SoyeLMf+3ZKzfkg1FAYIDtZg2IRrXTI2Dj7flta2OIMsqfv0rBb8sTkVWTgUAIDrCC9ddGYepl0c1afckIkdgAXkiImowVVVRUGiEogJBOm29e4lPvWULcnINFo9LInDV5Bg8cHu7xoZag0lWYTAo8PYSXSKhUxQVL759BKs3VC9lJQBQAUwZH4VH7m3vErESNVR98jVOtRMRUTWCICAo0AMhQR71TjplWbWadAKArABpGfrGhFhNUYkRefkGKIoKjSTAx1tymURuw7bcGkknYE46AWDx8nTsOVDg0JiInIkLK4iIyG4kSYCXlwi93vI8uyQC/v7aRt9r/eZsfPfrWRw7WQLAvHlp+qQYXDs1tlpveWdauDQVogiLyw5EEfj+t2T07h7oMskyUVNyjZ9MIiJqNsZdGgHJykiprABjhzeuXuP838/i6dcO48SpkqrHcvIM+OL7M3jilYMwmerZyqiJnEkus140XQF27MnHjfftxKbtuY4LjMhJmHgSEZFdXX9lHDw8xFrbI4oi0LOrDn17BjX4+slpZfh07hkA5m43F1JVYNvufCxZmdHg69uTrRubzqaU4fGXD7pEdyhVVbHnQAGee/Mwbrx3B+58eDd+WpSM4hKTs0OjZoCJJxER2VVMlDc++l9PhId6AjBPrVcOgA7uF4I3nu1W77WjF/pzeXqN0kkXEgTg9yWpDb6+PY0ZHm61P3mlym2+73x6Akaj80ZrVVXFe5+fxANP7cP6zdlITC7D4ePFmPPNadxwz3YkJpc6LTZqHrjGk4iI7K5Te3/88uVA7Nibj+OnSqDVChjcNwQJcZaL2APmTkeLl6dhycoM5OUbEBrsicnjInHFZVFV9R5PJ5VCtpKbqSpwNrXcnp9Og029PBq//ZWK0jKT1Sn3SkXFJmzemWe1y1RT+uufDPyxNA0AIF/QIVVVgYIiIx554QB+/mKgzaW1iC7GxJOIiJqEKAoY2CcYA/sE23R+Tl4F7nt8L9Iy9VUjgGVlZZjzzWks+ScDH7/WC4E6Lby9JasbdgC4TB/2kCAPfPi/nnjspYPIPlfD0xpRADIy7bfjvz5UVcWCP5ItHlcUICOrApt35GLYIOckxuT+XOMnk4iIWrzXPjiGjCx9tdaXKipHMMvw1pzjAIARg8OsJp2SJGDkkPonRhUGBf9uycHi5WnYsjPXbhuU2rf2w69fDcQDt7Wp81xFBXQBjd/x3xD5BUakpFkfKdZIAnbtL3BMQNQsccSTiIicLiWtHNt251s8rijAv1tzkJVTgUsvCcXXUV5Iz6yAfNHuIkEwjxpeMzWuXvdfvDwNn849g5LS8xtognRazLqrHUYPC6/XtY6dLMbvS1Kx+0ABBEHAgN5BmD4pBldPicWfK9JxNrW81r7yAODhIWLowJB63c9ebOkmo9p6IpEFHPEkIiKnO3y8qM5zVBU4cqIYWq2I91/pifhYbwDmEc7KNYc+3hLefK5bvdpxLl6ehrfmnKiWdAJAfqERz795BP9uybH5Wov+TsNtD+3GinVZyMiqQHqmHkv+ycAtD+zEyvVZuGdmG4tJJwDcck08/HydMyYUpNMiMtzT6jmyrKJHV52DIqLmiIknERE5na273KVz1Ykiw70w76N+eOfF7pg2IRqTL4vEEw90wKJ5g9G/t21rSgHz9HplaSZLPv7mFGzpLn30ZDHe/uQEAHOCVklWVCgq8Mp7RxEf44MXH+uMAH9zclm5493DQ8SdN7XCTTPibY7d3kRRwDVTYq0cB0KCPTB8kHNGZKl54FQ7ERE5Xa9uujo3DGk0Anp0Pj/aVt/NS7XZvjuvxkjnxdIy9Dh8vBhdO1rvQf3bX6mQJKFa0nkhAcDCZWn47x3tMHxQKDbvzENGph66AC2GDgxx2kjnhaZPisGRE8X4Z11Wta+HKAI+3hq8+Ww3aDQcs6KGc/53ORGRmzMYFazdmI3lazKRV2BAdIQXJl0WhcH9ghtVr7IlCQ32xNgREVi5PrPW5FMUgEljIxFgh1abF8ovNNp4nvX+8wCwe3+BxaQTMHds2rWvAACg1YpNWjIpv9CAhcvS8PfqTBQVGxEV4YUp46MxYUyk1R3/oijg2dmdMHJIGBYuS8PppFJ4e0sY0j8EkiTg6wWJkEQB/XoHYfzICPj6MI2g+uF3DBFRIxQWGTHr2X04cboUomDelXzmbCk2bMvFJf2D8cqTXeHhIn3DXd3D97RHZpYeew8VVo22Vf7dt1cQHritrd3vGRbiYdN54SHW1z4C5o1N9jinsVLSynHvE3tQUGisSuJPJZbinU9P4O/VGXj/lZ5WOyoJgoBhg0KrSiat35KDF948DFk2LxkQBGDjtlx89UMi3n2xOzp3sD4STHQh/jYkImqEV947ilOJ5m4ulRusK1/st+zMwxffWV8/SOf5eEv44NWeeO3prhgyIASd2vtj2KBQvPlcN7zzQnd4etrWfrI++vcORnCg5VFUQQDaJPigfRu/Oq/Vr1eQ1cLqogj079XwVqG2UFUVz75xCIUXJJ3mx81/Hz1ZjE/nnrb5eqcSS/DcG4dhNKlV39+qat7YXlpmwuznDqCo2LZRYyKAI55ERA2WnFaGLTvzLB5XVfMu51uvb2Vzz+6WTpKqj7Y1NY0kYMrlUfj2x7MWz/nvHe0g2DBUedXkGPy9qvYe8QLMI4nTJkQ3NFSbHDpWhBOnLbe1VBRg6aoM3P2f1jZNk//yp+XWo4oClJSZsHRVBq6bVr/yVdQ0FEXFrn35OHSsGKIIDOgdjE7t/Z0dVjUc8SQiaqDdNhTS1lcoOHqyuOmDoQYpK5fxy2LLyZWqAsl1FFWv1L61H578b0eIAqr1khdFQJQEvPRYZ8REeTc2ZKsOHS2uczrfYFCqRunrsml7rtV1q6oKbN6eW58QqYmcTirFdXdvx0PPHcC3PyXhq/mJuH32btzz2B7k5te9RtlROOJJRFaVp2Qgf9MuqKqKoMG94ZMQ4+yQXIYNFXbM5ymsuO2qVq7PRGmZbPG4IAA/L0rBlPFRNo16Xj46Ep07+OOPpWnYvb8AggAM6BOMKydE2yXpNBgVHD1RDJNJQesEXwTpqq9RFW0cTrK117rJVPf3rsHI729ny8mtwH1P7EVpmblCw4VvFg4fK8J/n96Hbz7o6xLrzZl4ElGtjAVFOHDPs0j/fcX5DEsQEHHFaPT4/BV4hDTtWjV30K1T3ZsqNBoB7dvWvT7QFRw8WoRf/0zBzr35UAH06R6IGVfEomcTFAw/m1qGw+emA/v0CERocN2bd5rCoWPFkEShRgekSpUjnmXlss07uFvF+WL23e3tGSYURcX835Px48JkFBWbkwtJFDBqWBj+e0c7BOrM61T79w6q8w2Rn68G7Vrb9j3Zub0fdu0vsFjmShQBrVbAH0tTMbBPcJOP6DbEkeNF+O2vVOw6UAAB5rW4V02KQcd2rjUF3Ri/LUlFaZmp1q+TrACJyWVYtykbl10a4fjgLsLEk4hqkCsM2DruFhTvO1p9WE9VkbVkLbaOuRlDNv0Cycf1XmQcqV1rP/ToEoBDR4sg11YCSAQuHxWBAD/n9N6uj4XL0vDOpyeqJWEbtuZg3eYcPHBbW1wz1XJh8frIzq3AK+8drSorBJifp7EjwvHwPR0cvhZWEmFegFkHZ5fFeufTE1i8PL3aY7KiYvWGLBw9WYwv3u4Dfz8NWsX5YkDvIOzcl19rEiIAmHFFjNWSSheaPjkGO/YWWDyuKMDeg4XYd6gQqgoMHxSCp2Z1comapIB5jfXbn5yoVl/1n3VZWL4mE48/0AGTxkY5OUL7WLG29jJklUQBWPVvlkskns4fcyUil5P+yzIU7T4EVa45BanKMooPHUfq/D+dEJnref6RzggL9ay2rq7y/zu182+SEkD2dvJMCd759FzHHeXCjjvmvz/6+pRNLS3rUlRixL2P7cXeA4XVHldkFUf/2IAvLnsWR5/7ANkrN0K19ipqR/16BVldwyiKQJcO/vD2ct7msGMni2sknZUUBUhNL8fvS86vU33u4c5VLUMrp94rp9ZHDQvDf65JsPneQ/qHYMbkmGrXqk3l+9ON23Px6IsHrD6njnL8lIVOUrIKVQXe+Og4TifZttbV1ZVYWS4CmCtuVI6UO5trvCUhIpeSPPd3WG8jIyD5298Qf8c1Do3LFUWEeeHbD/rhr3/SsWxVBvILjYgM98SU8dEYNzLC5pElZ1q4LM3qdLMkCfh9SSq6zG5cvcbFf6cjI1tfbRBdV5aDq3bPQXhJKmRBxKkdAk69JsO3fSv0++MT+HVq2sR9+KBQhId6IievotZvd0UBbpju3B3by1ZnWP36KAqwaHkabrnWnFAG6rT48p0+WL8lByvWZqKg0IiYKC9MviwKfXoE2rRWtZIgCHjwjrbo0VWHXxan4PDxYqiqavFXg6IAB44UYfuePAzu59zWmr8vsd5JShQF/LE0DY/ca99lEc4QG+mFk4mlFpdZSBIQF+MaM1RMPImoBn1apvXehapqPocAAP5+Glx/ZRyuv9I9S8rsPVhoMakBzCNEew8WWjxuqyUr06u9MHqY9Lhh+9vwqygAAEiqApwbuCk7nYwto2/CiH1L4BHa8JaYddFqRbz7Unc8+PQ+5OcbAcE8eleZ6N1+QyuMuCSsye5flx1787FyfZbVrw8A5OQaoKpqVVKp1YoYMzwcY4aHNzoGQRAwckgYRg4xPw+TbtiMgiLLtTtF0Tyd7ezEc/eBQuudpGQVuw/kOzCipjN1QjTemnPC4nFZBiZf5hrLCph4ElEN3nFRKDudbDn5FAV4xbnGLzFqPMmGWWRbd0Fbk5dfPVnpnroZAfq8WpdYqrIMQ04+zn71C9o9cbdN11cUFYVFRnh4iFUbgfLyDViyMh37DxdBFIG+PYJw+Zjq625bxfnix88GYMXaTKzfkoNyvYwObfww5fJotLdxE05TWL4mE6+8d9Smc/18NfUayWyM4jp62ysKUGBDi9GmZsvTITroOWtqE0ZH4p91WThwuBC1vUeZMj4K3Tu7RocpJp5EVEPczKuQu3ar5RMUFfG3znBcQNSkBvULQWJymcX3GZIIXGKH0avQEA8kp56vidklfYf1D1AUpC74s87Es6JCxo8LU/D70lTkF5iT255ddejbMxDf/3oW8gVdd7bszMPXCxLx1vPdq+3W9/XR4MqJMbhyomuUCysqNuKNj4/ZdK4kAhNGO27TSHioJ9Iz9ZbjkYCoCOdP6w7sE4w/l6fVuvEPMD9vA/s0j+ocWq2Id1/sjq8XJGLR3+koKzdPHYQEeeC6K2Nx9RWxDntjUhcmnkRUQ9T0cUj85AcU7NiPGr+1JREBPToh+rrJzgmO7G7a5dH4dXEKVFWtsUZMAABBwJUTG99x54pxUfjk29NV9/Aylta5odxYYL34foVBwUPP7ceBI0XVYj9w2LzT+mKqCuj1Mh554QB++mIAQoJs69VeXwajgrUbs7F8bSbyCwyIjvTG5MsiMbBPsE075JevzbSphqYoAj7eGlw9xT5VB2wxZXwUPv/ujMX1hLIMTLos0mHxWDJ9UjQWL0+r9Vjl9/W0Ca7xRsMePD0l3DuzLW67vhXOppVDEgXEx/pAY4fZCnty/VXvRORwoocHBi77GjHXXwFBc8E8rCQiesYEDPpnHiQv59RdJPuLivDCq091hUYjVNu5LIqApDF33ImP9Wn0faaMi0J8jHdVV59c3yjIgpWXIUmEX8fWVq/5658pNZJOALVON154rKJCxl//1L5TvLEKCo24Y/ZuvPzuUezam4+TZ0qxaVsOHn3xIJ557RBMprp37Ccll9mUoMZGeWPOG70QGe5lj9BtcuWEaLSO97W4y33S2Eh06eD8ad1Wcb547uHOkMSa39eVnaRio50/Mmtvnp4S2rf2Q5sEX5dLOgFAUFVbe284XlFREXQ6HQoLCxEQ4PxvYqKWqCIrF/lb9gCqisCBPeEV1fjNCuSasnMr8OfydOzclw9VNRd2v2JclF2TmoJCI97+5Dj+3ZKDhKxDuHbXB1bP7/Pj+4i66vJaj6mqium3bkNWTkWDYuncwR9fvtOnQR9rzezn9luuoykAN14Vh7tubmP1Gh99fQq//ZVaZ1mi5T9dAj9fx9eJLSox4uOvTuGfdVkwnYsxwF+Da6fG4sar4p1e9/RCKWnlWPh3Gnbty4eqqmjfxg+jhoVjQO9gl0zM3FF98jUmnkREDlZWLsNgUBDgr3GpF2hHys6twJHjRdD/7xUYVywHLn4lEgRETBqJvr9+DMHC7ie9XsaYGRsbHEO71r6Y+2G/Bn98bZKSy3DDvdbXrvp4S/jzu8HwslIbdP/hQtz7+F6Lx0UR6N0tEB+82rOhodpFUbERp5NKodGI6NjWD1oXaMlYG71expfzE/Hn8jSU683vCIICtbhuWhyunRrbqJ/DrJwK5BcYEBLs4bQOXM5Wn3yNazyJiBxk++48fPfLWew9t/YwOEiLKyfG4LppcW5R79OewkI8ETY4DOqid3H6/e448/5cVGRkAwA8QoPQ6v6b0PaxOy0mnQCg0YoQBevT6pZIooBunezfCnTX/nwIAqy2rSwrl3HsVInVVqTdOwegVzcd9h8urDFyKsB8/f9cE2+foBshwF+LXt0CnR2GVQajgtnP78fBo0XVnsv8AiM++fY0klPL8Nj9Heq9+ebg0SJ8Nu90tVJj/XsH4d5b2qB9G/dok+sMTfqb7rXXXkP//v3h7++P8PBwTJ06FceO2bZLj4ioOflrRTpmP38A+4+cf5HKyzfi6/mJmP3cflQYHNOpx9UIkoS2D9+OUWfWYcTBvzHiwDKMPrsB7Z++D6LW+hSyRhJwyYCQqjWj9SErKiaPi8Sajdl46Z0jeOp/h/D1gsQGT9tXsrXhklJHtiwIAv73dNeq5FSSBGgkAYIAeHiIeO7hzujTw312ZOflG7D/cCFOnC6p83O3t2WrMrD/cJHFr81f/5iP18eeAwW4/8m92H+4+ga2Xfvycdeje3D0hPVNcS1Zk061jx8/Htdeey369+8Pk8mEp556CgcPHsThw4fh6+tb58dzqp2ImoOcvApMv3WbxfV6ggDc/Z/WuGG680ewbFFUYsTCpWlYsjIDefkGBAd5YPJlkZg2IQb+fo6dSDt4tAj3Pr4HqlpzlLFy5PHCEUhJNBdqmHldPFauz0ZKWjlE8fx5UIHZ97TH1Msbtov/6Mli3P7QbqvneGgF/Pn9JTb1M1dVFYeOFWP95myU6xW0TvDBuEsjXKYXel0ys/X48KtT2LAlp2pkOjLcE7dd3wqXj65757teL2P1xmzsOVAAVQV6d9NhzPBwq8sULnbLgztxympXHwFjR4TjmYc62XQ9VVVx7Z3bkZapr/Waogi0a+2Hb97va3OM7s5l13hmZ2cjPDwc69evx/Dhw+s8n4knETUH3/2ShK/mJ1odDQsP9cTv3wx0mVp7luTkVuCex/ciM0tfbYpbEMy74z99s3eTlSiyZP3mbLz07lEYDErVWj1ZVtGhrR+uviIGf6/OxIEjhRAEAX17BuLqK2Lw3ucnkZKmt9gR6N0Xu2NAn4Z1TLrz4d04drK41vqRoghcMS66Xm0aVVXFngMFWLIyAxlZegQFemD8yAj07hGI9ZtzsHVnHmRFRef2/pg0NhJBgY59/i3Jya3AbQ/tRkGhodbn4t6Zbax2+zpyvAiPvHgAhUWmqlFtWTF3CnvzuW7o3tm2pRJjZ2xEud56L/NunQLw2Vu9bbrevkMFuO+JfXWeN/fDvmjnxAYEjuSyazwLC8+tawqu/Ye5oqICFRXnpzmKiuo39E1E5IpOJ5XVeU5WTgUMBgWenraP5DjDax8eQ1a2vsa6SlUFMrP0eP3DY3jr+e4OjWnEJWFY3DMIK9Zl4uSZUnh6iBg2MKSqL/n4UdVH1jZuz0FSSrmFq5mTwx9+S25w4vnofR1w16O7IRtqJrXt2/rh3pnWd7RfyGRS8OI7R7B2Y07VaK0oAus350AjCTDJatWI7YatOfhmQSKef6QzLh3ivDaflb75Mcli0gkAn887g/EjIxBcyxuVvHwDZj27vyphvPAapWUmzH7uABZ81h9hIXVv5vH301hNPEXBvCPfVqnplovnVzsvQ99iEs/6cNhqdkVRMGvWLAwZMgTdunWr9ZzXXnsNOp2u6k9cnHv2PSYiupCXp1jnSKa5ZqZrbzBKTS/Htt35FhMJWTF3BkrLsJzUNRU/Xw2mT4zB4/d3wKw726FvzyCLz/mWHXlWW4AqCrD7QAEqKqyPktWmwqDg1feP1lr8XRAAQ4VSrzWpXy9IxLpNOQDOJ1+VI+eVZYwUBVVLDUwmFc+9eRjHTzl3jWGFQcHyNZkWv1cA80ju8rWZtR77c0U6yvVyrbMEigJUGGQs+rv24vAXGzcy3GLNUcC8OW3sCNs7P9m6zMHf17XfRDqLw37L3XfffTh48CB++ukni+c8+eSTKCwsrPqTnJzsqPCIiJrM8MGhVusxSqKAIf1DXL6m4NGTtiUzR0+WNHEkjWMwKNa3nZ9jtKFz0MVWrjePutaWMKkqcOZsGVasy7LpWuV6Gb/9lWpLqOfvAfPGpJ8Xp9j+QU2goNAAg9H6TitRFCy23ly3Odvq0hRFQVVCXpfpk2Lg56upNfmURKBNgi9GXBJq07UAYEDvIPh4W08qg3Ra9Ohi/6oJzYFDEs/7778fS5Yswdq1axEba7mtl6enJwICAqr9ISJydwP7BKNtK99aR7oEAVCh4oarXH9jkXRu/aSfvgAhJWnwNNa+hMDaaKIr6NDWr84STOGhnsjJrUBmtm3TqpUWLbM+CicIwJKVtnVMOnKiuKrmZH3IsoqN23Lr/XH25OerQV3LlVVVhc7CFHdFRd2ft97GEenQYE98/FovREeauxRJIqqS0G6ddXj/5R7wqEf9US8vCbdcm2D1nNtvbAWNi89gOEuTrvFUVRUPPPAAFi5ciHXr1qF1a+utz4iImiNJEvDuSz3wyAv7ceJ0aVVipigqPLQinp3dCd06uf4b7Vb5x3DTtjcRm38SACALIo5E9sf6DlNR5B0CANBoBPSyUp/SFYwbFYHP5p2BwahYHE3MzTfgxvt2AgDat/HFrde1wrBB1kfF9hwoqHO0V1WB3DyDTXHW1bXImoaM1tqTr48Gl/QPwdaduVaXZowZUXsntI7t/JCWqbf4HEiigI7tbF8/2SbBFz9+1h+79hfg0NEiSJKA/r2C0LGdv83XuNB102JhMCqY+1MSZFmFKApQZBVarYC7bm6DKeMbVhWhJWjSxPO+++7DggULsHjxYvj7+yMjIwMAoNPp4O3d/PqjEhFZEhLkga/f64td+wuwcVsODAYF7Vr7YdxI9yiNk7F4FQ5d/QBiLsjUJFVBl4wdaJ17GHMHP4VinxBMGhsJXYDjWzjWR4CfFs890hnPvn4IonB+7eSFZZcuTHhOninFk68ewqP3tbeYUJSVmfDEKwdtu7+NG1natfaFJAn1TkBFEejgAgXMZ16XgG278yCoaq2lrsaOCEeruNpLK06bEINV/2ZbvLasqLhyYky94hEEAf16BqFfz8bXPxUEAbdck4Bpl0dj7aZs5OYbEBbiiZFDwhxeUszdNGk5JUsLu7/99lvccsstdX48yykREdlGn56F5G9+Rd7GXYAgIHTUIMTdMh0eoQ3bmX0hWV+BVXFDYSosrnVtpCKIOBbeGxm3P4lXn+rqNl2Yjp4oxvw/krFhaw5MJhX+vhoUl5osnq/RCFg8b3CtifXCZWl499MTNTp/1sbTQ8D8TwcgMtyrznNffvcIVq7PsrkwfaUXHu2MMcNrH010pN378/HSO0eRk2c4Xy8VwMSxkZh9d3urLTY/nXsa839PrvaGoPL/Z1wRgwdvb+vy5cdaCpet41lfTDyJiOqWuXQtdl/zIBSj6fyWZ0GA5OOFfos+Q+ilgxp1/bRflmHPDQ9ZP0kUMTp5E7zCG5/o2kpVVRw/VYKiEhOiwr0QG92wmTRVVVFRoWDSjZuhr2Nt4YO3t8XVU2ruVXjx7SNYvcG2BFEUgKsmx+DBO9rVeW5RiRH3P7EPZ86WAur5lvaVCZgoVvuSQ1WBqAhPaDUifH00GDUsDBPHRiLAz3mj0LKsYuuuPCSllMHbS8LQgSE2lUFSVRVrNmbjx4UpVZ2AOrTxw7XTYjF2RDiTThfisnU8iYjIvkqOn8GuGfdDNcnVRyNVFXK5Hjun3I1Lj6yAV7Tt5WIuVnriDASNBqrJ8mggFAX6s6kOSzzXbcrGp3NPIzXj/Oafnl11mHVnu3r3yRYEAcWlpjqTTgA4lVha47H6jt8oKrB8baZNiWeAnxafvdUbf61Ix6LlacjOqYDOX4sJYyPRvo0vlvyTgW2786EoKnT+WhQUGZGZXVGVjB49WYwFvyfjo9d6WpzWbmqSJGDIgBAMGRBSr48TBAGjh4Vj9LDwqpay7jKaTpYx8SQicmNJn8w/X8TxYooKuaICZ7/8GR2ef7DB99AE+ENV6t5BrNU1bKNGff29JgOvvnesxuMHjhTinsf24NO3eqN9PQt321rKKjv3fJOT00mlmP/7WazdZF6zWx+lZbbXCPXxlnDN1FhcM7XmSOvwQWFQVRV/rUjHm3NOAKjeL15VgcJiIx554QB++mKgy5fssoQJZ/PBryQRkRvLXLLGPNppiawgc+naRt0jctplMK/Mq50KAfqoBKSK9RvRaoiKChnvfX6y1mOKAhiMCuZ8fare162rpWKlyo1BO/fl47aHdmHV+ux6J52AuV+5Pf240HLdTkUBMrIqsHl740ssZedW4LtfkvDmx8fx+XencTqp5ghwU6owKMjM1qPEylpccm0c8SQicmNWp78rzzEYG3UP79hIxN82A2e/+qXGyKoKQICKZdET8d6s3bj/tja4dmrTdZ37d2suyqyMFioKsHNfATKy9DZt3qnkXUdB8EoxUd6oMCh49vXDMJlq7ta2hSAAUy+3X7md/AIjktOsd4vSSAJ2HyjA8MG2F0q/kKqq+O6Xs/h6fiIgnNs8rALf/5qMsSPC8dR/O1rdKNRYufkGfPtjIv5enYkKgwIBwMC+wZh5XQK6duQeEHfCEU8iIjcWOKg3BI3lpEnQSAga3KfR9+n6wbOIm3kVIAhQBBGyIEEFYBI9sKTbf3AsrBcA4OOvT2PnvvxG38+SzGy9TS0ns3Iq6j7pAkE6D/ToElBn0fNLh4Rh3aZsFJeYGpR0iiLQvo0fptkx8bQlDNXWEy34c0U6vvwhEYpqTu5lWYV8rgr/qn+z8N4XtY9C20NObgVun70bf61Ir1rrqQLYvicP9z6+F9t25zXZvcn+mHgSEbmxVvfdaHWqXTXJSLjn+kbfR9Rq0ePzV1D80XdY32EqtrUai7+73oQPR72NA7FDqs6TRODnRU3XrjEwQGu1//eF59XXzOtaWUwmRRG4pH8I2rf2w/HTJTatlfzPNfEICfKo+renh4ipl0fj4//1hJeX/fp4BwdqERnuaWUxhDlR7NHAwv6yrOLbH5MsHldVYMk/6cjJq1+yb6s5355GXp6hxtddUcxNGF5+9yhMpvovdyDn4FQ7EZEbCxnWHx2efwDHX/wIgiRBlc1JqKCRoJpkdHn3aQT06GS3++3K8MbW1uMtHpcVYNf+Arvd72LdOwdYLaouCObC6/GxPvW+dv9eQXju4U544+PjMFQokCQBqmouVj6obwheeLQzAMBDK9g0eDjt8mjMvK4VzpwthcmoICHWBz4+9n/ZFQQB106Nw/tWRh09PUT4+0lQVbXeZYhOnC5BTh3dlhQF2LQ91+4de4qKjVizMbtqdPViqgoUFBqxaXsuRlwSZtd7U9Ng4klE5ObaP3M/dP174MyH85C/cee5AvKD0fq/tyBkxEC73sum6eUmKA9dWmbCe5+dxMr1WRaTkMp86t5b2jT4PpddGoEhA0Kwcn0WzqaUwcdbwoghYdV2yQ/uF4Lvf022eA1BANrE+yIk2AOCINR7h31DXDkxGkdOFGHF2qxqBdcrGU0KHnr2APr2CMRrT3etVwJsy8YrUQD0DegrX5e0DMttMytJkoAzyWUYYfe7O9/ppFLs3JsPk6yia0d/9Oiic/v6pUw8iYiagfBxwxE+bniT36dnVx227c6zOiXd08692o1GBQ89ux9HTxZbLdAeGKDFY/d3QP/ejasl6uujsbr5p3vnAHTp6I+jJ2qPR1WBm6+Jd2iCIIoCnnmoE0YOCcPn353BmbNl1Y5XxrnnYAFeee8o/vd0N5uvHRfjDVEw1x+1RFGB1vH1H2Wui7cNSxIURYWPHZcuuILCIiNefPsItu/JhyCY38woivk5fuWJrkiIs/9z7Shc40lERDabNDYSWo1ocROOoqDWzj6NsWZjNg4ft550zpgcg4VzB2HYoIbt2q4PQRDwxjPd0CbBXJC9crOTKJqLTt11c2uMHub4dpWCIOCS/iEoK7e+6//frbk4m1pm8ZyLhQZ7YujAEIubukTRXB6qX6/G90C/WHysd50dqVQVDd6t74pMJgWznt1XtUlPVc+/cTibUob7ntyL3HzrSx9cGRNPIiKyWVCgB159qgs0klAtEZHODTjNvC4Bg/vZt57nkpXpEK0MHgoCcOxUMTQax72kBQV64Ov3+uL1Z7tizIgIDBsUguuvjMNPXwzATTPiHRbHxRKTy5CZbX2Tjyia12PWx3/vbIdAnUeN5FMSAY0k4tnZnSFa+yI1kCAIuP2GVlaOA+NHRdSrdJarW78lBydOl9b6RktWzOte/1ia6vjA7IRT7UREVC+D+4Xg+zn98cfSVGzYlgujSUH3TjpMnxSNXt0C7X6/zOwKq9O8qoo6k62mIEkChg4IxdABrjPaZjDWvc5SEIR6F72PCPPC1+/1wdyfk6pqaYoiMGxQKG65NgHtmnAd65jh4cgvMODjb05DUVRIonlzlyyrGDU0DI/e16HJ7u0M/6zLgijC4gi/ogB/r87EHTe2dmxgdsLEk4iI6i022hsP3tHOpn7jjRUW7IH0TL3VPUulZSZ8PT8Rk8dFITzUvl2B3ElslDe0WgFGo+UnS5bVevezB4DQEE88cm8HPHhHOxQVG+Hno7FrWShrZlwRizEjwrFibSbSMvTw99Ng9LDwquUOzUlhkdHqshIAKC5pXFMIZ+JUOxERubSJY6Pq3ChfUipj3i9JuOq2rVi6KsMxgbkgP18Nxo2MgGhlPWZYiAcG9mn4BiwPrYjQYE+HJZ2VgnQeuHZqHGbf3R533Ni6WSadABAb5QXJyrIFQYBbLy1g4klERC5t9PBwdGjrZzGZqmQuKA689sEx7DtU4JDYXNG9t7RBXLR3jedLkgCtRsTLT3SBZEMBfFvl5Rvwz7pMLF2VgVOJJXa7bks1eVyUxZJhAADVvi1XHY1T7URE5NI8PUR88EpPvDnnGNZtyqlz9FMSgQV/pKBn10CHxOdqAvy1+PztPvhpYTIWLU9HQaERWo2AMSPCccP0OLSKs89IYUWFjHc/P4nlqzOrJUrdOgXg2dmdEBNlfTc61a5HFx3Gj4rA8jWZNY6JItCpnT8mXRblhMjsQ1DVJqj0aydFRUXQ6XQoLCxEQECAs8MhIiIny8zW4/k3j+DQsSKrCagkCVi3cJjbF9tuLFVVYTCq0GoEu+46V1UVj7x4EDt259XY+CWJAnQ6Db79oF+1lqENUVxiwuoNWUjL1CPg3LrOqAj3nWa2lSyrmP/7Wfy8KAWFxSYAgIeHiEljI3H3f9rAx9u16pbWJ1/jiCdRM6bKMjIW/oOkz39E8eGT0Or8EH3NJMTfdR28ItlejtxPRJiXTcmMLKtQlPNlnloqQRDg6WH/5Hv3/gJs25VX6zFZUVFQaMSvf6bg7v/U7CJ1NqUMWbkVCAzQom0rX4tvDhYvT8MHX5yC0aRAEgUoqorP5p3BlPFReOju9tDYcbmAq5EkATdfnYDrpsXhVGIpTLKKNvFN03LV0dz/MyCiWikmE/Zc/xAyFv5jnnuUFRiycnHitU+R+Ml8DFo5z649vIkcpWM7P2zYmmOxX3ply0p7rmOk6laszYQkAbKFWvWKAixdmVEt8Tx4tAgffnkSh48XVz3WKs4H993apkbt19UbsvDWnBNV/zZd0DbzzxXp0EgCHrq7vZ0+G9el1Yro1N7f2WHYFTcXETVTZ977BhmLVpr/IV9Qm0NWYCwsxo5p90C19KpB5MImjY2CaCWpVFVgxhUxDoyo5cnNN1hMOisVFp0v+XPwaCEeeHIvjp4srnZOUkoZHnvpIP7dklP1mKqq+PKHRIvXVVVg4d9pyMlzfO1WajwmnkTNkGIy4cyH82BxEZwsQ382DVl/r3dsYER2EBzkgWdnd4IooNqoZuWM7ZjhYZgwJtJJ0bUM4aGedY4oB1+wJOLdz05WLX+4kKqa/7z96fGqUc0zZ8uQklZu9dqqCmzYWr/uS+QaONVO1Azpk9NRkZFj9RxBq0Hepl2ImDTKQVER2U/lJpOfFqZg4/ZcmEwK2rXyw1WTYzB+VITNG2ny8g1YuioDJ8+UwNNDxNCBobhkQEizXj9oDxPGROKvfyzXSxVF4Irx5p3Xp5NKcfyU9TJLeflG7NiTh8H9QlBaZqrz/qIAm84j18PEk6g5qqvgIQCogGDLeUQuqkuHALz0eJcGf/zfqzPw+kfHoSoqIJg34ixbnYmEWG+8+1IPRIQ1/93TDdWtUwDGDA/D6g3ZNSZWJBEID/PCVZPMyx0ys/U2XbOy7Wl0pDdEAVbbpMoKEBfj06DYybn4qkPUDHnHRcE73nqBYdVkQsjIQQ6KiMi17D5QgFffP2ae/lXNm2Hkc1O9KWnlmP3c/qp/N4Wc3Aps2ZmLnfvyode731prQRDwzOzOuGF6HLw8xQseBwb3C8Fnb/VGgL8WAKAL0Np0zcrzQoI8MGRACCQLGYogmM8d0r/h3ZfIeTjiSdQMCaKINrNvw6FZL9d+XJLg0y4BoaMGOzgyItcw/7ezEEXU2hNbVoCklHJs2ZWLoQNC7Xrf3HwD3v30BDZszaka0fPxljDjihjMvK6VW03xayQBd/+nDW6+OgEHjhTCaFLQvrVfjZHiTu38ERXhhfRMyyOfPt4SBvc9n0g+cHtb7D9ShJISU7Xi9JWTNE/P6giNhmNn7ohfNaJmKuGe6xF3+zUAAEFzrpihIACCAM+oMPRf/Dmn2qlFMhoVbN+dX2vSWUmSBGy08+aVohIj7nlsDzZuy602jVxWLuO7n8/i9Q+OwYV7uljk4y1hYJ9gDB0QWuvyBFEUcN/MmvU8L3T7ja2q9X6PDPfCfbe2QUyUFy4s89mjiw4f/a8nLukfUstVyB1wxJOomRJEEd0/eREx105E0hc/o+TQcWgCAxB99UTE3jQFGn8/Z4dINpDL9Uj7aQlSflgMQ1YufNrEIe62GYiYNIpvHBrIJKsWa4BWMnf8sZKZNsCvf6YiI0tfa8KrAli+NhNXTopGlw7Nr1PfpUPC8NzDnfDe5ydRXGKCIJh3pnt5ibjjhtaYMfl8+auMLD0eeeEAEpPLIEkCRAGQVcDXR8Kt17dqca1Qz6aWYdGyNOw+UABBAPr3DsbU8VGIjnTPlqRsmUlE5KIqsnKxdcxNKDlyCpW7LQRJgirLCJtwKfr+8hEkz8a1JHR3pafOInfNFqiyjMBBvaHr1bnOj1FVFTNu34aMLMt1IAUBuOeWNrj+yji7xTr1P1uQk2eweFySBEy+LBKP3NvBbvd0NQajgi0785CZrUeQzgNDB4bA+4KRzgqDgpvv24GMrIpqU+yA+Wui1YqY+0FfxMe2jI1FK9Zm4tX3j0LA+XLMomgeRX7psS4YPti+S0Eaqj75Gt8uExG5qD03PYzSE4nmf5x7Ea4s+p+9fD2OP/++cwJzAcb8QuyYdg/WdRqLA/c+h4MPvoiN/adi07BrUJaUavVjBUHAVZNjYK2Nu0YSMGG0/WqBqqqKXCtJJ2De3FS5s/tCZ86WYsPWHOw+UACTyb6jsI7moRUxYnAorr4iFmNHhFdLOgFg7cZspGboaySdgHmEVDap+OXPFEeF61SnEkvw6ntHzRvfLviyKwpgMql47s3DSMuwXu/UFTHxJCJyQcWHTphH8kyWehKqSPr8R5hKyxwbmAtQjEZsu/xWZF/YAOFcnlK48wC2XHo9DLn5Vq8xfWIM+vUMMi97vuBxSTSPrD01qxMCdbbtxraFIAgI8Le+uk0SgaDA8yPYJ06X4M6Hd+Om+3biyVcP4cGn9mHaLVuxeHma3eJqjPxCA75ekIgrZ27FqOkbcM2d2zD/97ONqq+5fnO21TcEsqJi9YbsBl/fnfy+JM3qc6EoKhYuc43vhfpg4klE5IJy12+v8xy5pAxFew47IBrXkrFoJQp3Hay15atqkqFPy8LZL3+2eg2tVsSbz3XDA7e1RVSEeUOMKACD+oVgzuu9MHZEuN3jnjAm0mqJXVkBxo803/d0UinueXxPjRaT+YVGvDXnBH5cmNygGEpKTTh6shink0qhWCuUWYf0TD1mPrgL835OQlZOBQwGBanpenw27wzufHg3CgqNdV+kFmV62WLDtUr6CvcrP9UQ23bnVRvpvJiiANv3WH+D5Yq4uYiIyAXpUyx3hbmQCy/TbzKpPyw2Dw9aelVWFCTP+wPtnrjb6nW0WhFXT4nF1VNiYTAqkEShzjaQjXHNlFj8vSYTRcXGGhuMRBHo3ysIvbsHAgA+m3caBoNicef9F9+dwcSxkQjws21UtqjYiE/mnsaKtZkwGs3fMxFhnrj56nhcMS4KgrWhtVq89M4R5BcYam2BmZxWjnc/P4GXHqt/cf/W8b7Ye6DA4pdWEICEFrK+05afbHf8+eeIJxGRCyo7a32dYqWAnnVvpmluKjJzLCed5xiy8+p1TQ+taHPSmZZRjvm/n8Xn353BslUZKLexAHxoiCc+fbMXOrb1r/a4KALjRkbg1Se7QhAE5BcasGVnntVyTyZZxRobp5xLSk2457E9WLYqoyrpBMydgt6acwJfz0+06TqVTiWW4MCRImt5P9ZtykZuvvU1rbW5YlyU1S+tqpqXSbQEvbvprH5PSiLQp3uQAyOyD454EhG5IKXctjaDGr+WMfpzIZ/WsSjad8Ty+ldBqLNzV0MYjQre/uQElq7KMO8sFgSYZBXvfX4Sj93fwabp+bhoH3z5bh8cP1WMoydLoNUK6N8rCKHBnlXn5OYZ6pxulkQBWTmWd+Vf6KeFyUhOK7eYyM79+SzGj4pEbLRt5XmOHC+u8xxFAY6fKsbgfvWrt9kmwRe339gKX/2QWFVyqZIgAAP7BOPyMfbb9OXKZkyOxYq1WRaPqyowbYL9v8+bGhNPIiIX5BESZH06GYDk59sianmqioKcVZtQsPMARK0GIZcOQvpvy61+TPwd11i+nqpiz8FCLF+TiZy8CoSHeOLy0RHo0UVndcr57U9OYNlq8xIIRQGUc5Oh5XoZL759BP5+Ggzqa1sbxw5t/dHhopHPSrZsapIVFcGBdZfSUlUVi5anWx09FUVg6ap03HWz9SLvlSSNbSPDktiwZQu3XJOAuGhvfP/rWZw8UwoACA32wFWTY3Dt1Fi36u7UGJ3a++Ohu9vhvc9OQhKFqp3+kiRAVVQ8+d+OSIhzvzeeTDyJiFxQ9PWTkfztb5ZPkETE/edKxwXUBCoMCtZtysbmHbkwGBW0b+OHyZdFISzk/Ohf4d4j2DXjfpQnpkDQSOY1bbICbWgQjDm1bKyQRAT07Iy4W6ZbvOdzbxzGpu25kCQBsqxCkgQsWZmBEYND8cKjnaHV1kzm0zLKsXSV5XW3ggB8NT/R5sTTmtBgT/TrGYjdBwosJoyiIGD0sLA6r2UwKHVu9FEUYNe+AlRUyPD0lKyeCwB9ewRVlpW1yMtTRLfOujqvZcnoYeEYPSwchUVGmGQVQTotxAYmsu5s+sQYdGnvj9+WpGLX/gIIEDCgTxCumhyD9q3dswkIE08iIhcUMmIgQscMQc6aLTUaiguSBMnfF60fmumk6BovOa0Ms57Zj8zsiqqe6Zu252LeT2fxxIMdcPnoSJQlpWLr6Bshl5prFV44tW7KL4JHeAhMpWVQzh0XPLSIuWEKurz1BCTvmq0bAeCjr05i8w5zK0xZVqv9/e/WHHw27zQeuL1djY9btznHYm93wDztefREMTKy9IgMr/3e9XHnza1x3+N7oapqrdPuN86Iq1Z6yRKtVoRWK1Rb21mbw8eLMf3WbXj7he7o1L72kdhK4aGeGD08HKs3ZNX6fAgCcOXEaPh4153E1kUXYL+SVu6qc4cAPDu7+TTRaf5zNEREbkgQBPT97WNEXzsRVcX8zv3t27ENLlk7Hz4J7rnJwmBUMOuZ/cjONa9RrExezIWyVbz6/jHsO1SAxA/nQS4tr71skizDkJWLHp+9gkGrv8fAFXMxJnkjen7xKrS62hOngkIjlvyTYXH9pKoCC5eloaS0Zh3KklITRBt2fjemhuWFunQIwHsv90B0RPUk1stTxJ03tcLtN7Sy6TqiKGDM8HBINuSARSVGzHp2H/Js2BT06H0d0KOL7tw9zI9J5/4ePigUd97U2qb4qOXhiCcRkYvS+Pqg97y30emVh5G9YgOUCgMCendB0ODe9S5/40rWbcqutUNPJUkEFvyRgjEL/qw16bzwxMw/V6HPgvdtuu/uAwUwydZH/gxGFXsPFWDogOqtCOOivev8WEkSEB7a+NHOSr26BeKnLwZg36FCpKSXw89HgwF9gus9knjj9His2ZANVbVcngkwJ/5lZTL+/Ccdt1yTYPWaPt4SPnilJ7bvycPfazKRl2dARLgnJo6NQu9u1tfKUsvGxJOIyMV5x0Uh/varnR2G3WzZmWd12lpWgC07czGiqMT6hWQFhtwCm+9ra7vJb39MQnJqOSaMjqya6r10SBje/eykxdJJkgiMHhYGfz/7vqwKgoBe3QLRq1tgg6+REOeDD17tgWdfP4zsXOujmYpqfmNQV+IJmBPtwf1C6r1znVo2TrUTEZFDGU1KneWCFAXwToiFtZ6BgkaCb7u6E6RKF9fPtOTYyRJ88u1pXHXbVuzeb97A5O0l4fEHOpjve1FIkgjodB6462bXnV7u1kmH374ehMCAuhPjigr37gdPro2JJxEROVRdCaAgAK3ifNDqrmutnqeaZMTfZvtIcEKcD3p311WtRbR6bRXQVyh49MWDyMw211QdMzwc77zYHZ3anY9fkgSMHh6Or97tg4gw+02zNwVJEtCji/XPXxIFdGjrnrulyT1wqp2IiBxq4phIfL0gESZT7cOeqgrMuCIG8SO6I/XnpSjcfbDWeqYJ990IXZ+u9br307M64e5H9yCvlnaPtcVhNClYvDy9arPMwD7BGNgnGJnZepSWyQgL8bT79HpTmjYxBv9uzbV4XFZUXDnR/YqSk/vgiCcRETlUcJAHnp7VCaKAaqNvlVPYI4eEYtLYKEjeXhj0z1y0uvdGSL7nu+p4Rkegy7tPo+t7z9T73pHhXvj2g7648ap4BAfWXapHUYANW3NqPB4R5oU2Cb5ulXQCQL+egZgx2VwN4cIlA5X/f/30OPTsGuj4wKjFEFQX7jBfVFQEnU6HwsJCBAQ0nxpWRM2VIa8AaT8tRfnZVHiEBiH66olN0rqQmoeDR4vw48JkbNqeC9mkonWCL2ZMjsGEMZE1elSbSstQejwRolYDv85tIdhSH8gGV922FRlZ1ltPxkZ546cvBtjlfq5AVVX8sy4LPy5MruoM1KGtH66/Mg6jh4VxRzrVW33yNSaeRGQXZz7+HkcefwOq0QRBo4GqyICiIuGe69H13aftlihQ81NZJL2yM01KWjkWLkvFlp15kBUVPbvoMH1SDDq2s21zUH288NZhrN2YU9WO8GKSJGDsiHA881Anu9/bFej1MgQBNnUsIrKkPvmae80REJFLSvlhEQ4/9ErVv1Xj+RZ9SZ8ugOjpgS5vPuGM0MgNCIJQNdW7cVsOnnn9MBRFrVqDmZFVgWWrMzHrzna4arJ9i+ZPnxSDVf9mWzwuyyqmN+M1j15e7pFw6vXm+qKL/05HVo4eAf5aTBgdiSsnRSNIV3cHJ3IdXONJRI2iKgqOP/+BlRNUJH78PQw5eY4LitxSVk4Fnn39MEwmtdrGn8qWlu9/cRIHjhTa9Z7dO+uqugCJF7wiVq49vevm1ujcgTNuzlRcYsI9j+/BR1+dwtmUMpTrFWRmV2DeL0m45YFdSM0od3aIVA9MPImoUYr2HkH52TSr56hGEzL/WuOgiMhdLV6eZnHKGzBPe/+yONXu973l2gS89Xw39O0RCK1GgFYroG/PILzzYnfcNCPe7vej+vn465M4lVgKVQUu/O5QFHMb1OffPOy02Kj+ONVORI1iLCqu+yRRhKmuLjTU4u3eX2C1xJEsq1UF3e2NHXhcU1GxESvWZlnpcqXi6IkSHD1ZXK2+KrkujngSUaP4tk2w2l0GAKAo8O3gul1diMg1nTxTApNsfQ+0IACHjhU5KCJqLCaeRNQo3nFRCBs71PKudVGAZ3Q4wi4b6tjAyO306RFYbZ3lxSRJQJ8eQY4LiJyustKBNapq7rhE7oGJJxE1Wtf3n4EmwK9m8imJEEQJPb9+neWUqE5TxkdbTSBkWcXVU2zb1a5PzcTRp97GqvhhWK7rifU9JyLxk/mQ9dZrdpJr6dTOHz7edf/u6N+Lb0jcBRNPImo03/atMHTr74icPh6C5vyLRMiIgRi85geEjRnixOhaHllWsXlHLj6dexqfzTuN7bvzoFjZtOMqwkM98fITXaDRCNU6GlUWk3/ornbo3llX53WKD53Av32uwKl3v0FFehbkMj1KjpzCoVkvY+vYm2EqLWuqT4HszMtLwlWTYyyu5hFFYOjAEMREedd+ArkcFpAnIrsyFhajIiMb2iAdPMO5WcPRTieV4vGXDyI9U1+VsMmyivgYb7z5XHfERrv+C3RVAfldeZBlFb266nDlRNsKyKuqivVdx6PsdDJUWa55giSi9X03ocs7TzVB5LYrLjFh+ZoMHDhSBEEwLzMYOyLCptG9lsYkq3jpnSNYsyEbkgjIijnhVBSgSwd/vPNiD7drXdrcsHMREVELlF9owI337kRxibHGLmBJBIICPfDDJ/3h59t8X6Rz1mzBtnG3WD1H8vXGmNTN0Pj6OCaoi+zal48nXjkEfYU5MRYAKCrg76fB2y90R9eO7vl6p6oqDh0rQkqaHn5+Evr3DLJbRyRVVbHnYCGW/JOOtAw9ggK1GD8yAkMGhkIjcX2ns7FzERFRC7R4eTqKi42obVZdVoDcfAOWrc7A1VfEOj44BynYsR+CJNU+2nmOXFqO0qOnoevbzYGRmaVllOPRlw7CaFRQOexT+eUqLTNh9nP78dPnAxAU6F7dePYdKsQbHx3D2dTzxdx9fSTMvC4B10yJbXT/d0EQ0Kd7IPp0D2xkpORsXONJRNRMrFqfVWvSWUlVgVX/ZjkuICcQNBJU1D2RJ2icM+7y+9I0mEznk84LKQpQXi7jr3/SHR9YIxw+XoRZz+xDSlr1DkKlZTI+/vo0vvvlrJMiI1fExJOIqJkoLTPVeU5Jad3nuLPQ0UPMw7tWeESEwK9rOwdFVN2/W3KsFslXVGDD1lzHBWQHn809A1lRLb7p+fanJBQWGR0bFLksJp5ERM1EQqyP9TqYItAqzjnrGh1F16szQi4daLV8V5vZtyGvUMbvS1Px3S9JWLspG0aj9WTVXmy5T4XB8jIBV5OTW4HdB+ruOLVmY7bjgiKXxjWeRETNxLQJ0di5r8DicVkBpo6PdlxATtJ7wfvYdvlMFO87WrX9WdBIUE0yYm+dgUWeQ7F45laoMBcol2UVOn8NnvxvRwwdGNqksXVs54/8nbkWB2UlCejc3n1aP+YVGOo8RxQF5OXXfZ4lqqpi78FCrNmYjdIyE2KjvTFxTCQiwrwafE1yHiaeRETNxLBBoRg5NBTrNuXUuoZwwugI9O/d/Atte4YFY+jmX5GxeBXSfloCQ14BfNu1QvytM/DlTh/8tSKj6vmRz7VjLCox4an/HcIHr/RE7ybcwHLVpGhs2m55Kl2WgWkTbCuSby8mWcXuffnILTAgNMgDvXsE2bxTPDio7k1QiqIiJLhhm6VKSk144pWD2HuwEJIkoLIQz9wfk3DPzDa4blpcg65LzsNySkREzYgsq/hpUTJ+WZyK3HOjTOGhnrh2WiyumhRjUwvC5io9U48Zt2+zeFwUgO5ddJjzei+oqop9hwpx4EgRRBHo2yMInewwEqmqKj7++hR+XpxaVYsSOF+X8tbrExAb5Y3te/KhKCq6dAjA+FERTVYCa9W/Wfjwy5PIKzi/BjMkyAP/vbMdRg0Ns+kaDz23H7v25VucbtdqBSz+bjAC/LT1jm/2c/ux08q1X3i0M8YMD6/3dcm+WMeTiKiFk2UVGdl6CAAiw71adMJZaf7vZ/H5d2esrkcEgDlv9MTbc07gzNmyqjWzigJ07xyAl5/ogtBgz0bFoarmNY8/L0rB4ePFEAD07KrDyKFh+OG3s8jONUASzWWWVBXw8hTx6pNdMaBPcKPue7GV67Pw4ttHLB5/5YkuuHRI3cnn8VPFuPuxvTCZlFqf23tntsH1V9Z/ZPLEmRLMfHCXxeOCYF7X/P2cfo0u10SNw8STiIjoIp/OPY2fF6XAJFt/2Qvw16C01FRjHaYkAtFR3pj7QV+7FUaXZRWCAJTrZVx313YUFNUs/i8IgEYjYO4H/ZBgp81hJlnFlTO3IC/f8m7z8FBP/Pb1QJvetBw+XoQ3Pz6Ok2dKqx7T+Wtw+42tMW1Cw9YVz/0pCd/8mFjnG4VfvxqIqAiu93QmFpAnIiK6SEyUd9WaTksEASguNtVaCVRWgOTUcqzakI2JYyLtElNlW9PlazORX2Cs9b6qCigy8OtfqXjk3vZ2ue+eAwVWk04AyMqpwP7DhejVLbDO63XpEIC5H/bD8VPFSM3Qw89Xg15dddBqG148p8IgQxQEKHXUZXWnKgDEckpERNRCjB4WBq2H5Zc9SRTg5SlaTXMEAfhnbabdY9uwNcfcO9MCWVGxbrP9ShLZuss8t5670Tu09cfIIWHo3yuoUUknALRt5Vfn6LSXp4hI7m53K0w8iYioRfD10eChO82F4y9eEiiJ5l7pGo31l0VVBQqL7V8MXa+vvZvRhexZazTUxl3mYSGNW8/aGMMHhyLAX1Pja1VJFIGJYyPh5WWfZQ/kGEw8iYioxZg8LgqvPNEF8THeVY+JAjBkQCi+eKc34mN8LCY6gHlUNC7a/kX4O7bzg2RlLaUoAu1a+drtfr26BSIsxHLyKQhAVIQXunVy3v4KD62IFx/rAkkSajw3ogi0ivPF7Te0dlJ01FBc40lERC3KpUPCMOKSUCSllKG0TEZ0hBeCAs1J2NTLo3DoWJHFj5UVFVeMj6r1mGIwIGftVhhy8uGTEIOgS/pAsNZK6gJTL4/G70vSLB5XFGD65FibrmULSRIw6672ePp/h2ocq0y8Z93ZzunVEPr3CsKX7/TB97+exfotOeZi/wFaTLs8CtddGQdfH6Yx7oa72omIiM4xmRQ8/PwB7D5QUGPqWxCAMcPD8dzDnWqU7zn79a84+vTbMOYWVD3m3SoW3T95EWFjh9p07wV/JOOTb09Xq+8pCObp/fGjIvD0rI52Lxv075YcfPjVSWRkVVQ9Fh3phf/e0Q5DBoTY9V6NZTIpMBgUeHtLLJ/kYlhOiYiIqIEqDAq+nn8Gi/5OR1m5ece0zl+Dq6fE4sar4qt2oldK+mwBDj7wYs0LCQIgChj49zcIHTnYpntv2p6LBX8kY9+hQgBA63gfXH1FLCaOjWyy0UdFUXHwaBFy8w0IDfZA144BTh/pJPfCxJOImpXig8eRv2UPIIoIGTEAvu0SnB0StQB6vYyklDKIooBWcT617tKWy8qxMnYI5OLSWq4AQBTg360jhu9aXK97G40KFBXwtLIL39kKi4zIzTcgMEBrU+tMar5Yx5OImgV9aib23Pww8v7dUe3x8Ikj0fOb1+ERHOicwKhF8PKS0LGd9TaZmUvWWk46AUBRUbz/KIoPn4R/l3Y237uxpYiaUmJyKT6fdwabtudCOTd01b9XIO68qTU6d+AgEVnnut/ZRNSiGQuLsfnS65G/eXeNY9nL/8W2cbdArqhfjUEie6vIyAZs2EBUkWG/GpwXkmUVm7bn4ssfzuCbHxNx6FgRmnIi8+SZEtwxew827zifdALArv0FuPfxvdh7sKDJ7k3NA0c8icglnf36F5SfTUW1V7dzVFlG0d4jyPh9OWKuv8IJ0bmHsqRUpP+yFIacfHjFRSPmuknwCAlydljNild0OOrs6QjAMyrc7vc+drIYT/3vEDKzK8zrTlXgmwVJ6NLRH/97qmuje8rX5u1PTqDCINf4lBUFgKrifx8cw89fDODmH7KIiScRuaSUeX/UmnRWEUWkfLewwYlnwc4DyF65EarRhMABPRA2digEqXkUolZlGYce/h+SPpkPiAIEUYIqm3DksdfR6dVH0Oahmc4OsdkInzgSmgA/mIpKaj9BFBHQszP8O7e1630zsvR48Ol9KNebNz9d2Ar02IlizHpmP775oC887Dhln5RchoNHLZeaUlQgLUOPvQcL0bt7oN3uS80LE08ickmG7DzrJygKKjJz6n3diswc7LrmQeRv2mVONAUBqskE74QY9P1tDnS9OjcwYtdx9Kl3zEmnqgKyClU2D0+pijn51AYFIO6W6U6OsnmQvL3Q+c0ncODuZ2oeFAUIooAu7zxp9/v++lcqyvU1Rx4Bc0/5xOQyrN+cg7Ej7DfSmpxeZtN5KWnlTDzJIq7xJCKX5B0XVbOv4QUESYJP6/oV1FYMBmy97BYUbNsLwDwyqJpMAAB9Sga2jrkJ5cnpDY7ZFRhy8nDmo3mw1n/x+IsfQpVlB0bVMKbSMhgLmnbNoj3E3zYDPb95A56RodUe923XCgOWfY2QYf3tfs+V6zKtzvCLIrD63yy73tPPxmLtPj7NY+aAmgYTTyJySfG3XwPAcsKhyjLibp1Rr2tmLFyJksMnoJpqJl2qLEMuKUPix9/VN1SXkrl0HVSjyeo5+pQMFO6u2bHGVWQuW4fNI67DisDe+CesP9a2G4UzH8yFYrL+eTlT7E1TMerMegz8Zy56L3gPl2z8BSMO/m1z/c76qqwvaomiAMWl9n2+unXWIUintXqOp4eIwX2D7Xpfal6YeBKRS4q5aSp0/XoAUi2/pkQBYeOHI/zyEfW6ZtovS63uQFZlGSnz/6xvqC7FVFxqbj5uy3ku6MyH87Bzyl3IPzcqDQDlZ9Nw+NHXsWvGAy6dfIoaDUJHDkb0jAkIGtizSTfYxER5W+8pLwmIj7VvT3mNJOD2G1tZPefGq+LgwzaWZAUTTyJySZKXJwat+BZx/5kOweP8KIvo7YXWD/wHfX+bU+/NQIa8wjp3IMvFFjaJuAm/Dq2sb8oCAEGAT9t4h8RTH6Unk3D4kdfM/5Av+jqpKrKWrEHK3D8cH5gLmjYh2tpqCsiyiisui7T7faeMj8a9M9tAqxEgCOZkVBDM7+duvCoO/7mGzR3IOr4tISKXpfH3Q4/PX0Gn1x5B0Z7DgCggsF93aPz9GnQ9vw6tUbB1T61T7QDMCVmbxidkpuISZK/aDLmkDH4d20DXv7vDysuEjr4EXrGR0Kdl1ZpkC5KEkFGD4JMQ45B46uPs179AEEXL609FAYmf/ID42692bGAuaOKYSKz6Nwv7DxfW+l7qqsnRTVbM/for48z335CF7JwKBOk8MHp4WJOUb6Lmh4knEbk8j+BAhI6+pNHXib/taiR/86uVM1TE33ltg6+vKgpOvPoJTr/9JeQyfdXjfl3bo9fXr0PXt1uDr20rQZLQ85vXsX3i7VAFVBs5FDQSNP5+6PbBc00eR0MUHzhmfdOToqLkyCnHBeTCtFoR77zQHd/+lIRFf6ehpNT8vEWEeeKG6XGYNiG6Se+vC9Bi+kTXe/NCro+92omoRTn435fMpYYuJooIGtwbA1fMheTZsL7Thx97A2fe+6bmAUmE5OmJIZt/hX/X9g26dn0VbN+PYy9+gJyVmwBVhaDRIGr6OHR4cRZ8XXCaHQB2XfMgMhattLocQvLzwfj8PQ6MyvUZjApS08shSQJio7wh2rDGl8ie2KudiMiCru8/C9/2rXH67a+gT80AAGj8fRF/57Xo8PyDDU46y5JSceb9b2s/KCtQDAYcf+kj9P35w4aGXi+BA3pg4NKvYcjJgyG3AJ6RYdDqrPcdd7aIK0Yj448VFo8LGgmRV45zYETuwUMronW8r7PDILIJRzyJqEVSZRklxxOhmkzwbZcAydurUdc7+cbnOPb8+zU3xVxAkESMzdoBbUDD1qg2d7K+Auu7T4A+Jb3mOlxBgKCRMHTr7wjo0ck5ARJRreqTr3FXOxG1SIIkwb9zWwR079jopBMAKjKyIVgp1QQAqqzAmJvf6Hs1V5KXJwb9MxfercyNAQSNBoLG3F1K8vFGvz8+YdJJ5OY41U5EZAde0eFVrSktETQSPEKDHBSRe/JpHYcRB5Yh++/1yPp7PRSDEbq+3RBzwxSOFBM1A5xqJyKyg/KUDKxpO9Lixhjz+sTx6DP/XQdHRkTUtLi5iIjIwbxjI9Hu8Ttx8rXPahwTJAmSjzc6PP+AEyJzf9mrNuHMe98iZ91WQFURdEkftJk1ExGTRjk7NJdgMik4m1oORVERF+MDT4/al3wcOFKInxelYOe+Aqiqih5ddLh6Siz69+IoPDkORzyJiOxEVVWcef9bnPjfpzAVFFU9HjioF3p89orDSik1J6fe/RpHH38TkCSgssanJAKygnZP3o2OLz3k3ACdSJZVLPgjGT8vTkFBoREA4OcrYdqEaMy8rhU8tOcT0EV/p+HtT05UPnUAzN2GFAW448ZW7DhEjVKffI2JJxGRnckVBuT9ux2mc52L/Lu0c3ZILstUWoa0n5Yif+seCJKEsDGXIGLKGIhaLQr3HMbGAdOsfvygVd8hZMRAB0XrOlRVxSvvHcWKtVk1jgkC0K9nEN56oTs0koDTSaX4z/07Ye3Ffs7rPdGza2CTxUvNG6faiYicSPL0QNjYoc4Ow+Xl/rsdO6+8F6bCYvPudQDJX/8Cr7goDFj6NZI+WwBBI1lscSpoJCTO+aFFJp479xXUmnQCgKoCO/bmY9X6LIwfFYGFy9IgipYrfUki8NtfaUw8ySFYTomIiByu9NRZbJ94O0zFpQAA1SRXJZgVaVnYdtnNyNu8y2LSWfkx+Vv3OiJcl/Pn8nRIVl7BRcE8vQ4A+w8XWisvC1kxr/8kcgSOeBIRkcMlzvkeqtFUaxUAVZZRkZkLQVP3S5TooW2K8Fze2dQyq8mkogIp6eUAAI1UdwtNyYZziOyBI55ERG5OVVXI5XooJpOzQ7FZ+m9/Q5Utj2YC5gLysFKUX9BIiJwyxt6huYUAfw2EOnJFP19z4j64X7C1pxGSCFzSP8SO0RFZxsSTiMhNKUYjTr8/F2s7jMHygJ7427cbtk+5C3kbdzo7tDrJ5XrrJ6gqNDp/SN6etSefggBBkpBwzw0AAGN+IUpPnYWpuKQJonU9l42IgLWtwaIIjB8ZAQCYcnk0tBqx1kRVAABBwPRJ0U0SJ9HFmHgSEVlhLCxG4pwfsGPq3dg+6XYcf/lj6FMznR0WFKMRO6bcjSOPvY7ypNRzD6rIWbEBW0bdiNQf/2qye6uqioId+5Hy/SKkL/ynQcleQPeOsLZIUdBICBrQAwOWfAWNn495q3Zl5iQKEL080Pf3OTAVl2LHlLvwT8RArOs0Fv+ED8Semx9B2enkhn56bmHsiHDERnnV+hRKogCdvxZTLo8CAISFeOKN57rB06N68imKgKQR8NLjXdAqztdBkVNLx3JKREQWFOzYj+0Tb4exsianqgKiCEES0WveW4ieMcFpsZ1+fy6OPPY6LA17CVotRif9C8+wYLvet3D3Iey77QkUHzxe9Zjo7YU2s29Fh+ceqLNffd7GnUj8ZD5y126BIcd63/qhOxZB16szjAVFSPluIXLWmgvIBw/ti7hbpqPk6GlsGz/TvDHpgml7QSNB8vPFkI0/w69jm8Z9wi4sO7cCT716CEdOFEMUAQECZEVFQqw3Xnu6G+Jjfaqdn19gwJKVGdixNx+qoqJXt0BMHheF8FBPJ30G1FywjicRUSMZ8wuxpsMY82jexbs4BAEQBQzd+gd0vTo7PDZVVbG2wxjzSKelX+GigE6vPoy2j9xht/sWHzmFTYOnQ9ZX1PKcAK3uuwld33vG4scff+kjnHj5Y6slkiqrmnd4/gG0f+Z+i9dSFQVrO45F+dm0WjcoCZKE4BEDMGjFXFs+NbelqioOHSvG7v35UFSgR+cA9O4eCKGuBaBEdlSffI1T7UREtUie+wdMhcW1Fz9UVQiCgDMfzXN8YACUcj3KE1MsJ50AIAgo2nfUrvc98fJHUPQGC88JkDjnB5SdqX2KO3PpWpx4+WPzqVZKJOn6dEWfnz6wmnQC5hqg5YkptSadgHlnfO6aLc1+yl0QBHTrFICbr07ALdckoE+PICad5NKYeBIR1SJr+XqriZ1qkpG1ZK0DIzpP0GrMhRqtnSMIEL3sN4VqKilFxh8rrO5EF0QRqQv+rPXYmfe+tbqmE4KAji8/hKFbfkPU9PF1xlN67My5nTHWlZw4U/dJROQwTDyJiGqhVBjqPsdodEAkNYlaLcIuGwZBkiyeo5pkREweZbd7GvOLoForHAkAooCKjJxaD+Vt2mW5dc45+dv22RyPxt8XVntAVp7nx00zRK6EiScR2Z1crkfx4ZMoPXUWLryM3KqggT2tJnaQJAT27+G4gC7S7vG7oFqYZhY0Enw7tkH4hEvtdj+PkEDzSKsVqqLAKzay9oM2jE7WZ4o4/PIRED09rJ7jGRmKwIE9bb4mETU9Jp5EZDemklIcfvR1rIwejH97TsS6TmOxrvNlSJ73h9sloPF3Xmc9ZllG6wdutulaqqLUWSy9voKH9kPPb984N+0umnfbn+t37tM2AQP//gaiDZ1/bCX5eCNqxuVV96iVoiLm+itqj3dYf+uJPICQEQNsjkcbpEPrB/9jNaFt/+wDdn0OiKjxmHgSkV3IZeXYOuZmnPloHuSSsqrHy04nY//tT1ZtLHEXvm3j0f2Tl8yFyi9Mts6tU2z14H8QPnGk1Wtk/rUam0fegGVeXbDMqws2DZmBtF+X2S0Jj71hCkYn/otOrzyE6KsnIOaGKej7+xwM3/sXvOOi7HKPC3V49gFIvj4WE8i2j95h8b5tHpppOfkWRUi+3oi9eVq94un48kNIuPdGc5UBSaxKwgWNhI6vPoz4O66p1/WIqOmxnBIR2cWpt77A0Wfes7jLGABGHFoOvw6tHRhV4+Vt2oUzH8xF9op/ocoKAgf0QKsH/oPIqWOtTg2feHUOjr/woTlRrVzbeK5UUOuHZqLzG4+75e7j4sMnceCeZ5G/eXfVY5rAALR74i60mX2b1c/p5Buf49gz71YvpySJkDw90f+vLxAy3PYRzwuVJaYg7aclMGTnwSs2CjHXT4ZnRGiDrkVE9cc6nkTkcKvbXAp9crrF44IkofWsW9D59cccGJVzFGzfj01DZlg9Z8Df3yBszBAHRWR/xUdOoeToKWj8fBA8rD8kG3fQF+zYj8RP56Ng616IXp6IuGI0Eu64Fl4xEU0cMRE1lfrka1z8QkSNpsqy1aSz8pzSk0kOisi5Ej9fYLVIuqCRkPTpfLdOPP07t4V/57b1/rjA/j3Qy4mbsojIuZh4ElHjiSJEby8o5XqLpwgaCRp/PwcG5TyFOw9YLZKummQU7DzgwIjIlRjyCpD8za9I/XEJTAVF8O3YBgl3XYeIyaPqbDlK5O6YeBJRowmCgOhrJiL1h0UWEy7VJCP6auf1Nm8oxWRC/pY9MOYXwrdNPPy7dajzY2yZdpa8vOwRHrmZkuNnsHX0jajIygUU80q38tRM5KzciMjp49D7h3e5E5+aNb61IiK7aPvwbRC0WvMGmosIkgRdv+4IGzfMCZE1XPLc37Gm9aXYOupG7Jp+H/7tPRkbB02vc7QyYsqYWp+HSoIkIXLqGDtHS65OVRTsnHYPDNn5VUknAODcbv+MP/7B6Xe+dlJ0RI7BxJOI7MKvU1sM/PsbeIQFAzC3dawsuxM8vD8GLPnSraYRE+f8gP13PIWKjOxqjxfuOYwtI29A4d4jFj82/rarofHzqT35FEUIHlok3H29vUOuUnzwOI48+Rb2znwcR59+ByVHTzXZvch2uWu3ovT4GctlpVQVZz6cB8VkcmxgRA7EXe1EZFeK0YjMJWtRtOcQRE8PhF9+KXR9ujo7rHoxFpVgVewQi2tWBUlEyKWDMHD5txavkb9tH3ZMvgPGgkJzAqoCUFVIfj7o98cnCL10kN3jVkwmHLjnWaTM/cO8uUk111dXZRkJ91yPru8/61bJf3Nz7Pn3cerNL6HWkViOOPg3/Dq2cVBURI3HXe1EdVBVFfoKBZ4eIkTR/WopujJRq0XUtMsQNe0yZ4fSYBl/rICit7xRSpUV5KzeDH1qpsUyQEEDe2LUmXVIW/AXctZtBVQVQZf0RexNU6HV+TdJ3MeeeRcp8xaaYzy31rZyZCHp0wXwDA9B+2fub5J7kw1srdvqhvVdiWzlkMRzzpw5eOutt5CRkYGePXvio48+woABDSsUTNQYefkGzP8jGUv+SUdpmQxPDxHjRkbgxqviEB3p7ezwyEXoUzMgSJo6R6b0aZYTTwDQ+Pog/o5rHNJBx1hQhMSPvwesTGKdeucbtJl9GyQffq/bW1m5jFX/ZuHI8SJoNCIG9Q3GoL7BkKTzSWTI8AE4+eonVq/jGRkKnzZxTR0ukdM0eeL5888/Y/bs2fjss88wcOBAvP/++xg3bhyOHTuG8PDwpr49UZWsnArc9chu5OYbqprrVBgULF2ZgTUbszDn9V5o26pllPsh6zzDQ23qre4RHuKAaGyTs3ozlAqD1XPkklLkbtiB8HHDHRRVy7B9dx6eef0wysrlqkRz4bI0xMd4450XeyAqwlzBIGTkIPh1aovSk4m1V38QgNb/vYW72qlZa/LFPu+++y7uuOMOzJw5E126dMFnn30GHx8ffPPNN019a6Jq3v7kOPIuSDoryYqKsnIZL7191G49tMm9RV55mbnvtyWiiKBL+sAnIcZxQdVBLrO8NOBCSnlFE0fSspxOKsXjLx9Eud6cSMqyClk2/x5JTS/Hg0/vQ4XB/EtHEAT0W/ip+Q3LBdPplZvwomZMQJuHbnXwZ0DkWE2aeBoMBuzatQtjxpwvGyKKIsaMGYMtW7bUOL+iogJFRUXV/hDZQ2a2Hpt35FW1zL6YogCnkkpx+HixYwMjl+QREoT2T99b+0FRgCCK6PS/RxwbVB38u3e06Ty/Lu2aOJKW5edFyVDU2lc4yAqQnqnHuk3nKyP4tkvAiH1L0fnNJ6Dr0xU+beMRNn4Y+i3+HL1/eLcqCSVqrpo08czJyYEsy4iIqL4GKiIiAhkZGTXOf+2116DT6ar+xMVxnQvZx+mkUpvOO3mmpIkjIXfR7sl70On1RyH5+VR73DsuGgOWfoXgIX2dFFntdL06Q9e3m8XERZAkBI8YAL8OrR0cWfO2dlNO1QhnbQQB+HdLTrXHtIEBaDPrFgzd9gdGHl2J/os+R8SESyE0k01Fislk01IVaplcaiHJk08+idmzZ1f9u6ioiMkn2YWnp22jCB4eLDVDZoIgoO3DtyPh7uuR/c9GmAqK4NMmDsHD+rtsSaKe37yBzcOvhVxaVm0NoSBJ0AYFoMfnrzoxuubJYLQwjXKOqqJqGr45U1UV6b8sw+kPvkXhjgOAICB4aF+0mX0bIiaNcnZ45EKaNPEMDQ2FJEnIzMys9nhmZiYiIyNrnO/p6QlPz7pbzRHVV9eOAfDz1aCk1PIuZUkUMKhPsAOjIneg8fVxm9JQ/l3aYdiOhTj5xhdInb8Yir4CorcX4v5zJdo+die846KcHWKz0yrOB6cSSy0WExBFoE2Cr2ODcjBVVXH4kdeQ+OG8800TVBV5m3cjb8NOdHhpFto/eY9zgySX0aRv2z08PNC3b1+sXr266jFFUbB69WoMHjy4KW9NVI2nh4gbplsePRcEYPK4SAQFejgwKiL782kdhx6fvYxx+btxWdZ2jMvbhW4fPc+ks4lcOTHGWgUrKApwxfjm/dznrNxoTjoBVNu9eW5R/fHn3kfhroNOiIxcUZPPF82ePRtffvkl5s2bhyNHjuCee+5BaWkpZs6c2dS3JqrmhulxmD4pGoB5dFMUUVX65NJLQvHgHdx0Qc2HqNFAG6RjaZ4mNmFMJC7pH1yj5nvlwN+9M9sgPsan5gc2I4mf/ABBY3k5k6CRkPj5jw6MiFxZk/9Guuaaa5CdnY3nnnsOGRkZ6NWrF5YvX15jwxFRUxNFAQ/d1R7TJkRj2aoMZOZUIEjngfEjI9CpfdN0kiH7U4xGZC1dh6J9RyB6eyFiwqXw79bB2WFRC6WRBPzvqa74bUkqfv0zFZnZ5nJVndv748ar4jFsUKiTI2x6hbsO1V6X9BzVJKNwx34HRkSujL3aicht5G7Ygd3XzYIhMweCRgNVVQBZQeiYS9D1vWfg274Vy9GQ06iqiqJiE7QaAT4+LWekeU27UShPSrV6TmD/Hhiy+VcHRUSOVp98zTW3ZhIRXaT44HFsn3ArDNl5AGBuZ3luDVnOqs1Y330CVsYOwfGXP4asZ5F0cjxBEKAL0LaopBMAIqeOtf6GTxQRMXWs4wIil8bEk4jcwsm3vjBP513ceuoCxpx8nHhlDrZdfiuTTyIHaXXvjeZOX2ItdUglERp/X8TNvMrxgZFLYuJJRC5PlWWk//q31XVkVRQF+Zt3IenT+U0fmAMoRiPksnK2cyWX5dMmDv0Xfw7Jx9tcIkQUAcmcXmh1ARj49zfwDGOpOjLjGk8icnlyWTmW63rV62O8W8Vi1InVdZ/oonLWbcWpt75EzspNgKrCp3UcWt1/ExLuuR6iVuvs8IhqMBYWI+X7RcjfvAuCKCLk0kGIvm4SNL7Ne1c/1S9fY+JJRC5PVVWsirmkan2nrS4vP+SW5YSSv/0N++96BoIonm89eK5eT9j44ej3+xwmn0TkMri5iIiaFUEQkHDXdeeLI9ryMVqtW+5w16dl4sC9zwGqWr3ftaoCqors5f/i7Je/OC9AIqJGYOJJRG6h9UO3wq9TW5uSSUEjIXLaZRAururtBpK//Q2qYn0iKnHO9w6KxrEy/lyFLaNvwjKfrvjbrzu2T74DOWu2ODssIrIjJp5E5Ba0AX64ZP0CxN1+NURvL8snCgIgCGj7yO2OC86OivYfg9UejKqK0uNnoJhMjgvKAY4+/Q52Tb8PeZt2QjWaoFQYkLNyE7aNuwWn35/r7PCIyE6YeBKR29AGBqD7xy9gbNpmDPhnLvy6mNucChqNuZwLAMnPB/1+/wS63l2cGWqDSV6eEOpYUiBoJLdcRmBJzurNOPXmF+Z/yOfLZVUuNTjy6Gso2n/UGaERkZ2536p7ImrxNH6+CBs5GMP3LkHev9uRuWQtFH0FAnp3QfQ1E916F23E5FFIXfCnxeOCRkLE5NFuuYzAksQ530PQSBbLZQkaCUmfLUD3T15ycGREZG9MPInIbQmCgJARAxEyYqCzQ7GbiClj4NM2HuVJqTUTMUGAqqhoM/s25wTXRAq276+z13f+1r2OC4iImgyn2omIXIio1WLg8m/h3SoWgHm0D5IIiAIErQa9f3gHQYN6OTdIOxM86i4NJXp5OCASImpqHPEkInIxPq1iMeLAMmQtXYfMJWugVBig690FsTdPg0dIULVzVVVF7tqtSP3xLxhz8+EdH4PYW6ZD16uzk6Kvv4jJo3H2ix8tj3qKIiImjXJsUETUJFhAnojITZlKy7DzynuRu2ZL1RrJyr/j77oO3T58rs6NSq6g5PgZ/Nt7MlSjqeaOflGE5OuNkUf+gWdEqHMCJCKrWECeiKgF2H/XM8hdvw0AqkYLK/8++/mPOPX2V06LrT78OrRGv9/mmKfTxXObps6VxdL4+2LAkq+YdBI1ExzxJCJyQ2VJqVjbfrTVmp/aYB3GJG+E6OEe6yMrsvOQMvc35G3abe71PXIQYm+eBq3O39mhEZEV9cnXuMaTiMgNZa/YUOc5xrxCFOw8iOBL+jggosbzDAtG20fvRNtHnR0JETUVTrUTEbkhxWAEbCjlqRgMTR8MEZGNOOJJ5IKMRgVnU8sAAPExPtBq+R6xJTPk5kMur4BnRAhErbn0kK53F6COnu6CRoJ/1w6OCJGIyCZMPIlciMmk4Ltfz+K3v1JRVGzuxR3gr8FVk2Nw84x4aDRMQFuSzKVrcfK1T1GwbR8AQBukQ/xd16HdE3ch6JI+8OvSHqXHTle1lryQIEmImjEBnmHBjg6biMgibi4ichGKouLp1w5h47bcGvtFBAEYOjAErz7ZFaLYfFolkmVJny3AwQdeBEQRUM73L4ckQtenGwav+g5lp5OxeeQNkItLqyefogiftvG4ZP2PTDyJqMmxnBKRG9qwLRcbttZMOgHzxuUNW3OxYVuu4wMjh9OnZ+HQQ6+Y/3Fh0gkAsoLCXQdx+oO58O/WAcN2LkLCvTdAc27nt2dUODo8ex+Gbv6VSScRuRxOtRO5iMV/p9UY3LqQKAJ/Lk/DiMGsZ9jcpcz7A6q19ZuKgqRP5qPdE3fDJyEGXd99Gl3ffRqqorhFwXgiarn4G4rIRSSnlVtMOgFzQno2tdxxAZHTFB8+WeeO9YqMbMilZdUeY9JJRK6OI55ELsLfT4P0TOvnBPg5/0fWkJOH5Hl/IHftVqiKiuBh/RA38yp4RYY5O7RmQ+PnA0EQocLKOxFRhOjpHoXhiYgq8e0xkYsYd2kEBCujXIIAjBsZ4biAapGzbivWtBuFo0+9jewVG5CzciOOv/Ah1rYbhcxl65waW3MSeeU4qCaTxeOCRkLE5FFVpZWIiNwFE08iFzFxbCTCQjwh1fJTKYlAWIgnJoyJdHxg5+hTM7Fzyt2Qy/XV60cqChSDEbtm3I/SE4lOi685CR01GLr+3SFIUs2D596dtHvsLgdHRUTUeEw8iVyEn68GH7/WE60TfAGYk83KJLR1gi8+fq0n/HydN9We9OVPkPUVtRctV1VAVpD4yQ+OD6wZEkQRA/78AroBPcz/1mggaDWAAEi+3uj7y0cIPHeMiMidsI4nkYtRVRX7Dxdh78ECAECvbjr06KKDYG0e3gHWdZ+A0qOnrJ7j3SoWo06sdlBEzZ+qqsjfvBuZf62GXF6BgO4dEX3tRGj8fJ0dGhFRlfrka87fqUBE1QiCgJ5ddejZVefsUKqosoyyU0l1n2c0OiCalkMQBAQP6YvgIX2dHQoRkV0w8aRmT6+XsXVXHopKTIiO9EKf7oHs/lNP2f9shGq0vNmlUuCgXk0fDDmdqijIXbsVBTv2Q9BICBs7DAE9Ozk7LCJyA0w8qdlSVRU/LUrBtz8moaz8fDvB8FBPPHZ/Bwzqy64utsrbuBOQJKCWnuAXipw2zkERNZ6qqshdtxVnv/wFJUdPQRukQ8y1kxB9/WRofH2cHZ7LKjpwDLtm3I+yU2chaCSoqoqjT76NkEsHoveC99ktiYis4uYiarbm/56MOd+crpZ0AkB2bgUee+kAdh8ocE5g7kgQbFpjGtTfPTa8qIqC/Xc8hW2X3YKMhStQfOAY8jbswIF7n8OGPlegPDnd2SG6pPKUDGwZdSPKElMBAKpJBmRzrdG8DTuxbfwtULjcgoisYOJJzVJxiQnfLEis9ZiqAlCBT7897dCY3FnIiAFW60oCgFdMJLwToh0UUeOc+WAuUub9AeBc8gSc+8YAys+mYef0++DC+y6dJvHj7yAXl9Y68q3KMor3H0Pm4lVOiIxaIpOsYv3mbHzy7Sl8Nu80dh8o4M+tG+BUOzVL/27NgcFo+ReQogJHThQjOa0McdGcVq1L6OhL4NuhNcpOnz2fqF2k9ayZtdeddDGKyYTT735j8bhqklG05xDyN+/mpp6LpM7/E6q15RaiiNSfliDqqssdFxS1SEdPFOOJVw4iJ88AjSRABfDDb8lo19oXbzzbDRFhXs4OkSzgiCc1S/kFhloLsdc8j9OCthBEEf0XfQaP8BBc2F5J0JgTzZgbp6L1gzc7K7x6KTuZhIqMbKvnCBoJuWu3OCgi92EsLLZ+gqLAmFfgkFio5crI0uPBp/chr8AAwDzyKcvmgYYzSaV48Kl9qKiwvh6dnIeJJzVL4aGelUvPrAoL8Wz6YJoJ3/atMGLfUnR+8wno+nWHb/tWCJ9wKQYs/Qo9v3kdgugev07U2grgN+K8lsSndSys9XUVNBJ827VyXEDUIv32Vyr0FTKUWn7HywqQmqHHmo3W31yS83CqnZqlYYNC4e0loVxf+7teUQR6dNEhKoLTMfWhDQxAm1m3oM2sW5wdSoP5tI2HNkgHY36hxXNUk4ygwb0dGJV7SLjrehya9bLF46pJRtytMxwYEdlD6ckkJH78PTIWr4RSYYCud1e0uv8mhI0f7vTGFbVZ+W9WrUlnJUEAVm/IxuWjnddimCxzjyEKonry9pLwwO1taz0mioBGEnH/rbUfp+ZN8vRAwj3XAxZquQqSBJ92CQgdfYmDI3N9cbdehcCBPWFpHUv8ndciiLVc3Ur2qk34t9ckJH22APqUDBiy85CzejN2XHEnDj30iktu1ikvtz6NrqpASWnddYfJOZh4UrN1xbgoPDu7E8JCPKo93q61Hz5+vSc6tfd3UmTVZedWYO/BApw4U+KSv+Sbo/ZP34uwMUPN/7hwiYAoQhsUgH6/f+I2SwccSfLyxMDl36L1fTdB8vWuetwzMgyd33wC3T563onRUX0Z8wux66r7oBiN1TaNVf5/0pwfkPbzUmeFZ1FcjLe1FR+QJKBVPDeNuir2aqdmT5ZVHDpWhKISI6IjvNEmwTX6XCenleGDL05h6668qsdiIr1w582tMXpYuBMjaxkUkwlpP/+/vfsOj6LO/wD+nplNb6SQXkjovRdRERQBsYAN7MIppx5iQU8BT/Fn98SzoGcvWFBOLIiiYAMVRZDegqGEhHTSNslm28z8/lgSicluNiE7s5t9v56HR7M72f1ANsl7v+Xz/RJ5r3yIuoO5MERFIOWqi5Dx9ysQlBDnseety8lFwfLPYSk5juCUBKRcPQ2h3VI99nyeYq8zoe7AEQgGCeH9ekA0cOWWrzny3NvY988nGluJNSOKiBrWH2f8ulLbwlrxxTdFeOL5P1xe89rTQ9G3F3ODVtqS1xg8iXRQUFSPG+dvg8lkb3ET1N3/6Inp5/lGT0xyjyrL2HPHI8h7ebmj7ZQoAIoKVVGQddcN6PPY3V65no46r21X3o6iT9bB5YJJAFPN+7yqVZpdVnHvQ7uxeXtlk8wsAFABzJyeink3cCmVltqS1ziXRORBqqrCZmv+Q/3lZUechk4AeP71Q6gzcY1SZ3Jg8XPIe2U5AEcIVW12x5SmquLwktdxeMlrOldI/satMCkKLjsZ6MEgCXjiXwNww1Xd0CUqoPH25MRg3HNrL9z6tywdq6PWcG6EyAMKiuux/ON8rP2hBGaLgqjIAEybkoQrpjumVDf8WuZykMFmVfDdT2W4aHKSRhWTJ9mqa3DkubcdwzFOHHzyVXSbdz2kYLb4Im3ETjjN9RpOSULsGSO8cr1zQICIWVdk4JrL01FSZoYkCkjoGsRZAx/gfa8mIh+Xc6QWs2/fii/WFcFscaTLaqMN763Mw5z523D4aF1rM1uQJAFFJfUaVEtaOP7tRihmi8tr7NU1qPhpi0YVtazuUB4OP/Mmch55AUUrv4JitepaD3lW8hXnIyA22mmXAsgysu66Qdui2sggCUhJDEFifDBDp4/giCdRB1JVFYuf3AezuXlzY0VxnLixYtWxVh9HUVRERgS0eh35BrnOvTcR7l7X0eR6M3bddB8KP/gCEEUIogjVbkdAbDSGvvMUuk46U5e6yLMMYaEY/eXr2DRlNuzVNY2bjASDBNUuo8/jdyP+vLN0rpI6GwZPog60c2818gqchwdZAX7ZXIF+vSOQnVPjcuSTO9s7j/C+7m10cPe6jrZj1j9R/Nm3jg8UBeqJF6atsgpbpt2M09YvR/TowbrURp4VNXwAJmSvQ/7bH6Pk8+8g15vRZcRAZNx0FSIH99G7POqEGDyJOlDO4VoIgvPuJAAgKyomjotHdk5Ni9cKAnDJBcmIj+Nav84iasRARAzsjZp9OWhpR5kgSehy2lCE99Z+U4RxZzaKP1nX8p2KCggqch59EaM+f1XbwkgzgbHR6H7Xjeh+1416l0J+gGs8qc2MNTZ89lUh3liei8++KoSxxqZ3SV4jIEB0GTob9OsViSfvH4AukY7p9Ia1+5IkYOa0VMy7oYcHqyStCYKAwW8+CSkkGIKh6U5iwSDBEBmOQS87P4rSkwpXftWsppOpsoyyr3+EzVirYVVE1FlxxJPcpqoqln+Sj9ffy4VdViGKAhRFxXOvHsQNV3fD1Zem+f3i7jHDY1od8YyKDECfHuEwGER8+vYYbNpagfzCeoSHGnDGmFhERwU6/2TyWVFD+uKMTR8j59H/ouijr6Da7RACA5By1UXouegWhGam6VKXrbK69XY5qgq5pg4BkeHaFEVEnRaDJ7nt4y8K8dLbRxo/lmVHurLZVby87AiCAkVcfpHvncDSkRLjg3HuWfH49sdSp+s3r7ksDQaDY4jTYBBxxmjPnZLjDqtNwfbdVaitsyMtOQQ9s8L9/g2Ep4T3zsLQd5Zg0MsPw1ZlREBMF93bJ4V1T29yXGJLpNAQBMRFa1QREXVmDJ7kFqtNwRvLc11e8+YHRzHtvGQEBvj3Co575vZCtdGG37ZVQpIco8Ki4FjaN+OilMZennpTVRUfrS7AWx8cRU3tn83qe2SG4d5bezU7bk5VVezaZ8TvOyohKyoG9o3EqKExkCSG1LaSQkMghYa0fqEGUq6ehuxFT0NVWj6wQJAkpM66FFIQR+KJ6NQxeJJbtu2qahJOWlJTa8fWnZU4bUSsRlV5p+BgCUseHIhd+6qxbn0pqo02JMYHY+rERGRlhMEuq9i4+Tj2HjBCEgWMHhaNwf2jNB9lfPejPLz6bm6z2w8frcPchTvxypKh6JnpmFotPW7Bgkf24I9DtY1BU5ZVJMYH4Yl/DUCPTE7B+qqg+Fj0fWoB9t35CP66TkQwSAhOTkDP+/6hY4VE1JkweJJbauvcO76xts71lJ2/EAQBg/t3weD+XZrcnp1TgwWP7MHxCisMkgAVjgDYMysMT94/ULOd7FXVNry5/GiL9ykKYLcreOWdI1iyeCDMZhm3LdqBohIzgD+XWACOQDpv0U68+8IIxMVyF76vyrz1WgR1jcEfDy1F3R+O5TRCgAHJM89Hn8f/iaB4/34zSUQdh8GT3JKSGOzedUnuXeePikvNuO2+nTBbHOHcflKAO5xbh9sW7cCyF0YiKNDzSxW++6kUsuJ8B5SiAJt+r0BllRUbt5TjWJHZ6XV1Jjs+/rIQN12X6alySQPJM89H0oypqMvJhVxrQmhmKgKio/Qui4g6Gf9ejEdu69MzApnpoU43v4oi0C0tFH17RmhbmA/5aHUBzJbmJxoBjvWfx4rM+P7nUk1qOV5hhSS2PrVfUWXFdz+Wudz0rCjAuvUlHVgd6UUQBIT3ykTUsP4MnUTkEQye5BZBEHDvvF4wGITGnpMNRNHRf/Leeb24G9qFbzc43+kOOJbXff9TmSa1xEYHuhzxbBABMww5e5FQdRSC4nwZhcnEJRbUnLm4DH88tBTr+0/Bt2lnYNOk61H0ydrGk5GIyP9wqp3cNqBPFF56ciheWnYYW3dWNd4+dGAX3HJ9FvpwtNMlU73rcKaq7q+lPVXnjOuKpW8carJe82Qhch1mlK7G7/1/wnirDeMB1AZGYlPWZGzJmNik76MgAKkp3rFDm7yHcWc2Np17HWzVNWh4x2Upq0D5D5uQdNkUDH3vPxAk543riahzYvCkNunTMwLPPTIYx8stKK+0IjY6kJtK3JSWEoKcw7VOm8tLkoCMtFBNaomOCsSsmel4o4UNRsGyGddsegpxpmKoJx3vGG41YmL2R4gylePbflc03q6qwCVTkzWpm3yDYrdjy8U3O047Onl080S/0KKP1yJq5NvoPv8GnSokIr1wqp3aJS42CL17RHh16Kw22vDeyjzMmb8N187dgof/sx97so261XPJ1GTXZ7jLKqZNTtKsnllXZGDu37IQFtp01Onsyp8QV1fc4pniADAy73vEG/MBOEY7Rw2NxrlnxXu8XvIdpV+uhzm/qDFoNqOqyH1+WauN64mo8+GIJ3VKOYdrcdt9O1FbZ28Me3nH6rH2h1Jce3m6Ljuwp5ydgO9+KsPvOyubBFABgApg5vTUZk3bPUkQBFx5cRoumZqMrbscJxelJoegaPIDMLtYgycLIgYf24jfRl+DS89PwXUz0htPYiICgIqNWyEEGKDanC8dMReUoP5YMUIzUjSsjIj0xuBJnY7FquCuxbtQZ7I3CXgNm2ne/SgPPTLDcM6Z2o7SGQwi/v3AALy7Mg8ff1GIaqMNAJCcGIyrL0vHhZMSNa2nQVCQhLEjHX0aVVXFkWPFLq+XVAXje9iwaNlpCPDzU6qoZe7uMRT+ulORiDo9Bk/qdNZvLENFlc3p/aIAfPDpMc2DJwAEBIj425XdcN3l6Sgps0CSBCR0DfKabgCCIMAQGQ67sdb5NQYJUcmxDJ3kVOyEMTj8nzedXyAICMlIQXBKgnZFEZFX4G8O6nS27qpyeX64ojpOEDKb9VtfZjCISEkKQWJ8sNeEzgYp10yHYHC+21i1y0i56kINKyJf03XSmQjtkeF817qqImv+3zjiSeSH+F1PnY6qqHC5i6fhOg1q8UVZd86GFBoCtBQaJAnRZ4xA3DljtS+MfIYgihj1+asITIht2nrrxBua9L9fgYybr9KrPCLSEYMndTqD+kU525ANwPF7MCsjDCHB7CHYktBuqTjt+/cR2s2x6UOQJMf6BAAJ50/AyFWvcKSKWhXWsxvO2rUG/Z5ehC5jhiK8Xw8kXjwJo79ZhgEvPOh1I/1EpA1BVd0YGtKJ0WhEVFQUqqurERnp2d2+VpuCnXuqYDIryEgNQbe0MI8+H3lOvVnGJbM3oa7ODmeH8yy6vTemTtRnM4+vUFUV5es3oXrbXoiBgYifMg7W+GSs+6EUxWVmREUG4Nxx8UhJYvN4IiJ/1pa85vfBU1VVfPjZMSxbkdfk1JgBfSJxz629kJXBAOqLdu2rxvzFu2C1Ko39qyXR0Zpy+nlJuOuWnhxxaaMPPs3Hy8uOQFFUSJIARVGhKMBFk5Mw/5aeMLhYV0v6UFUV5Rt+Q+Uv2yCIIuLOHouokQP52ieiDsXg2QavvXcEy1bkNbtdFIGQYAmvPzMMacnanCbjz44V1mPV14XYk22EQRIwengMLjg3CV2iAtr9mMWlZny6phDrN5bBbFXQMyscl56fjDHDY/z2F29BcT1Wri7Adz+Vob5eRmZ6KC4+PxmTzkpwuSHri2+K8MTzf7R4nyAAl12Ygtvn9PBU2dQOtdmH8Ptlt6LuwGHH2koVUGUZXUYOwvCPXuCOciLqMAyebiort+DS2ZucTsdKInD2mfFYfHffDn9u+tOX3xbjyecPAMKfp+sJgiP4L3lwIAb1i9K3wE5i9/5q3PnALtisSuMaWEFw7MM6fVQsHl3Yr8VG8LKs4rIbNqGs3Or0sSVJwGdvj0F0l0BPlU9tYCktx49DL4StvKrZ6UCCQUJIt1SM27rKsYmMiOgUtSWv+fUOgbU/lDiOjXFCVoDvfy6DqZ7HunnK3gNGPPH8AShq0yOdVRUwm2Xc/eDuxkbr1H5Wm4KFj+yF9aTQCfy5+f+XLeVYsepYi5+bc7jWZegEHOF045byjiqXTlHeqx/AWl7Z4pGUql2G6eBRFHz4hQ6V+RdbZTWOPPc2tl97F3besBCF/1sDxer6e4mos/Pr4Hm83ApRdD3lKssqg48HrVh1zOnXQFEdG4XWfOf6JB1q3fqNZagy2uDsJExVBVauLoDSwvC/qd75sYcNBAF8g+ZFCpZ/jtZaOxR+sFq7gvxQyerv8G3Gmdj3zydQ+NEaFCxfhe1X34n1fSej9o8jepdHpBu/Dp4x0QEt/qI9mSgCkRE84MlTfttaAVl2/jVQVeC3bRUaVtQ57TtQ0+rmn7JyKyoqm4/GpLqxxllVgYxUroX2FrZKo+sLVBXWimptivFDxp3Z2DrjNihmq+ObQ1ag2h1vzMwFJfht8izIpnqdqyTSh18Hz0njE5yOAAGONZ7jxsQhLJTB01Nc/fs3kO1euwzZZ4iiew3zW9pgFB8XhDEjYiA5+WkhCkDXuCCMGBx9akVShwntkeH4ojshGCSE987UsCL/cvjZN+HYzdX8u06VZZiPFaPwf2u0L4zIC/h18EyMD8blF6W0eJ8onjhX+6pu2hblZwb0iXT1+xGiCAzk5qJTNmpYjMuRZUEAMtNDnXYRuPOmHggPD2gWPkXREVbvn9/H5a540lbG3690+a5OtctIv3GmhhX5l+LPvm0c4WyRIKBk9ffaFUTkRfw6eALAvBu6Y/YVGQgKbPpPkZEaihceH8I+nh52+UUpLkc9BUHARZOTtCuokxo1NBoZqSFORy1VFbj60nSnbaZSEkPwxjPDcO74BBgMjmsEOALtS/8eimEDu3imcGqX5CvOdxxr2tK7OgFIueoixE4Yo31hfkKxWFxfoKqQ683aFEPkZfy6ndLJTCY7Nm+vRL1ZRkZaKPr2jPDbXo9ae3nZYby3Mh+i+OcgjSQ5wtD98/vi3LPi9S3QCbtdwc+/leOX3ytgszn6hE49J/GUeo+6YrUpWL+xDAcO1SLAIOC0ETEY1C/K7ddpYXE9brtvJ4pLLY1tlCRJgCyruPbydPz92m5uPZapXkZltRUR4QZEhnvm70qnTjZbkPPwCzj68nLYjbUAgMD4WGTdMQtZ829wHIVKHvHT6Etg3LHf6aizIEnIvHM2+j7+T40rI/IM9vEkXVisCvIKTBAFx4hxSz0hndm0tQIrVxdgT7YRkgSMHRmLyy9MQa/uER6suP2KSsy48/6dOFZkhnRi/aSqAgZJwL/m98E5Z3ZsWN6+uwr/emIvqo12GCQBKhwdF/r2jMAT9w9AbLR7/TPNZhnf/lSKH34uQ51JRvduYZg2Jclr/53p1MlmC+oOHAFEAeF9siAG8M2Cpx1751PsvGGB8wsEAeP3r0NY93TtiiLyIAZP0pTFquDN5bn4dE1hY0udLlEBmDktFVddktbp1v7ZbAqu+ccWFJeam3WsEeBYL/nSU0PRv3fHvGaP5psw+46tsNuUZocdSKKAjLRQvPnssDYFfSLyHFVRsP3qO1H08doTN5z4xj1xbm//5x5At39crV+BRB2MDeT9TEFRPX789Th+21YBs1nbXop2u4J7/m83Pvgkv0kfx6pqG1555wgefTYbXvzepl1+3HQcBcXNQyfgGPkUBGD5J/kd9nwffJYPWVZbPGFLVlQcPlqHjZvZvJ3IWwiiiKHv/QcDli5GWM9ujbfHjhuFUWveYOgkv8Y+QT6ssLgeT77wB7burGq8LTREwhUXp2LWzIxWm+N3hG82lGLrriqn969bX4qpExM7VaudjZvLm6xH/StZAX7+rRyqqnbIOuHvfypzuSNdFIHvN5bhrLFdT/m5iKhjCJKEjJuuRMZNV0KuN0MwSFzmQAQGT59VVm7BTXdvR3VN01OVTPUy3lx+FJVVVtx1Sy+P1/HZV0WNG1VaIokCPv+6yCPBs6rahlVfF2Ld+lIYa21ITw7BtPOScfaZ8a02Sz8VFovi9O/bQJZVKIpjk9Spam0UW1EAk4mnBhF5KykkWO8SiLwGp9p91Hsr82CscX4E4qdripCbX+fxOo4VmlyGMFlRkVdg6vDnzSsw4bpbt+CN93Nx9JgJlVU27M424qGns3Hvw3tgs7nRmb6demSGwdVApiAA6akhHba2NSUpxOXzSSKQzlODiIjIBzB4+iBZVvHlt8Uuj2KWJAFrvivxeC0R4a4HzQUBiIro2OklVVWx8NG9qDbamqx7bAjhm7dV4O0VRzv0OU92waQkCHCeBFUVuPzC1A57vkvOT3Z5v6wAF01ir1MiIvJ+DJ4+yGyRYTa7HtFTVRXHy1tpYtwBJk9IgKulpKoKnDu+41sLHc03OQ3eqgp8/EUhrB4a9ewaG4R75jmWMZzcn7thR/vYkTG4sAOb3k87LxmD+kU1+3duGAW98epuyEjjiCcREXk/Bk8fFBwkITDQ9ZdOEATEdHGvt+OpmH5eMqIimx+lCDjWd6Ymh+DccR0bPHftM0JqZeNUbZ0d+R6Y4m9w/sREPP/oIIwcEt049pmcGIzb5/TAY/cN6NA1poEBIv7z0CBcOyMdkRF/jjBnpIbigbv6YNYVGR32XKS/io1bse3K2/FNylh8kzoWO2b9E1W/79a7LCKiDsHNRT5IkgRMmZCAL78phtxSjx04puMnn53g8VqiuwTixSeGYOFje3E039TYTF1RgN49w/HYwv4ICurgE1IEwPEsrVzm4ZOnhg2KxrBB0bDLKmS70vF/z5MEBYqYc00mZl+RgdLjFgQEiIiLCeTpWp3M4Wfewv57noBgkBrP+i5c8SUKlq/GoFceQdrsy3SukIjo1DB4+qhrL093nD5Tb2+2wUgQgEnj49EzM1yTWtJTQ/HeiyOwfXcVdu83QhQFDB/cBf16eabp//BBXfD6e66viY4KQHpKiEee/68MkgCDRscPGgwikhO1+XuRtio37cD+e54AgMbQefL/77rpX+gyZigi+nbXpT4ioo7AqXYflZQQjP/+ewh6ZjUNlwEBAi6/KAULb+utaT2CIGDYoGhcPzMD116e7rHQCQAD+kSid4/wFqf3G8yYluo3J/koiortu6uw5tti/Lz5OCxWz+3ob4/OdoCAp+T+9z0IBudvYARJRN4rH2hYERFRx+OIpw/LTA/DG88Mxx+HanAotw6BgSJGDo1GZHjnblIsCAIeW9Qf8xbtRGGxubGP6InT6HDuWfG46pI0vcvUxJbtFfj3izkoKjE33hYeJuHGqzNx6QXJuk3Fm+plfPxFAT77qhClZRaEhEiYPCEBV1ycihSO2LaofMPmJiOdf6XaZZRv+E3DioiIOh6DZyfQq3sEenWP0LsMTSV0DcaypSOw9ocSfLOhFMYaG9JTQnHRlCSMGhrtF2sft++uwt0P7oHylxHF2joZz756ELKsYub0jmvr5K6aWjtuXbgDR47WNba7MtXL+HxtEdb+UILnHxuMPj386/XqDsHVEH7DNS5GRImIfAGDJ/mskGAJ089LxvTzXPe57KxefOswVFV12sD/1XeP4MJJiQgN1fbb/JV3DuNIXl2zs+VlWYXZIuP+J/bh6f8biPUby1BTa0dKUgjOOTO+1Z6wnV38lHHIf2ul81FPSULXSWdqWxQRUQfz75/0RD4qr8CE7Jwal9dYrAp+3FSOKRp0N2hQZ7JjzbfFTk/UUhSgqMSMq27eAlEEREGArKh4/rVDuOOmHrioA/uf+ppuc69F3psftXynIEA0SEj/+xXaFkVE1MH8Y/cFUSdTWWVt9RpRBCrcuK4j5R0zwWpzbzORogB22TFia7Up+PcLf2DDL2UertB7RfTviaHvPA3BIEE4uUuCJEIMDMDwj15AaEaKfgUSEXUAjngS+aC4mKBWr1EUIC7G84cInMwQ0P73soIAvPZeLsadFucXa3RbkjxjKrqMHIijr3yA8g2bIQgC4s49HelzrkBIaqLe5RERnTIGTyIflJIUggF9IrHvD6PTae2QYAlnjonTtK6s9DDExQTieEXbR1pVFcjNNyG/sB7pKf57BGhoZhr6PnGP3mUQEXkEp9qJfNStN3SHJApNzos/2dy/ZSEkWNtd0JIk4JrL0k/pMUwm5y2FiIjItzF4EvmoAX0i8dyjg5GZFtbk9tjoQNx3R2/ddvtfekFyYxsn6cSZ9ZLo3tS5JApISgj2WG1ERKQvQfXiY0WMRiOioqJQXV2NyEjPnYRD5MtUVUXO4VoUlZgRGRGAgf2iYJD0XyN5KLcWq9cVo7C4HpERARg/Ng6PPnsAtXX2FltASaKAs06Pw0P39NO+WCIiare25DUGTyLSzM+/Hceix/YCQJO1qZIoICoqAK89PRQJXTniSUTkS9qS1zjVTuQjsnNq8PGXjmMoC4rq9S6nXc4YHYfnHx2Mwf2jGm8zGARMmhCP1/8zjKGTiKiT44gnkZcrKKrHA//ehwMHaxvPpQeAM8fEYtHtfXz2xJ/KKitq6uyIiw7U/HQlIiLqOBzxJOokKqusuOXe7Th4uBYAmqyN3Li5HHc/uAt22WvfO7oU3SUQ6SmhDJ1ERH6EwZPIi338RQGqqm2QW+jVqSjA3gM1+GVzufaFERERtQOHGoi82JrvSpw2iAccx2Ku/aEE407TtlE8tay2zo6vvy/B7zsroSgqBvaNwgXnJiK6i7YnSBEReSsGTyIvZqyxubxfUbQ/j51atifbiLsf3IXaOrlxLe6vv1fgzQ9y8fCCfjhjFN8cEBFxqp3Ii8V3DYKrjpySCCQncie43iqrrbhr8S6Y6h2nLjWsxVVVwG5X8a/H9iE3v07HComIvAODJ5EXmz4lGa6Sp6wAF05K0q6gU1RVbcO+P4w4klcHL26o0WZfrCuGqV5ucVmEqgIqgJWrCzWvi4jI23CqnciLXTg5CV9+W4wjeXXNQo0A4OwzuzbpiemtSo9b8MIbh7D+l7LGv0dqcghuvLobJo6L17e4DvDLlvIWT2NqIMsqftp0HHf/o6d2RREReSGOeBJ5sZBgCS88PgRTzk5ocgxmWKiE62em4/67+kIQ9D8e05Xj5RbMmb8NG04KnQBwrLAeDz61Hx9/UaBfcR3EanOxA+wEu731a4iIOjuOeBJ5uYhwAxbd3gdzZ3fHodxaSJKAPj0iEBQk6V2aW15fnuu0JRQALH3jECaOi0dUZIC2hXWg/r0jcfBIHWQnPVUlEejXm4dgEBFxxJOogxlrbcgrMMFY63pHeltFRQZg2KBoDO7fxWdCp9ksY+0PJZAV5/PQsqxi7foSDavqeNPPS3YaOgHHWtzLLkzRsCIiIu/EEU+iDnIotxavvZeLjZsd6/1EATh9VCzmXJuJrIwwvctzm92u4OffyvH7zqoTvSgjcfYZXdsVdiuqrLDZXG8ikkQBhcXm9pbrFbIywnDH33vg2VcPQhTRuKSg4f+vvDgVo4fF6FskEZEXYPAk6gDZOTWYu2AHbHalcZOJogIbt5Rjy85KvPj4EPTuEaFvkW7Iza/DXYt3o6TMAunEmtLP1xZh6RuH8OT9AzCwb9s2MoWHtf4jRlVVREb4/o+iyy5MQWZ6KFZ8dgxbdlZCUYABfSIxY1oqxo2J1bs8IiKvIKhe3NOkLYfOE+lFVVXMum1rizvPAceoV49u4XjzueHaF+cma0UVqgvKcfOjuSirNzT7e4giEBQo4d0XRyAxvm19Q+98YBe2nghizix/aSTSU0PbUTkREemtLXmNazyJTtGBQ7U4lNty6AQcU61/HK5FzuFabQtzQ/X2fdgy7SZ8kzgGm4dNxaxPbsUF219DdF1pk+sUBbBYZXzyZdt3oN9wVTcIgoCWNt8LAjB5QjxDJxGRn2DwJDpFecdM7l1X4N51WqnYuBW/nDkTZWt/ajxqR1QV9Cneilm/PoqY2qIm1ysK8P3PZW1+ngF9IvHk/QMad61LkiOECgIwdWIi7p3X+9T/MkRE5BM8srAqNzcXDz/8ML7//nsUFxcjOTkZ11xzDe677z4EBgZ64imJdBMW6t63kbvXaUFVVez82wIoNjv+OlQrqQoE2YLJ+5bjg1F3NbnPbGlfL8oxw2Pw2dtjsHFzOY4eMyEkWMK40+KQ0JXHfRIR+ROP/CbMzs6Goih45ZVX0KNHD+zZswdz5sxBXV0dlixZ4omnJD9RWW3FscJ6BAdL6J4RBlHUv3n68EFdEBoiNZ7T3ZLwMAlDB3bRrqhWVPy0BabDeU7vF1UF3SoOoIupFFWhjpOFRBGntDvfYBBx1tiu7f58IiLyfR4JnlOmTMGUKVMaP87KysKBAwfw0ksvuQyeFosFFoul8WOj0eiJ8sgHlZVb8Pxrh7Dh1z9Pv0mMD8LsK7vh/ImJutYWHCzhuhnpeHnZEafXXDcjA0GB3rOypTb7sFvXxdaVNAZPRQEuPT/Zk2UREVEnp9lvwurqasTEuO5j9/jjjyMqKqrxT1pamkbVkTc7XmHB3+/ahh9/bXrkYnGpBY8/dwDvrXQ+cqeVqy9Nw3Uz0iGKjv6dhhPrGEURmDUzHVdenKp3iU0YIsPdus5iCG7cFDR5QjzGnRbnwaqIiKiz06Sd0sGDBzF8+HAsWbIEc+bMcXpdSyOeaWlpbKfk555+KQeff13o9MhFUQQ+eWsM4mKCtC2sBccrLPhmQynKK6yIiw3EuWclIDba+9Y126qM+Db1dCgWq9NragMj8cL4J5GcEoaZ01Ix/bxkr1jaQERE3qUt7ZTaNNW+YMECPPnkky6v2b9/P/r06dP4cUFBAaZMmYLLL7/cZegEgKCgIAQF6R8eyHtYbQrWfFvsNHQ2+Pr7ElxzWbo2RbkQFxOEKy/2/pH6gC6RyLx9Fg499Srg5K3nkMduxxezz0RkhAFCS72QiIiI2qhNwfOuu+7CrFmzXF6TlZXV+P+FhYWYMGECxo4di1dffbVdBZJ/MxptsFhdp05BEFBU4ttHLuqh90N3QK43I/eFdwFRgCCKUGUFgiig14O3o/ttVzNwEhFRh2pT8OzatSu6dnVvV2pBQQEmTJiA4cOH46233oIoes/GCvIdoaEGiILj+ElXIiMCtCmoExEkCf3/cx8yb5+FwhVfwlpajuDUJKRcdSGC4nnEIzVXvW0vcl96HxU//w5BkhB/3lnIuPkqhHXXf7aBiHyDR9Z4FhQUYPz48cjIyMCyZcsgSVLjfYmJ7u9A5pGZBACLHt2DjZvLXU63v/PCiFNq9UNErh154V3su/MRCAYJqt3ROkyQJEAUMPx/S5Fwwdk6V0hEevHYGk93ffPNNzh48CAOHjyI1NSmu3m9+Gh48lKzrsjAr79XQFXVZiOfggCcfUZXhk4iD6rYuBX77nwEABpDJwCosgwoArbOvA0TDnyLkFR9W5sRkffzyPz3rFmzoKpqi3+IGthlFRt+KcMDT+7DHffvxH9ezkHOkebnmffqHoGnHxqE2BjH7nBRdAROUQCmnpOI++7s0+xziKjjHFn6DgSD1PKdqgpVlpH/xv+0LYqIfJIm7ZTai1PtnVdltRV33r8LB4/UQRQdzcklUYCsqLjy4lT8Y3ZWs40tsqzit20VOJJXh5BgCWeMjkN8HLsgEHnausTRsJVXubwmeuwwjN3wgTYFEZFX0X2qnag1/3p8H44cNQH486hw+cQ8+gefHkNKUgimn9f0lBxJEjB2ZCzGjuTGFyIiIl/EreakueycGuzcW90YNFvy3so8KK1tZSciTcSOH+N8qh0AJBGxE8ZoVxAR+SwGT9Lcpm0VkFp55RWXWnCsqF6bgjzEbJZRUWmF3d5K93siL5c577omm4qaEAQIkoT0G2dqWxQR+SROtZPm7DbFsTvI2ZE5DdfZfXPEM+dILZZ9eBQ/bjoORQGCg0VcMDEJ181IR4wXHp9J1JqY04ej33/uw775j7bcTunD57ijnYjcwuBJmuvbKxKy7DpUhoZISEkM1qiijrNzbxXuuH8XFFltXLtqNiv4dE0BNmw6jleXDEXXWG6IIt+TOe86xJw+3NFA/sfNEAyGPxvI98jQuzwi8hEMnqS5McNjEB8XhOMVlsZwdjJRBKZNSUJQkIs1ZV5IllU8+NR+yPbm/UZlBaiosGDpG4fw0D399CmQ6BRFDeuPwa89pncZROTDuMaTNCdJAh5d2A/BQRJOPklVgGMGvm+vSNxwVTe9ymu3zdsrUFZudXq8p6wAGzYeR2W1VdvCiIiIvARHPMltdlnFr1vK8d1PZaiptSEtORQXTEpEj8zwNj9W316RePv54fjfqgKsW18Ck1lGUkIwLp6ajGlTkhEU6HvviY7kmRp7kjojKyryC+oRHeVfaz0VRYWpXkZwkAiDwfe+tkRE1DEYPMkt1UYb5j+wCwcO1TaGq993VGHlFwW46pJU3DKrecP31iQnhuCOm3rgjpt6eKhqbQUHiXDnOIaQYM8sIbDLKg4eroXFKiM9NdQrwm1NrR3LP8nHqq8LYayxQ5IEjBsTh+tmpKNnVtvfsBARkW9j8CS33P/kvsbjLP/a8H35J8eQlBCCi6cmO/t0v3D6qFg888pBl9ckdA1C924de668qqr4ZE0h3lmRh/JKxzS+JAoYf3ocbpvTA7E67aQ31thwyz3bkV9Y/+drRlbx46/H8dNvx/H0gwMxfHC0LrUREZE+OOdFrfrjUA227apyOYXMhu9AQtdgnHd2AlwN/M6amQFRbNvIcGtefz8Xz7x8sDF0Ao43Bes3luGmu7ehqtrWoc/nrlffPdIkdJ5cmyyrWPzUfths7HFKRORPGDypVb9tq2yyCaglJWUW5BWYtCnIi931j14Yf3ocAMcmKkkSIIqAKABzrumGCycndejzFRTXY9mKvBbvkxWg9LgF73/c8v2eZDLZsea7EqdvVlQVqKq24efN5doWRkREuuJUO7XKZldOrN90PaJp89GG7x0pKFDEw/f2R86MWny7oRTGGhuSEoJx3jmJHunfuebbYkiiI2S2RFGA1euKcMusrA4faXWlsMQMq9X1aKZBEnA4tw4TTu+qUVVERKQ3Bk9qVe/uEa02fA8OFpGWFKJRRd6vZ2Y4erZjt39bFZeYW3k7ANTWyTDVywgP0+7bPSio9ckURVUR6IPdC4iIqP34U59a1dDw3dl0uygCF01KQrCHdmuTc5GRAa12EzAYBM2/NqlJIUhLDnG53lVRgDNGx2pXFBER6Y7Bk1olSQIeW9QfQYESpL9M1woC0Kt7BG68JlOn6vzbuWfFuxyNlkRg4rh4GCTtptkBQBAEzL4yw2l7KVF0dAHITO/YHf5EROTdGDzJLX16RuDtpcMxfWoSwsMMEEUgOTEYc/+WhRceG4zQEI526qFvzwicPioWLS3fFEUgIEDEtZela18YgEnjE3DLrEyIgqMWUXS8iQGAoQO7YPFdfXSpi4iI9COoqjstr/VhNBoRFRWF6upqREZG6l0OkVeyWGT8+8UcrFtfAsAxCq0oQFJCMB78Z1/079227x1jrQ05h2ohigL69Iw45Yb3JWVmrPm2GMeKzAgPk3DOmfEY2DeyzQcOEBGRd2pLXmPwpEbFpWZ89PkxrNtQCpNJRmqyoyn8+RMTERDAwXFvV1xqxq+/V8BildE9IwzDB0e3aSe7yWTH0jcO4avvS2A/0aEgOEjEJecnY841mZ3+NVBVbcO+P4xQVaBf7wivOPmJiMgXMHhSm2Xn1OC2+3bCYpEbW/MIgqPf4tCBUVjy4CCfPD+d3GOxKpi7YAf+OFTTrPemIABjR8bi8fv6a9qSSSumehnPv3YQX39fAvuJ9bKSJGDy+Hjc8fceCA1l8w8iIlfakteYJAh2WcXCR/fAfFLoBNC4MWTnnmq8/eFRfYojTXz1XTGyc5qHTsDxOti4uRy/bavQvjAPs9sV3LV4F9Z8V9wYOgHH0Z5f/1CCO+7fBStPVyIi6jAMnn7OZLLjjfePoKzc6vSUGUUFPl1TyF/Andjna4tctj6SRGD1umLtCtLI9z+XYfd+Y4uvfUUB9v1Rg+9+LNW+MCKiTorB00/JsorX3juCC6/9Fe9+lN/q9bV1dhQVmzWojPRQXGp22voIcJyMVFRSr11BGvliXbHL42AFwXHyExERdQwuXvJTz716EJ+sKWzT50ga94Ik7XSJCoCxxu70flEAYrp0vs02JcfNTkf6Accyg+Iyi3YFERF1chzx9EP5haY2hU4BjtY8yYnBniuKdDX1nETXpwypwJSzE7UrSCNx0YEu/96CAMRGd77ATUSkFwZPP7T2+xJIbfjKqwCuvjStU+5oJoeLpiQhPi6o2clUgGN9Z8+sMIwfG+ex5zeZ7Ph8bRGWvn4Qr713BDmHaz32XCc7b2KiyyUGqgqcP7HzBW4iIr1wqt0PlVdaHUM5cN1JSxQdGyxmTkvBtClJ2hRHuogMD8CLTwzB/y3Zj937jU3uGz08Bv+6s4/H+nj+sLEMjz6bDbNZgUESoKrAshV5GDM8Gg/d08+j7YzOPSsBKz8vwOG8umZT7pIIpKeGYsqEBI89PxGRv2EfTz/0xvu5WPa/oy7XtgmCY6Rn+tRk9OkRoV1xpLucI7XYs98IQQCGD+6CtORQjz3Xtt1VuP2+nYDa/G2QKAIjh0Tj6f8b5LHnBwBjjQ2PP38AP20qb3L72JGxWHR7b3SJCvDo8xMR+bq25DWOePqhKWcn4C0XfTkl0XHNgtt6a1gVeYuemeHomRmuyXO9/cFRxxGfLbz9VRTgt22VyM6pQZ+ennvzExkRgMfvG4CiEjN27q2GChWD+kUhJTHEY89JROSvGDz9UEpSCC6/MAUfrS5odp8oAqEhBlw3M0OHynxPSZkZX3xTjCNH6xAUJGHcaXE4fVQsDOwA0KqaWju27a5yeY0kCfhhY5lHg2eDpIRgJCVwAx0RkScxePqpeTd2R1RkAJZ/kg9Tvdx4e79ekVhwWy+O9rjh0zWFeOblHODE0aKiAKz9oQTd0kLxzMOD0DU2SO8SvZrZIrd6jQA0eX0SEZFvY/D0U6IoYNYVGbhieiq276mC2aKgW1ooMtPD9C7NJ/z6ezmefinH8cGJaeKGExfzC+px9+LdeOv54ewE4EKXyACEhkgug6WsqMhI9dwaUyIi0haDp58LDpZw2ohYvcvwOe9+lNe46/+vZEXFoaN12LKjEqOHxWhfnBex2RRs+PU4Nm+vhCyr6NcrApMnJCA8zICAABEXTk7CR58fc7rRzWAQMGlCvLZFExGRxzB4ErWRyWTHrn1Gl9dIkoBftpT7dfA8mm/CnQ/sQulxCyTJsRxh3foSvLzsMB5e0B9jhsfg+pnp2PR7OfIL65uEz4ZQ/89/9EJkOHeVExF1FgyeRG1ks7vXgcxqddGvyosVFtfjq+9KUFRqRlSEAeeOT2hzSy1TvYzb7tuJqmorAEA+aTbdbFGw4JE9eOu54chMD8NLTw3FWx8cxRfrilBvdvyb9e0VidlXZGDMcP8N7kREnRGDJ1EbRYQbEBcTiOMVVqfXKLKKnlnatCTqKKqq4vX3c7FshWMZgeMoSQErVhVg3JhYLP5nPwQFutdEft36EsdBBS0+j+PPR58X4J5bHSOat8/pgZuvz8LxCgtCgiTE8JhKIqJOiUdmErWRKAq49IIUp2d8CwIQFCRicjtOvFFVFbKsz5kOn6wpxLIVeQAc09yyjMZafvqtHE//9w+3H+vn3467PANdllWs31jW5LagQBEpiSEMnUREnRhHPInaYeb0VPy+o9LRh/KkU3ckSQBU4MF/9kVYG4563La7Css/ycfmbRVQFKBHZhguvygV552doMnOeLus4p0TobMlqgp8/X0JbrwmE/FxrbeJMpsVl2egA4DV5ptLEYiIqP044knUDoEBIpY8OBDzbuyOpERH03FJEnDWaXF45emhOGN0nNuPterrQty2aCe2nAidAHAotw6PP3cAjz6bDaWlY306WM6hGqdT4w0UFfhlS7nLaxr06h4OycVPF1EEumt0OhIREXkPjngStVNAgIgZF6VixkWpsMsqJBEQXM0vt6CwuB5P/9fRD1Q+aQCwYbRw7Q+lGD0sBpPGt33avi3MltZHH0URsLhxHQBMm5KM/33e/GSsBooCXHZBitv1ERFR58ART6IOYJCENodOAPh8bZHjeB4nRAFY2cLRph0tIzUUYis/DRQFyOrm3gEDGWmhmPu3LABo8rgN/0QTz4rHOWd2bU+pRETkwzjiSaSjA4dqnTZPBxzT2zlHaj1eR0x0IM46rSt+/LWsychrA1EEEroGY/igLm4/5pUXpyEjNRTvf5yPnXurAQDpKSG4/KJUXDQ5iac6ERH5IQZPIh0FBogQBLjciBNg0GZi4vY53bH3gBHHKyxNwrAkCggIELD47r5tDotjR8Zi7MhYWG0KFFlFcLDUwVUTEZEv4VQ7kY7GjoxxGTolUcAZo7U50jQuNghvPDMMl1+YgtAQR0CUJAHnjOuK1/4zDAP6RLb7sQMDRIZOIiKCoKqtNT3Rj9FoRFRUFKqrqxEZ2f5fekTeqt4sY+bfN6Oq2tpsyl2Ao2foq08PRe82nhx0qmRZhaleRkiwCINGI65EROSb2pLX+BuFSEchwRKee2QQYro4mqY3nBgkCIDBIOD/7umreegEHCOdEeEGhk4iIupQXONJpLPM9DCseG00vv+5FJu2VsBuU9G3VwTOPzcR0VE8xYeIiDoPBk8iLxAUKOK8sxNx3tmJepdCRETkMZxHIyIiIiJNMHgSERERkSY41U5EPud4uQVffFuMo/kmhARLGD82DiOGRLMpPRGRl2PwJCKf8vGXBXju1YONHwuCgM/XFqFPzwgsWTwQXaICdKyOiIhc4VQ7EfmMn387jmdePghFQeMfWXa0Iv7jUA0WProHXtyamIjI7zF4EpHPWLYiD85m0xUF2L3fiD3ZRm2LIiIit3GqnYg6VL1Zxup1Rfj86yKUlFkQFWnA1ImJuGRqyilNg1dWW7E/p8blNZIk4KffyjGwb1S7n4eIiDyHwZOIOkxNrR3zFu3Aodw6QAVUOILo2x8cxedfF+G/Tw5BcmJIux7bYlFavUYAYLW2fh0REemDU+1E1GGef+0gDh+tg3oidDZQVKCyyooHl+xv92PHxQQiItz1e2W7rKJHt7B2PwcREXkWgycRdYiqahvWbSiF4mTAUVaAfQdq8Mch19PlzhgMIqaflwTRyU8tQQBCQyScMy6+XY9PRESex6l26hBH80344psiFBSbERFuwMQzu2L4YPZV9CcHc2sbd5i7svdADXp1j2jXc1w3IwO/76jCgYM1UE56KkkEIAhYfHdfhARL7XpsIiLyPAZPOiWqquLVd4/g3Y/yIYkCFFWFKAr48ptiDO4fhSfvH4DwML7M/IG7bzIkqf1vRkKCJSx9bDBWrDqGT74sRHmlFaIInDEmDtdcmoa+vSLb/dhEROR5TAR0SlZ9XYR3P8oHAMgnhqAaRr1276/GQ09n498PDNCtPtJO354RCAkWUW92vbln+OAup/Q8wcESrp+ZgetmpMNsURAQIMJwCmGWiIi0wzWe1G6yrOKd/+U5vV9RgF+2lCM3v07DqkgvIcESLrswBYKTDCiKwLgxsUhp5672vxIEASHBEkMnEZEPYfAkAIDJZEdxqRn1Ztntzzl6zITS4xaX14gC8MuWilMtj3zEDVd1w/ixXQEA0omp94bNQH16RGDRHX30Ko2IiLwAp9r93OGjdXhjeS5+2nQciuJYf3f2GV1xw1XdkJrsemTKanOjr6IowObGddQ5GAwiHrq3L7btSsLqdUUoKrEgJjoAUyYk4PTRcRydJCLycwyefiw7pwa3LtwBm01tbIEjyyq+/6kUv/5ejpefGopuac57IqYlhyAwQIDV5nwnsyyr6NU9vKNLJx1ZbQq++7EUq9cVobjMgtgugZg6MRFTzk5ASLAEQRAwfHA0hg+O1rtUIiLyMpxq91OqquKRZ7JhtSmNm4IayApgqpfx1Is5Lh8jLNSAKWcnOu2rKIpAfFwQRg2N6aiySWcmkx3zFu7Ao88ewO79RpSWWZB9sAZPv5SDOXdtQ2W1Ve8SiYjIizF4+qm9B2qQm29y2uxbUYCde6uRd8zk8nFumZWFbmlh+GsnHUkEAgNFPLyg3ym1zyHv8uxrBxvPS1dPvF9p+G/+MRMee/aATpUREZEvYPD0U0dbCZTuXhcRbsDL/x6Cv13VDXExgQCAoEARU89NwlvPDUf/3uyr2FlUVduw9gfXJxP9+nsFjhXWa1sYERH5DK7x9FPunu4SEtL6daGhBsy6IgOzrsiAXVYhiY5WN9S57M8xunUy0a591a1uTCMiIv/EEU8/NXpYNAIDXX/5IyMMGNQvqk2Pa5AEhk4/xy8/ERE5w+Dpp8JCDbjy4lSX11w3Ix2BAXyJkEO/XpFutUMa3L9tb1aIiMh/MFX4sRuu6obLLkwG4NiBbpAEiKKj6fv1M9Mxc5rrYEr+JSoyAFMnJrjsYnDG6Fgkd9DJRERE1PlwjacfE0UBd/y9J2ZOS8Pa9SWorLKia2wQJk9IQNfYIL3LIy8078YeOHqsHjv3VkMUHd0PBMGxsz0rPQwLb+utd4lEROTFBFVVW98toBOj0YioqChUV1cjMpK7o4m8gd2uYP0vx/H52iKUlJkRFx2IqecmYeK4eAS1sm6YiIg6n7bkNY54ElGbGAwiJo6Lx8Rx8XqXQkREPobDE0RERESkCQZPIiIiItIEgycRERERaYLBk4iIiIg0weBJRERERJpg8CQiIiIiTTB4EhEREZEmGDyJiIiISBMMnkRERESkCQZPIiIiItIEgycRERERaYLBk4iIiIg0weBJRERERJpg8CQiIiIiTTB4EhEREZEmGDyJiIiISBMMnkRERESkCQZPIiIiItKEQe8CiPxRXoEJhcVmRIQb0LdnBERR0Lsk8iGyrOK3bRXYurMSsgIM6BOJs06LQ0AAxxKIyLsxeBKdAlO9jO9+LMWho3UIDhJx5pg49OsVAUFoOUjmHK7F0y/lYE+2sfG2hK5BuOm6TEwan6BV2eTDjhXW4+7/241jhfWQJAECgJWrCxDTJQBP3j8AfXtF6l0iEZFTgqqqqt5FOGM0GhEVFYXq6mpERvKHKXmXnzYdx0NPZ6PeLMMgCVDhGIkaOjAKjy7qj8jwgCbXH8qtxU13b4fVpkBRmj/ePbf2wkWTk7QpnnySyWTH1XN/R0WFBfJfXkOiCAQHSXj3xRFI6BqsT4FE5Jfaktc4L0PUDnuyq3Hf43tRb5YBAHZZhSw73sPt3FuNBQ/vwV/f073w5mGnoRMAnn/9IEz1skfrJt+2dn0pyo43D50AoCiA2SLjky8LtS+MiMhNDJ5E7fDOijyn9ykKsGufETv2VDfeVlZuwZbtlU5DJwCYzQo2/FrWkWVSJ/P9z6VwsooDgOO19+2PpdoVRETURgyeRG1ksSr4dWuFyxApSQLW//JniCwrt7T6uJIkoKSs9evIf5lMMlpbHNUwCk9E5I0YPInayGJt/Zc/VBX1J02bd4kMcHGxg6Kobl1H/iurWxgkyfmQpygCmWmhGlZERNQ2DJ5EbRQeakBUKwFRUYGMkwJAcmLIid3uzj9HEgVMOL1rR5VJndC0KcmNa4lboijAxeenaFgREVHbMHgStZEoCrj4vCSILr57RFHA1HMSm9x2y6wsCAKchs9rZ6S3GmjJvw3oE4krpqcCaP46EgRg/Ng4nH0G37wQkfdi8CRqh6suTUf3bmHNwmdDH/j5N/dAdJfAJvcNHdgF/35gIOJimt4eFChizjXd8LcrMzxZMnUSc/+WhYW390ZackjjbV1jA/GP2Vl48J5+PIyAiLwa+3gStZPJZMfbK45i1ddFqDM51nP26x2BWTMzMHZkrNPPk2UVv++sbDy5aOyIGISG8iwHahtVVVFRZYOiqIiNDmTgJCLdtCWvMXgSnSKbTUF5pRVBQSKiowJb/wQiIqJOpC15jcMsRKcoIEBEYjxPiiEiImoN13gSERERkSYYPImIiIhIEwyeRERERKQJBk8iIiIi0gSDJxERERFpgsGTiIiIiDTB4ElEREREmmDwJCIiIiJNMHgSERERkSY8HjwtFguGDBkCQRCwY8cOTz8dEREREXkpjwfPe+65B8nJyZ5+GiIiIiLych4Nnl999RXWrVuHJUuWePJpiIiIiMgHGDz1wCUlJZgzZw4+++wzhIaGuvU5FosFFoul8WOj0eip8oiIiIhIYx4Z8VRVFbNmzcLNN9+MESNGuP15jz/+OKKiohr/pKWleaI8IiIiItJBm4LnggULIAiCyz/Z2dlYunQpampqsHDhwjYVs3DhQlRXVzf+yc/Pb9PnExEREZH3ElRVVd29uKysDOXl5S6vycrKwowZM7B69WoIgtB4uyzLkCQJV199NZYtW+bW8xmNRkRFRaG6uhqRkZHulklEREREGmlLXmtT8HRXXl5ek/WZhYWFmDx5MlauXInRo0cjNTXVrcdh8CQiIiLybm3Jax7ZXJSent7k4/DwcABA9+7d3Q6dRERERNS58OQiIiIiItKEx9opnaxbt27wwIw+EREREfkQTYJnezWEVfbzJCIiIvJODTnNnUFGrw6eNTU1AMB+nkRERERerqamBlFRUS6v8ciu9o6iKAoKCwsRERHRpDWTLzAajUhLS0N+fj535FOL+Bqh1vA1Qq7w9UGt0eo1oqoqampqkJycDFF0vX3Iq0c8RVH0+V3wkZGR/IFALvE1Qq3ha4Rc4euDWqPFa6S1kc4G3NVORERERJpg8CQiIiIiTTB4ekhQUBAWL16MoKAgvUshL8XXCLWGrxFyha8Pao03vka8enMREREREXUeHPEkIiIiIk0weBIRERGRJhg8iYiIiEgTDJ5EREREpAkGTyIiIiLSBIOnxiwWC4YMGQJBELBjxw69yyEvkJubixtuuAGZmZkICQlB9+7dsXjxYlitVr1LIx29+OKL6NatG4KDgzF69Ghs3rxZ75LISzz++OMYOXIkIiIiEB8fj+nTp+PAgQN6l0Ve6oknnoAgCLjjjjv0LgUAg6fm7rnnHiQnJ+tdBnmR7OxsKIqCV155BXv37sUzzzyDl19+GYsWLdK7NNLJihUrMH/+fCxevBjbtm3D4MGDMXnyZJSWlupdGnmBDRs2YO7cudi0aRO++eYb2Gw2TJo0CXV1dXqXRl5my5YteOWVVzBo0CC9S2nEPp4a+uqrrzB//nx8/PHH6N+/P7Zv344hQ4boXRZ5oaeeegovvfQSDh8+rHcppIPRo0dj5MiReOGFFwAAiqIgLS0N8+bNw4IFC3SujrxNWVkZ4uPjsWHDBowbN07vcshL1NbWYtiwYfjvf/+LRx55BEOGDMGzzz6rd1kc8dRKSUkJ5syZg3fffRehoaF6l0Nerrq6GjExMXqXQTqwWq3YunUrJk6c2HibKIqYOHEifv31Vx0rI29VXV0NAPyZQU3MnTsX559/fpOfJd7AoHcB/kBVVcyaNQs333wzRowYgdzcXL1LIi928OBBLF26FEuWLNG7FNLB8ePHIcsyEhISmtyekJCA7Oxsnaoib6UoCu644w6cfvrpGDBggN7lkJf48MMPsW3bNmzZskXvUprhiOcpWLBgAQRBcPknOzsbS5cuRU1NDRYuXKh3yaQhd18fJysoKMCUKVNw+eWXY86cOTpVTkS+Yu7cudizZw8+/PBDvUshL5Gfn4/bb78d77//PoKDg/Uupxmu8TwFZWVlKC8vd3lNVlYWZsyYgdWrV0MQhMbbZVmGJEm4+uqrsWzZMk+XSjpw9/URGBgIACgsLMT48eMxZswYvP322xBFvi/0R1arFaGhoVi5ciWmT5/eePv111+PqqoqrFq1Sr/iyKvceuutWLVqFX788UdkZmbqXQ55ic8++wwXX3wxJElqvE2WZQiCAFEUYbFYmtynNQZPDeTl5cFoNDZ+XFhYiMmTJ2PlypUYPXo0UlNTdayOvEFBQQEmTJiA4cOH47333tP1hwLpb/To0Rg1ahSWLl0KwDGdmp6ejltvvZWbiwiqqmLevHn49NNPsX79evTs2VPvksiL1NTU4OjRo01umz17Nvr06YN7771X9yUZXOOpgfT09CYfh4eHAwC6d+/O0EkoKCjA+PHjkZGRgSVLlqCsrKzxvsTERB0rI73Mnz8f119/PUaMGIFRo0bh2WefRV1dHWbPnq13aeQF5s6di+XLl2PVqlWIiIhAcXExACAqKgohISE6V0d6i4iIaBYuw8LCEBsbq3voBBg8iXT3zTff4ODBgzh48GCzNyKckPBPM2fORFlZGR544AEUFxdjyJAh+Prrr5ttOCL/9NJLLwEAxo8f3+T2t956C7NmzdK+IKI24FQ7EREREWmCuxeIiIiISBMMnkRERESkCQZPIiIiItIEgycRERERaYLBk4iIiIg0weBJRERERJpg8CQiIiIiTTB4EhEREZEmGDyJiIiISBMMnkRERESkCQZPIiIiItLE/wP0c3zNIEylggAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "ax.scatter(X[:,0],\n", " X[:,1],\n", " c=y,\n", - " cmap=cm.coolwarm)\n" + " cmap=cm.coolwarm)" ] }, { @@ -1026,31 +511,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "0c2690d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.812539Z", - "iopub.status.busy": "2023-07-26T00:00:08.812440Z", - "iopub.status.idle": "2023-07-26T00:00:08.816647Z", - "shell.execute_reply": "2023-07-26T00:00:08.816383Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
SVC(C=1, gamma=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "SVC(C=1, gamma=1)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "(X_train, \n", " X_test,\n", @@ -1060,7 +524,7 @@ " test_size=0.5,\n", " random_state=0)\n", "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1)\n", - "svm_rbf.fit(X_train, y_train)\n" + "svm_rbf.fit(X_train, y_train)" ] }, { @@ -1069,39 +533,21 @@ "metadata": {}, "source": [ "The plot shows that the resulting SVM has a decidedly non-linear\n", - "boundary. " + "boundary." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "3eb171e8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:08.818364Z", - "iopub.status.busy": "2023-07-26T00:00:08.818238Z", - "iopub.status.idle": "2023-07-26T00:00:09.069609Z", - "shell.execute_reply": "2023-07-26T00:00:09.069226Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "plot_svm(X_train,\n", " y_train,\n", " svm_rbf,\n", - " ax=ax)\n" + " ax=ax)" ] }, { @@ -1118,28 +564,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "9a6b905b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.071637Z", - "iopub.status.busy": "2023-07-26T00:00:09.071503Z", - "iopub.status.idle": "2023-07-26T00:00:09.226780Z", - "shell.execute_reply": "2023-07-26T00:00:09.226403Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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HiGtkScZPXz6HN+v7pt3eMjiK/9xzDq2Do7j38tL5lklBON0+fPOZ03BMCZ3A5GlEv9/fhOKMJKwrMUeoQqJLEz3zCUREtGBKM42zNEkXsCjAdO24A40D2FvfBxnTT/cZH0X9x9F21HdbF6ZYmuGNc32wOL1BR61FQcCTxzuULYpoATB4EhHFoRtX5s3SJF3GjSuD73R+7mQnxBAz6aIg4MWa2DndJ9acbB8O+f8vyTJOtQ8DIT7GRNGIwZOIKA5tXpSOa6qyAUw/sGZ8EPTOtQWozEsN+viWwdGQa0QlWUZTf+yc7hNrZEkOeI78tGtk5k6KPQyeRETxSBDw2R2L8S/bypGXqp+4uSQ9CV+4bjE+ckVpyIfrZ9nZLQAwaKLjR4gkxV8Cq8xLCXm/KABLcpIhhBoWJYpC3FxERBSnBFHAjSvzcOOKXNhcXgBzb4d05eJMPH2iK+R0/eUVmQtU6cWTJBl7zvTg2ZMdaBl0QBQEbChJwx1rC7GiMPhIbqy4tiobfz7YDI838MinJAO3rslXvC6i+YqOX1eJiCh8BAEmveaienDetDIfWrUQcJ2hKAgwJ2mwozJ7AYucO0mS8aPddfjV6+fROujw3ybLONIyjK88WY1XzvREpK6FZNJr8JUbqqAShWmbxMbfvmVVHq5eHLngT3SpOOJJRBTrZBnnemw40DAAl8eH4owkbF2ShSTdHL/FyzKsTi98sow0gwYQBOSk6vHd25bjO8/Vwur0Tpzs45PliSM35/z8C+zV2h7sPz/gL33K7eOjs7987TzWFqchwxRbpw9daH1pOn75vrV47mQXDjb2w+OTsTg7GTevzsWGkvSY76NKiUmQ5ehdGGOxWJCamop39jbAZEqOdDlERFHH5vTiwRdrcbJ9BKIgQIA/gGnUAj5/7RJcuTgr+INlGW+c7cNjR9vRMujfKJSdrMNta/Jx86p8iKIAt9eHffX9qO22QiUIWF2Yhk1lZqhUkZswu++vx9A6OBp0WacoAPdsLMJ7LytRtjCiBGWzWXHZ1nKMjIwgJSX0+mSOeBIRxSpZxg9eOIPTnf5+mlPXY3q8Mn68+yzSDNqgax7/fLAFfz/SPm3grNfqwu/eakJtlxX/sXMptGoVdlTlYEdVTlj/KXMmyyFDJ+Bf/9jQZ1euJiKaM67xJCKKUXXdFlR3WAJuABq/5e9H2gI+9nyPFX8/0u6/9oKHywD2ne/HvvP9C1jtwlHNMsUsCoA2is5bJ6JJ/MokIopR+88PhAxhkgwcbxuGwz3zaMwXa7pnOdkIeP5U54LUuaAEAZvK0kPWLsnAplIeJUkUjRg8iYhilMMjzek6V4Az2Zv67bOcbAQ0D0Zng/g71xUAQdqri4KATJM2oq2eiCg4Bk8iohhVlG6ANMv5NkatCskB2iglaVWYbU+0Xq2aR3XhszQ3Bf92/VKoRX+7J0GYbDOUYdLge7evgDZKaydKdNxcREQUo66pzMaf9jfDG2TkUhSAG1bmBdyBfnl5Bk62jwR9blEQcFUU94m8ekkWVhWkYk9tDxp6bVCrRGwsNePy8gxoGDqJohaDJxFRjDLpNfjU9gr84tV6CAKmna0uCkBxehLuXl8Y8LHbK3Pw9yNtGBr1zphy92/OEXDzqug+GSfNqMXdG4oiXQYRXQROtRMRxbBrl+XgW7cuR2XuZK/jJI0Kd6wpwIPvWhW0ybtBq8IP7lyF7GR/k3WVIExsVDLp1PjubcuRM+WMdyKihcARTyKiGLeuxIx1JWbYnB44PRLSDBqo59BOKD/NgN98cD0ONQ3iZNswJMioyk3G5RWZXCNJRGHB4ElEFCf857Ff3GNEUcDm8gxsLs8IT1Hz1G9zobp9GD4JqMxNRmF6UqRLIqJ5YPAkIqKoM+ry4qHXz2Pf+f5pa1dXF6bi89ctiapz2GVJxtHWIRxrGYJHkrE0OxlXLcmETsNRY6IL8ax2IiKKKj6fhPufrMbZbuu00An4d9tnmbT42T1rYQrQJkppvRYnvvXMabQNOSbWyPpkGUlaFe6/oQpritMiWyCRAi7mrHZuLiIioqhyqGkQtV0zQyfgP4++z+bC7tPdyhd2AY/Xh689WYOOYScAf+D0jY3lODw+fOfZ02gb4JnxRFMxeBIRkeJGRt14oboLj7zTilfO9Ew71vPVuh6IIbrbSzKw50yPAlWGtv/8ALoszoAnQMky4JOBp05E4bGjRBEU+XkKIiJKGLIk4y/vtOKxo+2QZBmiIMAny/jvvQ34+FWLsGtFLgZsnoCjnVMNjbqVKTiEtxv6IV7QP3UqSZaxr74fn75msbKFEUUxBk8iIlLMI4db8eiRton3x6emXV4JD71+Hnq1iOwUHRpDnCUvAMg0ai/qdUddXow4PEjWq2HSay65/qkcHmnWgOz2SQvyWkTxgsGTiIgUYXd58djR9pDX/PlgC/556yIcaBgIed2uFblzes2uYQf+72AL9p0fgCTLEACsLzHjA5tLUJ5tmmvpAZVkJOFk+0jIgFxoNszrNYjiDdd4EhGRIo40D8LtCz1E2Gt1IdmgwfoSc8B1nqIgoDjdgOuWzR482wdH8fm/n5gInQAgAzjWOowv/uMkznQGP6t+LnauyA0aOsdF+7GjREpj8CQiIkXYXF6E2DM0weHy4Ss3VuKmlXnQqiYfIQrAlRUZePBdq6DXzt4j89dvNMDhlmaEQ2ls9/n/21Pv3wV0iQrNSfjQltKJ2qYSBP+JUtdWZV/y8xPFI061ExGRInJT9JhLzMtN1UOrVuGTW8vxvstKcLbbAkmWUZFtgtk4t8bx3cMOnOoIPqIpyUC3xYnqDgtWFqbO8V8w010bCpGfpsdjR9tR32sDAGQYNbhldQFuW5MPlYrjO0RTMXgSES2AYbsbL53uxr76fjg9PpRmGnHjyjysK07zD38R1habkWHUYNDuCRhARUFAVa4JeWmT6yJNejXWl6Zf9Gu1DzvmdF3H8Oi8gicAXF6RicsrMmFzeuGVJKTqNRBC9YOao1GXFy9Ud+Glmm4M2N0w6dS4pioHt63Og/kiTm5yun1441wv9p3vh9MtoTQjCbtW5KEiZ35rXIkuBYMnEdE8NfTa8NWnquFw+yZ2OffZ3HinaRA7l+fgvu0VDJ/wnwv/6R2L8Z3nzkDA9DZEoiBAqxbwz9sqFuS1kuYwFQ8AhgU81nIhT1KyOjz40uOn0DHsmPh/GnZ48OTxdrxS24MfvmslCsyzn1vfOezAV56oxoDdDQH+Na71vTbsPtODd28oxAc3l/BzkxTFOQAionnweiV859nTY2sJJ28fX1e4+3QPXjod+Wbn0WJ9aTq+f/tKLMmZPAZZALCuOA3/efdqlGYaF+R1luYkI80Qum2SRhSwvuTiR1OV8D9vNaJj2DmjXZMkA1anFz/efXbW55AkGd96+jSGRj0AMDHKPP65+fcj7XjjbN9Clk00K454EhHNw9uNAxgc+8EeiADgqeMd2LU8hyNLY1YUpuLHd69Gr8UJi8ODDKP2oqaO50KlEnFFRSaer+4Kes2uFblRcd77hUZG3dh7rj/ojnlJltHQZ0d9txWLc5MDXgP4uwh0WZxB7xcE4PFj7di2NIufm2Fkc3rxWl0P3m4YgNMjYXGOCTesyENZ1sL8khVrou8rjogohpzutEA1dvpOIDKAjmEHLE4vUmYZgUs02Sl6ZKfow/LcXq+EN8/1hrxm1O0Ny2vPV/PA6KxtmgQA53pDB8/jrcOhPzdl/2tZnV4k83MzLJr77fjaUzWwOCbXNTf22/FiTTfu3VKCuzcURbS+SOBUOxERxZ2DTYOwunwhr3njXD9szugLn+o5bEySAajF0D/CpTn1EEDQYBopXcMO7Kvvw8GGgaj8+MyV2+vDN56ugdU5fTPd+C8VD7/dgoOzHJQQjzjiSUQ0DysKUkNO5woA8s0GpEThlG48axu0hxztAwCfJKPH4oRJH127uxfnmJCkUWHUEzw4CwDWFqeFfJ7K3GS8UN0d8hoBwKf/ehxbl2ThltX5yEkNzwj0XPRZXfjFq/U40TY8cZtGFHDjylx8+PIyqNWxNVZ24Hz/xPraQEQBePJ4OzaXZyhYVeTF1keRiCjKbF6UjgyjJuApO4B/ZOrONQVcQ6cwvUYFeQ4jfoY57n5Xklatwm1r8oM22xcF4IqKzFmXKVxRkYkUvTro5ybg//wcdnjw7Kku3Pe3Y6jrslxy3fMxMurGfzx2Eqfap/de9UgynjnVhZ+8fHZezf4j4XjbMMQQX/eSDJzpssLrlRSsKvIYPImI5kGtEvHNW1bAqJv+A378B86NK3Jx/fKcCFWXuDYvypixI3yq8XPU8yM4whfKPZuKsb0yC8Dk59L438vyUvCZaxbP+hxatQpfu6kKGpUYMgAB/ulft1fC956vhccbeolCODx9ohODdk/Ata2yDOxvGEBdd2RC8aWSJABz+OVnrksi4gXnfoiI5qksy4hfv38dXj7Tg331/XB4fCgbayC/ujCVo50RkJdmwNbFWXjrfF/AACoDeP9lxYp+bGo7R/B8dTca+2zQq1W4YnEmrl+WE3BjjygK+Py1S3DDyjy8croH3RYn0pK02LY0C+uLzXNuUF+Vn4qH3rcOz53qxFvn+jA4Grh5P+AfgRtxeHCgYQBblyp71OfLZ3pCbqgSBQGv1PaiMm9+zf6VVJWXjL3ngrerEgSgOD0JWnX0jbqHE4MnEdECSE3S4u4NRQm5SzViZBk1HRacbB+GLAOVecnTQtlnrq2AxyfhQOMAREEYa6AuQ4CAj19ZiisXZylW5x/3N+OJ4x0QBWEiYJ3vt+HxY+34wR0rA/cvFQRU5qagMjdlXi+fk6rHx65ahDvWFuBDfzwc8lqVIOBst1XZ4CnLsDiCr4UE/COygza3QgUtjG1Ls/GnA81weqWAqwRkGbh9TYHyhUUYgycRkYJGHXZsvLIMAHB4XxOSDInZy+9i2JxevFjThZdP92DY4YY5SYsrKjJxuGkQLYOjE6HSJ8vISdbhazcvQ2mmEVq1CvffVIWmPjvequ+D3e1FXooB2yuzkJqkVaz+N8724onjHQAwbVRPlgG7y4tvPVOD3927MeybZ2abbp+4bgGO+7wogoAUgwYjIcKnKAhINyr3MVsISTo1vnrTMnzn2dPwSpMfe1Hwjy5fX5WDa6uUHVmOBgyeREQUtQZtLnzp8Wr0WidP8ekaceKxo+0T10wNc302N77yZDUeet9amI3+pvRlWcYFadZ9rtuKl890o3vEiWSDBlsXZ2FjqRkqVejA+MSxDghC4L0xkgwM2D040DiAq5eEdwQ2LUmDYrMBbUOOoNPtPlnGmqK0sNYRyHXLcvDEsfag63IlWcY1MRjSVhel4VfvW4dnT3biQEM/3F4Zi7KMuGllPraUpyfkMhwGTyIimlWkRmp/8ep59FpdITcKTSXJMuwuL16o7sb7N5csSA2yJOPXexvwYk33xFS5KAjYV9+PxdkmfOe2FUFPQBp1edE0MBry+VWCgFPtw2EPnhAE3LWhED/dUx/wblEQkJemx/pic3jrCOC2Nfl4va4XQ6MzNxgJArC5LB1VefNbchApeWkGfHJrOT65tTzSpUQFBk8iohjUUzu/Hb45VdH/Q7xr2IGjrUMX/ThJBt4427dgwfPJ4x14saZ77LnlaX839Nnxk91n8a3blgd87Jz3Kyu0sXn70my0DTrwj6PtEyF6fDQ2w6TBt29ZPueNSwspLUmLH921Cr94tR4np7RUEgUBm8vM+ML1SxNydDAeMXgSESnI7Zns2WdzeJFkuPjnaN7dCV1PE8xZl/BgAEN9DjS3lqF0Z/4lPV4p53qsl/xYh3thWgJ5fdLE+sxAJFnG0dYhtA/aUZg+cxTYqFWh0GxAxyzT28vyFfpFQBBw7+WluKIiEy/WdKF5YBRJGhWurMjE1qVZ0Gkit8M6O0WP792xEnvP9uJ/DzSjz+aGJMs40DiIs38+gg9fXoptlWxNFusYPImIFOD1SfjboVY8faRx4raP/+8R7FhRiI9fVR50qvbCkU1Hqw3m/sNYtKUVGWuLL6mWgeOtaDzUj+bdG2Eonn5qTzSNhKouceRNFIB888L052wZGA256WX89Y60DAcMnhAE3LGmAL98/XzQx5p0aly5OHMhyp2z8mwT7tsxey9QpdV1WfCzV+pnTLcP2D34zz31cHtlXL8iN0LV0UIIa/B84IEH8MQTT6Curg4GgwGXX345fvjDH2Lp0qXhfFkiougiy/jPl89h//l++KaMePpkGa+f7Udjvx0/ums19BeMNgUa2cyS+5C1qRVZOzYCKWWXVE6WOQfAYfTV6+FunlxXGGoktHPIMfnPmeuCy3laUZA6rf3QXEkykJ2sxz//+Sh6LE4YdWpsX5qF29YUIDNZd1HP5ZXmdqqML8T/yfXLc3Cux4rdZ3qm/XtEAdCpRXzjlmXx2ctRli96evx3bzVBkuWga3p/v68J2yqz4vP/K0GENXju3bsXn/rUp7Bx40Z4vV585StfwfXXX48zZ87AaGQLESJKDKfaR7DvfH/A+yRZRnP/KJ59vhlXL50MgY5WGwocL6B0lxdac9K0x+gWXXroBACklCFrB5BSeg5Az8TN7qFRNB+oRfPuGyfC5+nWXrz7jhXTHv6pvx7HR7dWKrALW4trKrPwal3vnDcXCQAMGhXerO8DZP/SyRGHB8+c7MKeMz148F2rAvfMDKLInAStSoDbF7wASfafrx68KAH37ajA5vIMPF/dhaZeG9QqEetLzHjX+sJZj76MJcN2N5460YE9Z3pgcXqRqtfguuU5uG1NPtJmaWHVNezA2VmWV4x6fHincRBXhXsjFoVNWIPnSy+9NO39P/3pT8jOzsbRo0dx9dVXz7je5XLB5XJNvG+xxNbxWEREow77jNueP9EMeF2QZBmy1zlxu+x1QgKwqckOV0cHtMJk8/kM3WlkrnMgef2m+YXMYFLKoFsz/Xl1liaU4gRw4AU0774R0roUfO2p6hkP7bO68OPdZ+H2SLg2zMeB/tPWcvTaXDjZNjLR/3B81LDQbIDD5cXAqH8qPEWvRk6KHg19thmtiyRZhsPjwwMv1OI3H1w/55G4JJ0a11TlYPfp7oDhVxQE5KXqsapglhN1BAFritLQ0GtDfY8NA1YXXqjpxjuNA7h9XSFuW50fkU09C6lnxIn/eOwkhh2eif+rEacHTxxrx+t1vfjx3auRFWLEeWAODeJFYW7XUfRSdI3nyIh/p1p6enrA+x944AF8+9vfVrIkIqIFNd5yaC5aHv0oAKB57P0H9sy8xuecGfzCJqUMyesxET5//tuV8CYFv/y3bzXgqiWZYd2QotOo8J1bV+BY6xBere1Fv82FLJMO11RlY12xGTKAbosTkiwjRa/BR/54KEQvSKBzxIlT7SNYdRG9Kj98eSnO9VjR2G+fGEUF/KEzSavCl2+onDXISpKMB1+sw6GmwWmbjAZGPfj9viY099vwuWuXxPTO7Z+/eg7DDu+M/39JBoZGPfjlq/X4zu0rAj8Y/j6js5FkIC2J21NimWIfPUmS8LnPfQ5XXHEFVqwI/Il3//334wtf+MLE+xaLBUVFPH6OiCjc7PaxXpOqHIiVlUi2HMeWxtdwvnEZGi+4dnyk1u4B9p5pw5UBpj0Xss+nKArYUJqODaUzBy0EAPlp/jWw57qtIafEAX9YPNdju6jgmaRT44fvWoUXa7rxUk33xLrRayqzccuafGSaZl83eqChH+80DQa9/9W6Pmxbmo01EeihuRDaB0dR3RF8llKSZRxvG0bXsAN5aYG7MRSmJ2FRlhFN/faAzfYB/5rYzYuU3YhFC0ux4PmpT30KNTU12LdvX9BrdDoddLqLW/hNRBRNDu9rmnHboYYB7P3Ncdx11UG0mYB/+/rvAQD/+d2PQafV4DlvJe5aX4R3byxUutwJKRmXzfna8ZFaAPj8XwJfc/po73xLumhz2wUvQ626uFFFq8ODN876R1u3L83C1UuygoanYF6s7p5YKhCIKAh4qaY7ZoNnQ59tjtfZQ/7ffeyKMnz96RoAgVubfmBzCfRabiyKZYoEz/vuuw/PPfcc3nzzTRQWRu4bKxFRuI2P9PXUWiAd9k+TX5ahx9Ibj6PJlITd9snTS3aLK6FV6ZFp0uDOTaUwKnh+eDwqyUhC6ixnfksysK44bc7P+dyJDvx+fzN8sgzV2NrS/3unFetLzPjXreXITp3bxqD2odGQG6QkWUbbYOgTjqKZZpZjQyevCx36VxWl4Rs3L8NDb5xHn3VyLadRq8IHt5TgplXR3XuWZhfW4CnLMj796U/jySefxBtvvIGysjAskCciijLjobNixUFkr/RPQ7t9JXi7uxRCXde0ayuyjPjirkqkRjh0WgbemXjbK8l4sLEbxw60o3i0FX/46o+mXVvynj9AUOuhEQX8z4c2wqiPjhEotUrEXesK8fv9M0edAf+o4prCNBRnzG0ZwBt1vfjNW5PP5Z0y/3u0ZQgfe/gINpWZ8cmrypEzSwBN0qkxOBo8EAsAjEF6ucaCVYWpUIsCvCHStVYlYMVsm7AArC9Nx+/v3YjqzhH0WlxI0auxpjiNLZTiRFg/yz/1qU/hr3/9K55++mkkJyeju9t/5FhqaioMhks7cYOIKJr01FrgPNkBrXv6+r3x0KlbsxMAoAPwFQCNa/Ow+Kv+a370rlVYWTq/tjBDbi9eHrCgzelGkijiqnQTVpoMF71JxWic3EX0184BHPP4ICzOQNtJ74xrBbUeokaP915WjCxz9DScB/xnfneNOPDCtHPV/SOdizKN+OKuJXN6HlmS8X8HW2a97kjTEM52n8TP3rMmZI/QbUuy8NdDrSFHPbfFcIsgk16DG1bk4rnqroDrMwUBuGlVHoy6ucUOQRSwqjBtYYukqBDW4PnrX/8aALBt27Zpt//xj3/Ehz/84XC+NBFR2I2PbK7bcgwppReO5EyGzqlyUifDyaKs+W3AebZ3GP/V2gsZwPhE59N9w1hh0uPbFQUwzWGEaGJT0RivJOOxlm74vD5AB0jFM8NUYZMd5bfn4pYV6TPaRy3kpqJLIYgC/mV7Ba5bloOXTvege8SBZL0GW5dkYWOpGao5Tgm3DI6ix+qa9ToJgNXpxV/facFnrg0eanetyMWzp7pgdXpnNMQXBQHpRg22jx0HaXN68WpdD95pHITbK2Fxtgk3rMyd80htpHz0ijIM2N040DAwJfT7/76yIhP3bimNdIkUBQRZvsgjIRRksViQmpqKd/Y2wGRKjnQ5RJTAmnd3QhgamXab1j2IFVuOIWNtMcSSzXN6Hrt9dGIjj2XgnWkjjRfjwJAV327oCnifCGB1sgEPLp29K4hKv/KSXj+YSGwqCofjrUP4xtOn53y9RhTwt09uDtlaqm3Aju89X4vOESdUYyPSPllGSboBX795OXJS9Wjss+HrT9XA6vROa9skyTI+dmUZbl9bMJ9/VvjJMuq6LXiltg/Doy6Yjf7WV5U5yTHdKopCs9msuGxrOUZGRpCSEnoWJHYXlBARKaR5d6f/fPRNrdCnTu81aFw099C5kP7SOQgBgXf+SgCOWx2otzux2Bg/p+Io4UjzIB470obTXaFP0LmQR5Ix4vAgO0TwLMow4r8/sB7HWodQ22UFBP/ayFUFqYAgwOXx4RtPn4bN5Zv2cR0fIf39viYUmQ1YH6CtVNQQBFTmpaIyb/a1nJSYGDyJiKYINLJp9p3Dok2tFzWyGU4Dbi/OO0JPA4sADgzbZg2eUzcVAf6Q8/GaFgx4/Gs7fU4X3rzlo9Ou+cUnPgfJfDuuuLXk4ouPYi9Ud+HXbzTgUg4QEgTMaf2iIApYX5oeMDy+da4/5I58UQCePN4R3cGTaBYMnkREY8ZHNquunD6OKBi6IzayGYhLkma9RgTgnsN1gab67ynLxX+3BT5bHgByNstY2nQAHW+mTZzpHuv6bS78Zm8DgOC9NoMRBWBDqXnOG2eCOd42HLLXpyQDpzpGIEtyzB+vSYmLwZOIElLLI7UzbjP7zmHZdV1IXr/mgntyF+y8dKMxad7HYGZq1NCLApwhEpIXQKnh0qbZb882o9nhxkv9FgSKNxuv3gSz2Y7Ot0cAxEfw3HO6+5IeJwqAShDwvk3zH/29cNNRIPLYkZ2MnRSrGDyJKOG0PFKLfP1+lKydfvSeYOj2h84FCpnholWJuDEzFU/1DiPYmGaSKOAqs+mSnl8QBHy+JAfb01PwVHM3Xrvg/ky1Br5Leubo1TI4GnC97IXG19WOj0ymG7X4t+uWojz70v6vp1qam4z954OPNAvwd0IQOdpJMYzBk4jiWqCRzXz9fpRe7kXy+twL7lm4kc1w+0BBJo5ZRtHidE8LTOPNgr60KA/6ObYOCkgQsCYlCYvLc/HdsZsmduFbmhD8VO5L5/L48Oa5Puw91wer04tCswE7V+RObL4JJ71aBQEC5BDxUyUI+MOHN+BI8xAcXh+KzElYU5S2YEHw2qps/PlgMzzewFXIAJweH442D3KdJ8UsBk8iilvBRza9MTGyGYpRJeL/VRXj792DeK53GFaff+xzU6oR781LR6Uptg7pGLC58JUnqtE54oQg+KeUmwdG8WZ9P66tysZndiwO67rGzYsy8Gpd8FZQoiBgc3k60k06XL/iwl9YFoZJr8H9N1Th+8/XQpLlgGs9u0ac+NazZ3Df9grsDFMdFNqoy4vnq7vwUk03+m0umHRqXFOVjVvXFCDTFPwQAfJj8CSiuBBsZHPJTVroFsXuyGYoSSoRHy7IxIfyM2DzSdAJArTzGeWMFFnG95+rRbfFNf4ugMk1j6/U9qI4PQl3rCsMWwkbS80oNhvQPuycsdZyPO6+a234Xn/chtJ0/PK9a/GzV87hbI9txv3jYfS/3mjApjIzzEYGHSVZHR78x+On0DnsmPhYWJxePH2iC6/U9uJH71qJwvTobvQfaQyeRBTzuh7ej8WFDcgsn964WJevDXh6ULwRBAHJMXyOdV23FfV9M0PWVE8d78Ctq/PnfPLQxVKpRHz39hX45jOn0TwwOtHgXZJlaNQCvnh9JRbnKnOQSaHZgH7bbKcmyXilthd3b5j9kABaOL97qxGdw84Zo9GSLMPu8uHBl87iV+9dy2b5ITB4ElHU66m1wNEaOJjoepomz0W/cGQzZYsC1SUeV+M59Dcs3POdbBueOJ0nmMFRDzqHHSgK47GR6SYdfnHPWpxsH8Y7jYPw+CQsyjJi29JsJM2zVdLFcHokDNiD9/Mc1zowOus1tHAsDg/2nusP+nkqyTJaBkZR121FZV7o03sSGYMnEQEARh12bLzSP/18eF9TxM/cHjd+HvqaFe0B71ct6kVKaeBz0Wn+Lmz/5DqxG73VfWhp3wz9xoU5vlGS5Tm1BzraMoy8NAPUYVxOIIgC1hSbsabYHLbXmI1aJYTs5wkAAoSQx3PSwmsZGIVvlpZXggDU9zB4hsLgSRRDmnd3zuvxhmITcqqi5xviXP49up6myfPQzTkBriiJi/Wa0UhqOTjtfc/QCHqr+3C+ZjPEjSsX7HOpKi9l1h/oAPD7/U148ng7vnrTMixRaNo7EtQqERtL03G4eSjo6JpPlnF5eYbClSW2OeV8GWH9xSgeMHgSxYjxU3XyF6dd0uOt/U70HDahBwsXGOZjrv8e/aJTUXNUZSJxndiNoWodPN7J5QvWfj16+ssXNHQCwOrCNOSn6tFtcc3aRH3Y4cHXnqrGL9+7Djmp8XsO/V0bCnG4eXCib+hUoiCgLDMJa4vSIlBZ4irPMsGoVcHuDt3Fdl0ER8tjAYMnURSbOiJo7j88dl74pT2XZ2gEydU1OH8YaG6dOULodDsm3m59pRt6bXjb8Zj7D2PZdV0wLprtSobOcAs0stnxFlDfuwqyOXXyDhVg2Ljwo+aCKOBrN1Xh/idqYHV5Qk4xSzLg8sp49lQnPn7VrJ88MasyNwVf3lWFn+ypg8crQxzbrOKTZVRkGfH1W5bx2EyFadQq3LmuAH8+2BrwflEAtpRnxvUvRAuBwZMoSl04Iqhf1DqvkT9dCZCN3dAnH4NzyDvj/lGXc+LtqrTzSNKF95tn2tqumO+lGQ+srz8Je7t5xshmm6UYJfdUKVZHUYYRv3rfWrxQ3YUnjrfD5Q2ePiVZxt6zfXEdPAFgS0UG/lx0Gd4414fmfju0KhGXLcrAyoKUqN017fVKeLtxAEdbhuD1yajIMeGaymwkGzSRLm1B3L2+CD0jLrxc2zOxIW7872V5KfjsNYsjXWLUY/AkikLjoXP6COf8R/50a3Yiw3wQwPDEbbIM1I468Vb34MRtexY34MocM1aaksL28000r2HoVJKlCdJQz7Sb7I1daD6gRqdz5shm6T3Kn8GeZtTifZtLcKp9GKe7rCGvdXji7dDOwJJ0aty4Mi/SZaB72InDzQNweSWUZiZhXXH6jBObOocd+PpTNei1usZGaGW8eb4Pf367Gf9+fSW2VMT+mlRBFPDpaxdj14pc7KntQa/FhVSDGtuWZmNNURpHoeeAwZMoykwNnVk7Ni54OLswvP6xox+PjA5C0kwewfe0djGedepxsykVny7OjtrRFZojSxOsR09guHn6CGZnPTCsWqLoyOZcFGUYUdttC7reUxCAgrTYOpkpVrk8Pvzi1Xq8Vd8PCP7d9JIsI9OkxRd3LsWyfP8vLG6vD199shqDY22gpn7sPD4ZD75Uh5++e/WCnGkfDRbnJivW1zXecOsVURQZD53LrusKS+i8UK3NgUe6/COdU3/Ej7/9XP8IDlvYKzAmWZogtRyE1HIQ1qMncGZPHk4PLZ72ZyhzY9SFTgDYtTw35CYjWUZUjALGPVnGD1+qw77z/ZD97058XAbtbnz9qRq09NsBAG/V96Pf5g74cRu/5cnjHQoVTtGMI55EUWJq6AzH2ke7fWaAfLy5G5LDCRmAzzl5Usr42wKAx5q7sKxi5rSr0Zi0oPVRYHb7KFIyLgMAWAbemdv/+wUjnJ31wFDmRpTuVH76/FKUZ5tw59oCPBEgqIgCsLIgFddUZc/puUZG3fBKMtIMmrCdehSvzvZYcbh5KOB9kgx4JeAfR9vx7zuX4nDTYMjeo5Is42DjQBirpVjB4EkUBXpqLdD1NGHZrvBtuBkPL3Px5i0fnXj7NQA/CXDN1KbiFEUsTeh77TAaDxVjKHNso0MmYiZ0jvvIFaUoSDPgsaPt6LL4N76l6NW4aWUe7t5QOGuvxLfP9+PRI21o6POPyKUaNLh5ZR7uXF8AbQwfL6qkN8/1QSUIQXusSrKMffX9+Ny1i+HySiG7EQD+KXciBk+iKGHOMkBrTuKGG7p000Jn7IxwBiQIuH5FLq5cZMLGsd3rb+9tQIpp9nV1Tx3rwO/3N2HqPo8Rhwd/O9yKk+3D+M5ty6Fh+JyVzemd0UP0Qj5ZhtPjP1r0WOtQ0PApCkBpBmdJiMGTKCo4Wm3IkvvC+hqWgXdm3PZY9xD+r3NgYqp9fKTz6mf/AJVeBwHAnTlpuLcgM6y10TxYmibejJvQOdWUjW1zORGme9iJP+z3/59cGIIkGTjdacGLNd24dc3CHPdpd3mxu6Ybr9T2YGjUg0yTFjuX5+K6ZTkxf6RlTooeM9vXT2fUqmDUqnD9slw8djTwsbaA///+ltVx8jlJ88LgSRRh42s7sza1QrdoY9heJ9DawNuLdXjG4sCoT5p2u0qvg9qgh04U8K6SPBh18dGDL95ILQdhb+yC7MjF6IALLbVxFjovwe4z3RAEAXKIzUnPn+pakOA5aHPhS49Xo8fqxPjL2V1e/PbNRrx8uhs/uHMVTPrY/TF77bIcPHKkLej9oiBg5/JcCKKAnFQ9/nlrOf7rjYaJvpaAf504AFxekYEdlXNbl0vxLXa/IojiwPjazkVbwtM6aTapGhV+uKQQX63vwIXL/o0qEd+pyEc2Q2fE+CQZbw/bJt5/Y9CK6w16aEQRUstBDBxvReOhYgyrlgAA5MzUmAudPbWWkPc7nBfXVaFtcDT0jngAnSNOyJI8756LP91zDr1WF6a+3PibLYMO/PfeBvz7zqXzeo1Iyk7R430bi/GXQzNP6hEFAdnJOty1vnDithtW5iEv1YDHj7XjZNswZAD5ZgNuW52PnctzZ/T9jAfnuq04cL4fox4fCswG7FgaP83yw4XBkyjCcjJtyFhbHLG1nRVGPf68sgwvd/bh9bHbPlWcjRsKs6HnLuCIaXO48dX6dnSOTAbP/9fcg/8dsOE/kzqhP9MV89Pqzbs7oetpgjkrRE9Ox2TwtHolzLZKUK9RhdxdDQBalTDv0Nk+NIqT7SNB75dkGW/V9+PjV5Yhzaid12tF0j2bipBu1OKRI63os7oBACpBwNVLMvHRK8tmhKw1xWlYU5wGn0+CT5bjdiPXqMuLB1+sw/G2YajGloNIkPGn/c341LYKXLs8J8IVRi8GTyKCViVie/rkpo3rM1MYOiPI7pPwxbNtGPHOPJ1n7XA1jp8YQFrtcozmxHboNPcfxrJdXf5NdRfwyjIOj9hRMzh5gtGHTzXi+oIc/EthFvRBAs0V5RnYey74emlREHBlxfzXLNfNcrIS4A+f9b1WbCyL4RN7xjZ5XbcsBy2Do3B5fShIM8CkDz2qp1KJiM/I6fejl87iZPswAEzb9e+VZfz8tXqkGTXYUJoe5NGJjcGTiCiCAvVXfbZ3GP1Wfxugqf1VLxs4hdIuG04fKUJ/RjHef3UqRh32aY9NMhjDW/ACyl+cBuOimadpeSUZXznXhtM+J7wq58TtG/f1Yvc1erQ4XPjx0iJoxJm/HG0qS0eBScBbv7oDAFD6/r9C1OgB+PcpiQJwx7rCGY+7WHP9vUyI0lO/ekacePZkJ96q74PTK6E4PQk3rczD1UuyAk6JC6KA0szY+dwKp4ZeG462Bu5vCvg/xx451MbgGQSDJxFRBF1Mf9UH7/nhtPd/9ouZ15w+2jvfkiJu35AVNTbnjNvvXv0W1HuvQ3VpGl4c7satW2aO9qpUIr5x8zJc96ux9wUB4lgvSoNGhS/vqlyQALWyIA2CAIRYTgqtSkBlbsq8X2uh1XWN4OtPn4bbK0+shz3XY0VdtxVvNwzgP3YtZbP9EN5uHJi2gepCkuxvvj9sd8f0MotwYfAkIqKIsY+6IF4w6vt0a0/AE7Va8jS4A3uQ2bgRjqF2NJ6pRe77po+WJhmMSDNN/rC/aVUuoNKjPNuEqxZnLliLo8xkHa4oz8SBhv6A60lFAbh+WW7U7Wp3e3347nO1cF/Q8H387bcbB/DsqS7cvnZh2k3FI6fHh7mMYzs90uwXJaDo+oogIkowgfqr/qypG28M2SDDP70+PtK55LOPwVaeAhHAcmMSvrc09sNB2tr75nztlz76y7G3/nvyxv+afs2FI74fvLx09uUHsoxT7SN4ta4HAzY3MkxaXFOZg1WFqdP6iF7o0zsq0Gd14WyPdWJD0/hI2JrCNHzkytI5/9uUcuB8PyxOb9D7ZQBPn+jAbavz570BK14Vm5OCnuY0Tq8RkW7i7vZAGDyJCIC/zyePwVReoP6qd5bm4U2nv3+iVj85emcrT4Go8+8Av7M4L6bWc0Yrj9eHB1+qw6GmoYnQKAoCXqvrw6ZSM758Q2XQU46SdGo8+K6VONg4iNfqejBodyMrWYfrl+VgfUl6VLYPquu2hTwGEwD6bW4MO9wwG3UKVhY7rl6Shf/Z1wiXRwrYXl8UBFy/LCdud/TPF4MnEVGUqTIZcFeOGY/1BN7AsN2cgivMJoWrCo/h47+Cq2A93hi04rVBK0Y8XmhEAe1OD4DAJ2oBwD8VZeHGrFS4Tr2CvtP9aDizCbnv24yDDQP4xzv1E8//b4+ewLs2VeDaquyAo5e/39eMI83+/+fxNXvjfx9uGcLv9zXjn7eVB61frRJx5eJMXLk4Nk73mmsWVgXYuEV+eq0Kn792CX74Uh0ETG/dJQoC8tP0eO+mkojVF+0YPImIotAnCjOxyVKD5pbJ8Jmj1eCuomzclp0WtbulL5as1eBLLX1od/mDJlT+H0sqw8zRIpVeB41Bj3y9BjcX5cCgEmHcciuMht1I0p3A7oe8+KHOCHgnNya1DTnw81frUddlwX07KqaFT6vDg92nu4P2/JRlYPfpbnxgc/Gs7YNixdriNDx7qivo/YIAFJsNSImytanR5vKKTPzgjpV49HAbjrcNAwCSNCrsWpGLuzcURd3a3mjC/xkioigktb6D/Po+DNVXTtz22+UlSEqKj5HOcc/0DaNTPffRwnUpSfj3slwYpuy61q3ZCbX9WeS17scd+1fj8bzUiftk2X9s4+4zPdhQmo7N5ZM9NWs6LPCG6jQPf2un6nYLtlTEcC/OKdaXpKMgVY8uiyvgrmxZhv80ojj5xSaclhek4jsFqRh1eeHy+JBs0EDNbgCz4v8QEVGUmXYcZub6yTviLAwMeb1oc7oRau/v1NGRXy8rwfeXFELrdsNuH532568jZWhNBtwaL5J6HROPkb1OSB4n4HXiqSMNGHXYJ/7YR23++2bhleJnd7IoCvjWrcuRbvSP4I5/Roljn1vv3lCIbUt5pvrFSNKpYTbpGDrniCOeRERRZGroHMrciOKrU4FvRbqq8Oh1ume9Zur+6/yx6e6L6X3a8uhHJ95uBPDsj2Zes+jDT4R8jsXZ8TXKnJtmwK8/sB57z/Zh3/l+ONw+lGYaccOKXJTH2b+Vog+DJxFRlHEOrcJQ5mKU7syfcTJRPOk7N4SvffHrAIDte/4ClUEfkTqCNQMXBQFrClORmxbiLPkYpdeosHNFLnauyI10KZRgGDyJiEhxpTvz0fH45DKCRd4utKBsxnWaAKsLAvU+ffZkFx5+uxm3qOuQ0uPAvT/9XwBAyXv+AEGthygIuGFFDj585fTXGLS58c3nz6Pf5rpgdzKQYdLgM9cuvsR/IREFwgUJRBHW02/CwPFWSC0HI10KRZqlCQPHW9FZPwxDsX/KM8lgxOmjvTh9tDfu+nauvXHyzPQC18iM+wUAOzNTZ9xuNCbN+HPzuhKYU4x4SbUalpzJEUpBrYdKq4dWp8cdG8uRZDBO+1OYZcbP3rMG791YjEyTFmpRQIZRi3s2FuHn71mLDBN7WRItJI54EkVQTlUKmlvL0HioH0ArMgCIJZtnexjFI0sT+l47PLG2s7Qq+s74VooA/wk6q0wG3Jtvxr/N4TEmvRrfu2Mlvvn0aTwzPNkJ4Nb2Xry6pBRfu2kZ8oJMmScbNLjnsmLcc1nxgtRPRMExeBJFWOnOfDTv3oi+ej1SSnugY9/hhCQN9aCvfgmGMleidGd+pMtR3PaMZPQl6TDi8SFXp8aNWWm4ypwMtSjM+UStskwjfvehDXilug0vj922bbmAGzYuQ3FRWthqJ6K5Y/AkigKGYhPczVkAeiJdCkXY+BR7vDvdMYInDjdMvN/TYsFnr0rH4pzkeT2vVi1i65KsifcLMpJg13BVWcTIMuxuHzQqgUdIEgAGTyIiUthTxzrw+/1NgNc1cZuzpRv/NfQKbtiyBdfPcae13T460VrJMvBOwHPvKTK8XglPn+zEc6c60W/zt81aXZiKuzcUYTVHnxMafw0kIiLF1HVZ/KETmNbCyH0mGVucXXjx4NtoGxiNVHm0ALxeCd969jT+9+3midAJANUdFnz9qRrsOd0dweoo0hg8iaLEUJ8DluYRwNIU6VJIaZYm2Bu70NMfX9PsU08JGv/zxOEGwOuC5HFCnnKm+p+blmGwMQlZrgE8ebhpxslEdvulhVG9vhfOkx0L9U+iOXiuugun2kdwYWtUSZYhA/jV6w0YsrkCPpbiH6faiaLA9N3th5G1A0DKzJ6GFIcsTbAePYEze/LgyimLq93sG6+c++fwWwc+gbcOjL/3c3w+wDVz3WQ0Vf7lQNKRw6h/BCi5p+qiH08X77mTnbNcIePlMz14zyZ2EUhEHPEkihKlO/MxlLkRffVL4Go8F+lySCGuxnNoO1Lmb6GUgLvZw8FoTILPWQ2fsxrpW25FwVVAUUormnfPFogS16jDjuXrs7F8ffa8TsvyeH3osbow8xyo6VoGuZwiUXHEkyiKcHd7YnILWXG5m/3wvpnLRn67txGv1vb6p129zomz1K+6/H+wo7QX6vW9yKq8Eu/bHHo0zOWTsHfQOvH+kMeHYO31NeZUJGfqgaFL/qfQHKlEMegRpJMEaNUc90pUDJ5EUWaozwH30Ch0liZOt1NMC3TS0p0bF+H18xZAliFNuV2l0sFkU6FIdmPbpjIYjcFPDHptwIJftPTAZndM3PbR6ia8uywPHyvIhCAEOGeTFCGKAjaWmnG4eSho+JRkGZvLMhSujKIFf+UgiiI5VSlw5ZThzJ48WI+e4EYjijvFGUZ8cecSqAQB4pR82FqiRvVIAQq68qA/8kLQx789bMMPm7rhkKaHGgnAP7qH8MeOftjto1DpV0KlX3nJm5Lo0t21oRCAjEDxXxQEFJkN2FhqVrosihIMnkRRZnytZ9uRMq71pLh05eIs/OaD63HL6oKJ2+5cV4iPf2cbrOJWNB9Qw/r6kzN3ttvs+E19G3wOp/+Pc3JntM/pgs/hxKNNXeganlyjaLc7MNzZi4YzXL4S0pTRSa9XCnHh7CpzU/AfuyqhUQkQ4A+bqrFR6CKzHt+9fQVUKsaPRMWpdqIoxLWeFO9yUvX4wJZi/Hzs/fdsKkKSQYeMe6rQ8giAA/ux5obL5vx8b97y0Ym3l065Pa9428Tbp4/2zqvmeLX3bC8eebt+4v2P/+8R3LyuDO/ZWASd5tJOG7qiIhOrC9Pwal0Pmvrt0KpEbCpLx/piMwSRSyESGYMnUZTiWk9KVCXj4ZPQU2sBADhabZf8HIZiE3KCtOn6x5E2PPx2C2TPZE9Vu9uHx4+1o7pjBN+/Y8UlH3Vp0qtx25qC2S+khMLgSRSFxvt6ntnTj2U4geT1YPikhFJyTxWecL+EPP1BFG/2InnNKvTrC/CxmuaJa3xO18RI59XP/gEqvX9D0mdyUnHHqpsBAH/916ew+H2rFa9/IfTUWiAdrkZFpReYx5JIa5MTLSeLZ/Qx7Rp24OG3WwBgRvsjSQbO9ljxwqlu3L6O4ZEWDoMnUZQq3ZmP5t0bcWbPYawxn4NuDYMnJZal965DyyMGGI7th1Ffh5L1etxuduCszYFz6uk9TxerB6HR6JCkElF64tjE7fI1i5CkTwKiZKf7+AjmXEiHq7FiyzFkrJ1fo3XP0AhUR46h7mEHdCsn/99eOtUNU48DkixD8k2ul5W9zomOA08fa8L1VWkznjNQxwKiuWDwJIpipTvz0fVwJoCRSJdCFBElU9Z8ZrW3YJfXixxjO1AM1CJ94rot7S2QerOhs3lwuHVyc8xXn6hBVWEPvnpjJcym4C2alNC8uxPm/sNINuvndH3WlnPIWFsMsWTzvF5XVwIs27wSwP+b0/XjvVUBoBnAxl/PvIbrZelSMXgSxQCu9aR4lGQwzinAlNxThebdqejo879f2vY80rVtcKW68PrYNeoTRrx2vgKdLhW8Pue0x9f32vCVp2rwi3vWQHOJ6xXnKtiIpqPVhgLHCyi9zgvjorw5Ptv8QydRtGHwJIpyrpwyNB+oRSnXelICm3qcaN2zO5B/fDc+s9iH34/ddra9EvuyAEn2QvZ6J66VvU54AbT2OvBaTRuuWpo147kXatq45ZFaaN2DMGcZZtxXpKtB1lovktevicjXsGXgnRm3HW4awg931wEAfG4XXv32lwAAf/jyh3D8+AYIAlCSmYTLFmVgqN8Bd3Ypiq/NVbRuij8MnkRRzr/W80bgwAsMn3FKK/fB0ZoLBNl5TNO961tbZtz2wNMfDnjt1GnjL/wl8PPNZ9p4fITTebID+fr9WPIubdBrdYvWROxr12hMmnHb1csMeLK6B80DDrin3N6elYZVN5yDKAI3rsxDqmEIluYRNB6yofdNAwzFJlhhCbpTnigUBk+iGMDwGb805lRkLT4H6yEnmndvnDayR5E36rBj45X+r7XD+5qmjY62PFKLnFT/sZ1F2TUouEoL3ZqdEanzUoiigG/ftgLfe74Wp1snlyfsc5bBnGLEl2+oRHZhGgAgw3wQQCv66vVwN2dhqM+B5tYyfr7SRWPwJIoR4+FzdM9htliKI2LJZvhPrW5FzduZ6KkN3nOR/A7v8x8l63CM4urrlgMAtv7ro2gd8UGGf3p9fKSz5D1/gKDWQxQEbFuaiX/ZXjGv1546wrlsyQvIXjk5dR9LoXNcWpIWP7lrFY429uLlr/pv++yOClyzqgiaKacLjX+eppT2AOgZGwHtR/PujTAUmwCAn7c0JwyeRDFkaoslhs/4Mf5DPafehr5IFxMDAq3J3LWmBL870AkZwNQDHwW1HqLGv4v8pnVl81rP2f3Xg8jP9zfUXLTkdWSvzIrJsDmDIKAyN3ni3auWZE0LnePEks3QlfjfzlrUBOAwR0DpojF4EsUYhk8iv6m74r1eCQdbbDjdaUGgk8ZvWpmHytz5jciVLzuE0g3jzdTjJHReqpQyZO0AUkrPAeiBe2gUZ/b0c7kIzYrBkygGle7MR8sjSyA7LjxvhGLZ+FrPHmzntOVFUqtFfOvW5XjkUBueO9Y0cXuGSYt3byrDzasvMQzJk19jmcszEztsXiilbOJgC52lCctwAmf2HGb4pJBmjqUTEZHixJLNyFhbjEWbWqE/9vpFnXBDflq1CvdeXorffWjDxG3/9f51uHlNwcWfXCTLeKl/BP946NWJmz6vWowX+0amhVEak1KG5PVrsOy6Lpj7D6N5d2ekK6IoxRFPohglm1PRcrwfpYZuTrfHiakbjRoPvc6Rz0ukVk/ZFCNewlGZsoyHWnvxxpFebDNOPleny4OftfSg3u7EZ0qyo+YYzqiRUobk9ZgY+fSfuga4tekzzomnxMXgSRSj2GIpPjF8Rt4J6yie6RtGCoBAY5vP94/gCrMJ61Pj57xyozEJPmf1/J9oLHyuMZ8DMAL30CiaD6jR8ggYPgkAgydRTGP4jE+JEj5D9chUsoYLPdnaBbgcqDrfh8uqDmD8qHKf0wUAEAA83tKNyvKZR18GatSecC5Y+1mKE8CB/QyfBIDBkyjmMXzGpwvDZ3MrN2yEw3jwDaQdwONT3n/zlslTkF4D8MMAj1mQUcN4MjYCyvBJ4xg8ieIAw2d8mh4+gZZHlgDwr+8NFkL7XB681D+CFocbSWoRV6SZsDHVCDGB1iNObbNEUYDhk6Zg8CSKEwyf8Wk8fOpTuyA7ZIwOuNBSqw/YsuapniH8d1svBPjXJooAdvePoNygxw+WFCBNw2/5Fxo/BWmqv+3rgKv+LBbvOI0nUlZMjHRe/ewfoNLrIAC4Jzcd9+SnK1xtDGP4pDH8LkQURxg+45NYshnJZn9ASgGgSj48bQQUANocHpwaHMHlFzx2/9ZsNDmc+EZ9B35RVTyvndgtj9Re8mOB6AwagdaVbsvNxlttrdDrtVDpdRO3q/Q6aAx6JKlE3FmSC6OWP0IvCsMngcGTKO7wTPc4NeVjmLUD0KeemHaAwGDfMLZ4vNMeklY/Cuxdi/1bs3F21IlqmwMrky9t80vXw/uxuLABmeWXtsmpv8GClocHkXfvFRO3tTvcE2+3jrpRGYHNRYGY1CJ2ZqSgXTUzpKeoVfje4gKYGTovDcNnwuNXDlEc4rGacW7sh/c4q1fCvnOuGZddmdKPG44eB/auhQjg3FErUi6hBZDWPYiKFQf9Z5Mvyr2kknX5FmiqD+L8w4BFbcabA1a0jU42yf90XQtWZ1hx/6J8ZEQ41DlPdmBxdh0256YjMyMHr4/d/oXSHFxfkAW1yLNX5oXhM6ExeBLFKYbPODflY+lye3Be7ZtxyflF+fgw9mOL5SwEAEuNelyWarrolxIM3dCa53c2uW5NGbKxG8mFp/BMsx5lZT7kudx4/O/++1Na3Titc+Dfzrbi11UlMKhVl/xa89HySC3y9ftRcJUWujW7cLV9dOK+renJDJ0LheEzYTF4EsUxhs/EYFarkawSYfVJM+7706IrUOH1H1+4Li8DKeaLD55A7iV93tinhDYAwOKrcLj1NPZhEIAKXufkXVc3NOBNlKOjWItnO7pxc3bajOcLd5/PnloLtO5BLHmXlmeyK4HhMyExeBLFOYbP+KcSBdySnYa/dQ0GPGnnvDofBlHA2sJyQKXciF1KxmVzvva3T3524u1vj/25kBItksxZhrC/Bk2RUobk7WUoxZMMnwmCcwZECaB0Zz6GMjei7UgZpKGeSJdDYXBPbjqqjHpcuB1GBKAC8JXyfBgUDJ1EFyN5+x0ovdwLrXsQPbWW2R9AMYsjnkQJwlBsAma2LKQ4oVOJeHBJIZ7pG8bTPcPo83ihAnCl2YS7c9Ox2KhXvCbLwDszbvvmuU6ctI1Chv8Iyqk9Mrf5mrC0aRB1h9Zg8zWXIbsyWeGKKZK05iSYswwYiXQhFFYMnkQJxDrkhL2xy98TktPtcUenEnF3bjruzk2HR5KgFgQIETyxKNC55beV5KC6sWvG7Sq9DgcMa6DVn8F70s+j+1gmrPqVip5R7zzZgaLsmmm3GY1JPAZTQSm6GvScNAAKftxJWZx3IUoQOVUpGMrciOYDaliPngAsHP6MZxpRjGjoDOYKswnrkpNmLAkAAAFAf+46VFxWgRVbjkE6XK3YtOv4bvasFaPcWBQhukVLkLViFPn6/fM+rICiF4MnUQIp3ZmPDsONDJ8UMaIg4NsV+bgjOw2aKelTLQA3Z6Xh+4sLoSndjIy1xYqFz/Hd7KWXe5G8/Y6wvhaFkFKG5PVrUHq5l+EzjnGqnSjB8GQjijStSsQ/FWfjXWlJyBm77eFVi2A2JcHmlSAA0I2dUb8Cx1DzNtCD8E67m7MM0Jov7VQnWkBssRT3GDyJEhBbLFE0mLrL/g/tfdjn8MAj+6firjab8IH8dShYG/7wGWhtJ0UQw2dc41Q7UYIab7F0Zk8ep90p4l4dsMIz1oRUAvDmkA33nWlBQ+aasE67T1vbuWjJgj43zQOn3eMWgydRAmP4pGhxYeN7CYBblvGjpi6IxZeFJXz21FqQ5jvnX9u5fg1H/aPNlPCZ5jvH/p5xgsGTKMExfFKkdLs8Ie+XALQ6Pai1OyGWhGfDUbJZD+OiPIbOaJVSBuOiPOQvToOj1RbpamgBcI0nEXHNJ0VEv6jCtfsen/W6ZocbVSaDP3xi4dZ8Olpt0Ot7AKRe8nOQMjTqbgCLI10GLQCOeBIRAI58kvL04tx+BOnFyb5LCzXy2by7EwWOF5C1YhSiOWf2B1DEiOYcGAuHUOB4gWs94wBHPIloAkc+SUnLTHqYVCJsPinoNWoAG1KN024LNPI5LtQI6HhIdZ7sQIF+P9d2xoopu9xH95xD8+5UlO7Mj3RVdIkYPIloGobPIGQZZ0ddaBp1QScKWJdiRKpGFemqYppGFPHevHT8T3t/0GtuzEzFG4NWvDVohUOSsMigw03ZaVgyJXx2nDcAAIb6HEGn31seqYXWPQhzlgFF2TXIWsHQGVNSymBc1IP8xWkYGop0MTQfDJ5ENAPD53RNoy78uKkbDQ7XxG1qADdmpeGfijKhnuOUcSIZ8njxyoAF3U4PkjUqbDUnoyxJN+O6u3LMsHh9eLR7CCL8x2bK8G8sujLNhP3DNjzTPzJx/flRF14asOCeXDM+UnwZMgCklPp7cFqaR9B4qB892D7tNZwnO5Cv34/SHd6JJvG6RWsS+nM6VnGtZ+wTZFm+sItF1LBYLEhNTcU7extgMiVHuhyihNO8uxPm/sNYdl1Xwo4Odbnc+NSZVjh8EgJNCG8zm3B/Oaf9pnqsexC/b++HDP9GgvEgudVswhfLcqEJENS7XW680m9Fv8eLNI0K28zJ+FZDB3pc3oD/7wDwpbJc7MiYHN2UWg5i4Hgr+uqXwC1kTdyeoqtB1opRHocZ6yxNsB49geYDanQYbuR0exSx2ay4bGs5RkZGkJISesMfRzyJKCiOfAJ/7xoKGjoB4I0hG+6yO7HYqFe0rmi1Z8AybercN+W+N4ds0Iq9+Pey3BmPy9Vp8YGCjIn33x62ocvlDfo6AoBHuwaxIz0ZEPybj8bXfqaU9gDomXa9bg1DZ8ybttbzMJp3b2T4jEEMnkQUUiKHT0mW8crASNDQCQAqAK8OWBg8AciyjP/rCL5eU4Y/mH4oPwNZOk3I5zo2MgoVpgfXC5+r2emG1SchWT251lYs2QxdyUWXTrGCaz1jHhcmEdGsErXVkkuS4Z5lMZIEYNgbLB4llmaHG93u4KOU4w4Mz94IXJrjKjBv9K4WI6IAGDyJaE4SMXzqRQGGKT0kAxEAZGo5eQQATinU2LCfCMApzR4WlyUbgo52jsvWqGFWs7NAohHNOUgrrYW5/zCad3dGuhy6SAyeRDRniRY+BUHArszUkN8oJQA7M0KcfCPLaHO6UWd3YNgT3yOj+TrtrD9UJADFBu2sz3VVmgkpahGhYv+duWkT6zspgYyd4b7sui6GzxjE4ElEFyXRwue7c9ORplYF/WZ5a1YqioIEqf1DVnzidDM+XtOMz9a24Z6TDfju+U50u9zhKziCUjUqXGU2Bf2/EgCY1SpsSjEGuWKSViXi2xUF0AnCtOcbj5lXpZlwW7Z5nhVTzJpyhjvFFgZPIrpoU8Nn32uH4zp8pmvV+FlVMdalJE27PUkU8OH8DPxrcXbAx+3uH8F3GrrQ6vRM3CbDv77xM7Vt6HF5Aj4u1n2yKAvpavWMHy4i/BuxvlSWB9UsyxfGLTMZ8JsVJbgjJw2ZGjVMKhHLTHrcX5aLr5bnQeRoJ1HM4cIkIrok47vdGw8BwGFk7UDc7nbP0Wnw/SWF6Ha50exwQyf4A5BOFfh391GfhIdaegLeJwGwen34Y3sfvhyH/T8ztRr8clkx/tI5gJcHRiY2Z21KNeL9+RlYcpG7/3N1WnyyKBufLAoc8IkotjB4EtElS6TwCfhDUK5u9vWJewetcIXYPyPB39PyPq8PpjjcHJOuVePTpTn45+IsDHv8/0ZDkJBORImF3wmIaF7Gp90bDxXH/bT7XHW53LP+Vu8D0O+ZvfVQLNOIIrJ0GoZOCgvRnAO9+RQ3GMUYfjcgonlj+JzOpFLN2goIAJJV8TfaSaSYlDJk7diIRZtaGT5jCIMnES0Ihs9JV6ebEKpTpQBgmVGPDPb/JJqfsfBZUuWMdCU0RwyeRLRgGD79cnVa3JgZorcngA8VZCpUDRFR9GDwJKIFxfDp96nibNycmTrRd3J8Ut2kEvGN8jysuaA9ExFRIlAkeD700EMoLS2FXq/HZZddhkOHDinxskQUIQyfgFoU8OnSHPzfqjL8S1EWPpifga8sysUjqxfhcnNypMsjIoqIsAfPRx99FF/4whfwzW9+E8eOHcPq1auxc+dO9Pb2hvuliSiCGD79MrUa3J5jxnvzM7A1PQUakRNNRAvNWDjEDUYxIuzfAX/605/iE5/4BD7ykY9g2bJl+O///m8kJSXhD3/4Q7hfmogi7MLwKbUcjHRJRBRvxo7P5O722BDWLZVutxtHjx7F/fffP3GbKIq49tpr8fbbb8+43uVyweVyTbxvsVjCWR4RKWB6k/lWZAAQSzZHuCoiiidiyWZkAPBZnRg+NwIg/k4FixdhHfHs7++Hz+dDTk7OtNtzcnLQ3d094/oHHngAqampE3+KiorCWR4RKWTqyOfA8VaOfBJFoQ6nG2dsDvS6PJEu5ZKI5hwkZegiXQbNIqqayN1///34whe+MPG+xWJh+CSKE+Mjn331eqSU9kBXEumKiAgAjo+M4nftfTjvmJxxXG0y4J+KslBu1EewMopHYR3xzMzMhEqlQk9Pz7Tbe3p6kJubO+N6nU6HlJSUaX+IKH4Yik1wC1mRLoOIxhwctuH++nY0TAmdAFBtc+Dzda2ot7MxOy2ssAZPrVaL9evX49VXX524TZIkvPrqq9iyZUs4X5qIotRQnwOW5pGE3eVOFC18koyfN/dABmactCUB8MjAQ63sQEMLK+xT7V/4whfwoQ99CBs2bMCmTZvws5/9DHa7HR/5yEfC/dJEFGVyqlLQ3FqGxkP9AA4jaweAlLJIl0WUkI5ZRzHo9QW9XwJQa3eizelGkV6rXGEU18IePN/znvegr68P3/jGN9Dd3Y01a9bgpZdemrHhiIgSw/Rd7gyfRJHSPcdNRN0uD4MnLRhFNhfdd999uO+++5R4KSKKAeEKnx5Jwr4hG/YN2eCUJJQatLghKw2F/KFJNEOyam6r7ZLVPPSAFk5U7WonosSx0OGzx+XBl861ocvlhQD/mrVjllE81jOMjxdm4u7c9IUpnChOXJZmglYA3Bcu8JwiR6vG0iTubKeFw19jiChiFupYTUmW8dX6dvS4vAAmN0pIY3//rr0fB4as8y+YKI4YVCLen58Z8pqPFmRCEASFKqJEwOBJRBE1NXxaj564pOc4YhlFm9MzETQvJAB4tHvoUkskilv35JrxofyMienP8VBgEAV8oTQH2zLY1pAWFqfaiSjiSnfmo+WRJQC6Lunxh4ftUAEItj9XBlBnd8Luk2Cc47o2ooQgCHhffgZuyU7D/mEbRjw+ZGvV2JJmgp5fKxQGDJ5EFDWcIx4kW5oueq2nVw6xSG0KjyQDqkupjBacLEMGOI0bJZLVKuzKTI10GZQAGDyJKCrI5lQ0HirGpWw0WmLU44X+kZDXZGrUSOXu3Ig7PGLHY12DOGlzQAZQadTjzpw0bDUnAwyhRHGP34WJKCrMZ6PRtvRkGEQBoWLL7TlpHF2LsL93DeJr9R04NRY6AeCs3YkfNHbjN219wBxHrokodjF4ElHUuNTwaVCJ+Hp5PlQI/E1tY0oS7sg2L2itdHEa7E78vqMfAKZtAhuPmk/0DuOIZVTxuohIWQyeRBRVZuxyn2P4XJ9qxEPLS3BdRgr0ogAVgFK9Fp8tyca3KwqgFjnaGUnP9Y2EXF4rAnimd1ihaogoUrjGk4iizvgud9lxcVOvpQYdvlCWiy+U5YapMrpUdXZH0K4DgH8U9KzdqVQ5RBQhHPEkIqKw04qz/7jRcA0uUdxj8CSiqCSbU1G7T7io6XaKXpenmUJu/hIBXGE2KVUOEUUIgycRRaXxtZ5n9uQxfMaBnZmpITsPiABuzU5TsCIiigQGTyKKWqU78zGsWgLZwTWbsS5No8IDSwsnTo6aGkB1goDvLC5AgV4bmeKISDHcXERERIqoNBrwf6sW4dUBC05YRiEDWJ6sx3UZqUhW80gpokTA4ElEUW18recywwkkr8dFH6dJ0cWgEnFzdhpu5rQ6UULiVDtRHHC4fRgZdUOW4u/kl0Rf6ynLMk/0IaK4wRFPohh2rGUIfz/ShtOdFgBAmkGDm1bl4c51BdDG0dRl6c58NO/eiDN7DmMZEmDkU5bx5pANT/QModbuhABglcmAu/LSsSnVGOnqiIguGUc8iWLUyzXd+OYzp1HbZZm4bdjhwd8OteIbT9XA7Q3Vrjv2JMzIpyzjt+19+H5jF+rGGqrLAKptDny9vgOPdg1Gtj4ionlg8CSKQUN2Fx56owEAcOHsuiQDtd1WPHuyKwKVhdd4+BxuroI01BPpcsLimMWBx3uGAUyeYw5Mnm/+h45+1POEH6LpLE2wHj2BluP9ka6EZsHgSRSDXqntxfRYMp0kA8+d6uTawBj0dO9QyG/MIoDn+oYVqoYoBoyFzuYDanQ6r0DJPVWRrohCYPAkikEtA6OzXtNvc8PllWa9LhZ11g/D3tgVl9Pt5+xOhPqoSQDqbBzxJJrK3m5m6IwRDJ5EMUivFhH8DBg/UQDUYvydfR3vaz01c/iYaePw40o0X7I5NdIl0BwweBLFoM3lGfCFmEYXBQEbS9OhUsXnl3g8r/W8PM0U8huzAODytGSlyiEiWlDx+VOJKM6tKzajLDMJojBz5Mt/i4y7NhQqXRYtgNty0qAKMqApADCIAnZlpShaExHRQmHwJIpBoijg27cuR1lmEgBAJQhQCf7Jd41KwJd2VaIyN/7DSTyu9czTafHdikLop/xSMf6WUSXiB0sKYdawBTPROOvRE+hrtUa6DJojQZajd9urxWJBamoq3tnbAJOJU0tEM8gyTraP4J3GAbh9EkozjNhemQ2jLjGCSfPuThQ4XkDp5V4kr18TV03lrV4fXhkYQY3Vv5FobUoSrslIgSFOl08QXQrr609yN3sUsNmsuGxrOUZGRpCSEnrQIzF+OhHNgyzJsLm80KhE6LVRdhqQIGB1URpWF6VFupKI8J9odCOy2t+CcVEPxDgKnslqFe7ISccdOZGuBJBkGZ0uDyQZyNOpoREZfinypJaDY7vZVzF0xhAGT6Ig3F4fnjregedOdmHI4QEArMhPwbs3FGFtiTnC1dFUHm8ugOFIlxF/ZBlP9Q7jH91D6Pd4AQAmlYibs9Pwgbx0BlCKOI83l7vZYwy/axAF4PH68I2nT+Mv77ROhE4AONNlwTeeOY2Xa7ojWB1dKB7XekaDX7b04tdtfROhEwBsPgmPdg3ia+c64L3w2CwiolkweBIF8NypLpzpsgQ8jhIAHnqjAUM2l/KF0QylO/MxrFqC5gPquOzrGSm1Ngee6x8JeJ8M4ITNgVcGLMoWRUQxj8GT6EKyjGdPdc5y2qSMPbXx1T8ylpXcU4VO5xWwt5vjrq9npDzfNzxrP9Fne4cVqoZoJs/QCKz9PMUr1jB4El3A7ZPQZ3XPel3rgEOBamiu9KsLMNidFuky4kab0xPy6E4ZQKdr9q8TonBwndiNjreANksxSnfmR7ocuggMnkQXUIsiZj+RUIBOzS+faGMdcvrXetK8mVTiLIeyAkmqKOvykOBkWYbbJ2GW6ZqYNx4663s3cjd7DOKudqILiKL/uMnDzUOQgnwDl2QZWyrSFa6MQsmpSkHLySVoPtCHUjwZd309lbYtIwVHLKNB7xcB7Mhgf+Vo0Oxw4e9dg9g7aIUXgFmtws1ZaXhXrjnu+r4ydMa++PqMJFogd28oBCAHHPERBQGlGUlYV8zgGW3G13r21STB1Xgu0uXEtK1pJhTqNAF/SIgAklQibs1KU7gqulC1dRT3nWnB62OhEwCGvD78X9cAPl/bCrsv1IKJ2CK1HMRQtY6hM8YxeBIFsDQ3BV/eVQmN2n8M5fiRlABQmpmE79y6HOLs8/EUAfrVBbC4VkS6jJinVYn40dIiVCTpAPh/WIxPrGdp1fjx0iJk6TQRq48AryTjew1d8MqYsR5XBtDidON/2/sjUVrYsG9n7ONUO1EQWyoy8XBRGl6v60Vzvx0alYjLFqVjdWEaBIbOqDbU54B7aBS6SBdygT6XB0/1DuGVAStGfT7k67S4OTsNuzJTorIZe4ZWjV9WFeOM3YmjI6OQIKPSqMfGVCNEgV8Dkfb2sA3DXl/Q+yUAL/UP4yOFmXEx5e7fxa6f/A2IYhKDJ1EIRp0aN6/mjslY4l/rmY7mA2r/Ws/td0S6JABAg92JL55rg8MnT4xONTvd+FVrL14ftOCBxYXQRWM4EAQsMxmwzGSIdCV0gUaHCyoAwaMn4JKBbpcHZUnR9mvYxXGd2I3e6j60WTai9B5+T45lUfhdjohofqat9TyxO9LlQJZlfKehc1ronOqMzYmHO+NrSpTCTysImMv+dV2Mz9CMh84z527k2s44wOBJRHEpmtZ6HrGMotvtDdoXUwbwfN8IXBHYCGL3SehyueGIo00oieKyNFPIXqsAUKDTID+G1+IydMYfTrUTUdwa6ouOJv91NuesU6IOSUanglOiTaMu/G/nAA4O2yDDv2zuarMJHyrMRJ5Oq0gNND+LknRYn5KE45bRoAH0vfkZQIyux5VaDjJ0xiGOeBJRXMqpSoFbm45zz7sjPt2uFjCnKVGVQgGhzubAZ2pb8M5Y6AT8oXjvkA33nWlFm4MnEsWKryzKQ5VJD8D/y4OAyR/sH8rPwHUZKZEqbd48QyMY6NkO/eqCSJdCC4jBk4ji1vhaz463ENHwuT7VOOuUaKZajUK9AlOisowfNXXDE6AFjwRg1Cfh5y3d4a+DFoRJrcJPlxbhwcWFuD4zFVeZTXh3Xjr+tLIU78vPiHR5RDNwqp2I4pp+dQEszQ4UoCZiNSwx6rHcpEetzRk0gN6dZ1akRVGNzYEOlyfo/RKAapsTHU43CvScco8JgoC1qUlYm5oU6UoWzPjazp4RA0qqYnfUlmbiiCcRkQK+Xp6P4rEgNx4vx78B35qVituz0xSpo80ZPHROv47T7RQZ3FAU3zjiSURxzd/X04CUt4AC7IZuzc6I1GHWqPHQsmLsG7Jh75ANFq8XhXotbshKRaVRuR6Zhjm21jFEYUN7in8MnfGPwZOIFkS/zYVXzvSga8QBo1aNq5dkojI3JSp21JbcU4X6RwD78/uxJILhUy2K2JaRgm0R3PCxIdUIjQB4Qux2SlGLWM6G8aQwhs7EwOBJFOX6bS68XNONc702aEQB60vN2LYkG3pt9Jwb9/jRdjz8dvPYe/7z7Z891YV1RWm4/8aqqKi15J4qtDwCIMLhM9JMahXelWPGI91DQa95f14G1DHedJxiC0Nn4uBcClEUe6OuFx/70xE8eqQNR1uG8E7zIB56vQEff/gImvrskS4PAPBGXQ/+dKAZkoyxPzJ8sn847UT7MH6652yEK5w0vss9GlosRdKHCjJxW1YaAP8PATUm2/B8MC9DsfWmRABDZ6LhiCdRlDrbbcFPXzkHecqU6PjbVqcHX3+6Br+7d0NkRxNlGX871AYBgftUSjLwduMg2gdHUZgeHTtuS+6pQtfDg1iChkiXEjGiIOBfS7JxR24a3hiwYtjrQ6ZWjWvSU5Cu5Y8FUg5DZ+LhdxiiKPXU8U4IECAHiHSSDIw4PHjjXB92rciNQHV+HcMOdI44Q14jCsA7TYNREzxpUp5O6z/ZhigCGDoTE6faiaLU4ZZBSHLwHSACgCPNA8oVFIDLO/v53gIEuL2hDotUnlubjt7qvoSebieKBNeJ3RN/GDoTE0c8iaKUzxf6kEUZgMc7l4MYwyc3RQ+NKMAjBa/DJ8soyTAqWNXsSu6pwplHAM/w4Yi2WCJKJK4Tu9HxFtDSXg4AcGs3MnQmIAZPoiBkScbJ9mG0Do5Cr1ZhY5kZZqNOsdcvzzahvseKYJlOFIAlucmK1RNIkk6NHZXZ2FPbG3B0VhSAZL0Gm8rSI1BdaOMtljI7z0K3JtLVEMU314ndOPe8G53OK1ByL8NmImPwJAqgtnMEP3n5HHqtLoiCf02l+Aawc3kuPnnVIqjV4V+lcuvqfPx4d6gd4QJ2Lo/c+s5xH7q8FDWdFnSNOKaFZFEQIArAf+xcCrWKq3qIEtW00MkRzoTHnwZEF2jqs+NrT9Wg3+YCgIkwJcnAS6e78YvX6hWp4+rFmdi5LAeAf+RwnCgIEATgc9dWIDNZuRHYYJINGvzk7tW4c10hjGM77EVBwOXlGfjPd6/GqqK0yBY4i/4GC9d6EoUJQyddiCOeRBd45HArvBICTnHLMvD62T7cvb4QReFetygIuG9HBVYVpeGZE50432uDSgVsLE3H7WsL/KcCRQmTXo0PXV6KezeXwO72Qa8WFRkVnq/x6XZN9QvI5lpPogXF0EmBMHgSTeH0+HCwcSDoukrAP5q391w/PrBFgQ0zgoCrl2Th6iVZ4X+tBSCIAkz62Pq2Mr7RKK1wL3SWJiClLNIlEcUmSxNcjecAAO6hUTQfUDN00gyx9ROCKMxGXd6QoRPwtzGyOD2K1EPKkR2RXy9LFLMsTbAePYG+miRYXCsw1OeAW5vO0EkzMHgSTZGsV0OrEuAO0cpIhozsKFhbSQurv8ECXb4FujUc8SS6KGOh0z/CuRH61QUQS4GSquhZDkTRI/oXYcWhhl4bnjnRgWdOdKCh1xbpcmgKjVqFHVU5EAUh5HXbK7MVqoiUUHJPFVray9lUnuhiTQud/mn1nKoU5DB0UhAc8VTQgM2FH75Uh9ou68QuZUkGqvKS8aVdlcgwcRQtGtyzsQjvNA5gxOEN2JvyfZuK4/9jJcvos7nh8niRadJH9jx4heTdewXOPwykFVZzrSdRKJYmSEM9AAB7YxfXctJFYfBUiMPtw5cfr0avdXqLHgA4223D/Y9X4xfvXZsQP+CjXYZJh5/cvRq/fbMRh5oHMZ49M4wa3LOpGLuioHdmOB1sGMBfD7WgqX8UAKBVCdhRlYMPbi5BikET4erCy61N51pPolCmrOV0OnNgHcrDsGoJQyfNGYOnQl49040eixOBVg5KsoxuixOv1fXgxlX5itdGM2Wn6PG1m5dh0OZC+7ADBrUK5dkmiGLoKfhY91JNNx56/TymrjRw+2S8fLoHp9qG8ZO7VyM5zsMn13rSbLySDECGWkyQ1WoBRjg7DNfCUGYCyriWky4Og6dCXqvrm/2a2j4GzyiTbtIhPd6n1cdYHB78Zm8DAODCFQb+X45cePRIGz5+1aIIVKcM/eoCtBwehKb6IPt60gwHhqz4R/cQztidAIBygw535ppxbXoyMMu68Jg1NsI53Owf0eysB4YyN6J0J39W0aVh8FSIxekJONo5TgYw7HQrVQ7RDK/VBT5vfZwky3j5dDc+vKU0JprDX4qcqhSgyr/WE2D4pEl/6RzAw50DmBovGx0u/LipG3VWB+4ryY758Cm1HJxxm72xC2f25GEoc7H/hkwwdNK8MHgqJDdVjz6bK2iPSFEA8lINyhZFNEXnsAMCBCDEr0gOjwSL0xP3o8DjG40YPgkAztmdeLhzAMD0r47xt5/tH8EmswmbUhU4VCJMpJaDGDjeCufQqmm3c4STFhqDp0J2rcjFyfaRoPdLMrAzzjetUHRL0qpDjsoD/ub5ek1ibIAbD5/65GPIWsRd7ons+d5hiACkIPeLAJ7uGYq54Dl1hHPgeCsaDxVPjmyO4wgnLTAGT4VsWZSBtUVpONk+PGPUUxSAtUVmXF6eEZniiABctTgTjx9rD3r/+Odpki5xvm24csrgswb/P6HEcM7uDBo6AX8gPT/qUqqcBXHhCCdHNkkpifMTJMJUKhFfv7kK/3ewFS/UdMHp8X8b02tE3LQiD+/fXBz3O6ZJOZIko6ZjBIN2N9KNWqwoSJ3186s824SNpWYcbRma8cvR+CPv2VQYnoKj2OiwC67Gc9zlnsC0qtnXNGujcH1noDWb4waOt6Lm7XVw5Yx9XnNkkxTC4KkgjVqFj1xZhvdeVozmfjsAoDTTmDBTlxdFlmFzeSHL/mMsY33RvpL2n+/H/7zZgAH75HnyGUYNPnF1Oa6oyAz52P/YVYmfvnwWbzcOQhQEiIK/dYxBq8IXrluCyrzUcJcfVQzFJrQcLucu9wR3RZoJZ+2B2+EB/qn2K9NNSpY0K9eJ3bA0j8xYsznufF0mxI0rUcpWSKQwBs8I0GtUqMzjF3tAsoxXanvxxLF2tA05AAAFaQbcvrYAu5bnMIDO4sD5fjz4Yt2M2wfsHjz4Yh3uv6ESl4cIn3qNCl+5aRnaBux4u3EADo+E4nQDrqjIhFadeL8g5VSloAcrcf6wf61nhvkgxJLNkS6LFLYrKxWPdg9i1CfNmHIXAKgF4NbsNOULszQFvNnVeA691X04X7N5ckTzAoaNJh5rSRHB4ElB2ZxenO4cgU+SUZFtQnaKPrwvKMv47ZuNePZU17SWJZ3DDjz0+nmc77Hivh0VDJ9BSJKM377VGPKa377ViM2LMmaddi/KMKIoI7Y2SoRLTlUKmlvL4BzyAhiOdDkUASlqFR5cUoiv1ndgxOvD+MS7BEAvCvhWRQHydFpFa3Kd2I2h6sDdJUaHgZb2zci79wpFayKaCwZPmsHt9eEP+5rx8ulueMYW+wkANpSa8ekdFTAbw9NKp7rDgmdPdQEI3LJk95kebCnPwPrS9IV/cVmO+UB7utOCAVvoXrADNjdOd1qwsjCxpswXgrXfqcjr2O2jSMm4DABgGXgHRmOSIq9LoS026vHnlWXYO2TFCcsoJADLTQZcm5ECwxzWgM4qyOhlIK7Gczj3vBudzo2QzYG/lkvv5XpNik4MnjSNLMl44MU6HLtgg4kM4GjLML70WDV++p41MOkX/lPnheouiIIQtIm5KAh4oaZrwYKnw+3Dc6c68UJ1F/ptbiRpVNhemY3b1xQgNy3Mo7thMGif267auV5HkwzFJvQcNiH5zRoUXA1OtyconUrE9ZmpuD5zgX9xGzsdSHbMraVey3E3Op1X8Hx0ikkMnjTNyfZhHGkeCnifJMvosTrxYk0X7t5QtOCv3dRvn/XknKY++4K8ls3pxZcfP4W2odGJgD3q8eHFmm68XteLH9y5EuXZ0bVZYDbpcxyJzgjTiHU8m7rWEzjI8EkXNUI5m77XDqPxUDGGVUvm+IilDJ0Usxg8aZpXzvSGHHWUZGD36e6wBE+9ZvbpqoXqAPCnA01oG3LMaBskyTKcXgkPvliH335wPYQYanG1PD8FmSYtBmzugLtvBQAZJi2W53NDwaVg+KRx1tefnPPo5GxGB1xoqS1mD01KGAyeNE2/zRly1BEAhkbDc6b8lRWZY6Oege8XBf8182V3efFabfBzySVZRrfFiZPtw1hTbJ736ylFFAV88qpyPPBi7YyDL8fj8yevKo+pMB1tLgyf2StHFrzFkt03uW/a4pXALV5hMI/RSuvRE2g+oEanc+mClSNnpjJ0UsJg8KRpMkw6iIItZPg0G8Kze/P65bl44ngH7C5vwNOdDBoVblgx/1GGjiHHxKapYERBwPleW0wFTwDYUpGB+2+owv/sa0CfdfIXhMxkLT5xZTm2VPB0rPmaET4XqL+nyyfhd+39eLate+K2D59qxK6ibPxzUfbCbGAhSC0HYW/suuQRy5bjaq6vJJoHBk+aZkdlNt6s7w96vygA1y3LCctrpxg0+MEdK/GtZ05jwO6GamyXuU+WkaLX4Ju3LIfZNP/1iWrVXEb8ZKhj9Af9looMbF6UjtOdFgyNumFO8k+vc6Rz4cyYdp9nf0+fJOOb5ztwwuqAd8rvRD4Au/staHa48ZOlhdCIsfk5qZhZRjKloZ6JE3vc2kvdpMj1lUTzweBJ06wrNoc4U15ApkmLm1blhe31SzON+N2HNuBg4wCqO0Ygy/61i5eXZ0CzQA3MS9KTkJ6kweCoJ+g1kuxvHxWrBFHACrZMCqvx8GltsgGYX6ulA8M2HLc6At4nA6izO/HqoBW7Fno3dRxxndgN99BoyJHM0QEXamvWQdy4EiVsnk4UEQyeNI0gCvjazVX47d5GvFLbC9+UKffVhan47LWLYdJrwlqDWiXiysVZuHJxVlieX6UScdf6Qvz2rcCjI6IgYENJGgrN7J9IC89uH51x2zOtPZAc/iMZfc7Jdlfjbwtj11xlmPm1xz6f/tDZW92HM+dunPVa/cYCnthDFEGCLM+ykySCLBYLUlNT8c7eBphMyZEuJ+GMjLpR3eE/uWhJTjLy0gyRLmnhyDJ+t68JT5/onNjFP/73srxkfOOW5TDq+HsZhdZTa4F0uBoVKw6i4OqKOU23q/QrF7QGn7N6QZ8vKo31uQymryYJ9b0bOQVOFCE2mxWXbS3HyMgIUlJC/2LHn6wUVGqSNmyjjhEnCPj4VYtwbVUO9pzpRo/FCZNOg6uXZGFtURrXQ0aI3eXFqbZhuLwSSjONKM2M7j3dbLGkgLHQ6d9JHvwISIZOotjA4EkJrTTTiE9cXR7pMhKezyfhzwdb8ezJDrh9k5MwS3OS8blrF6MwPXqnky82fFoG3plx22sDVvy8pQeAf3r9zVs+CgC4+tk/QKX3b6j7eGEmbslOW+jyo4OlCa7GcwHvcg+NToROhkui2MfgSUQR99Dr5/FKbe+Mxvf1vTZ88bFT+Pk9a5CdEr3HmF5M+Ay0JnOnXo8XrA40O6b3yFXpddAY9MjVqXFbcW58tlSyNE2c3DPqC9yn161NZ+gkihMMnkQUUc39duyp7Q14nyTLGHX78NjRdvzr9gqFK7s485l216pE/HBpEX7S1I0Djuk75FclG/ClsryYDp1Sy0F4hkYC3mdpHkHjIZ7cQ5QoGDyJKKJer5vtmFYZr9b24J+3lkOM8rW38wmfKWoVvrO4AA0ZJoyf2P2rqhJUxngLJanlIAaOt6Lj/BUY6gvcMsqVU8bQSZQgGDyJKKL8R7CGbq7h9slweHwx0Wkg0LGa4+ZywlGubrJlUlGA9knRzHVi94zbLM0jqHl7HcSNFci7gW2MiBJd9H8XJ6K4lp6kBWacLj+dVi3AoFmYAwSUMDV8DvT425Bl5Ly+YMdrRqPxXpoDPdun3T7U54C4cSV7ZxIRAAZPIoqwHVXZePx4R9D7RUHAdVW5UT/NfqHx8Dk+3jl02IGFPNs9EgKNaI7rre7D+ZrNEDdOX4srloKhk4gmMHgSUUQVZxixa3kOXjrdM+M+URCQrFfjrvWFEahs/qYFrqorcP5hIFbDp+vEbnS8BVhcKwLez5FNIpoLBk+iOCJJMg409OOlmm50DDuQrFdjx9IcXLssByZ99H65/8u2CqQlafHkiQ64PNLE7VV5yfjsNYuRmayLYHULJ+/e6eHzQhENoyF6aQLAuefd6HReAf3qgoD3c2STiOaCR2YSxQmvV8L3XqjF0ZYhiAIgyf6VkxCADKMWD965Cjmp0dsLEwCcbh9qOofh8sooSU+K6sbx89H18H6Ys6YfQZuiq0HBVREKn2OnA7UdKYNbmHla2VCfg700iSgoHplJlID+drgVx1qHAPhDJzC2XUcGBu0ePPBiLX72njWAEL1rJfVaFTaUZkS6jLDLu/cK9NRapt3Wc9IA+/P7sQS7oTEHbqEkmnOAlLI5vYbUcnDO9dgbu3BmTx6GMlfCUGya+bqlQAlHM4loATB4EsUBt9eH5052Idj8hSTLaOizo67bgsq82O4LGS9mTEtXpaDlEaD/b4PIybTNuF6v70HWii4kr8es4XO8d2Zf/ZKQ143r6V/JXppEpAgGT6I40DbowKjHF/IaUQBqOhg8o1nJPVXoqbWgL8B9jlYb7AdeQClOwLho5kasqQaOt471zlw5p9cVy4BSjmgSkQIYPIkSSvROs5Nf0A06VSlo3n0jRvccRrI59Frdnv513GFORFGJwZMoDhSnG2DUqmB3Bx/1lGRgZSGDSCwr3ZmPntrtcM5ynVjGHeZEFJ0YPInigEatwi2r8/Ho4baA5/+IgoCKLCOW5rA7RKxjoCSiWCZGugAiWhjv2ViEy8rSAfiDJuCfWBcAZJm0uP/Gqqje0U5ERPGPI55EcUKtEvGVG6vwTtMgdp/pRtewEyadGtsrs7CjMgcGbeycdU5ERPGJwZMowjxeH061j8Dm8iI/1YDFOaZLHpkURAGbyzOwuTz+e2ESEVHsYfAkihRZxnMnO/GXQ22wubwTN5ekG3DfjsWozIvOtXxDdhd21/Rg3/l+uLwSyrONuGllPlYWpHAqn4iIQmLwJIqQx4914E8Hmmfc3jbkwFeeqMaP7lqFiijbDHS+x4qvPlUDp8c3cTpSr9WF/ecHcNuafHz8yjKGTyIiCoqbi4giwOb04v8OtgS8T5IBnywHDKWR5Pb68K1nz0wLnYD/VCQAePpEJ944G6j1ORERkR+DJ1EE7DvfD58U5HxL+MPnyfYRDNhcClYV2r76fow4PAhWtigAT57oULYoIiKKKWEJns3NzfjYxz6GsrIyGAwGlJeX45vf/Cbcbnc4Xo4o5gzZXRMtj0Jf51Ggmrmp6RiBKkTNkgw09tnhnOXoTiIiSlxhWeNZV1cHSZLwm9/8BhUVFaipqcEnPvEJ2O12/OQnPwnHSxLFFLNRNzFFHfo6jQLVzI089mdOFxIREQUQluC5a9cu7Nq1a+L9RYsW4ezZs/j1r3/N4EkE4MqKTPx2bwM8QeatRQFYWZCKDJNO4cqCW56fildqe4PeLwpASUYS9OwXSkREQSi2xnNkZATp6ekhr3G5XLBYLNP+EMUjk16N911WEvA+UQBUgoAPX16qbFGzuHpJJlL0aohBZtslGbhjbYGyRRERUUxRJHieP38ev/zlL/FP//RPIa974IEHkJqaOvGnqKhIifKIIuKu9QX4p6vKYNJNn3goTDPgB3euiLpWSlq1Ct+8ZTn0atW08Dm+VvW21XnYvjQ7QtUREVEsEGR5DgvNxnz5y1/GD3/4w5DX1NbWorKycuL9jo4ObN26Fdu2bcPvfve7kI91uVxwuSZ38VosFhQVFeGdvQ0wmaLrhzDRQhk/ucju9iEvRT+vk4uUMGRz4cWabhxo6IfTI6E824QbV+ZhdWFqVNdNREThYbNZcdnWcoyMjCAlJfThJxcVPPv6+jAwMBDymkWLFkGr1QIAOjs7sW3bNmzevBl/+tOfIIoXN8BqsViQmprK4ElEREQUpS4meF7U5qKsrCxkZWXN6dqOjg5s374d69evxx//+MeLDp1EREREFF/Csqu9o6MD27ZtQ0lJCX7yk5+gr2/yNJPc3NxwvCQRERERRbmwBM89e/bg/PnzOH/+PAoLC6fddxEz+0REREQUR8Iy//3hD38YsiwH/ENEREREiYkLL4mIiIhIEQyeRERERKQIBk8iIiIiUgSDJxEREREpgsGTiIiIiBTB4ElEREREimDwJCIiIiJFMHgSERERkSLCcnIREUUvWZLRPjQKjyQjP9UAvVYV6ZKIiChBMHgSJQpZxos13fjH0Tb0Wd0AAK1awPVVufjglhIk6fjtgIiIwos/aYgSxP++3YLHjrZPu83tlfFCTRfOdFvww3etgl7D0U8iIgofrvEkSgBtA/YZoXOcJAPN/XY8d7JT4aqIiCjRMHgSJYDdZ3ogCkLQ+yUZeLGmW8GKiIgoETF4EiWA7hEHJFkOeU2v1QXMcg0REdF8MHgSJQCjVh1yxBMADBoRmOUaIiKi+WDwJEoAVy3JCjniKQoCti3NVrAiIiJKRAyeRAlgfbEZS3OSA456ioK/rdIdawsiUBkRESUSBk+iBCCIAr5163KsLkwF4A+bqrEQmm7U4vu3r0RemiGSJRIRUQJgH0+iBGHSq/Gd21egud+OI81D8PoklGcbsb4kHaLItZ1ERBR+DJ5ECaY004jSTGOkyyAiogTEqXYiIiIiUgSDJxEREREpgsGTiIiIiBTB4ElEREREimDwJCIiIiJFMHgSERERkSIYPImIiIhIEQyeRBHi80mQpODnpxMREcUbNpAnUpIsY9/5fjx1vBNne6wQACzLS8btawuxuTwj0tURERGFFYMnkYL+uL8ZTxzvwPgJlTKA2m4rTr9Qi/dtKsJ7LyuJaH1EREThxKl2IoWcbBvGE8c7AABTZ9jH3/7roTac7bZEoDIiIiJlMHgSKeS5U50QBSHo/aIg4MXqLgUrIiIiUhaDJ5FCzvfaIMnBNxNJsoxzPTYFKyIiIlIWgyeRQjSq2b/ctGp+SRIRUfziTzkihVy+KGOWqXZgyyLubCciovjFXe1ECrlxVR6ere6E1zd9cxHgD516tQo7l+fO+jxer4S99X14qaYbvRYnUgwaXFOVg+uW5cCo45c0ERFFL454EikkO0WPb92yAjq1CAH+sDneVilJq8Z3bl+ONKM25HO4PD587alq/OyVepzrsWJw1IOWgVH8YV8TPvu34+i3usL/DyEiIrpEHB4hUtDKwlT86SOb8PrZXpzutECAgJWFqdi2JAt6rWrWx/9pfzNqu60AJkdNxwdP+2xu/Gh3HX501+owVU9ERDQ/DJ5ECkvSqXHTqnzctCr/oh436vLi5TPdM6bpx0myjNouKxr7bFiUZVqASomIiBYWp9qJYkRjnx1uX+iz3QUBONPJJvRERBSdGDyJYkSIDfGT5DleR0REFAEMnkQxojzbBL0m9JesDGBVQaoyBREREV0kBk+iGKHXqHDTirygI5qiIGBNUSqKMozKFkZERDRHDJ5EMeT9m4uxvsQMABPN6MeDaEGaHv92/dJIlUZERDQr7moniiEatQp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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "best_svm = grid.best_estimator_\n", "fig, ax = subplots(figsize=(8,8))\n", @@ -1297,7 +633,7 @@ " ax=ax)\n", "\n", "y_hat_test = best_svm.predict(X_test)\n", - "confusion_table(y_hat_test, y_test)\n" + "confusion_table(y_hat_test, y_test)" ] }, { @@ -1344,29 +680,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "0607fc41", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.558386Z", - "iopub.status.busy": "2023-07-26T00:00:09.558248Z", - "iopub.status.idle": "2023-07-26T00:00:09.656670Z", - "shell.execute_reply": "2023-07-26T00:00:09.656343Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "roc_curve(best_svm,\n", @@ -1374,7 +691,7 @@ " y_train,\n", " name='Training',\n", " color='r',\n", - " ax=ax);\n" + " ax=ax);" ] }, { @@ -1389,28 +706,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "5211a882", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.658742Z", - "iopub.status.busy": "2023-07-26T00:00:09.658594Z", - "iopub.status.idle": "2023-07-26T00:00:09.805114Z", - "shell.execute_reply": "2023-07-26T00:00:09.804797Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "svm_flex = SVC(kernel=\"rbf\", \n", " gamma=50,\n", @@ -1422,7 +721,7 @@ " y_train,\n", " name='Training $\\gamma=50$',\n", " color='r',\n", - " ax=ax);\n" + " ax=ax);" ] }, { @@ -1438,16 +737,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "12acc4ff", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.807019Z", - "iopub.status.busy": "2023-07-26T00:00:09.806879Z", - "iopub.status.idle": "2023-07-26T00:00:09.811100Z", - "shell.execute_reply": "2023-07-26T00:00:09.810783Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "roc_curve(svm_flex,\n", @@ -1456,7 +748,7 @@ " name='Test $\\gamma=50$',\n", " color='b',\n", " ax=ax)\n", - "fig;\n" + "fig;" ] }, { @@ -1469,28 +761,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "21c81913", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.812971Z", - "iopub.status.busy": "2023-07-26T00:00:09.812862Z", - "iopub.status.idle": "2023-07-26T00:00:09.916772Z", - "shell.execute_reply": "2023-07-26T00:00:09.916366Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", "for (X_, y_, c, name) in zip(\n", @@ -1504,7 +778,7 @@ " y_,\n", " name=name,\n", " ax=ax,\n", - " color=c)\n" + " color=c)" ] }, { @@ -1524,35 +798,17 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "2fff4fa8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:09.918766Z", - "iopub.status.busy": "2023-07-26T00:00:09.918630Z", - "iopub.status.idle": "2023-07-26T00:00:10.011719Z", - "shell.execute_reply": "2023-07-26T00:00:10.011390Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "rng = np.random.default_rng(123)\n", "X = np.vstack([X, rng.standard_normal((50, 2))])\n", "y = np.hstack([y, [0]*50])\n", "X[y==0,1] += 2\n", "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n" + "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);" ] }, { @@ -1565,29 +821,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "5396f2df", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:10.013499Z", - "iopub.status.busy": "2023-07-26T00:00:10.013374Z", - "iopub.status.idle": "2023-07-26T00:00:10.566891Z", - "shell.execute_reply": "2023-07-26T00:00:10.566514Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "svm_rbf_3 = SVC(kernel=\"rbf\",\n", " C=10,\n", @@ -1599,7 +836,7 @@ " y,\n", " svm_rbf_3,\n", " scatter_cmap=cm.tab10,\n", - " ax=ax)\n" + " ax=ax)" ] }, { @@ -1629,31 +866,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "f63c575e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:10.568858Z", - "iopub.status.busy": "2023-07-26T00:00:10.568713Z", - "iopub.status.idle": "2023-07-26T00:00:10.643719Z", - "shell.execute_reply": "2023-07-26T00:00:10.643399Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((63, 2308), (20, 2308))" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "Khan = load_data('Khan')\n", - "Khan['xtrain'].shape, Khan['xtest'].shape\n" + "Khan['xtrain'].shape, Khan['xtest'].shape" ] }, { @@ -1670,108 +889,20 @@ "large number of features relative to the number of observations. This\n", "suggests that we should use a linear kernel, because the additional\n", "flexibility that will result from using a polynomial or radial kernel \n", - "is unnecessary. " + "is unnecessary." ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "32091338", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:10.645715Z", - "iopub.status.busy": "2023-07-26T00:00:10.645573Z", - "iopub.status.idle": "2023-07-26T00:00:10.679454Z", - "shell.execute_reply": "2023-07-26T00:00:10.679131Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Truth1234
Predicted
18000
202300
300120
400020
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" - ], - "text/plain": [ - "Truth 1 2 3 4\n", - "Predicted \n", - "1 8 0 0 0\n", - "2 0 23 0 0\n", - "3 0 0 12 0\n", - "4 0 0 0 20" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "khan_linear = SVC(kernel='linear', C=10)\n", "khan_linear.fit(Khan['xtrain'], Khan['ytrain'])\n", "confusion_table(khan_linear.predict(Khan['xtrain']),\n", - " Khan['ytrain'])\n" + " Khan['ytrain'])" ] }, { @@ -1789,101 +920,13 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "d9058023", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T00:00:10.681354Z", - "iopub.status.busy": "2023-07-26T00:00:10.681208Z", - "iopub.status.idle": "2023-07-26T00:00:10.694453Z", - "shell.execute_reply": "2023-07-26T00:00:10.694155Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Truth1234
Predicted
13000
20620
30040
40005
\n", - "
" - ], - "text/plain": [ - "Truth 1 2 3 4\n", - "Predicted \n", - "1 3 0 0 0\n", - "2 0 6 2 0\n", - "3 0 0 4 0\n", - "4 0 0 0 5" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "confusion_table(khan_linear.predict(Khan['xtest']),\n", - " Khan['ytest'])\n" + " Khan['ytest'])" ] }, { @@ -1898,20 +941,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "ipynb,md:myst", "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" } }, "nbformat": 4, From d5c88a4b76fe88a2d6a57d5f5e2b438e805fc0d4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 25 Jul 2023 22:02:13 -0700 Subject: [PATCH 048/195] also building helpers and datasets so they are populated on github --- docs/{fix_labs.py => fix_and_run_notebooks.py} | 10 ++++++++++ 1 file changed, 10 insertions(+) rename docs/{fix_labs.py => fix_and_run_notebooks.py} (84%) diff --git a/docs/fix_labs.py b/docs/fix_and_run_notebooks.py similarity index 84% rename from docs/fix_labs.py rename to docs/fix_and_run_notebooks.py index 7e69d15..b40cd02 100644 --- a/docs/fix_labs.py +++ b/docs/fix_and_run_notebooks.py @@ -59,6 +59,16 @@ os.system(f'jupytext --sync {base}.ipynb; rm {base}.md') + cmd = f'jupyter nbconvert --execute --inplace {nbfile}' + if labname[:3] == 'Ch2': + cmd += ' --allow-errors' + os.system(cmd) + + +for nbfile in glob('source/helpers/*nb') + glob('source/datasets/*nb'): + cmd = f'jupyter nbconvert --execute --inplace {nbfile}' + os.system(cmd) + # add a warning for ridge # at ## Ridge Regression and the Lasso From d44e6e6f48fbb9db4c2ad4368f92df7b271f96b9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 09:12:09 -0400 Subject: [PATCH 049/195] cleaned up labs, colab button added --- .gitmodules | 3 + docs/ISLP_labs | 1 + docs/fix_and_run_notebooks.py | 50 +- docs/source/conf.py | 2 +- docs/source/datasets/Auto.ipynb | 48 +- docs/source/datasets/Bikeshare.ipynb | 48 +- docs/source/datasets/Boston.ipynb | 48 +- docs/source/datasets/BrainCancer.ipynb | 57 +- docs/source/datasets/Caravan.ipynb | 39 +- docs/source/datasets/Carseats.ipynb | 48 +- docs/source/datasets/College.ipynb | 48 +- docs/source/datasets/Credit.ipynb | 48 +- docs/source/datasets/Default.ipynb | 57 +- docs/source/datasets/Fund.ipynb | 39 +- docs/source/datasets/Hitters.ipynb | 48 +- docs/source/datasets/Khan.ipynb | 39 +- docs/source/datasets/NCI60.ipynb | 39 +- docs/source/datasets/NYSE.ipynb | 48 +- docs/source/datasets/OJ.ipynb | 48 +- docs/source/datasets/Portfolio.ipynb | 48 +- docs/source/datasets/Publication.ipynb | 48 +- docs/source/datasets/Smarket.ipynb | 48 +- docs/source/datasets/USArrests.ipynb | 173 +- docs/source/datasets/Wage.ipynb | 48 +- docs/source/datasets/Weekly.ipynb | 48 +- docs/source/helpers/cluster.ipynb | 63 +- docs/source/helpers/pygam.ipynb | 93 +- docs/source/helpers/survival.ipynb | 48 +- docs/source/helpers/svm.ipynb | 57 +- docs/source/installation.myst | 16 +- docs/source/labs/Ch10-deeplearning-lab.ipynb | 7566 ++++++++++++++++- docs/source/labs/Ch11-surv-lab.ipynb | 329 +- docs/source/labs/Ch12-unsup-lab.ipynb | 538 +- docs/source/labs/Ch13-multiple-lab.ipynb | 293 +- docs/source/labs/Ch14-surv-lab.ipynb | 995 +++ docs/source/labs/Ch2-statlearn-lab.ipynb | 1040 ++- docs/source/labs/Ch3-linreg-lab.ipynb | 338 +- docs/source/labs/Ch4-classification-lab.ipynb | 725 +- docs/source/labs/Ch5-resample-lab.ipynb | 248 +- docs/source/labs/Ch6-varselect-lab.ipynb | 529 +- docs/source/labs/Ch7-nonlin-lab.ipynb | 455 +- docs/source/labs/Ch8-baggboost-lab.ipynb | 329 +- docs/source/labs/Ch9-svm-lab.ipynb | 320 +- 43 files changed, 13872 insertions(+), 1281 deletions(-) create mode 100644 .gitmodules create mode 160000 docs/ISLP_labs create mode 100644 docs/source/labs/Ch14-surv-lab.ipynb diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..891a60a --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "docs/ISLP_labs"] + path = docs/ISLP_labs + url = https://github.com/intro-stat-learning/ISLP_labs diff --git a/docs/ISLP_labs b/docs/ISLP_labs new file mode 160000 index 0000000..322b7ec --- /dev/null +++ b/docs/ISLP_labs @@ -0,0 +1 @@ +Subproject commit 322b7ec7ac3906157f808da84c23b9388996f23c diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index b40cd02..13d7f51 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -4,14 +4,23 @@ for f in glob('source/labs/Ch14*'): os.remove(f) - +version = 'v1' +main = 'main' +os.system(f''' +cd ISLP_labs; +git checkout {version}; +cp * ../source/labs; +git checkout {main}; +pip install -r ../source/labs/frozen_requirements.txt; +pip install -r ../source/labs/torch_requirements.txt; +''') for nbfile in glob('source/labs/*nb'): base = os.path.splitext(nbfile)[0] labname = os.path.split(base)[1] colab_code = f''' - + Open In Colab @@ -40,36 +49,23 @@ if labname[:4] != 'Ch10': + # clear outputs for all but Ch10 os.system(f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') - os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') - - myst = open(f'{base}.md').read().strip() - - new_myst = [] - for l in myst.split('\n'): - if l.strip()[:9] != '# Chapter': - if 'Lab:' in l: - l = '# ' + l[6:] + '\n' + colab_code - new_myst.append(l) - - myst = '\n'.join(new_myst) # remove the "Chapter %d - - open(f'{base}.md', 'w').write(myst) - - os.system(f'jupytext --sync {base}.ipynb; rm {base}.md') + os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') - cmd = f'jupyter nbconvert --execute --inplace {nbfile}' - if labname[:3] == 'Ch2': - cmd += ' --allow-errors' - os.system(cmd) + myst = open(f'{base}.md').read().strip() + new_myst = [] + for l in myst.split('\n'): + if l.strip()[:9] != '# Chapter': + if 'Lab:' in l: + l = l.replace('Lab:', '') + '\n' + colab_code + new_myst.append(l) -for nbfile in glob('source/helpers/*nb') + glob('source/datasets/*nb'): - cmd = f'jupyter nbconvert --execute --inplace {nbfile}' - os.system(cmd) + myst = '\n'.join(new_myst) # remove the "Chapter %d + open(f'{base}.md', 'w').write(myst) -# add a warning for ridge -# at ## Ridge Regression and the Lasso + os.system(f'jupytext --sync {base}.ipynb; rm {base}.md') diff --git a/docs/source/conf.py b/docs/source/conf.py index d36b86a..f4521cd 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -28,7 +28,7 @@ graphviz_dot = '/opt/homebrew/bin/dot' numpydoc_class_members_toctree = False nb_execution_mode = "auto" -nb_execution_timeout = 60*3 #*100 +nb_execution_timeout = 60*20 #*100 nb_execution_excludepatterns = ['Ch10*'] nb_execution_allow_errors = True diff --git a/docs/source/datasets/Auto.ipynb b/docs/source/datasets/Auto.ipynb index b88ea02..b588844 100644 --- a/docs/source/datasets/Auto.ipynb +++ b/docs/source/datasets/Auto.ipynb @@ -44,7 +44,14 @@ "cell_type": "code", "execution_count": null, "id": "182ea1d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:43.883548Z", + "iopub.status.busy": "2023-07-26T12:47:43.883261Z", + "iopub.status.idle": "2023-07-26T12:47:44.433075Z", + "shell.execute_reply": "2023-07-26T12:47:44.432801Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -56,7 +63,14 @@ "cell_type": "code", "execution_count": null, "id": "979abd7e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:44.434662Z", + "iopub.status.busy": "2023-07-26T12:47:44.434558Z", + "iopub.status.idle": "2023-07-26T12:47:44.436577Z", + "shell.execute_reply": "2023-07-26T12:47:44.436322Z" + } + }, "outputs": [], "source": [ "Auto.shape" @@ -66,7 +80,14 @@ "cell_type": "code", "execution_count": null, "id": "7444c0f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:44.438047Z", + "iopub.status.busy": "2023-07-26T12:47:44.437943Z", + "iopub.status.idle": "2023-07-26T12:47:44.439951Z", + "shell.execute_reply": "2023-07-26T12:47:44.439712Z" + } + }, "outputs": [], "source": [ "Auto.columns" @@ -76,7 +97,14 @@ "cell_type": "code", "execution_count": null, "id": "59b6e919", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:44.441257Z", + "iopub.status.busy": "2023-07-26T12:47:44.441161Z", + "iopub.status.idle": "2023-07-26T12:47:44.449658Z", + "shell.execute_reply": "2023-07-26T12:47:44.449426Z" + } + }, "outputs": [], "source": [ "Auto.describe().iloc[:,:4]" @@ -91,6 +119,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Bikeshare.ipynb b/docs/source/datasets/Bikeshare.ipynb index ddb1053..ab42024 100644 --- a/docs/source/datasets/Bikeshare.ipynb +++ b/docs/source/datasets/Bikeshare.ipynb @@ -56,7 +56,14 @@ "cell_type": "code", "execution_count": null, "id": "bcdb89b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:19.462730Z", + "iopub.status.busy": "2023-07-26T12:47:19.461535Z", + "iopub.status.idle": "2023-07-26T12:47:20.022610Z", + "shell.execute_reply": "2023-07-26T12:47:20.022326Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -68,7 +75,14 @@ "cell_type": "code", "execution_count": null, "id": "72075fb0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:20.024144Z", + "iopub.status.busy": "2023-07-26T12:47:20.024034Z", + "iopub.status.idle": "2023-07-26T12:47:20.026016Z", + "shell.execute_reply": "2023-07-26T12:47:20.025777Z" + } + }, "outputs": [], "source": [ "Bikeshare.shape" @@ -78,7 +92,14 @@ "cell_type": "code", "execution_count": null, "id": "45396d69", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:20.027480Z", + "iopub.status.busy": "2023-07-26T12:47:20.027378Z", + "iopub.status.idle": "2023-07-26T12:47:20.029427Z", + "shell.execute_reply": "2023-07-26T12:47:20.029199Z" + } + }, "outputs": [], "source": [ "Bikeshare.columns" @@ -88,7 +109,14 @@ "cell_type": "code", "execution_count": null, "id": "26c24d9a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:20.030734Z", + "iopub.status.busy": "2023-07-26T12:47:20.030638Z", + "iopub.status.idle": "2023-07-26T12:47:20.042031Z", + "shell.execute_reply": "2023-07-26T12:47:20.041787Z" + } + }, "outputs": [], "source": [ "Bikeshare.describe().iloc[:,:4]" @@ -105,6 +133,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Boston.ipynb b/docs/source/datasets/Boston.ipynb index 569f5b4..027585a 100644 --- a/docs/source/datasets/Boston.ipynb +++ b/docs/source/datasets/Boston.ipynb @@ -49,7 +49,14 @@ "cell_type": "code", "execution_count": null, "id": "b8bb96f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:31.625524Z", + "iopub.status.busy": "2023-07-26T12:47:31.625196Z", + "iopub.status.idle": "2023-07-26T12:47:32.177553Z", + "shell.execute_reply": "2023-07-26T12:47:32.177240Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -61,7 +68,14 @@ "cell_type": "code", "execution_count": null, "id": "ab4b03f8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:32.179272Z", + "iopub.status.busy": "2023-07-26T12:47:32.179157Z", + "iopub.status.idle": "2023-07-26T12:47:32.181230Z", + "shell.execute_reply": "2023-07-26T12:47:32.180964Z" + } + }, "outputs": [], "source": [ "Boston.shape" @@ -71,7 +85,14 @@ "cell_type": "code", "execution_count": null, "id": "74890e1f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:32.182653Z", + "iopub.status.busy": "2023-07-26T12:47:32.182557Z", + "iopub.status.idle": "2023-07-26T12:47:32.184501Z", + "shell.execute_reply": "2023-07-26T12:47:32.184276Z" + } + }, "outputs": [], "source": [ "Boston.columns" @@ -81,7 +102,14 @@ "cell_type": "code", "execution_count": null, "id": "90ecf46f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:32.185826Z", + "iopub.status.busy": "2023-07-26T12:47:32.185735Z", + "iopub.status.idle": "2023-07-26T12:47:32.198310Z", + "shell.execute_reply": "2023-07-26T12:47:32.198074Z" + } + }, "outputs": [], "source": [ "Boston.describe()" @@ -98,6 +126,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/BrainCancer.ipynb b/docs/source/datasets/BrainCancer.ipynb index cb75946..89e8b2c 100644 --- a/docs/source/datasets/BrainCancer.ipynb +++ b/docs/source/datasets/BrainCancer.ipynb @@ -39,7 +39,14 @@ "cell_type": "code", "execution_count": null, "id": "519fa8cf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:09.619445Z", + "iopub.status.busy": "2023-07-26T12:47:09.618768Z", + "iopub.status.idle": "2023-07-26T12:47:10.149955Z", + "shell.execute_reply": "2023-07-26T12:47:10.149508Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -51,7 +58,14 @@ "cell_type": "code", "execution_count": null, "id": "ac7f1920", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:10.151658Z", + "iopub.status.busy": "2023-07-26T12:47:10.151541Z", + "iopub.status.idle": "2023-07-26T12:47:10.153944Z", + "shell.execute_reply": "2023-07-26T12:47:10.153658Z" + } + }, "outputs": [], "source": [ "BrainCancer.shape" @@ -61,7 +75,14 @@ "cell_type": "code", "execution_count": null, "id": "64b3177f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:10.155433Z", + "iopub.status.busy": "2023-07-26T12:47:10.155323Z", + "iopub.status.idle": "2023-07-26T12:47:10.157819Z", + "shell.execute_reply": "2023-07-26T12:47:10.157458Z" + } + }, "outputs": [], "source": [ "BrainCancer.columns" @@ -71,7 +92,14 @@ "cell_type": "code", "execution_count": null, "id": "8132496d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:10.159542Z", + "iopub.status.busy": "2023-07-26T12:47:10.159420Z", + "iopub.status.idle": "2023-07-26T12:47:10.166890Z", + "shell.execute_reply": "2023-07-26T12:47:10.166610Z" + } + }, "outputs": [], "source": [ "BrainCancer.describe()" @@ -81,7 +109,14 @@ "cell_type": "code", "execution_count": null, "id": "ed04719d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:10.168420Z", + "iopub.status.busy": "2023-07-26T12:47:10.168324Z", + "iopub.status.idle": "2023-07-26T12:47:10.171157Z", + "shell.execute_reply": "2023-07-26T12:47:10.170862Z" + } + }, "outputs": [], "source": [ "BrainCancer['diagnosis'].value_counts()" @@ -98,6 +133,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Caravan.ipynb b/docs/source/datasets/Caravan.ipynb index f093422..ab39457 100644 --- a/docs/source/datasets/Caravan.ipynb +++ b/docs/source/datasets/Caravan.ipynb @@ -27,7 +27,14 @@ "cell_type": "code", "execution_count": null, "id": "1f9a6aaa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:12.041705Z", + "iopub.status.busy": "2023-07-26T12:47:12.040979Z", + "iopub.status.idle": "2023-07-26T12:47:12.637566Z", + "shell.execute_reply": "2023-07-26T12:47:12.637297Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -39,7 +46,14 @@ "cell_type": "code", "execution_count": null, "id": "88755969", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:12.639146Z", + "iopub.status.busy": "2023-07-26T12:47:12.639031Z", + "iopub.status.idle": "2023-07-26T12:47:12.640881Z", + "shell.execute_reply": "2023-07-26T12:47:12.640666Z" + } + }, "outputs": [], "source": [ "Caravan.shape" @@ -49,7 +63,14 @@ "cell_type": "code", "execution_count": null, "id": "52ea2641", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:12.642281Z", + "iopub.status.busy": "2023-07-26T12:47:12.642186Z", + "iopub.status.idle": "2023-07-26T12:47:12.644243Z", + "shell.execute_reply": "2023-07-26T12:47:12.644020Z" + } + }, "outputs": [], "source": [ "Caravan.columns[:20]" @@ -66,6 +87,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Carseats.ipynb b/docs/source/datasets/Carseats.ipynb index dfd36d4..92ff1b4 100644 --- a/docs/source/datasets/Carseats.ipynb +++ b/docs/source/datasets/Carseats.ipynb @@ -37,7 +37,14 @@ "cell_type": "code", "execution_count": null, "id": "984643c9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:26.781289Z", + "iopub.status.busy": "2023-07-26T12:47:26.780964Z", + "iopub.status.idle": "2023-07-26T12:47:27.314225Z", + "shell.execute_reply": "2023-07-26T12:47:27.313885Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -49,7 +56,14 @@ "cell_type": "code", "execution_count": null, "id": "663f5f6a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:27.316055Z", + "iopub.status.busy": "2023-07-26T12:47:27.315854Z", + "iopub.status.idle": "2023-07-26T12:47:27.318176Z", + "shell.execute_reply": "2023-07-26T12:47:27.317912Z" + } + }, "outputs": [], "source": [ "Carseats.shape" @@ -59,7 +73,14 @@ "cell_type": "code", "execution_count": null, "id": "386299b2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:27.319606Z", + "iopub.status.busy": "2023-07-26T12:47:27.319504Z", + "iopub.status.idle": "2023-07-26T12:47:27.321648Z", + "shell.execute_reply": "2023-07-26T12:47:27.321403Z" + } + }, "outputs": [], "source": [ "Carseats.columns" @@ -69,7 +90,14 @@ "cell_type": "code", "execution_count": null, "id": "5c8c69c8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:27.323205Z", + "iopub.status.busy": "2023-07-26T12:47:27.323091Z", + "iopub.status.idle": "2023-07-26T12:47:27.331921Z", + "shell.execute_reply": "2023-07-26T12:47:27.331627Z" + } + }, "outputs": [], "source": [ "Carseats.describe().iloc[:,:4]" @@ -86,6 +114,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/College.ipynb b/docs/source/datasets/College.ipynb index af1027d..27a4d1d 100644 --- a/docs/source/datasets/College.ipynb +++ b/docs/source/datasets/College.ipynb @@ -58,7 +58,14 @@ "cell_type": "code", "execution_count": null, "id": "680ceb3e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:17.006699Z", + "iopub.status.busy": "2023-07-26T12:47:17.006226Z", + "iopub.status.idle": "2023-07-26T12:47:17.561114Z", + "shell.execute_reply": "2023-07-26T12:47:17.560739Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -70,7 +77,14 @@ "cell_type": "code", "execution_count": null, "id": "ccdf3e4f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:17.563075Z", + "iopub.status.busy": "2023-07-26T12:47:17.562947Z", + "iopub.status.idle": "2023-07-26T12:47:17.565074Z", + "shell.execute_reply": "2023-07-26T12:47:17.564824Z" + } + }, "outputs": [], "source": [ "College.shape" @@ -80,7 +94,14 @@ "cell_type": "code", "execution_count": null, "id": "09f59747", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:17.566389Z", + "iopub.status.busy": "2023-07-26T12:47:17.566297Z", + "iopub.status.idle": "2023-07-26T12:47:17.568257Z", + "shell.execute_reply": "2023-07-26T12:47:17.568025Z" + } + }, "outputs": [], "source": [ "College.columns" @@ -90,7 +111,14 @@ "cell_type": "code", "execution_count": null, "id": "6a48dfd5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:17.569585Z", + "iopub.status.busy": "2023-07-26T12:47:17.569492Z", + "iopub.status.idle": "2023-07-26T12:47:17.582384Z", + "shell.execute_reply": "2023-07-26T12:47:17.582154Z" + } + }, "outputs": [], "source": [ "College.describe().iloc[:,:4]" @@ -107,6 +135,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Credit.ipynb b/docs/source/datasets/Credit.ipynb index f5e51a9..d604aaa 100644 --- a/docs/source/datasets/Credit.ipynb +++ b/docs/source/datasets/Credit.ipynb @@ -43,7 +43,14 @@ "cell_type": "code", "execution_count": null, "id": "c4895446", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:39.024610Z", + "iopub.status.busy": "2023-07-26T12:47:39.024341Z", + "iopub.status.idle": "2023-07-26T12:47:39.593395Z", + "shell.execute_reply": "2023-07-26T12:47:39.593133Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -55,7 +62,14 @@ "cell_type": "code", "execution_count": null, "id": "c738c66b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:39.595074Z", + "iopub.status.busy": "2023-07-26T12:47:39.594871Z", + "iopub.status.idle": "2023-07-26T12:47:39.596893Z", + "shell.execute_reply": "2023-07-26T12:47:39.596667Z" + } + }, "outputs": [], "source": [ "Credit.shape" @@ -65,7 +79,14 @@ "cell_type": "code", "execution_count": null, "id": "d612f5a7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:39.598266Z", + "iopub.status.busy": "2023-07-26T12:47:39.598173Z", + "iopub.status.idle": "2023-07-26T12:47:39.600134Z", + "shell.execute_reply": "2023-07-26T12:47:39.599913Z" + } + }, "outputs": [], "source": [ "Credit.columns" @@ -75,7 +96,14 @@ "cell_type": "code", "execution_count": null, "id": "45633b1a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:39.601442Z", + "iopub.status.busy": "2023-07-26T12:47:39.601344Z", + "iopub.status.idle": "2023-07-26T12:47:39.609927Z", + "shell.execute_reply": "2023-07-26T12:47:39.609656Z" + } + }, "outputs": [], "source": [ "Credit.describe().iloc[:,:4]" @@ -92,6 +120,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Default.ipynb b/docs/source/datasets/Default.ipynb index 64357ef..8023d39 100644 --- a/docs/source/datasets/Default.ipynb +++ b/docs/source/datasets/Default.ipynb @@ -27,7 +27,14 @@ "cell_type": "code", "execution_count": null, "id": "ab810dee", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:36.566964Z", + "iopub.status.busy": "2023-07-26T12:47:36.566691Z", + "iopub.status.idle": "2023-07-26T12:47:37.127499Z", + "shell.execute_reply": "2023-07-26T12:47:37.127183Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -39,7 +46,14 @@ "cell_type": "code", "execution_count": null, "id": "086ef3a2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:37.129114Z", + "iopub.status.busy": "2023-07-26T12:47:37.129003Z", + "iopub.status.idle": "2023-07-26T12:47:37.131023Z", + "shell.execute_reply": "2023-07-26T12:47:37.130778Z" + } + }, "outputs": [], "source": [ "Default.shape" @@ -49,7 +63,14 @@ "cell_type": "code", "execution_count": null, "id": "6600c13b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:37.132471Z", + "iopub.status.busy": "2023-07-26T12:47:37.132373Z", + "iopub.status.idle": "2023-07-26T12:47:37.134281Z", + "shell.execute_reply": "2023-07-26T12:47:37.134067Z" + } + }, "outputs": [], "source": [ "Default.columns" @@ -59,7 +80,14 @@ "cell_type": "code", "execution_count": null, "id": "09e98840", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:37.135578Z", + "iopub.status.busy": "2023-07-26T12:47:37.135480Z", + "iopub.status.idle": "2023-07-26T12:47:37.141213Z", + "shell.execute_reply": "2023-07-26T12:47:37.140974Z" + } + }, "outputs": [], "source": [ "Default.describe()" @@ -69,7 +97,14 @@ "cell_type": "code", "execution_count": null, "id": "425f0cb1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:37.142597Z", + "iopub.status.busy": "2023-07-26T12:47:37.142519Z", + "iopub.status.idle": "2023-07-26T12:47:37.145148Z", + "shell.execute_reply": "2023-07-26T12:47:37.144915Z" + } + }, "outputs": [], "source": [ "Default['student'].value_counts()" @@ -86,6 +121,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Fund.ipynb b/docs/source/datasets/Fund.ipynb index fce1859..2e5dcb5 100644 --- a/docs/source/datasets/Fund.ipynb +++ b/docs/source/datasets/Fund.ipynb @@ -15,7 +15,14 @@ "cell_type": "code", "execution_count": null, "id": "5eba8e49", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:59.809785Z", + "iopub.status.busy": "2023-07-26T12:46:59.809389Z", + "iopub.status.idle": "2023-07-26T12:47:00.410897Z", + "shell.execute_reply": "2023-07-26T12:47:00.410627Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -27,7 +34,14 @@ "cell_type": "code", "execution_count": null, "id": "ced3b335", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:00.412492Z", + "iopub.status.busy": "2023-07-26T12:47:00.412385Z", + "iopub.status.idle": "2023-07-26T12:47:00.414444Z", + "shell.execute_reply": "2023-07-26T12:47:00.414168Z" + } + }, "outputs": [], "source": [ "Fund.shape" @@ -37,7 +51,14 @@ "cell_type": "code", "execution_count": null, "id": "bfff1ac6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:00.415891Z", + "iopub.status.busy": "2023-07-26T12:47:00.415789Z", + "iopub.status.idle": "2023-07-26T12:47:00.417755Z", + "shell.execute_reply": "2023-07-26T12:47:00.417529Z" + } + }, "outputs": [], "source": [ "Fund.columns" @@ -54,6 +75,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Hitters.ipynb b/docs/source/datasets/Hitters.ipynb index 6f261cd..5af634c 100644 --- a/docs/source/datasets/Hitters.ipynb +++ b/docs/source/datasets/Hitters.ipynb @@ -64,7 +64,14 @@ "cell_type": "code", "execution_count": null, "id": "4fa187f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:34.072657Z", + "iopub.status.busy": "2023-07-26T12:47:34.072382Z", + "iopub.status.idle": "2023-07-26T12:47:34.654518Z", + "shell.execute_reply": "2023-07-26T12:47:34.654230Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -76,7 +83,14 @@ "cell_type": "code", "execution_count": null, "id": "04535ffb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:34.656071Z", + "iopub.status.busy": "2023-07-26T12:47:34.655969Z", + "iopub.status.idle": "2023-07-26T12:47:34.657899Z", + "shell.execute_reply": "2023-07-26T12:47:34.657674Z" + } + }, "outputs": [], "source": [ "Hitters.shape" @@ -86,7 +100,14 @@ "cell_type": "code", "execution_count": null, "id": "6875aac6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:34.659335Z", + "iopub.status.busy": "2023-07-26T12:47:34.659236Z", + "iopub.status.idle": "2023-07-26T12:47:34.661182Z", + "shell.execute_reply": "2023-07-26T12:47:34.660944Z" + } + }, "outputs": [], "source": [ "Hitters.columns" @@ -96,7 +117,14 @@ "cell_type": "code", "execution_count": null, "id": "9e2cffc8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:34.662645Z", + "iopub.status.busy": "2023-07-26T12:47:34.662543Z", + "iopub.status.idle": "2023-07-26T12:47:34.674958Z", + "shell.execute_reply": "2023-07-26T12:47:34.674698Z" + } + }, "outputs": [], "source": [ "Hitters.describe().iloc[:,:4]" @@ -113,6 +141,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Khan.ipynb b/docs/source/datasets/Khan.ipynb index f12a5ca..c1ce7bf 100644 --- a/docs/source/datasets/Khan.ipynb +++ b/docs/source/datasets/Khan.ipynb @@ -43,7 +43,14 @@ "cell_type": "code", "execution_count": null, "id": "bfda6cad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:53.879692Z", + "iopub.status.busy": "2023-07-26T12:46:53.879072Z", + "iopub.status.idle": "2023-07-26T12:46:54.473904Z", + "shell.execute_reply": "2023-07-26T12:46:54.473562Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -55,7 +62,14 @@ "cell_type": "code", "execution_count": null, "id": "70514dc5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:54.475443Z", + "iopub.status.busy": "2023-07-26T12:46:54.475340Z", + "iopub.status.idle": "2023-07-26T12:46:54.477103Z", + "shell.execute_reply": "2023-07-26T12:46:54.476883Z" + } + }, "outputs": [], "source": [ "for X in ['xtest', 'xtrain']:\n", @@ -66,7 +80,14 @@ "cell_type": "code", "execution_count": null, "id": "e9df5de8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:54.478408Z", + "iopub.status.busy": "2023-07-26T12:46:54.478336Z", + "iopub.status.idle": "2023-07-26T12:46:54.480540Z", + "shell.execute_reply": "2023-07-26T12:46:54.480299Z" + } + }, "outputs": [], "source": [ "for Y in ['ytest', 'ytrain']:\n", @@ -84,6 +105,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/NCI60.ipynb b/docs/source/datasets/NCI60.ipynb index bbb576f..b38f981 100644 --- a/docs/source/datasets/NCI60.ipynb +++ b/docs/source/datasets/NCI60.ipynb @@ -26,7 +26,14 @@ "cell_type": "code", "execution_count": null, "id": "c88c2eaf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:07.189429Z", + "iopub.status.busy": "2023-07-26T12:47:07.188891Z", + "iopub.status.idle": "2023-07-26T12:47:07.734853Z", + "shell.execute_reply": "2023-07-26T12:47:07.734392Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -38,7 +45,14 @@ "cell_type": "code", "execution_count": null, "id": "0e6279ad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:07.736643Z", + "iopub.status.busy": "2023-07-26T12:47:07.736477Z", + "iopub.status.idle": "2023-07-26T12:47:07.740295Z", + "shell.execute_reply": "2023-07-26T12:47:07.739954Z" + } + }, "outputs": [], "source": [ "NCI60['labels'].value_counts()" @@ -48,7 +62,14 @@ "cell_type": "code", "execution_count": null, "id": "ed5ddd2f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:07.741963Z", + "iopub.status.busy": "2023-07-26T12:47:07.741866Z", + "iopub.status.idle": "2023-07-26T12:47:07.744496Z", + "shell.execute_reply": "2023-07-26T12:47:07.744146Z" + } + }, "outputs": [], "source": [ "NCI60['data'].shape" @@ -65,6 +86,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/NYSE.ipynb b/docs/source/datasets/NYSE.ipynb index 5f9dbd5..4fb6ea5 100644 --- a/docs/source/datasets/NYSE.ipynb +++ b/docs/source/datasets/NYSE.ipynb @@ -33,7 +33,14 @@ "cell_type": "code", "execution_count": null, "id": "fcff6c95", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:24.365935Z", + "iopub.status.busy": "2023-07-26T12:47:24.365648Z", + "iopub.status.idle": "2023-07-26T12:47:24.910157Z", + "shell.execute_reply": "2023-07-26T12:47:24.909886Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -45,7 +52,14 @@ "cell_type": "code", "execution_count": null, "id": "84426961", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:24.911976Z", + "iopub.status.busy": "2023-07-26T12:47:24.911859Z", + "iopub.status.idle": "2023-07-26T12:47:24.913899Z", + "shell.execute_reply": "2023-07-26T12:47:24.913685Z" + } + }, "outputs": [], "source": [ "NYSE.shape" @@ -55,7 +69,14 @@ "cell_type": "code", "execution_count": null, "id": "e6194a8c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:24.915295Z", + "iopub.status.busy": "2023-07-26T12:47:24.915180Z", + "iopub.status.idle": "2023-07-26T12:47:24.917209Z", + "shell.execute_reply": "2023-07-26T12:47:24.916991Z" + } + }, "outputs": [], "source": [ "NYSE.columns" @@ -65,7 +86,14 @@ "cell_type": "code", "execution_count": null, "id": "0c7bf3d7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:24.918571Z", + "iopub.status.busy": "2023-07-26T12:47:24.918468Z", + "iopub.status.idle": "2023-07-26T12:47:24.924914Z", + "shell.execute_reply": "2023-07-26T12:47:24.924671Z" + } + }, "outputs": [], "source": [ "NYSE.describe()" @@ -82,6 +110,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/OJ.ipynb b/docs/source/datasets/OJ.ipynb index e18a4de..55ffeb9 100644 --- a/docs/source/datasets/OJ.ipynb +++ b/docs/source/datasets/OJ.ipynb @@ -61,7 +61,14 @@ "cell_type": "code", "execution_count": null, "id": "609742da", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:14.553008Z", + "iopub.status.busy": "2023-07-26T12:47:14.551694Z", + "iopub.status.idle": "2023-07-26T12:47:15.102658Z", + "shell.execute_reply": "2023-07-26T12:47:15.102334Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -73,7 +80,14 @@ "cell_type": "code", "execution_count": null, "id": "6f195dcd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:15.104419Z", + "iopub.status.busy": "2023-07-26T12:47:15.104301Z", + "iopub.status.idle": "2023-07-26T12:47:15.106415Z", + "shell.execute_reply": "2023-07-26T12:47:15.106177Z" + } + }, "outputs": [], "source": [ "OJ.shape" @@ -83,7 +97,14 @@ "cell_type": "code", "execution_count": null, "id": "aaafb83b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:15.107821Z", + "iopub.status.busy": "2023-07-26T12:47:15.107723Z", + "iopub.status.idle": "2023-07-26T12:47:15.109747Z", + "shell.execute_reply": "2023-07-26T12:47:15.109486Z" + } + }, "outputs": [], "source": [ "OJ.columns" @@ -93,7 +114,14 @@ "cell_type": "code", "execution_count": null, "id": "774dfa86", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:15.111066Z", + "iopub.status.busy": "2023-07-26T12:47:15.110974Z", + "iopub.status.idle": "2023-07-26T12:47:15.123225Z", + "shell.execute_reply": "2023-07-26T12:47:15.122965Z" + } + }, "outputs": [], "source": [ "OJ.describe().iloc[:,:4]" @@ -110,6 +138,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Portfolio.ipynb b/docs/source/datasets/Portfolio.ipynb index 6d6a60d..1a6d711 100644 --- a/docs/source/datasets/Portfolio.ipynb +++ b/docs/source/datasets/Portfolio.ipynb @@ -22,7 +22,14 @@ "cell_type": "code", "execution_count": null, "id": "3adff220", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:02.309375Z", + "iopub.status.busy": "2023-07-26T12:47:02.308873Z", + "iopub.status.idle": "2023-07-26T12:47:02.849537Z", + "shell.execute_reply": "2023-07-26T12:47:02.849247Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -34,7 +41,14 @@ "cell_type": "code", "execution_count": null, "id": "b02a9e67", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:02.851392Z", + "iopub.status.busy": "2023-07-26T12:47:02.851244Z", + "iopub.status.idle": "2023-07-26T12:47:02.853779Z", + "shell.execute_reply": "2023-07-26T12:47:02.853348Z" + } + }, "outputs": [], "source": [ "Portfolio.shape" @@ -44,7 +58,14 @@ "cell_type": "code", "execution_count": null, "id": "3e83a0ed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:02.855660Z", + "iopub.status.busy": "2023-07-26T12:47:02.855540Z", + "iopub.status.idle": "2023-07-26T12:47:02.858065Z", + "shell.execute_reply": "2023-07-26T12:47:02.857779Z" + } + }, "outputs": [], "source": [ "Portfolio.columns" @@ -54,7 +75,14 @@ "cell_type": "code", "execution_count": null, "id": "3ebec412", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:02.859606Z", + "iopub.status.busy": "2023-07-26T12:47:02.859503Z", + "iopub.status.idle": "2023-07-26T12:47:02.865754Z", + "shell.execute_reply": "2023-07-26T12:47:02.865418Z" + } + }, "outputs": [], "source": [ "Portfolio.describe()" @@ -71,6 +99,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Publication.ipynb b/docs/source/datasets/Publication.ipynb index a4a6dfa..de4a449 100644 --- a/docs/source/datasets/Publication.ipynb +++ b/docs/source/datasets/Publication.ipynb @@ -45,7 +45,14 @@ "cell_type": "code", "execution_count": null, "id": "61d7c2b3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:29.196850Z", + "iopub.status.busy": "2023-07-26T12:47:29.196559Z", + "iopub.status.idle": "2023-07-26T12:47:29.727827Z", + "shell.execute_reply": "2023-07-26T12:47:29.727421Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -57,7 +64,14 @@ "cell_type": "code", "execution_count": null, "id": "4d72460d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:29.729844Z", + "iopub.status.busy": "2023-07-26T12:47:29.729686Z", + "iopub.status.idle": "2023-07-26T12:47:29.732275Z", + "shell.execute_reply": "2023-07-26T12:47:29.732008Z" + } + }, "outputs": [], "source": [ "Publication.shape" @@ -67,7 +81,14 @@ "cell_type": "code", "execution_count": null, "id": "fd34224c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:29.734028Z", + "iopub.status.busy": "2023-07-26T12:47:29.733885Z", + "iopub.status.idle": "2023-07-26T12:47:29.736365Z", + "shell.execute_reply": "2023-07-26T12:47:29.736014Z" + } + }, "outputs": [], "source": [ "Publication.columns" @@ -77,7 +98,14 @@ "cell_type": "code", "execution_count": null, "id": "51bfb0aa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:29.738169Z", + "iopub.status.busy": "2023-07-26T12:47:29.738046Z", + "iopub.status.idle": "2023-07-26T12:47:29.747027Z", + "shell.execute_reply": "2023-07-26T12:47:29.746722Z" + } + }, "outputs": [], "source": [ "Publication.describe().iloc[:,:4]" @@ -94,6 +122,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Smarket.ipynb b/docs/source/datasets/Smarket.ipynb index cced2a9..0be4dd9 100644 --- a/docs/source/datasets/Smarket.ipynb +++ b/docs/source/datasets/Smarket.ipynb @@ -41,7 +41,14 @@ "cell_type": "code", "execution_count": null, "id": "3d920337", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:21.928355Z", + "iopub.status.busy": "2023-07-26T12:47:21.927766Z", + "iopub.status.idle": "2023-07-26T12:47:22.480597Z", + "shell.execute_reply": "2023-07-26T12:47:22.480297Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -53,7 +60,14 @@ "cell_type": "code", "execution_count": null, "id": "25d90138", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:22.482125Z", + "iopub.status.busy": "2023-07-26T12:47:22.482016Z", + "iopub.status.idle": "2023-07-26T12:47:22.484017Z", + "shell.execute_reply": "2023-07-26T12:47:22.483801Z" + } + }, "outputs": [], "source": [ "Smarket.shape" @@ -63,7 +77,14 @@ "cell_type": "code", "execution_count": null, "id": "0e8c57de", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:22.485456Z", + "iopub.status.busy": "2023-07-26T12:47:22.485359Z", + "iopub.status.idle": "2023-07-26T12:47:22.487416Z", + "shell.execute_reply": "2023-07-26T12:47:22.487186Z" + } + }, "outputs": [], "source": [ "Smarket.columns" @@ -73,7 +94,14 @@ "cell_type": "code", "execution_count": null, "id": "2d455f1e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:22.488803Z", + "iopub.status.busy": "2023-07-26T12:47:22.488706Z", + "iopub.status.idle": "2023-07-26T12:47:22.497401Z", + "shell.execute_reply": "2023-07-26T12:47:22.497165Z" + } + }, "outputs": [], "source": [ "Smarket.describe().iloc[:,-4:]" @@ -90,6 +118,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/USArrests.ipynb b/docs/source/datasets/USArrests.ipynb index 1107424..d860098 100644 --- a/docs/source/datasets/USArrests.ipynb +++ b/docs/source/datasets/USArrests.ipynb @@ -28,9 +28,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "feab45d4-ce30-4ea9-800c-bbe9e7c11f6d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:56.351520Z", + "iopub.status.busy": "2023-07-26T12:46:56.350481Z", + "iopub.status.idle": "2023-07-26T12:46:58.021100Z", + "shell.execute_reply": "2023-07-26T12:46:58.019698Z" + } + }, "outputs": [], "source": [ "from statsmodels.datasets import get_rdataset\n", @@ -39,157 +46,51 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "bdfffad4-6ab1-45da-8d62-8a7c4326fb24", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 4)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:58.027241Z", + "iopub.status.busy": "2023-07-26T12:46:58.026857Z", + "iopub.status.idle": "2023-07-26T12:46:58.034424Z", + "shell.execute_reply": "2023-07-26T12:46:58.033781Z" } - ], + }, + "outputs": [], "source": [ "USArrests.shape" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "02f28a67-e8b9-4a17-ad0d-88672e1de26d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Murder', 'Assault', 'UrbanPop', 'Rape'], dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:58.038173Z", + "iopub.status.busy": "2023-07-26T12:46:58.037943Z", + "iopub.status.idle": "2023-07-26T12:46:58.041828Z", + "shell.execute_reply": "2023-07-26T12:46:58.041345Z" } - ], + }, + "outputs": [], "source": [ "USArrests.columns" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "711db396-64d6-4fbd-9be4-bebe4117216f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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max17.40000337.00000091.00000046.000000
\n", - "
" - ], - "text/plain": [ - " Murder Assault UrbanPop Rape\n", - "count 50.00000 50.000000 50.000000 50.000000\n", - "mean 7.78800 170.760000 65.540000 21.232000\n", - "std 4.35551 83.337661 14.474763 9.366385\n", - "min 0.80000 45.000000 32.000000 7.300000\n", - "25% 4.07500 109.000000 54.500000 15.075000\n", - "50% 7.25000 159.000000 66.000000 20.100000\n", - "75% 11.25000 249.000000 77.750000 26.175000\n", - "max 17.40000 337.000000 91.000000 46.000000" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:58.044543Z", + "iopub.status.busy": "2023-07-26T12:46:58.044381Z", + "iopub.status.idle": "2023-07-26T12:46:58.057559Z", + "shell.execute_reply": "2023-07-26T12:46:58.057142Z" } - ], + }, + "outputs": [], "source": [ "USArrests.describe()" ] @@ -216,7 +117,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Wage.ipynb b/docs/source/datasets/Wage.ipynb index b95d853..28bb484 100644 --- a/docs/source/datasets/Wage.ipynb +++ b/docs/source/datasets/Wage.ipynb @@ -53,7 +53,14 @@ "cell_type": "code", "execution_count": null, "id": "6832d321", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:04.731864Z", + "iopub.status.busy": "2023-07-26T12:47:04.731413Z", + "iopub.status.idle": "2023-07-26T12:47:05.295785Z", + "shell.execute_reply": "2023-07-26T12:47:05.295452Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -65,7 +72,14 @@ "cell_type": "code", "execution_count": null, "id": "1c1ad3f3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:05.297482Z", + "iopub.status.busy": "2023-07-26T12:47:05.297357Z", + "iopub.status.idle": "2023-07-26T12:47:05.299508Z", + "shell.execute_reply": "2023-07-26T12:47:05.299247Z" + } + }, "outputs": [], "source": [ "Wage.shape" @@ -75,7 +89,14 @@ "cell_type": "code", "execution_count": null, "id": "d56ab6a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:05.300989Z", + "iopub.status.busy": "2023-07-26T12:47:05.300875Z", + "iopub.status.idle": "2023-07-26T12:47:05.303024Z", + "shell.execute_reply": "2023-07-26T12:47:05.302786Z" + } + }, "outputs": [], "source": [ "Wage.columns" @@ -85,7 +106,14 @@ "cell_type": "code", "execution_count": null, "id": "5f021939", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:05.304606Z", + "iopub.status.busy": "2023-07-26T12:47:05.304487Z", + "iopub.status.idle": "2023-07-26T12:47:05.311771Z", + "shell.execute_reply": "2023-07-26T12:47:05.311522Z" + } + }, "outputs": [], "source": [ "Wage.describe()" @@ -102,6 +130,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/datasets/Weekly.ipynb b/docs/source/datasets/Weekly.ipynb index 69f26d6..15a1050 100644 --- a/docs/source/datasets/Weekly.ipynb +++ b/docs/source/datasets/Weekly.ipynb @@ -41,7 +41,14 @@ "cell_type": "code", "execution_count": null, "id": "d19dd431", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:41.468580Z", + "iopub.status.busy": "2023-07-26T12:47:41.468291Z", + "iopub.status.idle": "2023-07-26T12:47:41.999679Z", + "shell.execute_reply": "2023-07-26T12:47:41.999341Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -53,7 +60,14 @@ "cell_type": "code", "execution_count": null, "id": "17d2cda4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:42.002632Z", + "iopub.status.busy": "2023-07-26T12:47:42.002470Z", + "iopub.status.idle": "2023-07-26T12:47:42.004871Z", + "shell.execute_reply": "2023-07-26T12:47:42.004611Z" + } + }, "outputs": [], "source": [ "Weekly.shape" @@ -63,7 +77,14 @@ "cell_type": "code", "execution_count": null, "id": "f822715b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:42.006534Z", + "iopub.status.busy": "2023-07-26T12:47:42.006422Z", + "iopub.status.idle": "2023-07-26T12:47:42.008496Z", + "shell.execute_reply": "2023-07-26T12:47:42.008187Z" + } + }, "outputs": [], "source": [ "Weekly.columns" @@ -73,7 +94,14 @@ "cell_type": "code", "execution_count": null, "id": "9a5f4d04", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:47:42.010010Z", + "iopub.status.busy": "2023-07-26T12:47:42.009911Z", + "iopub.status.idle": "2023-07-26T12:47:42.019036Z", + "shell.execute_reply": "2023-07-26T12:47:42.018706Z" + } + }, "outputs": [], "source": [ "Weekly.describe().iloc[:,:4]" @@ -98,6 +126,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/helpers/cluster.ipynb b/docs/source/helpers/cluster.ipynb index 31798a0..4aa7de3 100644 --- a/docs/source/helpers/cluster.ipynb +++ b/docs/source/helpers/cluster.ipynb @@ -13,22 +13,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d5df152d", - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'sklearn'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AgglomerativeClustering\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhierarchy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dendrogram\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mISLP\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcluster\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compute_linkage\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:42.214971Z", + "iopub.status.busy": "2023-07-26T12:46:42.214537Z", + "iopub.status.idle": "2023-07-26T12:46:42.860533Z", + "shell.execute_reply": "2023-07-26T12:46:42.860243Z" } - ], + }, + "outputs": [], "source": [ "import numpy as np\n", "from sklearn.cluster import AgglomerativeClustering\n", @@ -46,9 +41,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0135c1fb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:42.862401Z", + "iopub.status.busy": "2023-07-26T12:46:42.862250Z", + "iopub.status.idle": "2023-07-26T12:46:42.864336Z", + "shell.execute_reply": "2023-07-26T12:46:42.864118Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", @@ -68,7 +70,14 @@ "cell_type": "code", "execution_count": null, "id": "17c52650", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:42.865831Z", + "iopub.status.busy": "2023-07-26T12:46:42.865731Z", + "iopub.status.idle": "2023-07-26T12:46:42.867386Z", + "shell.execute_reply": "2023-07-26T12:46:42.867147Z" + } + }, "outputs": [], "source": [ "clust = AgglomerativeClustering(distance_threshold=0,\n", @@ -80,7 +89,14 @@ "cell_type": "code", "execution_count": null, "id": "a3ae2622", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:42.868746Z", + "iopub.status.busy": "2023-07-26T12:46:42.868668Z", + "iopub.status.idle": "2023-07-26T12:46:42.872497Z", + "shell.execute_reply": "2023-07-26T12:46:42.872240Z" + } + }, "outputs": [], "source": [ "clust.fit(X)" @@ -98,7 +114,14 @@ "cell_type": "code", "execution_count": null, "id": "64e726a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:42.873930Z", + "iopub.status.busy": "2023-07-26T12:46:42.873845Z", + "iopub.status.idle": "2023-07-26T12:46:43.195508Z", + "shell.execute_reply": "2023-07-26T12:46:43.195084Z" + } + }, "outputs": [], "source": [ "linkage = compute_linkage(clust)\n", @@ -127,7 +150,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/helpers/pygam.ipynb b/docs/source/helpers/pygam.ipynb index aab61d1..b452294 100644 --- a/docs/source/helpers/pygam.ipynb +++ b/docs/source/helpers/pygam.ipynb @@ -16,7 +16,14 @@ "cell_type": "code", "execution_count": null, "id": "9a52fb27", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:47.820912Z", + "iopub.status.busy": "2023-07-26T12:46:47.820490Z", + "iopub.status.idle": "2023-07-26T12:46:48.577304Z", + "shell.execute_reply": "2023-07-26T12:46:48.577007Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -46,7 +53,14 @@ "cell_type": "code", "execution_count": null, "id": "4bddce77", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.579295Z", + "iopub.status.busy": "2023-07-26T12:46:48.579114Z", + "iopub.status.idle": "2023-07-26T12:46:48.581608Z", + "shell.execute_reply": "2023-07-26T12:46:48.581355Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", @@ -69,7 +83,14 @@ "cell_type": "code", "execution_count": null, "id": "3f8946e0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.583287Z", + "iopub.status.busy": "2023-07-26T12:46:48.583187Z", + "iopub.status.idle": "2023-07-26T12:46:48.618486Z", + "shell.execute_reply": "2023-07-26T12:46:48.614888Z" + } + }, "outputs": [], "source": [ "terms = [s(f, lam=0.01) for f in range(3)]\n", @@ -91,7 +112,14 @@ "cell_type": "code", "execution_count": null, "id": "c5b38706", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.624580Z", + "iopub.status.busy": "2023-07-26T12:46:48.624177Z", + "iopub.status.idle": "2023-07-26T12:46:48.814238Z", + "shell.execute_reply": "2023-07-26T12:46:48.808746Z" + } + }, "outputs": [], "source": [ "ax = plot(gam, 0)" @@ -109,7 +137,14 @@ "cell_type": "code", "execution_count": null, "id": "e4d2b6f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.825281Z", + "iopub.status.busy": "2023-07-26T12:46:48.824327Z", + "iopub.status.idle": "2023-07-26T12:46:48.897739Z", + "shell.execute_reply": "2023-07-26T12:46:48.897447Z" + } + }, "outputs": [], "source": [ "ax.scatter(X[:,0], \n", @@ -131,7 +166,14 @@ "cell_type": "code", "execution_count": null, "id": "82374baa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.899404Z", + "iopub.status.busy": "2023-07-26T12:46:48.899288Z", + "iopub.status.idle": "2023-07-26T12:46:48.916570Z", + "shell.execute_reply": "2023-07-26T12:46:48.915079Z" + } + }, "outputs": [], "source": [ "[degrees_of_freedom(X,\n", @@ -153,7 +195,14 @@ "cell_type": "code", "execution_count": null, "id": "0576d1f3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.924539Z", + "iopub.status.busy": "2023-07-26T12:46:48.924174Z", + "iopub.status.idle": "2023-07-26T12:46:48.955630Z", + "shell.execute_reply": "2023-07-26T12:46:48.954722Z" + } + }, "outputs": [], "source": [ "lam_vals = [approx_lam(X,\n", @@ -174,7 +223,14 @@ "cell_type": "code", "execution_count": null, "id": "3a8b546e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.961056Z", + "iopub.status.busy": "2023-07-26T12:46:48.960521Z", + "iopub.status.idle": "2023-07-26T12:46:48.989331Z", + "shell.execute_reply": "2023-07-26T12:46:48.987244Z" + } + }, "outputs": [], "source": [ "fixed_terms = [s(f, lam=l) for \n", @@ -189,7 +245,14 @@ "cell_type": "code", "execution_count": null, "id": "f2cfbea2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:48.995461Z", + "iopub.status.busy": "2023-07-26T12:46:48.994945Z", + "iopub.status.idle": "2023-07-26T12:46:49.130069Z", + "shell.execute_reply": "2023-07-26T12:46:49.129127Z" + } + }, "outputs": [], "source": [ "ax = plot(fixed_gam, 0)\n", @@ -210,6 +273,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/helpers/survival.ipynb b/docs/source/helpers/survival.ipynb index 7cb30a3..f90123e 100644 --- a/docs/source/helpers/survival.ipynb +++ b/docs/source/helpers/survival.ipynb @@ -15,7 +15,14 @@ "cell_type": "code", "execution_count": null, "id": "0932cabc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:45.058072Z", + "iopub.status.busy": "2023-07-26T12:46:45.057742Z", + "iopub.status.idle": "2023-07-26T12:46:45.657730Z", + "shell.execute_reply": "2023-07-26T12:46:45.657332Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -40,7 +47,14 @@ "cell_type": "code", "execution_count": null, "id": "d82896bb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:45.659634Z", + "iopub.status.busy": "2023-07-26T12:46:45.659493Z", + "iopub.status.idle": "2023-07-26T12:46:45.661327Z", + "shell.execute_reply": "2023-07-26T12:46:45.661109Z" + } + }, "outputs": [], "source": [ "cum_haz = lambda t: t\n", @@ -51,7 +65,14 @@ "cell_type": "code", "execution_count": null, "id": "c9f9d590", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:45.662631Z", + "iopub.status.busy": "2023-07-26T12:46:45.662534Z", + "iopub.status.idle": "2023-07-26T12:46:45.672267Z", + "shell.execute_reply": "2023-07-26T12:46:45.672017Z" + } + }, "outputs": [], "source": [ "T = np.array([sim_time(np.log(2), cum_haz, rng) for _ in range(500)])" @@ -69,7 +90,14 @@ "cell_type": "code", "execution_count": null, "id": "2d8478dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:45.673768Z", + "iopub.status.busy": "2023-07-26T12:46:45.673685Z", + "iopub.status.idle": "2023-07-26T12:46:45.934676Z", + "shell.execute_reply": "2023-07-26T12:46:45.934321Z" + } + }, "outputs": [], "source": [ "kmf = KaplanMeierFitter(label=\"Simulated data\")\n", @@ -111,6 +139,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/helpers/svm.ipynb b/docs/source/helpers/svm.ipynb index 593d840..eb950b5 100644 --- a/docs/source/helpers/svm.ipynb +++ b/docs/source/helpers/svm.ipynb @@ -14,7 +14,14 @@ "cell_type": "code", "execution_count": null, "id": "2746a357", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:51.026740Z", + "iopub.status.busy": "2023-07-26T12:46:51.026289Z", + "iopub.status.idle": "2023-07-26T12:46:51.779743Z", + "shell.execute_reply": "2023-07-26T12:46:51.779280Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -34,7 +41,14 @@ "cell_type": "code", "execution_count": null, "id": "4728535b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:51.781697Z", + "iopub.status.busy": "2023-07-26T12:46:51.781546Z", + "iopub.status.idle": "2023-07-26T12:46:51.783810Z", + "shell.execute_reply": "2023-07-26T12:46:51.783514Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", @@ -56,7 +70,14 @@ "cell_type": "code", "execution_count": null, "id": "74da6860", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:51.785373Z", + "iopub.status.busy": "2023-07-26T12:46:51.785272Z", + "iopub.status.idle": "2023-07-26T12:46:51.789605Z", + "shell.execute_reply": "2023-07-26T12:46:51.789351Z" + } + }, "outputs": [], "source": [ "svm = SVC(kernel='linear')\n", @@ -67,7 +88,14 @@ "cell_type": "code", "execution_count": null, "id": "d87b6f75", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:51.790987Z", + "iopub.status.busy": "2023-07-26T12:46:51.790907Z", + "iopub.status.idle": "2023-07-26T12:46:51.883284Z", + "shell.execute_reply": "2023-07-26T12:46:51.882993Z" + } + }, "outputs": [], "source": [ "plot(X, Y, svm)" @@ -89,7 +117,14 @@ "cell_type": "code", "execution_count": null, "id": "bc58956a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:46:51.884984Z", + "iopub.status.busy": "2023-07-26T12:46:51.884867Z", + "iopub.status.idle": "2023-07-26T12:46:52.011375Z", + "shell.execute_reply": "2023-07-26T12:46:52.011081Z" + } + }, "outputs": [], "source": [ "plot(X, Y, svm, features=(3, 4))" @@ -106,6 +141,18 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/installation.myst b/docs/source/installation.myst index d1c30aa..a1aaf6d 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -36,7 +36,6 @@ conda activate islp On windows, create a `Python` environment called `islp` in the Anaconda app. This can be done by selecting `Environments` on the left hand side of the app's screen. After creating the environment, open a terminal within that environment by clicking on the "Play" button. - # Installing `ISLP` Having completed the steps above, we use `pip` to install the `ISLP` package: @@ -103,7 +102,18 @@ pip install jupyterlab ### Windows -Either use the same `pip` command above or install JupyterLab from the `Home` tab. Ensure that the environment -is your `islp` environment. This information appears near the top left in the Anaconda `Home` page. +Either use the same `pip` command above or install JupyterLab from the +`Home` tab. Ensure that the environment is your `islp` +environment. This information appears near the top left in the +Anaconda `Home` page. + +# Google Colab + +The notebooks for the labs can be run in [Google +Colab](https://colab.research.google.com) with a few caveats: +- Labs that use files in the filesystem will require one to mount your + Google Drive. See [help](https://colab.research.google.com/notebooks/io.ipynb) +- The packages will have to be reinstalled each time a new runtime is started. +For most labs, inserting `pip install ISLP` at the top of the notebook will suffice, though Colab will ask you to restart after installation. diff --git a/docs/source/labs/Ch10-deeplearning-lab.ipynb b/docs/source/labs/Ch10-deeplearning-lab.ipynb index 61de0ed..75458eb 100644 --- a/docs/source/labs/Ch10-deeplearning-lab.ipynb +++ b/docs/source/labs/Ch10-deeplearning-lab.ipynb @@ -5,10 +5,13 @@ "id": "fd22c2e6", "metadata": {}, "source": [ + "# Deep Learning\n", "\n", - "# Chapter 10\n", + "\n", + " \"Open\n", + "\n", + "\n", "\n", - "# Lab: Deep Learning\n", "In this section we demonstrate how to fit the examples discussed\n", "in the text. We use the `Python` `torch` package, along with the\n", "`pytorch_lightning` package which provides utilities to simplify\n", @@ -23,11 +26,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "aeb913e2", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np, pandas as pd\n", @@ -60,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3a3e91e7", "metadata": {}, "outputs": [], @@ -68,7 +69,7 @@ "import torch\n", "from torch import nn\n", "from torch.optim import RMSprop\n", - "from torch.utils.data import TensorDataset\n" + "from torch.utils.data import TensorDataset" ] }, { @@ -86,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3f4fdb8c", "metadata": {}, "outputs": [], @@ -94,7 +95,7 @@ "from torchmetrics import (MeanAbsoluteError,\n", " R2Score)\n", "from torchinfo import summary\n", - "from torchvision.io import read_image\n" + "from torchvision.io import read_image" ] }, { @@ -111,13 +112,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "71cc6f03", "metadata": {}, "outputs": [], "source": [ "from pytorch_lightning import Trainer\n", - "from pytorch_lightning.loggers import CSVLogger\n" + "from pytorch_lightning.loggers import CSVLogger" ] }, { @@ -131,14 +132,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "fd1290f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n" + ] + } + ], "source": [ "from pytorch_lightning.utilities.seed import seed_everything\n", "seed_everything(0, workers=True)\n", - "torch.use_deterministic_algorithms(True, warn_only=True)\n" + "torch.use_deterministic_algorithms(True, warn_only=True)" ] }, { @@ -153,11 +162,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c85308d1", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "from torchvision.datasets import MNIST, CIFAR100\n", @@ -188,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4de9f03c", "metadata": {}, "outputs": [], @@ -196,7 +203,7 @@ "from ISLP.torch import (SimpleDataModule,\n", " SimpleModule,\n", " ErrorTracker,\n", - " rec_num_workers)\n" + " rec_num_workers)" ] }, { @@ -217,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4ef95fe3", "metadata": {}, "outputs": [], @@ -225,7 +232,7 @@ "from ISLP.torch.imdb import (load_lookup,\n", " load_tensor,\n", " load_sparse,\n", - " load_sequential)\n" + " load_sequential)" ] }, { @@ -246,15 +253,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "493821a9", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "from glob import glob\n", - "import json\n" + "import json" ] }, { @@ -268,15 +273,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a427e224", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "Hitters = load_data('Hitters').dropna()\n", - "n = Hitters.shape[0]\n" + "n = Hitters.shape[0]" ] }, { @@ -296,16 +299,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "feac4b15", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "model = MS(Hitters.columns.drop('Salary'), intercept=False)\n", "X = model.fit_transform(Hitters).to_numpy()\n", - "Y = Hitters['Salary'].to_numpy()\n" + "Y = Hitters['Salary'].to_numpy()" ] }, { @@ -332,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6a75a640", "metadata": {}, "outputs": [], @@ -357,10 +358,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "32d8de64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "259.71528833146243" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hit_lm = LinearRegression().fit(X_train, Y_train)\n", "Yhat_test = hit_lm.predict(X_test)\n", @@ -377,12 +389,12 @@ "The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work, we create a cross-validation grid and perform the cross-validation directly. \n", "\n", "We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,\n", - "and then fit the lasso without further normalization. " + "and then fit the lasso without further normalization." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "46786db6", "metadata": {}, "outputs": [], @@ -405,11 +417,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "2b55b38b", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "X_s = scaler.fit_transform(X_train)\n", @@ -430,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "cc44c559", "metadata": {}, "outputs": [], @@ -457,12 +467,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "1bd1fd13", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "257.2382010799497" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "trained_lasso = grid.best_estimator_\n", "Yhat_test = trained_lasso.predict(X_test)\n", @@ -483,12 +502,12 @@ "Typically this is done in `pytorch` by sub-classing a generic\n", "representation of a network, which is the approach we take here.\n", "Although this example is simple, we will go through the steps in some detail, since it will serve us well\n", - "for the more complex examples to follow.\n" + "for the more complex examples to follow." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "4b2ccc5a", "metadata": {}, "outputs": [], @@ -506,7 +525,7 @@ "\n", " def forward(self, x):\n", " x = self.flatten(x)\n", - " return torch.flatten(self.sequential(x))\n" + " return torch.flatten(self.sequential(x))" ] }, { @@ -546,12 +565,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "108ad1f1", "metadata": {}, "outputs": [], "source": [ - "hit_model = HittersModel(X.shape[1])\n" + "hit_model = HittersModel(X.shape[1])" ] }, { @@ -564,9 +583,7 @@ "for the weights and *intercept* of the map (often called the *bias*). This layer\n", "is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a\n", "linear map down to 1 dimension, again with a bias. The total number of\n", - "trainable parameters is therefore $50\\times 19+50+50+1=1051$.\n", - "\n", - " " + "trainable parameters is therefore $50\\times 19+50+50+1=1051$." ] }, { @@ -576,23 +593,62 @@ "source": [ "The package `torchinfo` provides a `summary()` function that neatly summarizes\n", "this information. We specify the size of the input and see the size\n", - "of each tensor as it passes through layers of the network. " + "of each tensor as it passes through layers of the network." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "2f20059e", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "HittersModel [175, 19] [175] --\n", + "├─Flatten: 1-1 [175, 19] [175, 19] --\n", + "├─Sequential: 1-2 [175, 19] [175, 1] --\n", + "│ └─Linear: 2-1 [175, 19] [175, 50] 1,000\n", + "│ └─ReLU: 2-2 [175, 50] [175, 50] --\n", + "│ └─Dropout: 2-3 [175, 50] [175, 50] --\n", + "│ └─Linear: 2-4 [175, 50] [175, 1] 51\n", + "===================================================================================================================\n", + "Total params: 1,051\n", + "Trainable params: 1,051\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.18\n", + "===================================================================================================================\n", + "Input size (MB): 0.01\n", + "Forward/backward pass size (MB): 0.07\n", + "Params size (MB): 0.00\n", + "Estimated Total Size (MB): 0.09\n", + "===================================================================================================================" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(hit_model, \n", " input_size=X_train.shape,\n", " col_names=['input_size',\n", " 'output_size',\n", - " 'num_params'])\n" + " 'num_params'])" ] }, { @@ -618,11 +674,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "6ca3030d", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "X_train_t = torch.tensor(X_train.astype(np.float32))\n", @@ -640,14 +694,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "86723b3e", "metadata": {}, "outputs": [], "source": [ "X_test_t = torch.tensor(X_test.astype(np.float32))\n", "Y_test_t = torch.tensor(Y_test.astype(np.float32))\n", - "hit_test = TensorDataset(X_test_t, Y_test_t)\n" + "hit_test = TensorDataset(X_test_t, Y_test_t)" ] }, { @@ -675,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "999279fd", "metadata": {}, "outputs": [], @@ -707,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "7bd9cc5c", "metadata": {}, "outputs": [], @@ -716,7 +770,7 @@ " hit_test,\n", " batch_size=32,\n", " num_workers=min(4, max_num_workers),\n", - " validation=hit_test)\n" + " validation=hit_test)" ] }, { @@ -735,13 +789,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "5be2f822", "metadata": {}, "outputs": [], "source": [ "hit_module = SimpleModule.regression(hit_model,\n", - " metrics={'mae':MeanAbsoluteError()})\n" + " metrics={'mae':MeanAbsoluteError()})" ] }, { @@ -762,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "87334d33", "metadata": {}, "outputs": [], @@ -793,12 +847,707 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "1a474999", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------\n", + "0 | model | HittersModel | 1.1 K \n", + "1 | loss | MSELoss | 0 \n", + "---------------------------------------\n", + "1.1 K Trainable params\n", + "0 Non-trainable params\n", + "1.1 K Total params\n", + "0.004 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7f1f820ee6fa4d81a3736b68611f3bac", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": 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"application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + } + ], "source": [ "hit_trainer = Trainer(deterministic=True,\n", " max_epochs=50,\n", @@ -825,14 +1574,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "e3b24643", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "hit_trainer.test(hit_module, datamodule=hit_dm)\n" + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ce9e57deabef4f55aba22efb507872f1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 103304.8515625\n", + " test_mae 224.26962280273438\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 103304.8515625, 'test_mae': 224.26962280273438}]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hit_trainer.test(hit_module, datamodule=hit_dm)" ] }, { @@ -852,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "f9b266e7", "metadata": {}, "outputs": [], @@ -871,11 +1655,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "c5752a0b", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "def summary_plot(results,\n", @@ -914,12 +1696,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "ff0c9fa0", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = subplots(1, 1, figsize=(6, 6))\n", "ax = summary_plot(hit_results,\n", @@ -950,26 +1741,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "1ee2b61b", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(224.2696, grad_fn=)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hit_model.eval() \n", "preds = hit_module(X_test_t)\n", "torch.abs(Y_test_t - preds).mean()" ] }, - { - "cell_type": "markdown", - "id": "8140eb0d", - "metadata": {}, - "source": [ - " " - ] - }, { "cell_type": "markdown", "id": "64fa7609", @@ -984,11 +1776,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "0ca069a3", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "del(Hitters,\n", @@ -999,7 +1789,7 @@ " X_test, X_train,\n", " Y_test, Y_train,\n", " X_test_t, Y_test_t,\n", - " hit_trainer, hit_module)\n" + " hit_trainer, hit_module)" ] }, { @@ -1017,10 +1807,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "af6a0a33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset MNIST\n", + " Number of datapoints: 60000\n", + " Root location: data\n", + " Split: Train\n", + " StandardTransform\n", + "Transform: ToTensor()" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(mnist_train, \n", " mnist_test) = [MNIST(root='data',\n", @@ -1028,7 +1834,7 @@ " download=True,\n", " transform=ToTensor())\n", " for train in [True, False]]\n", - "mnist_train\n" + "mnist_train" ] }, { @@ -1056,7 +1862,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "0df8a0c8", "metadata": {}, "outputs": [], @@ -1065,7 +1871,7 @@ " mnist_test,\n", " validation=0.2,\n", " num_workers=max_num_workers,\n", - " batch_size=256)\n" + " batch_size=256)" ] }, { @@ -1079,18 +1885,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "0c7dd6ee", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X: torch.Size([256, 1, 28, 28])\n", + "Y: torch.Size([256])\n", + "X: torch.Size([256, 1, 28, 28])\n", + "Y: torch.Size([256])\n" + ] + } + ], "source": [ "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", " print('Y: ', Y_.shape)\n", " if idx >= 1:\n", - " break\n" + " break" ] }, { @@ -1107,7 +1922,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "63e48c70", "metadata": {}, "outputs": [], @@ -1147,12 +1962,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "92405826", "metadata": {}, "outputs": [], "source": [ - "mnist_model = MNISTModel()\n" + "mnist_model = MNISTModel()" ] }, { @@ -1166,10 +1981,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "2c8f7a48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([256, 10])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mnist_model(X_).size()" ] @@ -1186,10 +2012,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "9f502df8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "MNISTModel [256, 1, 28, 28] [256, 10] --\n", + "├─Sequential: 1-1 [256, 1, 28, 28] [256, 10] --\n", + "│ └─Sequential: 2-1 [256, 1, 28, 28] [256, 256] --\n", + "│ │ └─Flatten: 3-1 [256, 1, 28, 28] [256, 784] --\n", + "│ │ └─Linear: 3-2 [256, 784] [256, 256] 200,960\n", + "│ │ └─ReLU: 3-3 [256, 256] [256, 256] --\n", + "│ │ └─Dropout: 3-4 [256, 256] [256, 256] --\n", + "│ └─Sequential: 2-2 [256, 256] [256, 128] --\n", + "│ │ └─Linear: 3-5 [256, 256] [256, 128] 32,896\n", + "│ │ └─ReLU: 3-6 [256, 128] [256, 128] --\n", + "│ │ └─Dropout: 3-7 [256, 128] [256, 128] --\n", + "│ └─Linear: 2-3 [256, 128] [256, 10] 1,290\n", + "===================================================================================================================\n", + "Total params: 235,146\n", + "Trainable params: 235,146\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 60.20\n", + "===================================================================================================================\n", + "Input size (MB): 0.80\n", + "Forward/backward pass size (MB): 0.81\n", + "Params size (MB): 0.94\n", + "Estimated Total Size (MB): 2.55\n", + "===================================================================================================================" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(mnist_model,\n", " input_data=X_,\n", @@ -1211,13 +2083,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "d1a7590f", "metadata": {}, "outputs": [], "source": [ "mnist_module = SimpleModule.classification(mnist_model)\n", - "mnist_logger = CSVLogger('logs', name='MNIST')\n" + "mnist_logger = CSVLogger('logs', name='MNIST')" ] }, { @@ -1230,19 +2102,495 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "e2fe6de3", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | MNISTModel | 235 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "235 K Trainable params\n", + "0 Non-trainable params\n", + "235 K Total params\n", + "0.941 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82f9178e94874e7e9cbb3b27cc1c351f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + 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"application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "mnist_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", " logger=mnist_logger,\n", " callbacks=[ErrorTracker()])\n", "mnist_trainer.fit(mnist_module,\n", - " datamodule=mnist_dm)\n" + " datamodule=mnist_dm)" ] }, { @@ -1277,12 +2625,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "73755987", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -1292,7 +2649,7 @@ " ylabel='Accuracy')\n", "ax.set_ylim([0.5, 1])\n", "ax.set_ylabel('Accuracy')\n", - "ax.set_xticks(np.linspace(0, 30, 7).astype(int));\n" + "ax.set_xticks(np.linspace(0, 30, 7).astype(int));" ] }, { @@ -1306,10 +2663,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "f5269c40", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b584fa2b09df4c20939181c016e85bc5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.9681000113487244\n", + " test_loss 0.14706705510616302\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.14706705510616302, 'test_accuracy': 0.9681000113487244}]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mnist_trainer.test(mnist_module,\n", " datamodule=mnist_dm)" @@ -1330,7 +2724,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "97a0b304", "metadata": {}, "outputs": [], @@ -1350,12 +2744,524 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "ea685183", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | MNIST_MLR | 7.9 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "7.9 K Trainable params\n", + "0 Non-trainable params\n", + "7.9 K Total params\n", + "0.031 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02fd79458dc34808bb403f8b8475a62d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9c827fce3589462294778b990fc78f2f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a9fcb28681b34dc6921cad2630ca648f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e2dd237b55d14ad4a871888e4ecde25d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5d8dc977673646d9ad09d0c5b7e4903a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "mlr_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", @@ -1373,12 +3279,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "c0bd63e3", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92b4035b1656456d88d929aac0d46d71", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.9240999817848206\n", + " test_loss 0.3186827003955841\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.3186827003955841, 'test_accuracy': 0.9240999817848206}]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mlr_trainer.test(mlr_module,\n", " datamodule=mnist_dm)" @@ -1397,11 +3338,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "6b0d3daa", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "del(mnist_test,\n", @@ -1428,10 +3367,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "67517b11", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], "source": [ "(cifar_train, \n", " cifar_test) = [CIFAR100(root=\"data\",\n", @@ -1442,7 +3390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "ee7b040e", "metadata": {}, "outputs": [], @@ -1473,18 +3421,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "bd9e74ad", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "cifar_dm = SimpleDataModule(cifar_train,\n", " cifar_test,\n", " validation=0.2,\n", " num_workers=max_num_workers,\n", - " batch_size=128)\n" + " batch_size=128)" ] }, { @@ -1497,18 +3443,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "a553b926", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X: torch.Size([128, 3, 32, 32])\n", + "Y: torch.Size([128])\n", + "X: torch.Size([128, 3, 32, 32])\n", + "Y: torch.Size([128])\n" + ] + } + ], "source": [ "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", " print('X: ', X_.shape)\n", " print('Y: ', Y_.shape)\n", " if idx >= 1:\n", - " break\n" + " break" ] }, { @@ -1525,12 +3480,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "94885e68", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = subplots(5, 5, figsize=(10,10))\n", "rng = np.random.default_rng(4)\n", @@ -1543,7 +3507,7 @@ " [1,2,0]),\n", " interpolation=None)\n", " axes[i,j].set_xticks([])\n", - " axes[i,j].set_yticks([])\n" + " axes[i,j].set_yticks([])" ] }, { @@ -1564,7 +3528,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "cdc2528a", "metadata": {}, "outputs": [], @@ -1584,7 +3548,7 @@ " self.pool = nn.MaxPool2d(kernel_size=(2,2))\n", "\n", " def forward(self, x):\n", - " return self.pool(self.activation(self.conv(x)))\n" + " return self.pool(self.activation(self.conv(x)))" ] }, { @@ -1609,7 +3573,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "845be1ae", "metadata": {}, "outputs": [], @@ -1632,7 +3596,7 @@ " def forward(self, x):\n", " val = self.conv(x)\n", " val = torch.flatten(val, start_dim=1)\n", - " return self.output(val)\n" + " return self.output(val)" ] }, { @@ -1645,12 +3609,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "be768cbb", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "CIFARModel [128, 3, 32, 32] [128, 100] --\n", + "├─Sequential: 1-1 [128, 3, 32, 32] [128, 256, 2, 2] --\n", + "│ └─BuildingBlock: 2-1 [128, 3, 32, 32] [128, 32, 16, 16] --\n", + "│ │ └─Conv2d: 3-1 [128, 3, 32, 32] [128, 32, 32, 32] 896\n", + "│ │ └─ReLU: 3-2 [128, 32, 32, 32] [128, 32, 32, 32] --\n", + "│ │ └─MaxPool2d: 3-3 [128, 32, 32, 32] [128, 32, 16, 16] --\n", + "│ └─BuildingBlock: 2-2 [128, 32, 16, 16] [128, 64, 8, 8] --\n", + "│ │ └─Conv2d: 3-4 [128, 32, 16, 16] [128, 64, 16, 16] 18,496\n", + "│ │ └─ReLU: 3-5 [128, 64, 16, 16] [128, 64, 16, 16] --\n", + "│ │ └─MaxPool2d: 3-6 [128, 64, 16, 16] [128, 64, 8, 8] --\n", + "│ └─BuildingBlock: 2-3 [128, 64, 8, 8] [128, 128, 4, 4] --\n", + "│ │ └─Conv2d: 3-7 [128, 64, 8, 8] [128, 128, 8, 8] 73,856\n", + "│ │ └─ReLU: 3-8 [128, 128, 8, 8] [128, 128, 8, 8] --\n", + "│ │ └─MaxPool2d: 3-9 [128, 128, 8, 8] [128, 128, 4, 4] --\n", + "│ └─BuildingBlock: 2-4 [128, 128, 4, 4] [128, 256, 2, 2] --\n", + "│ │ └─Conv2d: 3-10 [128, 128, 4, 4] [128, 256, 4, 4] 295,168\n", + "│ │ └─ReLU: 3-11 [128, 256, 4, 4] [128, 256, 4, 4] --\n", + "│ │ └─MaxPool2d: 3-12 [128, 256, 4, 4] [128, 256, 2, 2] --\n", + "├─Sequential: 1-2 [128, 1024] [128, 100] --\n", + "│ └─Dropout: 2-5 [128, 1024] [128, 1024] --\n", + "│ └─Linear: 2-6 [128, 1024] [128, 512] 524,800\n", + "│ └─ReLU: 2-7 [128, 512] [128, 512] --\n", + "│ └─Linear: 2-8 [128, 512] [128, 100] 51,300\n", + "===================================================================================================================\n", + "Total params: 964,516\n", + "Trainable params: 964,516\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 2.01\n", + "===================================================================================================================\n", + "Input size (MB): 1.57\n", + "Forward/backward pass size (MB): 63.54\n", + "Params size (MB): 3.86\n", + "Estimated Total Size (MB): 68.97\n", + "===================================================================================================================" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cifar_model = CIFARModel()\n", "summary(cifar_model,\n", @@ -1690,13 +3709,12 @@ "We saw earlier that entries of a module’s parameters are tensors. In passing\n", "the parameters to the optimizer we are doing more than\n", "simply passing arrays; part of the structure of the graph\n", - "is encoded in the tensors themselves.\n", - " " + "is encoded in the tensors themselves." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "67e3e2d9", "metadata": {}, "outputs": [], @@ -1704,22 +3722,498 @@ "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n", "cifar_module = SimpleModule.classification(cifar_model,\n", " optimizer=cifar_optimizer)\n", - "cifar_logger = CSVLogger('logs', name='CIFAR100')\n" + "cifar_logger = CSVLogger('logs', name='CIFAR100')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "1ea339ff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | CIFARModel | 964 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "964 K Trainable params\n", + "0 Non-trainable params\n", + "964 K Total params\n", + "3.858 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fdb66e85a34e49afafdf51191e247d54", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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"application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "cifar_trainer = Trainer(deterministic=True,\n", " max_epochs=30,\n", " logger=cifar_logger,\n", " callbacks=[ErrorTracker()])\n", "cifar_trainer.fit(cifar_module,\n", - " datamodule=cifar_dm)\n" + " datamodule=cifar_dm)" ] }, { @@ -1739,12 +4233,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "id": "0a9068d9", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "log_path = cifar_logger.experiment.metrics_file_path\n", "cifar_results = pd.read_csv(log_path)\n", @@ -1768,15 +4271,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "e71eff9b", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3e804034c1964ed38bbe2ca376491f54", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.40880000591278076\n", + " test_loss 2.4764862060546875\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 2.4764862060546875, 'test_accuracy': 0.40880000591278076}]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cifar_trainer.test(cifar_module,\n", - " datamodule=cifar_dm)\n" + " datamodule=cifar_dm)" ] }, { @@ -1801,12 +4339,508 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "58eae430", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | CIFARModel | 964 K \n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "964 K Trainable params\n", + "0 Non-trainable params\n", + "964 K Total params\n", + "3.858 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ebeb3882e14340e8b43876442c7e36d9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchmetrics/utilities/checks.py:49: UserWarning: MPS: no support for int64 min/max ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:1271.)\n", + " if ignore_index is None and target.min() < 0:\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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"output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3436eeffcb544f009e93641c9b125c58", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "try:\n", " for name, metric in cifar_module.metrics.items():\n", @@ -1855,12 +4889,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "638a1c8c", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([6, 3, 224, 224])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resize = Resize((232,232))\n", "crop = CenterCrop(224)\n", @@ -1883,19 +4934,226 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "7776ed1e", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "ResNet [6, 3, 224, 224] [6, 1000] --\n", + "├─Conv2d: 1-1 [6, 3, 224, 224] [6, 64, 112, 112] 9,408\n", + "├─BatchNorm2d: 1-2 [6, 64, 112, 112] [6, 64, 112, 112] 128\n", + "├─ReLU: 1-3 [6, 64, 112, 112] [6, 64, 112, 112] --\n", + "├─MaxPool2d: 1-4 [6, 64, 112, 112] [6, 64, 56, 56] --\n", + "├─Sequential: 1-5 [6, 64, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-1 [6, 64, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-1 [6, 64, 56, 56] [6, 64, 56, 56] 4,096\n", + "│ │ └─BatchNorm2d: 3-2 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-3 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-4 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-5 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-6 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-7 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-8 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─Sequential: 3-9 [6, 64, 56, 56] [6, 256, 56, 56] 16,896\n", + "│ │ └─ReLU: 3-10 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-2 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-11 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-12 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-13 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-14 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-15 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-16 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-17 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-18 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─ReLU: 3-19 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ └─Bottleneck: 2-3 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "│ │ └─Conv2d: 3-20 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-21 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-22 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-23 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", + "│ │ └─BatchNorm2d: 3-24 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", + "│ │ └─ReLU: 3-25 [6, 64, 56, 56] [6, 64, 56, 56] --\n", + "│ │ └─Conv2d: 3-26 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", + "│ │ └─BatchNorm2d: 3-27 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", + "│ │ └─ReLU: 3-28 [6, 256, 56, 56] [6, 256, 56, 56] --\n", + "├─Sequential: 1-6 [6, 256, 56, 56] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-4 [6, 256, 56, 56] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-29 [6, 256, 56, 56] [6, 128, 56, 56] 32,768\n", + "│ │ └─BatchNorm2d: 3-30 [6, 128, 56, 56] [6, 128, 56, 56] 256\n", + "│ │ └─ReLU: 3-31 [6, 128, 56, 56] [6, 128, 56, 56] --\n", + "│ │ └─Conv2d: 3-32 [6, 128, 56, 56] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-33 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-34 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-35 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-36 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─Sequential: 3-37 [6, 256, 56, 56] [6, 512, 28, 28] 132,096\n", + "│ │ └─ReLU: 3-38 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-5 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-39 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-40 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-41 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-42 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-43 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-44 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-45 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-46 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-47 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-6 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-48 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-49 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-50 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-51 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-52 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-53 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-54 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-55 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-56 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ └─Bottleneck: 2-7 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "│ │ └─Conv2d: 3-57 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-58 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-59 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-60 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", + "│ │ └─BatchNorm2d: 3-61 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", + "│ │ └─ReLU: 3-62 [6, 128, 28, 28] [6, 128, 28, 28] --\n", + "│ │ └─Conv2d: 3-63 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", + "│ │ └─BatchNorm2d: 3-64 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", + "│ │ └─ReLU: 3-65 [6, 512, 28, 28] [6, 512, 28, 28] --\n", + "├─Sequential: 1-7 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-8 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-66 [6, 512, 28, 28] [6, 256, 28, 28] 131,072\n", + "│ │ └─BatchNorm2d: 3-67 [6, 256, 28, 28] [6, 256, 28, 28] 512\n", + "│ │ └─ReLU: 3-68 [6, 256, 28, 28] [6, 256, 28, 28] --\n", + "│ │ └─Conv2d: 3-69 [6, 256, 28, 28] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-70 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-71 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-72 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-73 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─Sequential: 3-74 [6, 512, 28, 28] [6, 1024, 14, 14] 526,336\n", + "│ │ └─ReLU: 3-75 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-9 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-76 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-77 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-78 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-79 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-80 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-81 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-82 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-83 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-84 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-10 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-85 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-86 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-87 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-88 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-89 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-90 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-91 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-92 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-93 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-11 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-94 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-95 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-96 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-97 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-98 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-99 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-100 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-101 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-102 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-12 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-103 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-104 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-105 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-106 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-107 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-108 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-109 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-110 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-111 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ └─Bottleneck: 2-13 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "│ │ └─Conv2d: 3-112 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-113 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-114 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-115 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", + "│ │ └─BatchNorm2d: 3-116 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", + "│ │ └─ReLU: 3-117 [6, 256, 14, 14] [6, 256, 14, 14] --\n", + "│ │ └─Conv2d: 3-118 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", + "│ │ └─BatchNorm2d: 3-119 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", + "│ │ └─ReLU: 3-120 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", + "├─Sequential: 1-8 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-14 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-121 [6, 1024, 14, 14] [6, 512, 14, 14] 524,288\n", + "│ │ └─BatchNorm2d: 3-122 [6, 512, 14, 14] [6, 512, 14, 14] 1,024\n", + "│ │ └─ReLU: 3-123 [6, 512, 14, 14] [6, 512, 14, 14] --\n", + "│ │ └─Conv2d: 3-124 [6, 512, 14, 14] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-125 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-126 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-127 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-128 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─Sequential: 3-129 [6, 1024, 14, 14] [6, 2048, 7, 7] 2,101,248\n", + "│ │ └─ReLU: 3-130 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-15 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-131 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-132 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-133 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-134 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-135 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-136 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-137 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-138 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─ReLU: 3-139 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ └─Bottleneck: 2-16 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "│ │ └─Conv2d: 3-140 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-141 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-142 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-143 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", + "│ │ └─BatchNorm2d: 3-144 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", + "│ │ └─ReLU: 3-145 [6, 512, 7, 7] [6, 512, 7, 7] --\n", + "│ │ └─Conv2d: 3-146 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", + "│ │ └─BatchNorm2d: 3-147 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", + "│ │ └─ReLU: 3-148 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", + "├─AdaptiveAvgPool2d: 1-9 [6, 2048, 7, 7] [6, 2048, 1, 1] --\n", + "├─Linear: 1-10 [6, 2048] [6, 1000] 2,049,000\n", + "===================================================================================================================\n", + "Total params: 25,557,032\n", + "Trainable params: 25,557,032\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 24.54\n", + "===================================================================================================================\n", + "Input size (MB): 3.61\n", + "Forward/backward pass size (MB): 1066.99\n", + "Params size (MB): 102.23\n", + "Estimated Total Size (MB): 1172.83\n", + "===================================================================================================================" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", "summary(resnet_model,\n", " input_data=imgs,\n", " col_names=['input_size',\n", " 'output_size',\n", - " 'num_params'])\n" + " 'num_params'])" ] }, { @@ -1908,12 +5166,196 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "5c8e359d", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ResNet(\n", + " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 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BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", + ")" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resnet_model.eval()" ] @@ -1932,12 +5374,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "80f19869", "metadata": {}, "outputs": [], "source": [ - "img_preds = resnet_model(imgs)\n" + "img_preds = resnet_model(imgs)" ] }, { @@ -1953,13 +5395,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "6892597d", "metadata": {}, "outputs": [], "source": [ "img_probs = np.exp(np.asarray(img_preds.detach()))\n", - "img_probs /= img_probs.sum(1)[:,None]\n" + "img_probs /= img_probs.sum(1)[:,None]" ] }, { @@ -1972,7 +5414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "3eb1e1b0", "metadata": {}, "outputs": [], @@ -1982,7 +5424,7 @@ " labs.items()],\n", " columns=['idx', 'label'])\n", "class_labels = class_labels.set_index('idx')\n", - "class_labels = class_labels.sort_index()\n" + "class_labels = class_labels.sort_index()" ] }, { @@ -1997,19 +5439,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "0e89d251", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image: book_images/flamingo.jpg\n", + " label prob\n", + "0 flamingo 0.591760\n", + "1 spoonbill 0.012386\n", + "2 American_egret 0.002105\n", + "Image: book_images/hawk.jpg\n", + " label prob\n", + "0 great_grey_owl 0.287958\n", + "1 kite 0.039478\n", + "2 fountain 0.029384\n", + "Image: book_images/hawk_cropped.jpeg\n", + " label prob\n", + "0 kite 0.301841\n", + "1 jay 0.121659\n", + "2 magpie 0.015511\n", + "Image: book_images/huey.jpg\n", + " label prob\n", + "0 Lhasa 0.151153\n", + "1 Shih-Tzu 0.129804\n", + "2 Tibetan_terrier 0.102400\n", + "Image: book_images/kitty.jpg\n", + " label prob\n", + "0 tabby 0.173628\n", + "1 tiger_cat 0.110415\n", + "2 doormat 0.093447\n", + "Image: book_images/weaver.jpg\n", + " label prob\n", + "0 jacamar 0.287284\n", + "1 bee_eater 0.046768\n", + "2 bulbul 0.037507\n" + ] + } + ], "source": [ "for i, imgfile in enumerate(imgfiles):\n", " img_df = class_labels.copy()\n", " img_df['prob'] = img_probs[i]\n", " img_df = img_df.sort_values(by='prob', ascending=False)[:3]\n", " print(f'Image: {imgfile}')\n", - " print(img_df.reset_index().drop(columns=['idx']))\n" + " print(img_df.reset_index().drop(columns=['idx']))" ] }, { @@ -2026,11 +5503,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "69456669", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "del(cifar_test,\n", @@ -2064,24 +5539,33 @@ "* `load_sparse()`, a sparse matrix version usable by `sklearn`, since we will compare with a lasso fit;\n", "* `load_sequential()`, a padded\n", "version of the original sequence representation, limited to the last\n", - "500 words of each review.\n", - "\n" + "500 words of each review." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "70b3ece2", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n", + " 4468], dtype=int32)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(imdb_seq_train,\n", " imdb_seq_test) = load_sequential(root='data/IMDB')\n", "padded_sample = np.asarray(imdb_seq_train.tensors[0][0])\n", "sample_review = padded_sample[padded_sample > 0][:12]\n", - "sample_review[:12]\n" + "sample_review[:12]" ] }, { @@ -2103,10 +5587,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "349b2af7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\" this film was just brilliant casting location scenery story direction everyone's\"" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lookup = load_lookup(root='data/IMDB')\n", "' '.join(lookup[i] for i in sample_review)" @@ -2126,11 +5621,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "ffa8da37", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "max_num_workers=10\n", @@ -2140,7 +5633,7 @@ " imdb_test,\n", " validation=2000,\n", " num_workers=min(6, max_num_workers),\n", - " batch_size=512)\n" + " batch_size=512)" ] }, { @@ -2153,11 +5646,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "11eeeabb", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "class IMDBModel(nn.Module):\n", @@ -2177,7 +5668,7 @@ " self.activation,\n", " self.output]:\n", " val = _map(val)\n", - " return torch.flatten(val)\n" + " return torch.flatten(val)" ] }, { @@ -2190,17 +5681,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "5fe55a29", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "IMDBModel [25000, 10003] [25000] --\n", + "├─Linear: 1-1 [25000, 10003] [25000, 16] 160,064\n", + "├─ReLU: 1-2 [25000, 16] [25000, 16] --\n", + "├─Linear: 1-3 [25000, 16] [25000, 16] 272\n", + "├─ReLU: 1-4 [25000, 16] [25000, 16] --\n", + "├─Linear: 1-5 [25000, 16] [25000, 1] 17\n", + "===================================================================================================================\n", + "Total params: 160,353\n", + "Trainable params: 160,353\n", + "Non-trainable params: 0\n", + "Total mult-adds (G): 4.01\n", + "===================================================================================================================\n", + "Input size (MB): 1000.30\n", + "Forward/backward pass size (MB): 6.60\n", + "Params size (MB): 0.64\n", + "Estimated Total Size (MB): 1007.54\n", + "===================================================================================================================" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", "summary(imdb_model,\n", " input_size=imdb_test.tensors[0].size(),\n", " col_names=['input_size',\n", " 'output_size',\n", - " 'num_params'])\n" + " 'num_params'])" ] }, { @@ -2222,7 +5753,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "e1ebbc70", "metadata": {}, "outputs": [], @@ -2230,7 +5761,7 @@ "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n", "imdb_module = SimpleModule.binary_classification(\n", " imdb_model,\n", - " optimizer=imdb_optimizer)\n" + " optimizer=imdb_optimizer)" ] }, { @@ -2245,10 +5776,496 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "9feddeb2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | model | IMDBModel | 160 K \n", + "1 | loss | BCEWithLogitsLoss | 0 \n", + "--------------------------------------------\n", + "160 K Trainable params\n", + "0 Non-trainable params\n", + "160 K Total params\n", + "0.641 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1892: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", + " rank_zero_warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "44936d415c7e4ad6b38cd97c733a90be", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + 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"version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + } + ], "source": [ "imdb_logger = CSVLogger('logs', name='IMDB')\n", "imdb_trainer = Trainer(deterministic=True,\n", @@ -2269,12 +6286,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "887d7dad", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "03f177eef9fe4ed090eef15f0a005581", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8543599843978882\n", + " test_loss 1.1992340087890625\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 1.1992340087890625, 'test_accuracy': 0.8543599843978882}]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", "test_results" @@ -2294,7 +6346,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "id": "055a9aeb", "metadata": {}, "outputs": [], @@ -2303,7 +6355,7 @@ " (X_valid, Y_valid),\n", " (X_test, Y_test)) = load_sparse(validation=2000,\n", " random_state=0,\n", - " root='data/IMDB')\n" + " root='data/IMDB')" ] }, { @@ -2318,16 +6370,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "id": "e3e1e181", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "lam_max = np.abs(X_train.T * (Y_train - Y_train.mean())).max()\n", "lam_val = lam_max * np.exp(np.linspace(np.log(1),\n", - " np.log(1e-4), 50))\n" + " np.log(1e-4), 50))" ] }, { @@ -2338,23 +6388,21 @@ "With `LogisticRegression()` the regularization parameter\n", "$C$ is specified as the inverse of $\\lambda$. There are several\n", "solvers for logistic regression; here we use `liblinear` which\n", - "works well with the sparse input format. " + "works well with the sparse input format." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "dd64350c", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "logit = LogisticRegression(penalty='l1', \n", " C=1/lam_max,\n", " solver='liblinear',\n", " warm_start=True,\n", - " fit_intercept=True)\n" + " fit_intercept=True)" ] }, { @@ -2367,7 +6415,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "id": "0bf8b868", "metadata": {}, "outputs": [], @@ -2379,7 +6427,7 @@ " logit.C = 1/l\n", " logit.fit(X_train, Y_train)\n", " coefs.append(logit.coef_.copy())\n", - " intercepts.append(logit.intercept_)\n" + " intercepts.append(logit.intercept_)" ] }, { @@ -2393,15 +6441,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "id": "28256828", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "coefs = np.squeeze(coefs)\n", - "intercepts = np.squeeze(intercepts)\n" + "intercepts = np.squeeze(intercepts)" ] }, { @@ -2415,11 +6461,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "id": "a5945618", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "%%capture\n", @@ -2443,7 +6487,7 @@ " label=data_)\n", "axes[0].legend()\n", "axes[0].set_xlabel(r'$-\\log(\\lambda)$', fontsize=20)\n", - "axes[0].set_ylabel('Accuracy', fontsize=20)\n" + "axes[0].set_ylabel('Accuracy', fontsize=20)" ] }, { @@ -2458,12 +6502,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "id": "74704884", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", "summary_plot(imdb_results,\n", @@ -2493,11 +6547,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "id": "b1ecfca8", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "del(imdb_model,\n", @@ -2534,12 +6586,12 @@ "`ISLP` library. Notably, since more than 90% of the documents\n", "had fewer than 500 words, we set the document length to 500. For\n", "longer documents, we used the last 500 words, and for shorter\n", - "documents, we padded the front with blanks.\n" + "documents, we padded the front with blanks." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "5503cb3f", "metadata": {}, "outputs": [], @@ -2549,7 +6601,7 @@ " validation=2000,\n", " batch_size=300,\n", " num_workers=min(6, max_num_workers)\n", - " )\n" + " )" ] }, { @@ -2582,11 +6634,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "id": "8b683641", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "class LSTMModel(nn.Module):\n", @@ -2613,17 +6663,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "702aa8de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "LSTMModel [10, 500] [10] --\n", + "├─Embedding: 1-1 [10, 500] [10, 500, 32] 320,096\n", + "├─LSTM: 1-2 [10, 500, 32] [10, 500, 32] 8,448\n", + "├─Linear: 1-3 [10, 32] [10, 1] 33\n", + "===================================================================================================================\n", + "Total params: 328,577\n", + "Trainable params: 328,577\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 45.44\n", + "===================================================================================================================\n", + "Input size (MB): 50.00\n", + "Forward/backward pass size (MB): 2.56\n", + "Params size (MB): 1.31\n", + "Estimated Total Size (MB): 53.87\n", + "===================================================================================================================" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lstm_model = LSTMModel(X_test.shape[-1])\n", "summary(lstm_model,\n", " input_data=imdb_seq_train.tensors[0][:10],\n", " col_names=['input_size',\n", " 'output_size',\n", - " 'num_params'])\n" + " 'num_params'])" ] }, { @@ -2637,30 +6725,366 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "id": "e48aa272", "metadata": {}, "outputs": [], "source": [ "lstm_module = SimpleModule.binary_classification(lstm_model)\n", - "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')\n" + "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "143be7e8", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------------\n", + "0 | model | LSTMModel | 328 K \n", + "1 | loss | BCEWithLogitsLoss | 0 \n", + "--------------------------------------------\n", + "328 K Trainable params\n", + "0 Non-trainable params\n", + "328 K Total params\n", + "1.314 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1ee0aabd8caf403c93b8c4ba0a1c2507", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + 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"output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=20` reached.\n" + ] + } + ], "source": [ "lstm_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", " logger=lstm_logger,\n", " callbacks=[ErrorTracker()])\n", "lstm_trainer.fit(lstm_module,\n", - " datamodule=imdb_seq_dm)\n" + " datamodule=imdb_seq_dm)" ] }, { @@ -2674,10 +7098,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "id": "9272e2ba", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92a1cacbb1944d74b3f3c0670eb84b63", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8452399969100952\n", + " test_loss 0.7559056878089905\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.7559056878089905, 'test_accuracy': 0.8452399969100952}]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" ] @@ -2692,12 +7153,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "5d9d387e", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5, 1.0)" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", "fig, ax = subplots(1, 1, figsize=(6, 6))\n", @@ -2707,16 +7187,14 @@ " ylabel='Accuracy')\n", "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n", "ax.set_ylabel('Accuracy')\n", - "ax.set_ylim([0.5, 1])\n" + "ax.set_ylim([0.5, 1])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "7c6d1072", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "outputs": [], "source": [ "del(lstm_model,\n", @@ -2724,7 +7202,7 @@ " lstm_logger,\n", " imdb_seq_dm,\n", " imdb_seq_train,\n", - " imdb_seq_test)\n" + " imdb_seq_test)" ] }, { @@ -2740,7 +7218,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "id": "8e2d1d5c", "metadata": {}, "outputs": [], @@ -2751,7 +7229,7 @@ " with_mean=True,\n", " with_std=True).fit_transform(NYSE[cols]),\n", " columns=NYSE[cols].columns,\n", - " index=NYSE.index)\n" + " index=NYSE.index)" ] }, { @@ -2765,7 +7243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "6f6d0e12", "metadata": {}, "outputs": [], @@ -2776,7 +7254,7 @@ " newcol[lag:] = X[col].values[:-lag]\n", " X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n", "X.insert(len(X.columns), 'train', NYSE['train'])\n", - "X = X.dropna()\n" + "X = X.dropna()" ] }, { @@ -2790,16 +7268,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "id": "d3c961ec", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n", + " 'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n", + " 'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n", + " 'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n", + " dtype='object')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "Y, train = X['log_volume'], X['train']\n", "X = X.drop(columns=['train'] + cols)\n", - "X.columns\n" + "X.columns" ] }, { @@ -2813,10 +7304,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "id": "cb89da88", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.4128912938562521" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M = LinearRegression()\n", "M.fit(X[train], Y[train])\n", @@ -2835,11 +7337,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "id": "55d921d7", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "X_day = pd.merge(X, \n", @@ -2859,12 +7359,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "id": "4e87eeb9", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.45633025715446285" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M.fit(X_day[train], Y[train])\n", "M.score(X_day[~train], Y[~train])" @@ -2895,24 +7404,37 @@ "observation to be earliest in time, so we must reverse the current order. Hence we loop over\n", "`range(5,0,-1)` below, which is\n", "an example of using a `slice()` to index\n", - "iterable objects. The general notation is `start:end:step`. " + "iterable objects. The general notation is `start:end:step`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "id": "3689b318", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n", + " 'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n", + " 'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n", + " 'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n", + " dtype='object')" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ordered_cols = []\n", "for lag in range(5,0,-1):\n", " for col in cols:\n", " ordered_cols.append('{0}_{1}'.format(col, lag))\n", "X = X.reindex(columns=ordered_cols)\n", - "X.columns\n" + "X.columns" ] }, { @@ -2925,12 +7447,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "5633e521", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6046, 5, 3)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X_rnn = X.to_numpy().reshape((-1,5,3))\n", "X_rnn.shape" @@ -2952,7 +7483,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "979a6066", "metadata": {}, "outputs": [], @@ -2989,7 +7520,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "id": "3528f4e7", "metadata": {}, "outputs": [], @@ -2999,7 +7530,7 @@ " X_rnn_t = torch.tensor(X_rnn[mask].astype(np.float32))\n", " Y_t = torch.tensor(Y[mask].astype(np.float32))\n", " datasets.append(TensorDataset(X_rnn_t, Y_t))\n", - "nyse_train, nyse_test = datasets\n" + "nyse_train, nyse_test = datasets" ] }, { @@ -3012,18 +7543,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "id": "795c283e", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " action_fn=lambda data: sys.getsizeof(data.storage()),\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return super().__sizeof__() + self.nbytes()\n" + ] + }, + { + "data": { + "text/plain": [ + "===================================================================================================================\n", + "Layer (type:depth-idx) Input Shape Output Shape Param #\n", + "===================================================================================================================\n", + "NYSEModel [1770, 5, 3] [1770] --\n", + "├─RNN: 1-1 [1770, 5, 3] [1770, 5, 12] 204\n", + "├─Dropout: 1-2 [1770, 12] [1770, 12] --\n", + "├─Linear: 1-3 [1770, 12] [1770, 1] 13\n", + "===================================================================================================================\n", + "Total params: 217\n", + "Trainable params: 217\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 1.83\n", + "===================================================================================================================\n", + "Input size (MB): 0.11\n", + "Forward/backward pass size (MB): 0.86\n", + "Params size (MB): 0.00\n", + "Estimated Total Size (MB): 0.97\n", + "===================================================================================================================" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "summary(nyse_model,\n", " input_data=X_rnn_t,\n", " col_names=['input_size',\n", " 'output_size',\n", - " 'num_params'])\n" + " 'num_params'])" ] }, { @@ -3038,11 +7605,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "id": "31640121", - "metadata": { - "lines_to_next_cell": 0 - }, + "metadata": {}, "outputs": [], "source": [ "nyse_dm = SimpleDataModule(nyse_train,\n", @@ -3062,16 +7627,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "af6795f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64]) torch.Size([64])\n", + "torch.Size([64]) torch.Size([64])\n", + "torch.Size([64]) torch.Size([64])\n" + ] + } + ], "source": [ "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", " out = nyse_model(x)\n", " print(y.size(), out.size())\n", " if idx >= 2:\n", - " break\n" + " break" ] }, { @@ -3086,7 +7661,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "0d04344c", "metadata": {}, "outputs": [], @@ -3095,7 +7670,7 @@ " lr=0.001)\n", "nyse_module = SimpleModule.regression(nyse_model,\n", " optimizer=nyse_optimizer,\n", - " metrics={'r2':R2Score()})\n" + " metrics={'r2':R2Score()})" ] }, { @@ -3104,17 +7679,1870 @@ "metadata": {}, "source": [ "Fitting the model should by now be familiar.\n", - "The results on the test data are very similar to the linear AR model. " + "The results on the test data are very similar to the linear AR model." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "id": "485d7bb1", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------\n", + "0 | model | NYSEModel | 217 \n", + "1 | loss | MSELoss | 0 \n", + "------------------------------------\n", + "217 Trainable params\n", + "0 Non-trainable params\n", + "217 Total params\n", + "0.001 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bf9eb739cc954760813987d94e79fb8e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7c66168b0e85424c8abe2446f7f9e5a9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6998335a07b341f39fd4ebf1c93d162f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "076763c8702042028a8d216e15fef774", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, 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{ + "model_id": "4f1264a1828b4d55b74f0dccd97919c5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "59bf88e571724091ab98f7258dbe4780", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "632942952df344ee98d2423705e060c5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "152694e1fe214fe899cbeba080d5fec7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92e87ecd035e4a98af8940873c217a08", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5cfb6d3324224105bb51a11ee3610e99", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4b11d5fcedba4da68bec988d9c1af1a8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "39bf95953cd84ff1b5206301be0caba9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4507f5f3b42c4f79815e72a55cd57101", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c47d400fe5ac44098a50f94c59223d71", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b580013d7f0447a96878fa0982eda94", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "49e3e8b33931437bb03110aacac4c11d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82e34b2ab25a4d52bd58dd791fab350b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0233c4562c014826845209456bb8d5f4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Testing: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.6207807064056396\n", + " test_r2 0.41084879636764526\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.6207807064056396, 'test_r2': 0.41084879636764526}]" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nyse_trainer = Trainer(deterministic=True,\n", " max_epochs=200,\n", @@ -3143,7 +9571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "91d6633a", "metadata": {}, "outputs": [], @@ -3167,7 +9595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "87137503", "metadata": {}, "outputs": [], @@ -3176,7 +9604,7 @@ " day_test,\n", " num_workers=min(4, max_num_workers),\n", " validation=day_test,\n", - " batch_size=64)\n" + " batch_size=64)" ] }, { @@ -3189,7 +9617,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "3cf09a55", "metadata": {}, "outputs": [], @@ -3203,12 +9631,12 @@ " nn.Dropout(0.5),\n", " nn.Linear(32, 1))\n", " def forward(self, x):\n", - " return torch.flatten(self._forward(x))\n" + " return torch.flatten(self._forward(x))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "930576f3", "metadata": {}, "outputs": [], @@ -3218,7 +9646,7 @@ " lr=0.001)\n", "nl_module = SimpleModule.regression(nl_model,\n", " optimizer=nl_optimizer,\n", - " metrics={'r2':R2Score()})\n" + " metrics={'r2':R2Score()})" ] }, { @@ -3232,12 +9660,281 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "b9ae8d0e", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | NonLinearARModel | 705 \n", + "1 | loss | MSELoss | 0 \n", + "-------------------------------------------\n", + "705 Trainable params\n", + "0 Non-trainable params\n", + "705 Total params\n", + "0.003 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1e278bfc60e6404f98175752ac8f6482", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 0.5648325681686401\n", + " test_r2 0.4639463424682617\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'test_loss': 0.5648325681686401, 'test_r2': 0.4639463424682617}]" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nl_trainer = Trainer(deterministic=True,\n", " max_epochs=20,\n", @@ -3245,23 +9942,30 @@ "nl_trainer.fit(nl_module, datamodule=day_dm)\n", "nl_trainer.test(nl_module, datamodule=day_dm) " ] - }, - { - "cell_type": "markdown", - "id": "fd356e46", - "metadata": {}, - "source": [ - " \n", - "\n", - " \n" - ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,md:myst", + "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch11-surv-lab.ipynb b/docs/source/labs/Ch11-surv-lab.ipynb index f85f01f..07c4a5d 100644 --- a/docs/source/labs/Ch11-surv-lab.ipynb +++ b/docs/source/labs/Ch11-surv-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Survival Analysis\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -28,7 +28,14 @@ "cell_type": "code", "execution_count": null, "id": "91ac40fd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:03.064349Z", + "iopub.status.busy": "2023-07-26T05:18:03.064029Z", + "iopub.status.idle": "2023-07-26T05:18:04.312445Z", + "shell.execute_reply": "2023-07-26T05:18:04.312016Z" + } + }, "outputs": [], "source": [ "from matplotlib.pyplot import subplots\n", @@ -51,7 +58,14 @@ "cell_type": "code", "execution_count": null, "id": "99782418", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.315115Z", + "iopub.status.busy": "2023-07-26T05:18:04.314859Z", + "iopub.status.idle": "2023-07-26T05:18:04.379768Z", + "shell.execute_reply": "2023-07-26T05:18:04.379303Z" + } + }, "outputs": [], "source": [ "from lifelines import \\\n", @@ -77,7 +91,14 @@ "cell_type": "code", "execution_count": null, "id": "3137149a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.382594Z", + "iopub.status.busy": "2023-07-26T05:18:04.382386Z", + "iopub.status.idle": "2023-07-26T05:18:04.389761Z", + "shell.execute_reply": "2023-07-26T05:18:04.389375Z" + } + }, "outputs": [], "source": [ "BrainCancer = load_data('BrainCancer')\n", @@ -97,7 +118,14 @@ "cell_type": "code", "execution_count": null, "id": "45963c92", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.392289Z", + "iopub.status.busy": "2023-07-26T05:18:04.392117Z", + "iopub.status.idle": "2023-07-26T05:18:04.396285Z", + "shell.execute_reply": "2023-07-26T05:18:04.395840Z" + } + }, "outputs": [], "source": [ "BrainCancer['sex'].value_counts()" @@ -107,7 +135,14 @@ "cell_type": "code", "execution_count": null, "id": "73be61f6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.400201Z", + "iopub.status.busy": "2023-07-26T05:18:04.399975Z", + "iopub.status.idle": "2023-07-26T05:18:04.403967Z", + "shell.execute_reply": "2023-07-26T05:18:04.403581Z" + } + }, "outputs": [], "source": [ "BrainCancer['diagnosis'].value_counts()" @@ -117,7 +152,14 @@ "cell_type": "code", "execution_count": null, "id": "572f0b9e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.406232Z", + "iopub.status.busy": "2023-07-26T05:18:04.406110Z", + "iopub.status.idle": "2023-07-26T05:18:04.409773Z", + "shell.execute_reply": "2023-07-26T05:18:04.409374Z" + } + }, "outputs": [], "source": [ "BrainCancer['status'].value_counts()" @@ -152,7 +194,14 @@ "cell_type": "code", "execution_count": null, "id": "92c39707", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.411840Z", + "iopub.status.busy": "2023-07-26T05:18:04.411663Z", + "iopub.status.idle": "2023-07-26T05:18:04.539774Z", + "shell.execute_reply": "2023-07-26T05:18:04.539439Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -190,7 +239,14 @@ "cell_type": "code", "execution_count": null, "id": "3fc7848c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.542265Z", + "iopub.status.busy": "2023-07-26T05:18:04.542002Z", + "iopub.status.idle": "2023-07-26T05:18:04.674613Z", + "shell.execute_reply": "2023-07-26T05:18:04.674126Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -217,7 +273,14 @@ "cell_type": "code", "execution_count": null, "id": "bf30d26f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.679625Z", + "iopub.status.busy": "2023-07-26T05:18:04.679410Z", + "iopub.status.idle": "2023-07-26T05:18:04.743501Z", + "shell.execute_reply": "2023-07-26T05:18:04.743166Z" + } + }, "outputs": [], "source": [ "logrank_test(by_sex['Male']['time'],\n", @@ -243,7 +306,14 @@ "cell_type": "code", "execution_count": null, "id": "2ab78e07", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.746088Z", + "iopub.status.busy": "2023-07-26T05:18:04.745814Z", + "iopub.status.idle": "2023-07-26T05:18:04.773972Z", + "shell.execute_reply": "2023-07-26T05:18:04.773498Z" + } + }, "outputs": [], "source": [ "coxph = CoxPHFitter # shorthand\n", @@ -275,7 +345,14 @@ "cell_type": "code", "execution_count": null, "id": "4716b7b0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.776775Z", + "iopub.status.busy": "2023-07-26T05:18:04.776463Z", + "iopub.status.idle": "2023-07-26T05:18:04.783362Z", + "shell.execute_reply": "2023-07-26T05:18:04.782865Z" + } + }, "outputs": [], "source": [ "cox_fit.log_likelihood_ratio_test()" @@ -300,7 +377,14 @@ "cell_type": "code", "execution_count": null, "id": "c2767d88", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.785790Z", + "iopub.status.busy": "2023-07-26T05:18:04.785586Z", + "iopub.status.idle": "2023-07-26T05:18:04.824235Z", + "shell.execute_reply": "2023-07-26T05:18:04.823739Z" + } + }, "outputs": [], "source": [ "cleaned = BrainCancer.dropna()\n", @@ -338,7 +422,14 @@ "cell_type": "code", "execution_count": null, "id": "ede1d219", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.826852Z", + "iopub.status.busy": "2023-07-26T05:18:04.826662Z", + "iopub.status.idle": "2023-07-26T05:18:04.831013Z", + "shell.execute_reply": "2023-07-26T05:18:04.830464Z" + } + }, "outputs": [], "source": [ "levels = cleaned['diagnosis'].unique()\n", @@ -364,7 +455,14 @@ "cell_type": "code", "execution_count": null, "id": "dc032a71", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.833522Z", + "iopub.status.busy": "2023-07-26T05:18:04.833389Z", + "iopub.status.idle": "2023-07-26T05:18:04.840549Z", + "shell.execute_reply": "2023-07-26T05:18:04.840260Z" + } + }, "outputs": [], "source": [ "modal_df = pd.DataFrame(\n", @@ -386,7 +484,14 @@ "cell_type": "code", "execution_count": null, "id": "e7c1fe43", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.843157Z", + "iopub.status.busy": "2023-07-26T05:18:04.843004Z", + "iopub.status.idle": "2023-07-26T05:18:04.852876Z", + "shell.execute_reply": "2023-07-26T05:18:04.852545Z" + } + }, "outputs": [], "source": [ "modal_X = all_MS.transform(modal_df)\n", @@ -406,7 +511,14 @@ "cell_type": "code", "execution_count": null, "id": "f89fbed7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.855400Z", + "iopub.status.busy": "2023-07-26T05:18:04.855197Z", + "iopub.status.idle": "2023-07-26T05:18:04.864584Z", + "shell.execute_reply": "2023-07-26T05:18:04.864194Z" + } + }, "outputs": [], "source": [ "predicted_survival = fit_all.predict_survival_function(modal_X)\n", @@ -427,7 +539,14 @@ "cell_type": "code", "execution_count": null, "id": "8f0329b4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.867553Z", + "iopub.status.busy": "2023-07-26T05:18:04.867105Z", + "iopub.status.idle": "2023-07-26T05:18:04.987503Z", + "shell.execute_reply": "2023-07-26T05:18:04.986681Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -451,7 +570,14 @@ "cell_type": "code", "execution_count": null, "id": "3045bfc0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:04.990450Z", + "iopub.status.busy": "2023-07-26T05:18:04.990225Z", + "iopub.status.idle": "2023-07-26T05:18:05.181343Z", + "shell.execute_reply": "2023-07-26T05:18:05.178676Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -478,7 +604,14 @@ "cell_type": "code", "execution_count": null, "id": "d070f716", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.184743Z", + "iopub.status.busy": "2023-07-26T05:18:05.184569Z", + "iopub.status.idle": "2023-07-26T05:18:05.222538Z", + "shell.execute_reply": "2023-07-26T05:18:05.221996Z" + } + }, "outputs": [], "source": [ "posres_df = MS(['posres',\n", @@ -505,7 +638,14 @@ "cell_type": "code", "execution_count": null, "id": "2bbcdd0c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.225196Z", + "iopub.status.busy": "2023-07-26T05:18:05.224969Z", + "iopub.status.idle": "2023-07-26T05:18:05.270258Z", + "shell.execute_reply": "2023-07-26T05:18:05.269721Z" + } + }, "outputs": [], "source": [ "model = MS(Publication.columns.drop('mech'),\n", @@ -553,7 +693,14 @@ "cell_type": "code", "execution_count": null, "id": "b8ece43a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.272852Z", + "iopub.status.busy": "2023-07-26T05:18:05.272613Z", + "iopub.status.idle": "2023-07-26T05:18:05.277670Z", + "shell.execute_reply": "2023-07-26T05:18:05.277169Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(10)\n", @@ -584,7 +731,14 @@ "cell_type": "code", "execution_count": null, "id": "3e4f766f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.279951Z", + "iopub.status.busy": "2023-07-26T05:18:05.279774Z", + "iopub.status.idle": "2023-07-26T05:18:05.289519Z", + "shell.execute_reply": "2023-07-26T05:18:05.288948Z" + } + }, "outputs": [], "source": [ "model = MS(['Operators',\n", @@ -609,7 +763,14 @@ "cell_type": "code", "execution_count": null, "id": "72f42d14", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.292713Z", + "iopub.status.busy": "2023-07-26T05:18:05.292424Z", + "iopub.status.idle": "2023-07-26T05:18:05.299084Z", + "shell.execute_reply": "2023-07-26T05:18:05.298414Z" + } + }, "outputs": [], "source": [ "X[:5]" @@ -627,7 +788,14 @@ "cell_type": "code", "execution_count": null, "id": "8b921536", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.301800Z", + "iopub.status.busy": "2023-07-26T05:18:05.301580Z", + "iopub.status.idle": "2023-07-26T05:18:05.340807Z", + "shell.execute_reply": "2023-07-26T05:18:05.311843Z" + } + }, "outputs": [], "source": [ "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n", @@ -667,7 +835,14 @@ "cell_type": "code", "execution_count": null, "id": "96ce0f99", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.399214Z", + "iopub.status.busy": "2023-07-26T05:18:05.386941Z", + "iopub.status.idle": "2023-07-26T05:18:05.423513Z", + "shell.execute_reply": "2023-07-26T05:18:05.417411Z" + } + }, "outputs": [], "source": [ "cum_hazard = lambda t: 1e-5 * t**2 / 2" @@ -690,7 +865,14 @@ "cell_type": "code", "execution_count": null, "id": "63d78ff9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.450050Z", + "iopub.status.busy": "2023-07-26T05:18:05.449814Z", + "iopub.status.idle": "2023-07-26T05:18:05.587170Z", + "shell.execute_reply": "2023-07-26T05:18:05.586673Z" + } + }, "outputs": [], "source": [ "W = np.array([sim_time(l, cum_hazard, rng)\n", @@ -712,7 +894,14 @@ "cell_type": "code", "execution_count": null, "id": "fe008dbf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.590252Z", + "iopub.status.busy": "2023-07-26T05:18:05.589902Z", + "iopub.status.idle": "2023-07-26T05:18:05.596018Z", + "shell.execute_reply": "2023-07-26T05:18:05.595604Z" + } + }, "outputs": [], "source": [ "D['Failed'] = rng.choice([1, 0],\n", @@ -725,7 +914,14 @@ "cell_type": "code", "execution_count": null, "id": "c3a2bec7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.598122Z", + "iopub.status.busy": "2023-07-26T05:18:05.597969Z", + "iopub.status.idle": "2023-07-26T05:18:05.601009Z", + "shell.execute_reply": "2023-07-26T05:18:05.600579Z" + } + }, "outputs": [], "source": [ "D['Failed'].mean()" @@ -743,7 +939,14 @@ "cell_type": "code", "execution_count": null, "id": "2b27af56", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.603318Z", + "iopub.status.busy": "2023-07-26T05:18:05.603142Z", + "iopub.status.idle": "2023-07-26T05:18:05.827076Z", + "shell.execute_reply": "2023-07-26T05:18:05.826151Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -767,7 +970,14 @@ "cell_type": "code", "execution_count": null, "id": "9625598d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:05.830554Z", + "iopub.status.busy": "2023-07-26T05:18:05.830059Z", + "iopub.status.idle": "2023-07-26T05:18:06.008529Z", + "shell.execute_reply": "2023-07-26T05:18:06.008079Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -795,7 +1005,14 @@ "cell_type": "code", "execution_count": null, "id": "75a744ef", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:06.012649Z", + "iopub.status.busy": "2023-07-26T05:18:06.012037Z", + "iopub.status.idle": "2023-07-26T05:18:06.033832Z", + "shell.execute_reply": "2023-07-26T05:18:06.033320Z" + } + }, "outputs": [], "source": [ "multivariate_logrank_test(D['Wait time'],\n", @@ -815,7 +1032,14 @@ "cell_type": "code", "execution_count": null, "id": "9badb3e3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:06.036375Z", + "iopub.status.busy": "2023-07-26T05:18:06.036170Z", + "iopub.status.idle": "2023-07-26T05:18:06.055850Z", + "shell.execute_reply": "2023-07-26T05:18:06.055183Z" + } + }, "outputs": [], "source": [ "multivariate_logrank_test(D['Wait time'],\n", @@ -838,7 +1062,14 @@ "cell_type": "code", "execution_count": null, "id": "026e9ff8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:06.058057Z", + "iopub.status.busy": "2023-07-26T05:18:06.057906Z", + "iopub.status.idle": "2023-07-26T05:18:06.184883Z", + "shell.execute_reply": "2023-07-26T05:18:06.184356Z" + } + }, "outputs": [], "source": [ "X = MS(['Wait time',\n", @@ -861,7 +1092,14 @@ "cell_type": "code", "execution_count": null, "id": "7cab3789", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:06.187428Z", + "iopub.status.busy": "2023-07-26T05:18:06.187255Z", + "iopub.status.idle": "2023-07-26T05:18:06.315832Z", + "shell.execute_reply": "2023-07-26T05:18:06.314374Z" + } + }, "outputs": [], "source": [ "X = MS(['Wait time',\n", @@ -887,7 +1125,14 @@ "cell_type": "code", "execution_count": null, "id": "5cc4b898", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:06.320288Z", + "iopub.status.busy": "2023-07-26T05:18:06.319261Z", + "iopub.status.idle": "2023-07-26T05:18:06.585074Z", + "shell.execute_reply": "2023-07-26T05:18:06.577564Z" + } + }, "outputs": [], "source": [ "X = MS(D.columns,\n", @@ -921,6 +1166,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch12-unsup-lab.ipynb b/docs/source/labs/Ch12-unsup-lab.ipynb index 7c7f483..6b956a8 100644 --- a/docs/source/labs/Ch12-unsup-lab.ipynb +++ b/docs/source/labs/Ch12-unsup-lab.ipynb @@ -5,9 +5,9 @@ "id": "80f16ff6", "metadata": {}, "source": [ - "# : Unsupervised Learning\n", + " # Unsupervised Learning\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -23,7 +23,14 @@ "cell_type": "code", "execution_count": null, "id": "24559be0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:08.163850Z", + "iopub.status.busy": "2023-07-26T05:18:08.163726Z", + "iopub.status.idle": "2023-07-26T05:18:09.209261Z", + "shell.execute_reply": "2023-07-26T05:18:09.208715Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -48,7 +55,14 @@ "cell_type": "code", "execution_count": null, "id": "06fff57d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:09.212356Z", + "iopub.status.busy": "2023-07-26T05:18:09.212070Z", + "iopub.status.idle": "2023-07-26T05:18:09.327611Z", + "shell.execute_reply": "2023-07-26T05:18:09.327246Z" + } + }, "outputs": [], "source": [ "from sklearn.cluster import \\\n", @@ -78,7 +92,14 @@ "cell_type": "code", "execution_count": null, "id": "f425e07e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:09.329909Z", + "iopub.status.busy": "2023-07-26T05:18:09.329763Z", + "iopub.status.idle": "2023-07-26T05:18:10.559326Z", + "shell.execute_reply": "2023-07-26T05:18:10.558692Z" + } + }, "outputs": [], "source": [ "USArrests = get_rdataset('USArrests').data\n", @@ -97,7 +118,14 @@ "cell_type": "code", "execution_count": null, "id": "b127d014", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.561457Z", + "iopub.status.busy": "2023-07-26T05:18:10.561326Z", + "iopub.status.idle": "2023-07-26T05:18:10.564289Z", + "shell.execute_reply": "2023-07-26T05:18:10.563966Z" + } + }, "outputs": [], "source": [ "USArrests.columns" @@ -115,7 +143,14 @@ "cell_type": "code", "execution_count": null, "id": "c7343f72", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.566649Z", + "iopub.status.busy": "2023-07-26T05:18:10.566503Z", + "iopub.status.idle": "2023-07-26T05:18:10.570319Z", + "shell.execute_reply": "2023-07-26T05:18:10.569890Z" + } + }, "outputs": [], "source": [ "USArrests.mean()" @@ -135,7 +170,14 @@ "cell_type": "code", "execution_count": null, "id": "34501140", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.572610Z", + "iopub.status.busy": "2023-07-26T05:18:10.572485Z", + "iopub.status.idle": "2023-07-26T05:18:10.575894Z", + "shell.execute_reply": "2023-07-26T05:18:10.575543Z" + } + }, "outputs": [], "source": [ "USArrests.var()" @@ -167,7 +209,14 @@ "cell_type": "code", "execution_count": null, "id": "daf119e8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.578199Z", + "iopub.status.busy": "2023-07-26T05:18:10.578025Z", + "iopub.status.idle": "2023-07-26T05:18:10.581928Z", + "shell.execute_reply": "2023-07-26T05:18:10.581446Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler(with_std=True,\n", @@ -189,7 +238,14 @@ "cell_type": "code", "execution_count": null, "id": "a0eda7c9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.584181Z", + "iopub.status.busy": "2023-07-26T05:18:10.584070Z", + "iopub.status.idle": "2023-07-26T05:18:10.586440Z", + "shell.execute_reply": "2023-07-26T05:18:10.586052Z" + } + }, "outputs": [], "source": [ "pcaUS = PCA()" @@ -210,7 +266,14 @@ "cell_type": "code", "execution_count": null, "id": "1430fb3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.588957Z", + "iopub.status.busy": "2023-07-26T05:18:10.588809Z", + "iopub.status.idle": "2023-07-26T05:18:10.593315Z", + "shell.execute_reply": "2023-07-26T05:18:10.592971Z" + } + }, "outputs": [], "source": [ "pcaUS.fit(USArrests_scaled)" @@ -230,7 +293,14 @@ "cell_type": "code", "execution_count": null, "id": "6131d8d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.595542Z", + "iopub.status.busy": "2023-07-26T05:18:10.595388Z", + "iopub.status.idle": "2023-07-26T05:18:10.598527Z", + "shell.execute_reply": "2023-07-26T05:18:10.598057Z" + } + }, "outputs": [], "source": [ "pcaUS.mean_" @@ -249,7 +319,14 @@ "cell_type": "code", "execution_count": null, "id": "08246aad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.600656Z", + "iopub.status.busy": "2023-07-26T05:18:10.600536Z", + "iopub.status.idle": "2023-07-26T05:18:10.603024Z", + "shell.execute_reply": "2023-07-26T05:18:10.602498Z" + } + }, "outputs": [], "source": [ "scores = pcaUS.transform(USArrests_scaled)" @@ -270,7 +347,14 @@ "cell_type": "code", "execution_count": null, "id": "b682b632", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.605166Z", + "iopub.status.busy": "2023-07-26T05:18:10.605000Z", + "iopub.status.idle": "2023-07-26T05:18:10.607923Z", + "shell.execute_reply": "2023-07-26T05:18:10.607498Z" + } + }, "outputs": [], "source": [ "pcaUS.components_ " @@ -292,7 +376,14 @@ "cell_type": "code", "execution_count": null, "id": "c165e990", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.610624Z", + "iopub.status.busy": "2023-07-26T05:18:10.610442Z", + "iopub.status.idle": "2023-07-26T05:18:10.736284Z", + "shell.execute_reply": "2023-07-26T05:18:10.735735Z" + } + }, "outputs": [], "source": [ "i, j = 0, 1 # which components\n", @@ -323,7 +414,14 @@ "cell_type": "code", "execution_count": null, "id": "848c9f35", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.738926Z", + "iopub.status.busy": "2023-07-26T05:18:10.738771Z", + "iopub.status.idle": "2023-07-26T05:18:10.848441Z", + "shell.execute_reply": "2023-07-26T05:18:10.847972Z" + } + }, "outputs": [], "source": [ "scale_arrow = s_ = 2\n", @@ -352,7 +450,14 @@ "cell_type": "code", "execution_count": null, "id": "34fdfe21", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.852220Z", + "iopub.status.busy": "2023-07-26T05:18:10.851752Z", + "iopub.status.idle": "2023-07-26T05:18:10.859190Z", + "shell.execute_reply": "2023-07-26T05:18:10.857389Z" + } + }, "outputs": [], "source": [ "scores.std(0, ddof=1)" @@ -371,7 +476,14 @@ "cell_type": "code", "execution_count": null, "id": "31b43c57", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.861366Z", + "iopub.status.busy": "2023-07-26T05:18:10.861239Z", + "iopub.status.idle": "2023-07-26T05:18:10.864540Z", + "shell.execute_reply": "2023-07-26T05:18:10.863998Z" + } + }, "outputs": [], "source": [ "pcaUS.explained_variance_" @@ -390,7 +502,14 @@ "cell_type": "code", "execution_count": null, "id": "68e47d3a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.867030Z", + "iopub.status.busy": "2023-07-26T05:18:10.866863Z", + "iopub.status.idle": "2023-07-26T05:18:10.869949Z", + "shell.execute_reply": "2023-07-26T05:18:10.869455Z" + } + }, "outputs": [], "source": [ "pcaUS.explained_variance_ratio_" @@ -412,7 +531,14 @@ "cell_type": "code", "execution_count": null, "id": "e87fe198", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:10.872333Z", + "iopub.status.busy": "2023-07-26T05:18:10.872213Z", + "iopub.status.idle": "2023-07-26T05:18:11.030288Z", + "shell.execute_reply": "2023-07-26T05:18:11.029440Z" + } + }, "outputs": [], "source": [ "%%capture\n", @@ -440,7 +566,14 @@ "cell_type": "code", "execution_count": null, "id": "409fb0c6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.033250Z", + "iopub.status.busy": "2023-07-26T05:18:11.033097Z", + "iopub.status.idle": "2023-07-26T05:18:11.153804Z", + "shell.execute_reply": "2023-07-26T05:18:11.153245Z" + } + }, "outputs": [], "source": [ "ax = axes[1]\n", @@ -468,7 +601,14 @@ "cell_type": "code", "execution_count": null, "id": "e563e41b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.157597Z", + "iopub.status.busy": "2023-07-26T05:18:11.157216Z", + "iopub.status.idle": "2023-07-26T05:18:11.162432Z", + "shell.execute_reply": "2023-07-26T05:18:11.162026Z" + } + }, "outputs": [], "source": [ "a = np.array([1,2,8,-3])\n", @@ -498,7 +638,14 @@ "cell_type": "code", "execution_count": null, "id": "f83ad0bc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.164364Z", + "iopub.status.busy": "2023-07-26T05:18:11.164225Z", + "iopub.status.idle": "2023-07-26T05:18:11.167031Z", + "shell.execute_reply": "2023-07-26T05:18:11.166724Z" + } + }, "outputs": [], "source": [ "X = USArrests_scaled\n", @@ -520,7 +667,14 @@ "cell_type": "code", "execution_count": null, "id": "cb9bdc46", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.169269Z", + "iopub.status.busy": "2023-07-26T05:18:11.169096Z", + "iopub.status.idle": "2023-07-26T05:18:11.172167Z", + "shell.execute_reply": "2023-07-26T05:18:11.171624Z" + } + }, "outputs": [], "source": [ "V" @@ -530,7 +684,14 @@ "cell_type": "code", "execution_count": null, "id": "f23c101e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.174404Z", + "iopub.status.busy": "2023-07-26T05:18:11.174265Z", + "iopub.status.idle": "2023-07-26T05:18:11.177096Z", + "shell.execute_reply": "2023-07-26T05:18:11.176712Z" + } + }, "outputs": [], "source": [ "pcaUS.components_" @@ -548,7 +709,14 @@ "cell_type": "code", "execution_count": null, "id": "4cc49622", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.179219Z", + "iopub.status.busy": "2023-07-26T05:18:11.179054Z", + "iopub.status.idle": "2023-07-26T05:18:11.182070Z", + "shell.execute_reply": "2023-07-26T05:18:11.181699Z" + } + }, "outputs": [], "source": [ "(U * D[None,:])[:3]" @@ -558,7 +726,14 @@ "cell_type": "code", "execution_count": null, "id": "c96c9fe1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.184051Z", + "iopub.status.busy": "2023-07-26T05:18:11.183934Z", + "iopub.status.idle": "2023-07-26T05:18:11.186945Z", + "shell.execute_reply": "2023-07-26T05:18:11.186473Z" + } + }, "outputs": [], "source": [ "scores[:3]" @@ -582,7 +757,14 @@ "cell_type": "code", "execution_count": null, "id": "574409d6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.189376Z", + "iopub.status.busy": "2023-07-26T05:18:11.189199Z", + "iopub.status.idle": "2023-07-26T05:18:11.192254Z", + "shell.execute_reply": "2023-07-26T05:18:11.191873Z" + } + }, "outputs": [], "source": [ "n_omit = 20\n", @@ -615,7 +797,14 @@ "cell_type": "code", "execution_count": null, "id": "89f190ae", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.194569Z", + "iopub.status.busy": "2023-07-26T05:18:11.194458Z", + "iopub.status.idle": "2023-07-26T05:18:11.197204Z", + "shell.execute_reply": "2023-07-26T05:18:11.196762Z" + } + }, "outputs": [], "source": [ "def low_rank(X, M=1):\n", @@ -640,7 +829,14 @@ "cell_type": "code", "execution_count": null, "id": "322f339c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.199355Z", + "iopub.status.busy": "2023-07-26T05:18:11.199208Z", + "iopub.status.idle": "2023-07-26T05:18:11.201784Z", + "shell.execute_reply": "2023-07-26T05:18:11.201348Z" + } + }, "outputs": [], "source": [ "Xhat = Xna.copy()\n", @@ -661,7 +857,14 @@ "cell_type": "code", "execution_count": null, "id": "7e106d1a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.204020Z", + "iopub.status.busy": "2023-07-26T05:18:11.203890Z", + "iopub.status.idle": "2023-07-26T05:18:11.206286Z", + "shell.execute_reply": "2023-07-26T05:18:11.205915Z" + } + }, "outputs": [], "source": [ "thresh = 1e-7\n", @@ -693,7 +896,14 @@ "cell_type": "code", "execution_count": null, "id": "7cb05ce4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.208518Z", + "iopub.status.busy": "2023-07-26T05:18:11.208345Z", + "iopub.status.idle": "2023-07-26T05:18:11.212476Z", + "shell.execute_reply": "2023-07-26T05:18:11.211847Z" + } + }, "outputs": [], "source": [ "while rel_err > thresh:\n", @@ -725,7 +935,14 @@ "cell_type": "code", "execution_count": null, "id": "6f245188", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.214882Z", + "iopub.status.busy": "2023-07-26T05:18:11.214746Z", + "iopub.status.idle": "2023-07-26T05:18:11.217935Z", + "shell.execute_reply": "2023-07-26T05:18:11.217596Z" + } + }, "outputs": [], "source": [ "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]" @@ -764,7 +981,14 @@ "cell_type": "code", "execution_count": null, "id": "345fb41e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.220117Z", + "iopub.status.busy": "2023-07-26T05:18:11.219983Z", + "iopub.status.idle": "2023-07-26T05:18:11.222355Z", + "shell.execute_reply": "2023-07-26T05:18:11.221948Z" + } + }, "outputs": [], "source": [ "np.random.seed(0);\n", @@ -785,7 +1009,14 @@ "cell_type": "code", "execution_count": null, "id": "3a8c21a2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.224388Z", + "iopub.status.busy": "2023-07-26T05:18:11.224258Z", + "iopub.status.idle": "2023-07-26T05:18:11.262343Z", + "shell.execute_reply": "2023-07-26T05:18:11.261799Z" + } + }, "outputs": [], "source": [ "kmeans = KMeans(n_clusters=2,\n", @@ -805,7 +1036,14 @@ "cell_type": "code", "execution_count": null, "id": "e3e35b5d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.265599Z", + "iopub.status.busy": "2023-07-26T05:18:11.265438Z", + "iopub.status.idle": "2023-07-26T05:18:11.268983Z", + "shell.execute_reply": "2023-07-26T05:18:11.268283Z" + } + }, "outputs": [], "source": [ "kmeans.labels_" @@ -826,7 +1064,14 @@ "cell_type": "code", "execution_count": null, "id": "d928650a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.271491Z", + "iopub.status.busy": "2023-07-26T05:18:11.271353Z", + "iopub.status.idle": "2023-07-26T05:18:11.409914Z", + "shell.execute_reply": "2023-07-26T05:18:11.409365Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n", @@ -855,7 +1100,14 @@ "cell_type": "code", "execution_count": null, "id": "92e5175c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.412487Z", + "iopub.status.busy": "2023-07-26T05:18:11.412250Z", + "iopub.status.idle": "2023-07-26T05:18:11.548124Z", + "shell.execute_reply": "2023-07-26T05:18:11.547642Z" + } + }, "outputs": [], "source": [ "kmeans = KMeans(n_clusters=3,\n", @@ -885,7 +1137,14 @@ "cell_type": "code", "execution_count": null, "id": "4911ecc7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.551298Z", + "iopub.status.busy": "2023-07-26T05:18:11.551122Z", + "iopub.status.idle": "2023-07-26T05:18:11.570392Z", + "shell.execute_reply": "2023-07-26T05:18:11.569843Z" + } + }, "outputs": [], "source": [ "kmeans1 = KMeans(n_clusters=3,\n", @@ -939,7 +1198,14 @@ "cell_type": "code", "execution_count": null, "id": "1b42a700", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.573065Z", + "iopub.status.busy": "2023-07-26T05:18:11.572885Z", + "iopub.status.idle": "2023-07-26T05:18:11.577747Z", + "shell.execute_reply": "2023-07-26T05:18:11.577317Z" + } + }, "outputs": [], "source": [ "HClust = AgglomerativeClustering\n", @@ -962,7 +1228,14 @@ "cell_type": "code", "execution_count": null, "id": "50ef7eea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.580226Z", + "iopub.status.busy": "2023-07-26T05:18:11.580003Z", + "iopub.status.idle": "2023-07-26T05:18:11.583835Z", + "shell.execute_reply": "2023-07-26T05:18:11.583381Z" + } + }, "outputs": [], "source": [ "hc_avg = HClust(distance_threshold=0,\n", @@ -988,7 +1261,14 @@ "cell_type": "code", "execution_count": null, "id": "bf7a2408", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.586413Z", + "iopub.status.busy": "2023-07-26T05:18:11.586258Z", + "iopub.status.idle": "2023-07-26T05:18:11.591990Z", + "shell.execute_reply": "2023-07-26T05:18:11.591553Z" + } + }, "outputs": [], "source": [ "D = np.zeros((X.shape[0], X.shape[0]));\n", @@ -1024,7 +1304,14 @@ "cell_type": "code", "execution_count": null, "id": "a118c0ab", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.595666Z", + "iopub.status.busy": "2023-07-26T05:18:11.595400Z", + "iopub.status.idle": "2023-07-26T05:18:11.876074Z", + "shell.execute_reply": "2023-07-26T05:18:11.875555Z" + } + }, "outputs": [], "source": [ "cargs = {'color_threshold':-np.inf,\n", @@ -1051,7 +1338,14 @@ "cell_type": "code", "execution_count": null, "id": "b1ff41c0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:11.878711Z", + "iopub.status.busy": "2023-07-26T05:18:11.878497Z", + "iopub.status.idle": "2023-07-26T05:18:12.156685Z", + "shell.execute_reply": "2023-07-26T05:18:12.156217Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", @@ -1075,7 +1369,14 @@ "cell_type": "code", "execution_count": null, "id": "c2752a96", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.159239Z", + "iopub.status.busy": "2023-07-26T05:18:12.159059Z", + "iopub.status.idle": "2023-07-26T05:18:12.163542Z", + "shell.execute_reply": "2023-07-26T05:18:12.163127Z" + } + }, "outputs": [], "source": [ "cut_tree(linkage_comp, n_clusters=4).T" @@ -1097,7 +1398,14 @@ "cell_type": "code", "execution_count": null, "id": "1407f7a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.165971Z", + "iopub.status.busy": "2023-07-26T05:18:12.165813Z", + "iopub.status.idle": "2023-07-26T05:18:12.170809Z", + "shell.execute_reply": "2023-07-26T05:18:12.170332Z" + } + }, "outputs": [], "source": [ "cut_tree(linkage_comp, height=5)" @@ -1116,7 +1424,14 @@ "cell_type": "code", "execution_count": null, "id": "2d74f224", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.173359Z", + "iopub.status.busy": "2023-07-26T05:18:12.173198Z", + "iopub.status.idle": "2023-07-26T05:18:12.457460Z", + "shell.execute_reply": "2023-07-26T05:18:12.457026Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler()\n", @@ -1153,7 +1468,14 @@ "cell_type": "code", "execution_count": null, "id": "b7f7da12", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.461207Z", + "iopub.status.busy": "2023-07-26T05:18:12.460949Z", + "iopub.status.idle": "2023-07-26T05:18:12.680696Z", + "shell.execute_reply": "2023-07-26T05:18:12.680192Z" + } + }, "outputs": [], "source": [ "X = np.random.standard_normal((30, 3))\n", @@ -1186,7 +1508,14 @@ "cell_type": "code", "execution_count": null, "id": "b94424fc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.683099Z", + "iopub.status.busy": "2023-07-26T05:18:12.682918Z", + "iopub.status.idle": "2023-07-26T05:18:12.688821Z", + "shell.execute_reply": "2023-07-26T05:18:12.688474Z" + } + }, "outputs": [], "source": [ "NCI60 = load_data('NCI60')\n", @@ -1212,7 +1541,14 @@ "cell_type": "code", "execution_count": null, "id": "cea54566", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.691075Z", + "iopub.status.busy": "2023-07-26T05:18:12.690934Z", + "iopub.status.idle": "2023-07-26T05:18:12.693705Z", + "shell.execute_reply": "2023-07-26T05:18:12.693319Z" + } + }, "outputs": [], "source": [ "nci_data.shape" @@ -1230,7 +1566,14 @@ "cell_type": "code", "execution_count": null, "id": "4dac41bb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.695954Z", + "iopub.status.busy": "2023-07-26T05:18:12.695815Z", + "iopub.status.idle": "2023-07-26T05:18:12.700521Z", + "shell.execute_reply": "2023-07-26T05:18:12.700074Z" + } + }, "outputs": [], "source": [ "nci_labs.value_counts()" @@ -1252,7 +1595,14 @@ "cell_type": "code", "execution_count": null, "id": "d8ebadd6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.702785Z", + "iopub.status.busy": "2023-07-26T05:18:12.702646Z", + "iopub.status.idle": "2023-07-26T05:18:12.866311Z", + "shell.execute_reply": "2023-07-26T05:18:12.856469Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler()\n", @@ -1277,7 +1627,14 @@ "cell_type": "code", "execution_count": null, "id": "63b5efe3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:12.882679Z", + "iopub.status.busy": "2023-07-26T05:18:12.878318Z", + "iopub.status.idle": "2023-07-26T05:18:13.099177Z", + "shell.execute_reply": "2023-07-26T05:18:13.098712Z" + } + }, "outputs": [], "source": [ "cancer_types = list(np.unique(nci_labs))\n", @@ -1325,7 +1682,14 @@ "cell_type": "code", "execution_count": null, "id": "e20c3cc1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:13.101683Z", + "iopub.status.busy": "2023-07-26T05:18:13.101534Z", + "iopub.status.idle": "2023-07-26T05:18:13.327522Z", + "shell.execute_reply": "2023-07-26T05:18:13.327041Z" + } + }, "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", @@ -1379,7 +1743,14 @@ "cell_type": "code", "execution_count": null, "id": "622de805", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:13.330190Z", + "iopub.status.busy": "2023-07-26T05:18:13.330013Z", + "iopub.status.idle": "2023-07-26T05:18:13.332964Z", + "shell.execute_reply": "2023-07-26T05:18:13.332533Z" + } + }, "outputs": [], "source": [ "def plot_nci(linkage, ax, cut=-np.inf):\n", @@ -1410,7 +1781,14 @@ "cell_type": "code", "execution_count": null, "id": "54d40449", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:13.336193Z", + "iopub.status.busy": "2023-07-26T05:18:13.335972Z", + "iopub.status.idle": "2023-07-26T05:18:14.886877Z", + "shell.execute_reply": "2023-07-26T05:18:14.886190Z" + } + }, "outputs": [], "source": [ "fig, axes = plt.subplots(3, 1, figsize=(15,30)) \n", @@ -1444,7 +1822,14 @@ "cell_type": "code", "execution_count": null, "id": "dc80afc8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:14.891531Z", + "iopub.status.busy": "2023-07-26T05:18:14.890910Z", + "iopub.status.idle": "2023-07-26T05:18:14.903469Z", + "shell.execute_reply": "2023-07-26T05:18:14.903077Z" + } + }, "outputs": [], "source": [ "linkage_comp = compute_linkage(hc_comp)\n", @@ -1469,7 +1854,14 @@ "cell_type": "code", "execution_count": null, "id": "40ff59f9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:14.905992Z", + "iopub.status.busy": "2023-07-26T05:18:14.905760Z", + "iopub.status.idle": "2023-07-26T05:18:15.399751Z", + "shell.execute_reply": "2023-07-26T05:18:15.399237Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(10,10))\n", @@ -1500,7 +1892,14 @@ "cell_type": "code", "execution_count": null, "id": "1587e83b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:15.401922Z", + "iopub.status.busy": "2023-07-26T05:18:15.401768Z", + "iopub.status.idle": "2023-07-26T05:18:17.931480Z", + "shell.execute_reply": "2023-07-26T05:18:17.924183Z" + } + }, "outputs": [], "source": [ "nci_kmeans = KMeans(n_clusters=4, \n", @@ -1537,7 +1936,14 @@ "cell_type": "code", "execution_count": null, "id": "b09ceeab", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:17.955945Z", + "iopub.status.busy": "2023-07-26T05:18:17.954718Z", + "iopub.status.idle": "2023-07-26T05:18:18.488264Z", + "shell.execute_reply": "2023-07-26T05:18:18.487809Z" + } + }, "outputs": [], "source": [ "hc_pca = HClust(n_clusters=None,\n", @@ -1564,6 +1970,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch13-multiple-lab.ipynb b/docs/source/labs/Ch13-multiple-lab.ipynb index 5685600..f86882c 100644 --- a/docs/source/labs/Ch13-multiple-lab.ipynb +++ b/docs/source/labs/Ch13-multiple-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Multiple Testing\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "" @@ -25,7 +25,14 @@ "cell_type": "code", "execution_count": null, "id": "1f928b2d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:21.791802Z", + "iopub.status.busy": "2023-07-26T10:52:21.791660Z", + "iopub.status.idle": "2023-07-26T10:52:23.360588Z", + "shell.execute_reply": "2023-07-26T10:52:23.359991Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -48,7 +55,14 @@ "cell_type": "code", "execution_count": null, "id": "eb4b32aa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.363356Z", + "iopub.status.busy": "2023-07-26T10:52:23.362843Z", + "iopub.status.idle": "2023-07-26T10:52:23.365980Z", + "shell.execute_reply": "2023-07-26T10:52:23.365438Z" + } + }, "outputs": [], "source": [ "from scipy.stats import \\\n", @@ -79,7 +93,14 @@ "cell_type": "code", "execution_count": null, "id": "e12ac0cd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.368340Z", + "iopub.status.busy": "2023-07-26T10:52:23.368164Z", + "iopub.status.idle": "2023-07-26T10:52:23.371202Z", + "shell.execute_reply": "2023-07-26T10:52:23.370779Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(12)\n", @@ -102,7 +123,14 @@ "cell_type": "code", "execution_count": null, "id": "04d0f49e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.373512Z", + "iopub.status.busy": "2023-07-26T10:52:23.373319Z", + "iopub.status.idle": "2023-07-26T10:52:23.378383Z", + "shell.execute_reply": "2023-07-26T10:52:23.377960Z" + } + }, "outputs": [], "source": [ "result = ttest_1samp(X[:,0], 0)\n", @@ -131,7 +159,14 @@ "cell_type": "code", "execution_count": null, "id": "d1f0c695", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.380819Z", + "iopub.status.busy": "2023-07-26T10:52:23.380641Z", + "iopub.status.idle": "2023-07-26T10:52:23.408797Z", + "shell.execute_reply": "2023-07-26T10:52:23.408278Z" + } + }, "outputs": [], "source": [ "p_values = np.empty(100)\n", @@ -159,7 +194,14 @@ "cell_type": "code", "execution_count": null, "id": "7a9594a0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.411256Z", + "iopub.status.busy": "2023-07-26T10:52:23.411053Z", + "iopub.status.idle": "2023-07-26T10:52:23.422652Z", + "shell.execute_reply": "2023-07-26T10:52:23.422286Z" + } + }, "outputs": [], "source": [ "pd.crosstab(decision,\n", @@ -194,7 +236,14 @@ "cell_type": "code", "execution_count": null, "id": "25f7fc5d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.425157Z", + "iopub.status.busy": "2023-07-26T10:52:23.424798Z", + "iopub.status.idle": "2023-07-26T10:52:23.455752Z", + "shell.execute_reply": "2023-07-26T10:52:23.455052Z" + } + }, "outputs": [], "source": [ "true_mean = np.array([1]*50 + [0]*50)\n", @@ -233,7 +282,14 @@ "cell_type": "code", "execution_count": null, "id": "369b5bd3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.458631Z", + "iopub.status.busy": "2023-07-26T10:52:23.458434Z", + "iopub.status.idle": "2023-07-26T10:52:23.710141Z", + "shell.execute_reply": "2023-07-26T10:52:23.709635Z" + } + }, "outputs": [], "source": [ "m = np.linspace(1, 501)\n", @@ -271,7 +327,14 @@ "cell_type": "code", "execution_count": null, "id": "9ce7a19f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.713438Z", + "iopub.status.busy": "2023-07-26T10:52:23.713214Z", + "iopub.status.idle": "2023-07-26T10:52:23.754484Z", + "shell.execute_reply": "2023-07-26T10:52:23.754038Z" + } + }, "outputs": [], "source": [ "Fund = load_data('Fund')\n", @@ -321,7 +384,14 @@ "cell_type": "code", "execution_count": null, "id": "de6cffed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.756665Z", + "iopub.status.busy": "2023-07-26T10:52:23.756493Z", + "iopub.status.idle": "2023-07-26T10:52:23.759438Z", + "shell.execute_reply": "2023-07-26T10:52:23.759028Z" + } + }, "outputs": [], "source": [ "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n", @@ -341,7 +411,14 @@ "cell_type": "code", "execution_count": null, "id": "0de71500", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.761459Z", + "iopub.status.busy": "2023-07-26T10:52:23.761287Z", + "iopub.status.idle": "2023-07-26T10:52:23.764317Z", + "shell.execute_reply": "2023-07-26T10:52:23.763952Z" + } + }, "outputs": [], "source": [ "bonf, np.minimum(fund_mini_pvals * 5, 1)" @@ -364,7 +441,14 @@ "cell_type": "code", "execution_count": null, "id": "f7e87bdb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.766793Z", + "iopub.status.busy": "2023-07-26T10:52:23.766606Z", + "iopub.status.idle": "2023-07-26T10:52:23.810152Z", + "shell.execute_reply": "2023-07-26T10:52:23.809736Z" + } + }, "outputs": [], "source": [ "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]" @@ -383,7 +467,14 @@ "cell_type": "code", "execution_count": null, "id": "e88be376", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.812384Z", + "iopub.status.busy": "2023-07-26T10:52:23.812232Z", + "iopub.status.idle": "2023-07-26T10:52:23.815971Z", + "shell.execute_reply": "2023-07-26T10:52:23.815576Z" + } + }, "outputs": [], "source": [ "fund_mini.mean()" @@ -403,7 +494,14 @@ "cell_type": "code", "execution_count": null, "id": "41149af6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.817997Z", + "iopub.status.busy": "2023-07-26T10:52:23.817852Z", + "iopub.status.idle": "2023-07-26T10:52:23.821301Z", + "shell.execute_reply": "2023-07-26T10:52:23.820976Z" + } + }, "outputs": [], "source": [ "ttest_rel(fund_mini['Manager1'],\n", @@ -437,7 +535,14 @@ "cell_type": "code", "execution_count": null, "id": "61aabda7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:23.823367Z", + "iopub.status.busy": "2023-07-26T10:52:23.823190Z", + "iopub.status.idle": "2023-07-26T10:52:24.308715Z", + "shell.execute_reply": "2023-07-26T10:52:24.308222Z" + } + }, "outputs": [], "source": [ "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n", @@ -467,7 +572,14 @@ "cell_type": "code", "execution_count": null, "id": "cbcad4de", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.311170Z", + "iopub.status.busy": "2023-07-26T10:52:24.310960Z", + "iopub.status.idle": "2023-07-26T10:52:24.410742Z", + "shell.execute_reply": "2023-07-26T10:52:24.410212Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", @@ -490,7 +602,14 @@ "cell_type": "code", "execution_count": null, "id": "b5842190", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.413638Z", + "iopub.status.busy": "2023-07-26T10:52:24.413455Z", + "iopub.status.idle": "2023-07-26T10:52:24.827343Z", + "shell.execute_reply": "2023-07-26T10:52:24.826609Z" + } + }, "outputs": [], "source": [ "fund_pvalues = np.empty(2000)\n", @@ -512,7 +631,14 @@ "cell_type": "code", "execution_count": null, "id": "7c9d8bed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.830214Z", + "iopub.status.busy": "2023-07-26T10:52:24.830015Z", + "iopub.status.idle": "2023-07-26T10:52:24.834953Z", + "shell.execute_reply": "2023-07-26T10:52:24.834464Z" + } + }, "outputs": [], "source": [ "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n", @@ -538,7 +664,14 @@ "cell_type": "code", "execution_count": null, "id": "bfa39f7c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.837627Z", + "iopub.status.busy": "2023-07-26T10:52:24.837419Z", + "iopub.status.idle": "2023-07-26T10:52:24.840834Z", + "shell.execute_reply": "2023-07-26T10:52:24.840316Z" + } + }, "outputs": [], "source": [ "(fund_qvalues <= 0.1).sum()" @@ -563,7 +696,14 @@ "cell_type": "code", "execution_count": null, "id": "70b69b47", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.843470Z", + "iopub.status.busy": "2023-07-26T10:52:24.843237Z", + "iopub.status.idle": "2023-07-26T10:52:24.846889Z", + "shell.execute_reply": "2023-07-26T10:52:24.846276Z" + } + }, "outputs": [], "source": [ "(fund_pvalues <= 0.1 / 2000).sum()" @@ -594,7 +734,14 @@ "cell_type": "code", "execution_count": null, "id": "4c0ddea1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.849388Z", + "iopub.status.busy": "2023-07-26T10:52:24.849238Z", + "iopub.status.idle": "2023-07-26T10:52:24.852847Z", + "shell.execute_reply": "2023-07-26T10:52:24.852354Z" + } + }, "outputs": [], "source": [ "sorted_ = np.sort(fund_pvalues)\n", @@ -621,7 +768,14 @@ "cell_type": "code", "execution_count": null, "id": "0314eac9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:24.855721Z", + "iopub.status.busy": "2023-07-26T10:52:24.855334Z", + "iopub.status.idle": "2023-07-26T10:52:25.156072Z", + "shell.execute_reply": "2023-07-26T10:52:25.155438Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", @@ -652,7 +806,14 @@ "cell_type": "code", "execution_count": null, "id": "b59b8137", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:25.158998Z", + "iopub.status.busy": "2023-07-26T10:52:25.158769Z", + "iopub.status.idle": "2023-07-26T10:52:25.243020Z", + "shell.execute_reply": "2023-07-26T10:52:25.242512Z" + } + }, "outputs": [], "source": [ "Khan = load_data('Khan') \n", @@ -680,7 +841,14 @@ "cell_type": "code", "execution_count": null, "id": "96fb2f61", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:25.245359Z", + "iopub.status.busy": "2023-07-26T10:52:25.245172Z", + "iopub.status.idle": "2023-07-26T10:52:25.250139Z", + "shell.execute_reply": "2023-07-26T10:52:25.249644Z" + } + }, "outputs": [], "source": [ "D2 = D[lambda df:df['Y'] == 2]\n", @@ -714,7 +882,14 @@ "cell_type": "code", "execution_count": null, "id": "fdc229fa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:25.252510Z", + "iopub.status.busy": "2023-07-26T10:52:25.252319Z", + "iopub.status.idle": "2023-07-26T10:52:26.208474Z", + "shell.execute_reply": "2023-07-26T10:52:26.207894Z" + } + }, "outputs": [], "source": [ "B = 10000\n", @@ -746,7 +921,14 @@ "cell_type": "code", "execution_count": null, "id": "e3894695", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:26.210995Z", + "iopub.status.busy": "2023-07-26T10:52:26.210826Z", + "iopub.status.idle": "2023-07-26T10:52:26.458716Z", + "shell.execute_reply": "2023-07-26T10:52:26.458144Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", @@ -787,7 +969,14 @@ "cell_type": "code", "execution_count": null, "id": "3b7392cb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T10:52:26.461754Z", + "iopub.status.busy": "2023-07-26T10:52:26.461349Z", + "iopub.status.idle": "2023-07-26T12:21:43.650925Z", + "shell.execute_reply": "2023-07-26T12:21:43.650584Z" + } + }, "outputs": [], "source": [ "m, B = 100, 10000\n", @@ -826,7 +1015,14 @@ "cell_type": "code", "execution_count": null, "id": "cac15616", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:21:43.653105Z", + "iopub.status.busy": "2023-07-26T12:21:43.652856Z", + "iopub.status.idle": "2023-07-26T12:21:43.895651Z", + "shell.execute_reply": "2023-07-26T12:21:43.895292Z" + } + }, "outputs": [], "source": [ "cutoffs = np.sort(np.abs(T_vals))\n", @@ -860,7 +1056,14 @@ "cell_type": "code", "execution_count": null, "id": "9661eb10", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:21:43.897943Z", + "iopub.status.busy": "2023-07-26T12:21:43.897797Z", + "iopub.status.idle": "2023-07-26T12:21:43.901002Z", + "shell.execute_reply": "2023-07-26T12:21:43.900637Z" + } + }, "outputs": [], "source": [ "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])" @@ -879,7 +1082,14 @@ "cell_type": "code", "execution_count": null, "id": "18ad4900", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:21:43.902778Z", + "iopub.status.busy": "2023-07-26T12:21:43.902684Z", + "iopub.status.idle": "2023-07-26T12:21:43.905535Z", + "shell.execute_reply": "2023-07-26T12:21:43.905228Z" + } + }, "outputs": [], "source": [ "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])" @@ -899,7 +1109,14 @@ "cell_type": "code", "execution_count": null, "id": "28c276b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T12:21:43.907278Z", + "iopub.status.busy": "2023-07-26T12:21:43.907150Z", + "iopub.status.idle": "2023-07-26T12:21:43.988689Z", + "shell.execute_reply": "2023-07-26T12:21:43.988352Z" + } + }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", @@ -914,6 +1131,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch14-surv-lab.ipynb b/docs/source/labs/Ch14-surv-lab.ipynb new file mode 100644 index 0000000..031562d --- /dev/null +++ b/docs/source/labs/Ch14-surv-lab.ipynb @@ -0,0 +1,995 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c1af8696", + "metadata": {}, + "source": [ + "# Dummy\n", + "\n", + "## Survival Analysis\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "\n", + "\n", + "### Some preliminary details\n", + "\n", + "We will want to be able to see plots in the notebook. We\n", + "will therefore include the following as introduced in\n", + "Chapter 2:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bce7ad7", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "98e95e67", + "metadata": {}, + "source": [ + "#### New imports\n", + "\n", + "We again collect the new imports\n", + "needed for this lab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3741ca31", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from lifelines import \\\n", + " (KaplanMeierFitter,\n", + " CoxPHFitter)\n", + "from lifelines.statistics import \\\n", + " (logrank_test,\n", + " multivariate_logrank_test)\n", + "from ISLP.survival import sim_time" + ] + }, + { + "cell_type": "markdown", + "id": "c1dff6d8", + "metadata": {}, + "source": [ + "In this lab, we perform survival analyses on three separate data\n", + "sets. In the first section we analyze the `BrainCancer`\n", + "data .
\n", + "Next, we examine the `Publication`\n", + "data . Finally, we explore explores\n", + "a simulated call center data set.\n", + "\n", + "We begin by importing some of our libraries at this top\n", + "level. This makes the code more readable, as scanning the first few\n", + "lines of the notebook tell us what libraries are used in this\n", + "notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6402fd76", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from ISLP.models import ModelSpec as MS\n", + "from ISLP import load_data" + ] + }, + { + "cell_type": "markdown", + "id": "90eb295f", + "metadata": {}, + "source": [ + "### Brain Cancer Data" + ] + }, + { + "cell_type": "markdown", + "id": "26338f8e", + "metadata": {}, + "source": [ + "We begin with the `BrainCancer` data set, contained in the `ISLP` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b43f8c6f", + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer = load_data('BrainCancer')\n", + "BrainCancer.columns" + ] + }, + { + "cell_type": "markdown", + "id": "61eed2f0", + "metadata": {}, + "source": [ + "The rows index the 88 patients, while the columns contain the 8 predictors.\n", + "We first briefly examine the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4307ef61", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "BrainCancer['sex'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19c25475", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "BrainCancer['diagnosis'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9fa6491", + "metadata": {}, + "outputs": [], + "source": [ + "BrainCancer['status'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "bd1e89cb", + "metadata": {}, + "source": [ + "Before beginning an analysis, it is important to know how the\n", + "`status` variable has been coded. Most software\n", + "uses the convention that a `status` of 1 indicates an\n", + "uncensored observation, and a `status` of 0 indicates a censored\n", + "observation. But some scientists might use the opposite coding. For\n", + "the `BrainCancer` data set 35 patients died before the end of\n", + "the study.\n", + "\n", + "To begin the analysis, we re-create a Kaplan-Meier survival curve shown in the book. The main\n", + "package we will use for survival analysis\n", + "is `lifelines`.\n", + "The variable `time` corresponds to $y_i$, the time to the $i$th event (either censoring or\n", + "death). The first argument to `km.fit` are the event times with the\n", + "second argument being the censoring variable with a 1 indicating an observed\n", + "failure time. The `plot` method plots a curve with pointwise confidence\n", + "intervals. By default, they produce 90% confidence intervals. This can be changed\n", + "by setting the `alpha` argument to 1 minus the desired\n", + "confidence level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16b708ff", + "metadata": {}, + "outputs": [], + "source": [ + "km = KaplanMeierFitter()\n", + "km_brain = km.fit(BrainCancer['time'],\n", + " BrainCancer['status'])\n", + "km_brain.plot(label='Kaplan Meier estimate')" + ] + }, + { + "cell_type": "markdown", + "id": "71d6ef47", + "metadata": {}, + "source": [ + "Next we create Kaplan-Meier survival curves that are stratified by\n", + "`sex`, in order to reproduce a second figure from the book.\n", + "We do this by using the `groupby` method of a DataFrame.\n", + "This method returns a generator that can\n", + "be iterated over in the `for` loop. In this case,\n", + "the items in the `for` loop are 2-tuples representing\n", + "the groups: the first entry is the value\n", + "of the grouping column `sex` while the 2nd value\n", + "is the DataFrame consisting of all rows in the\n", + "DataFrame matching that value of `sex`.\n", + "We will want to use this data below\n", + "in the log-rank test, hence we store this\n", + "information in the dictionary `by_sex`. Finally,\n", + "we have also used the notion of\n", + " to automatically\n", + "label the different lines in the plot. String\n", + "interpolation is a powerful technique to format strings –\n", + "`python` has many ways to facilitate such operations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd07dfd6", + "metadata": {}, + "outputs": [], + "source": [ + "by_sex = {}\n", + "for sex, df in BrainCancer.groupby('sex'):\n", + " by_sex[sex] = df\n", + " km_sex = km.fit(df['time'],\n", + " df['status'])\n", + " km_sex.plot(label='Sex=%s' % sex)" + ] + }, + { + "cell_type": "markdown", + "id": "8d40bd2f", + "metadata": {}, + "source": [ + "As discussed in the book, we can perform a\n", + "log-rank test to compare the survival of males to females. We use\n", + "the `logrank_test` function from the `lifelines.statistics` module.\n", + "The first two arguments are the event times, with the second\n", + "denoting the corresponding (optional) censoring indicators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4e80bc9", + "metadata": {}, + "outputs": [], + "source": [ + "logrank_test(by_sex['Male']['time'],\n", + " by_sex['Female']['time'],\n", + " by_sex['Male']['status'],\n", + " by_sex['Female']['status'])" + ] + }, + { + "cell_type": "markdown", + "id": "3e025210", + "metadata": {}, + "source": [ + "The resulting $p$-value is $0.23$, indicating no evidence of a\n", + "difference in survival between the two sexes.\n", + "\n", + "Next, we fit Cox proportional hazards models using the `CoxPHFitter` estimator\n", + "from `lifelines`.\n", + "To begin, we consider a model that uses `sex` as the only predictor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b57ac5fa", + "metadata": {}, + "outputs": [], + "source": [ + "coxph = CoxPHFitter # shorthand\n", + "sex_df = BrainCancer[['time', 'status', 'sex']]\n", + "model_df = MS(['time', 'status', 'sex'],\n", + " intercept=False).fit_transform(sex_df)\n", + "cox_fit = coxph().fit(model_df,\n", + " 'time',\n", + " 'status')\n", + "cox_fit.summary[['coef', 'se(coef)', 'p']]" + ] + }, + { + "cell_type": "markdown", + "id": "da8b9eff", + "metadata": {}, + "source": [ + "The first argument to `fit` should be a data frame containing\n", + "at least the event time (the second argument `time` in this case)\n", + "as well as an optional censoring variable (the argument `status` in this case).\n", + "Note also that the Cox model does not include an intercept, which is why\n", + "we used the `intercept=False` argument to `ModelSpec` above.\n", + "It is possible to obtain the likelihood ratio test comparing this model to the one\n", + "with no features as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9991b523", + "metadata": {}, + "outputs": [], + "source": [ + "cox_fit.log_likelihood_ratio_test()" + ] + }, + { + "cell_type": "markdown", + "id": "fc76fd07", + "metadata": {}, + "source": [ + "Regardless of which test we use, we see that there is no clear\n", + "evidence for a difference in survival between males and females. As\n", + "we learned in this chapter, the score test from the Cox model is\n", + "exactly equal to the log rank test statistic!\n", + "\n", + "Now we fit a model that makes use of additional predictors. We first note\n", + "that one of our `diagnosis` values is missing, hence\n", + "we drop that observation before continuing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "391157a9", + "metadata": {}, + "outputs": [], + "source": [ + "cleaned = BrainCancer.dropna()\n", + "all_MS = MS(cleaned.columns,\n", + " intercept=False)\n", + "all_df = all_MS.fit_transform(cleaned)\n", + "fit_all = coxph().fit(all_df, \n", + " 'time', \n", + " 'status')\n", + "fit_all.summary[['coef', 'se(coef)', 'p']]" + ] + }, + { + "cell_type": "markdown", + "id": "18659bcd", + "metadata": {}, + "source": [ + "The `diagnosis` variable has been coded so that the baseline\n", + "corresponds to HG glioma. The results indicate that the risk associated with HG glioma\n", + "is more than eight times (i.e. $e^{2.15}=8.62$) the risk associated\n", + "with meningioma. In other words, after adjusting for the other\n", + "predictors, patients with HG glioma have much worse survival compared\n", + "to those with meningioma. In addition, larger values of the Karnofsky\n", + "index, `ki`, are associated with lower risk, i.e. longer survival.\n", + "\n", + "Finally, we plot estimated survival curves for each diagnosis category,\n", + "adjusting for the other predictors. To make these plots, we set the\n", + "values of the other predictors equal to the mean for quantitative variables\n", + "and equal to the mode for categorical. To do this, we use the\n", + "`apply()` method across rows (i.e. `axis=0`) with a function\n", + "`representative` that checks if a column is categorical\n", + "or not." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2c32bdf", + "metadata": {}, + "outputs": [], + "source": [ + "levels = cleaned['diagnosis'].unique()\n", + "def representative(series):\n", + " if hasattr(series.dtype, 'categories'):\n", + " return pd.Series.mode(series)\n", + " else:\n", + " return series.mean()\n", + "modal_data = cleaned.apply(representative, \n", + " axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "2f5e9767", + "metadata": {}, + "source": [ + "We make four\n", + "copies of the column means and assign the `diagnosis` column to be the four different\n", + "diagnoses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a6fc4b9", + "metadata": {}, + "outputs": [], + "source": [ + "modal_df = pd.DataFrame([modal_data.iloc[0] for _ in range(len(levels))])\n", + "modal_df['diagnosis'] = levels\n", + "modal_df" + ] + }, + { + "cell_type": "markdown", + "id": "dd013465", + "metadata": {}, + "source": [ + "We then construct the design matrix based on the model specification `all_MS` used to fit\n", + "the model, dropping the `time` and `status` variables as these are not\n", + "used to predict the survival functioin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1e6cb80", + "metadata": {}, + "outputs": [], + "source": [ + "modal_X = all_MS.transform(modal_df)#.drop(columns=['time', 'status'])\n", + "modal_X.index = levels\n", + "modal_X" + ] + }, + { + "cell_type": "markdown", + "id": "d98548ee", + "metadata": {}, + "source": [ + "The estimated survival\n", + "function can be found using the `predict_survival_function()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8753eaa2", + "metadata": {}, + "outputs": [], + "source": [ + "predicted_survival = fit_all.predict_survival_function(modal_X)\n", + "predicted_survival" + ] + }, + { + "cell_type": "markdown", + "id": "a814ddee", + "metadata": {}, + "source": [ + "This returns a data frame,\n", + "whose plot methods yields the different survival curves. To avoid clutter in\n", + "the plots, we do not display confidence intervals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddccc3ac", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", + "predicted_survival.plot(ax=ax);" + ] + }, + { + "cell_type": "markdown", + "id": "53997de5", + "metadata": {}, + "source": [ + "### Publication Data" + ] + }, + { + "cell_type": "markdown", + "id": "47103217", + "metadata": {}, + "source": [ + "The `Publication` data can be\n", + "found in the `ISLP` package.\n", + "We begin by plotting the Kaplan-Meier curves\n", + "stratified on the `posres` variable, which records whether the\n", + "study had a positive or negative result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01217472", + "metadata": {}, + "outputs": [], + "source": [ + "Publication = load_data('Publication')\n", + "by_result = {}\n", + "for result, df in Publication.groupby('posres'):\n", + " by_result[result] = df\n", + " km_result = km.fit(df['time'], df['status'])\n", + " km_result.plot(label='Result=%d' % result)" + ] + }, + { + "cell_type": "markdown", + "id": "7929b6e9", + "metadata": {}, + "source": [ + "As discussed previously, the $p$-values from fitting Cox’s\n", + "proportional hazards model to the `posres` variable are quite\n", + "large, providing no evidence of a difference in time-to-publication\n", + "between studies with positive versus negative results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e966bd6", + "metadata": {}, + "outputs": [], + "source": [ + "posres_df = MS(['posres',\n", + " 'time',\n", + " 'status'], intercept=False).fit_transform(Publication)\n", + "posres_fit = coxph().fit(posres_df,\n", + " 'time',\n", + " 'status')\n", + "posres_fit.summary[['coef', 'se(coef)', 'p']]" + ] + }, + { + "cell_type": "markdown", + "id": "1020fe19", + "metadata": {}, + "source": [ + "As expected, the log-rank test provides an identical conclusion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42572496", + "metadata": {}, + "outputs": [], + "source": [ + "logrank_test(by_result[0]['time'],\n", + " by_result[1]['time'],\n", + " by_result[0]['status'],\n", + " by_result[1]['status'])" + ] + }, + { + "cell_type": "markdown", + "id": "c06a3106", + "metadata": {}, + "source": [ + "However, the results change dramatically when we include other\n", + "predictors in the model. Here we have excluded the funding mechanism\n", + "variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c22a546", + "metadata": {}, + "outputs": [], + "source": [ + "model = MS(Publication.columns.drop('mech'),\n", + " intercept=False)\n", + "coxph().fit(model.fit_transform(Publication),\n", + " 'time',\n", + " 'status').summary[['coef', 'se(coef)', 'p']]" + ] + }, + { + "cell_type": "markdown", + "id": "2efd14bf", + "metadata": {}, + "source": [ + "We see that there are a number of statistically significant variables,\n", + "including whether the trial focused on a clinical endpoint, the impact\n", + "of the study, and whether the study had positive or negative results.\n", + "\n", + "### Call Center Data" + ] + }, + { + "cell_type": "markdown", + "id": "9dbf298f", + "metadata": {}, + "source": [ + "In this section, we will simulate survival data using the relationship\n", + "between cumulative hazard and\n", + "the survival function explored in an exercise.\n", + "Our simulated data will represent the observed\n", + "wait times (in seconds) for 2,000 customers who have phoned a call\n", + "center. In this context, censoring occurs if a customer hangs up\n", + "before his or her call is answered.\n", + "\n", + "There are three covariates: `Operators` (the number of call\n", + "center operators available at the time of the call, which can range\n", + "from $5$ to $15$), `Center` (either A, B, or C), and\n", + "`Time` of day (Morning, Afternoon, or Evening). We generate data\n", + "for these covariates so that all possibilities are equally likely: for\n", + "instance, morning, afternoon and evening calls are equally likely, and\n", + "any number of operators from $5$ to $15$ is equally likely. We use the\n", + "`ModelSpec` function from the `ISLP.models` package introduced\n", + "in Chapter 3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72e4c0c1", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(10)\n", + "N = 2000\n", + "Operators = np.random.choice(np.arange(5, 16),\n", + " N,\n", + " replace=True)\n", + "Center = np.random.choice(['A', 'B', 'C'], \n", + " N,\n", + " replace=True)\n", + "Time = np.random.choice(['Morn.', 'After.', 'Even.'], \n", + " N,\n", + " replace=True)\n", + "D = pd.DataFrame({'Operators': Operators,\n", + " 'Center': pd.Categorical(Center),\n", + " 'Time': pd.Categorical(Time)}) \n", + "model = MS(['Operators',\n", + " 'Center',\n", + " 'Time'],\n", + " intercept=False)\n", + "X = model.fit_transform(D)" + ] + }, + { + "cell_type": "markdown", + "id": "a3f7c728", + "metadata": {}, + "source": [ + "It is worthwhile to take a peek at the design matrix `X`, so\n", + "that we can be sure that we understand how the variables have been coded. By default,\n", + "the levels of categorical variables are sorted and the first column of the usual one-hot encoding\n", + "of the variable is dropped." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b8f4b9f", + "metadata": {}, + "outputs": [], + "source": [ + "X[:5]" + ] + }, + { + "cell_type": "markdown", + "id": "06cd05ca", + "metadata": {}, + "source": [ + "Next, we specify the coefficients and the hazard function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982ae944", + "metadata": {}, + "outputs": [], + "source": [ + "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n", + "true_linpred = X.dot(true_beta)\n", + "hazard = lambda t: 1e-5 * t" + ] + }, + { + "cell_type": "markdown", + "id": "5054aa25", + "metadata": {}, + "source": [ + "We use the function\n", + "`sim_time` from the `ISLP.survival` package. This function\n", + "uses the relationship between the survival function\n", + "and cumulative hazard $S(t) = \\exp(-H(t))$ and the specific\n", + "form of the cumulative hazard function in the Cox model\n", + "to simulate data based on values of the linear predictor\n", + "`true_linpred` and the cumulative hazard. We can generate\n", + "if we know the inverse of the cumulative hazard function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8939727", + "metadata": {}, + "outputs": [], + "source": [ + "cum_hazard = lambda t: 1e-5 * t**2 / 2" + ] + }, + { + "cell_type": "markdown", + "id": "c0ed581d", + "metadata": {}, + "source": [ + "Here, we have set the coefficient associated with `Operators` to\n", + "equal $0.04$; in other words, each additional operator leads to a\n", + "$e^{0.04}=1.041$-fold increase in the “risk” that the call will be\n", + "answered, given the `Center` and `Time` covariates. This\n", + "makes sense: the greater the number of operators at hand, the shorter\n", + "the wait time! The coefficient associated with `Center == B` is\n", + "$-0.3$, and `Center == A` is treated as the baseline. This means\n", + "that the risk of a call being answered at Center B is 0.74 times the\n", + "risk that it will be answered at Center A; in other words, the wait\n", + "times are a bit longer at Center B.\n", + "\n", + "We are now ready to generate data under the Cox proportional hazards\n", + "model. We’ll truncate the maximum time to 1000 seconds to keep\n", + "simulated wait times reasonable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a850809e", + "metadata": {}, + "outputs": [], + "source": [ + "W = np.array([sim_time(l, cum_hazard)\n", + " for l in true_linpred])\n", + "D['Wait time'] = np.clip(W, 0, 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "3f481113", + "metadata": {}, + "source": [ + "We now simulate our censoring variable, for which we assume\n", + "90% of calls were answered (`Failed==1`) before the\n", + "customer hungup (`Failed==0`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c93cd8c3", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "D['Failed'] = np.random.choice([1, 0],\n", + " N,\n", + " p=[0.9, 0.1])\n", + "D[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bac8ff69", + "metadata": {}, + "outputs": [], + "source": [ + "D['Failed'].mean()" + ] + }, + { + "cell_type": "markdown", + "id": "75699d8c", + "metadata": {}, + "source": [ + "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cafb21e9", + "metadata": {}, + "outputs": [], + "source": [ + "by_center = {}\n", + "for center, df in D.groupby('Center'):\n", + " by_center[center] = df\n", + " km_center = km.fit(df['Wait time'], df['Failed'])\n", + " km_center.plot(label='Center=%s' % center)\n", + "ax = plt.gca()\n", + "ax.set_title(\"Probability of Still Being on Hold\")" + ] + }, + { + "cell_type": "markdown", + "id": "6c477316", + "metadata": {}, + "source": [ + "Next, we stratify by `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d404c129", + "metadata": {}, + "outputs": [], + "source": [ + "by_time = {}\n", + "for time, df in D.groupby('Time'):\n", + " by_time[time] = df\n", + " km_time = km.fit(df['Wait time'], df['Failed'])\n", + " km_time.plot(label='Time=%s' % time)\n", + "ax = plt.gca()\n", + "ax.set_title(\"Probability of Still Being on Hold\")" + ] + }, + { + "cell_type": "markdown", + "id": "dcca1dbd", + "metadata": {}, + "source": [ + "It seems that calls at Call Center B take longer to be answered than\n", + "calls at Centers A and C. Similarly, it appears that wait times are\n", + "longest in the morning and shortest in the evening hours. We can use a\n", + "log-rank test to determine whether these differences are statistically\n", + "significant using `multivariate_logrank_test`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b928758d", + "metadata": {}, + "outputs": [], + "source": [ + "multivariate_logrank_test(D['Wait time'],\n", + " D['Center'],\n", + " D['Failed'])" + ] + }, + { + "cell_type": "markdown", + "id": "285f5644", + "metadata": {}, + "source": [ + "Next, we consider the effect of `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "976558e5", + "metadata": {}, + "outputs": [], + "source": [ + "multivariate_logrank_test(D['Wait time'],\n", + " D['Time'],\n", + " D['Failed'])" + ] + }, + { + "cell_type": "markdown", + "id": "e56b7df7", + "metadata": {}, + "source": [ + "As in the case of a categorical variable with 2 outcomes, these\n", + "results are similar to the likelihood ratio test\n", + "from the Cox proportional hazards model. First, let’s\n", + "look at the results for `Center`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36f7fa13", + "metadata": {}, + "outputs": [], + "source": [ + "X = MS(['Wait time',\n", + " 'Failed',\n", + " 'Center'],\n", + " intercept=False).fit_transform(D)\n", + "F = coxph().fit(X, 'Wait time', 'Failed')\n", + "F.log_likelihood_ratio_test()" + ] + }, + { + "cell_type": "markdown", + "id": "68ab32c8", + "metadata": {}, + "source": [ + "Next, the results for `Time`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e03b31e6", + "metadata": {}, + "outputs": [], + "source": [ + "X = MS(['Wait time',\n", + " 'Failed',\n", + " 'Time'],\n", + " intercept=False).fit_transform(D)\n", + "F = coxph().fit(X, 'Wait time', 'Failed')\n", + "F.log_likelihood_ratio_test()" + ] + }, + { + "cell_type": "markdown", + "id": "fda76031", + "metadata": {}, + "source": [ + "We find that differences between centers are highly significant, as\n", + "are differences between times of day.\n", + "\n", + "Finally, we fit a complete Cox’s proportional hazards model to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5022c12", + "metadata": {}, + "outputs": [], + "source": [ + "X = MS(D.columns,\n", + " intercept=False).fit_transform(D)\n", + "fit_queuing = coxph().fit(\n", + " X,\n", + " 'Wait time',\n", + " 'Failed')\n", + "fit_queuing.summary[['coef', 'se(coef)', 'p']]" + ] + }, + { + "cell_type": "markdown", + "id": "847ebaa7", + "metadata": {}, + "source": [ + "The $p$-values for Center B and evening time\n", + "are very small. It is also clear that the\n", + "hazard — that is, the instantaneous risk that a call will be\n", + "answered — increases with the number of operators. Since we\n", + "generated the data ourselves, we know that the true coefficients for\n", + "Center B, Center C, Operators\n", + "evening time, and morning time are $-0.3$,\n", + "$0$, $0.04$, and $0.2$, $-0.2$, respectively. The coefficient estimates\n", + "resulting from the Cox model are fairly accurate." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all", + "formats": "ipynb,md:myst", + "notebook_metadata_filter": "all" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/labs/Ch2-statlearn-lab.ipynb b/docs/source/labs/Ch2-statlearn-lab.ipynb index d49a478..1247790 100644 --- a/docs/source/labs/Ch2-statlearn-lab.ipynb +++ b/docs/source/labs/Ch2-statlearn-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Introduction to Python\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "" @@ -98,7 +98,14 @@ "cell_type": "code", "execution_count": null, "id": "88d0bfff", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.653253Z", + "iopub.status.busy": "2023-07-26T05:29:13.653107Z", + "iopub.status.idle": "2023-07-26T05:29:13.659825Z", + "shell.execute_reply": "2023-07-26T05:29:13.659469Z" + } + }, "outputs": [], "source": [ "print('fit a model with', 11, 'variables')" @@ -116,7 +123,14 @@ "cell_type": "code", "execution_count": null, "id": "ebe7aca8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.662377Z", + "iopub.status.busy": "2023-07-26T05:29:13.662207Z", + "iopub.status.idle": "2023-07-26T05:29:13.664797Z", + "shell.execute_reply": "2023-07-26T05:29:13.664422Z" + } + }, "outputs": [], "source": [ "print?" @@ -134,7 +148,14 @@ "cell_type": "code", "execution_count": null, "id": "df2f8168", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.666990Z", + "iopub.status.busy": "2023-07-26T05:29:13.666828Z", + "iopub.status.idle": "2023-07-26T05:29:13.670697Z", + "shell.execute_reply": "2023-07-26T05:29:13.670233Z" + } + }, "outputs": [], "source": [ "3 + 5" @@ -156,7 +177,14 @@ "cell_type": "code", "execution_count": null, "id": "4cc5c2c1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.673136Z", + "iopub.status.busy": "2023-07-26T05:29:13.672979Z", + "iopub.status.idle": "2023-07-26T05:29:13.675685Z", + "shell.execute_reply": "2023-07-26T05:29:13.675286Z" + } + }, "outputs": [], "source": [ "\"hello\" + \" \" + \"world\"" @@ -187,7 +215,14 @@ "cell_type": "code", "execution_count": null, "id": "e5d81180", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.677943Z", + "iopub.status.busy": "2023-07-26T05:29:13.677800Z", + "iopub.status.idle": "2023-07-26T05:29:13.680585Z", + "shell.execute_reply": "2023-07-26T05:29:13.680100Z" + } + }, "outputs": [], "source": [ "x = [3, 4, 5]\n", @@ -210,7 +245,14 @@ "cell_type": "code", "execution_count": null, "id": "425a714c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.682740Z", + "iopub.status.busy": "2023-07-26T05:29:13.682602Z", + "iopub.status.idle": "2023-07-26T05:29:13.685374Z", + "shell.execute_reply": "2023-07-26T05:29:13.684966Z" + } + }, "outputs": [], "source": [ "y = [4, 9, 7]\n", @@ -265,7 +307,14 @@ "cell_type": "code", "execution_count": null, "id": "2f88669a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.688017Z", + "iopub.status.busy": "2023-07-26T05:29:13.687702Z", + "iopub.status.idle": "2023-07-26T05:29:13.902613Z", + "shell.execute_reply": "2023-07-26T05:29:13.901721Z" + } + }, "outputs": [], "source": [ "import numpy as np " @@ -293,7 +342,14 @@ "cell_type": "code", "execution_count": null, "id": "e8ec382a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.908393Z", + "iopub.status.busy": "2023-07-26T05:29:13.907922Z", + "iopub.status.idle": "2023-07-26T05:29:13.914563Z", + "shell.execute_reply": "2023-07-26T05:29:13.912356Z" + } + }, "outputs": [], "source": [ "x = np.array([3, 4, 5])\n", @@ -324,7 +380,14 @@ "cell_type": "code", "execution_count": null, "id": "04de39ad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.919758Z", + "iopub.status.busy": "2023-07-26T05:29:13.919275Z", + "iopub.status.idle": "2023-07-26T05:29:13.929618Z", + "shell.execute_reply": "2023-07-26T05:29:13.928417Z" + } + }, "outputs": [], "source": [ "x + y" @@ -343,7 +406,14 @@ "cell_type": "code", "execution_count": null, "id": "fee5d798", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.934509Z", + "iopub.status.busy": "2023-07-26T05:29:13.934172Z", + "iopub.status.idle": "2023-07-26T05:29:13.941331Z", + "shell.execute_reply": "2023-07-26T05:29:13.939888Z" + } + }, "outputs": [], "source": [ "x = np.array([[1, 2], [3, 4]])\n", @@ -365,7 +435,14 @@ "cell_type": "code", "execution_count": null, "id": "5871ca00", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.946610Z", + "iopub.status.busy": "2023-07-26T05:29:13.945948Z", + "iopub.status.idle": "2023-07-26T05:29:13.952982Z", + "shell.execute_reply": "2023-07-26T05:29:13.951422Z" + } + }, "outputs": [], "source": [ "x.ndim" @@ -385,7 +462,14 @@ "cell_type": "code", "execution_count": null, "id": "5a32759c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.958221Z", + "iopub.status.busy": "2023-07-26T05:29:13.957674Z", + "iopub.status.idle": "2023-07-26T05:29:13.965373Z", + "shell.execute_reply": "2023-07-26T05:29:13.964551Z" + } + }, "outputs": [], "source": [ "x.dtype" @@ -406,7 +490,14 @@ "cell_type": "code", "execution_count": null, "id": "55f01b16", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.970795Z", + "iopub.status.busy": "2023-07-26T05:29:13.970020Z", + "iopub.status.idle": "2023-07-26T05:29:13.977698Z", + "shell.execute_reply": "2023-07-26T05:29:13.976971Z" + } + }, "outputs": [], "source": [ "np.array([[1, 2], [3.0, 4]]).dtype" @@ -426,7 +517,14 @@ "cell_type": "code", "execution_count": null, "id": "2a9de618", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.983003Z", + "iopub.status.busy": "2023-07-26T05:29:13.982433Z", + "iopub.status.idle": "2023-07-26T05:29:13.987748Z", + "shell.execute_reply": "2023-07-26T05:29:13.986808Z" + } + }, "outputs": [], "source": [ "np.array?" @@ -444,7 +542,14 @@ "cell_type": "code", "execution_count": null, "id": "33e3e6ea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:13.997637Z", + "iopub.status.busy": "2023-07-26T05:29:13.996561Z", + "iopub.status.idle": "2023-07-26T05:29:14.002488Z", + "shell.execute_reply": "2023-07-26T05:29:14.001948Z" + } + }, "outputs": [], "source": [ "np.array([[1, 2], [3, 4]], float).dtype" @@ -463,7 +568,14 @@ "cell_type": "code", "execution_count": null, "id": "623f8434", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.004972Z", + "iopub.status.busy": "2023-07-26T05:29:14.004832Z", + "iopub.status.idle": "2023-07-26T05:29:14.007701Z", + "shell.execute_reply": "2023-07-26T05:29:14.007253Z" + } + }, "outputs": [], "source": [ "x.shape" @@ -487,7 +599,14 @@ "cell_type": "code", "execution_count": null, "id": "235738a7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.010160Z", + "iopub.status.busy": "2023-07-26T05:29:14.009950Z", + "iopub.status.idle": "2023-07-26T05:29:14.013139Z", + "shell.execute_reply": "2023-07-26T05:29:14.012702Z" + } + }, "outputs": [], "source": [ "x = np.array([1, 2, 3, 4])\n", @@ -506,7 +625,14 @@ "cell_type": "code", "execution_count": null, "id": "7f3494e1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.015464Z", + "iopub.status.busy": "2023-07-26T05:29:14.015347Z", + "iopub.status.idle": "2023-07-26T05:29:14.018540Z", + "shell.execute_reply": "2023-07-26T05:29:14.017992Z" + } + }, "outputs": [], "source": [ "x = np.array([1, 2, 3, 4])\n", @@ -533,7 +659,14 @@ "cell_type": "code", "execution_count": null, "id": "0ca8f936", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.020924Z", + "iopub.status.busy": "2023-07-26T05:29:14.020756Z", + "iopub.status.idle": "2023-07-26T05:29:14.023635Z", + "shell.execute_reply": "2023-07-26T05:29:14.023118Z" + } + }, "outputs": [], "source": [ "x = np.array([1, 2, 3, 4, 5, 6])\n", @@ -565,7 +698,14 @@ "cell_type": "code", "execution_count": null, "id": "23fba624", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.026491Z", + "iopub.status.busy": "2023-07-26T05:29:14.026156Z", + "iopub.status.idle": "2023-07-26T05:29:14.029404Z", + "shell.execute_reply": "2023-07-26T05:29:14.028872Z" + } + }, "outputs": [], "source": [ "x_reshape[0, 0] " @@ -584,7 +724,14 @@ "cell_type": "code", "execution_count": null, "id": "1a1df926", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.031676Z", + "iopub.status.busy": "2023-07-26T05:29:14.031560Z", + "iopub.status.idle": "2023-07-26T05:29:14.034395Z", + "shell.execute_reply": "2023-07-26T05:29:14.033993Z" + } + }, "outputs": [], "source": [ "x_reshape[1, 2] " @@ -605,7 +752,14 @@ "cell_type": "code", "execution_count": null, "id": "4bfcc1e1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.036755Z", + "iopub.status.busy": "2023-07-26T05:29:14.036603Z", + "iopub.status.idle": "2023-07-26T05:29:14.039645Z", + "shell.execute_reply": "2023-07-26T05:29:14.039150Z" + } + }, "outputs": [], "source": [ "print('x before we modify x_reshape:\\n', x)\n", @@ -636,7 +790,14 @@ "cell_type": "code", "execution_count": null, "id": "973c562a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.042240Z", + "iopub.status.busy": "2023-07-26T05:29:14.042111Z", + "iopub.status.idle": "2023-07-26T05:29:14.226949Z", + "shell.execute_reply": "2023-07-26T05:29:14.226329Z" + } + }, "outputs": [], "source": [ "my_tuple = (3, 4, 5)\n", @@ -656,7 +817,14 @@ "cell_type": "code", "execution_count": null, "id": "56b5c382", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.229928Z", + "iopub.status.busy": "2023-07-26T05:29:14.229597Z", + "iopub.status.idle": "2023-07-26T05:29:14.233213Z", + "shell.execute_reply": "2023-07-26T05:29:14.232504Z" + } + }, "outputs": [], "source": [ "x_reshape.shape, x_reshape.ndim, x_reshape.T" @@ -678,7 +846,14 @@ "cell_type": "code", "execution_count": null, "id": "2e624622", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.235702Z", + "iopub.status.busy": "2023-07-26T05:29:14.235564Z", + "iopub.status.idle": "2023-07-26T05:29:14.238916Z", + "shell.execute_reply": "2023-07-26T05:29:14.238262Z" + } + }, "outputs": [], "source": [ "np.sqrt(x)" @@ -696,7 +871,14 @@ "cell_type": "code", "execution_count": null, "id": "6f45bd25", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.241171Z", + "iopub.status.busy": "2023-07-26T05:29:14.241044Z", + "iopub.status.idle": "2023-07-26T05:29:14.244021Z", + "shell.execute_reply": "2023-07-26T05:29:14.243599Z" + } + }, "outputs": [], "source": [ "x**2" @@ -714,7 +896,14 @@ "cell_type": "code", "execution_count": null, "id": "6980f628", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.246394Z", + "iopub.status.busy": "2023-07-26T05:29:14.246080Z", + "iopub.status.idle": "2023-07-26T05:29:14.249174Z", + "shell.execute_reply": "2023-07-26T05:29:14.248827Z" + } + }, "outputs": [], "source": [ "x**0.5" @@ -741,7 +930,14 @@ "cell_type": "code", "execution_count": null, "id": "89e32100", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.251585Z", + "iopub.status.busy": "2023-07-26T05:29:14.251451Z", + "iopub.status.idle": "2023-07-26T05:29:14.254814Z", + "shell.execute_reply": "2023-07-26T05:29:14.254443Z" + } + }, "outputs": [], "source": [ "x = np.random.normal(size=50)\n", @@ -760,7 +956,14 @@ "cell_type": "code", "execution_count": null, "id": "e3428155", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.257670Z", + "iopub.status.busy": "2023-07-26T05:29:14.257502Z", + "iopub.status.idle": "2023-07-26T05:29:14.259844Z", + "shell.execute_reply": "2023-07-26T05:29:14.259473Z" + } + }, "outputs": [], "source": [ "y = x + np.random.normal(loc=50, scale=1, size=50)" @@ -779,7 +982,14 @@ "cell_type": "code", "execution_count": null, "id": "df70172e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.262323Z", + "iopub.status.busy": "2023-07-26T05:29:14.262139Z", + "iopub.status.idle": "2023-07-26T05:29:14.266530Z", + "shell.execute_reply": "2023-07-26T05:29:14.266141Z" + } + }, "outputs": [], "source": [ "np.corrcoef(x, y)" @@ -800,7 +1010,14 @@ "cell_type": "code", "execution_count": null, "id": "1d996956", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.269539Z", + "iopub.status.busy": "2023-07-26T05:29:14.269386Z", + "iopub.status.idle": "2023-07-26T05:29:14.272569Z", + "shell.execute_reply": "2023-07-26T05:29:14.271780Z" + } + }, "outputs": [], "source": [ "print(np.random.normal(scale=5, size=2))\n", @@ -826,7 +1043,14 @@ "cell_type": "code", "execution_count": null, "id": "37b3a30b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.275409Z", + "iopub.status.busy": "2023-07-26T05:29:14.275284Z", + "iopub.status.idle": "2023-07-26T05:29:14.279259Z", + "shell.execute_reply": "2023-07-26T05:29:14.278925Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1303)\n", @@ -856,7 +1080,14 @@ "cell_type": "code", "execution_count": null, "id": "b4f99e0e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.282826Z", + "iopub.status.busy": "2023-07-26T05:29:14.282508Z", + "iopub.status.idle": "2023-07-26T05:29:14.285616Z", + "shell.execute_reply": "2023-07-26T05:29:14.285268Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(3)\n", @@ -868,7 +1099,14 @@ "cell_type": "code", "execution_count": null, "id": "dbd70319", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.287760Z", + "iopub.status.busy": "2023-07-26T05:29:14.287646Z", + "iopub.status.idle": "2023-07-26T05:29:14.290801Z", + "shell.execute_reply": "2023-07-26T05:29:14.290395Z" + } + }, "outputs": [], "source": [ "np.var(y), y.var(), np.mean((y - y.mean())**2)" @@ -887,7 +1125,14 @@ "cell_type": "code", "execution_count": null, "id": "bf281844", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.292985Z", + "iopub.status.busy": "2023-07-26T05:29:14.292843Z", + "iopub.status.idle": "2023-07-26T05:29:14.295775Z", + "shell.execute_reply": "2023-07-26T05:29:14.295412Z" + } + }, "outputs": [], "source": [ "np.sqrt(np.var(y)), np.std(y)" @@ -906,7 +1151,14 @@ "cell_type": "code", "execution_count": null, "id": "f751b1dd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.297946Z", + "iopub.status.busy": "2023-07-26T05:29:14.297809Z", + "iopub.status.idle": "2023-07-26T05:29:14.300890Z", + "shell.execute_reply": "2023-07-26T05:29:14.300481Z" + } + }, "outputs": [], "source": [ "X = rng.standard_normal((10, 3))\n", @@ -925,7 +1177,14 @@ "cell_type": "code", "execution_count": null, "id": "cbf92de5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.303121Z", + "iopub.status.busy": "2023-07-26T05:29:14.303004Z", + "iopub.status.idle": "2023-07-26T05:29:14.306153Z", + "shell.execute_reply": "2023-07-26T05:29:14.305697Z" + } + }, "outputs": [], "source": [ "X.mean(axis=0)" @@ -943,7 +1202,14 @@ "cell_type": "code", "execution_count": null, "id": "efacc213", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.308317Z", + "iopub.status.busy": "2023-07-26T05:29:14.308171Z", + "iopub.status.idle": "2023-07-26T05:29:14.311023Z", + "shell.execute_reply": "2023-07-26T05:29:14.310704Z" + } + }, "outputs": [], "source": [ "X.mean(0)" @@ -986,7 +1252,14 @@ "cell_type": "code", "execution_count": null, "id": "637fb67d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.313130Z", + "iopub.status.busy": "2023-07-26T05:29:14.312981Z", + "iopub.status.idle": "2023-07-26T05:29:14.818758Z", + "shell.execute_reply": "2023-07-26T05:29:14.818230Z" + } + }, "outputs": [], "source": [ "from matplotlib.pyplot import subplots\n", @@ -1010,7 +1283,14 @@ "cell_type": "code", "execution_count": null, "id": "109d681d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.821437Z", + "iopub.status.busy": "2023-07-26T05:29:14.821210Z", + "iopub.status.idle": "2023-07-26T05:29:14.906268Z", + "shell.execute_reply": "2023-07-26T05:29:14.905878Z" + } + }, "outputs": [], "source": [ "output = subplots(figsize=(8, 8))\n", @@ -1030,7 +1310,14 @@ "cell_type": "code", "execution_count": null, "id": "e905982c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.908936Z", + "iopub.status.busy": "2023-07-26T05:29:14.908699Z", + "iopub.status.idle": "2023-07-26T05:29:14.995469Z", + "shell.execute_reply": "2023-07-26T05:29:14.994686Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1059,7 +1346,14 @@ "cell_type": "code", "execution_count": null, "id": "27133a4e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:14.997995Z", + "iopub.status.busy": "2023-07-26T05:29:14.997801Z", + "iopub.status.idle": "2023-07-26T05:29:15.089963Z", + "shell.execute_reply": "2023-07-26T05:29:15.089604Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1081,7 +1375,14 @@ "cell_type": "code", "execution_count": null, "id": "ef41f90d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.092622Z", + "iopub.status.busy": "2023-07-26T05:29:15.092487Z", + "iopub.status.idle": "2023-07-26T05:29:15.212584Z", + "shell.execute_reply": "2023-07-26T05:29:15.211918Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1111,7 +1412,14 @@ "cell_type": "code", "execution_count": null, "id": "16a9cb99", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.215419Z", + "iopub.status.busy": "2023-07-26T05:29:15.215027Z", + "iopub.status.idle": "2023-07-26T05:29:15.330192Z", + "shell.execute_reply": "2023-07-26T05:29:15.328644Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1134,7 +1442,14 @@ "cell_type": "code", "execution_count": null, "id": "6707420e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.334119Z", + "iopub.status.busy": "2023-07-26T05:29:15.333963Z", + "iopub.status.idle": "2023-07-26T05:29:15.405814Z", + "shell.execute_reply": "2023-07-26T05:29:15.405400Z" + } + }, "outputs": [], "source": [ "fig.set_size_inches(12,3)\n", @@ -1160,7 +1475,14 @@ "cell_type": "code", "execution_count": null, "id": "6c78ef8b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.408333Z", + "iopub.status.busy": "2023-07-26T05:29:15.408192Z", + "iopub.status.idle": "2023-07-26T05:29:15.732292Z", + "shell.execute_reply": "2023-07-26T05:29:15.731605Z" + } + }, "outputs": [], "source": [ "fig, axes = subplots(nrows=2,\n", @@ -1181,7 +1503,14 @@ "cell_type": "code", "execution_count": null, "id": "a18402fb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.734889Z", + "iopub.status.busy": "2023-07-26T05:29:15.734697Z", + "iopub.status.idle": "2023-07-26T05:29:15.959530Z", + "shell.execute_reply": "2023-07-26T05:29:15.959066Z" + } + }, "outputs": [], "source": [ "axes[0,1].plot(x, y, 'o')\n", @@ -1212,7 +1541,14 @@ "cell_type": "code", "execution_count": null, "id": "80e18950", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:15.962416Z", + "iopub.status.busy": "2023-07-26T05:29:15.962231Z", + "iopub.status.idle": "2023-07-26T05:29:17.048215Z", + "shell.execute_reply": "2023-07-26T05:29:17.047676Z" + } + }, "outputs": [], "source": [ "fig.savefig(\"Figure.png\", dpi=400)\n", @@ -1231,7 +1567,14 @@ "cell_type": "code", "execution_count": null, "id": "fe8c404d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.051216Z", + "iopub.status.busy": "2023-07-26T05:29:17.051042Z", + "iopub.status.idle": "2023-07-26T05:29:17.330983Z", + "shell.execute_reply": "2023-07-26T05:29:17.330378Z" + } + }, "outputs": [], "source": [ "axes[0,1].set_xlim([-1,1])\n", @@ -1262,7 +1605,14 @@ "cell_type": "code", "execution_count": null, "id": "a99034cb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.333539Z", + "iopub.status.busy": "2023-07-26T05:29:17.333339Z", + "iopub.status.idle": "2023-07-26T05:29:17.452013Z", + "shell.execute_reply": "2023-07-26T05:29:17.451505Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1284,7 +1634,14 @@ "cell_type": "code", "execution_count": null, "id": "c613a5df", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.454682Z", + "iopub.status.busy": "2023-07-26T05:29:17.454485Z", + "iopub.status.idle": "2023-07-26T05:29:17.604571Z", + "shell.execute_reply": "2023-07-26T05:29:17.604143Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1311,7 +1668,14 @@ "cell_type": "code", "execution_count": null, "id": "bef21527", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.607805Z", + "iopub.status.busy": "2023-07-26T05:29:17.607474Z", + "iopub.status.idle": "2023-07-26T05:29:17.731227Z", + "shell.execute_reply": "2023-07-26T05:29:17.730768Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1340,7 +1704,14 @@ "cell_type": "code", "execution_count": null, "id": "4435ed50", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.733591Z", + "iopub.status.busy": "2023-07-26T05:29:17.733437Z", + "iopub.status.idle": "2023-07-26T05:29:17.736896Z", + "shell.execute_reply": "2023-07-26T05:29:17.736428Z" + } + }, "outputs": [], "source": [ "seq1 = np.linspace(0, 10, 11)\n", @@ -1361,7 +1732,14 @@ "cell_type": "code", "execution_count": null, "id": "7a2c664a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.739070Z", + "iopub.status.busy": "2023-07-26T05:29:17.738902Z", + "iopub.status.idle": "2023-07-26T05:29:17.742002Z", + "shell.execute_reply": "2023-07-26T05:29:17.741608Z" + } + }, "outputs": [], "source": [ "seq2 = np.arange(0, 10)\n", @@ -1384,7 +1762,14 @@ "cell_type": "code", "execution_count": null, "id": "810eafdf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.744281Z", + "iopub.status.busy": "2023-07-26T05:29:17.744099Z", + "iopub.status.idle": "2023-07-26T05:29:17.747188Z", + "shell.execute_reply": "2023-07-26T05:29:17.746807Z" + } + }, "outputs": [], "source": [ "\"hello world\"[3:6]" @@ -1403,7 +1788,14 @@ "cell_type": "code", "execution_count": null, "id": "f7a9f652", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.749618Z", + "iopub.status.busy": "2023-07-26T05:29:17.749428Z", + "iopub.status.idle": "2023-07-26T05:29:17.752320Z", + "shell.execute_reply": "2023-07-26T05:29:17.751846Z" + } + }, "outputs": [], "source": [ "\"hello world\"[slice(3,6)]" @@ -1432,7 +1824,14 @@ "cell_type": "code", "execution_count": null, "id": "24427538", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.754673Z", + "iopub.status.busy": "2023-07-26T05:29:17.754528Z", + "iopub.status.idle": "2023-07-26T05:29:17.758000Z", + "shell.execute_reply": "2023-07-26T05:29:17.757315Z" + } + }, "outputs": [], "source": [ "A = np.array(np.arange(16)).reshape((4, 4))\n", @@ -1452,7 +1851,14 @@ "cell_type": "code", "execution_count": null, "id": "18684ed5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.760251Z", + "iopub.status.busy": "2023-07-26T05:29:17.760111Z", + "iopub.status.idle": "2023-07-26T05:29:17.763156Z", + "shell.execute_reply": "2023-07-26T05:29:17.762782Z" + } + }, "outputs": [], "source": [ "A[1,2]" @@ -1475,7 +1881,14 @@ "cell_type": "code", "execution_count": null, "id": "34d27b73", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.765326Z", + "iopub.status.busy": "2023-07-26T05:29:17.765144Z", + "iopub.status.idle": "2023-07-26T05:29:17.768118Z", + "shell.execute_reply": "2023-07-26T05:29:17.767768Z" + } + }, "outputs": [], "source": [ "A[[1,3]]" @@ -1495,7 +1908,14 @@ "cell_type": "code", "execution_count": null, "id": "1bb799c5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.770122Z", + "iopub.status.busy": "2023-07-26T05:29:17.769970Z", + "iopub.status.idle": "2023-07-26T05:29:17.773095Z", + "shell.execute_reply": "2023-07-26T05:29:17.772652Z" + } + }, "outputs": [], "source": [ "A[:,[0,2]]" @@ -1515,7 +1935,14 @@ "cell_type": "code", "execution_count": null, "id": "de853e7c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.775350Z", + "iopub.status.busy": "2023-07-26T05:29:17.775182Z", + "iopub.status.idle": "2023-07-26T05:29:17.778160Z", + "shell.execute_reply": "2023-07-26T05:29:17.777807Z" + } + }, "outputs": [], "source": [ "A[[1,3],[0,2]]" @@ -1533,7 +1960,14 @@ "cell_type": "code", "execution_count": null, "id": "e71bf672", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.780228Z", + "iopub.status.busy": "2023-07-26T05:29:17.780099Z", + "iopub.status.idle": "2023-07-26T05:29:17.783205Z", + "shell.execute_reply": "2023-07-26T05:29:17.782588Z" + } + }, "outputs": [], "source": [ "np.array([A[1,0],A[3,2]])" @@ -1551,7 +1985,14 @@ "cell_type": "code", "execution_count": null, "id": "458fe5c9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.785646Z", + "iopub.status.busy": "2023-07-26T05:29:17.785525Z", + "iopub.status.idle": "2023-07-26T05:29:17.812781Z", + "shell.execute_reply": "2023-07-26T05:29:17.812287Z" + } + }, "outputs": [], "source": [ "A[[1,3],[0,2,3]]" @@ -1571,7 +2012,14 @@ "cell_type": "code", "execution_count": null, "id": "703832ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.815878Z", + "iopub.status.busy": "2023-07-26T05:29:17.815556Z", + "iopub.status.idle": "2023-07-26T05:29:17.819079Z", + "shell.execute_reply": "2023-07-26T05:29:17.818526Z" + } + }, "outputs": [], "source": [ "A[[1,3]][:,[0,2]]" @@ -1592,7 +2040,14 @@ "cell_type": "code", "execution_count": null, "id": "dbf328b7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.821663Z", + "iopub.status.busy": "2023-07-26T05:29:17.821485Z", + "iopub.status.idle": "2023-07-26T05:29:17.825110Z", + "shell.execute_reply": "2023-07-26T05:29:17.824596Z" + } + }, "outputs": [], "source": [ "idx = np.ix_([1,3],[0,2,3])\n", @@ -1615,7 +2070,14 @@ "cell_type": "code", "execution_count": null, "id": "65784eb0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.827692Z", + "iopub.status.busy": "2023-07-26T05:29:17.827506Z", + "iopub.status.idle": "2023-07-26T05:29:17.830968Z", + "shell.execute_reply": "2023-07-26T05:29:17.830460Z" + } + }, "outputs": [], "source": [ "A[1:4:2,0:3:2]" @@ -1646,7 +2108,14 @@ "cell_type": "code", "execution_count": null, "id": "3ddb0d83", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.833837Z", + "iopub.status.busy": "2023-07-26T05:29:17.833652Z", + "iopub.status.idle": "2023-07-26T05:29:17.836642Z", + "shell.execute_reply": "2023-07-26T05:29:17.836240Z" + } + }, "outputs": [], "source": [ "keep_rows = np.zeros(A.shape[0], bool)\n", @@ -1665,7 +2134,14 @@ "cell_type": "code", "execution_count": null, "id": "90e72009", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.839230Z", + "iopub.status.busy": "2023-07-26T05:29:17.839048Z", + "iopub.status.idle": "2023-07-26T05:29:17.844524Z", + "shell.execute_reply": "2023-07-26T05:29:17.841903Z" + } + }, "outputs": [], "source": [ "keep_rows[[1,3]] = True\n", @@ -1686,7 +2162,14 @@ "cell_type": "code", "execution_count": null, "id": "fb3b6534", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.847591Z", + "iopub.status.busy": "2023-07-26T05:29:17.847413Z", + "iopub.status.idle": "2023-07-26T05:29:17.851043Z", + "shell.execute_reply": "2023-07-26T05:29:17.850650Z" + } + }, "outputs": [], "source": [ "np.all(keep_rows == np.array([0,1,0,1]))" @@ -1714,7 +2197,14 @@ "cell_type": "code", "execution_count": null, "id": "b146db22", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.854162Z", + "iopub.status.busy": "2023-07-26T05:29:17.853950Z", + "iopub.status.idle": "2023-07-26T05:29:17.860504Z", + "shell.execute_reply": "2023-07-26T05:29:17.860073Z" + } + }, "outputs": [], "source": [ "A[np.array([0,1,0,1])]" @@ -1732,7 +2222,14 @@ "cell_type": "code", "execution_count": null, "id": "6bf7b4a3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.864709Z", + "iopub.status.busy": "2023-07-26T05:29:17.864481Z", + "iopub.status.idle": "2023-07-26T05:29:17.869279Z", + "shell.execute_reply": "2023-07-26T05:29:17.868135Z" + } + }, "outputs": [], "source": [ "A[keep_rows]" @@ -1760,7 +2257,14 @@ "cell_type": "code", "execution_count": null, "id": "288011d9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.875531Z", + "iopub.status.busy": "2023-07-26T05:29:17.874719Z", + "iopub.status.idle": "2023-07-26T05:29:17.880224Z", + "shell.execute_reply": "2023-07-26T05:29:17.879831Z" + } + }, "outputs": [], "source": [ "keep_cols = np.zeros(A.shape[1], bool)\n", @@ -1781,7 +2285,14 @@ "cell_type": "code", "execution_count": null, "id": "3e3b1954", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.885549Z", + "iopub.status.busy": "2023-07-26T05:29:17.885336Z", + "iopub.status.idle": "2023-07-26T05:29:17.890989Z", + "shell.execute_reply": "2023-07-26T05:29:17.890528Z" + } + }, "outputs": [], "source": [ "idx_mixed = np.ix_([1,3], keep_cols)\n", @@ -1845,7 +2356,14 @@ "cell_type": "code", "execution_count": null, "id": "d02bfb28", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:17.893962Z", + "iopub.status.busy": "2023-07-26T05:29:17.893772Z", + "iopub.status.idle": "2023-07-26T05:29:18.126793Z", + "shell.execute_reply": "2023-07-26T05:29:18.126353Z" + } + }, "outputs": [], "source": [ "import pandas as pd\n", @@ -1865,7 +2383,14 @@ "cell_type": "code", "execution_count": null, "id": "ca6090af", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.129214Z", + "iopub.status.busy": "2023-07-26T05:29:18.129036Z", + "iopub.status.idle": "2023-07-26T05:29:18.133861Z", + "shell.execute_reply": "2023-07-26T05:29:18.133327Z" + } + }, "outputs": [], "source": [ "Auto = pd.read_csv('Auto.data', delim_whitespace=True)" @@ -1893,7 +2418,14 @@ "cell_type": "code", "execution_count": null, "id": "7220aa87", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.136921Z", + "iopub.status.busy": "2023-07-26T05:29:18.136464Z", + "iopub.status.idle": "2023-07-26T05:29:18.140452Z", + "shell.execute_reply": "2023-07-26T05:29:18.139936Z" + } + }, "outputs": [], "source": [ "Auto['horsepower']" @@ -1914,7 +2446,14 @@ "cell_type": "code", "execution_count": null, "id": "ca1939f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.142925Z", + "iopub.status.busy": "2023-07-26T05:29:18.142803Z", + "iopub.status.idle": "2023-07-26T05:29:18.146461Z", + "shell.execute_reply": "2023-07-26T05:29:18.145934Z" + } + }, "outputs": [], "source": [ "np.unique(Auto['horsepower'])" @@ -1942,7 +2481,14 @@ "cell_type": "code", "execution_count": null, "id": "cfb084e7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.149045Z", + "iopub.status.busy": "2023-07-26T05:29:18.148875Z", + "iopub.status.idle": "2023-07-26T05:29:18.153953Z", + "shell.execute_reply": "2023-07-26T05:29:18.153625Z" + } + }, "outputs": [], "source": [ "Auto = pd.read_csv('Auto.data',\n", @@ -1964,7 +2510,14 @@ "cell_type": "code", "execution_count": null, "id": "2e02b6a5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.156574Z", + "iopub.status.busy": "2023-07-26T05:29:18.155920Z", + "iopub.status.idle": "2023-07-26T05:29:18.159130Z", + "shell.execute_reply": "2023-07-26T05:29:18.158724Z" + } + }, "outputs": [], "source": [ "Auto.shape" @@ -1985,7 +2538,14 @@ "cell_type": "code", "execution_count": null, "id": "d391f8d0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.161446Z", + "iopub.status.busy": "2023-07-26T05:29:18.161286Z", + "iopub.status.idle": "2023-07-26T05:29:18.165230Z", + "shell.execute_reply": "2023-07-26T05:29:18.164876Z" + } + }, "outputs": [], "source": [ "Auto_new = Auto.dropna()\n", @@ -2006,7 +2566,14 @@ "cell_type": "code", "execution_count": null, "id": "a222ca60", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.167288Z", + "iopub.status.busy": "2023-07-26T05:29:18.167156Z", + "iopub.status.idle": "2023-07-26T05:29:18.171499Z", + "shell.execute_reply": "2023-07-26T05:29:18.171112Z" + } + }, "outputs": [], "source": [ "Auto = Auto_new # overwrite the previous value\n", @@ -2029,7 +2596,14 @@ "cell_type": "code", "execution_count": null, "id": "a75921ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.173985Z", + "iopub.status.busy": "2023-07-26T05:29:18.173779Z", + "iopub.status.idle": "2023-07-26T05:29:18.180116Z", + "shell.execute_reply": "2023-07-26T05:29:18.179681Z" + } + }, "outputs": [], "source": [ "Auto[:3]" @@ -2047,7 +2621,14 @@ "cell_type": "code", "execution_count": null, "id": "7469ff2b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.183321Z", + "iopub.status.busy": "2023-07-26T05:29:18.183157Z", + "iopub.status.idle": "2023-07-26T05:29:18.201363Z", + "shell.execute_reply": "2023-07-26T05:29:18.200939Z" + } + }, "outputs": [], "source": [ "idx_80 = Auto['year'] > 80\n", @@ -2066,7 +2647,14 @@ "cell_type": "code", "execution_count": null, "id": "ac455404", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.203476Z", + "iopub.status.busy": "2023-07-26T05:29:18.203352Z", + "iopub.status.idle": "2023-07-26T05:29:18.209504Z", + "shell.execute_reply": "2023-07-26T05:29:18.209134Z" + } + }, "outputs": [], "source": [ "Auto[['mpg', 'horsepower']]" @@ -2085,7 +2673,14 @@ "cell_type": "code", "execution_count": null, "id": "f41235d4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.211995Z", + "iopub.status.busy": "2023-07-26T05:29:18.211814Z", + "iopub.status.idle": "2023-07-26T05:29:18.215209Z", + "shell.execute_reply": "2023-07-26T05:29:18.214581Z" + } + }, "outputs": [], "source": [ "Auto.index" @@ -2104,7 +2699,14 @@ "cell_type": "code", "execution_count": null, "id": "31caba24", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.217421Z", + "iopub.status.busy": "2023-07-26T05:29:18.217292Z", + "iopub.status.idle": "2023-07-26T05:29:18.225169Z", + "shell.execute_reply": "2023-07-26T05:29:18.224781Z" + } + }, "outputs": [], "source": [ "Auto_re = Auto.set_index('name')\n", @@ -2115,7 +2717,14 @@ "cell_type": "code", "execution_count": null, "id": "594bedfa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.227488Z", + "iopub.status.busy": "2023-07-26T05:29:18.227297Z", + "iopub.status.idle": "2023-07-26T05:29:18.230280Z", + "shell.execute_reply": "2023-07-26T05:29:18.229861Z" + } + }, "outputs": [], "source": [ "Auto_re.columns" @@ -2137,7 +2746,14 @@ "cell_type": "code", "execution_count": null, "id": "9a59b083", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.232510Z", + "iopub.status.busy": "2023-07-26T05:29:18.232342Z", + "iopub.status.idle": "2023-07-26T05:29:18.238242Z", + "shell.execute_reply": "2023-07-26T05:29:18.237748Z" + } + }, "outputs": [], "source": [ "rows = ['amc rebel sst', 'ford torino']\n", @@ -2156,7 +2772,14 @@ "cell_type": "code", "execution_count": null, "id": "54457a11", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.240468Z", + "iopub.status.busy": "2023-07-26T05:29:18.240293Z", + "iopub.status.idle": "2023-07-26T05:29:18.245745Z", + "shell.execute_reply": "2023-07-26T05:29:18.245311Z" + } + }, "outputs": [], "source": [ "Auto_re.iloc[[3,4]]" @@ -2174,7 +2797,14 @@ "cell_type": "code", "execution_count": null, "id": "8a4f3faf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.247984Z", + "iopub.status.busy": "2023-07-26T05:29:18.247834Z", + "iopub.status.idle": "2023-07-26T05:29:18.254533Z", + "shell.execute_reply": "2023-07-26T05:29:18.254012Z" + } + }, "outputs": [], "source": [ "Auto_re.iloc[:,[0,2,3]]" @@ -2193,7 +2823,14 @@ "cell_type": "code", "execution_count": null, "id": "8f1e6e56", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.256972Z", + "iopub.status.busy": "2023-07-26T05:29:18.256754Z", + "iopub.status.idle": "2023-07-26T05:29:18.261662Z", + "shell.execute_reply": "2023-07-26T05:29:18.261232Z" + } + }, "outputs": [], "source": [ "Auto_re.iloc[[3,4],[0,2,3]]" @@ -2211,7 +2848,14 @@ "cell_type": "code", "execution_count": null, "id": "f1d0e795", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.263825Z", + "iopub.status.busy": "2023-07-26T05:29:18.263676Z", + "iopub.status.idle": "2023-07-26T05:29:18.268126Z", + "shell.execute_reply": "2023-07-26T05:29:18.267724Z" + } + }, "outputs": [], "source": [ "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]" @@ -2233,7 +2877,14 @@ "cell_type": "code", "execution_count": null, "id": "392f10a1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.270321Z", + "iopub.status.busy": "2023-07-26T05:29:18.270149Z", + "iopub.status.idle": "2023-07-26T05:29:18.277370Z", + "shell.execute_reply": "2023-07-26T05:29:18.276917Z" + } + }, "outputs": [], "source": [ "idx_80 = Auto_re['year'] > 80\n", @@ -2252,7 +2903,14 @@ "cell_type": "code", "execution_count": null, "id": "5f5b7906", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.279435Z", + "iopub.status.busy": "2023-07-26T05:29:18.279296Z", + "iopub.status.idle": "2023-07-26T05:29:18.287423Z", + "shell.execute_reply": "2023-07-26T05:29:18.286943Z" + } + }, "outputs": [], "source": [ "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]" @@ -2275,7 +2933,14 @@ "cell_type": "code", "execution_count": null, "id": "b13c5728", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.289828Z", + "iopub.status.busy": "2023-07-26T05:29:18.289661Z", + "iopub.status.idle": "2023-07-26T05:29:18.296360Z", + "shell.execute_reply": "2023-07-26T05:29:18.295846Z" + } + }, "outputs": [], "source": [ "Auto_re.loc[lambda df: (df['year'] > 80) & (df['mpg'] > 30),\n", @@ -2298,7 +2963,14 @@ "cell_type": "code", "execution_count": null, "id": "d5a2c3be", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.298956Z", + "iopub.status.busy": "2023-07-26T05:29:18.298779Z", + "iopub.status.idle": "2023-07-26T05:29:18.306530Z", + "shell.execute_reply": "2023-07-26T05:29:18.305978Z" + } + }, "outputs": [], "source": [ "Auto_re.loc[lambda df: (df['displacement'] < 300)\n", @@ -2330,7 +3002,14 @@ "cell_type": "code", "execution_count": null, "id": "a6278441", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.308697Z", + "iopub.status.busy": "2023-07-26T05:29:18.308549Z", + "iopub.status.idle": "2023-07-26T05:29:18.311058Z", + "shell.execute_reply": "2023-07-26T05:29:18.310729Z" + } + }, "outputs": [], "source": [ "total = 0\n", @@ -2358,7 +3037,14 @@ "cell_type": "code", "execution_count": null, "id": "2aa065a8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.313814Z", + "iopub.status.busy": "2023-07-26T05:29:18.313627Z", + "iopub.status.idle": "2023-07-26T05:29:18.316245Z", + "shell.execute_reply": "2023-07-26T05:29:18.315821Z" + } + }, "outputs": [], "source": [ "total = 0\n", @@ -2394,7 +3080,14 @@ "cell_type": "code", "execution_count": null, "id": "832c1b7d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.318251Z", + "iopub.status.busy": "2023-07-26T05:29:18.318060Z", + "iopub.status.idle": "2023-07-26T05:29:18.321951Z", + "shell.execute_reply": "2023-07-26T05:29:18.321594Z" + } + }, "outputs": [], "source": [ "total = 0\n", @@ -2434,7 +3127,14 @@ "cell_type": "code", "execution_count": null, "id": "2ca2e7cd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.324105Z", + "iopub.status.busy": "2023-07-26T05:29:18.323988Z", + "iopub.status.idle": "2023-07-26T05:29:18.330121Z", + "shell.execute_reply": "2023-07-26T05:29:18.329787Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", @@ -2453,7 +3153,14 @@ "cell_type": "code", "execution_count": null, "id": "d8ae1dcc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.332238Z", + "iopub.status.busy": "2023-07-26T05:29:18.331972Z", + "iopub.status.idle": "2023-07-26T05:29:18.335469Z", + "shell.execute_reply": "2023-07-26T05:29:18.335131Z" + } + }, "outputs": [], "source": [ "for col in D.columns:\n", @@ -2490,7 +3197,14 @@ "cell_type": "code", "execution_count": null, "id": "30a8a663", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.338919Z", + "iopub.status.busy": "2023-07-26T05:29:18.338751Z", + "iopub.status.idle": "2023-07-26T05:29:18.449864Z", + "shell.execute_reply": "2023-07-26T05:29:18.449132Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2509,7 +3223,14 @@ "cell_type": "code", "execution_count": null, "id": "81fdedb1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.452711Z", + "iopub.status.busy": "2023-07-26T05:29:18.452267Z", + "iopub.status.idle": "2023-07-26T05:29:18.556826Z", + "shell.execute_reply": "2023-07-26T05:29:18.556309Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2532,7 +3253,14 @@ "cell_type": "code", "execution_count": null, "id": "61850515", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.559255Z", + "iopub.status.busy": "2023-07-26T05:29:18.559034Z", + "iopub.status.idle": "2023-07-26T05:29:18.680294Z", + "shell.execute_reply": "2023-07-26T05:29:18.679833Z" + } + }, "outputs": [], "source": [ "ax = Auto.plot.scatter('horsepower', 'mpg');\n", @@ -2553,7 +3281,14 @@ "cell_type": "code", "execution_count": null, "id": "f2e0c165", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.682678Z", + "iopub.status.busy": "2023-07-26T05:29:18.682519Z", + "iopub.status.idle": "2023-07-26T05:29:18.726894Z", + "shell.execute_reply": "2023-07-26T05:29:18.726384Z" + } + }, "outputs": [], "source": [ "fig = ax.figure\n", @@ -2577,7 +3312,14 @@ "cell_type": "code", "execution_count": null, "id": "35c72e77", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.729555Z", + "iopub.status.busy": "2023-07-26T05:29:18.729403Z", + "iopub.status.idle": "2023-07-26T05:29:18.935439Z", + "shell.execute_reply": "2023-07-26T05:29:18.934984Z" + } + }, "outputs": [], "source": [ "fig, axes = subplots(ncols=3, figsize=(15, 5))\n", @@ -2607,7 +3349,14 @@ "cell_type": "code", "execution_count": null, "id": "2b6fb1ad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.937842Z", + "iopub.status.busy": "2023-07-26T05:29:18.937680Z", + "iopub.status.idle": "2023-07-26T05:29:18.941712Z", + "shell.execute_reply": "2023-07-26T05:29:18.941344Z" + } + }, "outputs": [], "source": [ "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n", @@ -2627,7 +3376,14 @@ "cell_type": "code", "execution_count": null, "id": "1f19f48c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:18.944261Z", + "iopub.status.busy": "2023-07-26T05:29:18.944131Z", + "iopub.status.idle": "2023-07-26T05:29:19.068255Z", + "shell.execute_reply": "2023-07-26T05:29:19.067715Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2646,7 +3402,14 @@ "cell_type": "code", "execution_count": null, "id": "929b6901", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:19.071259Z", + "iopub.status.busy": "2023-07-26T05:29:19.071061Z", + "iopub.status.idle": "2023-07-26T05:29:19.195022Z", + "shell.execute_reply": "2023-07-26T05:29:19.194542Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2665,7 +3428,14 @@ "cell_type": "code", "execution_count": null, "id": "8d48f1e9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:19.197491Z", + "iopub.status.busy": "2023-07-26T05:29:19.197276Z", + "iopub.status.idle": "2023-07-26T05:29:19.318988Z", + "shell.execute_reply": "2023-07-26T05:29:19.318622Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2688,7 +3458,14 @@ "cell_type": "code", "execution_count": null, "id": "400690e6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:19.321439Z", + "iopub.status.busy": "2023-07-26T05:29:19.321240Z", + "iopub.status.idle": "2023-07-26T05:29:20.519159Z", + "shell.execute_reply": "2023-07-26T05:29:20.518700Z" + } + }, "outputs": [], "source": [ "pd.plotting.scatter_matrix(Auto);" @@ -2707,7 +3484,14 @@ "cell_type": "code", "execution_count": null, "id": "6bbeca8b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:20.521548Z", + "iopub.status.busy": "2023-07-26T05:29:20.521429Z", + "iopub.status.idle": "2023-07-26T05:29:20.804202Z", + "shell.execute_reply": "2023-07-26T05:29:20.803825Z" + } + }, "outputs": [], "source": [ "pd.plotting.scatter_matrix(Auto[['mpg',\n", @@ -2727,7 +3511,14 @@ "cell_type": "code", "execution_count": null, "id": "1727f604", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:20.809656Z", + "iopub.status.busy": "2023-07-26T05:29:20.809455Z", + "iopub.status.idle": "2023-07-26T05:29:20.816572Z", + "shell.execute_reply": "2023-07-26T05:29:20.816157Z" + } + }, "outputs": [], "source": [ "Auto[['mpg', 'weight']].describe()" @@ -2745,7 +3536,14 @@ "cell_type": "code", "execution_count": null, "id": "39bf4091", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:29:20.819204Z", + "iopub.status.busy": "2023-07-26T05:29:20.818991Z", + "iopub.status.idle": "2023-07-26T05:29:20.824216Z", + "shell.execute_reply": "2023-07-26T05:29:20.823857Z" + } + }, "outputs": [], "source": [ "Auto['cylinders'].describe()\n", @@ -2769,6 +3567,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch3-linreg-lab.ipynb b/docs/source/labs/Ch3-linreg-lab.ipynb index ab4e953..faefdc7 100644 --- a/docs/source/labs/Ch3-linreg-lab.ipynb +++ b/docs/source/labs/Ch3-linreg-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Linear Regression\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -22,7 +22,14 @@ "cell_type": "code", "execution_count": null, "id": "ca5277a6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:48.562599Z", + "iopub.status.busy": "2023-07-26T05:16:48.562472Z", + "iopub.status.idle": "2023-07-26T05:16:49.062186Z", + "shell.execute_reply": "2023-07-26T05:16:49.061609Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -47,7 +54,14 @@ "cell_type": "code", "execution_count": null, "id": "675f24e6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.064676Z", + "iopub.status.busy": "2023-07-26T05:16:49.064483Z", + "iopub.status.idle": "2023-07-26T05:16:49.739307Z", + "shell.execute_reply": "2023-07-26T05:16:49.738853Z" + } + }, "outputs": [], "source": [ "import statsmodels.api as sm" @@ -72,7 +86,14 @@ "cell_type": "code", "execution_count": null, "id": "a0ee23c2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.741758Z", + "iopub.status.busy": "2023-07-26T05:16:49.741609Z", + "iopub.status.idle": "2023-07-26T05:16:49.744969Z", + "shell.execute_reply": "2023-07-26T05:16:49.744551Z" + } + }, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence \\\n", @@ -96,7 +117,14 @@ "cell_type": "code", "execution_count": null, "id": "b35eb887", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.747166Z", + "iopub.status.busy": "2023-07-26T05:16:49.746991Z", + "iopub.status.idle": "2023-07-26T05:16:49.867818Z", + "shell.execute_reply": "2023-07-26T05:16:49.867356Z" + } + }, "outputs": [], "source": [ "from ISLP import load_data\n", @@ -121,7 +149,14 @@ "cell_type": "code", "execution_count": null, "id": "961908f7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.870378Z", + "iopub.status.busy": "2023-07-26T05:16:49.870231Z", + "iopub.status.idle": "2023-07-26T05:16:49.874355Z", + "shell.execute_reply": "2023-07-26T05:16:49.873851Z" + } + }, "outputs": [], "source": [ "dir()" @@ -147,7 +182,14 @@ "cell_type": "code", "execution_count": null, "id": "662caa15", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.876620Z", + "iopub.status.busy": "2023-07-26T05:16:49.876476Z", + "iopub.status.idle": "2023-07-26T05:16:49.880485Z", + "shell.execute_reply": "2023-07-26T05:16:49.880074Z" + } + }, "outputs": [], "source": [ "A = np.array([3,5,11])\n", @@ -167,7 +209,14 @@ "cell_type": "code", "execution_count": null, "id": "ebb7d126", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.882618Z", + "iopub.status.busy": "2023-07-26T05:16:49.882477Z", + "iopub.status.idle": "2023-07-26T05:16:49.885324Z", + "shell.execute_reply": "2023-07-26T05:16:49.884886Z" + } + }, "outputs": [], "source": [ "A.sum()" @@ -198,7 +247,14 @@ "cell_type": "code", "execution_count": null, "id": "1ea46cee", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.887855Z", + "iopub.status.busy": "2023-07-26T05:16:49.887580Z", + "iopub.status.idle": "2023-07-26T05:16:49.892800Z", + "shell.execute_reply": "2023-07-26T05:16:49.892442Z" + } + }, "outputs": [], "source": [ "Boston = load_data(\"Boston\")\n", @@ -222,7 +278,14 @@ "cell_type": "code", "execution_count": null, "id": "26c0ba88", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.895108Z", + "iopub.status.busy": "2023-07-26T05:16:49.894938Z", + "iopub.status.idle": "2023-07-26T05:16:49.901385Z", + "shell.execute_reply": "2023-07-26T05:16:49.900996Z" + } + }, "outputs": [], "source": [ "X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),\n", @@ -242,7 +305,14 @@ "cell_type": "code", "execution_count": null, "id": "d4dd511b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.903600Z", + "iopub.status.busy": "2023-07-26T05:16:49.903426Z", + "iopub.status.idle": "2023-07-26T05:16:49.906235Z", + "shell.execute_reply": "2023-07-26T05:16:49.905900Z" + } + }, "outputs": [], "source": [ "y = Boston['medv']\n", @@ -269,7 +339,14 @@ "cell_type": "code", "execution_count": null, "id": "eef9f8e3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.908149Z", + "iopub.status.busy": "2023-07-26T05:16:49.908028Z", + "iopub.status.idle": "2023-07-26T05:16:49.973872Z", + "shell.execute_reply": "2023-07-26T05:16:49.973443Z" + } + }, "outputs": [], "source": [ "summarize(results)" @@ -315,7 +392,14 @@ "cell_type": "code", "execution_count": null, "id": "557170d4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.976562Z", + "iopub.status.busy": "2023-07-26T05:16:49.976352Z", + "iopub.status.idle": "2023-07-26T05:16:49.982733Z", + "shell.execute_reply": "2023-07-26T05:16:49.982315Z" + } + }, "outputs": [], "source": [ "design = MS(['lstat'])\n", @@ -339,7 +423,14 @@ "cell_type": "code", "execution_count": null, "id": "b83ec097", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.985396Z", + "iopub.status.busy": "2023-07-26T05:16:49.985228Z", + "iopub.status.idle": "2023-07-26T05:16:49.992390Z", + "shell.execute_reply": "2023-07-26T05:16:49.991020Z" + } + }, "outputs": [], "source": [ "design = MS(['lstat'])\n", @@ -372,7 +463,14 @@ "cell_type": "code", "execution_count": null, "id": "d4dce5f6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:49.995458Z", + "iopub.status.busy": "2023-07-26T05:16:49.995307Z", + "iopub.status.idle": "2023-07-26T05:16:50.003927Z", + "shell.execute_reply": "2023-07-26T05:16:50.003569Z" + } + }, "outputs": [], "source": [ "results.summary()" @@ -391,7 +489,14 @@ "cell_type": "code", "execution_count": null, "id": "a0edf555", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.006397Z", + "iopub.status.busy": "2023-07-26T05:16:50.006253Z", + "iopub.status.idle": "2023-07-26T05:16:50.009669Z", + "shell.execute_reply": "2023-07-26T05:16:50.009037Z" + } + }, "outputs": [], "source": [ "results.params" @@ -413,7 +518,14 @@ "cell_type": "code", "execution_count": null, "id": "fdc5a3f3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.012044Z", + "iopub.status.busy": "2023-07-26T05:16:50.011931Z", + "iopub.status.idle": "2023-07-26T05:16:50.016842Z", + "shell.execute_reply": "2023-07-26T05:16:50.016427Z" + } + }, "outputs": [], "source": [ "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n", @@ -433,7 +545,14 @@ "cell_type": "code", "execution_count": null, "id": "2c6acbf0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.019127Z", + "iopub.status.busy": "2023-07-26T05:16:50.018964Z", + "iopub.status.idle": "2023-07-26T05:16:50.022350Z", + "shell.execute_reply": "2023-07-26T05:16:50.021918Z" + } + }, "outputs": [], "source": [ "new_predictions = results.get_prediction(newX);\n", @@ -452,7 +571,14 @@ "cell_type": "code", "execution_count": null, "id": "c472ef33", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.024574Z", + "iopub.status.busy": "2023-07-26T05:16:50.024433Z", + "iopub.status.idle": "2023-07-26T05:16:50.027837Z", + "shell.execute_reply": "2023-07-26T05:16:50.027439Z" + } + }, "outputs": [], "source": [ "new_predictions.conf_int(alpha=0.05)" @@ -470,7 +596,14 @@ "cell_type": "code", "execution_count": null, "id": "3e2ffc7a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.029978Z", + "iopub.status.busy": "2023-07-26T05:16:50.029838Z", + "iopub.status.idle": "2023-07-26T05:16:50.032847Z", + "shell.execute_reply": "2023-07-26T05:16:50.032491Z" + } + }, "outputs": [], "source": [ "new_predictions.conf_int(obs=True, alpha=0.05)" @@ -509,7 +642,14 @@ "cell_type": "code", "execution_count": null, "id": "4e56a1d3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.035185Z", + "iopub.status.busy": "2023-07-26T05:16:50.035076Z", + "iopub.status.idle": "2023-07-26T05:16:50.037782Z", + "shell.execute_reply": "2023-07-26T05:16:50.037389Z" + } + }, "outputs": [], "source": [ "def abline(ax, b, m):\n", @@ -535,7 +675,14 @@ "cell_type": "code", "execution_count": null, "id": "7f43ffe7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.040033Z", + "iopub.status.busy": "2023-07-26T05:16:50.039836Z", + "iopub.status.idle": "2023-07-26T05:16:50.042447Z", + "shell.execute_reply": "2023-07-26T05:16:50.042026Z" + } + }, "outputs": [], "source": [ "def abline(ax, b, m, *args, **kwargs):\n", @@ -567,7 +714,14 @@ "cell_type": "code", "execution_count": null, "id": "3f7b67c9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.044790Z", + "iopub.status.busy": "2023-07-26T05:16:50.044610Z", + "iopub.status.idle": "2023-07-26T05:16:50.159032Z", + "shell.execute_reply": "2023-07-26T05:16:50.158476Z" + } + }, "outputs": [], "source": [ "ax = Boston.plot.scatter('lstat', 'medv')\n", @@ -611,7 +765,14 @@ "cell_type": "code", "execution_count": null, "id": "b35a2fd3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.161898Z", + "iopub.status.busy": "2023-07-26T05:16:50.161592Z", + "iopub.status.idle": "2023-07-26T05:16:50.274404Z", + "shell.execute_reply": "2023-07-26T05:16:50.273842Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(8,8))[1]\n", @@ -640,7 +801,14 @@ "cell_type": "code", "execution_count": null, "id": "82673b80", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.276799Z", + "iopub.status.busy": "2023-07-26T05:16:50.276637Z", + "iopub.status.idle": "2023-07-26T05:16:50.384102Z", + "shell.execute_reply": "2023-07-26T05:16:50.383325Z" + } + }, "outputs": [], "source": [ "infl = results.get_influence()\n", @@ -679,7 +847,14 @@ "cell_type": "code", "execution_count": null, "id": "54596dc4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.387053Z", + "iopub.status.busy": "2023-07-26T05:16:50.386874Z", + "iopub.status.idle": "2023-07-26T05:16:50.400398Z", + "shell.execute_reply": "2023-07-26T05:16:50.399938Z" + } + }, "outputs": [], "source": [ "X = MS(['lstat', 'age']).fit_transform(Boston)\n", @@ -704,7 +879,14 @@ "cell_type": "code", "execution_count": null, "id": "75c78238", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.402974Z", + "iopub.status.busy": "2023-07-26T05:16:50.402819Z", + "iopub.status.idle": "2023-07-26T05:16:50.406561Z", + "shell.execute_reply": "2023-07-26T05:16:50.406042Z" + } + }, "outputs": [], "source": [ "terms = Boston.columns.drop('medv')\n", @@ -724,7 +906,14 @@ "cell_type": "code", "execution_count": null, "id": "f14b9e1a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.409092Z", + "iopub.status.busy": "2023-07-26T05:16:50.408922Z", + "iopub.status.idle": "2023-07-26T05:16:50.427019Z", + "shell.execute_reply": "2023-07-26T05:16:50.426146Z" + } + }, "outputs": [], "source": [ "X = MS(terms).fit_transform(Boston)\n", @@ -748,7 +937,14 @@ "cell_type": "code", "execution_count": null, "id": "0a2714b1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.430858Z", + "iopub.status.busy": "2023-07-26T05:16:50.430692Z", + "iopub.status.idle": "2023-07-26T05:16:50.449487Z", + "shell.execute_reply": "2023-07-26T05:16:50.448800Z" + } + }, "outputs": [], "source": [ "minus_age = Boston.columns.drop(['medv', 'age']) \n", @@ -792,7 +988,14 @@ "cell_type": "code", "execution_count": null, "id": "961c9128", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.454906Z", + "iopub.status.busy": "2023-07-26T05:16:50.454582Z", + "iopub.status.idle": "2023-07-26T05:16:50.464023Z", + "shell.execute_reply": "2023-07-26T05:16:50.463522Z" + } + }, "outputs": [], "source": [ "vals = [VIF(X, i)\n", @@ -818,7 +1021,14 @@ "cell_type": "code", "execution_count": null, "id": "4886f9e9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.466225Z", + "iopub.status.busy": "2023-07-26T05:16:50.466081Z", + "iopub.status.idle": "2023-07-26T05:16:50.472696Z", + "shell.execute_reply": "2023-07-26T05:16:50.472188Z" + } + }, "outputs": [], "source": [ "vals = []\n", @@ -843,7 +1053,14 @@ "cell_type": "code", "execution_count": null, "id": "b54d2da1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.475003Z", + "iopub.status.busy": "2023-07-26T05:16:50.474857Z", + "iopub.status.idle": "2023-07-26T05:16:50.488206Z", + "shell.execute_reply": "2023-07-26T05:16:50.487746Z" + } + }, "outputs": [], "source": [ "X = MS(['lstat',\n", @@ -870,7 +1087,14 @@ "cell_type": "code", "execution_count": null, "id": "1b71633a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.490521Z", + "iopub.status.busy": "2023-07-26T05:16:50.490379Z", + "iopub.status.idle": "2023-07-26T05:16:50.504489Z", + "shell.execute_reply": "2023-07-26T05:16:50.504059Z" + } + }, "outputs": [], "source": [ "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n", @@ -907,7 +1131,14 @@ "cell_type": "code", "execution_count": null, "id": "6d30a306", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.506891Z", + "iopub.status.busy": "2023-07-26T05:16:50.506725Z", + "iopub.status.idle": "2023-07-26T05:16:50.513731Z", + "shell.execute_reply": "2023-07-26T05:16:50.513303Z" + } + }, "outputs": [], "source": [ "anova_lm(results1, results3)" @@ -946,7 +1177,14 @@ "cell_type": "code", "execution_count": null, "id": "9a5ec13f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.516035Z", + "iopub.status.busy": "2023-07-26T05:16:50.515894Z", + "iopub.status.idle": "2023-07-26T05:16:50.640835Z", + "shell.execute_reply": "2023-07-26T05:16:50.640080Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(8,8))[1]\n", @@ -983,7 +1221,14 @@ "cell_type": "code", "execution_count": null, "id": "09bbc0c6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.643135Z", + "iopub.status.busy": "2023-07-26T05:16:50.643008Z", + "iopub.status.idle": "2023-07-26T05:16:50.649451Z", + "shell.execute_reply": "2023-07-26T05:16:50.649047Z" + } + }, "outputs": [], "source": [ "Carseats = load_data('Carseats')\n", @@ -1013,7 +1258,14 @@ "cell_type": "code", "execution_count": null, "id": "2e1da1fa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:50.651966Z", + "iopub.status.busy": "2023-07-26T05:16:50.651596Z", + "iopub.status.idle": "2023-07-26T05:16:50.678716Z", + "shell.execute_reply": "2023-07-26T05:16:50.678209Z" + } + }, "outputs": [], "source": [ "allvars = list(Carseats.columns.drop('Sales'))\n", @@ -1050,6 +1302,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch4-classification-lab.ipynb b/docs/source/labs/Ch4-classification-lab.ipynb index b8e8dd8..c5f4d47 100644 --- a/docs/source/labs/Ch4-classification-lab.ipynb +++ b/docs/source/labs/Ch4-classification-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Logistic Regression, LDA, QDA, and KNN\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "" @@ -38,7 +38,14 @@ "cell_type": "code", "execution_count": null, "id": "71523a7f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:52.147230Z", + "iopub.status.busy": "2023-07-26T05:16:52.147074Z", + "iopub.status.idle": "2023-07-26T05:16:53.319384Z", + "shell.execute_reply": "2023-07-26T05:16:53.318779Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -62,7 +69,14 @@ "cell_type": "code", "execution_count": null, "id": "901ed7c5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.322482Z", + "iopub.status.busy": "2023-07-26T05:16:53.322239Z", + "iopub.status.idle": "2023-07-26T05:16:53.343077Z", + "shell.execute_reply": "2023-07-26T05:16:53.342596Z" + } + }, "outputs": [], "source": [ "from ISLP import confusion_table\n", @@ -89,7 +103,14 @@ "cell_type": "code", "execution_count": null, "id": "8b56ee0f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.345526Z", + "iopub.status.busy": "2023-07-26T05:16:53.345383Z", + "iopub.status.idle": "2023-07-26T05:16:53.358625Z", + "shell.execute_reply": "2023-07-26T05:16:53.357838Z" + } + }, "outputs": [], "source": [ "Smarket = load_data('Smarket')\n", @@ -109,7 +130,14 @@ "cell_type": "code", "execution_count": null, "id": "62d49b47", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.361002Z", + "iopub.status.busy": "2023-07-26T05:16:53.360735Z", + "iopub.status.idle": "2023-07-26T05:16:53.364261Z", + "shell.execute_reply": "2023-07-26T05:16:53.363501Z" + } + }, "outputs": [], "source": [ "Smarket.columns" @@ -132,7 +160,14 @@ "cell_type": "code", "execution_count": null, "id": "bc34e7b7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.366253Z", + "iopub.status.busy": "2023-07-26T05:16:53.366125Z", + "iopub.status.idle": "2023-07-26T05:16:53.372926Z", + "shell.execute_reply": "2023-07-26T05:16:53.372328Z" + } + }, "outputs": [], "source": [ "Smarket.corr()" @@ -154,7 +189,14 @@ "cell_type": "code", "execution_count": null, "id": "9f075144", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.376785Z", + "iopub.status.busy": "2023-07-26T05:16:53.376551Z", + "iopub.status.idle": "2023-07-26T05:16:53.491023Z", + "shell.execute_reply": "2023-07-26T05:16:53.490615Z" + } + }, "outputs": [], "source": [ "Smarket.plot(y='Volume');" @@ -182,7 +224,14 @@ "cell_type": "code", "execution_count": null, "id": "2ab15aa3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.493500Z", + "iopub.status.busy": "2023-07-26T05:16:53.493302Z", + "iopub.status.idle": "2023-07-26T05:16:53.566736Z", + "shell.execute_reply": "2023-07-26T05:16:53.566181Z" + } + }, "outputs": [], "source": [ "allvars = Smarket.columns.drop(['Today', 'Direction', 'Year'])\n", @@ -217,7 +266,14 @@ "cell_type": "code", "execution_count": null, "id": "0faa6a6c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.569154Z", + "iopub.status.busy": "2023-07-26T05:16:53.568975Z", + "iopub.status.idle": "2023-07-26T05:16:53.572426Z", + "shell.execute_reply": "2023-07-26T05:16:53.572078Z" + } + }, "outputs": [], "source": [ "results.params" @@ -236,7 +292,14 @@ "cell_type": "code", "execution_count": null, "id": "108aff98", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.574612Z", + "iopub.status.busy": "2023-07-26T05:16:53.574492Z", + "iopub.status.idle": "2023-07-26T05:16:53.578308Z", + "shell.execute_reply": "2023-07-26T05:16:53.577846Z" + } + }, "outputs": [], "source": [ "results.pvalues" @@ -262,7 +325,14 @@ "cell_type": "code", "execution_count": null, "id": "54b4b96c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.580578Z", + "iopub.status.busy": "2023-07-26T05:16:53.580429Z", + "iopub.status.idle": "2023-07-26T05:16:53.583750Z", + "shell.execute_reply": "2023-07-26T05:16:53.583333Z" + } + }, "outputs": [], "source": [ "probs = results.predict()\n", @@ -286,7 +356,14 @@ "cell_type": "code", "execution_count": null, "id": "46c2887c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.586085Z", + "iopub.status.busy": "2023-07-26T05:16:53.585953Z", + "iopub.status.idle": "2023-07-26T05:16:53.588706Z", + "shell.execute_reply": "2023-07-26T05:16:53.588288Z" + } + }, "outputs": [], "source": [ "labels = np.array(['Down']*1250)\n", @@ -311,7 +388,14 @@ "cell_type": "code", "execution_count": null, "id": "0f816c4d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.590957Z", + "iopub.status.busy": "2023-07-26T05:16:53.590766Z", + "iopub.status.idle": "2023-07-26T05:16:53.598268Z", + "shell.execute_reply": "2023-07-26T05:16:53.597798Z" + } + }, "outputs": [], "source": [ "confusion_table(labels, Smarket.Direction)" @@ -336,7 +420,14 @@ "cell_type": "code", "execution_count": null, "id": "23668517", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.600706Z", + "iopub.status.busy": "2023-07-26T05:16:53.600511Z", + "iopub.status.idle": "2023-07-26T05:16:53.603872Z", + "shell.execute_reply": "2023-07-26T05:16:53.603421Z" + } + }, "outputs": [], "source": [ "(507+145)/1250, np.mean(labels == Smarket.Direction)" @@ -372,7 +463,14 @@ "cell_type": "code", "execution_count": null, "id": "86a02d7c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.606055Z", + "iopub.status.busy": "2023-07-26T05:16:53.605931Z", + "iopub.status.idle": "2023-07-26T05:16:53.609917Z", + "shell.execute_reply": "2023-07-26T05:16:53.609576Z" + } + }, "outputs": [], "source": [ "train = (Smarket.Year < 2005)\n", @@ -419,7 +517,14 @@ "cell_type": "code", "execution_count": null, "id": "f23b1c7b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.612185Z", + "iopub.status.busy": "2023-07-26T05:16:53.612009Z", + "iopub.status.idle": "2023-07-26T05:16:53.618057Z", + "shell.execute_reply": "2023-07-26T05:16:53.617645Z" + } + }, "outputs": [], "source": [ "X_train, X_test = X.loc[train], X.loc[~train]\n", @@ -449,7 +554,14 @@ "cell_type": "code", "execution_count": null, "id": "82f849b9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.620635Z", + "iopub.status.busy": "2023-07-26T05:16:53.620266Z", + "iopub.status.idle": "2023-07-26T05:16:53.623107Z", + "shell.execute_reply": "2023-07-26T05:16:53.622634Z" + } + }, "outputs": [], "source": [ "D = Smarket.Direction\n", @@ -470,7 +582,14 @@ "cell_type": "code", "execution_count": null, "id": "e216583a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.625601Z", + "iopub.status.busy": "2023-07-26T05:16:53.625081Z", + "iopub.status.idle": "2023-07-26T05:16:53.631930Z", + "shell.execute_reply": "2023-07-26T05:16:53.631398Z" + } + }, "outputs": [], "source": [ "labels = np.array(['Down']*252)\n", @@ -490,7 +609,14 @@ "cell_type": "code", "execution_count": null, "id": "5538dc17", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.634500Z", + "iopub.status.busy": "2023-07-26T05:16:53.634318Z", + "iopub.status.idle": "2023-07-26T05:16:53.638177Z", + "shell.execute_reply": "2023-07-26T05:16:53.637615Z" + } + }, "outputs": [], "source": [ "np.mean(labels == L_test), np.mean(labels != L_test)" @@ -529,7 +655,14 @@ "cell_type": "code", "execution_count": null, "id": "016110f9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.640626Z", + "iopub.status.busy": "2023-07-26T05:16:53.640465Z", + "iopub.status.idle": "2023-07-26T05:16:53.650498Z", + "shell.execute_reply": "2023-07-26T05:16:53.649952Z" + } + }, "outputs": [], "source": [ "model = MS(['Lag1', 'Lag2']).fit(Smarket)\n", @@ -558,7 +691,14 @@ "cell_type": "code", "execution_count": null, "id": "70984cd6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.653494Z", + "iopub.status.busy": "2023-07-26T05:16:53.652768Z", + "iopub.status.idle": "2023-07-26T05:16:53.656801Z", + "shell.execute_reply": "2023-07-26T05:16:53.656039Z" + } + }, "outputs": [], "source": [ "(35+106)/252,106/(106+76)" @@ -594,7 +734,14 @@ "cell_type": "code", "execution_count": null, "id": "4b745fd8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.659702Z", + "iopub.status.busy": "2023-07-26T05:16:53.659530Z", + "iopub.status.idle": "2023-07-26T05:16:53.664523Z", + "shell.execute_reply": "2023-07-26T05:16:53.664140Z" + } + }, "outputs": [], "source": [ "newdata = pd.DataFrame({'Lag1':[1.2, 1.5],\n", @@ -625,7 +772,14 @@ "cell_type": "code", "execution_count": null, "id": "cd533313", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.668435Z", + "iopub.status.busy": "2023-07-26T05:16:53.668202Z", + "iopub.status.idle": "2023-07-26T05:16:53.670675Z", + "shell.execute_reply": "2023-07-26T05:16:53.670317Z" + } + }, "outputs": [], "source": [ "lda = LDA(store_covariance=True)" @@ -646,7 +800,14 @@ "cell_type": "code", "execution_count": null, "id": "953dc6ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.673288Z", + "iopub.status.busy": "2023-07-26T05:16:53.673119Z", + "iopub.status.idle": "2023-07-26T05:16:53.681150Z", + "shell.execute_reply": "2023-07-26T05:16:53.680147Z" + } + }, "outputs": [], "source": [ "X_train, X_test = [M.drop(columns=['intercept'])\n", @@ -680,7 +841,14 @@ "cell_type": "code", "execution_count": null, "id": "8917085e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.683917Z", + "iopub.status.busy": "2023-07-26T05:16:53.683495Z", + "iopub.status.idle": "2023-07-26T05:16:53.686711Z", + "shell.execute_reply": "2023-07-26T05:16:53.686302Z" + } + }, "outputs": [], "source": [ "lda.means_" @@ -701,7 +869,14 @@ "cell_type": "code", "execution_count": null, "id": "efb3e9bd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.689572Z", + "iopub.status.busy": "2023-07-26T05:16:53.689426Z", + "iopub.status.idle": "2023-07-26T05:16:53.693007Z", + "shell.execute_reply": "2023-07-26T05:16:53.692236Z" + } + }, "outputs": [], "source": [ "lda.classes_" @@ -720,7 +895,14 @@ "cell_type": "code", "execution_count": null, "id": "efce91fb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.698435Z", + "iopub.status.busy": "2023-07-26T05:16:53.698251Z", + "iopub.status.idle": "2023-07-26T05:16:53.701054Z", + "shell.execute_reply": "2023-07-26T05:16:53.700738Z" + } + }, "outputs": [], "source": [ "lda.priors_" @@ -738,7 +920,14 @@ "cell_type": "code", "execution_count": null, "id": "e33cd6c5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.703175Z", + "iopub.status.busy": "2023-07-26T05:16:53.702968Z", + "iopub.status.idle": "2023-07-26T05:16:53.706047Z", + "shell.execute_reply": "2023-07-26T05:16:53.705603Z" + } + }, "outputs": [], "source": [ "lda.scalings_" @@ -757,7 +946,14 @@ "cell_type": "code", "execution_count": null, "id": "dc8a5178", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.708404Z", + "iopub.status.busy": "2023-07-26T05:16:53.708240Z", + "iopub.status.idle": "2023-07-26T05:16:53.711274Z", + "shell.execute_reply": "2023-07-26T05:16:53.710854Z" + } + }, "outputs": [], "source": [ "lda_pred = lda.predict(X_test)" @@ -777,7 +973,14 @@ "cell_type": "code", "execution_count": null, "id": "31a6782b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.713574Z", + "iopub.status.busy": "2023-07-26T05:16:53.713379Z", + "iopub.status.idle": "2023-07-26T05:16:53.718661Z", + "shell.execute_reply": "2023-07-26T05:16:53.718207Z" + } + }, "outputs": [], "source": [ "confusion_table(lda_pred, L_test)" @@ -799,7 +1002,14 @@ "cell_type": "code", "execution_count": null, "id": "da1bffa3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.720875Z", + "iopub.status.busy": "2023-07-26T05:16:53.720726Z", + "iopub.status.idle": "2023-07-26T05:16:53.724610Z", + "shell.execute_reply": "2023-07-26T05:16:53.724076Z" + } + }, "outputs": [], "source": [ "lda_prob = lda.predict_proba(X_test)\n", @@ -824,7 +1034,14 @@ "cell_type": "code", "execution_count": null, "id": "3d7eae3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.726893Z", + "iopub.status.busy": "2023-07-26T05:16:53.726728Z", + "iopub.status.idle": "2023-07-26T05:16:53.730223Z", + "shell.execute_reply": "2023-07-26T05:16:53.729787Z" + } + }, "outputs": [], "source": [ "np.all(\n", @@ -851,7 +1068,14 @@ "cell_type": "code", "execution_count": null, "id": "18fa175c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.732568Z", + "iopub.status.busy": "2023-07-26T05:16:53.732400Z", + "iopub.status.idle": "2023-07-26T05:16:53.735369Z", + "shell.execute_reply": "2023-07-26T05:16:53.734923Z" + } + }, "outputs": [], "source": [ "np.sum(lda_prob[:,0] > 0.9)" @@ -898,7 +1122,14 @@ "cell_type": "code", "execution_count": null, "id": "62499aad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.737641Z", + "iopub.status.busy": "2023-07-26T05:16:53.737376Z", + "iopub.status.idle": "2023-07-26T05:16:53.742371Z", + "shell.execute_reply": "2023-07-26T05:16:53.741972Z" + } + }, "outputs": [], "source": [ "qda = QDA(store_covariance=True)\n", @@ -917,7 +1148,14 @@ "cell_type": "code", "execution_count": null, "id": "296fdd89", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.744511Z", + "iopub.status.busy": "2023-07-26T05:16:53.744359Z", + "iopub.status.idle": "2023-07-26T05:16:53.747331Z", + "shell.execute_reply": "2023-07-26T05:16:53.746954Z" + } + }, "outputs": [], "source": [ "qda.means_, qda.priors_" @@ -936,7 +1174,14 @@ "cell_type": "code", "execution_count": null, "id": "7fbbefb0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.749983Z", + "iopub.status.busy": "2023-07-26T05:16:53.749838Z", + "iopub.status.idle": "2023-07-26T05:16:53.753016Z", + "shell.execute_reply": "2023-07-26T05:16:53.752436Z" + } + }, "outputs": [], "source": [ "qda.covariance_[0]" @@ -958,7 +1203,14 @@ "cell_type": "code", "execution_count": null, "id": "03094dcd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.754995Z", + "iopub.status.busy": "2023-07-26T05:16:53.754855Z", + "iopub.status.idle": "2023-07-26T05:16:53.760423Z", + "shell.execute_reply": "2023-07-26T05:16:53.760086Z" + } + }, "outputs": [], "source": [ "qda_pred = qda.predict(X_test)\n", @@ -978,7 +1230,14 @@ "cell_type": "code", "execution_count": null, "id": "25022d1a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.762503Z", + "iopub.status.busy": "2023-07-26T05:16:53.762361Z", + "iopub.status.idle": "2023-07-26T05:16:53.765264Z", + "shell.execute_reply": "2023-07-26T05:16:53.764918Z" + } + }, "outputs": [], "source": [ "np.mean(qda_pred == L_test)" @@ -1015,7 +1274,14 @@ "cell_type": "code", "execution_count": null, "id": "f73eb202", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.767345Z", + "iopub.status.busy": "2023-07-26T05:16:53.767204Z", + "iopub.status.idle": "2023-07-26T05:16:53.771774Z", + "shell.execute_reply": "2023-07-26T05:16:53.771427Z" + } + }, "outputs": [], "source": [ "NB = GaussianNB()\n", @@ -1034,7 +1300,14 @@ "cell_type": "code", "execution_count": null, "id": "8e860cc3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.773771Z", + "iopub.status.busy": "2023-07-26T05:16:53.773609Z", + "iopub.status.idle": "2023-07-26T05:16:53.776397Z", + "shell.execute_reply": "2023-07-26T05:16:53.776019Z" + } + }, "outputs": [], "source": [ "NB.classes_" @@ -1052,7 +1325,14 @@ "cell_type": "code", "execution_count": null, "id": "c28bf3b5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.778509Z", + "iopub.status.busy": "2023-07-26T05:16:53.778344Z", + "iopub.status.idle": "2023-07-26T05:16:53.781156Z", + "shell.execute_reply": "2023-07-26T05:16:53.780778Z" + } + }, "outputs": [], "source": [ "NB.class_prior_" @@ -1072,7 +1352,14 @@ "cell_type": "code", "execution_count": null, "id": "84a9f086", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.783409Z", + "iopub.status.busy": "2023-07-26T05:16:53.783220Z", + "iopub.status.idle": "2023-07-26T05:16:53.786972Z", + "shell.execute_reply": "2023-07-26T05:16:53.786412Z" + } + }, "outputs": [], "source": [ "NB.theta_" @@ -1090,7 +1377,14 @@ "cell_type": "code", "execution_count": null, "id": "885f5165", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.789289Z", + "iopub.status.busy": "2023-07-26T05:16:53.789145Z", + "iopub.status.idle": "2023-07-26T05:16:53.792294Z", + "shell.execute_reply": "2023-07-26T05:16:53.791863Z" + } + }, "outputs": [], "source": [ "NB.var_" @@ -1110,7 +1404,14 @@ "cell_type": "code", "execution_count": null, "id": "61473094", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.794766Z", + "iopub.status.busy": "2023-07-26T05:16:53.794585Z", + "iopub.status.idle": "2023-07-26T05:16:53.798951Z", + "shell.execute_reply": "2023-07-26T05:16:53.798539Z" + } + }, "outputs": [], "source": [ "X_train[L_train == 'Down'].mean()" @@ -1128,7 +1429,14 @@ "cell_type": "code", "execution_count": null, "id": "db203a23", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.801373Z", + "iopub.status.busy": "2023-07-26T05:16:53.801183Z", + "iopub.status.idle": "2023-07-26T05:16:53.805590Z", + "shell.execute_reply": "2023-07-26T05:16:53.805195Z" + } + }, "outputs": [], "source": [ "X_train[L_train == 'Down'].var(ddof=0)" @@ -1147,7 +1455,14 @@ "cell_type": "code", "execution_count": null, "id": "b387227e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.808068Z", + "iopub.status.busy": "2023-07-26T05:16:53.807902Z", + "iopub.status.idle": "2023-07-26T05:16:53.813920Z", + "shell.execute_reply": "2023-07-26T05:16:53.813575Z" + } + }, "outputs": [], "source": [ "nb_labels = NB.predict(X_test)\n", @@ -1168,7 +1483,14 @@ "cell_type": "code", "execution_count": null, "id": "f14ebe61", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.816106Z", + "iopub.status.busy": "2023-07-26T05:16:53.815946Z", + "iopub.status.idle": "2023-07-26T05:16:53.819614Z", + "shell.execute_reply": "2023-07-26T05:16:53.819281Z" + } + }, "outputs": [], "source": [ "NB.predict_proba(X_test)[:5]" @@ -1195,7 +1517,14 @@ "cell_type": "code", "execution_count": null, "id": "41a12f4a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.821819Z", + "iopub.status.busy": "2023-07-26T05:16:53.821654Z", + "iopub.status.idle": "2023-07-26T05:16:53.835418Z", + "shell.execute_reply": "2023-07-26T05:16:53.834963Z" + } + }, "outputs": [], "source": [ "knn1 = KNeighborsClassifier(n_neighbors=1)\n", @@ -1218,7 +1547,14 @@ "cell_type": "code", "execution_count": null, "id": "1dd2f3e4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.840858Z", + "iopub.status.busy": "2023-07-26T05:16:53.840380Z", + "iopub.status.idle": "2023-07-26T05:16:53.844994Z", + "shell.execute_reply": "2023-07-26T05:16:53.844563Z" + } + }, "outputs": [], "source": [ "(83+43)/252, np.mean(knn1_pred == L_test)" @@ -1237,7 +1573,14 @@ "cell_type": "code", "execution_count": null, "id": "8ec72efc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.848486Z", + "iopub.status.busy": "2023-07-26T05:16:53.848323Z", + "iopub.status.idle": "2023-07-26T05:16:53.859379Z", + "shell.execute_reply": "2023-07-26T05:16:53.859009Z" + } + }, "outputs": [], "source": [ "knn3 = KNeighborsClassifier(n_neighbors=3)\n", @@ -1273,7 +1616,14 @@ "cell_type": "code", "execution_count": null, "id": "98fef4ed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.862002Z", + "iopub.status.busy": "2023-07-26T05:16:53.861826Z", + "iopub.status.idle": "2023-07-26T05:16:53.880744Z", + "shell.execute_reply": "2023-07-26T05:16:53.880217Z" + } + }, "outputs": [], "source": [ "Caravan = load_data('Caravan')\n", @@ -1296,7 +1646,14 @@ "cell_type": "code", "execution_count": null, "id": "1af72c0b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.883491Z", + "iopub.status.busy": "2023-07-26T05:16:53.883258Z", + "iopub.status.idle": "2023-07-26T05:16:53.886241Z", + "shell.execute_reply": "2023-07-26T05:16:53.885825Z" + } + }, "outputs": [], "source": [ "348 / 5822" @@ -1314,7 +1671,14 @@ "cell_type": "code", "execution_count": null, "id": "9ea841e4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.888642Z", + "iopub.status.busy": "2023-07-26T05:16:53.888457Z", + "iopub.status.idle": "2023-07-26T05:16:53.891757Z", + "shell.execute_reply": "2023-07-26T05:16:53.891278Z" + } + }, "outputs": [], "source": [ "feature_df = Caravan.drop(columns=['Purchase'])" @@ -1356,7 +1720,14 @@ "cell_type": "code", "execution_count": null, "id": "6e26b14f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.893952Z", + "iopub.status.busy": "2023-07-26T05:16:53.893809Z", + "iopub.status.idle": "2023-07-26T05:16:53.896350Z", + "shell.execute_reply": "2023-07-26T05:16:53.895913Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler(with_mean=True,\n", @@ -1387,7 +1758,14 @@ "cell_type": "code", "execution_count": null, "id": "bd178f38", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.898581Z", + "iopub.status.busy": "2023-07-26T05:16:53.898422Z", + "iopub.status.idle": "2023-07-26T05:16:53.907100Z", + "shell.execute_reply": "2023-07-26T05:16:53.906252Z" + } + }, "outputs": [], "source": [ "scaler.fit(feature_df)\n", @@ -1407,7 +1785,14 @@ "cell_type": "code", "execution_count": null, "id": "18929b37", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.909697Z", + "iopub.status.busy": "2023-07-26T05:16:53.909376Z", + "iopub.status.idle": "2023-07-26T05:16:53.917328Z", + "shell.execute_reply": "2023-07-26T05:16:53.916861Z" + } + }, "outputs": [], "source": [ "feature_std = pd.DataFrame(\n", @@ -1434,7 +1819,14 @@ "cell_type": "code", "execution_count": null, "id": "9d439d0b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.919758Z", + "iopub.status.busy": "2023-07-26T05:16:53.919493Z", + "iopub.status.idle": "2023-07-26T05:16:53.924297Z", + "shell.execute_reply": "2023-07-26T05:16:53.923909Z" + } + }, "outputs": [], "source": [ "(X_train,\n", @@ -1460,7 +1852,14 @@ "cell_type": "code", "execution_count": null, "id": "9b8c0171", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.926683Z", + "iopub.status.busy": "2023-07-26T05:16:53.926533Z", + "iopub.status.idle": "2023-07-26T05:16:53.979689Z", + "shell.execute_reply": "2023-07-26T05:16:53.979266Z" + } + }, "outputs": [], "source": [ "knn1 = KNeighborsClassifier(n_neighbors=1)\n", @@ -1494,7 +1893,14 @@ "cell_type": "code", "execution_count": null, "id": "f31b6e26", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.982811Z", + "iopub.status.busy": "2023-07-26T05:16:53.982601Z", + "iopub.status.idle": "2023-07-26T05:16:53.989512Z", + "shell.execute_reply": "2023-07-26T05:16:53.989071Z" + } + }, "outputs": [], "source": [ "confusion_table(knn1_pred, y_test)" @@ -1515,7 +1921,14 @@ "cell_type": "code", "execution_count": null, "id": "4a8c37aa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.992418Z", + "iopub.status.busy": "2023-07-26T05:16:53.992103Z", + "iopub.status.idle": "2023-07-26T05:16:53.995535Z", + "shell.execute_reply": "2023-07-26T05:16:53.995078Z" + } + }, "outputs": [], "source": [ "9/(53+9)" @@ -1540,7 +1953,14 @@ "cell_type": "code", "execution_count": null, "id": "153662f8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:53.999510Z", + "iopub.status.busy": "2023-07-26T05:16:53.999082Z", + "iopub.status.idle": "2023-07-26T05:16:54.130823Z", + "shell.execute_reply": "2023-07-26T05:16:54.130318Z" + } + }, "outputs": [], "source": [ "for K in range(1,6):\n", @@ -1587,7 +2007,14 @@ "cell_type": "code", "execution_count": null, "id": "55b99a3f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:54.134280Z", + "iopub.status.busy": "2023-07-26T05:16:54.133924Z", + "iopub.status.idle": "2023-07-26T05:16:54.790553Z", + "shell.execute_reply": "2023-07-26T05:16:54.788632Z" + } + }, "outputs": [], "source": [ "logit = LogisticRegression(C=1e10, solver='liblinear')\n", @@ -1620,7 +2047,14 @@ "cell_type": "code", "execution_count": null, "id": "0c2ea9f5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:54.800869Z", + "iopub.status.busy": "2023-07-26T05:16:54.800067Z", + "iopub.status.idle": "2023-07-26T05:16:54.901396Z", + "shell.execute_reply": "2023-07-26T05:16:54.824967Z" + } + }, "outputs": [], "source": [ "logit_labels = np.where(logit_pred[:,1]>0.25, 'Yes', 'No')\n", @@ -1631,7 +2065,14 @@ "cell_type": "code", "execution_count": null, "id": "3a6bf0a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.014712Z", + "iopub.status.busy": "2023-07-26T05:16:55.014300Z", + "iopub.status.idle": "2023-07-26T05:16:55.022432Z", + "shell.execute_reply": "2023-07-26T05:16:55.021283Z" + } + }, "outputs": [], "source": [ "9/(20+9)" @@ -1652,7 +2093,14 @@ "cell_type": "code", "execution_count": null, "id": "769300df", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.026479Z", + "iopub.status.busy": "2023-07-26T05:16:55.026122Z", + "iopub.status.idle": "2023-07-26T05:16:55.040298Z", + "shell.execute_reply": "2023-07-26T05:16:55.039107Z" + } + }, "outputs": [], "source": [ "Bike = load_data('Bikeshare')" @@ -1670,7 +2118,14 @@ "cell_type": "code", "execution_count": null, "id": "02b351f5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.043729Z", + "iopub.status.busy": "2023-07-26T05:16:55.043547Z", + "iopub.status.idle": "2023-07-26T05:16:55.047018Z", + "shell.execute_reply": "2023-07-26T05:16:55.046428Z" + } + }, "outputs": [], "source": [ "Bike.shape, Bike.columns" @@ -1690,7 +2145,14 @@ "cell_type": "code", "execution_count": null, "id": "926c908d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.049121Z", + "iopub.status.busy": "2023-07-26T05:16:55.049011Z", + "iopub.status.idle": "2023-07-26T05:16:55.569728Z", + "shell.execute_reply": "2023-07-26T05:16:55.567494Z" + } + }, "outputs": [], "source": [ "X = MS(['mnth',\n", @@ -1726,7 +2188,14 @@ "cell_type": "code", "execution_count": null, "id": "064b24d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.575305Z", + "iopub.status.busy": "2023-07-26T05:16:55.575124Z", + "iopub.status.idle": "2023-07-26T05:16:55.582612Z", + "shell.execute_reply": "2023-07-26T05:16:55.580612Z" + } + }, "outputs": [], "source": [ "hr_encode = contrast('hr', 'sum')\n", @@ -1745,7 +2214,14 @@ "cell_type": "code", "execution_count": null, "id": "27c1aa54", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:55.591223Z", + "iopub.status.busy": "2023-07-26T05:16:55.589141Z", + "iopub.status.idle": "2023-07-26T05:16:56.075120Z", + "shell.execute_reply": "2023-07-26T05:16:56.073529Z" + } + }, "outputs": [], "source": [ "X2 = MS([mnth_encode,\n", @@ -1787,7 +2263,14 @@ "cell_type": "code", "execution_count": null, "id": "dd0114c8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.092324Z", + "iopub.status.busy": "2023-07-26T05:16:56.087118Z", + "iopub.status.idle": "2023-07-26T05:16:56.137844Z", + "shell.execute_reply": "2023-07-26T05:16:56.135218Z" + } + }, "outputs": [], "source": [ "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)" @@ -1806,7 +2289,14 @@ "cell_type": "code", "execution_count": null, "id": "292585a1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.147457Z", + "iopub.status.busy": "2023-07-26T05:16:56.146798Z", + "iopub.status.idle": "2023-07-26T05:16:56.159395Z", + "shell.execute_reply": "2023-07-26T05:16:56.157990Z" + } + }, "outputs": [], "source": [ "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)" @@ -1830,7 +2320,14 @@ "cell_type": "code", "execution_count": null, "id": "e05e0575", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.169355Z", + "iopub.status.busy": "2023-07-26T05:16:56.168352Z", + "iopub.status.idle": "2023-07-26T05:16:56.190269Z", + "shell.execute_reply": "2023-07-26T05:16:56.186463Z" + } + }, "outputs": [], "source": [ "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", @@ -1849,7 +2346,14 @@ "cell_type": "code", "execution_count": null, "id": "9f04a25e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.213429Z", + "iopub.status.busy": "2023-07-26T05:16:56.210143Z", + "iopub.status.idle": "2023-07-26T05:16:56.249114Z", + "shell.execute_reply": "2023-07-26T05:16:56.246382Z" + } + }, "outputs": [], "source": [ "months = Bike['mnth'].dtype.categories\n", @@ -1875,7 +2379,14 @@ "cell_type": "code", "execution_count": null, "id": "20c7c054", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.310254Z", + "iopub.status.busy": "2023-07-26T05:16:56.306244Z", + "iopub.status.idle": "2023-07-26T05:16:56.486315Z", + "shell.execute_reply": "2023-07-26T05:16:56.485755Z" + } + }, "outputs": [], "source": [ "fig_month, ax_month = subplots(figsize=(8,8))\n", @@ -1899,7 +2410,14 @@ "cell_type": "code", "execution_count": null, "id": "3f83f4bf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.489411Z", + "iopub.status.busy": "2023-07-26T05:16:56.489143Z", + "iopub.status.idle": "2023-07-26T05:16:56.493292Z", + "shell.execute_reply": "2023-07-26T05:16:56.492665Z" + } + }, "outputs": [], "source": [ "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", @@ -1921,7 +2439,14 @@ "cell_type": "code", "execution_count": null, "id": "86e9a741", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.496145Z", + "iopub.status.busy": "2023-07-26T05:16:56.495966Z", + "iopub.status.idle": "2023-07-26T05:16:56.609790Z", + "shell.execute_reply": "2023-07-26T05:16:56.609280Z" + } + }, "outputs": [], "source": [ "fig_hr, ax_hr = subplots(figsize=(8,8))\n", @@ -1949,7 +2474,14 @@ "cell_type": "code", "execution_count": null, "id": "a9448278", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:56.612317Z", + "iopub.status.busy": "2023-07-26T05:16:56.612179Z", + "iopub.status.idle": "2023-07-26T05:16:57.099663Z", + "shell.execute_reply": "2023-07-26T05:16:57.097888Z" + } + }, "outputs": [], "source": [ "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()" @@ -1967,7 +2499,14 @@ "cell_type": "code", "execution_count": null, "id": "ec8505be", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:57.113476Z", + "iopub.status.busy": "2023-07-26T05:16:57.110476Z", + "iopub.status.idle": "2023-07-26T05:16:57.156419Z", + "shell.execute_reply": "2023-07-26T05:16:57.148075Z" + } + }, "outputs": [], "source": [ "S_pois = summarize(M_pois)\n", @@ -1993,7 +2532,14 @@ "cell_type": "code", "execution_count": null, "id": "0b8a5edf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:57.169051Z", + "iopub.status.busy": "2023-07-26T05:16:57.167599Z", + "iopub.status.idle": "2023-07-26T05:16:57.482769Z", + "shell.execute_reply": "2023-07-26T05:16:57.482137Z" + } + }, "outputs": [], "source": [ "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", @@ -2023,7 +2569,14 @@ "cell_type": "code", "execution_count": null, "id": "d487c7ed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:57.485339Z", + "iopub.status.busy": "2023-07-26T05:16:57.484975Z", + "iopub.status.idle": "2023-07-26T05:16:57.605612Z", + "shell.execute_reply": "2023-07-26T05:16:57.605058Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -2061,6 +2614,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch5-resample-lab.ipynb b/docs/source/labs/Ch5-resample-lab.ipynb index 8d35e84..df0614f 100644 --- a/docs/source/labs/Ch5-resample-lab.ipynb +++ b/docs/source/labs/Ch5-resample-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Cross-Validation and the Bootstrap\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -23,7 +23,14 @@ "cell_type": "code", "execution_count": null, "id": "60fad148", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:58.904948Z", + "iopub.status.busy": "2023-07-26T05:16:58.904833Z", + "iopub.status.idle": "2023-07-26T05:16:59.817459Z", + "shell.execute_reply": "2023-07-26T05:16:59.816977Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -47,7 +54,14 @@ "cell_type": "code", "execution_count": null, "id": "2478aeb4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.820190Z", + "iopub.status.busy": "2023-07-26T05:16:59.819944Z", + "iopub.status.idle": "2023-07-26T05:16:59.822616Z", + "shell.execute_reply": "2023-07-26T05:16:59.822236Z" + } + }, "outputs": [], "source": [ "from functools import partial\n", @@ -83,7 +97,14 @@ "cell_type": "code", "execution_count": null, "id": "99c95faf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.824651Z", + "iopub.status.busy": "2023-07-26T05:16:59.824498Z", + "iopub.status.idle": "2023-07-26T05:16:59.830661Z", + "shell.execute_reply": "2023-07-26T05:16:59.830199Z" + } + }, "outputs": [], "source": [ "Auto = load_data('Auto')\n", @@ -104,7 +125,14 @@ "cell_type": "code", "execution_count": null, "id": "41b0717d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.832904Z", + "iopub.status.busy": "2023-07-26T05:16:59.832740Z", + "iopub.status.idle": "2023-07-26T05:16:59.837926Z", + "shell.execute_reply": "2023-07-26T05:16:59.837164Z" + } + }, "outputs": [], "source": [ "hp_mm = MS(['horsepower'])\n", @@ -127,7 +155,14 @@ "cell_type": "code", "execution_count": null, "id": "d7ea3c0d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.840693Z", + "iopub.status.busy": "2023-07-26T05:16:59.840426Z", + "iopub.status.idle": "2023-07-26T05:16:59.847201Z", + "shell.execute_reply": "2023-07-26T05:16:59.846618Z" + } + }, "outputs": [], "source": [ "X_valid = hp_mm.transform(Auto_valid)\n", @@ -153,7 +188,14 @@ "cell_type": "code", "execution_count": null, "id": "a02a2d05", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.849828Z", + "iopub.status.busy": "2023-07-26T05:16:59.849638Z", + "iopub.status.idle": "2023-07-26T05:16:59.852938Z", + "shell.execute_reply": "2023-07-26T05:16:59.852364Z" + } + }, "outputs": [], "source": [ "def evalMSE(terms,\n", @@ -189,7 +231,14 @@ "cell_type": "code", "execution_count": null, "id": "51d93dea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.855432Z", + "iopub.status.busy": "2023-07-26T05:16:59.855267Z", + "iopub.status.idle": "2023-07-26T05:16:59.867911Z", + "shell.execute_reply": "2023-07-26T05:16:59.867517Z" + } + }, "outputs": [], "source": [ "MSE = np.zeros(3)\n", @@ -215,7 +264,14 @@ "cell_type": "code", "execution_count": null, "id": "83432f06", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.870663Z", + "iopub.status.busy": "2023-07-26T05:16:59.870321Z", + "iopub.status.idle": "2023-07-26T05:16:59.884091Z", + "shell.execute_reply": "2023-07-26T05:16:59.883749Z" + } + }, "outputs": [], "source": [ "Auto_train, Auto_valid = train_test_split(Auto,\n", @@ -280,7 +336,14 @@ "cell_type": "code", "execution_count": null, "id": "bcfc433f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:16:59.886618Z", + "iopub.status.busy": "2023-07-26T05:16:59.886459Z", + "iopub.status.idle": "2023-07-26T05:17:00.662796Z", + "shell.execute_reply": "2023-07-26T05:17:00.662228Z" + } + }, "outputs": [], "source": [ "hp_model = sklearn_sm(sm.OLS,\n", @@ -329,7 +392,14 @@ "cell_type": "code", "execution_count": null, "id": "f951ffc8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:00.665287Z", + "iopub.status.busy": "2023-07-26T05:17:00.665106Z", + "iopub.status.idle": "2023-07-26T05:17:01.422477Z", + "shell.execute_reply": "2023-07-26T05:17:01.422022Z" + } + }, "outputs": [], "source": [ "cv_error = np.zeros(5)\n", @@ -367,7 +437,14 @@ "cell_type": "code", "execution_count": null, "id": "e3610b5a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.424821Z", + "iopub.status.busy": "2023-07-26T05:17:01.424682Z", + "iopub.status.idle": "2023-07-26T05:17:01.427733Z", + "shell.execute_reply": "2023-07-26T05:17:01.427314Z" + } + }, "outputs": [], "source": [ "A = np.array([3, 5, 9])\n", @@ -389,7 +466,14 @@ "cell_type": "code", "execution_count": null, "id": "1627460d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.429812Z", + "iopub.status.busy": "2023-07-26T05:17:01.429700Z", + "iopub.status.idle": "2023-07-26T05:17:01.457253Z", + "shell.execute_reply": "2023-07-26T05:17:01.456715Z" + } + }, "outputs": [], "source": [ "cv_error = np.zeros(5)\n", @@ -435,7 +519,14 @@ "cell_type": "code", "execution_count": null, "id": "8a636468", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.460096Z", + "iopub.status.busy": "2023-07-26T05:17:01.459902Z", + "iopub.status.idle": "2023-07-26T05:17:01.466995Z", + "shell.execute_reply": "2023-07-26T05:17:01.466500Z" + } + }, "outputs": [], "source": [ "validation = ShuffleSplit(n_splits=1,\n", @@ -460,7 +551,14 @@ "cell_type": "code", "execution_count": null, "id": "746aeccd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.468986Z", + "iopub.status.busy": "2023-07-26T05:17:01.468832Z", + "iopub.status.idle": "2023-07-26T05:17:01.495719Z", + "shell.execute_reply": "2023-07-26T05:17:01.495353Z" + } + }, "outputs": [], "source": [ "validation = ShuffleSplit(n_splits=10,\n", @@ -516,7 +614,14 @@ "cell_type": "code", "execution_count": null, "id": "daa53d0c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.498336Z", + "iopub.status.busy": "2023-07-26T05:17:01.498116Z", + "iopub.status.idle": "2023-07-26T05:17:01.504124Z", + "shell.execute_reply": "2023-07-26T05:17:01.503427Z" + } + }, "outputs": [], "source": [ "Portfolio = load_data('Portfolio')\n", @@ -542,7 +647,14 @@ "cell_type": "code", "execution_count": null, "id": "578c9564", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.507072Z", + "iopub.status.busy": "2023-07-26T05:17:01.506919Z", + "iopub.status.idle": "2023-07-26T05:17:01.510768Z", + "shell.execute_reply": "2023-07-26T05:17:01.510184Z" + } + }, "outputs": [], "source": [ "alpha_func(Portfolio, range(100))" @@ -563,7 +675,14 @@ "cell_type": "code", "execution_count": null, "id": "5754d6d5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.512875Z", + "iopub.status.busy": "2023-07-26T05:17:01.512759Z", + "iopub.status.idle": "2023-07-26T05:17:01.516640Z", + "shell.execute_reply": "2023-07-26T05:17:01.516258Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(0)\n", @@ -587,7 +706,14 @@ "cell_type": "code", "execution_count": null, "id": "8320a49c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.519014Z", + "iopub.status.busy": "2023-07-26T05:17:01.518877Z", + "iopub.status.idle": "2023-07-26T05:17:01.521841Z", + "shell.execute_reply": "2023-07-26T05:17:01.521433Z" + } + }, "outputs": [], "source": [ "def boot_SE(func,\n", @@ -624,7 +750,14 @@ "cell_type": "code", "execution_count": null, "id": "e656aa1f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.523933Z", + "iopub.status.busy": "2023-07-26T05:17:01.523801Z", + "iopub.status.idle": "2023-07-26T05:17:01.855578Z", + "shell.execute_reply": "2023-07-26T05:17:01.855121Z" + } + }, "outputs": [], "source": [ "alpha_SE = boot_SE(alpha_func,\n", @@ -672,7 +805,14 @@ "cell_type": "code", "execution_count": null, "id": "c5d14195", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.857907Z", + "iopub.status.busy": "2023-07-26T05:17:01.857723Z", + "iopub.status.idle": "2023-07-26T05:17:01.860405Z", + "shell.execute_reply": "2023-07-26T05:17:01.859990Z" + } + }, "outputs": [], "source": [ "def boot_OLS(model_matrix, response, D, idx):\n", @@ -699,7 +839,14 @@ "cell_type": "code", "execution_count": null, "id": "7e0523f0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.862645Z", + "iopub.status.busy": "2023-07-26T05:17:01.862482Z", + "iopub.status.idle": "2023-07-26T05:17:01.864654Z", + "shell.execute_reply": "2023-07-26T05:17:01.864299Z" + } + }, "outputs": [], "source": [ "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')" @@ -724,7 +871,14 @@ "cell_type": "code", "execution_count": null, "id": "32836e93", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.866661Z", + "iopub.status.busy": "2023-07-26T05:17:01.866518Z", + "iopub.status.idle": "2023-07-26T05:17:01.885141Z", + "shell.execute_reply": "2023-07-26T05:17:01.884669Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(0)\n", @@ -747,7 +901,14 @@ "cell_type": "code", "execution_count": null, "id": "14ce3afa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:01.887739Z", + "iopub.status.busy": "2023-07-26T05:17:01.887564Z", + "iopub.status.idle": "2023-07-26T05:17:03.352633Z", + "shell.execute_reply": "2023-07-26T05:17:03.352273Z" + } + }, "outputs": [], "source": [ "hp_se = boot_SE(hp_func,\n", @@ -776,7 +937,14 @@ "cell_type": "code", "execution_count": null, "id": "6b1213ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:03.356355Z", + "iopub.status.busy": "2023-07-26T05:17:03.356184Z", + "iopub.status.idle": "2023-07-26T05:17:03.453023Z", + "shell.execute_reply": "2023-07-26T05:17:03.452622Z" + } + }, "outputs": [], "source": [ "hp_model.fit(Auto, Auto['mpg'])\n", @@ -827,7 +995,14 @@ "cell_type": "code", "execution_count": null, "id": "af99b778", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:03.455469Z", + "iopub.status.busy": "2023-07-26T05:17:03.455201Z", + "iopub.status.idle": "2023-07-26T05:17:05.757186Z", + "shell.execute_reply": "2023-07-26T05:17:05.756703Z" + } + }, "outputs": [], "source": [ "quad_model = MS([poly('horsepower', 2, raw=True)])\n", @@ -849,7 +1024,14 @@ "cell_type": "code", "execution_count": null, "id": "0206281e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:05.759524Z", + "iopub.status.busy": "2023-07-26T05:17:05.759220Z", + "iopub.status.idle": "2023-07-26T05:17:05.769493Z", + "shell.execute_reply": "2023-07-26T05:17:05.768995Z" + } + }, "outputs": [], "source": [ "M = sm.OLS(Auto['mpg'],\n", @@ -863,6 +1045,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch6-varselect-lab.ipynb b/docs/source/labs/Ch6-varselect-lab.ipynb index 7d79f3a..c84a41a 100644 --- a/docs/source/labs/Ch6-varselect-lab.ipynb +++ b/docs/source/labs/Ch6-varselect-lab.ipynb @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": null, - "id": "394db52b", + "id": "d985770e", "metadata": { "tags": [ "hide-cell" @@ -34,7 +34,7 @@ "```\n", "\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -48,7 +48,14 @@ "cell_type": "code", "execution_count": null, "id": "564883b2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:07.048830Z", + "iopub.status.busy": "2023-07-26T05:17:07.048718Z", + "iopub.status.idle": "2023-07-26T05:17:08.239907Z", + "shell.execute_reply": "2023-07-26T05:17:08.239413Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -76,7 +83,14 @@ "cell_type": "code", "execution_count": null, "id": "9aa8d3ee", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:08.242431Z", + "iopub.status.busy": "2023-07-26T05:17:08.242231Z", + "iopub.status.idle": "2023-07-26T05:17:09.939443Z", + "shell.execute_reply": "2023-07-26T05:17:09.938977Z" + } + }, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", @@ -120,7 +134,14 @@ "cell_type": "code", "execution_count": null, "id": "05371e33", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.942405Z", + "iopub.status.busy": "2023-07-26T05:17:09.942257Z", + "iopub.status.idle": "2023-07-26T05:17:09.952925Z", + "shell.execute_reply": "2023-07-26T05:17:09.952485Z" + } + }, "outputs": [], "source": [ "Hitters = load_data('Hitters')\n", @@ -141,7 +162,14 @@ "cell_type": "code", "execution_count": null, "id": "aa5bc5b8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.955373Z", + "iopub.status.busy": "2023-07-26T05:17:09.955215Z", + "iopub.status.idle": "2023-07-26T05:17:09.959055Z", + "shell.execute_reply": "2023-07-26T05:17:09.958533Z" + } + }, "outputs": [], "source": [ "Hitters = Hitters.dropna();\n", @@ -163,7 +191,14 @@ "cell_type": "code", "execution_count": null, "id": "10494837", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.961940Z", + "iopub.status.busy": "2023-07-26T05:17:09.961736Z", + "iopub.status.idle": "2023-07-26T05:17:09.964700Z", + "shell.execute_reply": "2023-07-26T05:17:09.964271Z" + } + }, "outputs": [], "source": [ "def nCp(sigma2, estimator, X, Y):\n", @@ -187,7 +222,14 @@ "cell_type": "code", "execution_count": null, "id": "b8173cef", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.967518Z", + "iopub.status.busy": "2023-07-26T05:17:09.967341Z", + "iopub.status.idle": "2023-07-26T05:17:09.983184Z", + "shell.execute_reply": "2023-07-26T05:17:09.982693Z" + } + }, "outputs": [], "source": [ "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n", @@ -208,7 +250,14 @@ "cell_type": "code", "execution_count": null, "id": "0cc8d7bd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.985554Z", + "iopub.status.busy": "2023-07-26T05:17:09.985389Z", + "iopub.status.idle": "2023-07-26T05:17:09.987653Z", + "shell.execute_reply": "2023-07-26T05:17:09.987280Z" + } + }, "outputs": [], "source": [ "neg_Cp = partial(nCp, sigma2)" @@ -238,7 +287,14 @@ "cell_type": "code", "execution_count": null, "id": "440b4c50", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.989896Z", + "iopub.status.busy": "2023-07-26T05:17:09.989765Z", + "iopub.status.idle": "2023-07-26T05:17:09.992061Z", + "shell.execute_reply": "2023-07-26T05:17:09.991502Z" + } + }, "outputs": [], "source": [ "strategy = Stepwise.first_peak(design,\n", @@ -262,7 +318,14 @@ "cell_type": "code", "execution_count": null, "id": "b33a8617", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:09.994597Z", + "iopub.status.busy": "2023-07-26T05:17:09.994397Z", + "iopub.status.idle": "2023-07-26T05:17:10.882860Z", + "shell.execute_reply": "2023-07-26T05:17:10.882342Z" + } + }, "outputs": [], "source": [ "hitters_MSE = sklearn_selected(OLS,\n", @@ -283,7 +346,14 @@ "cell_type": "code", "execution_count": null, "id": "e7f554cf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:10.885149Z", + "iopub.status.busy": "2023-07-26T05:17:10.884996Z", + "iopub.status.idle": "2023-07-26T05:17:11.423862Z", + "shell.execute_reply": "2023-07-26T05:17:11.423408Z" + } + }, "outputs": [], "source": [ "hitters_Cp = sklearn_selected(OLS,\n", @@ -322,7 +392,14 @@ "cell_type": "code", "execution_count": null, "id": "fb7091b8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:11.426460Z", + "iopub.status.busy": "2023-07-26T05:17:11.426313Z", + "iopub.status.idle": "2023-07-26T05:17:11.428837Z", + "shell.execute_reply": "2023-07-26T05:17:11.428385Z" + } + }, "outputs": [], "source": [ "strategy = Stepwise.fixed_steps(design,\n", @@ -343,7 +420,14 @@ "cell_type": "code", "execution_count": null, "id": "56adc728", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:11.431149Z", + "iopub.status.busy": "2023-07-26T05:17:11.431007Z", + "iopub.status.idle": "2023-07-26T05:17:11.925588Z", + "shell.execute_reply": "2023-07-26T05:17:11.925166Z" + } + }, "outputs": [], "source": [ "full_path.fit(Hitters, Y)\n", @@ -369,7 +453,14 @@ "cell_type": "code", "execution_count": null, "id": "387267d9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:11.928605Z", + "iopub.status.busy": "2023-07-26T05:17:11.928329Z", + "iopub.status.idle": "2023-07-26T05:17:12.168674Z", + "shell.execute_reply": "2023-07-26T05:17:12.165742Z" + } + }, "outputs": [], "source": [ "mse_fig, ax = subplots(figsize=(8,8))\n", @@ -419,7 +510,14 @@ "cell_type": "code", "execution_count": null, "id": "0047ba88", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:12.180601Z", + "iopub.status.busy": "2023-07-26T05:17:12.179903Z", + "iopub.status.idle": "2023-07-26T05:17:14.657028Z", + "shell.execute_reply": "2023-07-26T05:17:14.656647Z" + } + }, "outputs": [], "source": [ "K = 5\n", @@ -453,7 +551,14 @@ "cell_type": "code", "execution_count": null, "id": "27b0eb63", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:14.659368Z", + "iopub.status.busy": "2023-07-26T05:17:14.659240Z", + "iopub.status.idle": "2023-07-26T05:17:14.663478Z", + "shell.execute_reply": "2023-07-26T05:17:14.663025Z" + } + }, "outputs": [], "source": [ "cv_mse = []\n", @@ -477,7 +582,14 @@ "cell_type": "code", "execution_count": null, "id": "a45fe587", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:14.665854Z", + "iopub.status.busy": "2023-07-26T05:17:14.665694Z", + "iopub.status.idle": "2023-07-26T05:17:14.764103Z", + "shell.execute_reply": "2023-07-26T05:17:14.763688Z" + } + }, "outputs": [], "source": [ "ax.errorbar(np.arange(n_steps), \n", @@ -504,7 +616,14 @@ "cell_type": "code", "execution_count": null, "id": "c71c2079", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:14.766668Z", + "iopub.status.busy": "2023-07-26T05:17:14.766176Z", + "iopub.status.idle": "2023-07-26T05:17:15.254311Z", + "shell.execute_reply": "2023-07-26T05:17:15.253748Z" + } + }, "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1, \n", @@ -530,7 +649,14 @@ "cell_type": "code", "execution_count": null, "id": "2ff34edf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:15.257176Z", + "iopub.status.busy": "2023-07-26T05:17:15.256855Z", + "iopub.status.idle": "2023-07-26T05:17:15.361521Z", + "shell.execute_reply": "2023-07-26T05:17:15.360872Z" + } + }, "outputs": [], "source": [ "ax.plot(np.arange(n_steps), \n", @@ -563,7 +689,14 @@ "cell_type": "code", "execution_count": null, "id": "845f3fa0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:15.364952Z", + "iopub.status.busy": "2023-07-26T05:17:15.364689Z", + "iopub.status.idle": "2023-07-26T05:17:15.379444Z", + "shell.execute_reply": "2023-07-26T05:17:15.379028Z" + } + }, "outputs": [], "source": [ "D = design.fit_transform(Hitters)\n", @@ -584,7 +717,14 @@ "cell_type": "code", "execution_count": null, "id": "e9f943e7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:15.381427Z", + "iopub.status.busy": "2023-07-26T05:17:15.381284Z", + "iopub.status.idle": "2023-07-26T05:17:18.000267Z", + "shell.execute_reply": "2023-07-26T05:17:17.999357Z" + } + }, "outputs": [], "source": [ "path = fit_path(X, \n", @@ -604,7 +744,14 @@ "cell_type": "code", "execution_count": null, "id": "657ac171", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.003862Z", + "iopub.status.busy": "2023-07-26T05:17:18.003705Z", + "iopub.status.idle": "2023-07-26T05:17:18.006563Z", + "shell.execute_reply": "2023-07-26T05:17:18.006222Z" + } + }, "outputs": [], "source": [ "path[3]" @@ -643,7 +790,14 @@ "cell_type": "code", "execution_count": null, "id": "e576ad54", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.008980Z", + "iopub.status.busy": "2023-07-26T05:17:18.008783Z", + "iopub.status.idle": "2023-07-26T05:17:18.078116Z", + "shell.execute_reply": "2023-07-26T05:17:18.077656Z" + } + }, "outputs": [], "source": [ "Xs = X - X.mean(0)[None,:]\n", @@ -685,7 +839,14 @@ "cell_type": "code", "execution_count": null, "id": "3ef15192", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.080752Z", + "iopub.status.busy": "2023-07-26T05:17:18.080552Z", + "iopub.status.idle": "2023-07-26T05:17:18.094265Z", + "shell.execute_reply": "2023-07-26T05:17:18.093783Z" + } + }, "outputs": [], "source": [ "soln_path = pd.DataFrame(soln_array.T,\n", @@ -709,7 +870,14 @@ "cell_type": "code", "execution_count": null, "id": "40b67c85", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.097009Z", + "iopub.status.busy": "2023-07-26T05:17:18.096807Z", + "iopub.status.idle": "2023-07-26T05:17:18.336481Z", + "shell.execute_reply": "2023-07-26T05:17:18.335993Z" + } + }, "outputs": [], "source": [ "path_fig, ax = subplots(figsize=(8,8))\n", @@ -735,7 +903,14 @@ "cell_type": "code", "execution_count": null, "id": "d14bd86c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.338709Z", + "iopub.status.busy": "2023-07-26T05:17:18.338591Z", + "iopub.status.idle": "2023-07-26T05:17:18.342588Z", + "shell.execute_reply": "2023-07-26T05:17:18.342104Z" + } + }, "outputs": [], "source": [ "beta_hat = soln_path.loc[soln_path.index[39]]\n", @@ -754,7 +929,14 @@ "cell_type": "code", "execution_count": null, "id": "759e215a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.344904Z", + "iopub.status.busy": "2023-07-26T05:17:18.344761Z", + "iopub.status.idle": "2023-07-26T05:17:18.347862Z", + "shell.execute_reply": "2023-07-26T05:17:18.347417Z" + } + }, "outputs": [], "source": [ "np.linalg.norm(beta_hat)" @@ -774,7 +956,14 @@ "cell_type": "code", "execution_count": null, "id": "b58d5ccb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.350171Z", + "iopub.status.busy": "2023-07-26T05:17:18.350022Z", + "iopub.status.idle": "2023-07-26T05:17:18.353142Z", + "shell.execute_reply": "2023-07-26T05:17:18.352734Z" + } + }, "outputs": [], "source": [ "beta_hat = soln_path.loc[soln_path.index[59]]\n", @@ -796,7 +985,14 @@ "cell_type": "code", "execution_count": null, "id": "74b5cae2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.355548Z", + "iopub.status.busy": "2023-07-26T05:17:18.355367Z", + "iopub.status.idle": "2023-07-26T05:17:18.370341Z", + "shell.execute_reply": "2023-07-26T05:17:18.369949Z" + } + }, "outputs": [], "source": [ "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n", @@ -817,7 +1013,14 @@ "cell_type": "code", "execution_count": null, "id": "a89989c3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.372736Z", + "iopub.status.busy": "2023-07-26T05:17:18.372583Z", + "iopub.status.idle": "2023-07-26T05:17:18.375751Z", + "shell.execute_reply": "2023-07-26T05:17:18.375223Z" + } + }, "outputs": [], "source": [ "np.linalg.norm(ridge.coef_)" @@ -845,7 +1048,14 @@ "cell_type": "code", "execution_count": null, "id": "3eb41945", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.377750Z", + "iopub.status.busy": "2023-07-26T05:17:18.377627Z", + "iopub.status.idle": "2023-07-26T05:17:18.385518Z", + "shell.execute_reply": "2023-07-26T05:17:18.385025Z" + } + }, "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", @@ -877,7 +1087,14 @@ "cell_type": "code", "execution_count": null, "id": "635bc363", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.387843Z", + "iopub.status.busy": "2023-07-26T05:17:18.387682Z", + "iopub.status.idle": "2023-07-26T05:17:18.396692Z", + "shell.execute_reply": "2023-07-26T05:17:18.396288Z" + } + }, "outputs": [], "source": [ "ridge.alpha = 1e10\n", @@ -907,7 +1124,14 @@ "cell_type": "code", "execution_count": null, "id": "e117267f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.399267Z", + "iopub.status.busy": "2023-07-26T05:17:18.399145Z", + "iopub.status.idle": "2023-07-26T05:17:18.894315Z", + "shell.execute_reply": "2023-07-26T05:17:18.893964Z" + } + }, "outputs": [], "source": [ "param_grid = {'ridge__alpha': lambdas}\n", @@ -932,7 +1156,14 @@ "cell_type": "code", "execution_count": null, "id": "0037e9e4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:18.896714Z", + "iopub.status.busy": "2023-07-26T05:17:18.896538Z", + "iopub.status.idle": "2023-07-26T05:17:22.201859Z", + "shell.execute_reply": "2023-07-26T05:17:22.201489Z" + } + }, "outputs": [], "source": [ "grid = skm.GridSearchCV(pipe, \n", @@ -957,7 +1188,14 @@ "cell_type": "code", "execution_count": null, "id": "71f0f5c5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:22.203851Z", + "iopub.status.busy": "2023-07-26T05:17:22.203701Z", + "iopub.status.idle": "2023-07-26T05:17:22.319890Z", + "shell.execute_reply": "2023-07-26T05:17:22.318803Z" + } + }, "outputs": [], "source": [ "ridge_fig, ax = subplots(figsize=(8,8))\n", @@ -983,7 +1221,14 @@ "cell_type": "code", "execution_count": null, "id": "fb265379", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:22.322704Z", + "iopub.status.busy": "2023-07-26T05:17:22.322434Z", + "iopub.status.idle": "2023-07-26T05:17:25.602424Z", + "shell.execute_reply": "2023-07-26T05:17:25.601831Z" + } + }, "outputs": [], "source": [ "grid_r2 = skm.GridSearchCV(pipe, \n", @@ -1004,7 +1249,14 @@ "cell_type": "code", "execution_count": null, "id": "79616f48", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:25.604857Z", + "iopub.status.busy": "2023-07-26T05:17:25.604672Z", + "iopub.status.idle": "2023-07-26T05:17:25.718682Z", + "shell.execute_reply": "2023-07-26T05:17:25.718164Z" + } + }, "outputs": [], "source": [ "r2_fig, ax = subplots(figsize=(8,8))\n", @@ -1034,7 +1286,14 @@ "cell_type": "code", "execution_count": null, "id": "11e55883", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:25.721504Z", + "iopub.status.busy": "2023-07-26T05:17:25.721335Z", + "iopub.status.idle": "2023-07-26T05:17:26.086495Z", + "shell.execute_reply": "2023-07-26T05:17:26.085843Z" + } + }, "outputs": [], "source": [ "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n", @@ -1058,7 +1317,14 @@ "cell_type": "code", "execution_count": null, "id": "d107f961", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.090198Z", + "iopub.status.busy": "2023-07-26T05:17:26.090011Z", + "iopub.status.idle": "2023-07-26T05:17:26.211437Z", + "shell.execute_reply": "2023-07-26T05:17:26.210939Z" + } + }, "outputs": [], "source": [ "tuned_ridge = pipeCV.named_steps['ridge']\n", @@ -1087,7 +1353,14 @@ "cell_type": "code", "execution_count": null, "id": "21d65d1f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.214203Z", + "iopub.status.busy": "2023-07-26T05:17:26.213975Z", + "iopub.status.idle": "2023-07-26T05:17:26.217529Z", + "shell.execute_reply": "2023-07-26T05:17:26.216950Z" + } + }, "outputs": [], "source": [ "np.min(tuned_ridge.mse_path_.mean(1))" @@ -1108,7 +1381,14 @@ "cell_type": "code", "execution_count": null, "id": "11373de9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.219927Z", + "iopub.status.busy": "2023-07-26T05:17:26.219801Z", + "iopub.status.idle": "2023-07-26T05:17:26.222854Z", + "shell.execute_reply": "2023-07-26T05:17:26.222347Z" + } + }, "outputs": [], "source": [ "tuned_ridge.coef_" @@ -1150,7 +1430,14 @@ "cell_type": "code", "execution_count": null, "id": "72181964", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.225411Z", + "iopub.status.busy": "2023-07-26T05:17:26.225192Z", + "iopub.status.idle": "2023-07-26T05:17:26.228812Z", + "shell.execute_reply": "2023-07-26T05:17:26.228380Z" + } + }, "outputs": [], "source": [ "outer_valid = skm.ShuffleSplit(n_splits=1, \n", @@ -1170,7 +1457,14 @@ "cell_type": "code", "execution_count": null, "id": "4c3054b5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.231904Z", + "iopub.status.busy": "2023-07-26T05:17:26.231669Z", + "iopub.status.idle": "2023-07-26T05:17:26.556426Z", + "shell.execute_reply": "2023-07-26T05:17:26.556096Z" + } + }, "outputs": [], "source": [ "results = skm.cross_validate(pipeCV, \n", @@ -1201,7 +1495,14 @@ "cell_type": "code", "execution_count": null, "id": "72bb0c1d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.560895Z", + "iopub.status.busy": "2023-07-26T05:17:26.560717Z", + "iopub.status.idle": "2023-07-26T05:17:26.616139Z", + "shell.execute_reply": "2023-07-26T05:17:26.615605Z" + } + }, "outputs": [], "source": [ "lassoCV = skl.ElasticNetCV(n_alphas=100, \n", @@ -1218,7 +1519,14 @@ "cell_type": "code", "execution_count": null, "id": "69adc7ac", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.618605Z", + "iopub.status.busy": "2023-07-26T05:17:26.618409Z", + "iopub.status.idle": "2023-07-26T05:17:26.629654Z", + "shell.execute_reply": "2023-07-26T05:17:26.629177Z" + } + }, "outputs": [], "source": [ "lambdas, soln_array = skl.Lasso.path(Xs, \n", @@ -1244,7 +1552,14 @@ "cell_type": "code", "execution_count": null, "id": "2aa75789", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.632157Z", + "iopub.status.busy": "2023-07-26T05:17:26.631965Z", + "iopub.status.idle": "2023-07-26T05:17:26.833444Z", + "shell.execute_reply": "2023-07-26T05:17:26.832719Z" + } + }, "outputs": [], "source": [ "path_fig, ax = subplots(figsize=(8,8))\n", @@ -1268,7 +1583,14 @@ "cell_type": "code", "execution_count": null, "id": "e38c9bed", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.836589Z", + "iopub.status.busy": "2023-07-26T05:17:26.836233Z", + "iopub.status.idle": "2023-07-26T05:17:26.841023Z", + "shell.execute_reply": "2023-07-26T05:17:26.840686Z" + } + }, "outputs": [], "source": [ "np.min(tuned_lasso.mse_path_.mean(1))" @@ -1286,7 +1608,14 @@ "cell_type": "code", "execution_count": null, "id": "53c42724", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.843812Z", + "iopub.status.busy": "2023-07-26T05:17:26.843647Z", + "iopub.status.idle": "2023-07-26T05:17:26.968792Z", + "shell.execute_reply": "2023-07-26T05:17:26.968349Z" + } + }, "outputs": [], "source": [ "lassoCV_fig, ax = subplots(figsize=(8,8))\n", @@ -1315,7 +1644,14 @@ "cell_type": "code", "execution_count": null, "id": "5f4942ba", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.971271Z", + "iopub.status.busy": "2023-07-26T05:17:26.971065Z", + "iopub.status.idle": "2023-07-26T05:17:26.974897Z", + "shell.execute_reply": "2023-07-26T05:17:26.973908Z" + } + }, "outputs": [], "source": [ "tuned_lasso.coef_" @@ -1363,7 +1699,14 @@ "cell_type": "code", "execution_count": null, "id": "72a876bb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.977635Z", + "iopub.status.busy": "2023-07-26T05:17:26.977456Z", + "iopub.status.idle": "2023-07-26T05:17:26.983252Z", + "shell.execute_reply": "2023-07-26T05:17:26.982747Z" + } + }, "outputs": [], "source": [ "pca = PCA(n_components=2)\n", @@ -1389,7 +1732,14 @@ "cell_type": "code", "execution_count": null, "id": "e0e821c6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.985960Z", + "iopub.status.busy": "2023-07-26T05:17:26.985773Z", + "iopub.status.idle": "2023-07-26T05:17:26.991831Z", + "shell.execute_reply": "2023-07-26T05:17:26.991247Z" + } + }, "outputs": [], "source": [ "pipe = Pipeline([('scaler', scaler), \n", @@ -1414,7 +1764,14 @@ "cell_type": "code", "execution_count": null, "id": "1ac6886c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:26.994661Z", + "iopub.status.busy": "2023-07-26T05:17:26.994482Z", + "iopub.status.idle": "2023-07-26T05:17:27.138949Z", + "shell.execute_reply": "2023-07-26T05:17:27.138448Z" + } + }, "outputs": [], "source": [ "param_grid = {'pca__n_components': range(1, 20)}\n", @@ -1437,7 +1794,14 @@ "cell_type": "code", "execution_count": null, "id": "5e0c5a96", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.141752Z", + "iopub.status.busy": "2023-07-26T05:17:27.141555Z", + "iopub.status.idle": "2023-07-26T05:17:27.257460Z", + "shell.execute_reply": "2023-07-26T05:17:27.256920Z" + } + }, "outputs": [], "source": [ "pcr_fig, ax = subplots(figsize=(8,8))\n", @@ -1474,7 +1838,14 @@ "cell_type": "code", "execution_count": null, "id": "9df0fb7f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.260143Z", + "iopub.status.busy": "2023-07-26T05:17:27.259963Z", + "iopub.status.idle": "2023-07-26T05:17:27.267101Z", + "shell.execute_reply": "2023-07-26T05:17:27.266627Z" + } + }, "outputs": [], "source": [ "Xn = np.zeros((X.shape[0], 1))\n", @@ -1501,7 +1872,14 @@ "cell_type": "code", "execution_count": null, "id": "458c21a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.269230Z", + "iopub.status.busy": "2023-07-26T05:17:27.269087Z", + "iopub.status.idle": "2023-07-26T05:17:27.272035Z", + "shell.execute_reply": "2023-07-26T05:17:27.271586Z" + } + }, "outputs": [], "source": [ "pipe.named_steps['pca'].explained_variance_ratio_" @@ -1534,7 +1912,14 @@ "cell_type": "code", "execution_count": null, "id": "f49d464e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.274642Z", + "iopub.status.busy": "2023-07-26T05:17:27.274472Z", + "iopub.status.idle": "2023-07-26T05:17:27.279195Z", + "shell.execute_reply": "2023-07-26T05:17:27.278730Z" + } + }, "outputs": [], "source": [ "pls = PLSRegression(n_components=2, \n", @@ -1555,7 +1940,14 @@ "cell_type": "code", "execution_count": null, "id": "8e118b5b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.281479Z", + "iopub.status.busy": "2023-07-26T05:17:27.281289Z", + "iopub.status.idle": "2023-07-26T05:17:27.395617Z", + "shell.execute_reply": "2023-07-26T05:17:27.395127Z" + } + }, "outputs": [], "source": [ "param_grid = {'n_components':range(1, 20)}\n", @@ -1578,7 +1970,14 @@ "cell_type": "code", "execution_count": null, "id": "0f98407c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:27.398116Z", + "iopub.status.busy": "2023-07-26T05:17:27.397950Z", + "iopub.status.idle": "2023-07-26T05:17:27.513229Z", + "shell.execute_reply": "2023-07-26T05:17:27.512778Z" + } + }, "outputs": [], "source": [ "pls_fig, ax = subplots(figsize=(8,8))\n", @@ -1607,6 +2006,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch7-nonlin-lab.ipynb b/docs/source/labs/Ch7-nonlin-lab.ipynb index 54807a8..de4c75b 100644 --- a/docs/source/labs/Ch7-nonlin-lab.ipynb +++ b/docs/source/labs/Ch7-nonlin-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Non-Linear Modeling\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -22,7 +22,14 @@ "cell_type": "code", "execution_count": null, "id": "77af2dc4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:29.006535Z", + "iopub.status.busy": "2023-07-26T05:17:29.006365Z", + "iopub.status.idle": "2023-07-26T05:17:30.236882Z", + "shell.execute_reply": "2023-07-26T05:17:30.236365Z" + } + }, "outputs": [], "source": [ "import numpy as np, pandas as pd\n", @@ -49,7 +56,14 @@ "cell_type": "code", "execution_count": null, "id": "b57d1d51", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.240347Z", + "iopub.status.busy": "2023-07-26T05:17:30.240127Z", + "iopub.status.idle": "2023-07-26T05:17:30.254879Z", + "shell.execute_reply": "2023-07-26T05:17:30.254420Z" + } + }, "outputs": [], "source": [ "from pygam import (s as s_gam,\n", @@ -81,7 +95,14 @@ "cell_type": "code", "execution_count": null, "id": "65f0e562", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.257519Z", + "iopub.status.busy": "2023-07-26T05:17:30.257098Z", + "iopub.status.idle": "2023-07-26T05:17:30.265239Z", + "shell.execute_reply": "2023-07-26T05:17:30.264877Z" + } + }, "outputs": [], "source": [ "Wage = load_data('Wage')\n", @@ -104,7 +125,14 @@ "cell_type": "code", "execution_count": null, "id": "a391fae6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.267640Z", + "iopub.status.busy": "2023-07-26T05:17:30.267444Z", + "iopub.status.idle": "2023-07-26T05:17:30.380537Z", + "shell.execute_reply": "2023-07-26T05:17:30.378736Z" + } + }, "outputs": [], "source": [ "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n", @@ -147,7 +175,14 @@ "cell_type": "code", "execution_count": null, "id": "d672f40e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.388859Z", + "iopub.status.busy": "2023-07-26T05:17:30.387288Z", + "iopub.status.idle": "2023-07-26T05:17:30.401783Z", + "shell.execute_reply": "2023-07-26T05:17:30.398614Z" + } + }, "outputs": [], "source": [ "age_grid = np.linspace(age.min(),\n", @@ -175,7 +210,14 @@ "cell_type": "code", "execution_count": null, "id": "56174265", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.414085Z", + "iopub.status.busy": "2023-07-26T05:17:30.411883Z", + "iopub.status.idle": "2023-07-26T05:17:30.434639Z", + "shell.execute_reply": "2023-07-26T05:17:30.432466Z" + } + }, "outputs": [], "source": [ "def plot_wage_fit(age_df, \n", @@ -224,7 +266,14 @@ "cell_type": "code", "execution_count": null, "id": "1a10422f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.447621Z", + "iopub.status.busy": "2023-07-26T05:17:30.446550Z", + "iopub.status.idle": "2023-07-26T05:17:30.730675Z", + "shell.execute_reply": "2023-07-26T05:17:30.730133Z" + } + }, "outputs": [], "source": [ "plot_wage_fit(age_df, \n", @@ -264,7 +313,14 @@ "cell_type": "code", "execution_count": null, "id": "8105e672", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.732769Z", + "iopub.status.busy": "2023-07-26T05:17:30.732623Z", + "iopub.status.idle": "2023-07-26T05:17:30.781558Z", + "shell.execute_reply": "2023-07-26T05:17:30.779955Z" + } + }, "outputs": [], "source": [ "models = [MS([poly('age', degree=d)]) \n", @@ -304,7 +360,14 @@ "cell_type": "code", "execution_count": null, "id": "095fca7f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.790773Z", + "iopub.status.busy": "2023-07-26T05:17:30.789916Z", + "iopub.status.idle": "2023-07-26T05:17:30.829956Z", + "shell.execute_reply": "2023-07-26T05:17:30.827755Z" + } + }, "outputs": [], "source": [ "summarize(M)" @@ -324,7 +387,14 @@ "cell_type": "code", "execution_count": null, "id": "0b576607", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.839811Z", + "iopub.status.busy": "2023-07-26T05:17:30.837704Z", + "iopub.status.idle": "2023-07-26T05:17:30.856244Z", + "shell.execute_reply": "2023-07-26T05:17:30.854399Z" + } + }, "outputs": [], "source": [ "(-11.983)**2" @@ -346,7 +416,14 @@ "cell_type": "code", "execution_count": null, "id": "7242df77", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.866948Z", + "iopub.status.busy": "2023-07-26T05:17:30.865892Z", + "iopub.status.idle": "2023-07-26T05:17:30.922189Z", + "shell.execute_reply": "2023-07-26T05:17:30.920195Z" + } + }, "outputs": [], "source": [ "models = [MS(['education', poly('age', degree=d)])\n", @@ -375,7 +452,14 @@ "cell_type": "code", "execution_count": null, "id": "c7972aea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:30.932685Z", + "iopub.status.busy": "2023-07-26T05:17:30.932050Z", + "iopub.status.idle": "2023-07-26T05:17:31.040926Z", + "shell.execute_reply": "2023-07-26T05:17:31.003239Z" + } + }, "outputs": [], "source": [ "X = poly_age.transform(Wage)\n", @@ -399,7 +483,14 @@ "cell_type": "code", "execution_count": null, "id": "84f172f2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.067292Z", + "iopub.status.busy": "2023-07-26T05:17:31.066686Z", + "iopub.status.idle": "2023-07-26T05:17:31.149946Z", + "shell.execute_reply": "2023-07-26T05:17:31.109686Z" + } + }, "outputs": [], "source": [ "newX = poly_age.transform(age_df)\n", @@ -419,7 +510,14 @@ "cell_type": "code", "execution_count": null, "id": "fcf376bd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.174590Z", + "iopub.status.busy": "2023-07-26T05:17:31.158361Z", + "iopub.status.idle": "2023-07-26T05:17:31.302984Z", + "shell.execute_reply": "2023-07-26T05:17:31.302329Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -465,7 +563,14 @@ "cell_type": "code", "execution_count": null, "id": "c4d00900", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.305474Z", + "iopub.status.busy": "2023-07-26T05:17:31.305329Z", + "iopub.status.idle": "2023-07-26T05:17:31.336068Z", + "shell.execute_reply": "2023-07-26T05:17:31.334266Z" + } + }, "outputs": [], "source": [ "cut_age = pd.qcut(age, 4)\n", @@ -513,7 +618,14 @@ "cell_type": "code", "execution_count": null, "id": "d3817102", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.345711Z", + "iopub.status.busy": "2023-07-26T05:17:31.343152Z", + "iopub.status.idle": "2023-07-26T05:17:31.444719Z", + "shell.execute_reply": "2023-07-26T05:17:31.422743Z" + } + }, "outputs": [], "source": [ "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n", @@ -537,7 +649,14 @@ "cell_type": "code", "execution_count": null, "id": "c06c0f27", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.504387Z", + "iopub.status.busy": "2023-07-26T05:17:31.502827Z", + "iopub.status.idle": "2023-07-26T05:17:31.545517Z", + "shell.execute_reply": "2023-07-26T05:17:31.543597Z" + } + }, "outputs": [], "source": [ "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n", @@ -558,7 +677,14 @@ "cell_type": "code", "execution_count": null, "id": "8cb32db0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.554111Z", + "iopub.status.busy": "2023-07-26T05:17:31.553059Z", + "iopub.status.idle": "2023-07-26T05:17:31.605969Z", + "shell.execute_reply": "2023-07-26T05:17:31.601942Z" + } + }, "outputs": [], "source": [ "bs_age = MS([bs('age',\n", @@ -590,7 +716,14 @@ "cell_type": "code", "execution_count": null, "id": "f26b715f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.613882Z", + "iopub.status.busy": "2023-07-26T05:17:31.613189Z", + "iopub.status.idle": "2023-07-26T05:17:31.632577Z", + "shell.execute_reply": "2023-07-26T05:17:31.631063Z" + } + }, "outputs": [], "source": [ "BSpline(df=6).fit(age).internal_knots_" @@ -616,7 +749,14 @@ "cell_type": "code", "execution_count": null, "id": "d6109816", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.640827Z", + "iopub.status.busy": "2023-07-26T05:17:31.640229Z", + "iopub.status.idle": "2023-07-26T05:17:31.697215Z", + "shell.execute_reply": "2023-07-26T05:17:31.695639Z" + } + }, "outputs": [], "source": [ "bs_age0 = MS([bs('age',\n", @@ -660,7 +800,14 @@ "cell_type": "code", "execution_count": null, "id": "7c73a3da", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.706944Z", + "iopub.status.busy": "2023-07-26T05:17:31.705664Z", + "iopub.status.idle": "2023-07-26T05:17:31.757111Z", + "shell.execute_reply": "2023-07-26T05:17:31.754514Z" + } + }, "outputs": [], "source": [ "ns_age = MS([ns('age', df=5)]).fit(Wage)\n", @@ -680,7 +827,14 @@ "cell_type": "code", "execution_count": null, "id": "d73789b4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.766336Z", + "iopub.status.busy": "2023-07-26T05:17:31.764642Z", + "iopub.status.idle": "2023-07-26T05:17:31.949222Z", + "shell.execute_reply": "2023-07-26T05:17:31.947680Z" + } + }, "outputs": [], "source": [ "plot_wage_fit(age_df,\n", @@ -710,7 +864,14 @@ "cell_type": "code", "execution_count": null, "id": "3e70b87d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:31.962335Z", + "iopub.status.busy": "2023-07-26T05:17:31.958770Z", + "iopub.status.idle": "2023-07-26T05:17:32.190885Z", + "shell.execute_reply": "2023-07-26T05:17:32.188959Z" + } + }, "outputs": [], "source": [ "X_age = np.asarray(age).reshape((-1,1))\n", @@ -736,7 +897,14 @@ "cell_type": "code", "execution_count": null, "id": "efe03b12", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:32.212724Z", + "iopub.status.busy": "2023-07-26T05:17:32.212362Z", + "iopub.status.idle": "2023-07-26T05:17:33.141120Z", + "shell.execute_reply": "2023-07-26T05:17:33.140497Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -764,7 +932,14 @@ "cell_type": "code", "execution_count": null, "id": "acff2af2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:33.143621Z", + "iopub.status.busy": "2023-07-26T05:17:33.143494Z", + "iopub.status.idle": "2023-07-26T05:17:34.781067Z", + "shell.execute_reply": "2023-07-26T05:17:34.758430Z" + } + }, "outputs": [], "source": [ "gam_opt = gam.gridsearch(X_age, y)\n", @@ -793,7 +968,14 @@ "cell_type": "code", "execution_count": null, "id": "a2d25550", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:34.825629Z", + "iopub.status.busy": "2023-07-26T05:17:34.825425Z", + "iopub.status.idle": "2023-07-26T05:17:35.011756Z", + "shell.execute_reply": "2023-07-26T05:17:35.006694Z" + } + }, "outputs": [], "source": [ "age_term = gam.terms[0]\n", @@ -816,7 +998,14 @@ "cell_type": "code", "execution_count": null, "id": "bc6322de", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:35.024537Z", + "iopub.status.busy": "2023-07-26T05:17:35.022461Z", + "iopub.status.idle": "2023-07-26T05:17:35.863008Z", + "shell.execute_reply": "2023-07-26T05:17:35.862422Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -860,7 +1049,14 @@ "cell_type": "code", "execution_count": null, "id": "703d2570", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:35.865454Z", + "iopub.status.busy": "2023-07-26T05:17:35.865140Z", + "iopub.status.idle": "2023-07-26T05:17:35.892627Z", + "shell.execute_reply": "2023-07-26T05:17:35.889738Z" + } + }, "outputs": [], "source": [ "ns_age = NaturalSpline(df=4).fit(age)\n", @@ -891,7 +1087,14 @@ "cell_type": "code", "execution_count": null, "id": "766a7b1f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:35.908556Z", + "iopub.status.busy": "2023-07-26T05:17:35.902581Z", + "iopub.status.idle": "2023-07-26T05:17:36.057026Z", + "shell.execute_reply": "2023-07-26T05:17:36.055296Z" + } + }, "outputs": [], "source": [ "age_grid = np.linspace(age.min(),\n", @@ -935,7 +1138,14 @@ "cell_type": "code", "execution_count": null, "id": "2a9ed841", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.068318Z", + "iopub.status.busy": "2023-07-26T05:17:36.067032Z", + "iopub.status.idle": "2023-07-26T05:17:36.214624Z", + "shell.execute_reply": "2023-07-26T05:17:36.214052Z" + } + }, "outputs": [], "source": [ "year_grid = np.linspace(2003, 2009, 100)\n", @@ -978,7 +1188,14 @@ "cell_type": "code", "execution_count": null, "id": "ccf068b1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.216725Z", + "iopub.status.busy": "2023-07-26T05:17:36.216587Z", + "iopub.status.idle": "2023-07-26T05:17:36.383722Z", + "shell.execute_reply": "2023-07-26T05:17:36.379206Z" + } + }, "outputs": [], "source": [ "gam_full = LinearGAM(s_gam(0) +\n", @@ -1007,7 +1224,14 @@ "cell_type": "code", "execution_count": null, "id": "38b719f1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.404487Z", + "iopub.status.busy": "2023-07-26T05:17:36.401365Z", + "iopub.status.idle": "2023-07-26T05:17:36.620141Z", + "shell.execute_reply": "2023-07-26T05:17:36.619756Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -1032,7 +1256,14 @@ "cell_type": "code", "execution_count": null, "id": "02142f6e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.622262Z", + "iopub.status.busy": "2023-07-26T05:17:36.622071Z", + "iopub.status.idle": "2023-07-26T05:17:36.728386Z", + "shell.execute_reply": "2023-07-26T05:17:36.725088Z" + } + }, "outputs": [], "source": [ "age_term = gam_full.terms[0]\n", @@ -1057,7 +1288,14 @@ "cell_type": "code", "execution_count": null, "id": "94587b05", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.738456Z", + "iopub.status.busy": "2023-07-26T05:17:36.737408Z", + "iopub.status.idle": "2023-07-26T05:17:36.927722Z", + "shell.execute_reply": "2023-07-26T05:17:36.917522Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -1081,7 +1319,14 @@ "cell_type": "code", "execution_count": null, "id": "bba4c757", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:36.975410Z", + "iopub.status.busy": "2023-07-26T05:17:36.975007Z", + "iopub.status.idle": "2023-07-26T05:17:37.089224Z", + "shell.execute_reply": "2023-07-26T05:17:37.088696Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1110,7 +1355,14 @@ "cell_type": "code", "execution_count": null, "id": "32368085", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.091963Z", + "iopub.status.busy": "2023-07-26T05:17:37.091763Z", + "iopub.status.idle": "2023-07-26T05:17:37.181347Z", + "shell.execute_reply": "2023-07-26T05:17:37.177854Z" + } + }, "outputs": [], "source": [ "gam_0 = LinearGAM(age_term + f_gam(2, lam=0))\n", @@ -1137,7 +1389,14 @@ "cell_type": "code", "execution_count": null, "id": "e7ba9957", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.190026Z", + "iopub.status.busy": "2023-07-26T05:17:37.188398Z", + "iopub.status.idle": "2023-07-26T05:17:37.218825Z", + "shell.execute_reply": "2023-07-26T05:17:37.206670Z" + } + }, "outputs": [], "source": [ "anova_gam(gam_0, gam_linear, gam_full)" @@ -1164,7 +1423,14 @@ "cell_type": "code", "execution_count": null, "id": "ffc0099a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.237108Z", + "iopub.status.busy": "2023-07-26T05:17:37.233509Z", + "iopub.status.idle": "2023-07-26T05:17:37.451340Z", + "shell.execute_reply": "2023-07-26T05:17:37.449175Z" + } + }, "outputs": [], "source": [ "gam_0 = LinearGAM(year_term +\n", @@ -1189,7 +1455,14 @@ "cell_type": "code", "execution_count": null, "id": "08026a6b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.465361Z", + "iopub.status.busy": "2023-07-26T05:17:37.464408Z", + "iopub.status.idle": "2023-07-26T05:17:37.476379Z", + "shell.execute_reply": "2023-07-26T05:17:37.473563Z" + } + }, "outputs": [], "source": [ "gam_full.summary()" @@ -1209,7 +1482,14 @@ "cell_type": "code", "execution_count": null, "id": "9191d615", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.488339Z", + "iopub.status.busy": "2023-07-26T05:17:37.487573Z", + "iopub.status.idle": "2023-07-26T05:17:37.520050Z", + "shell.execute_reply": "2023-07-26T05:17:37.516172Z" + } + }, "outputs": [], "source": [ "Yhat = gam_full.predict(Xgam)" @@ -1228,7 +1508,14 @@ "cell_type": "code", "execution_count": null, "id": "92007a5f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:37.533259Z", + "iopub.status.busy": "2023-07-26T05:17:37.531878Z", + "iopub.status.idle": "2023-07-26T05:17:38.910096Z", + "shell.execute_reply": "2023-07-26T05:17:38.906703Z" + } + }, "outputs": [], "source": [ "gam_logit = LogisticGAM(age_term + \n", @@ -1241,7 +1528,14 @@ "cell_type": "code", "execution_count": null, "id": "4dd6aa2f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:38.924790Z", + "iopub.status.busy": "2023-07-26T05:17:38.922174Z", + "iopub.status.idle": "2023-07-26T05:17:39.053438Z", + "shell.execute_reply": "2023-07-26T05:17:39.047514Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1266,7 +1560,14 @@ "cell_type": "code", "execution_count": null, "id": "5a6e8754", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.066631Z", + "iopub.status.busy": "2023-07-26T05:17:39.064606Z", + "iopub.status.idle": "2023-07-26T05:17:39.115103Z", + "shell.execute_reply": "2023-07-26T05:17:39.111772Z" + } + }, "outputs": [], "source": [ "pd.crosstab(Wage['high_earn'], Wage['education'])" @@ -1293,7 +1594,14 @@ "cell_type": "code", "execution_count": null, "id": "c92b60be", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.164808Z", + "iopub.status.busy": "2023-07-26T05:17:39.164544Z", + "iopub.status.idle": "2023-07-26T05:17:39.168909Z", + "shell.execute_reply": "2023-07-26T05:17:39.168184Z" + } + }, "outputs": [], "source": [ "only_hs = Wage['education'] == '1. < HS Grad'\n", @@ -1319,7 +1627,14 @@ "cell_type": "code", "execution_count": null, "id": "e525e8d0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.173520Z", + "iopub.status.busy": "2023-07-26T05:17:39.173179Z", + "iopub.status.idle": "2023-07-26T05:17:39.334235Z", + "shell.execute_reply": "2023-07-26T05:17:39.329778Z" + } + }, "outputs": [], "source": [ "gam_logit_ = LogisticGAM(age_term +\n", @@ -1341,7 +1656,14 @@ "cell_type": "code", "execution_count": null, "id": "c3e66cf9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.345416Z", + "iopub.status.busy": "2023-07-26T05:17:39.344660Z", + "iopub.status.idle": "2023-07-26T05:17:39.468357Z", + "shell.execute_reply": "2023-07-26T05:17:39.465567Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1357,7 +1679,14 @@ "cell_type": "code", "execution_count": null, "id": "fd924348", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.479166Z", + "iopub.status.busy": "2023-07-26T05:17:39.477744Z", + "iopub.status.idle": "2023-07-26T05:17:39.605116Z", + "shell.execute_reply": "2023-07-26T05:17:39.604574Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1372,7 +1701,14 @@ "cell_type": "code", "execution_count": null, "id": "2d3ec90a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.607454Z", + "iopub.status.busy": "2023-07-26T05:17:39.607274Z", + "iopub.status.idle": "2023-07-26T05:17:39.737422Z", + "shell.execute_reply": "2023-07-26T05:17:39.736830Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8, 8))\n", @@ -1401,7 +1737,14 @@ "cell_type": "code", "execution_count": null, "id": "4f2bc0eb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:39.740507Z", + "iopub.status.busy": "2023-07-26T05:17:39.740171Z", + "iopub.status.idle": "2023-07-26T05:17:39.902495Z", + "shell.execute_reply": "2023-07-26T05:17:39.902127Z" + } + }, "outputs": [], "source": [ "lowess = sm.nonparametric.lowess\n", @@ -1427,6 +1770,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch8-baggboost-lab.ipynb b/docs/source/labs/Ch8-baggboost-lab.ipynb index 04147cd..43349ee 100644 --- a/docs/source/labs/Ch8-baggboost-lab.ipynb +++ b/docs/source/labs/Ch8-baggboost-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Tree-Based Methods\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -20,7 +20,14 @@ "cell_type": "code", "execution_count": null, "id": "4cc7120f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:41.271445Z", + "iopub.status.busy": "2023-07-26T05:17:41.271226Z", + "iopub.status.idle": "2023-07-26T05:17:42.756473Z", + "shell.execute_reply": "2023-07-26T05:17:42.756027Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -45,7 +52,14 @@ "cell_type": "code", "execution_count": null, "id": "864fa15e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.759051Z", + "iopub.status.busy": "2023-07-26T05:17:42.758860Z", + "iopub.status.idle": "2023-07-26T05:17:42.832228Z", + "shell.execute_reply": "2023-07-26T05:17:42.831597Z" + } + }, "outputs": [], "source": [ "from sklearn.tree import (DecisionTreeClassifier as DTC,\n", @@ -85,7 +99,14 @@ "cell_type": "code", "execution_count": null, "id": "bfb4c83b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.836883Z", + "iopub.status.busy": "2023-07-26T05:17:42.836681Z", + "iopub.status.idle": "2023-07-26T05:17:42.843170Z", + "shell.execute_reply": "2023-07-26T05:17:42.842647Z" + } + }, "outputs": [], "source": [ "Carseats = load_data('Carseats')\n", @@ -109,7 +130,14 @@ "cell_type": "code", "execution_count": null, "id": "5d9e7a14", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.845711Z", + "iopub.status.busy": "2023-07-26T05:17:42.845521Z", + "iopub.status.idle": "2023-07-26T05:17:42.856897Z", + "shell.execute_reply": "2023-07-26T05:17:42.856518Z" + } + }, "outputs": [], "source": [ "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n", @@ -136,7 +164,14 @@ "cell_type": "code", "execution_count": null, "id": "970c4515", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.859418Z", + "iopub.status.busy": "2023-07-26T05:17:42.859055Z", + "iopub.status.idle": "2023-07-26T05:17:42.866816Z", + "shell.execute_reply": "2023-07-26T05:17:42.866395Z" + } + }, "outputs": [], "source": [ "clf = DTC(criterion='entropy',\n", @@ -166,7 +201,14 @@ "cell_type": "code", "execution_count": null, "id": "2d9a51dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.868805Z", + "iopub.status.busy": "2023-07-26T05:17:42.868636Z", + "iopub.status.idle": "2023-07-26T05:17:42.872257Z", + "shell.execute_reply": "2023-07-26T05:17:42.871848Z" + } + }, "outputs": [], "source": [ "accuracy_score(High, clf.predict(X))" @@ -194,7 +236,14 @@ "cell_type": "code", "execution_count": null, "id": "cbc04b67", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.874181Z", + "iopub.status.busy": "2023-07-26T05:17:42.874073Z", + "iopub.status.idle": "2023-07-26T05:17:42.878254Z", + "shell.execute_reply": "2023-07-26T05:17:42.877678Z" + } + }, "outputs": [], "source": [ "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n", @@ -219,7 +268,14 @@ "cell_type": "code", "execution_count": null, "id": "809830c7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:42.880423Z", + "iopub.status.busy": "2023-07-26T05:17:42.880256Z", + "iopub.status.idle": "2023-07-26T05:17:43.233297Z", + "shell.execute_reply": "2023-07-26T05:17:43.232754Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(12,12))[1]\n", @@ -248,7 +304,14 @@ "cell_type": "code", "execution_count": null, "id": "abe8c7fc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.235787Z", + "iopub.status.busy": "2023-07-26T05:17:43.235629Z", + "iopub.status.idle": "2023-07-26T05:17:43.238645Z", + "shell.execute_reply": "2023-07-26T05:17:43.238197Z" + } + }, "outputs": [], "source": [ "print(export_text(clf,\n", @@ -276,7 +339,14 @@ "cell_type": "code", "execution_count": null, "id": "f7a1f736", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.241246Z", + "iopub.status.busy": "2023-07-26T05:17:43.241128Z", + "iopub.status.idle": "2023-07-26T05:17:43.248339Z", + "shell.execute_reply": "2023-07-26T05:17:43.247882Z" + } + }, "outputs": [], "source": [ "validation = skm.ShuffleSplit(n_splits=1,\n", @@ -305,7 +375,14 @@ "cell_type": "code", "execution_count": null, "id": "c663a623", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.250854Z", + "iopub.status.busy": "2023-07-26T05:17:43.250675Z", + "iopub.status.idle": "2023-07-26T05:17:43.253625Z", + "shell.execute_reply": "2023-07-26T05:17:43.253117Z" + } + }, "outputs": [], "source": [ "(X_train,\n", @@ -330,7 +407,14 @@ "cell_type": "code", "execution_count": null, "id": "c2f2a403", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.256072Z", + "iopub.status.busy": "2023-07-26T05:17:43.255891Z", + "iopub.status.idle": "2023-07-26T05:17:43.261392Z", + "shell.execute_reply": "2023-07-26T05:17:43.260822Z" + } + }, "outputs": [], "source": [ "clf = DTC(criterion='entropy', random_state=0)\n", @@ -351,7 +435,14 @@ "cell_type": "code", "execution_count": null, "id": "b2505658", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.264289Z", + "iopub.status.busy": "2023-07-26T05:17:43.264075Z", + "iopub.status.idle": "2023-07-26T05:17:43.268404Z", + "shell.execute_reply": "2023-07-26T05:17:43.267912Z" + } + }, "outputs": [], "source": [ "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n", @@ -373,7 +464,14 @@ "cell_type": "code", "execution_count": null, "id": "09a7bd33", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.270876Z", + "iopub.status.busy": "2023-07-26T05:17:43.270706Z", + "iopub.status.idle": "2023-07-26T05:17:43.553527Z", + "shell.execute_reply": "2023-07-26T05:17:43.553080Z" + } + }, "outputs": [], "source": [ "grid = skm.GridSearchCV(clf,\n", @@ -397,7 +495,14 @@ "cell_type": "code", "execution_count": null, "id": "a75dea32", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:43.556004Z", + "iopub.status.busy": "2023-07-26T05:17:43.555851Z", + "iopub.status.idle": "2023-07-26T05:17:44.479893Z", + "shell.execute_reply": "2023-07-26T05:17:44.479062Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(12, 12))[1]\n", @@ -420,7 +525,14 @@ "cell_type": "code", "execution_count": null, "id": "3f1e3d19", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.483391Z", + "iopub.status.busy": "2023-07-26T05:17:44.483211Z", + "iopub.status.idle": "2023-07-26T05:17:44.486537Z", + "shell.execute_reply": "2023-07-26T05:17:44.486111Z" + } + }, "outputs": [], "source": [ "best_.tree_.n_leaves" @@ -441,7 +553,14 @@ "cell_type": "code", "execution_count": null, "id": "30f5c2f9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.489160Z", + "iopub.status.busy": "2023-07-26T05:17:44.489048Z", + "iopub.status.idle": "2023-07-26T05:17:44.498333Z", + "shell.execute_reply": "2023-07-26T05:17:44.497705Z" + } + }, "outputs": [], "source": [ "print(accuracy_score(High_test,\n", @@ -473,7 +592,14 @@ "cell_type": "code", "execution_count": null, "id": "1ca6bc7a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.500404Z", + "iopub.status.busy": "2023-07-26T05:17:44.500198Z", + "iopub.status.idle": "2023-07-26T05:17:44.510037Z", + "shell.execute_reply": "2023-07-26T05:17:44.509664Z" + } + }, "outputs": [], "source": [ "Boston = load_data(\"Boston\")\n", @@ -496,7 +622,14 @@ "cell_type": "code", "execution_count": null, "id": "15cfb553", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.512140Z", + "iopub.status.busy": "2023-07-26T05:17:44.511989Z", + "iopub.status.idle": "2023-07-26T05:17:44.515238Z", + "shell.execute_reply": "2023-07-26T05:17:44.514701Z" + } + }, "outputs": [], "source": [ "(X_train,\n", @@ -520,7 +653,14 @@ "cell_type": "code", "execution_count": null, "id": "d18820a3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.517521Z", + "iopub.status.busy": "2023-07-26T05:17:44.517330Z", + "iopub.status.idle": "2023-07-26T05:17:44.858666Z", + "shell.execute_reply": "2023-07-26T05:17:44.858062Z" + } + }, "outputs": [], "source": [ "reg = DTR(max_depth=3)\n", @@ -556,7 +696,14 @@ "cell_type": "code", "execution_count": null, "id": "619f8d94", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.861023Z", + "iopub.status.busy": "2023-07-26T05:17:44.860905Z", + "iopub.status.idle": "2023-07-26T05:17:44.914455Z", + "shell.execute_reply": "2023-07-26T05:17:44.913988Z" + } + }, "outputs": [], "source": [ "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n", @@ -584,7 +731,14 @@ "cell_type": "code", "execution_count": null, "id": "779a14b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.917435Z", + "iopub.status.busy": "2023-07-26T05:17:44.917262Z", + "iopub.status.idle": "2023-07-26T05:17:44.920852Z", + "shell.execute_reply": "2023-07-26T05:17:44.920436Z" + } + }, "outputs": [], "source": [ "best_ = grid.best_estimator_\n", @@ -611,7 +765,14 @@ "cell_type": "code", "execution_count": null, "id": "2b04030b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:44.923340Z", + "iopub.status.busy": "2023-07-26T05:17:44.923189Z", + "iopub.status.idle": "2023-07-26T05:17:45.244796Z", + "shell.execute_reply": "2023-07-26T05:17:45.244381Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(12,12))[1]\n", @@ -644,7 +805,14 @@ "cell_type": "code", "execution_count": null, "id": "7dedce31", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:45.248119Z", + "iopub.status.busy": "2023-07-26T05:17:45.247930Z", + "iopub.status.idle": "2023-07-26T05:17:45.416858Z", + "shell.execute_reply": "2023-07-26T05:17:45.416407Z" + } + }, "outputs": [], "source": [ "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n", @@ -666,7 +834,14 @@ "cell_type": "code", "execution_count": null, "id": "1f2c7336", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:45.418831Z", + "iopub.status.busy": "2023-07-26T05:17:45.418689Z", + "iopub.status.idle": "2023-07-26T05:17:45.525786Z", + "shell.execute_reply": "2023-07-26T05:17:45.525224Z" + } + }, "outputs": [], "source": [ "ax = subplots(figsize=(8,8))[1]\n", @@ -691,7 +866,14 @@ "cell_type": "code", "execution_count": null, "id": "0bba2439", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:45.528244Z", + "iopub.status.busy": "2023-07-26T05:17:45.528120Z", + "iopub.status.idle": "2023-07-26T05:17:46.372586Z", + "shell.execute_reply": "2023-07-26T05:17:46.372196Z" + } + }, "outputs": [], "source": [ "bag_boston = RF(max_features=X_train.shape[1],\n", @@ -721,7 +903,14 @@ "cell_type": "code", "execution_count": null, "id": "90457fbe", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:46.375077Z", + "iopub.status.busy": "2023-07-26T05:17:46.374893Z", + "iopub.status.idle": "2023-07-26T05:17:46.498984Z", + "shell.execute_reply": "2023-07-26T05:17:46.498519Z" + } + }, "outputs": [], "source": [ "RF_boston = RF(max_features=6,\n", @@ -745,7 +934,14 @@ "cell_type": "code", "execution_count": null, "id": "8aae1857", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:46.501498Z", + "iopub.status.busy": "2023-07-26T05:17:46.501332Z", + "iopub.status.idle": "2023-07-26T05:17:46.511203Z", + "shell.execute_reply": "2023-07-26T05:17:46.510809Z" + } + }, "outputs": [], "source": [ "feature_imp = pd.DataFrame(\n", @@ -795,7 +991,14 @@ "cell_type": "code", "execution_count": null, "id": "887b4fa9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:46.513662Z", + "iopub.status.busy": "2023-07-26T05:17:46.513485Z", + "iopub.status.idle": "2023-07-26T05:17:50.160797Z", + "shell.execute_reply": "2023-07-26T05:17:50.160288Z" + } + }, "outputs": [], "source": [ "boost_boston = GBR(n_estimators=5000,\n", @@ -819,7 +1022,14 @@ "cell_type": "code", "execution_count": null, "id": "6c56696c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:50.163556Z", + "iopub.status.busy": "2023-07-26T05:17:50.163389Z", + "iopub.status.idle": "2023-07-26T05:17:50.751622Z", + "shell.execute_reply": "2023-07-26T05:17:50.751113Z" + } + }, "outputs": [], "source": [ "test_error = np.zeros_like(boost_boston.train_score_)\n", @@ -851,7 +1061,14 @@ "cell_type": "code", "execution_count": null, "id": "8a636f63", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:50.753880Z", + "iopub.status.busy": "2023-07-26T05:17:50.753719Z", + "iopub.status.idle": "2023-07-26T05:17:50.765303Z", + "shell.execute_reply": "2023-07-26T05:17:50.764814Z" + } + }, "outputs": [], "source": [ "y_hat_boost = boost_boston.predict(X_test);\n", @@ -874,7 +1091,14 @@ "cell_type": "code", "execution_count": null, "id": "4e2ab3c7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:50.767698Z", + "iopub.status.busy": "2023-07-26T05:17:50.767534Z", + "iopub.status.idle": "2023-07-26T05:17:53.090781Z", + "shell.execute_reply": "2023-07-26T05:17:53.090355Z" + } + }, "outputs": [], "source": [ "boost_boston = GBR(n_estimators=5000,\n", @@ -920,7 +1144,14 @@ "cell_type": "code", "execution_count": null, "id": "b0b41c04", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:53.093602Z", + "iopub.status.busy": "2023-07-26T05:17:53.093377Z", + "iopub.status.idle": "2023-07-26T05:17:54.986360Z", + "shell.execute_reply": "2023-07-26T05:17:54.985944Z" + } + }, "outputs": [], "source": [ "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n", @@ -939,7 +1170,14 @@ "cell_type": "code", "execution_count": null, "id": "fda8fffc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:54.988616Z", + "iopub.status.busy": "2023-07-26T05:17:54.988463Z", + "iopub.status.idle": "2023-07-26T05:17:55.919353Z", + "shell.execute_reply": "2023-07-26T05:17:55.918371Z" + } + }, "outputs": [], "source": [ "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n", @@ -959,7 +1197,14 @@ "cell_type": "code", "execution_count": null, "id": "be77f0e4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:55.922114Z", + "iopub.status.busy": "2023-07-26T05:17:55.921834Z", + "iopub.status.idle": "2023-07-26T05:17:55.926261Z", + "shell.execute_reply": "2023-07-26T05:17:55.925458Z" + } + }, "outputs": [], "source": [ "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n", @@ -973,6 +1218,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/source/labs/Ch9-svm-lab.ipynb b/docs/source/labs/Ch9-svm-lab.ipynb index 4a7215e..7d8ebe5 100644 --- a/docs/source/labs/Ch9-svm-lab.ipynb +++ b/docs/source/labs/Ch9-svm-lab.ipynb @@ -7,7 +7,7 @@ "source": [ "# Support Vector Machines\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", @@ -22,7 +22,14 @@ "cell_type": "code", "execution_count": null, "id": "eeaa5be0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:57.286672Z", + "iopub.status.busy": "2023-07-26T05:17:57.286340Z", + "iopub.status.idle": "2023-07-26T05:17:58.454461Z", + "shell.execute_reply": "2023-07-26T05:17:58.453915Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -44,7 +51,14 @@ "cell_type": "code", "execution_count": null, "id": "41a59634", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.457424Z", + "iopub.status.busy": "2023-07-26T05:17:58.457095Z", + "iopub.status.idle": "2023-07-26T05:17:58.490405Z", + "shell.execute_reply": "2023-07-26T05:17:58.490061Z" + } + }, "outputs": [], "source": [ "from sklearn.svm import SVC\n", @@ -65,7 +79,14 @@ "cell_type": "code", "execution_count": null, "id": "c9a175d7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.493151Z", + "iopub.status.busy": "2023-07-26T05:17:58.492879Z", + "iopub.status.idle": "2023-07-26T05:17:58.496409Z", + "shell.execute_reply": "2023-07-26T05:17:58.495164Z" + } + }, "outputs": [], "source": [ "roc_curve = RocCurveDisplay.from_estimator # shorthand" @@ -98,7 +119,14 @@ "cell_type": "code", "execution_count": null, "id": "a7216b47", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.499025Z", + "iopub.status.busy": "2023-07-26T05:17:58.498850Z", + "iopub.status.idle": "2023-07-26T05:17:58.613732Z", + "shell.execute_reply": "2023-07-26T05:17:58.613136Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", @@ -124,7 +152,14 @@ "cell_type": "code", "execution_count": null, "id": "ed329198", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.616777Z", + "iopub.status.busy": "2023-07-26T05:17:58.616601Z", + "iopub.status.idle": "2023-07-26T05:17:58.621468Z", + "shell.execute_reply": "2023-07-26T05:17:58.621124Z" + } + }, "outputs": [], "source": [ "svm_linear = SVC(C=10, kernel='linear')\n", @@ -146,7 +181,14 @@ "cell_type": "code", "execution_count": null, "id": "95494b8b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.623538Z", + "iopub.status.busy": "2023-07-26T05:17:58.623369Z", + "iopub.status.idle": "2023-07-26T05:17:58.810058Z", + "shell.execute_reply": "2023-07-26T05:17:58.809447Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -173,7 +215,14 @@ "cell_type": "code", "execution_count": null, "id": "98c2236f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.812870Z", + "iopub.status.busy": "2023-07-26T05:17:58.812658Z", + "iopub.status.idle": "2023-07-26T05:17:58.967044Z", + "shell.execute_reply": "2023-07-26T05:17:58.966462Z" + } + }, "outputs": [], "source": [ "svm_linear_small = SVC(C=0.1, kernel='linear')\n", @@ -200,7 +249,14 @@ "cell_type": "code", "execution_count": null, "id": "b498f594", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.969920Z", + "iopub.status.busy": "2023-07-26T05:17:58.969735Z", + "iopub.status.idle": "2023-07-26T05:17:58.973409Z", + "shell.execute_reply": "2023-07-26T05:17:58.972682Z" + } + }, "outputs": [], "source": [ "svm_linear.coef_" @@ -219,7 +275,14 @@ "cell_type": "code", "execution_count": null, "id": "b65e80d6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:58.975865Z", + "iopub.status.busy": "2023-07-26T05:17:58.975747Z", + "iopub.status.idle": "2023-07-26T05:17:59.006899Z", + "shell.execute_reply": "2023-07-26T05:17:59.006545Z" + } + }, "outputs": [], "source": [ "kfold = skm.KFold(5, \n", @@ -248,7 +311,14 @@ "cell_type": "code", "execution_count": null, "id": "bba8fad7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.010681Z", + "iopub.status.busy": "2023-07-26T05:17:59.010231Z", + "iopub.status.idle": "2023-07-26T05:17:59.014165Z", + "shell.execute_reply": "2023-07-26T05:17:59.013759Z" + } + }, "outputs": [], "source": [ "grid.cv_results_[('mean_test_score')]" @@ -270,7 +340,14 @@ "cell_type": "code", "execution_count": null, "id": "ad64269d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.017638Z", + "iopub.status.busy": "2023-07-26T05:17:59.017453Z", + "iopub.status.idle": "2023-07-26T05:17:59.020397Z", + "shell.execute_reply": "2023-07-26T05:17:59.019642Z" + } + }, "outputs": [], "source": [ "X_test = rng.standard_normal((20, 2))\n", @@ -292,7 +369,14 @@ "cell_type": "code", "execution_count": null, "id": "5107fca1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.026576Z", + "iopub.status.busy": "2023-07-26T05:17:59.026365Z", + "iopub.status.idle": "2023-07-26T05:17:59.033664Z", + "shell.execute_reply": "2023-07-26T05:17:59.033173Z" + } + }, "outputs": [], "source": [ "best_ = grid.best_estimator_\n", @@ -315,7 +399,14 @@ "cell_type": "code", "execution_count": null, "id": "0320d9e0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.035936Z", + "iopub.status.busy": "2023-07-26T05:17:59.035645Z", + "iopub.status.idle": "2023-07-26T05:17:59.041475Z", + "shell.execute_reply": "2023-07-26T05:17:59.041054Z" + } + }, "outputs": [], "source": [ "svm_ = SVC(C=0.001,\n", @@ -342,7 +433,14 @@ "cell_type": "code", "execution_count": null, "id": "84d7e778", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.043762Z", + "iopub.status.busy": "2023-07-26T05:17:59.043557Z", + "iopub.status.idle": "2023-07-26T05:17:59.144334Z", + "shell.execute_reply": "2023-07-26T05:17:59.143881Z" + } + }, "outputs": [], "source": [ "X[y==1] += 1.9;\n", @@ -362,7 +460,14 @@ "cell_type": "code", "execution_count": null, "id": "abb1f8be", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.147029Z", + "iopub.status.busy": "2023-07-26T05:17:59.146868Z", + "iopub.status.idle": "2023-07-26T05:17:59.154787Z", + "shell.execute_reply": "2023-07-26T05:17:59.153508Z" + } + }, "outputs": [], "source": [ "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n", @@ -385,7 +490,14 @@ "cell_type": "code", "execution_count": null, "id": "2e4ed2f5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.157293Z", + "iopub.status.busy": "2023-07-26T05:17:59.157100Z", + "iopub.status.idle": "2023-07-26T05:17:59.287805Z", + "shell.execute_reply": "2023-07-26T05:17:59.287264Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -411,7 +523,14 @@ "cell_type": "code", "execution_count": null, "id": "164a611c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.291112Z", + "iopub.status.busy": "2023-07-26T05:17:59.290933Z", + "iopub.status.idle": "2023-07-26T05:17:59.296989Z", + "shell.execute_reply": "2023-07-26T05:17:59.296618Z" + } + }, "outputs": [], "source": [ "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n", @@ -434,7 +553,14 @@ "cell_type": "code", "execution_count": null, "id": "c67591a1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.299723Z", + "iopub.status.busy": "2023-07-26T05:17:59.299515Z", + "iopub.status.idle": "2023-07-26T05:17:59.442236Z", + "shell.execute_reply": "2023-07-26T05:17:59.441618Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -468,7 +594,14 @@ "cell_type": "code", "execution_count": null, "id": "322be574", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.445260Z", + "iopub.status.busy": "2023-07-26T05:17:59.445043Z", + "iopub.status.idle": "2023-07-26T05:17:59.448424Z", + "shell.execute_reply": "2023-07-26T05:17:59.447969Z" + } + }, "outputs": [], "source": [ "X = rng.standard_normal((200, 2))\n", @@ -489,7 +622,14 @@ "cell_type": "code", "execution_count": null, "id": "04fda182", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.451843Z", + "iopub.status.busy": "2023-07-26T05:17:59.451105Z", + "iopub.status.idle": "2023-07-26T05:17:59.552259Z", + "shell.execute_reply": "2023-07-26T05:17:59.551787Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -513,7 +653,14 @@ "cell_type": "code", "execution_count": null, "id": "0c2690d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.554419Z", + "iopub.status.busy": "2023-07-26T05:17:59.554263Z", + "iopub.status.idle": "2023-07-26T05:17:59.559300Z", + "shell.execute_reply": "2023-07-26T05:17:59.558839Z" + } + }, "outputs": [], "source": [ "(X_train, \n", @@ -540,7 +687,14 @@ "cell_type": "code", "execution_count": null, "id": "3eb171e8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.561548Z", + "iopub.status.busy": "2023-07-26T05:17:59.561354Z", + "iopub.status.idle": "2023-07-26T05:17:59.836831Z", + "shell.execute_reply": "2023-07-26T05:17:59.836408Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -566,7 +720,14 @@ "cell_type": "code", "execution_count": null, "id": "9a6b905b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:17:59.839191Z", + "iopub.status.busy": "2023-07-26T05:17:59.838970Z", + "iopub.status.idle": "2023-07-26T05:18:00.004302Z", + "shell.execute_reply": "2023-07-26T05:18:00.003731Z" + } + }, "outputs": [], "source": [ "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1e5)\n", @@ -592,7 +753,14 @@ "cell_type": "code", "execution_count": null, "id": "5ab01d6c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.007020Z", + "iopub.status.busy": "2023-07-26T05:18:00.006845Z", + "iopub.status.idle": "2023-07-26T05:18:00.113455Z", + "shell.execute_reply": "2023-07-26T05:18:00.112946Z" + } + }, "outputs": [], "source": [ "kfold = skm.KFold(5, \n", @@ -622,7 +790,14 @@ "cell_type": "code", "execution_count": null, "id": "166a6acb", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.116122Z", + "iopub.status.busy": "2023-07-26T05:18:00.115942Z", + "iopub.status.idle": "2023-07-26T05:18:00.360989Z", + "shell.execute_reply": "2023-07-26T05:18:00.360361Z" + } + }, "outputs": [], "source": [ "best_svm = grid.best_estimator_\n", @@ -682,7 +857,14 @@ "cell_type": "code", "execution_count": null, "id": "0607fc41", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.363471Z", + "iopub.status.busy": "2023-07-26T05:18:00.363318Z", + "iopub.status.idle": "2023-07-26T05:18:00.469265Z", + "shell.execute_reply": "2023-07-26T05:18:00.468690Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -708,7 +890,14 @@ "cell_type": "code", "execution_count": null, "id": "5211a882", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.471742Z", + "iopub.status.busy": "2023-07-26T05:18:00.471575Z", + "iopub.status.idle": "2023-07-26T05:18:00.629647Z", + "shell.execute_reply": "2023-07-26T05:18:00.629119Z" + } + }, "outputs": [], "source": [ "svm_flex = SVC(kernel=\"rbf\", \n", @@ -739,7 +928,14 @@ "cell_type": "code", "execution_count": null, "id": "12acc4ff", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.632175Z", + "iopub.status.busy": "2023-07-26T05:18:00.631994Z", + "iopub.status.idle": "2023-07-26T05:18:00.636710Z", + "shell.execute_reply": "2023-07-26T05:18:00.636238Z" + } + }, "outputs": [], "source": [ "roc_curve(svm_flex,\n", @@ -763,7 +959,14 @@ "cell_type": "code", "execution_count": null, "id": "21c81913", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.639004Z", + "iopub.status.busy": "2023-07-26T05:18:00.638813Z", + "iopub.status.idle": "2023-07-26T05:18:00.753454Z", + "shell.execute_reply": "2023-07-26T05:18:00.752986Z" + } + }, "outputs": [], "source": [ "fig, ax = subplots(figsize=(8,8))\n", @@ -800,7 +1003,14 @@ "cell_type": "code", "execution_count": null, "id": "2fff4fa8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.756116Z", + "iopub.status.busy": "2023-07-26T05:18:00.755939Z", + "iopub.status.idle": "2023-07-26T05:18:00.855998Z", + "shell.execute_reply": "2023-07-26T05:18:00.855206Z" + } + }, "outputs": [], "source": [ "rng = np.random.default_rng(123)\n", @@ -823,7 +1033,14 @@ "cell_type": "code", "execution_count": null, "id": "5396f2df", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:00.861579Z", + "iopub.status.busy": "2023-07-26T05:18:00.861401Z", + "iopub.status.idle": "2023-07-26T05:18:01.455936Z", + "shell.execute_reply": "2023-07-26T05:18:01.455227Z" + } + }, "outputs": [], "source": [ "svm_rbf_3 = SVC(kernel=\"rbf\",\n", @@ -868,7 +1085,14 @@ "cell_type": "code", "execution_count": null, "id": "f63c575e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:01.458214Z", + "iopub.status.busy": "2023-07-26T05:18:01.458015Z", + "iopub.status.idle": "2023-07-26T05:18:01.539063Z", + "shell.execute_reply": "2023-07-26T05:18:01.538275Z" + } + }, "outputs": [], "source": [ "Khan = load_data('Khan')\n", @@ -896,7 +1120,14 @@ "cell_type": "code", "execution_count": null, "id": "32091338", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:01.541953Z", + "iopub.status.busy": "2023-07-26T05:18:01.541758Z", + "iopub.status.idle": "2023-07-26T05:18:01.578259Z", + "shell.execute_reply": "2023-07-26T05:18:01.577863Z" + } + }, "outputs": [], "source": [ "khan_linear = SVC(kernel='linear', C=10)\n", @@ -922,7 +1153,14 @@ "cell_type": "code", "execution_count": null, "id": "d9058023", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-26T05:18:01.582842Z", + "iopub.status.busy": "2023-07-26T05:18:01.582631Z", + "iopub.status.idle": "2023-07-26T05:18:01.598298Z", + "shell.execute_reply": "2023-07-26T05:18:01.597510Z" + } + }, "outputs": [], "source": [ "confusion_table(khan_linear.predict(Khan['xtest']),\n", @@ -943,6 +1181,18 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md:myst", "main_language": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" } }, "nbformat": 4, From 5cccd68ff59c1717800d6f162aa26a401c4a2d36 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 09:25:49 -0400 Subject: [PATCH 050/195] update readthedocs --- .readthedocs.yaml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3655b23..3bd28b2 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -12,7 +12,13 @@ build: python: "3.9" apt_packages: - r-base - + jobs: + pre_install: + - python docs/fix_and_run_notebooks.py + +submodules: + include: all + # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/source/conf.py From b1ac032df7baa3081400736448409df7e242f3ba Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 09:40:37 -0400 Subject: [PATCH 051/195] make it pre_build instead of pre_install --- .readthedocs.yaml | 3 ++- docs/fix_and_run_notebooks.py | 15 +++++++++------ docs/source/installation.myst | 4 ++-- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3bd28b2..901b279 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -13,7 +13,8 @@ build: apt_packages: - r-base jobs: - pre_install: + pre_build: + - pip install nbformat - python docs/fix_and_run_notebooks.py submodules: diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 13d7f51..12dc0a0 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -1,21 +1,24 @@ import os, nbformat from glob import glob -for f in glob('source/labs/Ch14*'): +import __main__ +dirname = os.path.split(__main__.__file__)[0] + +for f in glob(os.path.join(dirname, 'source', 'labs', 'Ch14*')): os.remove(f) version = 'v1' main = 'main' os.system(f''' -cd ISLP_labs; +cd {dirname}/ISLP_labs; git checkout {version}; -cp * ../source/labs; +cp * {dirname}/source/labs; git checkout {main}; -pip install -r ../source/labs/frozen_requirements.txt; -pip install -r ../source/labs/torch_requirements.txt; +pip install -r {dirname}/source/labs/frozen_requirements.txt; +pip install -r {dirname}/source/labs/torch_requirements.txt; ''') -for nbfile in glob('source/labs/*nb'): +for nbfile in glob(os.path.join(dirname, 'source', 'labs', '*nb')): base = os.path.splitext(nbfile)[0] labname = os.path.split(base)[1] diff --git a/docs/source/installation.myst b/docs/source/installation.myst index a1aaf6d..054cee6 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,7 +61,7 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/frozen_requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/frozen_requirements.txt ``` ## Torch requirements @@ -84,7 +84,7 @@ current verisons of the labs. --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/torch_requirements.txt ``` ## Jupyter From 500715b045d34cf4026a0254cfe7850a7005fb44 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 09:50:50 -0400 Subject: [PATCH 052/195] adding jupytext --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 901b279..bf4c424 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -14,7 +14,7 @@ build: - r-base jobs: pre_build: - - pip install nbformat + - pip install nbformat jupytext - python docs/fix_and_run_notebooks.py submodules: From d147f5102c0c6c6deb59ad2cc1d234daf0f98a57 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 10:32:52 -0400 Subject: [PATCH 053/195] removing labs from library repo, they are in a submodule --- docs/fix_and_run_notebooks.py | 9 +- docs/source/labs/Auto.csv | 393 - docs/source/labs/Auto.data | 398 - docs/source/labs/Ch10-deeplearning-lab.ipynb | 9973 ----------------- docs/source/labs/Ch11-surv-lab.ipynb | 1185 -- docs/source/labs/Ch12-unsup-lab.ipynb | 1989 ---- docs/source/labs/Ch13-multiple-lab.ipynb | 1150 -- docs/source/labs/Ch14-surv-lab.ipynb | 995 -- docs/source/labs/Ch2-statlearn-lab.ipynb | 3586 ------ docs/source/labs/Ch3-linreg-lab.ipynb | 1321 --- docs/source/labs/Ch4-classification-lab.ipynb | 2633 ----- docs/source/labs/Ch5-resample-lab.ipynb | 1064 -- docs/source/labs/Ch6-varselect-lab.ipynb | 2025 ---- docs/source/labs/Ch7-nonlin-lab.ipynb | 1789 --- docs/source/labs/Ch8-baggboost-lab.ipynb | 1237 -- docs/source/labs/Ch9-svm-lab.ipynb | 1200 -- 16 files changed, 6 insertions(+), 30941 deletions(-) delete mode 100644 docs/source/labs/Auto.csv delete mode 100644 docs/source/labs/Auto.data delete mode 100644 docs/source/labs/Ch10-deeplearning-lab.ipynb delete mode 100644 docs/source/labs/Ch11-surv-lab.ipynb delete mode 100644 docs/source/labs/Ch12-unsup-lab.ipynb delete mode 100644 docs/source/labs/Ch13-multiple-lab.ipynb delete mode 100644 docs/source/labs/Ch14-surv-lab.ipynb delete mode 100644 docs/source/labs/Ch2-statlearn-lab.ipynb delete mode 100644 docs/source/labs/Ch3-linreg-lab.ipynb delete mode 100644 docs/source/labs/Ch4-classification-lab.ipynb delete mode 100644 docs/source/labs/Ch5-resample-lab.ipynb delete mode 100644 docs/source/labs/Ch6-varselect-lab.ipynb delete mode 100644 docs/source/labs/Ch7-nonlin-lab.ipynb delete mode 100644 docs/source/labs/Ch8-baggboost-lab.ipynb delete mode 100644 docs/source/labs/Ch9-svm-lab.ipynb diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 12dc0a0..de76437 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -3,19 +3,22 @@ import __main__ dirname = os.path.split(__main__.__file__)[0] +print(dirname) for f in glob(os.path.join(dirname, 'source', 'labs', 'Ch14*')): os.remove(f) - + print(f) + stop + version = 'v1' main = 'main' os.system(f''' cd {dirname}/ISLP_labs; git checkout {version}; -cp * {dirname}/source/labs; +mkdir -p {dirname}/source/labs; +cp -r * {dirname}/source/labs; git checkout {main}; pip install -r {dirname}/source/labs/frozen_requirements.txt; -pip install -r {dirname}/source/labs/torch_requirements.txt; ''') for nbfile in glob(os.path.join(dirname, 'source', 'labs', '*nb')): diff --git a/docs/source/labs/Auto.csv b/docs/source/labs/Auto.csv deleted file mode 100644 index 78a4921..0000000 --- a/docs/source/labs/Auto.csv +++ /dev/null @@ -1,393 +0,0 @@ -mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name -18.0,8,307.0,130,3504,12.0,70,1,chevrolet chevelle malibu -15.0,8,350.0,165,3693,11.5,70,1,buick skylark 320 -18.0,8,318.0,150,3436,11.0,70,1,plymouth satellite -16.0,8,304.0,150,3433,12.0,70,1,amc rebel sst -17.0,8,302.0,140,3449,10.5,70,1,ford torino -15.0,8,429.0,198,4341,10.0,70,1,ford galaxie 500 -14.0,8,454.0,220,4354,9.0,70,1,chevrolet impala -14.0,8,440.0,215,4312,8.5,70,1,plymouth fury iii -14.0,8,455.0,225,4425,10.0,70,1,pontiac catalina -15.0,8,390.0,190,3850,8.5,70,1,amc ambassador dpl -15.0,8,383.0,170,3563,10.0,70,1,dodge challenger se -14.0,8,340.0,160,3609,8.0,70,1,plymouth 'cuda 340 -15.0,8,400.0,150,3761,9.5,70,1,chevrolet monte carlo -14.0,8,455.0,225,3086,10.0,70,1,buick estate wagon (sw) -24.0,4,113.0,95,2372,15.0,70,3,toyota corona mark ii -22.0,6,198.0,95,2833,15.5,70,1,plymouth duster -18.0,6,199.0,97,2774,15.5,70,1,amc hornet -21.0,6,200.0,85,2587,16.0,70,1,ford maverick -27.0,4,97.0,88,2130,14.5,70,3,datsun pl510 -26.0,4,97.0,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan -25.0,4,110.0,87,2672,17.5,70,2,peugeot 504 -24.0,4,107.0,90,2430,14.5,70,2,audi 100 ls -25.0,4,104.0,95,2375,17.5,70,2,saab 99e -26.0,4,121.0,113,2234,12.5,70,2,bmw 2002 -21.0,6,199.0,90,2648,15.0,70,1,amc gremlin -10.0,8,360.0,215,4615,14.0,70,1,ford f250 -10.0,8,307.0,200,4376,15.0,70,1,chevy c20 -11.0,8,318.0,210,4382,13.5,70,1,dodge d200 -9.0,8,304.0,193,4732,18.5,70,1,hi 1200d -27.0,4,97.0,88,2130,14.5,71,3,datsun pl510 -28.0,4,140.0,90,2264,15.5,71,1,chevrolet vega 2300 -25.0,4,113.0,95,2228,14.0,71,3,toyota corona -19.0,6,232.0,100,2634,13.0,71,1,amc gremlin -16.0,6,225.0,105,3439,15.5,71,1,plymouth satellite custom -17.0,6,250.0,100,3329,15.5,71,1,chevrolet chevelle malibu -19.0,6,250.0,88,3302,15.5,71,1,ford torino 500 -18.0,6,232.0,100,3288,15.5,71,1,amc matador -14.0,8,350.0,165,4209,12.0,71,1,chevrolet impala -14.0,8,400.0,175,4464,11.5,71,1,pontiac catalina brougham -14.0,8,351.0,153,4154,13.5,71,1,ford galaxie 500 -14.0,8,318.0,150,4096,13.0,71,1,plymouth fury iii -12.0,8,383.0,180,4955,11.5,71,1,dodge monaco (sw) -13.0,8,400.0,170,4746,12.0,71,1,ford country squire (sw) -13.0,8,400.0,175,5140,12.0,71,1,pontiac safari (sw) -18.0,6,258.0,110,2962,13.5,71,1,amc hornet sportabout (sw) -22.0,4,140.0,72,2408,19.0,71,1,chevrolet vega (sw) -19.0,6,250.0,100,3282,15.0,71,1,pontiac firebird -18.0,6,250.0,88,3139,14.5,71,1,ford mustang -23.0,4,122.0,86,2220,14.0,71,1,mercury capri 2000 -28.0,4,116.0,90,2123,14.0,71,2,opel 1900 -30.0,4,79.0,70,2074,19.5,71,2,peugeot 304 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200-sx -20.3,5,131.0,103,2830,15.9,78,2,audi 5000 -17.0,6,163.0,125,3140,13.6,78,2,volvo 264gl -21.6,4,121.0,115,2795,15.7,78,2,saab 99gle -16.2,6,163.0,133,3410,15.8,78,2,peugeot 604sl -31.5,4,89.0,71,1990,14.9,78,2,volkswagen scirocco -29.5,4,98.0,68,2135,16.6,78,3,honda accord lx -21.5,6,231.0,115,3245,15.4,79,1,pontiac lemans v6 -19.8,6,200.0,85,2990,18.2,79,1,mercury zephyr 6 -22.3,4,140.0,88,2890,17.3,79,1,ford fairmont 4 -20.2,6,232.0,90,3265,18.2,79,1,amc concord dl 6 -20.6,6,225.0,110,3360,16.6,79,1,dodge aspen 6 -17.0,8,305.0,130,3840,15.4,79,1,chevrolet caprice classic -17.6,8,302.0,129,3725,13.4,79,1,ford ltd landau -16.5,8,351.0,138,3955,13.2,79,1,mercury grand marquis -18.2,8,318.0,135,3830,15.2,79,1,dodge st. regis -16.9,8,350.0,155,4360,14.9,79,1,buick estate wagon (sw) -15.5,8,351.0,142,4054,14.3,79,1,ford country squire (sw) -19.2,8,267.0,125,3605,15.0,79,1,chevrolet malibu classic (sw) -18.5,8,360.0,150,3940,13.0,79,1,chrysler lebaron town @ country (sw) -31.9,4,89.0,71,1925,14.0,79,2,vw rabbit custom -34.1,4,86.0,65,1975,15.2,79,3,maxda glc deluxe -35.7,4,98.0,80,1915,14.4,79,1,dodge colt hatchback custom -27.4,4,121.0,80,2670,15.0,79,1,amc spirit dl -25.4,5,183.0,77,3530,20.1,79,2,mercedes benz 300d -23.0,8,350.0,125,3900,17.4,79,1,cadillac eldorado -27.2,4,141.0,71,3190,24.8,79,2,peugeot 504 -23.9,8,260.0,90,3420,22.2,79,1,oldsmobile cutlass salon brougham -34.2,4,105.0,70,2200,13.2,79,1,plymouth horizon -34.5,4,105.0,70,2150,14.9,79,1,plymouth horizon tc3 -31.8,4,85.0,65,2020,19.2,79,3,datsun 210 -37.3,4,91.0,69,2130,14.7,79,2,fiat strada custom -28.4,4,151.0,90,2670,16.0,79,1,buick skylark limited -28.8,6,173.0,115,2595,11.3,79,1,chevrolet citation -26.8,6,173.0,115,2700,12.9,79,1,oldsmobile omega brougham -33.5,4,151.0,90,2556,13.2,79,1,pontiac phoenix -41.5,4,98.0,76,2144,14.7,80,2,vw rabbit -38.1,4,89.0,60,1968,18.8,80,3,toyota corolla tercel -32.1,4,98.0,70,2120,15.5,80,1,chevrolet chevette -37.2,4,86.0,65,2019,16.4,80,3,datsun 310 -28.0,4,151.0,90,2678,16.5,80,1,chevrolet citation -26.4,4,140.0,88,2870,18.1,80,1,ford fairmont -24.3,4,151.0,90,3003,20.1,80,1,amc concord -19.1,6,225.0,90,3381,18.7,80,1,dodge aspen -34.3,4,97.0,78,2188,15.8,80,2,audi 4000 -29.8,4,134.0,90,2711,15.5,80,3,toyota corona liftback -31.3,4,120.0,75,2542,17.5,80,3,mazda 626 -37.0,4,119.0,92,2434,15.0,80,3,datsun 510 hatchback -32.2,4,108.0,75,2265,15.2,80,3,toyota corolla -46.6,4,86.0,65,2110,17.9,80,3,mazda glc -27.9,4,156.0,105,2800,14.4,80,1,dodge colt -40.8,4,85.0,65,2110,19.2,80,3,datsun 210 -44.3,4,90.0,48,2085,21.7,80,2,vw rabbit c (diesel) -43.4,4,90.0,48,2335,23.7,80,2,vw dasher (diesel) -36.4,5,121.0,67,2950,19.9,80,2,audi 5000s (diesel) -30.0,4,146.0,67,3250,21.8,80,2,mercedes-benz 240d -44.6,4,91.0,67,1850,13.8,80,3,honda civic 1500 gl -33.8,4,97.0,67,2145,18.0,80,3,subaru dl -29.8,4,89.0,62,1845,15.3,80,2,vokswagen rabbit -32.7,6,168.0,132,2910,11.4,80,3,datsun 280-zx -23.7,3,70.0,100,2420,12.5,80,3,mazda rx-7 gs -35.0,4,122.0,88,2500,15.1,80,2,triumph tr7 coupe -32.4,4,107.0,72,2290,17.0,80,3,honda accord -27.2,4,135.0,84,2490,15.7,81,1,plymouth reliant -26.6,4,151.0,84,2635,16.4,81,1,buick skylark -25.8,4,156.0,92,2620,14.4,81,1,dodge aries wagon (sw) -23.5,6,173.0,110,2725,12.6,81,1,chevrolet citation -30.0,4,135.0,84,2385,12.9,81,1,plymouth reliant -39.1,4,79.0,58,1755,16.9,81,3,toyota starlet -39.0,4,86.0,64,1875,16.4,81,1,plymouth champ -35.1,4,81.0,60,1760,16.1,81,3,honda civic 1300 -32.3,4,97.0,67,2065,17.8,81,3,subaru -37.0,4,85.0,65,1975,19.4,81,3,datsun 210 mpg -37.7,4,89.0,62,2050,17.3,81,3,toyota tercel -34.1,4,91.0,68,1985,16.0,81,3,mazda glc 4 -34.7,4,105.0,63,2215,14.9,81,1,plymouth horizon 4 -34.4,4,98.0,65,2045,16.2,81,1,ford escort 4w -29.9,4,98.0,65,2380,20.7,81,1,ford escort 2h -33.0,4,105.0,74,2190,14.2,81,2,volkswagen jetta -33.7,4,107.0,75,2210,14.4,81,3,honda prelude -32.4,4,108.0,75,2350,16.8,81,3,toyota corolla -32.9,4,119.0,100,2615,14.8,81,3,datsun 200sx -31.6,4,120.0,74,2635,18.3,81,3,mazda 626 -28.1,4,141.0,80,3230,20.4,81,2,peugeot 505s turbo diesel -30.7,6,145.0,76,3160,19.6,81,2,volvo diesel -25.4,6,168.0,116,2900,12.6,81,3,toyota cressida -24.2,6,146.0,120,2930,13.8,81,3,datsun 810 maxima -22.4,6,231.0,110,3415,15.8,81,1,buick century -26.6,8,350.0,105,3725,19.0,81,1,oldsmobile cutlass ls -20.2,6,200.0,88,3060,17.1,81,1,ford granada gl -17.6,6,225.0,85,3465,16.6,81,1,chrysler lebaron salon -28.0,4,112.0,88,2605,19.6,82,1,chevrolet cavalier -27.0,4,112.0,88,2640,18.6,82,1,chevrolet cavalier wagon -34.0,4,112.0,88,2395,18.0,82,1,chevrolet cavalier 2-door -31.0,4,112.0,85,2575,16.2,82,1,pontiac j2000 se hatchback -29.0,4,135.0,84,2525,16.0,82,1,dodge aries se -27.0,4,151.0,90,2735,18.0,82,1,pontiac phoenix -24.0,4,140.0,92,2865,16.4,82,1,ford fairmont futura -36.0,4,105.0,74,1980,15.3,82,2,volkswagen rabbit l -37.0,4,91.0,68,2025,18.2,82,3,mazda glc custom l -31.0,4,91.0,68,1970,17.6,82,3,mazda glc custom -38.0,4,105.0,63,2125,14.7,82,1,plymouth horizon miser -36.0,4,98.0,70,2125,17.3,82,1,mercury lynx l -36.0,4,120.0,88,2160,14.5,82,3,nissan stanza xe -36.0,4,107.0,75,2205,14.5,82,3,honda accord -34.0,4,108.0,70,2245,16.9,82,3,toyota corolla -38.0,4,91.0,67,1965,15.0,82,3,honda civic -32.0,4,91.0,67,1965,15.7,82,3,honda civic (auto) -38.0,4,91.0,67,1995,16.2,82,3,datsun 310 gx -25.0,6,181.0,110,2945,16.4,82,1,buick century limited -38.0,6,262.0,85,3015,17.0,82,1,oldsmobile cutlass ciera (diesel) -26.0,4,156.0,92,2585,14.5,82,1,chrysler lebaron medallion -22.0,6,232.0,112,2835,14.7,82,1,ford granada l -32.0,4,144.0,96,2665,13.9,82,3,toyota celica gt -36.0,4,135.0,84,2370,13.0,82,1,dodge charger 2.2 -27.0,4,151.0,90,2950,17.3,82,1,chevrolet camaro -27.0,4,140.0,86,2790,15.6,82,1,ford mustang gl -44.0,4,97.0,52,2130,24.6,82,2,vw pickup -32.0,4,135.0,84,2295,11.6,82,1,dodge rampage -28.0,4,120.0,79,2625,18.6,82,1,ford ranger -31.0,4,119.0,82,2720,19.4,82,1,chevy s-10 diff --git a/docs/source/labs/Auto.data b/docs/source/labs/Auto.data deleted file mode 100644 index f863a85..0000000 --- a/docs/source/labs/Auto.data +++ /dev/null @@ -1,398 +0,0 @@ -mpg cylinders displacement horsepower weight acceleration year origin name -18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu" -15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320" -18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite" -16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst" -17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino" -15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500" -14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala" -14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii" -14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina" -15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl" -15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se" -14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340" -15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo" -14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)" -24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii" -22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster" -18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet" -21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick" -27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510" -26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan" -25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504" -24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls" -25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e" -26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002" -21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin" -10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250" -10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20" -11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200" -9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d" -27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510" -28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300" -25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona" -25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto" -19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin" -16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom" -17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu" -19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500" -18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador" -14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala" -14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham" -14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500" -14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii" -12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)" -13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)" -13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)" -18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)" -22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)" -19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird" -18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang" -23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000" -28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900" -30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304" -30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b" -31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200" -35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200" -27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111" -26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket" -24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop" -25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop" -23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3" -20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega" -21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout" -13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala" -14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina" -15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii" -14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500" -17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst" -11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis" -13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom" -12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale" -13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal" -19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe" -15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)" -13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)" -13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)" -14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)" -18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)" -22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)" -21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)" -26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)" -22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)" -28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)" -23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)" -28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)" -27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)" -13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350" -14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador" -13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu" -14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino" -15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom" -12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham" -13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" -13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" -14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" -13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" -12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" -13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" -18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" -16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" -18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" -18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" -23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" -26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" -11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" -12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" -13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" -12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" -18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" -20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" -21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" -22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" -18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" -19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" -21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" -26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" -15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" -16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" -29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" -24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" -20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" -19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" -15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" -24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" -20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" -11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" -20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" -21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" -19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" -15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" -31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" -26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" -32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" -25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" -16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" -16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" -18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" -16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" -13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" -14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" -14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" -14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" -29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" -26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" -26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" -31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" -32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710" -28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt" -24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128" -26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc" -24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic" -26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru" -31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9" -19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom" -18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova" -15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch" -15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" -16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" -15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" -16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" -14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" -17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" -16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" -15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" -18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" -21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" -20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" -13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" -29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" -23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" -20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" -23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" -24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" -25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" -24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" -18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" -29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" -19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" -23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" -23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" -22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" -25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" -33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" -28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" -25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" -25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" -26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" -27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" -17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" -16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" -15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" -14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" -22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" -22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" -24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" -22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" -29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" -24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" -29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" -33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" -20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" -18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" -18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" -17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" -29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" -32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" -28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" -26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" -20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" -13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" -19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" -19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" -16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" -16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" -13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" -13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" -13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" -31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" -30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" -36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" -25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" -33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" -17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" -17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" -15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" -15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" -17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" -20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" -19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" -18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" -16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" -15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" -15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" -16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" -29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" -24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" -26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" -25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" -30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" -33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" -30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" -30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" -22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" -21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" -21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" -43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" -36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" -32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" -39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" -36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" -19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" -19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" -20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" -19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" -20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" -20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" -25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" -20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" -19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" -20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" -20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" -18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" -18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" -19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" -17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" -18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" -17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" -30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" -27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" -27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" -30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" -21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" -23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" -23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" -23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" -20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" -17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" -21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" -16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" -31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" -29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" -21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" -19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" -22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" -20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" -20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" -17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" -17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" -16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" -18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" -16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" -15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" -19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" -18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" -31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" -34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" -35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" -27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" -25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" -23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" -27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" -23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" -34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" -34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" -31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" -37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" -28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" -28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" -26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" -33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" -41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" -38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" -32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" -37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" -28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" -26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" -24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" -19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" -34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" -29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" -31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" -37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" -32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" -46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" -27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" -40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" -44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" -43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" -36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" -30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" -44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" -40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" -33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" -29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" -32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" -23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" -35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" -23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" -32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" -27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" -26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" -25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" -23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" -30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" -39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" -39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" -35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" -32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" -37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" -37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" -34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" -34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" -34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" -29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" -33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" -34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" -33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" -32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" -32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" -31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" -28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" -30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" -25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" -24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" -22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" -26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" -20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" -17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" -28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" -27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" -34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" -31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" -29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" -27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" -24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" -36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" -37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" -31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" -38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" -36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" -36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" -36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" -34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" -38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" -32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" -38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" -25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" -38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" -26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" -22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" -32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" -36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" -27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" -27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" -44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" -32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" -28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" -31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/docs/source/labs/Ch10-deeplearning-lab.ipynb b/docs/source/labs/Ch10-deeplearning-lab.ipynb deleted file mode 100644 index 75458eb..0000000 --- a/docs/source/labs/Ch10-deeplearning-lab.ipynb +++ /dev/null @@ -1,9973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fd22c2e6", - "metadata": {}, - "source": [ - "# Deep Learning\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this section we demonstrate how to fit the examples discussed\n", - "in the text. We use the `Python` `torch` package, along with the\n", - "`pytorch_lightning` package which provides utilities to simplify\n", - "fitting and evaluating models. This code can be impressively fast\n", - "with certain special processors, such as Apple’s new M1 chip. The package is well-structured, flexible, and will feel comfortable\n", - "to `Python` users. A good companion is the site\n", - "[pytorch.org/tutorials](https://pytorch.org/tutorials/beginner/basics/intro.html).\n", - "Much of our code is adapted from there, as well as the `pytorch_lightning` documentation. {The precise URLs at the time of writing are and .}\n", - "\n", - "We start with several standard imports that we have seen in other contexts." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "aeb913e2", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np, pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "from sklearn.linear_model import \\\n", - " (LinearRegression,\n", - " LogisticRegression,\n", - " Lasso)\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import KFold\n", - "from sklearn.pipeline import Pipeline\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec as MS\n", - "from sklearn.model_selection import \\\n", - " (train_test_split,\n", - " GridSearchCV)" - ] - }, - { - "cell_type": "markdown", - "id": "36a4ac8d", - "metadata": {}, - "source": [ - "### Torch-Specific Imports\n", - "There are a number of imports for `torch`. (These are not\n", - "included with `ISLP`, so must be installed separately.)\n", - "First we import the main library\n", - "and essential tools used to specify sequentially-structured networks." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3a3e91e7", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torch import nn\n", - "from torch.optim import RMSprop\n", - "from torch.utils.data import TensorDataset" - ] - }, - { - "cell_type": "markdown", - "id": "6e89c676", - "metadata": {}, - "source": [ - "There are several other helper packages for `torch`. For instance,\n", - "the `torchmetrics` package has utilities to compute\n", - "various metrics to evaluate performance when fitting\n", - "a model. The `torchinfo` package provides a useful\n", - "summary of the layers of a model. We use the `read_image()`\n", - "function when loading test images in Section 10.9.4." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3f4fdb8c", - "metadata": {}, - "outputs": [], - "source": [ - "from torchmetrics import (MeanAbsoluteError,\n", - " R2Score)\n", - "from torchinfo import summary\n", - "from torchvision.io import read_image" - ] - }, - { - "cell_type": "markdown", - "id": "b7b12e5c", - "metadata": {}, - "source": [ - "The package `pytorch_lightning` is a somewhat higher-level\n", - "interface to `torch` that simplifies the specification and\n", - "fitting of\n", - "models by reducing the amount of boilerplate code needed\n", - "(compared to using `torch` alone)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71cc6f03", - "metadata": {}, - "outputs": [], - "source": [ - "from pytorch_lightning import Trainer\n", - "from pytorch_lightning.loggers import CSVLogger" - ] - }, - { - "cell_type": "markdown", - "id": "8394fc0a", - "metadata": {}, - "source": [ - "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n", - "where possible." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fd1290f1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 0\n" - ] - } - ], - "source": [ - "from pytorch_lightning.utilities.seed import seed_everything\n", - "seed_everything(0, workers=True)\n", - "torch.use_deterministic_algorithms(True, warn_only=True)" - ] - }, - { - "cell_type": "markdown", - "id": "f29a6c10", - "metadata": {}, - "source": [ - "We will use several datasets shipped with `torchvision` for our\n", - "examples: a pretrained network for image classification,\n", - "as well as some transforms used for preprocessing." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c85308d1", - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.datasets import MNIST, CIFAR100\n", - "from torchvision.models import (resnet50,\n", - " ResNet50_Weights)\n", - "from torchvision.transforms import (Resize,\n", - " Normalize,\n", - " CenterCrop,\n", - " ToTensor)" - ] - }, - { - "cell_type": "markdown", - "id": "6ca24f46", - "metadata": {}, - "source": [ - "We have provided a few utilities in `ISLP` specifically for this lab.\n", - "The `SimpleDataModule` and `SimpleModule` are simple\n", - "versions of objects used in `pytorch_lightning`, the\n", - "high-level module for fitting `torch` models. Although more advanced\n", - "uses such as computing on graphical processing units (GPUs) and parallel data processing\n", - "are possible in this module, we will not be focusing much on these\n", - "in this lab. The `ErrorTracker` handles\n", - "collections of targets and predictions over each mini-batch\n", - "in the validation or test stage, allowing computation\n", - "of the metric over the entire validation or test data set." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4de9f03c", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.torch import (SimpleDataModule,\n", - " SimpleModule,\n", - " ErrorTracker,\n", - " rec_num_workers)" - ] - }, - { - "cell_type": "markdown", - "id": "d81a775a", - "metadata": {}, - "source": [ - "In addition we have included some helper\n", - "functions to load the\n", - "`IMDb` database, as well as a lookup that maps integers\n", - "to particular keys in the database. We’ve included\n", - "a slightly modified copy of the preprocessed\n", - "`IMDb` data from `keras`, a separate package\n", - "for fitting deep learning models. This saves us significant\n", - "preprocessing and allows us to focus on specifying and fitting\n", - "the models themselves." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4ef95fe3", - "metadata": {}, - "outputs": [], - "source": [ - "from ISLP.torch.imdb import (load_lookup,\n", - " load_tensor,\n", - " load_sparse,\n", - " load_sequential)" - ] - }, - { - "cell_type": "markdown", - "id": "d8d760ad", - "metadata": {}, - "source": [ - "Finally, we introduce some utility imports not directly related to\n", - "`torch`.\n", - "The `glob()` function from the `glob` module is used\n", - "to find all files matching wildcard characters, which we will use\n", - "in our example applying the `ResNet50` model\n", - "to some of our own images.\n", - "The `json` module will be used to load\n", - "a JSON file for looking up classes to identify the labels of the\n", - "pictures in the `ResNet50` example." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "493821a9", - "metadata": {}, - "outputs": [], - "source": [ - "from glob import glob\n", - "import json" - ] - }, - { - "cell_type": "markdown", - "id": "c415e4e5", - "metadata": {}, - "source": [ - "## Single Layer Network on Hitters Data\n", - "We start by fitting the models in Section 10.6 on the `Hitters` data." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a427e224", - "metadata": {}, - "outputs": [], - "source": [ - "Hitters = load_data('Hitters').dropna()\n", - "n = Hitters.shape[0]" - ] - }, - { - "cell_type": "markdown", - "id": "f1ca2542", - "metadata": {}, - "source": [ - " We will fit two linear models (least squares and lasso) and compare their performance\n", - "to that of a neural network. For this comparison we will use mean absolute error on a validation dataset.\n", - "\\begin{equation*}\n", - "\\begin{split}\n", - "\\mbox{MAE}(y,\\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n |y_i-\\hat{y}_i|.\n", - "\\end{split}\n", - "\\end{equation*}\n", - "We set up the model matrix and the response." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "feac4b15", - "metadata": {}, - "outputs": [], - "source": [ - "model = MS(Hitters.columns.drop('Salary'), intercept=False)\n", - "X = model.fit_transform(Hitters).to_numpy()\n", - "Y = Hitters['Salary'].to_numpy()" - ] - }, - { - "cell_type": "markdown", - "id": "ad0d90b5", - "metadata": {}, - "source": [ - "The `to_numpy()` method above converts `pandas`\n", - "data frames or series to `numpy` arrays.\n", - "We do this because we will need to use `sklearn` to fit the lasso model,\n", - "and it requires this conversion. \n", - "We also use a linear regression method from `sklearn`, rather than the method\n", - "in Chapter~3 from `statsmodels`, to facilitate the comparisons." - ] - }, - { - "cell_type": "markdown", - "id": "35acacc2", - "metadata": {}, - "source": [ - "We now split the data into test and training, fixing the random\n", - "state used by `sklearn` to do the split." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6a75a640", - "metadata": {}, - "outputs": [], - "source": [ - "(X_train, \n", - " X_test,\n", - " Y_train,\n", - " Y_test) = train_test_split(X,\n", - " Y,\n", - " test_size=1/3,\n", - " random_state=1)" - ] - }, - { - "cell_type": "markdown", - "id": "edc24bfb", - "metadata": {}, - "source": [ - "### Linear Models\n", - "We fit the linear model and evaluate the test error directly." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "32d8de64", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "259.71528833146243" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hit_lm = LinearRegression().fit(X_train, Y_train)\n", - "Yhat_test = hit_lm.predict(X_test)\n", - "np.abs(Yhat_test - Y_test).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "d60340ef", - "metadata": {}, - "source": [ - "Next we fit the lasso using `sklearn`. We are using\n", - "mean absolute error to select and evaluate a model, rather than mean squared error.\n", - "The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work, we create a cross-validation grid and perform the cross-validation directly. \n", - "\n", - "We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,\n", - "and then fit the lasso without further normalization." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "46786db6", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler(with_mean=True, with_std=True)\n", - "lasso = Lasso(warm_start=True, max_iter=30000)\n", - "standard_lasso = Pipeline(steps=[('scaler', scaler),\n", - " ('lasso', lasso)])" - ] - }, - { - "cell_type": "markdown", - "id": "e7ff854a", - "metadata": {}, - "source": [ - "We need to create a grid of values for $\\lambda$. As is common practice, \n", - "we choose a grid of 100 values of $\\lambda$, uniform on the log scale from `lam_max` down to `0.01*lam_max`. Here `lam_max` is the smallest value of\n", - "$\\lambda$ with an all-zero solution. This value equals the largest absolute inner-product between any predictor and the (centered) response. {The derivation of this result is beyond the scope of this book.}" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2b55b38b", - "metadata": {}, - "outputs": [], - "source": [ - "X_s = scaler.fit_transform(X_train)\n", - "n = X_s.shape[0]\n", - "lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n\n", - "param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), 100))\n", - " * lam_max}" - ] - }, - { - "cell_type": "markdown", - "id": "977dea00", - "metadata": {}, - "source": [ - "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n", - "We now perform cross-validation using this sequence of $\\lambda$ values." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cc44c559", - "metadata": {}, - "outputs": [], - "source": [ - "cv = KFold(10,\n", - " shuffle=True,\n", - " random_state=1)\n", - "grid = GridSearchCV(lasso,\n", - " param_grid,\n", - " cv=cv,\n", - " scoring='neg_mean_absolute_error')\n", - "grid.fit(X_train, Y_train);" - ] - }, - { - "cell_type": "markdown", - "id": "fca88ebb", - "metadata": {}, - "source": [ - "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n", - "performance on `X_test` and `Y_test`, which were not used in\n", - "cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1bd1fd13", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "257.2382010799497" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trained_lasso = grid.best_estimator_\n", - "Yhat_test = trained_lasso.predict(X_test)\n", - "np.fabs(Yhat_test - Y_test).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "f19cd1f2", - "metadata": {}, - "source": [ - "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n", - "\n", - "### Specifying a Network: Classes and Inheritance\n", - "To fit the neural network, we first set up a model structure\n", - "that describes the network.\n", - "Doing so requires us to define new classes specific to the model we wish to fit.\n", - "Typically this is done in `pytorch` by sub-classing a generic\n", - "representation of a network, which is the approach we take here.\n", - "Although this example is simple, we will go through the steps in some detail, since it will serve us well\n", - "for the more complex examples to follow." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4b2ccc5a", - "metadata": {}, - "outputs": [], - "source": [ - "class HittersModel(nn.Module):\n", - "\n", - " def __init__(self, input_size):\n", - " super(HittersModel, self).__init__()\n", - " self.flatten = nn.Flatten()\n", - " self.sequential = nn.Sequential(\n", - " nn.Linear(input_size, 50),\n", - " nn.ReLU(),\n", - " nn.Dropout(0.4),\n", - " nn.Linear(50, 1))\n", - "\n", - " def forward(self, x):\n", - " x = self.flatten(x)\n", - " return torch.flatten(self.sequential(x))" - ] - }, - { - "cell_type": "markdown", - "id": "5da217b2", - "metadata": {}, - "source": [ - "The `class` statement identifies the code chunk as a\n", - "declaration for a class `HittersModel`\n", - "that inherits from the base class `nn.Module`. This base\n", - "class is ubiquitous in `torch` and represents the\n", - "mappings in the neural networks.\n", - "\n", - "Indented beneath the `class` statement are the methods of this class:\n", - "in this case `__init__` and `forward`. The `__init__` method is\n", - "called when an instance of the class is created as in the cell\n", - "below. In the methods, `self` always refers to an instance of the\n", - "class. In the `__init__` method, we have attached two objects to\n", - "`self` as attributes: `flatten` and `sequential`. These are used in\n", - "the `forward` method to describe the map that this module implements.\n", - "\n", - "There is one additional line in the `__init__` method, which\n", - "is a call to\n", - "`super()`. This function allows subclasses (i.e. `HittersModel`)\n", - "to access methods of the class they inherit from. For example,\n", - "the class `nn.Module` has its own `__init__` method, which is different from\n", - "the `HittersModel.__init__()` method we’ve written above.\n", - "Using `super()` allows us to call the method of the base class. For\n", - "`torch` models, we will always be making this `super()` call as it is necessary\n", - "for the model to be properly interpreted by `torch`.\n", - "\n", - "The object `nn.Module` has more methods than simply `__init__` and `forward`. These\n", - "methods are directly accessible to `HittersModel` instances because of this inheritance.\n", - "One such method we will see shortly is the `eval()` method, used\n", - "to disable dropout for when we want to evaluate the model on test data." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "108ad1f1", - "metadata": {}, - "outputs": [], - "source": [ - "hit_model = HittersModel(X.shape[1])" - ] - }, - { - "cell_type": "markdown", - "id": "46d750dd", - "metadata": {}, - "source": [ - "The object `self.sequential` is a composition of four maps. The\n", - "first maps the 19 features of `Hitters` to 50 dimensions, introducing $50\\times 19+50$ parameters\n", - "for the weights and *intercept* of the map (often called the *bias*). This layer\n", - "is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a\n", - "linear map down to 1 dimension, again with a bias. The total number of\n", - "trainable parameters is therefore $50\\times 19+50+50+1=1051$." - ] - }, - { - "cell_type": "markdown", - "id": "e0c250c5", - "metadata": {}, - "source": [ - "The package `torchinfo` provides a `summary()` function that neatly summarizes\n", - "this information. We specify the size of the input and see the size\n", - "of each tensor as it passes through layers of the network." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2f20059e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "HittersModel [175, 19] [175] --\n", - "├─Flatten: 1-1 [175, 19] [175, 19] --\n", - "├─Sequential: 1-2 [175, 19] [175, 1] --\n", - "│ └─Linear: 2-1 [175, 19] [175, 50] 1,000\n", - "│ └─ReLU: 2-2 [175, 50] [175, 50] --\n", - "│ └─Dropout: 2-3 [175, 50] [175, 50] --\n", - "│ └─Linear: 2-4 [175, 50] [175, 1] 51\n", - "===================================================================================================================\n", - "Total params: 1,051\n", - "Trainable params: 1,051\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 0.18\n", - "===================================================================================================================\n", - "Input size (MB): 0.01\n", - "Forward/backward pass size (MB): 0.07\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.09\n", - "===================================================================================================================" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary(hit_model, \n", - " input_size=X_train.shape,\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "05a0eb81", - "metadata": {}, - "source": [ - "We have truncated the end of the output slightly, here and in subsequent uses.\n", - "\n", - "We now need to transform our training data into a form accessible to `torch`.\n", - "The basic\n", - "datatype in `torch` is a `tensor`, which is very similar\n", - "to an `ndarray` from early chapters.\n", - "We also note here that `torch` typically\n", - "works with 32-bit (*single precision*)\n", - "rather than 64-bit (*double precision*) floating point numbers.\n", - "We therefore convert our data to `np.float32` before\n", - "forming the tensor.\n", - "The $X$ and $Y$ tensors are then arranged into a `Dataset`\n", - "recognized by `torch`\n", - "using `TensorDataset()`." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6ca3030d", - "metadata": {}, - "outputs": [], - "source": [ - "X_train_t = torch.tensor(X_train.astype(np.float32))\n", - "Y_train_t = torch.tensor(Y_train.astype(np.float32))\n", - "hit_train = TensorDataset(X_train_t, Y_train_t)" - ] - }, - { - "cell_type": "markdown", - "id": "7924f53d", - "metadata": {}, - "source": [ - "We do the same for the test data." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "86723b3e", - "metadata": {}, - "outputs": [], - "source": [ - "X_test_t = torch.tensor(X_test.astype(np.float32))\n", - "Y_test_t = torch.tensor(Y_test.astype(np.float32))\n", - "hit_test = TensorDataset(X_test_t, Y_test_t)" - ] - }, - { - "cell_type": "markdown", - "id": "0d055846", - "metadata": {}, - "source": [ - "Finally, this dataset is passed to a `DataLoader()` which ultimately\n", - "passes data into our network. While this may seem\n", - "like a lot of overhead, this structure is helpful for more\n", - "complex tasks where data may live on different machines,\n", - "or where data must be passed to a GPU.\n", - "We provide a helper function `SimpleDataModule()` in `ISLP` to make this task easier for\n", - "standard usage.\n", - "One of its arguments is `num_workers`, which indicates\n", - "how many processes we will use\n", - "for loading the data. For small\n", - "data like `Hitters` this will have little effect, but\n", - "it does provide an advantage for the `MNIST` and `CIFAR100` examples below.\n", - "The `torch` package will inspect the process running and determine a\n", - "maximum number of workers. {This depends on the computing hardware and the number of cores available.} We’ve included a function\n", - "`rec_num_workers()` to compute this so we know how many\n", - "workers might be reasonable (here the max was 16)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "999279fd", - "metadata": {}, - "outputs": [], - "source": [ - "max_num_workers = rec_num_workers()" - ] - }, - { - "cell_type": "markdown", - "id": "9fe60b31", - "metadata": {}, - "source": [ - "The general training setup in `pytorch_lightning` involves\n", - "training, validation and test data. These are each\n", - "represented by different data loaders. During each epoch,\n", - "we run a training step to learn the model and a validation\n", - "step to track the error. The test data is typically\n", - "used at the end of training to evaluate the model.\n", - "\n", - "In this case, as we had split only into test and training,\n", - "we’ll use the test data as validation data with the\n", - "argument `validation=hit_test`. The\n", - "`validation` argument can be a float between 0 and 1, an\n", - "integer, or a\n", - "`Dataset`. If a float (respectively, integer), it is interpreted\n", - "as a percentage (respectively number) of the *training* observations to be used for validation.\n", - "If it is a `Dataset`, it is passed directly to a data loader." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "7bd9cc5c", - "metadata": {}, - "outputs": [], - "source": [ - "hit_dm = SimpleDataModule(hit_train,\n", - " hit_test,\n", - " batch_size=32,\n", - " num_workers=min(4, max_num_workers),\n", - " validation=hit_test)" - ] - }, - { - "cell_type": "markdown", - "id": "9be1f578", - "metadata": {}, - "source": [ - "Next we must provide a `pytorch_lightning` module that controls\n", - "the steps performed during the training process. We provide methods for our\n", - "`SimpleModule()` that simply record the value\n", - "of the loss function and any additional\n", - "metrics at the end of each epoch. These operations\n", - "are controlled by the methods `SimpleModule.[training/test/validation]_step()`, though\n", - "we will not be modifying these in our examples." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5be2f822", - "metadata": {}, - "outputs": [], - "source": [ - "hit_module = SimpleModule.regression(hit_model,\n", - " metrics={'mae':MeanAbsoluteError()})" - ] - }, - { - "cell_type": "markdown", - "id": "78e707d8", - "metadata": {}, - "source": [ - " By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in\n", - "(10.23).\n", - "We have also asked for mean absolute error to be tracked as well\n", - "in the metrics that are logged.\n", - "\n", - "We log our results via `CSVLogger()`, which in this case stores the results in a CSV file within a directory `logs/hitters`. After the fitting is complete, this allows us to load the\n", - "results as a `pd.DataFrame()` and visualize them below. There are\n", - "several ways to log the results within `pytorch_lightning`, though\n", - "we will not cover those here in detail." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "87334d33", - "metadata": {}, - "outputs": [], - "source": [ - "hit_logger = CSVLogger('logs', name='hitters')" - ] - }, - { - "cell_type": "markdown", - "id": "07663639", - "metadata": {}, - "source": [ - "Finally we are ready to train our model and log the results. We\n", - "use the `Trainer()` object from `pytorch_lightning`\n", - "to do this work. The argument `datamodule=hit_dm` tells the trainer\n", - "how training/validation/test logs are produced,\n", - "while the first argument `hit_module`\n", - "specifies the network architecture\n", - "as well as the training/validation/test steps.\n", - "The `callbacks` argument allows for\n", - "several tasks to be carried out at various\n", - "points while training a model. Here\n", - "our `ErrorTracker()` callback will enable\n", - "us to compute validation error while training\n", - "and, finally, the test error.\n", - "We now fit the model for 50 epochs." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "1a474999", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "---------------------------------------\n", - "0 | model | HittersModel | 1.1 K \n", - "1 | loss | MSELoss | 0 \n", - "---------------------------------------\n", - "1.1 K Trainable params\n", - "0 Non-trainable params\n", - "1.1 K Total params\n", - "0.004 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7f1f820ee6fa4d81a3736b68611f3bac", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - 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"model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - } - ], - "source": [ - "hit_trainer = Trainer(deterministic=True,\n", - " max_epochs=50,\n", - " log_every_n_steps=5,\n", - " logger=hit_logger,\n", - " callbacks=[ErrorTracker()])\n", - "hit_trainer.fit(hit_module, datamodule=hit_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "5e154e47", - "metadata": {}, - "source": [ - "At each step of SGD, the algorithm randomly selects 32 training observations for\n", - "the computation of the gradient. Recall from Section 10.7\n", - "that an epoch amounts to the number of SGD steps required to process $n$\n", - "observations. Since the training set has\n", - "$n=175$, and we specified a `batch_size` of 32 in the construction of `hit_dm`, an epoch is $175/32=5.5$ SGD steps.\n", - "\n", - "After having fit the model, we can evaluate performance on our test\n", - "data using the `test()` method of our trainer." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e3b24643", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ce9e57deabef4f55aba22efb507872f1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 103304.8515625\n", - " test_mae 224.26962280273438\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 103304.8515625, 'test_mae': 224.26962280273438}]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hit_trainer.test(hit_module, datamodule=hit_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "0220713b", - "metadata": {}, - "source": [ - "The results of the fit have been logged into a CSV file. We can find the\n", - "results specific to this run in the `experiment.metrics_file_path`\n", - "attribute of our logger. Note that each time the model is fit, the logger will output\n", - "results into a new subdirectory of our directory `logs/hitters`.\n", - "\n", - "We now create a plot of the MAE (mean absolute error) as a function of\n", - "the number of epochs.\n", - "First we retrieve the logged summaries." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "f9b266e7", - "metadata": {}, - "outputs": [], - "source": [ - "hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path)" - ] - }, - { - "cell_type": "markdown", - "id": "b7ff26d7", - "metadata": {}, - "source": [ - "Since we will produce similar plots in later examples, we write a\n", - "simple generic function to produce this plot." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "c5752a0b", - "metadata": {}, - "outputs": [], - "source": [ - "def summary_plot(results,\n", - " ax,\n", - " col='loss',\n", - " valid_legend='Validation',\n", - " training_legend='Training',\n", - " ylabel='Loss',\n", - " fontsize=20):\n", - " for (column,\n", - " color,\n", - " label) in zip([f'train_{col}_epoch',\n", - " f'valid_{col}'],\n", - " ['black',\n", - " 'red'],\n", - " [training_legend,\n", - " valid_legend]):\n", - " results.plot(x='epoch',\n", - " y=column,\n", - " label=label,\n", - " marker='o',\n", - " color=color,\n", - " ax=ax)\n", - " ax.set_xlabel('Epoch')\n", - " ax.set_ylabel(ylabel)\n", - " return ax" - ] - }, - { - "cell_type": "markdown", - "id": "874c2611", - "metadata": {}, - "source": [ - "We now set up our axes, and use our function to produce the MAE plot." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "ff0c9fa0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = subplots(1, 1, figsize=(6, 6))\n", - "ax = summary_plot(hit_results,\n", - " ax,\n", - " col='mae',\n", - " ylabel='MAE',\n", - " valid_legend='Validation (=Test)')\n", - "ax.set_ylim([0, 400])\n", - "ax.set_xticks(np.linspace(0, 50, 11).astype(int));" - ] - }, - { - "cell_type": "markdown", - "id": "212c64a3", - "metadata": {}, - "source": [ - "We can predict directly from the final model, and\n", - "evaluate its performance on the test data.\n", - "Before fitting, we call the `eval()` method\n", - "of `hit_model`.\n", - "This tells\n", - "`torch` to effectively consider this model to be fitted, so that\n", - "we can use it to predict on new data. For our model here,\n", - "the biggest change is that the dropout layers will\n", - "be turned off, i.e. no weights will be randomly\n", - "dropped in predicting on new data." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1ee2b61b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(224.2696, grad_fn=)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hit_model.eval() \n", - "preds = hit_module(X_test_t)\n", - "torch.abs(Y_test_t - preds).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "64fa7609", - "metadata": {}, - "source": [ - "### Cleanup\n", - "In setting up our data module, we had initiated\n", - "several worker processes that will remain running.\n", - "We delete all references to the torch objects to ensure these processes\n", - "will be killed." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "0ca069a3", - "metadata": {}, - "outputs": [], - "source": [ - "del(Hitters,\n", - " hit_model, hit_dm,\n", - " hit_logger,\n", - " hit_test, hit_train,\n", - " X, Y,\n", - " X_test, X_train,\n", - " Y_test, Y_train,\n", - " X_test_t, Y_test_t,\n", - " hit_trainer, hit_module)" - ] - }, - { - "cell_type": "markdown", - "id": "ac321b7f", - "metadata": {}, - "source": [ - "## Multilayer Network on the MNIST Digit Data\n", - "The `torchvision` package comes with a number of example datasets,\n", - "including the `MNIST` digit data. Our first step is to retrieve\n", - "the training and test data sets; the `MNIST()` function within\n", - "`torchvision.datasets` is provided for this purpose. The\n", - "data will be downloaded the first time this function is executed, and stored in the directory `data/MNIST`." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "af6a0a33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset MNIST\n", - " Number of datapoints: 60000\n", - " Root location: data\n", - " Split: Train\n", - " StandardTransform\n", - "Transform: ToTensor()" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(mnist_train, \n", - " mnist_test) = [MNIST(root='data',\n", - " train=train,\n", - " download=True,\n", - " transform=ToTensor())\n", - " for train in [True, False]]\n", - "mnist_train" - ] - }, - { - "cell_type": "markdown", - "id": "6c8aa040", - "metadata": {}, - "source": [ - "There are 60,000 images in the training data and 10,000 in the test\n", - "data. The images are $28\\times 28$, and stored as a matrix of pixels. We\n", - "need to transform each one into a vector.\n", - "\n", - "Neural networks are somewhat sensitive to the scale of the inputs, much as ridge and\n", - "lasso regularization are affected by scaling. Here the inputs are eight-bit\n", - "grayscale values between 0 and 255, so we rescale to the unit\n", - "interval. {Note: eight bits means $2^8$, which equals 256. Since the convention\n", - "is to start at $0$, the possible values range from $0$ to $255$.}\n", - "This transformation, along with some reordering\n", - "of the axes, is performed by the `ToTensor()` transform\n", - "from the `torchvision.transforms` package.\n", - "\n", - "As in our `Hitters` example, we form a data module\n", - "from the training and test datasets, setting aside 20%\n", - "of the training images for validation." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0df8a0c8", - "metadata": {}, - "outputs": [], - "source": [ - "mnist_dm = SimpleDataModule(mnist_train,\n", - " mnist_test,\n", - " validation=0.2,\n", - " num_workers=max_num_workers,\n", - " batch_size=256)" - ] - }, - { - "cell_type": "markdown", - "id": "3dc50b18", - "metadata": {}, - "source": [ - "Let’s take a look at the data that will get fed into our network. We loop through the first few\n", - "chunks of the test dataset, breaking after 2 batches:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "0c7dd6ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n", - "X: torch.Size([256, 1, 28, 28])\n", - "Y: torch.Size([256])\n" - ] - } - ], - "source": [ - "for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()):\n", - " print('X: ', X_.shape)\n", - " print('Y: ', Y_.shape)\n", - " if idx >= 1:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "cc80f21e", - "metadata": {}, - "source": [ - "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n", - "Here the `1` indicates a single channel (greyscale). For RGB images such as `CIFAR100` below,\n", - "we will see that the `1` in the size will be replaced by `3` for the three RGB channels.\n", - "\n", - "Now we are ready to specify our neural network." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "63e48c70", - "metadata": {}, - "outputs": [], - "source": [ - "class MNISTModel(nn.Module):\n", - " def __init__(self):\n", - " super(MNISTModel, self).__init__()\n", - " self.layer1 = nn.Sequential(\n", - " nn.Flatten(),\n", - " nn.Linear(28*28, 256),\n", - " nn.ReLU(),\n", - " nn.Dropout(0.4))\n", - " self.layer2 = nn.Sequential(\n", - " nn.Linear(256, 128),\n", - " nn.ReLU(),\n", - " nn.Dropout(0.3))\n", - " self._forward = nn.Sequential(\n", - " self.layer1,\n", - " self.layer2,\n", - " nn.Linear(128, 10))\n", - " def forward(self, x):\n", - " return self._forward(x)" - ] - }, - { - "cell_type": "markdown", - "id": "d5a5ffef", - "metadata": {}, - "source": [ - "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n", - "256 dimensions where we apply a ReLU activation with 40% dropout.\n", - "A second layer maps the first layer’s output down to\n", - "128 dimensions, applying a ReLU activation with 30% dropout. Finally,\n", - "the 128 dimensions are mapped down to 10, the number of classes in the\n", - "`MNIST` data." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "92405826", - "metadata": {}, - "outputs": [], - "source": [ - "mnist_model = MNISTModel()" - ] - }, - { - "cell_type": "markdown", - "id": "b16bd23a", - "metadata": {}, - "source": [ - "We can check that the model produces output of expected size based\n", - "on our existing batch `X_` above." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "2c8f7a48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([256, 10])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mnist_model(X_).size()" - ] - }, - { - "cell_type": "markdown", - "id": "da0de828", - "metadata": {}, - "source": [ - "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n", - "a tensor of correct shape. In this case, we pass through the final\n", - "batched `X_` from above." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "9f502df8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "MNISTModel [256, 1, 28, 28] [256, 10] --\n", - "├─Sequential: 1-1 [256, 1, 28, 28] [256, 10] --\n", - "│ └─Sequential: 2-1 [256, 1, 28, 28] [256, 256] --\n", - "│ │ └─Flatten: 3-1 [256, 1, 28, 28] [256, 784] --\n", - "│ │ └─Linear: 3-2 [256, 784] [256, 256] 200,960\n", - "│ │ └─ReLU: 3-3 [256, 256] [256, 256] --\n", - "│ │ └─Dropout: 3-4 [256, 256] [256, 256] --\n", - "│ └─Sequential: 2-2 [256, 256] [256, 128] --\n", - "│ │ └─Linear: 3-5 [256, 256] [256, 128] 32,896\n", - "│ │ └─ReLU: 3-6 [256, 128] [256, 128] --\n", - "│ │ └─Dropout: 3-7 [256, 128] [256, 128] --\n", - "│ └─Linear: 2-3 [256, 128] [256, 10] 1,290\n", - "===================================================================================================================\n", - "Total params: 235,146\n", - "Trainable params: 235,146\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 60.20\n", - "===================================================================================================================\n", - "Input size (MB): 0.80\n", - "Forward/backward pass size (MB): 0.81\n", - "Params size (MB): 0.94\n", - "Estimated Total Size (MB): 2.55\n", - "===================================================================================================================" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary(mnist_model,\n", - " input_data=X_,\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "f98b70bb", - "metadata": {}, - "source": [ - "Having set up both the model and the data module, fitting this model is\n", - "now almost identical to the `Hitters` example. In contrast to our regression model, here we will use the\n", - "`SimpleModule.classification()` method which\n", - "uses the cross-entropy loss function instead of mean squared error." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "d1a7590f", - "metadata": {}, - "outputs": [], - "source": [ - "mnist_module = SimpleModule.classification(mnist_model)\n", - "mnist_logger = CSVLogger('logs', name='MNIST')" - ] - }, - { - "cell_type": "markdown", - "id": "ca665912", - "metadata": {}, - "source": [ - "Now we are ready to go. The final step is to supply training data, and fit the model." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "e2fe6de3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNISTModel | 235 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "235 K Trainable params\n", - "0 Non-trainable params\n", - "235 K Total params\n", - "0.941 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82f9178e94874e7e9cbb3b27cc1c351f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - 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"Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], - "source": [ - "mnist_trainer = Trainer(deterministic=True,\n", - " max_epochs=30,\n", - " logger=mnist_logger,\n", - " callbacks=[ErrorTracker()])\n", - "mnist_trainer.fit(mnist_module,\n", - " datamodule=mnist_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "6b4a4e0c", - "metadata": {}, - "source": [ - "We have suppressed the output here, which is a progress report on the\n", - "fitting of the model, grouped by epoch. This is very useful, since on\n", - "large datasets fitting can take time. Fitting this model took 245\n", - "seconds on a MacBook Pro with an Apple M1 Pro chip with 10 cores and 16 GB of RAM.\n", - "Here we specified a\n", - "validation split of 20%, so training is actually performed on\n", - "80% of the 60,000 observations in the training set. This is an\n", - "alternative to actually supplying validation data, like we did for the `Hitters` data.\n", - "SGD uses batches\n", - "of 256 observations in computing the gradient, and doing the\n", - "arithmetic, we see that an epoch corresponds to 188 gradient steps." - ] - }, - { - "cell_type": "markdown", - "id": "25528d72", - "metadata": {}, - "source": [ - "`SimpleModule.classification()` includes\n", - "an accuracy metric by default. Other\n", - "classification metrics can be added from `torchmetrics`.\n", - "We will use our `summary_plot()` function to display \n", - "accuracy across epochs." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "73755987", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n", - "fig, ax = subplots(1, 1, figsize=(6, 6))\n", - "summary_plot(mnist_results,\n", - " ax,\n", - " col='accuracy',\n", - " ylabel='Accuracy')\n", - "ax.set_ylim([0.5, 1])\n", - "ax.set_ylabel('Accuracy')\n", - "ax.set_xticks(np.linspace(0, 30, 7).astype(int));" - ] - }, - { - "cell_type": "markdown", - "id": "ba63a8da", - "metadata": {}, - "source": [ - "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n", - "97% accuracy on the test data." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "f5269c40", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b584fa2b09df4c20939181c016e85bc5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9681000113487244\n", - " test_loss 0.14706705510616302\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.14706705510616302, 'test_accuracy': 0.9681000113487244}]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mnist_trainer.test(mnist_module,\n", - " datamodule=mnist_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "bca75042", - "metadata": {}, - "source": [ - "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n", - "regression. For LDA we refer the reader to Section 4.7.3.\n", - "Although we could use the `sklearn` function `LogisticRegression()` to fit \n", - "multiclass logistic regression, we are set up here to fit such a model\n", - "with `torch`.\n", - "We just have an input layer and an output layer, and omit the hidden layers!" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "97a0b304", - "metadata": {}, - "outputs": [], - "source": [ - "class MNIST_MLR(nn.Module):\n", - " def __init__(self):\n", - " super(MNIST_MLR, self).__init__()\n", - " self.linear = nn.Sequential(nn.Flatten(),\n", - " nn.Linear(784, 10))\n", - " def forward(self, x):\n", - " return self.linear(x)\n", - "\n", - "mlr_model = MNIST_MLR()\n", - "mlr_module = SimpleModule.classification(mlr_model)\n", - "mlr_logger = CSVLogger('logs', name='MNIST_MLR')" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "ea685183", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | MNIST_MLR | 7.9 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "7.9 K Trainable params\n", - "0 Non-trainable params\n", - "7.9 K Total params\n", - "0.031 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02fd79458dc34808bb403f8b8475a62d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - 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"`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a9fcb28681b34dc6921cad2630ca648f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e2dd237b55d14ad4a871888e4ecde25d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5d8dc977673646d9ad09d0c5b7e4903a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], - "source": [ - "mlr_trainer = Trainer(deterministic=True,\n", - " max_epochs=30,\n", - " callbacks=[ErrorTracker()])\n", - "mlr_trainer.fit(mlr_module, datamodule=mnist_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "c376e402", - "metadata": {}, - "source": [ - "We fit the model just as before and compute the test results." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c0bd63e3", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "92b4035b1656456d88d929aac0d46d71", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.9240999817848206\n", - " test_loss 0.3186827003955841\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.3186827003955841, 'test_accuracy': 0.9240999817848206}]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlr_trainer.test(mlr_module,\n", - " datamodule=mnist_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "e0e99a86", - "metadata": {}, - "source": [ - "The accuracy is above 90% even for this pretty simple model.\n", - "\n", - "As in the `Hitters` example, we delete some of\n", - "the objects we created above." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "6b0d3daa", - "metadata": {}, - "outputs": [], - "source": [ - "del(mnist_test,\n", - " mnist_train,\n", - " mnist_model,\n", - " mnist_dm,\n", - " mnist_trainer,\n", - " mnist_module,\n", - " mnist_results,\n", - " mlr_model,\n", - " mlr_module,\n", - " mlr_trainer)" - ] - }, - { - "cell_type": "markdown", - "id": "1e370989", - "metadata": {}, - "source": [ - "## Convolutional Neural Networks\n", - "In this section we fit a CNN to the `CIFAR100` data, which is available in the `torchvision`\n", - "package. It is arranged in a similar fashion as the `MNIST` data." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "67517b11", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Files already downloaded and verified\n", - "Files already downloaded and verified\n" - ] - } - ], - "source": [ - "(cifar_train, \n", - " cifar_test) = [CIFAR100(root=\"data\",\n", - " train=train,\n", - " download=True)\n", - " for train in [True, False]]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "ee7b040e", - "metadata": {}, - "outputs": [], - "source": [ - "transform = ToTensor()\n", - "cifar_train_X = torch.stack([transform(x) for x in\n", - " cifar_train.data])\n", - "cifar_test_X = torch.stack([transform(x) for x in\n", - " cifar_test.data])\n", - "cifar_train = TensorDataset(cifar_train_X,\n", - " torch.tensor(cifar_train.targets))\n", - "cifar_test = TensorDataset(cifar_test_X,\n", - " torch.tensor(cifar_test.targets))" - ] - }, - { - "cell_type": "markdown", - "id": "2bba84d8", - "metadata": {}, - "source": [ - "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n", - "each three-color image is represented as a set of three channels, each of which consists of\n", - "$32\\times 32$ eight-bit pixels. We standardize as we did for the\n", - "digits, but keep the array structure. This is accomplished with the `ToTensor()` transform.\n", - "\n", - "Creating the data module is similar to the `MNIST` example." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "bd9e74ad", - "metadata": {}, - "outputs": [], - "source": [ - "cifar_dm = SimpleDataModule(cifar_train,\n", - " cifar_test,\n", - " validation=0.2,\n", - " num_workers=max_num_workers,\n", - " batch_size=128)" - ] - }, - { - "cell_type": "markdown", - "id": "6ee8883b", - "metadata": {}, - "source": [ - "We again look at the shape of typical batches in our data loaders." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "a553b926", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n", - "X: torch.Size([128, 3, 32, 32])\n", - "Y: torch.Size([128])\n" - ] - } - ], - "source": [ - "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n", - " print('X: ', X_.shape)\n", - " print('Y: ', Y_.shape)\n", - " if idx >= 1:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "2b25a138", - "metadata": {}, - "source": [ - "Before we start, we look at some of the training images; similar code produced\n", - "Figure 10.5 on page 445. The example below also illustrates\n", - "that `TensorDataset` objects can be indexed with integers --- we are choosing\n", - "random images from the training data by indexing `cifar_train`. In order to display correctly,\n", - "we must reorder the dimensions by a call to `np.transpose()`." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "94885e68", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = subplots(5, 5, figsize=(10,10))\n", - "rng = np.random.default_rng(4)\n", - "indices = rng.choice(np.arange(len(cifar_train)), 25,\n", - " replace=False).reshape((5,5))\n", - "for i in range(5):\n", - " for j in range(5):\n", - " idx = indices[i,j]\n", - " axes[i,j].imshow(np.transpose(cifar_train[idx][0],\n", - " [1,2,0]),\n", - " interpolation=None)\n", - " axes[i,j].set_xticks([])\n", - " axes[i,j].set_yticks([])" - ] - }, - { - "cell_type": "markdown", - "id": "04fda6b8", - "metadata": {}, - "source": [ - "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n", - "\n", - "We specify a moderately-sized CNN for\n", - "demonstration purposes, similar in structure to Figure 10.8.\n", - "We use several layers, each consisting of convolution, ReLU, and max-pooling steps.\n", - "We first define a module that defines one of these layers. As in our\n", - "previous examples, we overwrite the `__init__()` and `forward()` methods\n", - "of `nn.Module`. This user-defined module can now be used in ways just like\n", - "`nn.Linear()` or `nn.Dropout()`." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "cdc2528a", - "metadata": {}, - "outputs": [], - "source": [ - "class BuildingBlock(nn.Module):\n", - "\n", - " def __init__(self,\n", - " in_channels,\n", - " out_channels):\n", - "\n", - " super(BuildingBlock, self).__init__()\n", - " self.conv = nn.Conv2d(in_channels=in_channels,\n", - " out_channels=out_channels,\n", - " kernel_size=(3,3),\n", - " padding='same')\n", - " self.activation = nn.ReLU()\n", - " self.pool = nn.MaxPool2d(kernel_size=(2,2))\n", - "\n", - " def forward(self, x):\n", - " return self.pool(self.activation(self.conv(x)))" - ] - }, - { - "cell_type": "markdown", - "id": "31891282", - "metadata": {}, - "source": [ - "Notice that we used the `padding = \"same\"` argument to\n", - "`nn.Conv2d()`, which ensures that the output channels have the\n", - "same dimension as the input channels. There are 32 channels in the first\n", - "hidden layer, in contrast to the three channels in the input layer. We\n", - "use a $3\\times 3$ convolution filter for each channel in all the layers. Each\n", - "convolution is followed by a max-pooling layer over $2\\times2$\n", - "blocks.\n", - "\n", - "In forming our deep learning model for the `CIFAR100` data, we use several of our `BuildingBlock()`\n", - "modules sequentially. This simple example\n", - "illustrates some of the power of `torch`. Users can\n", - "define modules of their own, which can be combined in other\n", - "modules. Ultimately, everything is fit by a generic trainer." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "845be1ae", - "metadata": {}, - "outputs": [], - "source": [ - "class CIFARModel(nn.Module):\n", - "\n", - " def __init__(self):\n", - " super(CIFARModel, self).__init__()\n", - " sizes = [(3,32),\n", - " (32,64),\n", - " (64,128),\n", - " (128,256)]\n", - " self.conv = nn.Sequential(*[BuildingBlock(in_, out_)\n", - " for in_, out_ in sizes])\n", - "\n", - " self.output = nn.Sequential(nn.Dropout(0.5),\n", - " nn.Linear(2*2*256, 512),\n", - " nn.ReLU(),\n", - " nn.Linear(512, 100))\n", - " def forward(self, x):\n", - " val = self.conv(x)\n", - " val = torch.flatten(val, start_dim=1)\n", - " return self.output(val)" - ] - }, - { - "cell_type": "markdown", - "id": "1ed2c036", - "metadata": {}, - "source": [ - "We build the model and look at the summary. (We had created examples of `X_` earlier.)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "be768cbb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "CIFARModel [128, 3, 32, 32] [128, 100] --\n", - "├─Sequential: 1-1 [128, 3, 32, 32] [128, 256, 2, 2] --\n", - "│ └─BuildingBlock: 2-1 [128, 3, 32, 32] [128, 32, 16, 16] --\n", - "│ │ └─Conv2d: 3-1 [128, 3, 32, 32] [128, 32, 32, 32] 896\n", - "│ │ └─ReLU: 3-2 [128, 32, 32, 32] [128, 32, 32, 32] --\n", - "│ │ └─MaxPool2d: 3-3 [128, 32, 32, 32] [128, 32, 16, 16] --\n", - "│ └─BuildingBlock: 2-2 [128, 32, 16, 16] [128, 64, 8, 8] --\n", - "│ │ └─Conv2d: 3-4 [128, 32, 16, 16] [128, 64, 16, 16] 18,496\n", - "│ │ └─ReLU: 3-5 [128, 64, 16, 16] [128, 64, 16, 16] --\n", - "│ │ └─MaxPool2d: 3-6 [128, 64, 16, 16] [128, 64, 8, 8] --\n", - "│ └─BuildingBlock: 2-3 [128, 64, 8, 8] [128, 128, 4, 4] --\n", - "│ │ └─Conv2d: 3-7 [128, 64, 8, 8] [128, 128, 8, 8] 73,856\n", - "│ │ └─ReLU: 3-8 [128, 128, 8, 8] [128, 128, 8, 8] --\n", - "│ │ └─MaxPool2d: 3-9 [128, 128, 8, 8] [128, 128, 4, 4] --\n", - "│ └─BuildingBlock: 2-4 [128, 128, 4, 4] [128, 256, 2, 2] --\n", - "│ │ └─Conv2d: 3-10 [128, 128, 4, 4] [128, 256, 4, 4] 295,168\n", - "│ │ └─ReLU: 3-11 [128, 256, 4, 4] [128, 256, 4, 4] --\n", - "│ │ └─MaxPool2d: 3-12 [128, 256, 4, 4] [128, 256, 2, 2] --\n", - "├─Sequential: 1-2 [128, 1024] [128, 100] --\n", - "│ └─Dropout: 2-5 [128, 1024] [128, 1024] --\n", - "│ └─Linear: 2-6 [128, 1024] [128, 512] 524,800\n", - "│ └─ReLU: 2-7 [128, 512] [128, 512] --\n", - "│ └─Linear: 2-8 [128, 512] [128, 100] 51,300\n", - "===================================================================================================================\n", - "Total params: 964,516\n", - "Trainable params: 964,516\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 2.01\n", - "===================================================================================================================\n", - "Input size (MB): 1.57\n", - "Forward/backward pass size (MB): 63.54\n", - "Params size (MB): 3.86\n", - "Estimated Total Size (MB): 68.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cifar_model = CIFARModel()\n", - "summary(cifar_model,\n", - " input_data=X_,\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "6320d294", - "metadata": {}, - "source": [ - "The total number of trainable parameters is 964,516.\n", - "By studying the size of the parameters, we can see that the channels halve in both\n", - "dimensions\n", - "after each of these max-pooling operations. After the last of these we\n", - "have a layer with 256 channels of dimension $2\\times 2$. These are then\n", - "flattened to a dense layer of size 1,024;\n", - "in other words, each of the $2\\times 2$ matrices is turned into a\n", - "$4$-vector, and put side-by-side in one layer. This is followed by a\n", - "dropout regularization layer, then\n", - "another dense layer of size 512, and finally, the\n", - "output layer.\n", - "\n", - "Up to now, we have been using a default\n", - "optimizer in `SimpleModule()`. For these data,\n", - "experiments show that a smaller learning rate performs\n", - "better than the default 0.01. We use a\n", - "custom optimizer here with a learning rate of 0.001.\n", - "Besides this, the logging and training\n", - "follow a similar pattern to our previous examples. The optimizer\n", - "takes an argument `params` that informs\n", - "the optimizer which parameters are involved in SGD (stochastic gradient descent).\n", - "\n", - "We saw earlier that entries of a module’s parameters are tensors. In passing\n", - "the parameters to the optimizer we are doing more than\n", - "simply passing arrays; part of the structure of the graph\n", - "is encoded in the tensors themselves." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "67e3e2d9", - "metadata": {}, - "outputs": [], - "source": [ - "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n", - "cifar_module = SimpleModule.classification(cifar_model,\n", - " optimizer=cifar_optimizer)\n", - "cifar_logger = CSVLogger('logs', name='CIFAR100')" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "1ea339ff", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fdb66e85a34e49afafdf51191e247d54", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - 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"Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], - "source": [ - "cifar_trainer = Trainer(deterministic=True,\n", - " max_epochs=30,\n", - " logger=cifar_logger,\n", - " callbacks=[ErrorTracker()])\n", - "cifar_trainer.fit(cifar_module,\n", - " datamodule=cifar_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "977e27d9", - "metadata": {}, - "source": [ - "This model takes 10 minutes or more to run and achieves about 42% accuracy on the test\n", - "data. Although this is not terrible for 100-class data (a random\n", - "classifier gets 1% accuracy), searching the web we see results around\n", - "75%. Typically it takes a lot of architecture carpentry,\n", - "fiddling with regularization, and time, to achieve such results.\n", - "\n", - "Let’s take a look at the validation and training accuracy\n", - "across epochs." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "0a9068d9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "log_path = cifar_logger.experiment.metrics_file_path\n", - "cifar_results = pd.read_csv(log_path)\n", - "fig, ax = subplots(1, 1, figsize=(6, 6))\n", - "summary_plot(cifar_results,\n", - " ax,\n", - " col='accuracy',\n", - " ylabel='Accuracy')\n", - "ax.set_xticks(np.linspace(0, 10, 6).astype(int))\n", - "ax.set_ylabel('Accuracy')\n", - "ax.set_ylim([0, 1]);" - ] - }, - { - "cell_type": "markdown", - "id": "9c48006a", - "metadata": {}, - "source": [ - "Finally, we evaluate our model on our test data." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "e71eff9b", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3e804034c1964ed38bbe2ca376491f54", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.40880000591278076\n", - " test_loss 2.4764862060546875\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 2.4764862060546875, 'test_accuracy': 0.40880000591278076}]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cifar_trainer.test(cifar_module,\n", - " datamodule=cifar_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "2d03b891", - "metadata": {}, - "source": [ - "### Hardware Acceleration\n", - "As deep learning has become ubiquitous in machine learning, hardware\n", - "manufacturers have produced special libraries that can\n", - "often speed up the gradient-descent steps.\n", - "\n", - "For instance, Mac OS devices with the M1 chip may have the *Metal* programming framework\n", - "enabled, which can speed up the `torch` \n", - "computations. We present an example of how to use this acceleration.\n", - "\n", - "The main changes are to the `Trainer()` call as well as to the metrics\n", - "that will be evaluated on the data. These metrics must be told where\n", - "the data will be located at evaluation time. This is\n", - "accomplished with a call to the `to()` method of the metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "58eae430", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | CIFARModel | 964 K \n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "964 K Trainable params\n", - "0 Non-trainable params\n", - "964 K Total params\n", - "3.858 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ebeb3882e14340e8b43876442c7e36d9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchmetrics/utilities/checks.py:49: UserWarning: MPS: no support for int64 min/max ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:1271.)\n", - " if ignore_index is None and target.min() < 0:\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - 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"Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3436eeffcb544f009e93641c9b125c58", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "try:\n", - " for name, metric in cifar_module.metrics.items():\n", - " cifar_module.metrics[name] = metric.to('mps')\n", - " cifar_trainer_mps = Trainer(accelerator='mps',\n", - " deterministic=True,\n", - " max_epochs=30)\n", - " cifar_trainer_mps.fit(cifar_module,\n", - " datamodule=cifar_dm)\n", - " cifar_trainer_mps.test(cifar_module,\n", - " datamodule=cifar_dm)\n", - "except:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "0c7227ef", - "metadata": {}, - "source": [ - "This yields approximately two- or three-fold acceleration for each epoch.\n", - "We have protected this code block using `try:` and `except:`\n", - "clauses; if it works, we get the speedup, if it fails, nothing happens." - ] - }, - { - "cell_type": "markdown", - "id": "4328a190", - "metadata": {}, - "source": [ - "## Using Pretrained CNN Models\n", - "We now show how to use a CNN pretrained on the `imagenet` database to classify natural\n", - "images, and demonstrate how we produced Figure 10.10.\n", - "We copied six JPEG images from a digital photo album into the\n", - "directory `book_images`. These images are available\n", - "from the data section of , the ISLP book website. Download `book_images.zip`; when\n", - "clicked it creates the `book_images` directory. \n", - "\n", - "The pretrained network we use is called `resnet50`; specification details can be found on the web.\n", - "We will read in the images, and\n", - "convert them into the array format expected by the `torch`\n", - "software to match the specifications in `resnet50`. \n", - "The conversion involves a resize, a crop and then a predefined standardization for each of the three channels.\n", - "We now read in the images and preprocess them." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "638a1c8c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "torch.Size([6, 3, 224, 224])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "resize = Resize((232,232))\n", - "crop = CenterCrop(224)\n", - "normalize = Normalize([0.485,0.456,0.406],\n", - " [0.229,0.224,0.225])\n", - "imgfiles = sorted([f for f in glob('book_images/*')])\n", - "imgs = torch.stack([torch.div(crop(resize(read_image(f))), 255)\n", - " for f in imgfiles])\n", - "imgs = normalize(imgs)\n", - "imgs.size()" - ] - }, - { - "cell_type": "markdown", - "id": "a2c5aec2", - "metadata": {}, - "source": [ - "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "7776ed1e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "ResNet [6, 3, 224, 224] [6, 1000] --\n", - "├─Conv2d: 1-1 [6, 3, 224, 224] [6, 64, 112, 112] 9,408\n", - "├─BatchNorm2d: 1-2 [6, 64, 112, 112] [6, 64, 112, 112] 128\n", - "├─ReLU: 1-3 [6, 64, 112, 112] [6, 64, 112, 112] --\n", - "├─MaxPool2d: 1-4 [6, 64, 112, 112] [6, 64, 56, 56] --\n", - "├─Sequential: 1-5 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-1 [6, 64, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-1 [6, 64, 56, 56] [6, 64, 56, 56] 4,096\n", - "│ │ └─BatchNorm2d: 3-2 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-3 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-4 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-5 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-6 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-7 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-8 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─Sequential: 3-9 [6, 64, 56, 56] [6, 256, 56, 56] 16,896\n", - "│ │ └─ReLU: 3-10 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-2 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-11 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-12 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-13 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-14 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-15 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-16 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-17 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-18 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-19 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ └─Bottleneck: 2-3 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "│ │ └─Conv2d: 3-20 [6, 256, 56, 56] [6, 64, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-21 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-22 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-23 [6, 64, 56, 56] [6, 64, 56, 56] 36,864\n", - "│ │ └─BatchNorm2d: 3-24 [6, 64, 56, 56] [6, 64, 56, 56] 128\n", - "│ │ └─ReLU: 3-25 [6, 64, 56, 56] [6, 64, 56, 56] --\n", - "│ │ └─Conv2d: 3-26 [6, 64, 56, 56] [6, 256, 56, 56] 16,384\n", - "│ │ └─BatchNorm2d: 3-27 [6, 256, 56, 56] [6, 256, 56, 56] 512\n", - "│ │ └─ReLU: 3-28 [6, 256, 56, 56] [6, 256, 56, 56] --\n", - "├─Sequential: 1-6 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-4 [6, 256, 56, 56] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-29 [6, 256, 56, 56] [6, 128, 56, 56] 32,768\n", - "│ │ └─BatchNorm2d: 3-30 [6, 128, 56, 56] [6, 128, 56, 56] 256\n", - "│ │ └─ReLU: 3-31 [6, 128, 56, 56] [6, 128, 56, 56] --\n", - "│ │ └─Conv2d: 3-32 [6, 128, 56, 56] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-33 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-34 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-35 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-36 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─Sequential: 3-37 [6, 256, 56, 56] [6, 512, 28, 28] 132,096\n", - "│ │ └─ReLU: 3-38 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-5 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-39 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-40 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-41 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-42 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-43 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-44 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-45 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-46 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-47 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-6 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-48 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-49 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-50 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-51 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-52 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-53 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-54 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-55 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-56 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ └─Bottleneck: 2-7 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "│ │ └─Conv2d: 3-57 [6, 512, 28, 28] [6, 128, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-58 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-59 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-60 [6, 128, 28, 28] [6, 128, 28, 28] 147,456\n", - "│ │ └─BatchNorm2d: 3-61 [6, 128, 28, 28] [6, 128, 28, 28] 256\n", - "│ │ └─ReLU: 3-62 [6, 128, 28, 28] [6, 128, 28, 28] --\n", - "│ │ └─Conv2d: 3-63 [6, 128, 28, 28] [6, 512, 28, 28] 65,536\n", - "│ │ └─BatchNorm2d: 3-64 [6, 512, 28, 28] [6, 512, 28, 28] 1,024\n", - "│ │ └─ReLU: 3-65 [6, 512, 28, 28] [6, 512, 28, 28] --\n", - "├─Sequential: 1-7 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-8 [6, 512, 28, 28] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-66 [6, 512, 28, 28] [6, 256, 28, 28] 131,072\n", - "│ │ └─BatchNorm2d: 3-67 [6, 256, 28, 28] [6, 256, 28, 28] 512\n", - "│ │ └─ReLU: 3-68 [6, 256, 28, 28] [6, 256, 28, 28] --\n", - "│ │ └─Conv2d: 3-69 [6, 256, 28, 28] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-70 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-71 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-72 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-73 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─Sequential: 3-74 [6, 512, 28, 28] [6, 1024, 14, 14] 526,336\n", - "│ │ └─ReLU: 3-75 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-9 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-76 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-77 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-78 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-79 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-80 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-81 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-82 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-83 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-84 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-10 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-85 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-86 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-87 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-88 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-89 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-90 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-91 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-92 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-93 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-11 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-94 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-95 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-96 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-97 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-98 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-99 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-100 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-101 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-102 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-12 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-103 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-104 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-105 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-106 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-107 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-108 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-109 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-110 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-111 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ └─Bottleneck: 2-13 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "│ │ └─Conv2d: 3-112 [6, 1024, 14, 14] [6, 256, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-113 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-114 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-115 [6, 256, 14, 14] [6, 256, 14, 14] 589,824\n", - "│ │ └─BatchNorm2d: 3-116 [6, 256, 14, 14] [6, 256, 14, 14] 512\n", - "│ │ └─ReLU: 3-117 [6, 256, 14, 14] [6, 256, 14, 14] --\n", - "│ │ └─Conv2d: 3-118 [6, 256, 14, 14] [6, 1024, 14, 14] 262,144\n", - "│ │ └─BatchNorm2d: 3-119 [6, 1024, 14, 14] [6, 1024, 14, 14] 2,048\n", - "│ │ └─ReLU: 3-120 [6, 1024, 14, 14] [6, 1024, 14, 14] --\n", - "├─Sequential: 1-8 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-14 [6, 1024, 14, 14] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-121 [6, 1024, 14, 14] [6, 512, 14, 14] 524,288\n", - "│ │ └─BatchNorm2d: 3-122 [6, 512, 14, 14] [6, 512, 14, 14] 1,024\n", - "│ │ └─ReLU: 3-123 [6, 512, 14, 14] [6, 512, 14, 14] --\n", - "│ │ └─Conv2d: 3-124 [6, 512, 14, 14] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-125 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-126 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-127 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-128 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─Sequential: 3-129 [6, 1024, 14, 14] [6, 2048, 7, 7] 2,101,248\n", - "│ │ └─ReLU: 3-130 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-15 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-131 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-132 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-133 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-134 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-135 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-136 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-137 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-138 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-139 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ └─Bottleneck: 2-16 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "│ │ └─Conv2d: 3-140 [6, 2048, 7, 7] [6, 512, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-141 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-142 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-143 [6, 512, 7, 7] [6, 512, 7, 7] 2,359,296\n", - "│ │ └─BatchNorm2d: 3-144 [6, 512, 7, 7] [6, 512, 7, 7] 1,024\n", - "│ │ └─ReLU: 3-145 [6, 512, 7, 7] [6, 512, 7, 7] --\n", - "│ │ └─Conv2d: 3-146 [6, 512, 7, 7] [6, 2048, 7, 7] 1,048,576\n", - "│ │ └─BatchNorm2d: 3-147 [6, 2048, 7, 7] [6, 2048, 7, 7] 4,096\n", - "│ │ └─ReLU: 3-148 [6, 2048, 7, 7] [6, 2048, 7, 7] --\n", - "├─AdaptiveAvgPool2d: 1-9 [6, 2048, 7, 7] [6, 2048, 1, 1] --\n", - "├─Linear: 1-10 [6, 2048] [6, 1000] 2,049,000\n", - "===================================================================================================================\n", - "Total params: 25,557,032\n", - "Trainable params: 25,557,032\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 24.54\n", - "===================================================================================================================\n", - "Input size (MB): 3.61\n", - "Forward/backward pass size (MB): 1066.99\n", - "Params size (MB): 102.23\n", - "Estimated Total Size (MB): 1172.83\n", - "===================================================================================================================" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n", - "summary(resnet_model,\n", - " input_data=imgs,\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "0d29ab45", - "metadata": {}, - "source": [ - "We set the mode to `eval()` to ensure that the model is ready to predict on new data." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "5c8e359d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ResNet(\n", - " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", - " (layer1): Sequential(\n", - " (0): Bottleneck(\n", - " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " (downsample): Sequential(\n", - " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - " )\n", - " (1): Bottleneck(\n", - " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " (2): Bottleneck(\n", - " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - 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" (relu): ReLU(inplace=True)\n", - " )\n", - " (2): Bottleneck(\n", - " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", - " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", - " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (relu): ReLU(inplace=True)\n", - " )\n", - " )\n", - " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", - " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", - ")" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "resnet_model.eval()" - ] - }, - { - "cell_type": "markdown", - "id": "228a6411", - "metadata": {}, - "source": [ - "Inspecting the output above, we see that when setting up the\n", - "`resnet_model`, the authors defined a `Bottleneck`, much like our\n", - "`BuildingBlock` module.\n", - "\n", - "We now feed our six images through the fitted network." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "80f19869", - "metadata": {}, - "outputs": [], - "source": [ - "img_preds = resnet_model(imgs)" - ] - }, - { - "cell_type": "markdown", - "id": "e7bd653f", - "metadata": {}, - "source": [ - "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n", - "the probabilities by applying the softmax to the logits in `img_preds`. Note that\n", - "we have had to call the `detach()` method on the tensor `img_preds` in order to convert\n", - "it to our a more familiar `ndarray`." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "6892597d", - "metadata": {}, - "outputs": [], - "source": [ - "img_probs = np.exp(np.asarray(img_preds.detach()))\n", - "img_probs /= img_probs.sum(1)[:,None]" - ] - }, - { - "cell_type": "markdown", - "id": "b0f07763", - "metadata": {}, - "source": [ - "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "3eb1e1b0", - "metadata": {}, - "outputs": [], - "source": [ - "labs = json.load(open('imagenet_class_index.json'))\n", - "class_labels = pd.DataFrame([(int(k), v[1]) for k, v in \n", - " labs.items()],\n", - " columns=['idx', 'label'])\n", - "class_labels = class_labels.set_index('idx')\n", - "class_labels = class_labels.sort_index()" - ] - }, - { - "cell_type": "markdown", - "id": "7779e914", - "metadata": {}, - "source": [ - "We’ll now construct a data frame for each image file\n", - "with the labels with the three highest probabilities as\n", - "estimated by the model above." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "0e89d251", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image: book_images/flamingo.jpg\n", - " label prob\n", - "0 flamingo 0.591760\n", - "1 spoonbill 0.012386\n", - "2 American_egret 0.002105\n", - "Image: book_images/hawk.jpg\n", - " label prob\n", - "0 great_grey_owl 0.287958\n", - "1 kite 0.039478\n", - "2 fountain 0.029384\n", - "Image: book_images/hawk_cropped.jpeg\n", - " label prob\n", - "0 kite 0.301841\n", - "1 jay 0.121659\n", - "2 magpie 0.015511\n", - "Image: book_images/huey.jpg\n", - " label prob\n", - "0 Lhasa 0.151153\n", - "1 Shih-Tzu 0.129804\n", - "2 Tibetan_terrier 0.102400\n", - "Image: book_images/kitty.jpg\n", - " label prob\n", - "0 tabby 0.173628\n", - "1 tiger_cat 0.110415\n", - "2 doormat 0.093447\n", - "Image: book_images/weaver.jpg\n", - " label prob\n", - "0 jacamar 0.287284\n", - "1 bee_eater 0.046768\n", - "2 bulbul 0.037507\n" - ] - } - ], - "source": [ - "for i, imgfile in enumerate(imgfiles):\n", - " img_df = class_labels.copy()\n", - " img_df['prob'] = img_probs[i]\n", - " img_df = img_df.sort_values(by='prob', ascending=False)[:3]\n", - " print(f'Image: {imgfile}')\n", - " print(img_df.reset_index().drop(columns=['idx']))" - ] - }, - { - "cell_type": "markdown", - "id": "7f9d3843", - "metadata": {}, - "source": [ - "We see that the model\n", - "is quite confident about `Flamingo.jpg`, but a little less so for the\n", - "other images.\n", - "\n", - "We end this section with our usual cleanup." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "69456669", - "metadata": {}, - "outputs": [], - "source": [ - "del(cifar_test,\n", - " cifar_train,\n", - " cifar_dm,\n", - " cifar_module,\n", - " cifar_logger,\n", - " cifar_optimizer,\n", - " cifar_trainer)" - ] - }, - { - "cell_type": "markdown", - "id": "64b33c4d", - "metadata": {}, - "source": [ - "## IMDB Document Classification\n", - "We now implement models for sentiment classification (Section 10.4) on the `IMDB`\n", - "dataset. As mentioned above code block~8, we are using\n", - "a preprocessed version of the `IMDB` dataset found in the\n", - "`keras` package. As `keras` uses `tensorflow`, a different\n", - "tensor and deep learning library, we have\n", - "converted the data to be suitable for `torch`. The\n", - "code used to convert from `keras` is\n", - "available in the module `ISLP.torch._make_imdb`. It\n", - "requires some of the `keras` packages to run. These data use a dictionary of size 10,000.\n", - "\n", - "We have stored three different representations of the review data for this lab:\n", - "\n", - "* `load_tensor()`, a sparse tensor version usable by `torch`;\n", - "* `load_sparse()`, a sparse matrix version usable by `sklearn`, since we will compare with a lasso fit;\n", - "* `load_sequential()`, a padded\n", - "version of the original sequence representation, limited to the last\n", - "500 words of each review." - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "70b3ece2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n", - " 4468], dtype=int32)" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(imdb_seq_train,\n", - " imdb_seq_test) = load_sequential(root='data/IMDB')\n", - "padded_sample = np.asarray(imdb_seq_train.tensors[0][0])\n", - "sample_review = padded_sample[padded_sample > 0][:12]\n", - "sample_review[:12]" - ] - }, - { - "cell_type": "markdown", - "id": "d6b73c18", - "metadata": {}, - "source": [ - "The datasets `imdb_seq_train` and `imdb_seq_test` are\n", - "both instances of the class `TensorDataset`. The\n", - "tensors used to construct them can be found in the `tensors` attribute, with\n", - "the first tensor the features `X` and the second the outcome `Y`.\n", - "We have taken the first row of features and stored it as `padded_sample`. In the preprocessing\n", - "used to form these data, sequences were padded with 0s in the beginning if they were\n", - "not long enough, hence we remove this padding by restricting to entries where\n", - "`padded_sample > 0`. We then provide the first 12 words of the sample review.\n", - "\n", - "We can find these words in the `lookup` dictionary from the `ISLP.torch.imdb` module." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "349b2af7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\" this film was just brilliant casting location scenery story direction everyone's\"" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lookup = load_lookup(root='data/IMDB')\n", - "' '.join(lookup[i] for i in sample_review)" - ] - }, - { - "cell_type": "markdown", - "id": "99d5a108", - "metadata": {}, - "source": [ - "For our first model, we have created a binary feature for each\n", - "of the 10,000 possible words in the dataset, with an entry of one\n", - "in the $i,j$ entry if word $j$ appears in review $i$. As most reviews\n", - "are quite short, such a feature matrix has over 98% zeros. These data\n", - "are accessed using `load_tensor()` from the `ISLP` library." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "ffa8da37", - "metadata": {}, - "outputs": [], - "source": [ - "max_num_workers=10\n", - "(imdb_train,\n", - " imdb_test) = load_tensor(root='data/IMDB')\n", - "imdb_dm = SimpleDataModule(imdb_train,\n", - " imdb_test,\n", - " validation=2000,\n", - " num_workers=min(6, max_num_workers),\n", - " batch_size=512)" - ] - }, - { - "cell_type": "markdown", - "id": "e53eea99", - "metadata": {}, - "source": [ - "We’ll use a two-layer model for our first model." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "11eeeabb", - "metadata": {}, - "outputs": [], - "source": [ - "class IMDBModel(nn.Module):\n", - "\n", - " def __init__(self, input_size):\n", - " super(IMDBModel, self).__init__()\n", - " self.dense1 = nn.Linear(input_size, 16)\n", - " self.activation = nn.ReLU()\n", - " self.dense2 = nn.Linear(16, 16)\n", - " self.output = nn.Linear(16, 1)\n", - "\n", - " def forward(self, x):\n", - " val = x\n", - " for _map in [self.dense1,\n", - " self.activation,\n", - " self.dense2,\n", - " self.activation,\n", - " self.output]:\n", - " val = _map(val)\n", - " return torch.flatten(val)" - ] - }, - { - "cell_type": "markdown", - "id": "943bf8d4", - "metadata": {}, - "source": [ - "We now instantiate our model and look at a summary." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "5fe55a29", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "IMDBModel [25000, 10003] [25000] --\n", - "├─Linear: 1-1 [25000, 10003] [25000, 16] 160,064\n", - "├─ReLU: 1-2 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-3 [25000, 16] [25000, 16] 272\n", - "├─ReLU: 1-4 [25000, 16] [25000, 16] --\n", - "├─Linear: 1-5 [25000, 16] [25000, 1] 17\n", - "===================================================================================================================\n", - "Total params: 160,353\n", - "Trainable params: 160,353\n", - "Non-trainable params: 0\n", - "Total mult-adds (G): 4.01\n", - "===================================================================================================================\n", - "Input size (MB): 1000.30\n", - "Forward/backward pass size (MB): 6.60\n", - "Params size (MB): 0.64\n", - "Estimated Total Size (MB): 1007.54\n", - "===================================================================================================================" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n", - "summary(imdb_model,\n", - " input_size=imdb_test.tensors[0].size(),\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "a49a0145", - "metadata": {}, - "source": [ - "We’ll again use\n", - "a smaller learning rate for these data,\n", - "hence we pass an `optimizer` to the\n", - "`SimpleModule`. \n", - "Since the reviews are classified into\n", - "positive or negative sentiment, we use\n", - "`SimpleModule.binary_classification()`. {Our use of\n", - " `binary_classification()` instead of `classification()` is\n", - " due to some subtlety in how `torchmetrics.Accuracy()` works,\n", - "as well as the data type of the targets.}" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "e1ebbc70", - "metadata": {}, - "outputs": [], - "source": [ - "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n", - "imdb_module = SimpleModule.binary_classification(\n", - " imdb_model,\n", - " optimizer=imdb_optimizer)" - ] - }, - { - "cell_type": "markdown", - "id": "18076d4c", - "metadata": {}, - "source": [ - "Having loaded the datasets into a data module\n", - "and created a `SimpleModule`, the remaining steps\n", - "are familiar." - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "9feddeb2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | IMDBModel | 160 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "160 K Trainable params\n", - "0 Non-trainable params\n", - "160 K Total params\n", - "0.641 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1892: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "44936d415c7e4ad6b38cd97c733a90be", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - 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}, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=30` reached.\n" - ] - } - ], - "source": [ - "imdb_logger = CSVLogger('logs', name='IMDB')\n", - "imdb_trainer = Trainer(deterministic=True,\n", - " max_epochs=30,\n", - " logger=imdb_logger,\n", - " callbacks=[ErrorTracker()])\n", - "imdb_trainer.fit(imdb_module,\n", - " datamodule=imdb_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "f28e6658", - "metadata": {}, - "source": [ - "Evaluating the test error yields roughly 86% accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "887d7dad", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "03f177eef9fe4ed090eef15f0a005581", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8543599843978882\n", - " test_loss 1.1992340087890625\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 1.1992340087890625, 'test_accuracy': 0.8543599843978882}]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n", - "test_results" - ] - }, - { - "cell_type": "markdown", - "id": "d2f87545", - "metadata": {}, - "source": [ - "### Comparison to Lasso\n", - "We now fit a lasso logistic regression model\n", - " using `LogisticRegression()` from `sklearn`. Since `sklearn` does not recognize\n", - "the sparse tensors of `torch`, we use a sparse\n", - "matrix that is recognized by `sklearn.`" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "055a9aeb", - "metadata": {}, - "outputs": [], - "source": [ - "((X_train, Y_train),\n", - " (X_valid, Y_valid),\n", - " (X_test, Y_test)) = load_sparse(validation=2000,\n", - " random_state=0,\n", - " root='data/IMDB')" - ] - }, - { - "cell_type": "markdown", - "id": "d8324d03", - "metadata": {}, - "source": [ - "Similar to what we did in\n", - "Section 10.9.1,\n", - "we construct a series of 50 values for the lasso reguralization parameter $\\lambda$." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "e3e1e181", - "metadata": {}, - "outputs": [], - "source": [ - "lam_max = np.abs(X_train.T * (Y_train - Y_train.mean())).max()\n", - "lam_val = lam_max * np.exp(np.linspace(np.log(1),\n", - " np.log(1e-4), 50))" - ] - }, - { - "cell_type": "markdown", - "id": "73e7d53e", - "metadata": {}, - "source": [ - "With `LogisticRegression()` the regularization parameter\n", - "$C$ is specified as the inverse of $\\lambda$. There are several\n", - "solvers for logistic regression; here we use `liblinear` which\n", - "works well with the sparse input format." - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "dd64350c", - "metadata": {}, - "outputs": [], - "source": [ - "logit = LogisticRegression(penalty='l1', \n", - " C=1/lam_max,\n", - " solver='liblinear',\n", - " warm_start=True,\n", - " fit_intercept=True)" - ] - }, - { - "cell_type": "markdown", - "id": "f0e9739a", - "metadata": {}, - "source": [ - "The path of 50 values takes approximately 40 seconds to run." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "0bf8b868", - "metadata": {}, - "outputs": [], - "source": [ - "coefs = []\n", - "intercepts = []\n", - "\n", - "for l in lam_val:\n", - " logit.C = 1/l\n", - " logit.fit(X_train, Y_train)\n", - " coefs.append(logit.coef_.copy())\n", - " intercepts.append(logit.intercept_)" - ] - }, - { - "cell_type": "markdown", - "id": "95f4fbff", - "metadata": {}, - "source": [ - "The coefficient and intercepts have an extraneous dimension which can be removed\n", - "by the `np.squeeze()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "28256828", - "metadata": {}, - "outputs": [], - "source": [ - "coefs = np.squeeze(coefs)\n", - "intercepts = np.squeeze(intercepts)" - ] - }, - { - "cell_type": "markdown", - "id": "94e746f6", - "metadata": {}, - "source": [ - "We’ll now make a plot to compare our neural network results with the\n", - "lasso." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "a5945618", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "fig, axes = subplots(1, 2, figsize=(16, 8), sharey=True)\n", - "for ((X_, Y_),\n", - " data_,\n", - " color) in zip([(X_train, Y_train),\n", - " (X_valid, Y_valid),\n", - " (X_test, Y_test)],\n", - " ['Training', 'Validation', 'Test'],\n", - " ['black', 'red', 'blue']):\n", - " linpred_ = X_ * coefs.T + intercepts[None,:]\n", - " label_ = np.array(linpred_ > 0)\n", - " accuracy_ = np.array([np.mean(Y_ == l) for l in label_.T])\n", - " axes[0].plot(-np.log(lam_val / X_train.shape[0]),\n", - " accuracy_,\n", - " '.--',\n", - " color=color,\n", - " markersize=13,\n", - " linewidth=2,\n", - " label=data_)\n", - "axes[0].legend()\n", - "axes[0].set_xlabel(r'$-\\log(\\lambda)$', fontsize=20)\n", - "axes[0].set_ylabel('Accuracy', fontsize=20)" - ] - }, - { - "cell_type": "markdown", - "id": "7dc7e494", - "metadata": {}, - "source": [ - "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n", - "when making a complex figure, since the steps can be spread across two or more cells.\n", - "We now add a plot of the lasso accuracy, and display the composed figure by simply entering its name at the end of the cell." - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "74704884", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qbW1VJBLRxo0bU35LcOSRRyZ+3nPPPTVixAiFw+Ee2w8dOjTxhybZ00Pi7dvb27V27VqdcMIJidtdLpeOPfZYxWKxXh0fAOyuUq2fvGnTJr377rv62c9+psbGRgruAAB6JVPTvCsqKrRo0aJuC2vU1tb2SWENALlr6NCh+uKLLxy1bW5u1tSpU1O2e/TRRx19CT906FBH+3WCXErmkLjsY1dffbWuvvrqpG3iJexTefTRR+Xz+TIVmiM7VrS65ppr9OSTT+r222/XQQcdpCFDhqiyslJbtmxJ2k9hYWGX3w3DSPqH0V17p+tNAAC2rZ+cyhFHHEHBHQBAr8Wnea9Zs6bb83TDMFRaWupomndFRUXiC7TW1laVlJTI6/Uy0hIYgAzDcFxZ+9RTT3X0PnTqqadm/f2EXErmkLjMAT6fL2+m6S1dulQXXXRRYljxF198offffz+rMRQVFWnMmDF68cUXE49JNBrVSy+9pIkTJ2Y1FgDIVfX19YlRkz0xTVPBYFBVVVWJgjvZ/oIMAJCfMj3N2+VyafLkyX0RKoDdVD4tN0EuZddRnCcHGIahxsZGeTweSUpUd41vc2ma3sEHH6yGhga98sorWrZsmc4///x+GVJ8xRVX6Oabb1ZjY6PefPNNzZo1S21tbTnxGAFALgiHw46K7iSbagIA2L1Fo1EFAgE98MADCgQCSSuAdyc+zXvHpbFKS0u1aNEipnkD6HP58j5ELmXXMeIyR+TLNL0777xTl1xyiTwej0aNGqXrrruuT4sD9eS6667TRx99pAsvvFAul0uXXXaZTjvttJz4JgUAcoGT9Y9N05Tb7c5CNACAXNPQ0NDtupJz5szp1Qd9pnkD6G/58D5ELmXXGVa+T3bPko6ODhUVFam9vX2nD4ObNm3SihUrtP/++2vw4MH9FOHAFovFdNhhh+m8887Tz3/+84z1y3MLIF85XT85EAgwPXwASXY+g/zAc4hMaGhoUGVl5U7rnsUHS+TSKCUAuy8+b/e//sil9PZchhGXyEsrV67U//3f/8nn82nz5s26++67tWLFCp1//vn9HRoA9DnLshQMBlVfX58YoX/BBRfI6/Umlhnx+Xw6+eST1dLS0u00lFxaPxkAkD3RaFSzZs3qtliDZVkyDEOzZ8+W3+/P6RE4AIDey8dcColL5KWCggItWLBA11xzjSzL0uGHH66nnnpKhx12WH+HBgB9qq2tLTEVJl58x+Vyad68edp33331yiuvqLi4WIZhaPHixTu1jW9zaf1kAED2hEKhLtPDd2RZllavXq1QKESxHADYzeRjLoXEJfLS+PHjtXTp0v4OAwCyyrIs+f1+tbS0SFKi+E68mMKqVas0ZcoUvfzyyzIMI2/WTwYAZE9ra2tG2wEA8kc+5lJIXAIAkCeCwaBCoVDSNsuWLVNzc3Ni3UrDMOTz+VjHEgAgSSopKcloOwAA+lJBfwcAAACcqa+vT6xh2RPTNFVXV5eliAAA/SEajSoQCOiBBx5QIBBIjLx3wuv1qrS0tMdR94ZhaPz48fJ6vZkKFwCAXUbiEgCAPBEOhxPTw3sSiUQUDoezFBEAINsaGho0YcIETZkyReeff76mTJmiCRMmqKGhwdH9XS6X5syZI0k7JS/jv9fW1lKYBwCQE0hcAgCQByzL0scff5yynWmacrvdWYgIAJBtDQ0Nqqys3Km4zpo1a1RZWek4eVlRUaFFixZp3LhxXa4vLS3VokWLVFFRkbGYAQBIB2tcAgCQBzZu3Kg1a9akbBeJRFRVVZWFiAAA2RSNRjVr1ixZlrXTbZZlyTAMzZ49W36/39FoyYqKCvn9foVCIbW2tqqkpERer5eRlgCAnELiEgCAHGFZloLBoOrr6xMVwGfMmCGfz6ehQ4fqiSee0BFHHKFYLNbtB1eXyyWPx6OysrJ+iB4A0JdCodBOIy23Z1mWVq9erVAopMmTJzvq0+VyOW4LAEB/YKp4LrEsKRCQamqkadPsbSBgX5+jJk+erNmzZyd+nzBhgmpra5PexzAMPfzww2nvO1P9AEAuaGtrk8/n05QpU7Rw4UL9/e9/18KFCzVlyhT5fD61tbXpsMMO0/Lly3XyySdLUqJQT3zr8XjU2NjYY8EFAED+am1tzWg7ANitRKN2/uSBB+xtL4qW9QdyKc4x4jJXtLVJfr8UCkmmKUUi9nbePMnrlRobpeLijO6yvLxcnZ2devzxx3e6LRQKqaysTMuWLdORRx7puM8XX3xRe+65ZybD1M9+9jM9/PDDeuWVV7pc39raquIMPyYA0B8sy5Lf71dLS4skJQrwxLctLS3y+/0KBoM69NBDFQwG1dzcrLq6usTIzKqqKpWVlZG0BIDdVElJSUbbAcBuo6FBmjVL2n5UemmpNGeO1Adr9pJLyS4Sl7nAsuyk5dYPrIpXjI1vW1rs24NBKYMfSGfOnKlzzz1XH3zwgUpLS7vcNn/+fB133HG9+kOTpNGjR2csvlTGjh2btX0BQF8KBoMKhUI93h6NRhUKhdTc3CyfzyfDMOTz+eTz+bIYJQCgP3m9XpWWlmrNmjXdLhdiGIZKS0vl9Xr7IToA6CcNDVJl5c4zVdessa9ftCjjyUtyKdnFVPFcEAzaIy17Gsocjdq3NzdndLdnnXWWRo8erQULFnS5/osvvtDf/vY3nX322frWt76lcePGaejQoTriiCP0wAMPJO1zx+HNb7/9tsrKyjR48GB9+ctf1pNPPrnTfa677jodcsghGjp0qA444AD9+Mc/VmdnpyRpwYIFuvHGG7Vs2TIZhiHDMBLx7ji8+d///re+9rWvaciQIdp777112WWX6YsvvkjcftFFF+nss8/W7bffrpKSEu2999763ve+l9gXAPSX+vr6xHTvnpimqbq6uixFBADINS6XS3PmzJGknUbXx3+vra2luA6A/GZZ0vr1zi4dHdKVV3a/vF78ulmz7HZO+nO4TB+5lOzmUhhx2dfuvNO+JNPWZo+kTPVHMnVq99PFr77avvSSaZq68MILtWDBAv3whz9MnPD87W9/UzQa1YwZM/S3v/1N1113nUaMGKFHHnlEVVVVOvDAA3XCCSek7D8Wi6miokJjxozR888/r/b29i5rOMQNHz5cCxYs0D777KN///vfuvTSSzV8+HBde+21mj59upYvX67HH39cTz31lCSpqKhopz7Wr1+v0047TSeddJJefPFFhcNh1dTU6PLLL+/yZtLU1KSSkhI1NTXpnXfe0fTp0zVx4kRdeumlvX78ACBT1q5dm5gW3pNIJKJwOJyliAAAuaiiokKLFi3SrFmzuhTqKS0tVW1trSr6YEokAGTVhg3SsGGZ6cuy7Onj3eQQuvXFF5KD6drkUrKbSyFx2dc6OuwhypmwYYN96W4fu+iSSy7RbbfdpmAwmKgoOH/+fJ177rnab7/9dM011yTaXnHFFXriiSf017/+1dEf21NPPaX//Oc/euKJJ7TPPvtIkn75y1/qjDPO6NLuRz/6UeLnCRMm6JprrtGf//xnXXvttRoyZIiGDRsm0zSTDme+//77tWnTJt13332JdSHuvvtulZeX65ZbbtGYMWMkScXFxbr77rvlcrl06KGH6swzz9TTTz9N4hJAv/nss8+0fPnylO1M05Tb7c5CRACAXFZRUSG/369QKKTW1laVlJTI6/Uy0hIAsohcSvZyKSQu+9qIEdK4ccnbtLVJGzemHnE5dGj3Iy5HjNjl8A499FB5PB7de++9mjx5st555x2FQiHddNNNikaj+uUvf6m//vWvWrNmjbZs2aLNmzdr6NChjvp+4403NH78+MQfmiSddNJJO7X7y1/+orvuukvvvvuuvvjiC0UiEY3o5TG98cYbOuqoo7osZjtp0iTFYjG9+eabiT+2r3zlK11O6kpKSvTvf/+7V/sCgEx54YUXNH36dL3//vsp20YiEVVVVfV9UACAnOdyuRIflAFgtzJ0qD3y0YnmZntmaiqPPiqVlTnbt0PkUrKXSyFx2decTOMOBKQpU1L39eijUh8UYpg5c6auuOIK/fa3v9X8+fN14IEHyufz6ZZbbtGcOXNUW1urI444Qnvuuadmz56tLVu2ZGzfzz77rC644ALdeOONOu2001RUVKQ///nPuuOOOzK2j+0VFhZ2+d0wDMVisT7ZFwBIdsXwYDCo+vr6RAXwGTNmyOfz6ZFHHkkkLU3TVCwW6/Y9yeVyyePxqMzJCRcAIKfFC64xWhIAumEYjqZrS5JOPdWuHr5mTfcDwQzDvv3UU6U+eJ8ll5KdXArFeXKBzyd5vT3/Iblc9u199IH1vPPOU0FBge6//37dd999uuSSS2QYhpYuXSq/368ZM2boqKOO0gEHHKC33nrLcb+HHXaYVq9erdbW1sR1zz33XJc2LS0t2m+//fTDH/5Qxx13nA4++GCtXLmyS5tBgwYp2lPhou32tWzZMq1fvz5x3dKlS1VQUKAvfelLjmMGgExqa2uTz+fTlClTtHDhQv3973/XwoULNWXKFPl8Pl1++eUqKyvTSSedpJdeekmTJk2SpEShnvjW4/GosbFxp2IMAID80tDQoAkTJmjKlCk6//zzNWXKFE2YMEENDQ39HRoA5B+XS9patEw7nifHf6+t7ZOkpUQuJVtIXOYCw5AaGyWPx/49Xlk2vvV47Nv76APrsGHDNH36dN1www1qbW3VRRddJEk6+OCD9eSTT6qlpUVvvPGGvv3tb2vt2rWO+z3llFN0yCGHqLq6WsuWLVMoFNIPf/jDLm0OPvhgrVq1Sn/+85/17rvv6q677tJDDz3Upc2ECRO0YsUKvfLKK/rkk0+0efPmnfZ1wQUXaPDgwaqurtby5cvV1NSkK664QlVVVYmhzQCQTZZlye/3q6WlRZISxXfi25aWFp177rl68MEHFQwGdcQRRygYDCoQCKi6ulrl5eWqrq5WIBBQMBhUcXdLhQAA8kZDQ4MqKyu7FNWRpDVr1qiyspLkJQDsiooKadGinZfoKy21r+/DomXkUrKDxGWuKC6WgkF72nh1tVRebm8DAfv6Pv7AOnPmTLW1tem0005LrKPwox/9SMccc4xOO+00TZ48WWPHjtXZZ5/tuM+CggI99NBD2rhxo0444QTV1NToF7/4RZc206ZN01VXXaXLL79cEydOVEtLi3784x93aXPuuefq9NNP15QpUzR69Gg98MADO+1r6NCheuKJJ7Ru3Todf/zxqqys1Ne//nXdfffdvX8wACADgsGgQqFQj99yxqcKvvbaa4mpF4ZhyOfzae7cuVq8eLHmzp0rn8/HSEsAyHPRaFSzZs2S1c1Uxvh1s2fPTjkyBgDQjYoK6f33paYm6f777e2KFX2atIwjl9L3DKu7/57YSUdHh4qKitTe3r7TYqebNm3SihUrtP/++2vw4MH9FCH6As8tgF1VU1OjhQsXJkZYdsc0TVVXV2vu3LlZjAwDWbLzGeQHnsP8FAgENMXBmvZNTU0U3QEwYPB5e/eV7Lnt7bkMIy4BAOgD4XA4adJSsqeNh8PhLEUEAOgv269Tlol2AAAMFCQuAQDoA/HCOqnauN3uLEQDAOhPJSUlGW0HAMBAQeISAIAMa2xs1GOPPZayXSQSUVVVVRYiAgD0J6/Xq9LS0h7XLDYMQ+PHj5fX681yZAAA5LbUw0EAAMBOLMtSMBhUfX29wuGw3G63LrjgAj3//PP6wQ9+0G0Bhu25XC55PB6VlZVlKWIAQH9xuVyaM2eOKisrZRhGl/8R8WRmbW2tXC5Xf4UIAEBOInEJAEAvtbW1ye/3KxQKyTRNRSIRmaapefPmdWlXUVGhjz76SC0tLV3aRSIReTweNTY2UjEcAAaIiooKLVq0SLNmzdIHH3yQuL60tFS1tbWqyEL1WwDIRdSM3v1k8jklcQkAQC9YliW/36+WlhZJShTgiW8LCgoUi8V000036Uc/+pEkqbm5WXV1dYmRmVVVVSorKyNpCQADTEVFReKLr9bWVpWUlMjr9TLSEsCAVFhYKEnasGGDhgwZ0s/RIJO2bNkiSRn5/0biEgCAXggGgwqFQj3eHovFJKlLYtLn88nn82UlPgBA34hGoxlJOLpcLk2ePDnzAQJAnnG5XBo5cqTC4bAkaejQoXyxvxuIxWL6+OOPNXToUEcFS1MhcQkAQC/U19cnpnv3xDRN1dXVkawEgN1EQ0NDt1O858yZwxRvAEjD2LFjJSmRvMTuoaCgQPvuu29GEtEkLgEA6IVwOJw0aSnZ08Y5+QKA3UNDQ4MqKyt3Wq9rzZo1qqys1KJFi0heAsAuMgxDJSUlcrvd6uzs7O9wkCGDBg1SQUFBRvoicZlDLEsKBqX6eikcltxuacYMyeeTGC0NALnB7XYn1rHsiWmacrvdWYwKANAXotGoZs2a1W2RAcuyZBiGZs+eLb/fzzqVAJAGl8vF+yi6lZn0J9LW1mYnKKdMkRYulP7+d3s7ZYp9fVtb5vdpGEbSy89+9rO0+n744YczFisA5IpUSUvJHnFZVVWVpYgAAH0lFAp1mR6+I8uytHr16qRrHwMAgF3HiMscYFmS3y9tLVCr+AzE+Lalxb49GMzsyMvW1tbEz3/5y1/0k5/8RG+++WbiumHDhmVuZwCQ5yzL0vXXX68//vGPSdu5XC55PB6VlZVlKTIAQF/Z/nw5E+0AAEDvMOIyBwSDUigkRaPd3x6N2rc3N2d2v2PHjk1cioqKZBhGl+v+/Oc/67DDDtPgwYN16KGH6ne/+13ivlu2bNHll1+ukpISDR48WPvtt59uvvlmSdKECRMkSeecc44Mw0j8DgD5KhqN6rLLLtOtt96auC7+3havlBffejweNTY2UhERAHYDJSUlGW0HAAB6hxGXfezOO+1LMm1t9kjKbpbO6WLqVKm4eOfrr77avmTSn/70J/3kJz/R3XffraOPPlovv/yyLr30Uu25556qrq7WXXfdpcWLF+uvf/2r9t13X61evVqrV6+WJL344otyu92aP3++Tj/9dNapAJA3LMtSMBhUfX29wuGw3G63zjvvPP3hD3/Qgw8+KMleCuOee+7RZZddpubmZtXV1SXaVlVVqaysjKQlAOwmvF6vSktLtWbNmm7XuTQMQ6WlpfJ6vf0QHQAAuz8Sl32so0NasyYzfW3YYF+620em/fSnP9Udd9yRqJC4//776/XXX9f//u//qrq6WqtWrdLBBx+sk08+WYZhaL/99kvcd/To0ZKkkSNHauzYsZkPDgD6QFtbm/x+v0KhkEzTVCQSkWmamjdvXmI0ZWFhoerq6jR9+nRJks/nk8/n68+wAQB9yOVyac6cOaqsrJRhGF2Sl/EvqWpra/miHgCAPkLiso+NGCGNG5e8TVubtHFj6hGXQ4d2P+JyxIhdj68769ev17vvvquZM2fq0ksvTVwfiURUVFQkSbrooov0jW98Q1/60pd0+umn66yzztKpp56a2UAAIEssy5Lf71fL1sWGI1sXGY5vY7GYCgsLtXjxYp1++un9FicAIPsqKiq0aNEizZo1q0uhntLSUtXW1ia+6AcAAJlH4rKPOZnGHQjY1cNTefRRu8J4X/viiy8kSX/84x914okndrkt/m3yMcccoxUrVuixxx7TU089pfPOO0+nnHKKFi1a1PcBAkCGBYPBpBVhY7GYYrGYhgwZksWoAACZEI1GFQqF1NraqpKSEnm93l6PkKyoqEiMyk+nHwAA0DskLnOAzyd5vXb18O4K9LhckscjZatA7ZgxY7TPPvvovffe0wUXXNBjuxEjRmj69OmaPn26Kisrdfrpp2vdunXaa6+9VFhYqGhP1YYAIMfU19cnpof3xDRN1dXVMTUcAPJIQ0NDtyMl58yZ0+uRki6XS5MnT85whAAAIBkSlznAMKTGRsnvt6uHm6YUiWzbejz27dms9XDjjTfqyiuvVFFRkU4//XRt3rxZ//znP9XW1qarr75ad955p0pKSnT00UeroKBAf/vb3zR27FiNHDlSkl1t9+mnn9akSZO0xx57qLi7Oe4AkCPC4XDSpKVkTxsPh8NZiggAkK6GhgZVVlbuVFRnzZo1qqys1KJFi5jmDQBAjivo7wBgKy6WgkF72nh1tVRebm8DAfv6bOf9ampqNHfuXM2fP19HHHGEfD6fFixYoP3331+SNHz4cN1666067rjjdPzxx+v999/Xo48+qoIC+yV1xx136Mknn9T48eN19NFHZzd4AOglt9udcrqfaZpyu91ZiggAkI5oNKpZs2Z1Wwk8ft3s2bOZIQQAQI4zrO7+m2MnHR0dKioqUnt7u0bsUA1n06ZNWrFihfbff38NHjy4nyJEX+C5BQaGxx57TFOnTk3ZLhAIMFUceS3Z+QzyA8+hM4FAQFMcLCLf1NTE9G8AALKot+cyTBUHAAxomzZt0m233Za0jcvlksfjUVm2FhsGAKSltbU1o+0AAED/YKo4AGDA6uzs1De/+U01NTVJUmK6uGmaXbYej0eNjY0ysrnYMABgl5WUlGS0HQAA6B+MuAQADEjRaFQzZszQP/7xD0nSnnvuqSeffFJbtmxRXV2dwuGw3G63qqqqVFZWRtISAPKI1+tVaWmp1qxZ0+06l4ZhqLS0VF6vtx+iAwAATpG4BADs9izLUjAYVH19fSIhuWHDBv31r3+VJA0ePFj/+Mc/dNJJJ0kS61gCQJ5zuVyaM2eOKisrZRhGl+Rl/Iuo2tralIXZAABA/yJxmUHUOdr98JwC+a+trU1+v1+hUEimaSoSiSS2gwcPViQS0YMPPkhxBgDYzVRUVGjRokWaNWuWPvjgg8T1paWlqq2tVUVFRT9GBwAAnCBxmQGFhYWSpA0bNmjIkCH9HA0yacOGDZK2PccA8otlWfL7/WppaZEkRSKRLtvOzk4deuihOuOMM/otRgBA36moqEh8edXa2qqSkhJ5vV5GWgIAkCdIXGaAy+XSyJEjFQ6HJUlDhw5lLbQ8Z1mWNmzYoHA4rJEjR3JyC+SpYDCoUCjU4+3RaFSvvfaampubmR4OALspl8vFqHoAAPIUicsMGTt2rCQlkpfYPYwcOTLx3ALIP/X19Ylp4T0xTVN1dXUkLgEAAAAgx5C4zBDDMFRSUiK3263Ozs7+DgcZUFhYyEhLIM+Fw+GkSUvJnjbOl04AAAAAkHtIXGaYy+Ui2QUAecQ0Tbnd7v4OAwAAAACwg4L+DgAAgL4wd+5cPfbYYynbRSIRVVVVZSEiAAAAAEBvMOISAJC3LMtSMBhUfX29wuGw3G63zjvvPC1atEh//OMfU97f5XLJ4/GorKwsC9ECAAAAAHqDxCUAIC+1tbXJ7/crFAolCvC4XC7NmzevS7vLLrtMr7/+upYsWZJoF996PB41NjbKMIx+OgoAQDLRaFShUEitra0qKSmR1+tlWSYAAAYQEpcAgLxjWZb8fr9aWlokKVGAJxqNJtoYhqEFCxbowgsvlGVZam5uVl1dXWJkZlVVlcrKykhaAkCOamho0KxZs/TBBx8kristLdWcOXNUUVHRj5EBAIBsIXEJAMg7wWBQoVAoaRvLsrTffvtJspOYPp9PPp8vG+EBANLU0NCgyspKWZbV5fo1a9aosrJSixYtInkJAMAAQHEeAEDeqa+vl2km/+7NNE3V1dVlKSIAQKZEo1HNmjVrp6SlpMR1s2fP7jLKHgAA7J5IXAIA8k44HE5MD+9JJBJROBzOUkQAgEwJhUJdpofvyLIsrV69OuXIewAAkP9IXAIA8s6oUaNSrk1pmqbcbneWIgIAZEpra2tG2wEAgPxF4hIAkFc2b96st956q9sphNuLRCKqqqrKUlQAgEwpKSnJaDsAAJC/SFwCAPLGxo0bdc4552jp0qVJ27lcLnm9XpWVlWUpMgBApni9XpWWlvY4st4wDI0fP15erzfLkQEAgGwjcQkAyAvr169XeXm5HnvsMUnSkCFDdMQRR0hSolBPfOvxeNTY2JhyOjkAIPe4XC7NmTNHknZ6H4//XltbK5fLlfXYAABAdiUvyQoAQJZZlqVgMKj6+nqFw2G53W6dc845+tWvfqUlS5ZIkoYNG6ZHH31UJ598spqbm1VXV5doW1VVpbKyMpKWAJDHKioqtGjRIs2aNatLoZ7S0lLV1taqoqKiH6MDAADZYlipFgmDJKmjo0NFRUVqb2/XiBEj+jscANgttbW1ye/3KxQKyTRNRSIRuVwuRaPRRJuioiI9/vjj+upXv9qPkQL5ifOZ/DfQnsNoNKpQKKTW1laVlJTI6/Uy0hIAgDzW23MZRlwCAHKCZVny+/1qaWmRZBfXkdQlaWmapp5++mkde+yx/RIjACC7XC6XJk+e3N9hAACAfkLiEgCQE4LBoEKhUNI2kUhEX3zxRZYiAgAAAAD0J4rzAAByQn19faK4Tk9M01RdXV2WIgIAAAAA9CcSlwCAnBAOhxPTw3sSiUQUDoezFBEAAAAAoD+RuAQA5AS3262CguT/lkzTlNvtzlJEAAAAAID+ROISANDvLMtSQUGBYrFY0naRSERVVVVZigoAAAAA0J8ozgMA6FednZ367//+b82bNy9pO5fLJY/Ho7KysixFBgAAAADoTyQuAQBZYVmWgsGg6uvrFQ6H5Xa7dfbZZ+vXv/61nnnmmUS7/fbbTytXrpRpmopEIomtx+NRY2OjDMPox6MAAAAAAGQLiUsAQJ9ra2uT3+9XKBRKJCJdLleXUZZ77LGHFixYoOnTp6u5uVl1dXWJBGdVVZXKyspIWgIAAADAAELiEgDQpyzLkt/vV0tLiyQlKodHo9FEG9M09fTTT2vSpEmSJJ/PJ5/Pl/1gAQAAAAA5g8QlAKBPBYNBhUKhpG0ikUgioQkAAAAAgERVcQBAH6uvr5dpJv+ezDRN1dXVZSkiAAAAAEA+IHEJAOhTa9euTTmaMhKJKBwOZykiAAAAAEA+IHEJAOgzL774op5//vmU7UzTlNvtzkJEAAAAAIB8wRqXAIBdZlmWgsGg6uvrExXAZ8yYocMOO0w//OEPde+998qyrJT9RCIRVVVVZSFiAAAAAEC+IHEJANglbW1t8vv9CoVCMk1TkUhEpmlq3rx5crlcXaqGDx06VJs2bVIsFtupH5fLJY/Ho7KysmyGDwDoQ9FoVKFQSK2trSopKZHX65XL5ervsAAAQJ5hqjgAoNcsy5Lf71dLS4skJdawjG/jScvhw4fr17/+tVasWKFJkyZJUqJQT3zr8XjU2NgowzCyegwAgL7R0NCgCRMmaMqUKTr//PM1ZcoUTZgwQQ0NDf0dGgAAyDOMuAQA9FowGFQoFErZbuHChTrnnHMS92lublZdXV1iWnlVVZXKyspIWgLAbqKhoUGVlZU7LROyZs0aVVZWatGiRaqoqOin6AAAQL4hcQkA6LX6+vrE9PCemKapRx55JJG4NAxDPp9PPp8vW2ECALIoGo1q1qxZ3a5tbFmWDMPQ7Nmz5ff7mTYOAAAcYao4AKDX1q5dmzRpKdnTxsPhcJYiAgD0t1AopA8++KDH2y3L0urVqx2N2AcAAJAYcQkA6EZP1cKPO+443XjjjXrrrbccjbh0u91ZjBoA0J9aW1sz2g4AAIDEJQCgi2TVwvfYYw9t3rzZUT+RSERVVVV9HC0AIFeUlJRktB0AAABTxQEACamqhceTloWFhTrooIN6XKPM5XLJ6/WqrKwsC1EDAHKB1+tVaWlpjwXXDMPQ+PHj5fV6sxwZAADIVyQuAQAJ8Wrh0Wg0abv58+frhRdekMfjkWRPC99+6/F41NjYSLVwABhAXC6X5syZI0k7vf/Hf6+traUwDwAAcIzEJQAgIV4tPBnTNNXU1KTi4mIFg0EFAgFVV1ervLxc1dXVCgQCCgaDKi4uzlLUAIBcUVFRoUWLFmncuHFdri8tLdWiRYtUUVHRT5EBAIB8xBqXAICEcDjcq2rhhmHI5/PJ5/NlIzwAQB6oqKhIrJXc2tqqkpISeb1eRloCAIBeI3EJAEhwu91UCwcApM3lcmny5Mn9HQYAAMhzTBUHgAHCsiwFAgHV1NRo2rRpqqmpUSAQ0Pr16xNtZsyY4WjEJdXCAQAAAAB9jRGXADAAtLW1JabtxUdUmqapefPmqbCwUH/5y190zjnnyOfzyev1qqWlpdsCPS6XSx6Ph2rhAAAAAIA+l7cjLn/7299qwoQJGjx4sE488US98MILPbbt7OzUTTfdpAMPPFCDBw/WUUcdpccffzyL0QJA/7EsS36/Xy0tLZKUGFEZ33Z2dur888/X+vXrZRiGGhsbqRYOAAAAAOh3eZm4/Mtf/qKrr75aP/3pT/XSSy/pqKOO0mmnnZYoFrGjH/3oR/rf//1f/eY3v9Hrr7+u73znOzrnnHP08ssvZzlyAMi+YDCoUCjU7QjKuE2bNumpp56SJKqFAwAAAABygmFZltXfQfTWiSeeqOOPP1533323JCkWi2n8+PG64oordP311+/Ufp999tEPf/hDfe9730tcd+6552rIkCGqr693tM+Ojg4VFRWpvb1dI0aMyMyBAEAW1NTUaOHChSkL7lRXV2vu3LlZjAxAtnE+k/94DgEAQD7r7blM3q1xuWXLFv3rX//SDTfckLiuoKBAp5xyip599tlu77N582YNHjy4y3VDhgzRkiVLetzP5s2btXnz5sTvHR0daUYOAP0jHA47KrjT06h1AED/4ZwUAAAMZHk3VfyTTz5RNBrVmDFjulw/ZswYffTRR93e57TTTtOdd96pt99+W7FYTE8++aQaGhrU2tra435uvvlmFRUVJS7jx4/P6HEAQLZ8+OGHKduYpim3252FaAAAvZG356TRqBQISA88YG+TLFcCAADQk7xLXO6KOXPm6OCDD9ahhx6qQYMG6fLLL9fFF1+sgoKeD/+GG25Qe3t74rJ69eosRgwAzlmWpUAgoJqaGk2bNk01NTUKBAKKrwRy5ZVXpuwjEomoqqqqr0MFAPRSXp6TNjRIEyZIU6ZI559vbydMsK8HAADohbybKj5q1Ci5XC6tXbu2y/Vr167V2LFju73P6NGj9fDDD2vTpk369NNPtc8+++j666/XAQcc0ON+9thjD+2xxx4ZjR0AMq2trU1+v1+hUEimaSoSicg0Tc2bN09er1eNjY2qqqrS//zP/+jdd99VLBbbqQ+XyyWPx6OysrJ+OAIAQDJ5d07a0CBVVko7LqO/Zo19/aJFUkVF/8QGAADyTt6NuBw0aJCOPfZYPf3004nrYrGYnn76aZ100klJ7zt48GCNGzdOkUhEDz74oPx+f1+HCwB9xrIs+f1+tbS0SFJiHcv4tqWlJfE+9/zzz2vSpEmS7Gnh2289Ho8aGxtlGEZW4wcA7GaiUWnWrJ2TltK262bPzv60caatAwCQt/JuxKUkXX311aqurtZxxx2nE044QbW1tVq/fr0uvvhiSdKFF16ocePG6eabb5Zkf2Bfs2aNJk6cqDVr1uhnP/uZYrGYrr322v48DABISzAYVCgU6vH2aDSqUCik5uZm+Xw+BYNBNTc3q66uTuFwWG63W1VVVSorKyNpCQBIXygkffBBz7dblrR6td1u8uTsxNTQYCdTt4+rtFSaM4eRnwAA5IG8TFxOnz5dH3/8sX7yk5/oo48+0sSJE/X4448nCvasWrWqy/qVmzZt0o9+9CO99957GjZsmKZOnaq6ujqNHDmyn44AANJXX1+fmB7eE9M0VVdXJ5/PJ8Mw5PP55PP5shglAGDASFL4cpfapYtp6wAA5L28TFxK0uWXX67LL7+829sCgUCX330+n15//fUsRAUA2RMOh5MmLSV72ng4HM5SRACAAa2kJLPt0pFq2rph2NPW/X7J5er7eAAAwC7JuzUuAQBSe3u73njjjZTtTNOU2+3OQkQAgAHP67WnYfe0/IhhSOPH2+36Wm+mrQMAgJxF4hIAcpBlWQoEAqqpqdG0adNUU1OjQCAgy7IUDAZ15JFH6p133knZTyQSUVVVVRYiBgAMeC6XvXaktHPyMv57bW12Rjjm2rR1AACwS/J2qjgA7K7a2trk9/sVCoUSa1iapql58+bJ6/WqrKxMq1atkiS5XC5ZlqVYLLZTPy6XSx6PR2VlZdk+BABAvopG7VGIra32lG6vt3eJxooKe+3I7gri1NZmb03JXJq2DgAAdhmJSwDIIZZlye/3q6WlRZISa1jGty0tLbIsS1/96lc1ePBg3XXXXfre9763U5IzEonI4/GosbGRiuEAAGcyVYG7osJeOzKdBGi64tPW16zpfp1Lw7Bvz8a0dQAAsMtIXAJADgkGgwolWW8rGo1qyZIlamxs1FlnnaWCggIFg0E1Nzerrq5O4XBYbrdbVVVVKisrI2kJAHAm0xW4XS5p8uSMhtgr8WnrlZV2knL748r2tHUAALDLSFwCQA6pr69PjJjsiWmaWrx4saZNmyZJMgxDPp9PPp8vW2ECAHYnuVyBO52p67kybR0AAOwyEpcAkEPWrl2bNGkp2dPGw+FwliICAOz2elOBO5ujKDMxdT0Xpq0DAIBdRuISALIoXhW8vr4+Ma17xowZ8nq9amxsTKxtmYxpmnK73VmIFgAwIORiBe5MTl3v72nrAABgl5G4BIAsSVYtfOjQodqwYYOjfiKRiKqqqvo4WgDAgJFrFbhzeeo6AADIqoL+DgAABoJU1cK3T1oOGzZMBQXdvz27XC55vV6VlZX1ccQAgAEjXoG7p4JuhiGNH5+9Cty9mboOAAB2ayQuASAL4tXCo9Fo0na33HKLVq5cqUmTJkmyp4Vvv/V4PGpsbKRaOAAgc+IVuKWdk5f9UYE7F6euS/ZI0EBAeuABe5vifzoAAEgfU8UBIAvq6+vlcrmSJi5N09Rbb72lvfbaS8FgUM3Nzaqrq0ushVlVVaWysrK8TVpalhQMSvX1Ujgsud3SjBmSz9fzIB8AQJbkUgXuXJu6LmWmUFBcOpXSAQAYYAzL6m7xGOyoo6NDRUVFam9v14gRI/o7HAB5ZOPGjZo4caLeeuutlG3Ly8u1ePHiLESVGU6TkW1t24q6mqYUiWzber1SY6NUXNy7PgH0Hucz+a/Pn8NcSKpFo9KECXYhnu4+qhiGnTRcsSI7sfVUKCj+T6k3hYIymQAFACAP9fZchsSlQ5zoA+hJT5XCfT6fHnnkEV155ZVasWJFyn5M01R1dbXmzp2bhaiTc5I8dJqMtCz7fi0t3c+qc7kkj8fe32efOU9wSpIVsxS8a5nq7/lc4fZBchdt0Yz/Hi7flUfJKNguy0k2FJDE+czuYMA8h/FkodQ1YbgrycJ0xJOoPa252ZskaiYToAAA5CkSl31kwJwkAuiVniqFRyIR7bXXXlq3bl2v+gsEAvL5fH0UrTNOEpIjRzpPRgaD0pQpqffb1CT95CfO+jQMqW3FZ/JPXKlQx1Ey1amITJmKKKJCeUcsU+Mr+6l4/5G9G+4J7OY4n8l/A+o57G504vjx2Z26Hgg4/yc2eXLPt2cyAQoAQB7r7bkMa1wCwC5KVSl8+6Slz+fTF198oVdeeaXbdS5dLpc8Hk+fVgt3MujQsuwc39ZD0tZDSWxbWqQzzpAuuSR5Mdf4TMPDDrP3lYppShdfLL3/fuo+m5ulMq8l/8SVaun4ih2fCrtsWzq+Iv/E1xRcN0KG3y9raYuC8qk+MkNhueWOhDVD9fItXSLD709kQxmYCQA5pKJi2xdP/TV1PVOFgnpTKT1ZAhQAgAGGxCUA7KJ4pfBUfvzjH+vGG2/UZ5991uPozL6uFt7ToMN587oOOgwGUyckn3/evjjx5pvO2kUi0sqVztr+z/9IPzxzmUIdE3tsE5WpUMdRap52q44M/Vt+PaOQyrqMzJynGnljzWoM+VXc3Ky2I33y+y2FQoZMI6qIVSDTiGnePJe8XkuNjQYDMwEg21yu/k3kZapQUC5XSu/vNU0BAEiCxCUA7KL6+vpE4rEnpmnqww8/lGEYKi4u7pNq4alGCToZRRkfdFhfLxUUSLHYLoXSLZer+6nf24snU53o6JDq7/l8axKysOf9qlMLnx6nd7RYLTpJUjcjM+WRX40K3Dtf/rc8anmuQJJLEcv+0BbftoRi8k+NKdhS2GV0qqPRmb0ZxsmQTwDILV6vPYU7VaEgrzd5P7t7pfRMIIkKAOgGiUsA2EXhcDhp0lKyp42Ht5srbRiGfD5fxtaxdDKSctkyZ9O6m5vtXJmTpOWee0obNyZva5pSdbWdd0u1PFh8ucme1rfc3rhxlsLLNiZNWkpSVIWq2/xNRTQoSRtTIZXpW/e1KpSkv6hcCj3nUnPQkm+ysfVxdzA60+lQV/WyLQAgO1wuO5FXWbnt28C4+BdKtbWpE2yZSoBmSk+Fgtassa/PdqGgXEuiAgByRkF/BwAA+ciyLH366adypfigYpqm3G53H8WQeiTlpEnS5Zen7ss0pbo6e4Bfqs9epmkPAEyV4IxEpKoqu22yQRMul337jTemTlpK0pVXGnIP2yBTnSnbJktabu+vmp6yjalO1d32kf24T+1US8h+AOxRmcYOozM7ZcUcDnW1LOfDYuMfMC3LLhhRUyNNm2ZvA4HuPwwDANJTUWEn8saN63p9aanzBF88ASrtPIK+NwnQTIhG7SRhd/8z4tfNnu3sn3ImxJOoO64BGk+iNjT0rr9o1P6f+MAD9nZXjyNT/QAA0kJVcYcGVAVHAJLs5GQwGFR9fX1iWveMGTP05S9/WTU1Nfr73//uqJ++qhTutNCpU+Xl0tVX96IC+A861fJsgaLa+UOWS1F5ToopuNSeWt3TCMWItW2E4siRkm+Ssz6Dt7+oKdcenzJOlxFV1MrUh0BL5e7ndfU9h2jKuXulbB349cvyXXWMLMkuDKSthYG0tTCQgjIk6be/tYewXnRR6hACAenII6mSjl3G+Uz+4znsR5mYyrw7VUrPhExXW8/UyE1GgAJAn6GqOABkQFtbW7eFdObNm6fCwkJ1dm4b7VdQUKBYN8MP06kU7mSpw/r63q0NmYxp2vuIj47sacq2yyV5PJKvzFKj5ZdfNygkb5eiNxEVyqOlarR+JUOPSDJUrDYFLb+aZahO1QprlNz6RFVaqDLLkqFGSSMd9+n7f8fJ+z/L1NLxFUW7+VfmUkSeEa/p4HOP1ML50W4ToXEFimn0oM/08ZYRiiX5t2gqInd4uerPXa4CXZSibafq7gjrSNco+aMPdl8YSM1qlF/F3/uefacdpyDuFGiBdOut0scfSy+9ZF+XbMFS1sQEgMzKRKGg3alS+vZ2NambyWrrmZr+nslp9JlatzPX+gGAbLLgSHt7uyXJam9v7+9QAPSxWCxmeb1ey+VyWZJ6vIwaNcr685//bHm9XkuSZZpml63X67XWrVvX6/2vW2dZXq89f9g0u269Xvv2l16yrP32i88xTn459FBn7QIB5/u3mposS7JikhVQmTVTf7TK1WjN1B+tgMqsWLzTRx+1rFjMvqPL1f2OXS7LmjTJsurqnPW5NdB177VZ3hGv2PFpiyXFtm4tyzviFWvde23xMFNefn3i/c4eI5VZ5Wp00DZmjdQ6az+tsFzq7P6w1Wl5Fdx2XJm8xJ9M+wVtP18zZ1pWebm9bWqyr8eAw/lM/uM5RNqc/nNsanLW34MPWlZpadf7lpba16dy//3OYrn//uT9RCI7x7D9xTAsa/x4u102+kn3ccnlfiIR+7Vx//321slj0ZdyLR70HZ7r3UZvz2XUx/HsNjhJBAaOpqYmK1nCMn55cOuJXiwWswKBgDVz5kyrvLzcmjlzphUIBKzYLiSGUuX4Cgosa9gw5/kr07SsSy5JnTf0ervmsWIxO/e1fa4rENiuzcyZ27KZqS69CdjJAc2cuS3OaMwK1L5szTyk2Sof85w185BmK1D7shWLxnZ4PGM9HHvM8notK/p0k+VVMHWSseJca+YedYkEaSYugf0utKyDDrKf3Ew9RpdcYh+8oyw0BhLOZ/IfzyHSFk/OGUZmknPd9WMY9iVVUixTSdRc6yfdxyWX+8lE8jNTci0e9CzdpGMuPtckUncZics+wkkiMHDMnDkzMWqyp4tpmtbM7RJomeL0fLlXibHALuSvko3S6+y0rLPOynygTi/l5b16TB0deyxmrfvqGZZXzfbtO47gVNBa99UzLCsWs5qm3uo41AJFkt5uaos1c+qHzp/4khJn7U46ybLGjbOssWN7TojumLFmZOaAwPlM/uM5REbEE1k7JrN6k8jKxOjETCVRMzVyMxP95Nroz0z1k6nkZ6ZkMp5MJaByKZGVS8eUbtIx11578ZhyaQRzLr32HCBx2Uc4SQQGjvLycsvJiMvyXibQLCt1bsjpQMa997ase+6x81NOR1KmHEUZlyzTd9hh9ujAU09NHahh2ImzIUOcfQgYOrTnDy7xyw4jLnvzuKc89nXrrNjJXnuaujHPnqZuzLOnqZ+8Lbsbe8bZ6MxTjv3UwWHHrBNPjNkjRL1eK1bgsprk6zJNvkk+K1aw9cm85JLUj7tpWtZXvuLsMZd2MbONfMX5TP7jOYRlWX2XTBg/3vkH70yPTkwniZpLIy5zKZZM9ZPJKfSZsDtP6Y8fXy6MTsxEP+kmHfvitZeJxzeXRjDn0mvPIRKXfYSTRGDg6KsRl6lyQ6++allHHOHs3DKeM814vinVXPX45YADnAUazxY6SbZNneq8z77iJMPpcHTmzEtiltnDFPUdLxMnWtYfaz+3Tk6xZqfjDx/Tpjmbel5QsGtrCSBvcT6T/3gOkTMfUjM1yrGnY+pNEjVTIzcz0U8ujf7MVD+ZXhfVstJ77e2uU/rjfeXC6MRM9JOJpGMurcmbqWOKx5Erz9P2fWVpOj6Jyz7CSSKwe4jFYlZTU1OX9SibmpqsjRs3Wr/61a+sjz/+2PEal4FeJNCc5gOl1G12HHToeCSlk6nATk8ODj/csr76VWfJrt6ccORLAs3B6Mxdm/affC3O+MhMR4/RGWc42+lJJzlr15cJY2QN5zP5j+dwgMulKZOZTihkahRUOiM3M9FPLo2UzFQ/mUxSW1b6CZLdcUp//HHJhdGJmeon1157mXj/zKURzLn02uslEpd9hJNEIP+tW7euxwrggwcPtiRZNTU1KauKu1wuy+v19qr4TqbXrux1Dsnp0MxLLkmdOXW57HZO+0yVtd0+2ZZPU5ZTZIydHPZhh1nWccf18nl3+hg5GenqclnWIYc4GxHbB2u6Ivs4n8l/PIcDWK5O181EkZ9MSXfkZib6yaXRn5nqJ5NJ6lxJHuVSYtiycmt0Yqb6yaXRvpl6/8ylY8q1x6YXensuUyAAGAAsy5Lf71dLS4skKRKJdNlu2rRJkjR//nx98MEHamxslMfjkSSZptll6/F41NjYKMMwtvYtBQJSTY00bZq9DQTs6+Pq6yWXK3WcxxwjHXdcz21dLsnrlcrKenXwkt8vbT12bT3mxLalxb7dsqS1a6VoNHl/0aj08cdScbEUDNoHW10tlZfb20DAvr642G5vGFJjo7T18dTWxzGx9Xjs2w3DeZ+5wDAkn0+aO1davNje+nz29XJ22EuXSi+8IJ1xRuJuPTJNqa5OUnGxrEBQgV+/rJoDntG0Mc+p5oBnFPj1y7IC2z1GM2Zse457Eo1Ko0albheJSOFw8jYAgL4VCkkffNDz7ZYlrV5tt8sGl0uaM8f+ecd/YvHfa2udnQBlSkWF9P77UlOTdP/99nbFCvv6bPWTqccll/rxeqXS0p5PVgxDGj/ebpdMNCrNmtX1JDkuft3s2anPRTMRT2tr8n04bZepfjLx951rx1RS4qyfZO0y9drL1PtnJo4p156nXPvf0g2z3/YMAFkUDAYVcvBm+4c//EHjx49P3Ke5uVl1dXUKh8Nyu92qqqpSWVlZImnZ1mbn/EIhO7EUidjbefPs/5+NjXYeKRxOfQ4mSePGSQsXdt9nJNI1x9eLg0/+jyYatW9vbpbGjrU77+6EMs40Jbfb/jmevPP5kscQT0g2N9vZt3DY7qOqys7Cbn9ATvvMA04P2zSTP+SS/fw/8oh0333SH/5gaOnSidteH59K866SvA3bXnPy+ewXYUtL9y8+l8t+QR18sJ09TZW83J5l2QdWX7/toGbM6JK47VU7AEBqmfqQmkkVFdKiRXYyavsPvqWldjKstwnDTHC5pMmT+7efTD0uudJPPPlZWbnzeWJvkqi9SZAke+wzEU8mElCZ7CcTf9+5dkzxpOOaNd2f6BqGfXuypGOmXnuZev/MxDHl2vOUi/9bdpSxsZ67OablAPmtLwru9GYG9MyZvVu70vG6lc4O3vlUYKdTDljvMKOcVpNPddlpGdCta3E2yddlLc4m+bZVSu/NOgbV1Zb1xhvOpqr3dtq/kzVYkTbOZ/Ifz+EA1hcFUjIlS5Vo806mHpdc6SfdqfjZWCszX6f0Z3LtxFw5JsvK7Nqz6bz2+mK5g109plx7nvrhfwtrXPYRThKB/FZeXm4lS1rGL+Xxct0O9CbH16/5wPJyZzsvL+9dNhYZ01droNq5Q7vgj2lELCm2dWtfv26d1bvKUfHXQE8Vy+Ovj2i0d6+j3iQ5nSY4SYR2i/OZ/MdzOIDl4pqSGHhyoRp4puLJlYJO8ePIxN93Lh3T9n1lYu3ZdJ7rTL9/pntMufQ89cP/FhKXfYSTRCC39VQtPF5AZ+bMmVZBQYGVyRGXvRnI2G/5wI0bLevYY1OfIG4/3DOfCuTsJpy8Pk4+2bJOO63nc4q0XnPJnvOTT7asW26xrJEjnX3YkOziTU7axYcRZ7p4E6/hHnE+k/94Dge4TCYTgGzLxeR7LhR02r6PXBidmOl+LCs3RmVn+v2zv0cwZ7KfLP9v6e25jGFZltXHs9F3Cx0dHSoqKlJ7e7tGjBjR3+EA2E5bW5v8fr9CoZBM01QkEklsvV6vGhsbtWzZMk2ZMiVlX4FAQL6taytaVvLl+aZNk/7+99TxlZfbtVt6Wg8zEum6Hmav9RTo+vXSlVdK773nrJ9AYNu6kpblbD1KZIyT10d1tfPX3NVXSw5e8tue9lTP+ccf24G8/bYUi6V7uPbBHX+8vUbQqlWp2zc1ST/5Seo1OwMBe12sVO2CwW2v5VR/7LsRzmfyH88h1NCw81qF48f335qSQG80NNjrFUr2/9+4+P/bRYuy/zqOr/fe2mqvB+j17lpRqUz0k6m/71w6plySa++fufQ8ZfGx6e25DIlLhzhJBHKTZVny+XxqaWlRtJskhcvlksfjUSAQ0OTJk1O2CwaDMgwjZRLp+uulyy6zcy7JmKadbJo7Nx5vhvOByQLdd9+uCaGeiu50l8hBv0j1+qipsYs3JaujE3/NSdK993b/lO/Ydu5ch7k7p9l6p/bf367Smoph2KXXH300ddtzz5UefDB1u3jGts++UchNnM/kP55DSNr9kgkYWHIteZRr+PvuWzy+PcvSY0Piso9wkgjkpkAg4Hgk5ZFHHplyZGZxcbEsy85nJBuw5aRC+LZ991GBbCeBmqZ04onSL38p3XDDgEnO7K4CAeejKO+4w1mOceJEe3DDxRc7eHk4yZwWFEh77SWtW5d8ZKZpSgcdJL31lrMRnPvuK334Ye+qn/fEMKTDD7e/fbjnHunNN52NzuyLkZlZHu3J+Uz+4zkEsFsgeQQMWCQu+wgniUBuqqmp0cKFCxVJkswwTVPV1dWaO3euLMtSc3Oz6urqFA6H5Xa7VVVVpbKyMhlbkwROk0OStMceUmdn93mXtAYyOklmOA20qcmeOsv077znJFcdf81demnqHGNcaan9uSFl7i4YcPaa+/WvpauuSt0uEJDuu88ONNm3AQUF0rhx0urVqfvsC4GAdOSRmR+Z2Q+jPTmfyX88hwAAIJ/19lzGzEJMANBn1q5dmzRpKUmRSEThcFiSZBiGfD5fYh3L7tTXb8sd9MTlkiZNkv72N3uZnu7yDh6PnXfokhN0kpDsKZkxb962ZMb69dK116Z+gEzT3tfkyXb/Pl8fDf9ENhiG/fT3lOva/jU3Y4b9knFi+5laO4oPiGhulv134/XKWtqiYOxk1WuGwnLLrbBmqF6+giUyJnnstVUbGlJnWMvK7L+Je+9NHmAsJh1xhJ1dTfWHOXq0/beViXU4JfvBve8+e23Plhb7ungM8W1Li/2k9OZbCsuy75PJPgEAAIDdDIlLAHnrnXfe0YsvvpiynWmacrvdjvtduzb1KLVoVCoqsvOOwaDDgYxOEpIjRyZPZixdKk2YIHV0ODuYSMQOCruN4mJnr7mtOcakucPDD7fbL1+eevb3H/8o+XyG2hYuln/iSoU6jpKpTkVkylRE81Qj77Blaly4n4oLCpxnWJ0E6vFI11yTeo3LaFS67jpnoz1vv91+AJctS94uEpHeeEN69tnk+92W3bWvS/UlRTBo36c3fQIAAAADDIlLADnNsiwFg0HV19cnpnbPmDFDxcXF8ng82rBhQ8o+IpGIqqqqtvbXcy7Bsuy1/pYsSR2Xadr3lRwOZHQ6uurGG5MnM2Ix50nLHQPFbsPJa87p6MzqaumVV5LvLxaT7r9f+uwz6Z13Ruqd9UWSpIgKu2xb1h8pf7VhDxJ0mmF1GujIkc4SnE5He159tZ2QfO211NWOPv3U2TDshQuTF/zZ/kuKuXPtjHCqdUDr6khcAgAAYMBijUuHWE8IyL62trakxXSi0ahaWlo0ePBgbdmyRbFuEgDbVwv/7DOjx9zIoYfaucU333QeX6+K7jhdj3LQIGnLltTtRoywqy4uWOBs3yQ+BqxMVCrvrV16yTlZg9XpmpBO2zn9u/R4tn3pkIzLZVdef+89exhrd4lTw5CGDZM+/9zZ41JeLi1e7KytA5zP5D+eQwAAkM8oztNHOEkEssuyLPl8PrW0tCjazYd/l8ulo48+Wh6PR9dcc40uuOCCpNXCR44sTlrUZEfDh9vLSGas6E6ms0Pl5XbyxWmlFtbIQw+c5u5Gj5Y+/jh1O9O0R3HOndtHBbOdFply0s5ptaODD7bXucxkdteJ7R/MDOF8Jv/xHAIAgHxGcR4Au4VgMKhQkinT0WhU//znP3X77bdr/PjxCgaDSauFBwLJZ2DHfeUrUm2tdMwx0tln96LoTirhsLOkh8uVOrMan/7dm0otQA+cLjHZ1GTn/FINPIxEpH/9yx50eNFFyWdL71LBbKdFpjI5n37ZstQFhCR74dvPP7engKf6e99zT/vbkWQiETvZCgAAAAxQJC4B5KT6+vrEqMmemKapuro6+Xy+lNXCnVYKP/FE6ZRT7N8dF91xMqzM7U4dgGlKp56augDJ9skMp+sIAj1wmrtzuaTDDpNeeCF1Tu6VV6QDD9z2u5OC2X0yOtMJJ39DTrO7zzwjfeMb9jDWVKZMkdrbnVVeBwAAAAYoEpcAclI4HE6atJTsojthhxWzP/zQWaXw7afCOhrYlawIx5e/bFf6KS62MzDz5iUPIBKRvv99e8RWb5IZTkegAT1wmv928jJ2YseC2U5q2ezS6EynUv0NOc3umqadsV2yJPWXFGPG2NPPGTENAAAA9IjEJYCcs2XLFr399tsp25mmKffWitk9jdYqK5P++lf7ttT97VCAO9UQsFSVwl9/XZo82R5+5nTEls/H9G/0Cyf5bycv48MPt29bvjz5/uIFs8vKkv8ZdTc6s19kMrsbHzXNiGmk4ZNPpM2be3+/YcOkIUN67nNXV78fOtReAaE769Y5W1+6O4MH2+tOd+ezz6TOzl3rd9Age3WH7rS3O6uT153CQmnkyO5v+/xzadOmXevX5ZL22qv729avlzZs2LV+DUMaNar72zZulL74Ytf6ley1kbuzebPU0bHr/e69t70ix462bLGfu11VXGz/b9pRJGJ/wbariors19uOYjHp0093vd8RI6Q99uj+NidrUveE9wgb7xE23iO24T3Clon3CKc1KhMsONLe3m5Jstrb2/s7FGC3tnbtWsvr9VqSHF0CgYC1bp1leb2WJVmWaXbdFhXZW6eXQGBrIMk69Xrt25uanHXa1OS8z7hYzA5m5kzLKi+3t4GAfT3Qj5y8jMvLnf1plJc7/zNK/G3muljMfiBcru4PxOWyb++nv2XOZ/Jf/DmU2nv1/y1+ufvunvseNar3/cUvP/1pz/1++cu73u93v9tzvz7frvdbWdlzv5WVu96vz9dzv9/97q73++Uv99zvT3+66/2OGtVzv3ffvev9Sj33+9e/ptdvONx9v07/n/R0Wb68+36XL0+v3/hp4I7C4fT6/etfe36M0+mX9wj7wnuEfeE9YtuF9wj7kpn3iN6djzLiEkDWWZalYDCo+vr6RCGdGTNmqKioSGeffbZWrVolSTIMQ4ZhKNZNaW+XyyWPxyOvt0yTJ/c8Wmv7b9X22ktqb7cUje48isnlsuTxGPYMbMtKPQTszDPtr5viIy974nLZIzYnT+7d6CqmfyNHOXkZO13S1e22/zwKCuxvlZO1rauz/xwsq5/WwnSKolkAAABAxpC4BJBVbW1t8vv9CoVCieI7pmlq3rx5crlcim6dJ1JSUqL77rtPN910005tI5GIPB6PGhsb1dxsOKoW/stfSt/5r8/kn7hSoY6jZKpTEZkyFVFEhfLs+aoaF+4nwxgpBYLJS5BHo9Kzzzo74GjUzq7EkZDEbiDVy7g3s6XvuCN50jLe9u237alkZ5/dj2thOsUUcAAAACAjSFwCyBrLsuT3+9WydSRjvPhOfGttHbl4wgkn6KGHHtI+++yjr3/962publZdXV1idGZVVZXKyspkGMbWauGWIpGeEwGmy9K770jF1dMU/KJFzZqkOlUpLLfcCqtKdSr7YqmMiiOkH/9Yuu02ZyMpnSzGs9PCmcDuz+mSrmVldl7PyZ9Tc7N06KF28lJKvRZmv4/M5EsKAAAAIG2GZSX7ZI64jo4OFRUVqb29XSNGjOjvcIC8FAgENGXKlJTt/u///k/f+MY3JCVPPkiSf5qlv/8jVRbCUvnRa7T45fFpHsEOSkulDz5I3S4QIHmBAaenSuGRSNfRkYGA5OBtwbFAQDrySGf7juv3JGcWcT6T/+LP4bvvtmv48N4/hxTesFF4w0bhjW0ovGHjPcLGe4SN94hteI+wZaY4T4cOPND5+SiJS4c40QfSV1NTo4ULFyZGWHbHNE1VV1dr7ty5SRMf++9vn/wcO65Vf3pilCIq7LlPdapaCzVXl2buYExTuvBCe/5qqmFl/V4OGegflpV6trRl2QnCZH9GEybYJ7Gtrak/2EyYYPe9cmX3U9B3/LN0mmCNx5rvCU7OZ/IfzyEAAMhnvT2XYao4gKwJh8NJk5aSPW08HA6nrI+zYoW9nRx9TRGdkrxPFapKdc6CPPRQ6ZxzpJtvTt4uErETl8mGdlGEAwOck9nSTmvZVFc7G+D8/vvJb49G7f00NNgjPc8+O3kdrvj0888+6z7GnFtfEwAAANiNkLgEkDWjR49WQUFBt1XC40zTlNvtVjBFfZy4NeuGyqtmtcijaDdvaS5F5FGLyob8U9qUYt1K05QmTZJ+8QtpyRJnC/QZBkU4gDRlqlK5U6Ypffe7XetmdSee5AwGpZ/8xFmCkz95AAAAIHNIXALIis2bN6u1tTVp0lKyR1xWVVWpri510R2Xy9JeRVHN++Rc+aMPKqSynauFq0WNrnNlTJksPfpo8iDjZY6dDgGLZygowgGkLROVyiXpqKOkZcuSt4lEnK/JVFBg1+tK9kVKPMHZ3LwtfqfTyneH6ecAAABAXyFxCaDPrV27VhUVFYlq4j1xuVzyeDwqKyvTbbclT1pKdrIgvPdhKn7rEwXlU7PKdq4WrmYZUUnf/7694rWTUZSSsyFgALLGaaXygw+WXnst+chM05T23ddeQPyTT5LvNxaTli9PPdrT5bLfKny+ntfN3HFaudN2AAAAwEBF4hJAxliWpWAwqPr6eoXDYbndbp1wwgm66aabtGbNGknSHnvsoQkTJujNN9+UaZqKRCKJrcfjUWNjowzD0B4dH0saJannBKGpiNzFnZLXK6OlRb5os3xq7toons3w+Xo3ilJiJCWQQ5wOhF62TLr33uR9RSLS739vJxoXLkydkLSs1FPUo1HpT3+yK6O+/LK0atW2fW2/jU8rDwSSr+PL9HMAAACAxCWADGlra5Pf71coFEokIl0ul+ZtN7dz3Lhxevjhh3XMMcfqrruW6Z57Pld7+yAVFW3Rf//3cF155VEqKLA/oV8xfIEa9P2k+0wU3XGakGQUJZDXnPwJOx2ZWVZmJyRTTT+PRqUjjrArmqdKXm7aJD30UOr+QiF7/0uXpm63/fRzAAAAYKAxLCtZpQrE9bZcOzCQWJYln8+nlpYWRbvLFEgaPny4/vOf/2jIkH16zDGefLK0eLGdnLDOmCrf49enLLoTPOt2GX9fbGcgSEgCUM9TsCORrlOwLctOCqZKct54o/S1r6Xe7+DBdvIyU0zTrqY+d27m+uR8Jv/xHAIAgHzW23OZgizEBGA3FwwGFQqFekxaStLnn3+ut956e+vUSPv7kh2nRi5daslfHpM15y4ZgSY1yi+P7HmUpjolWVu32lZ0Z4zbvnN8qNXcuXb2c+5cqlsAA1R8ZGYgYCf+ysvtbSBgXx9fNzI+/dzjsX83za7b+IDtyZPthKfL1f3+XC779s8/t9tmSiSSuvI5AAAAsDtjqjiAtNXX1yemh/fENE3ddtsLCoV86mndSssyFFpqqHnpg/Jpk4q1KXXRnaqqvjkoAHnN6RK1TleQcLIihWlKBx4oLVmSet3MceOkNWu6H+kZZ5p2LAAAAMBAReISQNrC4XDSpKUkRSIRLf/3UTKNiCJWz289LkVUN/y78n3eLI0eLWPduuRFd+JVwAFgFzlJcjpNcM6Y4WzdzKuusi/JRCJ8NwMAAICBjcQlgLQNGjQoZRvTNLXn5qKkSUtJisql8L7HSXUvSRMm9K4KOAD0IScJTqfFga68UmpocFZECAAAABioWOMSQFpeeOEFPfnkkynbRSIRjZclKXk9MFMRuTs/lI4+2vlCdQCQI5yum1lQ4Kwd380AAABgIGPEJYBd9tRTT+nss8/W+vXrt7vWJ2mGJLeksKR6FRQs0Ve+crme/Pfx6ml9y7iIClW196OSvPYVTheqA4Ac4XRaudN2AAAAwEBF4hLALlm0aJHOP/98dXbaVb5POOE0vfHGr/T55xMldcp+e4lIqtGwYcv04IP76byvfqBX1u0ne9Tlzp/IXYrIo2dVdtjHWTsOAOgLTr9z4bsZAAAAoGckLgEkZVmWgsGg6uvrFQ6H5Xa7VVRUpDvvvDPRxu8/W598skgbNsRXnyjssl2//kjNnGlo7v+8pcXfna9n9HUtkVemOhWRKVMRRVQoj1rUKL+MCx/O6jECAAAAAIDcQ+ISQI/a2trk9/sVCoVkmqYikYgKCgoUi8USbS6++GJdcMEfdcoprh77iUYNhULSF/sv1426UT/TjWpWmepUpbDcciusKtWprGCpjElUowAAAAAAACQuAfTAsiz5/X61tLRIsovrSOqStBw3bpzmzp2ryy4rSBT77olZEFPdfTH5ZE8S96lZPrOla6XwSV6qUQAAAAAAAEkkLgH0IBgMKhQKJW2zZs0ahUIhhcO+pElLSYrEDIXltn/51a+kr36VahQAAAAAAKBHJC4BdKu+vj4xPbwnpmmqrq5OQ4akriphKiK3wtK3viVde+22ihQAAAAAAADdIHEJoFvhcDhp0lKyp4+/+WaRli9P3V9Ehao68W1p7lxGVQIAAAAAgJRIXALo1t577y3DMGRZVo9tCgoq9Oyzv1Q0Gr/Gkr2CZVculyWPx1BZ8JbubgYAAAAAANhJQX8HACD3bNq0Sf/5z3+2S1r6JP1RUuPWrU/SBMVif1Y0uock6aQTOvXV4a9Jkkx1SrK2biXPnq+qceFnDLQEAAAAAACOMeISQBcbN27U2Wefreeee07SSNnJyjJJnbLfMiKSaiQ1a7/95mvlyst0/vmW7l15ugatD6pZk1SnKoXlllthValOZV8slVHtkYJBpokDAAAAAABHSFwCSFi/fr2mTZumZ555RpJUUPAPxWInbr21cIftJJWUnKTf/EY6a1hQxtfs+/jULJ+au3YckxQKSc3NFOQBAAAAAACOkLgEBijLshQMBlVfX69wOKzi4mK98sorevXVVyVJQ4acoY0bJyXpwaXnnnNpxAjJqKuXTFNKVszHNKW6OhKXAAAAAADAERKXwADU1tYmv9+vUCgk0zR3qh4+YsQI+XwL9NhjDnOR4XDyhpJ9ezicgegBAAAAAMBAQHEeYICxLEt+v18tLS2StFPSUpIOOOAAWdZo57lIt1sqSPF2Ypp2OwAAAAAAAAdIXAIDTDAYVCgUUjQa7bHNK6+8oljsI+e5yH32kWKx5I0jEamqqvcBAwAAAACAAYnEJTDA1NfXyzSTrxLhcpl65523neUij3lNuuWW5A1dLsnrlcrKehktAAAAAAAYqEhcAgNMOBzudnr4Nqai0d/prbeSJxntXKSlst+fL23ZYl85ZszWLsyuW49HamyUDCO94AEAAAAAwIBBcR5ggHG73SooKFAsFpPkkzRDkltSWNKDkmZJOj3RfsIE6f33txUNj2/tXKQh4/O/S2edJY0aJT32mPTcc3bFnnDYnkdeVWWPtCRpCQAAAAAAeoHEJTDAHHTQQYrFRkhqlFQmqVP2W0FEUs3WrVRYGNN99xVo+nSpuTlJLrJ4X2nJEikalfbYQ/L57AsAAAAAAEAaSFwCA8hrr72m//mfX0h6RJJn67WFO2wNGUan/u//TE2eLMmy5LOC8qleUliyRkvRCyRNkbR1FOWIEdk6BAAAAAAAMECQuAQGiI8//ljl5eVav/5Y2SMte+KSZbns0ZRtbZLfL4VC2+aIFxRI995rzxX/xz+k4uIsHQEAAAAAABhIKM4DDABbtmzRueeeqxUrVshe0zJZcR47R1l3n2UnLVta7CvjBX3ipcZbWqTycsmy+ixuAAAAAAAwcJG4BHZzlmXpO9/5jkKhkCRpjz32VarB1pGIFP7Pp/ZIy2i054ZLl9oLYAIAAAAAAGQYU8WB3YhlWQoGg6qvr1c4HJbb7VZhYaHmz58vSRo8eLBOPfVo/eMf2wZOdsc0Jfcnb2ybHp6sYV0dxXgAAAAAAEDGkbgEdhNtbW3y+/0KhUIyTVORSCSxjVuwYIFeemm0Fi9O3lckIlXt/aj0VvIp5fbQzHAGogcAAAAAAOiKxCWwG7AsS36/Xy1b16OMJyvj24KCAo0fP16ff36ebr01eV8ul113p2z026l3bJqS251W7AAAAAAAAN1hjUtgNxAMBhUKhRTtYT3KWCymlStP1aWXGonr9tnH3ppm163HIzX+bo2Mf/w99Y4jEamqKp3QAQAAAAAAukXiEtgN1NfXyzSTDaD+jqQ/JH77f/9PWr3KUuDXL6v6gJDKxzyv6gNCCvz6ZQUDlooPHyfNnJl8py6X5PVKZWUZOQYAAAAAAIDtMVUc2A2Ew+Ht1rL0SZohyS0pLGmRpOsSbb//femW69tkTPHLFwrJFy/A86kpXRWRGrxSY6N0++3SyJFSMCi1tGwr1BPfejx2O8PYMRwAAAAAAIC0kbgEdgNut1su1yhFow9KKpPUKfvPOyKpRtILklw68sh/65ZfnSFjst9ORkrbqobHty0tkt9vJyx/+UvJsqTmZrt6eDhsr2lZVWWPtCRpCQAAAAAA+ohhWZbV30Hkg46ODhUVFam9vV0jRozo73CALpqaAvra1wokedT99xERSS/qmWe2aIphSVOmpO40EJB8vozGCQDoX5zP5D+eQwAAkM96ey7DGpfAbiAW88oeadnTIGpT0kkyjDKpvn5bJZ6emKY9whIAAAAAAKCfkLgEdgM33PCa7OnhPTNNS/X1hj3dO7EeZg8iEbsdAAAAAABAPyFxCeS5559/Xi++uFJSYdJ2kYhh5yIHDUrdqWnaa1kCAAAAAAD0ExKXQB777LPP9F//9V+S1ir1iEvJPegz6ZlnUnccidgFeAAAAAAAAPo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- "text/plain": [ - "
" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n", - "summary_plot(imdb_results,\n", - " axes[1],\n", - " col='accuracy',\n", - " ylabel='Accuracy')\n", - "axes[1].set_xticks(np.linspace(0, 30, 7).astype(int))\n", - "axes[1].set_ylabel('Accuracy', fontsize=20)\n", - "axes[1].set_xlabel('Epoch', fontsize=20)\n", - "axes[1].set_ylim([0.5, 1]);\n", - "axes[1].axhline(test_results[0]['test_accuracy'],\n", - " color='blue',\n", - " linestyle='--',\n", - " linewidth=3)\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "7898334d", - "metadata": {}, - "source": [ - "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$, as it does for the neural network.\n", - "\n", - "Once again, we end with a cleanup." - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "b1ecfca8", - "metadata": {}, - "outputs": [], - "source": [ - "del(imdb_model,\n", - " imdb_trainer,\n", - " imdb_logger,\n", - " imdb_dm,\n", - " imdb_train,\n", - " imdb_test)" - ] - }, - { - "cell_type": "markdown", - "id": "0169cd1b", - "metadata": {}, - "source": [ - "## Recurrent Neural Networks\n", - "In this lab we fit the models illustrated in\n", - "Section 10.5." - ] - }, - { - "cell_type": "markdown", - "id": "6e7f96cc", - "metadata": {}, - "source": [ - "### Sequential Models for Document Classification\n", - "Here we fit a simple LSTM RNN for sentiment prediction to\n", - "the `IMDb` movie-review data, as discussed in Section 10.5.1.\n", - "For an RNN we use the sequence of words in a document, taking their\n", - "order into account. We loaded the preprocessed \n", - "data at the beginning of\n", - "Section 10.9.5.\n", - "A script that details the preprocessing can be found in the\n", - "`ISLP` library. Notably, since more than 90% of the documents\n", - "had fewer than 500 words, we set the document length to 500. For\n", - "longer documents, we used the last 500 words, and for shorter\n", - "documents, we padded the front with blanks." - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "5503cb3f", - "metadata": {}, - "outputs": [], - "source": [ - "imdb_seq_dm = SimpleDataModule(imdb_seq_train,\n", - " imdb_seq_test,\n", - " validation=2000,\n", - " batch_size=300,\n", - " num_workers=min(6, max_num_workers)\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "356b76f2", - "metadata": {}, - "source": [ - "The first layer of the RNN is an embedding layer of size 32, which will be\n", - "learned during training. This layer one-hot encodes each document\n", - "as a matrix of dimension $500 \\times 10,003$, and then maps these\n", - "$10,003$ dimensions down to $32$. {The extra 3 dimensions\n", - "correspond to commonly occurring non-word entries in the reviews.}\n", - " Since each word is represented by an\n", - "integer, this is effectively achieved by the creation of an embedding\n", - "matrix of size $10,003\\times 32$; each of the 500 integers in the\n", - "document are then mapped to the appropriate 32 real numbers by\n", - "indexing the appropriate rows of this matrix." - ] - }, - { - "cell_type": "markdown", - "id": "14ddd9a9", - "metadata": {}, - "source": [ - "The second layer is an LSTM with 32 units, and the output\n", - "layer is a single logit for the binary classification task.\n", - "In the last line of the `forward()` method below,\n", - "we take the last 32-dimensional output of the LSTM and map it to our response." - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "8b683641", - "metadata": {}, - "outputs": [], - "source": [ - "class LSTMModel(nn.Module):\n", - " def __init__(self, input_size):\n", - " super(LSTMModel, self).__init__()\n", - " self.embedding = nn.Embedding(input_size, 32)\n", - " self.lstm = nn.LSTM(input_size=32,\n", - " hidden_size=32,\n", - " batch_first=True)\n", - " self.dense = nn.Linear(32, 1)\n", - " def forward(self, x):\n", - " val, (h_n, c_n) = self.lstm(self.embedding(x))\n", - " return torch.flatten(self.dense(val[:,-1]))" - ] - }, - { - "cell_type": "markdown", - "id": "b7903383", - "metadata": {}, - "source": [ - "We instantiate and take a look at the summary of the model, using the\n", - "first 10 documents in the corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "702aa8de", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "LSTMModel [10, 500] [10] --\n", - "├─Embedding: 1-1 [10, 500] [10, 500, 32] 320,096\n", - "├─LSTM: 1-2 [10, 500, 32] [10, 500, 32] 8,448\n", - "├─Linear: 1-3 [10, 32] [10, 1] 33\n", - "===================================================================================================================\n", - "Total params: 328,577\n", - "Trainable params: 328,577\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 45.44\n", - "===================================================================================================================\n", - "Input size (MB): 50.00\n", - "Forward/backward pass size (MB): 2.56\n", - "Params size (MB): 1.31\n", - "Estimated Total Size (MB): 53.87\n", - "===================================================================================================================" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lstm_model = LSTMModel(X_test.shape[-1])\n", - "summary(lstm_model,\n", - " input_data=imdb_seq_train.tensors[0][:10],\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "f663b95c", - "metadata": {}, - "source": [ - "The 10,003 is suppressed in the summary, but we see it in the\n", - "parameter count, since $10,003\\times 32=320,096$." - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "e48aa272", - "metadata": {}, - "outputs": [], - "source": [ - "lstm_module = SimpleModule.binary_classification(lstm_model)\n", - "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "143be7e8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------------\n", - "0 | model | LSTMModel | 328 K \n", - "1 | loss | BCEWithLogitsLoss | 0 \n", - "--------------------------------------------\n", - "328 K Trainable params\n", - "0 Non-trainable params\n", - "328 K Total params\n", - "1.314 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1ee0aabd8caf403c93b8c4ba0a1c2507", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - 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"version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=20` reached.\n" - ] - } - ], - "source": [ - "lstm_trainer = Trainer(deterministic=True,\n", - " max_epochs=20,\n", - " logger=lstm_logger,\n", - " callbacks=[ErrorTracker()])\n", - "lstm_trainer.fit(lstm_module,\n", - " datamodule=imdb_seq_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "55dc2b66", - "metadata": {}, - "source": [ - "The rest is now similar to other networks we have fit. We\n", - "track the test performance as the network is fit, and see that it attains 85% accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "9272e2ba", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "92a1cacbb1944d74b3f3c0670eb84b63", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_accuracy 0.8452399969100952\n", - " test_loss 0.7559056878089905\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.7559056878089905, 'test_accuracy': 0.8452399969100952}]" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "eee55195", - "metadata": {}, - "source": [ - "We once again show the learning progress, followed by cleanup." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "5d9d387e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.5, 1.0)" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n", - "fig, ax = subplots(1, 1, figsize=(6, 6))\n", - "summary_plot(lstm_results,\n", - " ax,\n", - " col='accuracy',\n", - " ylabel='Accuracy')\n", - "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n", - "ax.set_ylabel('Accuracy')\n", - "ax.set_ylim([0.5, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "7c6d1072", - "metadata": {}, - "outputs": [], - "source": [ - "del(lstm_model,\n", - " lstm_trainer,\n", - " lstm_logger,\n", - " imdb_seq_dm,\n", - " imdb_seq_train,\n", - " imdb_seq_test)" - ] - }, - { - "cell_type": "markdown", - "id": "7d179385", - "metadata": {}, - "source": [ - "### Time Series Prediction\n", - "We now show how to fit the models in Section 10.5.2\n", - "for time series prediction.\n", - "We first load and standardize the data." - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "8e2d1d5c", - "metadata": {}, - "outputs": [], - "source": [ - "NYSE = load_data('NYSE')\n", - "cols = ['DJ_return', 'log_volume', 'log_volatility']\n", - "X = pd.DataFrame(StandardScaler(\n", - " with_mean=True,\n", - " with_std=True).fit_transform(NYSE[cols]),\n", - " columns=NYSE[cols].columns,\n", - " index=NYSE.index)" - ] - }, - { - "cell_type": "markdown", - "id": "6ad4a748", - "metadata": {}, - "source": [ - "Next we set up the lagged versions of the data, dropping\n", - "any rows with missing values using the `dropna()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "6f6d0e12", - "metadata": {}, - "outputs": [], - "source": [ - "for lag in range(1, 6):\n", - " for col in cols:\n", - " newcol = np.zeros(X.shape[0]) * np.nan\n", - " newcol[lag:] = X[col].values[:-lag]\n", - " X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n", - "X.insert(len(X.columns), 'train', NYSE['train'])\n", - "X = X.dropna()" - ] - }, - { - "cell_type": "markdown", - "id": "91a924d2", - "metadata": {}, - "source": [ - "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n", - "`log_volatility` to predict only from previous day’s data." - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "d3c961ec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n", - " 'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n", - " 'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n", - " dtype='object')" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y, train = X['log_volume'], X['train']\n", - "X = X.drop(columns=['train'] + cols)\n", - "X.columns" - ] - }, - { - "cell_type": "markdown", - "id": "1c5f8d2f", - "metadata": {}, - "source": [ - "We first fit a simple linear model and compute the $R^2$ on the test data using\n", - "the `score()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "cb89da88", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4128912938562521" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = LinearRegression()\n", - "M.fit(X[train], Y[train])\n", - "M.score(X[~train], Y[~train])" - ] - }, - { - "cell_type": "markdown", - "id": "b92425ab", - "metadata": {}, - "source": [ - "We refit this model, including the factor variable `day_of_week`.\n", - "For a categorical series in `pandas`, we can form the indicators\n", - "using the `get_dummies()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "55d921d7", - "metadata": {}, - "outputs": [], - "source": [ - "X_day = pd.merge(X, \n", - " pd.get_dummies(NYSE['day_of_week']),\n", - " on='date')" - ] - }, - { - "cell_type": "markdown", - "id": "afab736a", - "metadata": {}, - "source": [ - " Note that we do not have\n", - "to reinstantiate the linear regression model\n", - "as its `fit()` method accepts a design matrix and a response directly." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "4e87eeb9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.45633025715446285" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M.fit(X_day[train], Y[train])\n", - "M.score(X_day[~train], Y[~train])" - ] - }, - { - "cell_type": "markdown", - "id": "43309e3b", - "metadata": {}, - "source": [ - "This model achieves an $R^2$ of about 46%." - ] - }, - { - "cell_type": "markdown", - "id": "93c61d80", - "metadata": {}, - "source": [ - "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n", - "versions of each feature as indicated by the `input_shape` argument\n", - "to the layer `nn.RNN()` below. We first\n", - "ensure the columns of our data frame are such that a reshaped\n", - "matrix will have the variables correctly lagged. We use the\n", - "`reindex()` method to do this.\n", - "\n", - "For an input shape `(5,3)`, each row represents a lagged version of the three variables.\n", - "The `nn.RNN()` layer also expects the first row of each\n", - "observation to be earliest in time, so we must reverse the current order. Hence we loop over\n", - "`range(5,0,-1)` below, which is\n", - "an example of using a `slice()` to index\n", - "iterable objects. The general notation is `start:end:step`." - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "3689b318", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n", - " 'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n", - " 'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n", - " 'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n", - " dtype='object')" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ordered_cols = []\n", - "for lag in range(5,0,-1):\n", - " for col in cols:\n", - " ordered_cols.append('{0}_{1}'.format(col, lag))\n", - "X = X.reindex(columns=ordered_cols)\n", - "X.columns" - ] - }, - { - "cell_type": "markdown", - "id": "9dbcf9f7", - "metadata": {}, - "source": [ - "We now reshape the data." - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "5633e521", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6046, 5, 3)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_rnn = X.to_numpy().reshape((-1,5,3))\n", - "X_rnn.shape" - ] - }, - { - "cell_type": "markdown", - "id": "bd3ae3ff", - "metadata": {}, - "source": [ - "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n", - "\n", - "Now we are ready to proceed with the RNN, which uses 12 hidden units, and 10%\n", - "dropout.\n", - "After passing through the RNN, we extract the final time point as `val[:,-1]`\n", - "in `forward()` below. This gets passed through a 10% dropout and then flattened through\n", - "a linear layer." - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "979a6066", - "metadata": {}, - "outputs": [], - "source": [ - "class NYSEModel(nn.Module):\n", - " def __init__(self):\n", - " super(NYSEModel, self).__init__()\n", - " self.rnn = nn.RNN(3,\n", - " 12,\n", - " batch_first=True)\n", - " self.dense = nn.Linear(12, 1)\n", - " self.dropout = nn.Dropout(0.1)\n", - " def forward(self, x):\n", - " val, h_n = self.rnn(x)\n", - " val = self.dense(self.dropout(val[:,-1]))\n", - " return torch.flatten(val)\n", - "nyse_model = NYSEModel()" - ] - }, - { - "cell_type": "markdown", - "id": "14671ab2", - "metadata": {}, - "source": [ - "We fit the model in a similar fashion to previous networks. We\n", - "supply the `fit` function with test data as validation data, so that when\n", - "we monitor its progress and plot the history function we can see the\n", - "progress on the test data. Of course we should not use this as a basis for\n", - "early stopping, since then the test performance would be biased.\n", - "\n", - "We form the training dataset similar to\n", - "our `Hitters` example." - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "3528f4e7", - "metadata": {}, - "outputs": [], - "source": [ - "datasets = []\n", - "for mask in [train, ~train]:\n", - " X_rnn_t = torch.tensor(X_rnn[mask].astype(np.float32))\n", - " Y_t = torch.tensor(Y[mask].astype(np.float32))\n", - " datasets.append(TensorDataset(X_rnn_t, Y_t))\n", - "nyse_train, nyse_test = datasets" - ] - }, - { - "cell_type": "markdown", - "id": "1a731099", - "metadata": {}, - "source": [ - "Following our usual pattern, we inspect the summary." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "795c283e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " action_fn=lambda data: sys.getsizeof(data.storage()),\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return super().__sizeof__() + self.nbytes()\n" - ] - }, - { - "data": { - "text/plain": [ - "===================================================================================================================\n", - "Layer (type:depth-idx) Input Shape Output Shape Param #\n", - "===================================================================================================================\n", - "NYSEModel [1770, 5, 3] [1770] --\n", - "├─RNN: 1-1 [1770, 5, 3] [1770, 5, 12] 204\n", - "├─Dropout: 1-2 [1770, 12] [1770, 12] --\n", - "├─Linear: 1-3 [1770, 12] [1770, 1] 13\n", - "===================================================================================================================\n", - "Total params: 217\n", - "Trainable params: 217\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 1.83\n", - "===================================================================================================================\n", - "Input size (MB): 0.11\n", - "Forward/backward pass size (MB): 0.86\n", - "Params size (MB): 0.00\n", - "Estimated Total Size (MB): 0.97\n", - "===================================================================================================================" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary(nyse_model,\n", - " input_data=X_rnn_t,\n", - " col_names=['input_size',\n", - " 'output_size',\n", - " 'num_params'])" - ] - }, - { - "cell_type": "markdown", - "id": "650e0ec7", - "metadata": {}, - "source": [ - "\\newpage\n", - "We again put the two datasets into a data module, with a\n", - "batch size of 64." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "31640121", - "metadata": {}, - "outputs": [], - "source": [ - "nyse_dm = SimpleDataModule(nyse_train,\n", - " nyse_test,\n", - " num_workers=min(4, max_num_workers),\n", - " validation=nyse_test,\n", - " batch_size=64)" - ] - }, - { - "cell_type": "markdown", - "id": "1de7ae89", - "metadata": {}, - "source": [ - "We run some data through our model to be sure the sizes match up correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "af6795f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n", - "torch.Size([64]) torch.Size([64])\n" - ] - } - ], - "source": [ - "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n", - " out = nyse_model(x)\n", - " print(y.size(), out.size())\n", - " if idx >= 2:\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "eecb6d9c", - "metadata": {}, - "source": [ - "We follow our previous example for setting up a trainer for a\n", - "regression problem, requesting the $R^2$ metric\n", - "to be be computed at each epoch." - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "0d04344c", - "metadata": {}, - "outputs": [], - "source": [ - "nyse_optimizer = RMSprop(nyse_model.parameters(),\n", - " lr=0.001)\n", - "nyse_module = SimpleModule.regression(nyse_model,\n", - " optimizer=nyse_optimizer,\n", - " metrics={'r2':R2Score()})" - ] - }, - { - "cell_type": "markdown", - "id": "9a10444d", - "metadata": {}, - "source": [ - "Fitting the model should by now be familiar.\n", - "The results on the test data are very similar to the linear AR model." - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "485d7bb1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n", - " rank_zero_warn(\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------\n", - "0 | model | NYSEModel | 217 \n", - "1 | loss | MSELoss | 0 \n", - "------------------------------------\n", - "217 Trainable params\n", - "0 Non-trainable params\n", - "217 Total params\n", - "0.001 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bf9eb739cc954760813987d94e79fb8e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - 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"to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4b11d5fcedba4da68bec988d9c1af1a8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "39bf95953cd84ff1b5206301be0caba9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4507f5f3b42c4f79815e72a55cd57101", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c47d400fe5ac44098a50f94c59223d71", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8b580013d7f0447a96878fa0982eda94", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "49e3e8b33931437bb03110aacac4c11d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82e34b2ab25a4d52bd58dd791fab350b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0233c4562c014826845209456bb8d5f4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 0.6207807064056396\n", - " test_r2 0.41084879636764526\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.6207807064056396, 'test_r2': 0.41084879636764526}]" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nyse_trainer = Trainer(deterministic=True,\n", - " max_epochs=200,\n", - " callbacks=[ErrorTracker()])\n", - "nyse_trainer.fit(nyse_module,\n", - " datamodule=nyse_dm)\n", - "nyse_trainer.test(nyse_module,\n", - " datamodule=nyse_dm)" - ] - }, - { - "cell_type": "markdown", - "id": "dbd13ee3", - "metadata": {}, - "source": [ - "We could also fit a model without the `nn.RNN()` layer by just\n", - "using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the \n", - "hidden layer, this would be equivalent to our earlier linear AR model. \n", - "\n", - "Instead we will fit a nonlinear AR model using the feature set `X_day` that includes the `day_of_week` indicators.\n", - "To do so, we\n", - "must first create our test and training datasets and a corresponding\n", - "data module. This may seem a little burdensome, but is part of the\n", - "general pipeline for `torch`." - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "91d6633a", - "metadata": {}, - "outputs": [], - "source": [ - "datasets = []\n", - "for mask in [train, ~train]:\n", - " X_day_t = torch.tensor(\n", - " np.asarray(X_day[mask]).astype(np.float32))\n", - " Y_t = torch.tensor(np.asarray(Y[mask]).astype(np.float32))\n", - " datasets.append(TensorDataset(X_day_t, Y_t))\n", - "day_train, day_test = datasets" - ] - }, - { - "cell_type": "markdown", - "id": "b5cf941c", - "metadata": {}, - "source": [ - "Creating a data module follows a familiar pattern." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "87137503", - "metadata": {}, - "outputs": [], - "source": [ - "day_dm = SimpleDataModule(day_train,\n", - " day_test,\n", - " num_workers=min(4, max_num_workers),\n", - " validation=day_test,\n", - " batch_size=64)" - ] - }, - { - "cell_type": "markdown", - "id": "2575f374", - "metadata": {}, - "source": [ - "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer with 32 units. The remaining steps are familiar." - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "3cf09a55", - "metadata": {}, - "outputs": [], - "source": [ - "class NonLinearARModel(nn.Module):\n", - " def __init__(self):\n", - " super(NonLinearARModel, self).__init__()\n", - " self._forward = nn.Sequential(nn.Flatten(),\n", - " nn.Linear(20, 32),\n", - " nn.ReLU(),\n", - " nn.Dropout(0.5),\n", - " nn.Linear(32, 1))\n", - " def forward(self, x):\n", - " return torch.flatten(self._forward(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "930576f3", - "metadata": {}, - "outputs": [], - "source": [ - "nl_model = NonLinearARModel()\n", - "nl_optimizer = RMSprop(nl_model.parameters(),\n", - " lr=0.001)\n", - "nl_module = SimpleModule.regression(nl_model,\n", - " optimizer=nl_optimizer,\n", - " metrics={'r2':R2Score()})" - ] - }, - { - "cell_type": "markdown", - "id": "a0e3043c", - "metadata": {}, - "source": [ - "We continue with the usual training steps, fit the model,\n", - "and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`." - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "b9ae8d0e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | NonLinearARModel | 705 \n", - "1 | loss | MSELoss | 0 \n", - "-------------------------------------------\n", - "705 Trainable params\n", - "0 Non-trainable params\n", - "705 Total params\n", - "0.003 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1e278bfc60e6404f98175752ac8f6482", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - 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"To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " Test metric DataLoader 0\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - " test_loss 0.5648325681686401\n", - " test_r2 0.4639463424682617\n", - "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 0.5648325681686401, 'test_r2': 0.4639463424682617}]" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nl_trainer = Trainer(deterministic=True,\n", - " max_epochs=20,\n", - " callbacks=[ErrorTracker()])\n", - "nl_trainer.fit(nl_module, datamodule=day_dm)\n", - "nl_trainer.test(nl_module, datamodule=day_dm) " - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch11-surv-lab.ipynb b/docs/source/labs/Ch11-surv-lab.ipynb deleted file mode 100644 index 07c4a5d..0000000 --- a/docs/source/labs/Ch11-surv-lab.ipynb +++ /dev/null @@ -1,1185 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c7f4eb5a", - "metadata": {}, - "source": [ - "# Survival Analysis\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - " In this lab, we perform survival analyses on three separate data\n", - "sets. In Section 11.8.1 we analyze the `BrainCancer` \n", - "data that was first described in Section 11.3. In Section 11.8.2, we examine the `Publication` \n", - "data from Section 11.5.4. Finally, Section 11.8.3 explores\n", - "a simulated call-center data set.\n", - "\n", - "We begin by importing some of our libraries at this top\n", - "level. This makes the code more readable, as scanning the first few\n", - "lines of the notebook tell us what libraries are used in this\n", - "notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91ac40fd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:03.064349Z", - "iopub.status.busy": "2023-07-26T05:18:03.064029Z", - "iopub.status.idle": "2023-07-26T05:18:04.312445Z", - "shell.execute_reply": "2023-07-26T05:18:04.312016Z" - } - }, - "outputs": [], - "source": [ - "from matplotlib.pyplot import subplots\n", - "import numpy as np\n", - "import pandas as pd\n", - "from ISLP.models import ModelSpec as MS\n", - "from ISLP import load_data" - ] - }, - { - "cell_type": "markdown", - "id": "a3dbcbbf", - "metadata": {}, - "source": [ - "We also collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99782418", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.315115Z", - "iopub.status.busy": "2023-07-26T05:18:04.314859Z", - "iopub.status.idle": "2023-07-26T05:18:04.379768Z", - "shell.execute_reply": "2023-07-26T05:18:04.379303Z" - } - }, - "outputs": [], - "source": [ - "from lifelines import \\\n", - " (KaplanMeierFitter,\n", - " CoxPHFitter)\n", - "from lifelines.statistics import \\\n", - " (logrank_test,\n", - " multivariate_logrank_test)\n", - "from ISLP.survival import sim_time" - ] - }, - { - "cell_type": "markdown", - "id": "2c538d28", - "metadata": {}, - "source": [ - "## Brain Cancer Data\n", - "\n", - "We begin with the `BrainCancer` data set, contained in the `ISLP` package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3137149a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.382594Z", - "iopub.status.busy": "2023-07-26T05:18:04.382386Z", - "iopub.status.idle": "2023-07-26T05:18:04.389761Z", - "shell.execute_reply": "2023-07-26T05:18:04.389375Z" - } - }, - "outputs": [], - "source": [ - "BrainCancer = load_data('BrainCancer')\n", - "BrainCancer.columns" - ] - }, - { - "cell_type": "markdown", - "id": "e798f172", - "metadata": {}, - "source": [ - "The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n", - "We first briefly examine the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45963c92", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.392289Z", - "iopub.status.busy": "2023-07-26T05:18:04.392117Z", - "iopub.status.idle": "2023-07-26T05:18:04.396285Z", - "shell.execute_reply": "2023-07-26T05:18:04.395840Z" - } - }, - "outputs": [], - "source": [ - "BrainCancer['sex'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73be61f6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.400201Z", - "iopub.status.busy": "2023-07-26T05:18:04.399975Z", - "iopub.status.idle": "2023-07-26T05:18:04.403967Z", - "shell.execute_reply": "2023-07-26T05:18:04.403581Z" - } - }, - "outputs": [], - "source": [ - "BrainCancer['diagnosis'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "572f0b9e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.406232Z", - "iopub.status.busy": "2023-07-26T05:18:04.406110Z", - "iopub.status.idle": "2023-07-26T05:18:04.409773Z", - "shell.execute_reply": "2023-07-26T05:18:04.409374Z" - } - }, - "outputs": [], - "source": [ - "BrainCancer['status'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "fbd132de", - "metadata": {}, - "source": [ - "Before beginning an analysis, it is important to know how the\n", - "`status` variable has been coded. Most software\n", - "uses the convention that a `status` of 1 indicates an\n", - "uncensored observation (often death), and a `status` of 0 indicates a censored\n", - "observation. But some scientists might use the opposite coding. For\n", - "the `BrainCancer` data set 35 patients died before the end of\n", - "the study, so we are using the conventional coding.\n", - "\n", - "To begin the analysis, we re-create the Kaplan-Meier survival curve shown in Figure 11.2. The main\n", - "package we will use for survival analysis\n", - "is `lifelines`.\n", - "The variable `time` corresponds to $y_i$, the time to the $i$th event (either censoring or\n", - "death). The first argument to `km.fit` is the event time, and the\n", - "second argument is the censoring variable, with a 1 indicating an observed\n", - "failure time. The `plot()` method produces a survival curve with pointwise confidence\n", - "intervals. By default, these are 90% confidence intervals, but this can be changed\n", - "by setting the `alpha` argument to one minus the desired\n", - "confidence level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92c39707", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.411840Z", - "iopub.status.busy": "2023-07-26T05:18:04.411663Z", - "iopub.status.idle": "2023-07-26T05:18:04.539774Z", - "shell.execute_reply": "2023-07-26T05:18:04.539439Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "km = KaplanMeierFitter()\n", - "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n", - "km_brain.plot(label='Kaplan Meier estimate', ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "f037665b", - "metadata": {}, - "source": [ - "Next we create Kaplan-Meier survival curves that are stratified by\n", - "`sex`, in order to reproduce Figure 11.3.\n", - "We do this using the `groupby()` method of a dataframe.\n", - "This method returns a generator that can\n", - "be iterated over in the `for` loop. In this case,\n", - "the items in the `for` loop are 2-tuples representing\n", - "the groups: the first entry is the value\n", - "of the grouping column `sex` while the second value\n", - "is the dataframe consisting of all rows in the\n", - "dataframe matching that value of `sex`.\n", - "We will want to use this data below\n", - "in the log-rank test, hence we store this\n", - "information in the dictionary `by_sex`. Finally,\n", - "we have also used the notion of\n", - " *string interpolation* to automatically\n", - "label the different lines in the plot. String\n", - "interpolation is a powerful technique to format strings ---\n", - "`Python` has many ways to facilitate such operations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fc7848c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.542265Z", - "iopub.status.busy": "2023-07-26T05:18:04.542002Z", - "iopub.status.idle": "2023-07-26T05:18:04.674613Z", - "shell.execute_reply": "2023-07-26T05:18:04.674126Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "by_sex = {}\n", - "for sex, df in BrainCancer.groupby('sex'):\n", - " by_sex[sex] = df\n", - " km_sex = km.fit(df['time'], df['status'])\n", - " km_sex.plot(label='Sex=%s' % sex, ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "c0c1a16a", - "metadata": {}, - "source": [ - "As discussed in Section 11.4, we can perform a\n", - "log-rank test to compare the survival of males to females. We use\n", - "the `logrank_test()` function from the `lifelines.statistics` module.\n", - "The first two arguments are the event times, with the second\n", - "denoting the corresponding (optional) censoring indicators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf30d26f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.679625Z", - "iopub.status.busy": "2023-07-26T05:18:04.679410Z", - "iopub.status.idle": "2023-07-26T05:18:04.743501Z", - "shell.execute_reply": "2023-07-26T05:18:04.743166Z" - } - }, - "outputs": [], - "source": [ - "logrank_test(by_sex['Male']['time'],\n", - " by_sex['Female']['time'],\n", - " by_sex['Male']['status'],\n", - " by_sex['Female']['status'])" - ] - }, - { - "cell_type": "markdown", - "id": "e270649c", - "metadata": {}, - "source": [ - "The resulting $p$-value is $0.23$, indicating no evidence of a\n", - "difference in survival between the two sexes.\n", - "\n", - "Next, we use the `CoxPHFitter()` estimator\n", - "from `lifelines` to fit Cox proportional hazards models.\n", - "To begin, we consider a model that uses `sex` as the only predictor." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ab78e07", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.746088Z", - "iopub.status.busy": "2023-07-26T05:18:04.745814Z", - "iopub.status.idle": "2023-07-26T05:18:04.773972Z", - "shell.execute_reply": "2023-07-26T05:18:04.773498Z" - } - }, - "outputs": [], - "source": [ - "coxph = CoxPHFitter # shorthand\n", - "sex_df = BrainCancer[['time', 'status', 'sex']]\n", - "model_df = MS(['time', 'status', 'sex'],\n", - " intercept=False).fit_transform(sex_df)\n", - "cox_fit = coxph().fit(model_df,\n", - " 'time',\n", - " 'status')\n", - "cox_fit.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "b58b93ae", - "metadata": {}, - "source": [ - "The first argument to `fit` should be a data frame containing\n", - "at least the event time (the second argument `time` in this case),\n", - "as well as an optional censoring variable (the argument `status` in this case).\n", - "Note also that the Cox model does not include an intercept, which is why\n", - "we used the `intercept=False` argument to `ModelSpec` above.\n", - "The `summary()` method delivers many columns; we chose to abbreviate its output here.\n", - "It is possible to obtain the likelihood ratio test comparing this model to the one\n", - "with no features as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4716b7b0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.776775Z", - "iopub.status.busy": "2023-07-26T05:18:04.776463Z", - "iopub.status.idle": "2023-07-26T05:18:04.783362Z", - "shell.execute_reply": "2023-07-26T05:18:04.782865Z" - } - }, - "outputs": [], - "source": [ - "cox_fit.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "2820f486", - "metadata": {}, - "source": [ - "Regardless of which test we use, we see that there is no clear\n", - "evidence for a difference in survival between males and females. As\n", - "we learned in this chapter, the score test from the Cox model is\n", - "exactly equal to the log rank test statistic!\n", - "\n", - "Now we fit a model that makes use of additional predictors. We first note\n", - "that one of our `diagnosis` values is missing, hence\n", - "we drop that observation before continuing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2767d88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.785790Z", - "iopub.status.busy": "2023-07-26T05:18:04.785586Z", - "iopub.status.idle": "2023-07-26T05:18:04.824235Z", - "shell.execute_reply": "2023-07-26T05:18:04.823739Z" - } - }, - "outputs": [], - "source": [ - "cleaned = BrainCancer.dropna()\n", - "all_MS = MS(cleaned.columns, intercept=False)\n", - "all_df = all_MS.fit_transform(cleaned)\n", - "fit_all = coxph().fit(all_df,\n", - " 'time',\n", - " 'status')\n", - "fit_all.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "eee4ab1f", - "metadata": {}, - "source": [ - " The `diagnosis` variable has been coded so that the baseline\n", - "corresponds to HG glioma. The results indicate that the risk associated with HG glioma\n", - "is more than eight times (i.e. $e^{2.15}=8.62$) the risk associated\n", - "with meningioma. In other words, after adjusting for the other\n", - "predictors, patients with HG glioma have much worse survival compared\n", - "to those with meningioma. In addition, larger values of the Karnofsky\n", - "index, `ki`, are associated with lower risk, i.e. longer survival.\n", - "\n", - "Finally, we plot estimated survival curves for each diagnosis category,\n", - "adjusting for the other predictors. To make these plots, we set the\n", - "values of the other predictors equal to the mean for quantitative variables\n", - "and equal to the mode for categorical. To do this, we use the\n", - "`apply()` method across rows (i.e. `axis=0`) with a function\n", - "`representative` that checks if a column is categorical\n", - "or not." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ede1d219", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.826852Z", - "iopub.status.busy": "2023-07-26T05:18:04.826662Z", - "iopub.status.idle": "2023-07-26T05:18:04.831013Z", - "shell.execute_reply": "2023-07-26T05:18:04.830464Z" - } - }, - "outputs": [], - "source": [ - "levels = cleaned['diagnosis'].unique()\n", - "def representative(series):\n", - " if hasattr(series.dtype, 'categories'):\n", - " return pd.Series.mode(series)\n", - " else:\n", - " return series.mean()\n", - "modal_data = cleaned.apply(representative, axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "e1c307ae", - "metadata": {}, - "source": [ - "We make four\n", - "copies of the column means and assign the `diagnosis` column to be the four different\n", - "diagnoses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc032a71", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.833522Z", - "iopub.status.busy": "2023-07-26T05:18:04.833389Z", - "iopub.status.idle": "2023-07-26T05:18:04.840549Z", - "shell.execute_reply": "2023-07-26T05:18:04.840260Z" - } - }, - "outputs": [], - "source": [ - "modal_df = pd.DataFrame(\n", - " [modal_data.iloc[0] for _ in range(len(levels))])\n", - "modal_df['diagnosis'] = levels\n", - "modal_df" - ] - }, - { - "cell_type": "markdown", - "id": "84da2586", - "metadata": {}, - "source": [ - "We then construct the model matrix based on the model specification `all_MS` used to fit\n", - "the model, and name the rows according to the levels of `diagnosis`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7c1fe43", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.843157Z", - "iopub.status.busy": "2023-07-26T05:18:04.843004Z", - "iopub.status.idle": "2023-07-26T05:18:04.852876Z", - "shell.execute_reply": "2023-07-26T05:18:04.852545Z" - } - }, - "outputs": [], - "source": [ - "modal_X = all_MS.transform(modal_df)\n", - "modal_X.index = levels\n", - "modal_X" - ] - }, - { - "cell_type": "markdown", - "id": "3cfe1ec4", - "metadata": {}, - "source": [ - "We can use the `predict_survival_function()` method to obtain the estimated survival function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f89fbed7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.855400Z", - "iopub.status.busy": "2023-07-26T05:18:04.855197Z", - "iopub.status.idle": "2023-07-26T05:18:04.864584Z", - "shell.execute_reply": "2023-07-26T05:18:04.864194Z" - } - }, - "outputs": [], - "source": [ - "predicted_survival = fit_all.predict_survival_function(modal_X)\n", - "predicted_survival" - ] - }, - { - "cell_type": "markdown", - "id": "29afd641", - "metadata": {}, - "source": [ - "This returns a data frame,\n", - "whose plot methods yields the different survival curves. To avoid clutter in\n", - "the plots, we do not display confidence intervals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f0329b4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.867553Z", - "iopub.status.busy": "2023-07-26T05:18:04.867105Z", - "iopub.status.idle": "2023-07-26T05:18:04.987503Z", - "shell.execute_reply": "2023-07-26T05:18:04.986681Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "predicted_survival.plot(ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "12723ce5", - "metadata": {}, - "source": [ - "## Publication Data\n", - "The `Publication` data presented in Section 11.5.4 can be\n", - "found in the `ISLP` package.\n", - "We first reproduce Figure 11.5 by plotting the Kaplan-Meier curves\n", - "stratified on the `posres` variable, which records whether the\n", - "study had a positive or negative result." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3045bfc0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:04.990450Z", - "iopub.status.busy": "2023-07-26T05:18:04.990225Z", - "iopub.status.idle": "2023-07-26T05:18:05.181343Z", - "shell.execute_reply": "2023-07-26T05:18:05.178676Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "Publication = load_data('Publication')\n", - "by_result = {}\n", - "for result, df in Publication.groupby('posres'):\n", - " by_result[result] = df\n", - " km_result = km.fit(df['time'], df['status'])\n", - " km_result.plot(label='Result=%d' % result, ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "6fcb22f7", - "metadata": {}, - "source": [ - "As discussed previously, the $p$-values from fitting Cox’s\n", - "proportional hazards model to the `posres` variable are quite\n", - "large, providing no evidence of a difference in time-to-publication\n", - "between studies with positive versus negative results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d070f716", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.184743Z", - "iopub.status.busy": "2023-07-26T05:18:05.184569Z", - "iopub.status.idle": "2023-07-26T05:18:05.222538Z", - "shell.execute_reply": "2023-07-26T05:18:05.221996Z" - } - }, - "outputs": [], - "source": [ - "posres_df = MS(['posres',\n", - " 'time',\n", - " 'status'],\n", - " intercept=False).fit_transform(Publication)\n", - "posres_fit = coxph().fit(posres_df,\n", - " 'time',\n", - " 'status')\n", - "posres_fit.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "513a55b1", - "metadata": {}, - "source": [ - "However, the results change dramatically when we include other\n", - "predictors in the model. Here we exclude the funding mechanism\n", - "variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bbcdd0c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.225196Z", - "iopub.status.busy": "2023-07-26T05:18:05.224969Z", - "iopub.status.idle": "2023-07-26T05:18:05.270258Z", - "shell.execute_reply": "2023-07-26T05:18:05.269721Z" - } - }, - "outputs": [], - "source": [ - "model = MS(Publication.columns.drop('mech'),\n", - " intercept=False)\n", - "coxph().fit(model.fit_transform(Publication),\n", - " 'time',\n", - " 'status').summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "75bb8aa6", - "metadata": {}, - "source": [ - "We see that there are a number of statistically significant variables,\n", - "including whether the trial focused on a clinical endpoint, the impact\n", - "of the study, and whether the study had positive or negative results." - ] - }, - { - "cell_type": "markdown", - "id": "bfe236e5", - "metadata": {}, - "source": [ - "## Call Center Data\n", - "\n", - "In this section, we will simulate survival data using the relationship\n", - "between cumulative hazard and\n", - "the survival function explored in Exercise 8.\n", - "Our simulated data will represent the observed\n", - "wait times (in seconds) for 2,000 customers who have phoned a call\n", - "center. In this context, censoring occurs if a customer hangs up\n", - "before his or her call is answered.\n", - "\n", - "There are three covariates: `Operators` (the number of call\n", - "center operators available at the time of the call, which can range\n", - "from $5$ to $15$), `Center` (either A, B, or C), and\n", - "`Time` of day (Morning, Afternoon, or Evening). We generate data\n", - "for these covariates so that all possibilities are equally likely: for\n", - "instance, morning, afternoon and evening calls are equally likely, and\n", - "any number of operators from $5$ to $15$ is equally likely." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8ece43a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.272852Z", - "iopub.status.busy": "2023-07-26T05:18:05.272613Z", - "iopub.status.idle": "2023-07-26T05:18:05.277670Z", - "shell.execute_reply": "2023-07-26T05:18:05.277169Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(10)\n", - "N = 2000\n", - "Operators = rng.choice(np.arange(5, 16),\n", - " N,\n", - " replace=True)\n", - "Center = rng.choice(['A', 'B', 'C'],\n", - " N,\n", - " replace=True)\n", - "Time = rng.choice(['Morn.', 'After.', 'Even.'],\n", - " N,\n", - " replace=True)\n", - "D = pd.DataFrame({'Operators': Operators,\n", - " 'Center': pd.Categorical(Center),\n", - " 'Time': pd.Categorical(Time)})" - ] - }, - { - "cell_type": "markdown", - "id": "c93e44f3", - "metadata": {}, - "source": [ - "We then build a model matrix (omitting the intercept)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e4f766f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.279951Z", - "iopub.status.busy": "2023-07-26T05:18:05.279774Z", - "iopub.status.idle": "2023-07-26T05:18:05.289519Z", - "shell.execute_reply": "2023-07-26T05:18:05.288948Z" - } - }, - "outputs": [], - "source": [ - "model = MS(['Operators',\n", - " 'Center',\n", - " 'Time'],\n", - " intercept=False)\n", - "X = model.fit_transform(D)" - ] - }, - { - "cell_type": "markdown", - "id": "cad1ed19", - "metadata": {}, - "source": [ - "It is worthwhile to take a peek at the model matrix `X`, so\n", - "that we can be sure that we understand how the variables have been coded. By default,\n", - "the levels of categorical variables are sorted and, as usual, the first column of the one-hot encoding\n", - "of the variable is dropped." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72f42d14", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.292713Z", - "iopub.status.busy": "2023-07-26T05:18:05.292424Z", - "iopub.status.idle": "2023-07-26T05:18:05.299084Z", - "shell.execute_reply": "2023-07-26T05:18:05.298414Z" - } - }, - "outputs": [], - "source": [ - "X[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "38c40ae1", - "metadata": {}, - "source": [ - "Next, we specify the coefficients and the hazard function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b921536", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.301800Z", - "iopub.status.busy": "2023-07-26T05:18:05.301580Z", - "iopub.status.idle": "2023-07-26T05:18:05.340807Z", - "shell.execute_reply": "2023-07-26T05:18:05.311843Z" - } - }, - "outputs": [], - "source": [ - "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n", - "true_linpred = X.dot(true_beta)\n", - "hazard = lambda t: 1e-5 * t" - ] - }, - { - "cell_type": "markdown", - "id": "a0698ffd", - "metadata": {}, - "source": [ - "Here, we have set the coefficient associated with `Operators` to\n", - "equal $0.04$; in other words, each additional operator leads to a\n", - "$e^{0.04}=1.041$-fold increase in the “risk” that the call will be\n", - "answered, given the `Center` and `Time` covariates. This\n", - "makes sense: the greater the number of operators at hand, the shorter\n", - "the wait time! The coefficient associated with `Center == B` is\n", - "$-0.3$, and `Center == A` is treated as the baseline. This means\n", - "that the risk of a call being answered at Center B is 0.74 times the\n", - "risk that it will be answered at Center A; in other words, the wait\n", - "times are a bit longer at Center B.\n", - "\n", - "Recall from Section 2.3.7 the use of `lambda`\n", - "for creating short functions on the fly.\n", - "We use the function\n", - "`sim_time()` from the `ISLP.survival` package. This function\n", - "uses the relationship between the survival function\n", - "and cumulative hazard $S(t) = \\exp(-H(t))$ and the specific\n", - "form of the cumulative hazard function in the Cox model\n", - "to simulate data based on values of the linear predictor\n", - "`true_linpred` and the cumulative hazard. \n", - " We need to provide the cumulative hazard function, which we do here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96ce0f99", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.399214Z", - "iopub.status.busy": "2023-07-26T05:18:05.386941Z", - "iopub.status.idle": "2023-07-26T05:18:05.423513Z", - "shell.execute_reply": "2023-07-26T05:18:05.417411Z" - } - }, - "outputs": [], - "source": [ - "cum_hazard = lambda t: 1e-5 * t**2 / 2" - ] - }, - { - "cell_type": "markdown", - "id": "1956e4c2", - "metadata": {}, - "source": [ - "We are now ready to generate data under the Cox proportional hazards\n", - "model. We truncate the maximum time to 1000 seconds to keep\n", - "simulated wait times reasonable. The function\n", - "`sim_time()` takes a linear predictor,\n", - "a cumulative hazard function and a\n", - "random number generator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63d78ff9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.450050Z", - "iopub.status.busy": "2023-07-26T05:18:05.449814Z", - "iopub.status.idle": "2023-07-26T05:18:05.587170Z", - "shell.execute_reply": "2023-07-26T05:18:05.586673Z" - } - }, - "outputs": [], - "source": [ - "W = np.array([sim_time(l, cum_hazard, rng)\n", - " for l in true_linpred])\n", - "D['Wait time'] = np.clip(W, 0, 1000)" - ] - }, - { - "cell_type": "markdown", - "id": "035e4ecf", - "metadata": {}, - "source": [ - "We now simulate our censoring variable, for which we assume\n", - "90% of calls were answered (`Failed==1`) before the\n", - "customer hung up (`Failed==0`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe008dbf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.590252Z", - "iopub.status.busy": "2023-07-26T05:18:05.589902Z", - "iopub.status.idle": "2023-07-26T05:18:05.596018Z", - "shell.execute_reply": "2023-07-26T05:18:05.595604Z" - } - }, - "outputs": [], - "source": [ - "D['Failed'] = rng.choice([1, 0],\n", - " N,\n", - " p=[0.9, 0.1])\n", - "D[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3a2bec7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.598122Z", - "iopub.status.busy": "2023-07-26T05:18:05.597969Z", - "iopub.status.idle": "2023-07-26T05:18:05.601009Z", - "shell.execute_reply": "2023-07-26T05:18:05.600579Z" - } - }, - "outputs": [], - "source": [ - "D['Failed'].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "207937e5", - "metadata": {}, - "source": [ - "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b27af56", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.603318Z", - "iopub.status.busy": "2023-07-26T05:18:05.603142Z", - "iopub.status.idle": "2023-07-26T05:18:05.827076Z", - "shell.execute_reply": "2023-07-26T05:18:05.826151Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "by_center = {}\n", - "for center, df in D.groupby('Center'):\n", - " by_center[center] = df\n", - " km_center = km.fit(df['Wait time'], df['Failed'])\n", - " km_center.plot(label='Center=%s' % center, ax=ax)\n", - "ax.set_title(\"Probability of Still Being on Hold\")" - ] - }, - { - "cell_type": "markdown", - "id": "be6d37f7", - "metadata": {}, - "source": [ - "Next, we stratify by `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9625598d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:05.830554Z", - "iopub.status.busy": "2023-07-26T05:18:05.830059Z", - "iopub.status.idle": "2023-07-26T05:18:06.008529Z", - "shell.execute_reply": "2023-07-26T05:18:06.008079Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "by_time = {}\n", - "for time, df in D.groupby('Time'):\n", - " by_time[time] = df\n", - " km_time = km.fit(df['Wait time'], df['Failed'])\n", - " km_time.plot(label='Time=%s' % time, ax=ax)\n", - "ax.set_title(\"Probability of Still Being on Hold\")" - ] - }, - { - "cell_type": "markdown", - "id": "1408ebc0", - "metadata": {}, - "source": [ - "It seems that calls at Call Center B take longer to be answered than\n", - "calls at Centers A and C. Similarly, it appears that wait times are\n", - "longest in the morning and shortest in the evening hours. We can use a\n", - "log-rank test to determine whether these differences are statistically\n", - "significant using the function `multivariate_logrank_test()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75a744ef", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:06.012649Z", - "iopub.status.busy": "2023-07-26T05:18:06.012037Z", - "iopub.status.idle": "2023-07-26T05:18:06.033832Z", - "shell.execute_reply": "2023-07-26T05:18:06.033320Z" - } - }, - "outputs": [], - "source": [ - "multivariate_logrank_test(D['Wait time'],\n", - " D['Center'],\n", - " D['Failed'])" - ] - }, - { - "cell_type": "markdown", - "id": "be5055e4", - "metadata": {}, - "source": [ - "Next, we consider the effect of `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9badb3e3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:06.036375Z", - "iopub.status.busy": "2023-07-26T05:18:06.036170Z", - "iopub.status.idle": "2023-07-26T05:18:06.055850Z", - "shell.execute_reply": "2023-07-26T05:18:06.055183Z" - } - }, - "outputs": [], - "source": [ - "multivariate_logrank_test(D['Wait time'],\n", - " D['Time'],\n", - " D['Failed'])" - ] - }, - { - "cell_type": "markdown", - "id": "64b2bc33", - "metadata": {}, - "source": [ - "As in the case of a categorical variable with 2 levels, these\n", - "results are similar to the likelihood ratio test\n", - "from the Cox proportional hazards model. First, we\n", - "look at the results for `Center`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "026e9ff8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:06.058057Z", - "iopub.status.busy": "2023-07-26T05:18:06.057906Z", - "iopub.status.idle": "2023-07-26T05:18:06.184883Z", - "shell.execute_reply": "2023-07-26T05:18:06.184356Z" - } - }, - "outputs": [], - "source": [ - "X = MS(['Wait time',\n", - " 'Failed',\n", - " 'Center'],\n", - " intercept=False).fit_transform(D)\n", - "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "4ed54fe0", - "metadata": {}, - "source": [ - "Next, we look at the results for `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cab3789", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:06.187428Z", - "iopub.status.busy": "2023-07-26T05:18:06.187255Z", - "iopub.status.idle": "2023-07-26T05:18:06.315832Z", - "shell.execute_reply": "2023-07-26T05:18:06.314374Z" - } - }, - "outputs": [], - "source": [ - "X = MS(['Wait time',\n", - " 'Failed',\n", - " 'Time'],\n", - " intercept=False).fit_transform(D)\n", - "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "2d250dc9", - "metadata": {}, - "source": [ - "We find that differences between centers are highly significant, as\n", - "are differences between times of day.\n", - "\n", - "Finally, we fit Cox's proportional hazards model to the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cc4b898", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:06.320288Z", - "iopub.status.busy": "2023-07-26T05:18:06.319261Z", - "iopub.status.idle": "2023-07-26T05:18:06.585074Z", - "shell.execute_reply": "2023-07-26T05:18:06.577564Z" - } - }, - "outputs": [], - "source": [ - "X = MS(D.columns,\n", - " intercept=False).fit_transform(D)\n", - "fit_queuing = coxph().fit(\n", - " X,\n", - " 'Wait time',\n", - " 'Failed')\n", - "fit_queuing.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "bec9d61d", - "metadata": {}, - "source": [ - "The $p$-values for Center B and evening time\n", - "are very small. It is also clear that the\n", - "hazard --- that is, the instantaneous risk that a call will be\n", - "answered --- increases with the number of operators. Since we\n", - "generated the data ourselves, we know that the true coefficients for\n", - " `Operators`, `Center = B`, `Center = C`, \n", - "`Time = Even.` and `Time = Morn.` are $0.04$, $-0.3$,\n", - "$0$, $0.2$, and $-0.2$, respectively. The coefficient estimates\n", - "from the fitted Cox model are fairly accurate." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch12-unsup-lab.ipynb b/docs/source/labs/Ch12-unsup-lab.ipynb deleted file mode 100644 index 6b956a8..0000000 --- a/docs/source/labs/Ch12-unsup-lab.ipynb +++ /dev/null @@ -1,1989 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "80f16ff6", - "metadata": {}, - "source": [ - " # Unsupervised Learning\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this lab we demonstrate PCA and clustering on several datasets.\n", - "As in other labs, we import some of our libraries at this top\n", - "level. This makes the code more readable, as scanning the first few\n", - "lines of the notebook tell us what libraries are used in this\n", - "notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24559be0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:08.163850Z", - "iopub.status.busy": "2023-07-26T05:18:08.163726Z", - "iopub.status.idle": "2023-07-26T05:18:09.209261Z", - "shell.execute_reply": "2023-07-26T05:18:09.208715Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from statsmodels.datasets import get_rdataset\n", - "from sklearn.decomposition import PCA\n", - "from sklearn.preprocessing import StandardScaler\n", - "from ISLP import load_data" - ] - }, - { - "cell_type": "markdown", - "id": "59b24a4b", - "metadata": {}, - "source": [ - "We also collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06fff57d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:09.212356Z", - "iopub.status.busy": "2023-07-26T05:18:09.212070Z", - "iopub.status.idle": "2023-07-26T05:18:09.327611Z", - "shell.execute_reply": "2023-07-26T05:18:09.327246Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.cluster import \\\n", - " (KMeans,\n", - " AgglomerativeClustering)\n", - "from scipy.cluster.hierarchy import \\\n", - " (dendrogram,\n", - " cut_tree)\n", - "from ISLP.cluster import compute_linkage" - ] - }, - { - "cell_type": "markdown", - "id": "f091de06", - "metadata": {}, - "source": [ - "## Principal Components Analysis\n", - "In this lab, we perform PCA on `USArrests`, a data set in the\n", - "`R` computing environment.\n", - "We retrieve the data using `get_rdataset()`, which can fetch data from\n", - "many standard `R` packages.\n", - "\n", - "The rows of the data set contain the 50 states, in alphabetical order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f425e07e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:09.329909Z", - "iopub.status.busy": "2023-07-26T05:18:09.329763Z", - "iopub.status.idle": "2023-07-26T05:18:10.559326Z", - "shell.execute_reply": "2023-07-26T05:18:10.558692Z" - } - }, - "outputs": [], - "source": [ - "USArrests = get_rdataset('USArrests').data\n", - "USArrests" - ] - }, - { - "cell_type": "markdown", - "id": "e2942890", - "metadata": {}, - "source": [ - "The columns of the data set contain the four variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b127d014", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.561457Z", - "iopub.status.busy": "2023-07-26T05:18:10.561326Z", - "iopub.status.idle": "2023-07-26T05:18:10.564289Z", - "shell.execute_reply": "2023-07-26T05:18:10.563966Z" - } - }, - "outputs": [], - "source": [ - "USArrests.columns" - ] - }, - { - "cell_type": "markdown", - "id": "68718331", - "metadata": {}, - "source": [ - "We first briefly examine the data. We notice that the variables have vastly different means." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7343f72", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.566649Z", - "iopub.status.busy": "2023-07-26T05:18:10.566503Z", - "iopub.status.idle": "2023-07-26T05:18:10.570319Z", - "shell.execute_reply": "2023-07-26T05:18:10.569890Z" - } - }, - "outputs": [], - "source": [ - "USArrests.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "171a1ee0", - "metadata": {}, - "source": [ - "Dataframes have several useful methods for computing\n", - "column-wise summaries. We can also examine the\n", - "variance of the four variables using the `var()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34501140", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.572610Z", - "iopub.status.busy": "2023-07-26T05:18:10.572485Z", - "iopub.status.idle": "2023-07-26T05:18:10.575894Z", - "shell.execute_reply": "2023-07-26T05:18:10.575543Z" - } - }, - "outputs": [], - "source": [ - "USArrests.var()" - ] - }, - { - "cell_type": "markdown", - "id": "5634db88", - "metadata": {}, - "source": [ - "Not surprisingly, the variables also have vastly different variances.\n", - "The `UrbanPop` variable measures the percentage of the population\n", - "in each state living in an urban area, which is not a comparable\n", - "number to the number of rapes in each state per 100,000 individuals.\n", - "PCA looks for derived variables that account for most of the variance in the data set.\n", - "If we do not scale the variables before performing PCA, then the principal components\n", - "would mostly be driven by the\n", - "`Assault` variable, since it has by far the largest\n", - "variance. So if the variables are measured in different units or vary widely in scale, it is recommended to standardize the variables to have standard deviation one before performing PCA.\n", - "Typically we set the means to zero as well.\n", - "\n", - "This scaling can be done via the `StandardScaler()` transform imported above. We first `fit` the\n", - "scaler, which computes the necessary means and standard\n", - "deviations and then apply it to our data using the\n", - "`transform` method. As before, we combine these steps using the `fit_transform()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "daf119e8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.578199Z", - "iopub.status.busy": "2023-07-26T05:18:10.578025Z", - "iopub.status.idle": "2023-07-26T05:18:10.581928Z", - "shell.execute_reply": "2023-07-26T05:18:10.581446Z" - } - }, - "outputs": [], - "source": [ - "scaler = StandardScaler(with_std=True,\n", - " with_mean=True)\n", - "USArrests_scaled = scaler.fit_transform(USArrests)" - ] - }, - { - "cell_type": "markdown", - "id": "dd6e5f5b", - "metadata": {}, - "source": [ - "Having scaled the data, we can then\n", - "perform principal components analysis using the `PCA()` transform\n", - "from the `sklearn.decomposition` package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0eda7c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.584181Z", - "iopub.status.busy": "2023-07-26T05:18:10.584070Z", - "iopub.status.idle": "2023-07-26T05:18:10.586440Z", - "shell.execute_reply": "2023-07-26T05:18:10.586052Z" - } - }, - "outputs": [], - "source": [ - "pcaUS = PCA()" - ] - }, - { - "cell_type": "markdown", - "id": "e5758ee5", - "metadata": {}, - "source": [ - "(By default, the `PCA()` transform centers the variables to have\n", - "mean zero though it does not scale them.) The transform `pcaUS`\n", - "can be used to find the PCA\n", - "`scores` returned by `fit()`. Once the `fit` method has been called, the `pcaUS` object also contains a number of useful quantities." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1430fb3c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.588957Z", - "iopub.status.busy": "2023-07-26T05:18:10.588809Z", - "iopub.status.idle": "2023-07-26T05:18:10.593315Z", - "shell.execute_reply": "2023-07-26T05:18:10.592971Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.fit(USArrests_scaled)" - ] - }, - { - "cell_type": "markdown", - "id": "56890b11", - "metadata": {}, - "source": [ - "After fitting, the `mean_` attribute corresponds to the means\n", - "of the variables. In this case, since we centered and scaled the data with\n", - "`scaler()` the means will all be 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6131d8d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.595542Z", - "iopub.status.busy": "2023-07-26T05:18:10.595388Z", - "iopub.status.idle": "2023-07-26T05:18:10.598527Z", - "shell.execute_reply": "2023-07-26T05:18:10.598057Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.mean_" - ] - }, - { - "cell_type": "markdown", - "id": "3c1c34e7", - "metadata": {}, - "source": [ - "The scores can be computed using the `transform()` method\n", - "of `pcaUS` after it has been fit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08246aad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.600656Z", - "iopub.status.busy": "2023-07-26T05:18:10.600536Z", - "iopub.status.idle": "2023-07-26T05:18:10.603024Z", - "shell.execute_reply": "2023-07-26T05:18:10.602498Z" - } - }, - "outputs": [], - "source": [ - "scores = pcaUS.transform(USArrests_scaled)" - ] - }, - { - "cell_type": "markdown", - "id": "574ed47f", - "metadata": {}, - "source": [ - "We will plot these scores a bit further down.\n", - "The `components_` attribute provides the principal component loadings:\n", - "each row of `pcaUS.components_` contains the corresponding\n", - "principal component loading vector." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b682b632", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.605166Z", - "iopub.status.busy": "2023-07-26T05:18:10.605000Z", - "iopub.status.idle": "2023-07-26T05:18:10.607923Z", - "shell.execute_reply": "2023-07-26T05:18:10.607498Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.components_ " - ] - }, - { - "cell_type": "markdown", - "id": "fc8bfc99", - "metadata": {}, - "source": [ - "The `biplot` is a common visualization method used with\n", - "PCA. It is not built in as a standard\n", - "part of `sklearn`, though there are python\n", - "packages that do produce such plots. Here we\n", - "make a simple biplot manually." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c165e990", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.610624Z", - "iopub.status.busy": "2023-07-26T05:18:10.610442Z", - "iopub.status.idle": "2023-07-26T05:18:10.736284Z", - "shell.execute_reply": "2023-07-26T05:18:10.735735Z" - } - }, - "outputs": [], - "source": [ - "i, j = 0, 1 # which components\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "ax.scatter(scores[:,0], scores[:,1])\n", - "ax.set_xlabel('PC%d' % (i+1))\n", - "ax.set_ylabel('PC%d' % (j+1))\n", - "for k in range(pcaUS.components_.shape[1]):\n", - " ax.arrow(0, 0, pcaUS.components_[i,k], pcaUS.components_[j,k])\n", - " ax.text(pcaUS.components_[i,k],\n", - " pcaUS.components_[j,k],\n", - " USArrests.columns[k])" - ] - }, - { - "cell_type": "markdown", - "id": "eaef3e98", - "metadata": {}, - "source": [ - "Notice that this figure is a reflection of Figure 12.1 through the $y$-axis. Recall that the\n", - "principal components are only unique up to a sign change, so we can\n", - "reproduce that figure by flipping the\n", - "signs of the second set of scores and loadings.\n", - "We also increase the length of the arrows to emphasize the loadings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "848c9f35", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.738926Z", - "iopub.status.busy": "2023-07-26T05:18:10.738771Z", - "iopub.status.idle": "2023-07-26T05:18:10.848441Z", - "shell.execute_reply": "2023-07-26T05:18:10.847972Z" - } - }, - "outputs": [], - "source": [ - "scale_arrow = s_ = 2\n", - "scores[:,1] *= -1\n", - "pcaUS.components_[1] *= -1 # flip the y-axis\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "ax.scatter(scores[:,0], scores[:,1])\n", - "ax.set_xlabel('PC%d' % (i+1))\n", - "ax.set_ylabel('PC%d' % (j+1))\n", - "for k in range(pcaUS.components_.shape[1]):\n", - " ax.arrow(0, 0, s_*pcaUS.components_[i,k], s_*pcaUS.components_[j,k])\n", - " ax.text(s_*pcaUS.components_[i,k],\n", - " s_*pcaUS.components_[j,k],\n", - " USArrests.columns[k])" - ] - }, - { - "cell_type": "markdown", - "id": "e380e98d", - "metadata": {}, - "source": [ - "The standard deviations of the principal component scores are as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34fdfe21", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.852220Z", - "iopub.status.busy": "2023-07-26T05:18:10.851752Z", - "iopub.status.idle": "2023-07-26T05:18:10.859190Z", - "shell.execute_reply": "2023-07-26T05:18:10.857389Z" - } - }, - "outputs": [], - "source": [ - "scores.std(0, ddof=1)" - ] - }, - { - "cell_type": "markdown", - "id": "c24b8ca2", - "metadata": {}, - "source": [ - "The variance of each score can be extracted directly from the `pcaUS` object via\n", - "the `explained_variance_` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31b43c57", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.861366Z", - "iopub.status.busy": "2023-07-26T05:18:10.861239Z", - "iopub.status.idle": "2023-07-26T05:18:10.864540Z", - "shell.execute_reply": "2023-07-26T05:18:10.863998Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.explained_variance_" - ] - }, - { - "cell_type": "markdown", - "id": "d2dcf543", - "metadata": {}, - "source": [ - "The proportion of variance explained by each principal \n", - "component (PVE) is stored as `explained_variance_ratio_`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68e47d3a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.867030Z", - "iopub.status.busy": "2023-07-26T05:18:10.866863Z", - "iopub.status.idle": "2023-07-26T05:18:10.869949Z", - "shell.execute_reply": "2023-07-26T05:18:10.869455Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.explained_variance_ratio_" - ] - }, - { - "cell_type": "markdown", - "id": "831b19b7", - "metadata": {}, - "source": [ - "We see that the first principal component explains 62.0% of the\n", - "variance in the data, the next principal component explains 24.7%\n", - "of the variance, and so forth.\n", - "We can plot the PVE explained by each component, as well as the cumulative PVE. We first\n", - "plot the proportion of variance explained." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e87fe198", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:10.872333Z", - "iopub.status.busy": "2023-07-26T05:18:10.872213Z", - "iopub.status.idle": "2023-07-26T05:18:11.030288Z", - "shell.execute_reply": "2023-07-26T05:18:11.029440Z" - } - }, - "outputs": [], - "source": [ - "%%capture\n", - "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", - "ticks = np.arange(pcaUS.n_components_)+1\n", - "ax = axes[0]\n", - "ax.plot(ticks,\n", - " pcaUS.explained_variance_ratio_,\n", - " marker='o')\n", - "ax.set_xlabel('Principal Component');\n", - "ax.set_ylabel('Proportion of Variance Explained')\n", - "ax.set_ylim([0,1])\n", - "ax.set_xticks(ticks)" - ] - }, - { - "cell_type": "markdown", - "id": "2ed96395", - "metadata": {}, - "source": [ - "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "409fb0c6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.033250Z", - "iopub.status.busy": "2023-07-26T05:18:11.033097Z", - "iopub.status.idle": "2023-07-26T05:18:11.153804Z", - "shell.execute_reply": "2023-07-26T05:18:11.153245Z" - } - }, - "outputs": [], - "source": [ - "ax = axes[1]\n", - "ax.plot(ticks,\n", - " pcaUS.explained_variance_ratio_.cumsum(),\n", - " marker='o')\n", - "ax.set_xlabel('Principal Component')\n", - "ax.set_ylabel('Cumulative Proportion of Variance Explained')\n", - "ax.set_ylim([0, 1])\n", - "ax.set_xticks(ticks)\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "496fb6be", - "metadata": {}, - "source": [ - "The result is similar to that shown in Figure 12.3. Note\n", - "that the method `cumsum()` computes the cumulative sum of\n", - "the elements of a numeric vector. For instance:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e563e41b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.157597Z", - "iopub.status.busy": "2023-07-26T05:18:11.157216Z", - "iopub.status.idle": "2023-07-26T05:18:11.162432Z", - "shell.execute_reply": "2023-07-26T05:18:11.162026Z" - } - }, - "outputs": [], - "source": [ - "a = np.array([1,2,8,-3])\n", - "np.cumsum(a)" - ] - }, - { - "cell_type": "markdown", - "id": "6794b1a3", - "metadata": {}, - "source": [ - "## Matrix Completion\n", - "\n", - "We now re-create the analysis carried out on the `USArrests` data in\n", - "Section 12.3.\n", - "\n", - "We saw in Section 12.2.2 that solving the optimization\n", - "problem (12.6) on a centered data matrix $\\bf X$ is\n", - "equivalent to computing the first $M$ principal\n", - "components of the data. We use our scaled\n", - "and centered `USArrests` data as $\\bf X$ below. The *singular value decomposition* \n", - "(SVD) is a general algorithm for solving\n", - "(12.6)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f83ad0bc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.164364Z", - "iopub.status.busy": "2023-07-26T05:18:11.164225Z", - "iopub.status.idle": "2023-07-26T05:18:11.167031Z", - "shell.execute_reply": "2023-07-26T05:18:11.166724Z" - } - }, - "outputs": [], - "source": [ - "X = USArrests_scaled\n", - "U, D, V = np.linalg.svd(X, full_matrices=False)\n", - "U.shape, D.shape, V.shape" - ] - }, - { - "cell_type": "markdown", - "id": "f9c71e57", - "metadata": {}, - "source": [ - "The `np.linalg.svd()` function returns three components, `U`, `D` and `V`. The matrix `V` is equivalent to the\n", - "loading matrix from principal components (up to an unimportant sign flip). Using the `full_matrices=False` option ensures that\n", - "for a tall matrix the shape of `U` is the same as the shape of `X`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb9bdc46", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.169269Z", - "iopub.status.busy": "2023-07-26T05:18:11.169096Z", - "iopub.status.idle": "2023-07-26T05:18:11.172167Z", - "shell.execute_reply": "2023-07-26T05:18:11.171624Z" - } - }, - "outputs": [], - "source": [ - "V" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f23c101e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.174404Z", - "iopub.status.busy": "2023-07-26T05:18:11.174265Z", - "iopub.status.idle": "2023-07-26T05:18:11.177096Z", - "shell.execute_reply": "2023-07-26T05:18:11.176712Z" - } - }, - "outputs": [], - "source": [ - "pcaUS.components_" - ] - }, - { - "cell_type": "markdown", - "id": "a5d9e0ae", - "metadata": {}, - "source": [ - "The matrix `U` corresponds to a *standardized* version of the PCA score matrix (each column standardized to have sum-of-squares one). If we multiply each column of `U` by the corresponding element of `D`, we recover the PCA scores exactly (up to a meaningless sign flip)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cc49622", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.179219Z", - "iopub.status.busy": "2023-07-26T05:18:11.179054Z", - "iopub.status.idle": "2023-07-26T05:18:11.182070Z", - "shell.execute_reply": "2023-07-26T05:18:11.181699Z" - } - }, - "outputs": [], - "source": [ - "(U * D[None,:])[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c96c9fe1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.184051Z", - "iopub.status.busy": "2023-07-26T05:18:11.183934Z", - "iopub.status.idle": "2023-07-26T05:18:11.186945Z", - "shell.execute_reply": "2023-07-26T05:18:11.186473Z" - } - }, - "outputs": [], - "source": [ - "scores[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "6b7002cb", - "metadata": {}, - "source": [ - "While it would be possible to carry out this lab using the `PCA()` estimator,\n", - "here we use the `np.linalg.svd()` function in order to illustrate its use.\n", - "\n", - "We now omit 20 entries in the $50\\times 4$ data matrix at random. We do so\n", - "by first selecting 20 rows (states) at random, and then selecting one\n", - "of the four entries in each row at random. This ensures that every row has\n", - "at least three observed values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "574409d6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.189376Z", - "iopub.status.busy": "2023-07-26T05:18:11.189199Z", - "iopub.status.idle": "2023-07-26T05:18:11.192254Z", - "shell.execute_reply": "2023-07-26T05:18:11.191873Z" - } - }, - "outputs": [], - "source": [ - "n_omit = 20\n", - "np.random.seed(15)\n", - "r_idx = np.random.choice(np.arange(X.shape[0]),\n", - " n_omit,\n", - " replace=False)\n", - "c_idx = np.random.choice(np.arange(X.shape[1]),\n", - " n_omit,\n", - " replace=True)\n", - "Xna = X.copy()\n", - "Xna[r_idx, c_idx] = np.nan" - ] - }, - { - "cell_type": "markdown", - "id": "fd4a5fdf", - "metadata": {}, - "source": [ - "Here the array `r_idx`\n", - "contains 20 integers from 0 to 49; this represents the states (rows of `X`) that are selected to contain missing values. And `c_idx` contains\n", - "20 integers from 0 to 3, representing the features (columns in `X`) that contain the missing values for each of the selected states.\n", - "\n", - "We now write some code to implement Algorithm 12.1. \n", - "We first write a function that takes in a matrix, and returns an approximation to the matrix using the `svd()` function.\n", - "This will be needed in Step 2 of Algorithm 12.1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89f190ae", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.194569Z", - "iopub.status.busy": "2023-07-26T05:18:11.194458Z", - "iopub.status.idle": "2023-07-26T05:18:11.197204Z", - "shell.execute_reply": "2023-07-26T05:18:11.196762Z" - } - }, - "outputs": [], - "source": [ - "def low_rank(X, M=1):\n", - " U, D, V = np.linalg.svd(X)\n", - " L = U[:,:M] * D[None,:M]\n", - " return L.dot(V[:M])" - ] - }, - { - "cell_type": "markdown", - "id": "a04129d0", - "metadata": {}, - "source": [ - "To conduct Step 1 of the algorithm, we initialize `Xhat` --- this is $\\tilde{\\bf X}$ in Algorithm 12.1 --- by replacing\n", - "the missing values with the column means of the non-missing entries. These are stored in\n", - "`Xbar` below after running `np.nanmean()` over the row axis.\n", - "We make a copy so that when we assign values to `Xhat` below we do not also overwrite the\n", - "values in `Xna`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "322f339c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.199355Z", - "iopub.status.busy": "2023-07-26T05:18:11.199208Z", - "iopub.status.idle": "2023-07-26T05:18:11.201784Z", - "shell.execute_reply": "2023-07-26T05:18:11.201348Z" - } - }, - "outputs": [], - "source": [ - "Xhat = Xna.copy()\n", - "Xbar = np.nanmean(Xhat, axis=0)\n", - "Xhat[r_idx, c_idx] = Xbar[c_idx]" - ] - }, - { - "cell_type": "markdown", - "id": "357041e8", - "metadata": {}, - "source": [ - "Before we begin Step 2, we set ourselves up to measure the progress of our\n", - "iterations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e106d1a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.204020Z", - "iopub.status.busy": "2023-07-26T05:18:11.203890Z", - "iopub.status.idle": "2023-07-26T05:18:11.206286Z", - "shell.execute_reply": "2023-07-26T05:18:11.205915Z" - } - }, - "outputs": [], - "source": [ - "thresh = 1e-7\n", - "rel_err = 1\n", - "count = 0\n", - "ismiss = np.isnan(Xna)\n", - "mssold = np.mean(Xhat[~ismiss]**2)\n", - "mss0 = np.mean(Xna[~ismiss]**2)" - ] - }, - { - "cell_type": "markdown", - "id": "105ac73f", - "metadata": {}, - "source": [ - "Here `ismiss` is a logical matrix with the same dimensions as `Xna`;\n", - "a given element is `True` if the corresponding matrix element is missing. The notation `~ismiss` negates this boolean vector. This is useful\n", - "because it allows us to access both the missing and non-missing entries. We store the mean of the squared non-missing elements in `mss0`.\n", - "We store the mean squared error of the non-missing elements of the old version of `Xhat` in `mssold` (which currently\n", - "agrees with `mss0`). We plan to store the mean squared error of the non-missing elements of the current version of `Xhat` in `mss`, and will then\n", - "iterate Step 2 of Algorithm 12.1 until the *relative error*, defined as\n", - "`(mssold - mss) / mss0`, falls below `thresh = 1e-7`.\n", - " {Algorithm 12.1 tells us to iterate Step 2 until (12.14) is no longer decreasing. Determining whether (12.14) is decreasing requires us only to keep track of `mssold - mss`. However, in practice, we keep track of `(mssold - mss) / mss0` instead: this makes it so that the number of iterations required for Algorithm 12.1 to converge does not depend on whether we multiplied the raw data $\\bf X$ by a constant factor.}\n", - "\n", - "In Step 2(a) of Algorithm 12.1, we approximate `Xhat` using `low_rank()`; we call this `Xapp`. In Step 2(b), we use `Xapp` to update the estimates for elements in `Xhat` that are missing in `Xna`. Finally, in Step 2(c), we compute the relative error. These three steps are contained in the following `while` loop:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cb05ce4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.208518Z", - "iopub.status.busy": "2023-07-26T05:18:11.208345Z", - "iopub.status.idle": "2023-07-26T05:18:11.212476Z", - "shell.execute_reply": "2023-07-26T05:18:11.211847Z" - } - }, - "outputs": [], - "source": [ - "while rel_err > thresh:\n", - " count += 1\n", - " # Step 2(a)\n", - " Xapp = low_rank(Xhat, M=1)\n", - " # Step 2(b)\n", - " Xhat[ismiss] = Xapp[ismiss]\n", - " # Step 2(c)\n", - " mss = np.mean(((Xna - Xapp)[~ismiss])**2)\n", - " rel_err = (mssold - mss) / mss0\n", - " mssold = mss\n", - " print(\"Iteration: {0}, MSS:{1:.3f}, Rel.Err {2:.2e}\"\n", - " .format(count, mss, rel_err))" - ] - }, - { - "cell_type": "markdown", - "id": "5cd8edf2", - "metadata": {}, - "source": [ - "We see that after eight iterations, the relative error has fallen below `thresh = 1e-7`, and so the algorithm terminates. When this happens, the mean squared error of the non-missing elements equals 0.381.\n", - "\n", - "Finally, we compute the correlation between the 20 imputed values\n", - "and the actual values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f245188", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.214882Z", - "iopub.status.busy": "2023-07-26T05:18:11.214746Z", - "iopub.status.idle": "2023-07-26T05:18:11.217935Z", - "shell.execute_reply": "2023-07-26T05:18:11.217596Z" - } - }, - "outputs": [], - "source": [ - "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]" - ] - }, - { - "cell_type": "markdown", - "id": "f22444ff", - "metadata": {}, - "source": [ - "In this lab, we implemented Algorithm 12.1 ourselves for didactic purposes. However, a reader who wishes to apply matrix completion to their data might look to more specialized `Python` implementations." - ] - }, - { - "cell_type": "markdown", - "id": "7da441a1", - "metadata": {}, - "source": [ - "## Clustering" - ] - }, - { - "cell_type": "markdown", - "id": "ef4903d6", - "metadata": {}, - "source": [ - "### $K$-Means Clustering\n", - "\n", - "The estimator `sklearn.cluster.KMeans()` performs $K$-means clustering in\n", - "`Python`. We begin with a simple simulated example in which there\n", - "truly are two clusters in the data: the first 25 observations have a\n", - "mean shift relative to the next 25 observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "345fb41e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.220117Z", - "iopub.status.busy": "2023-07-26T05:18:11.219983Z", - "iopub.status.idle": "2023-07-26T05:18:11.222355Z", - "shell.execute_reply": "2023-07-26T05:18:11.221948Z" - } - }, - "outputs": [], - "source": [ - "np.random.seed(0);\n", - "X = np.random.standard_normal((50,2));\n", - "X[:25,0] += 3;\n", - "X[:25,1] -= 4;" - ] - }, - { - "cell_type": "markdown", - "id": "6ed3587f", - "metadata": {}, - "source": [ - "We now perform $K$-means clustering with $K=2$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a8c21a2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.224388Z", - "iopub.status.busy": "2023-07-26T05:18:11.224258Z", - "iopub.status.idle": "2023-07-26T05:18:11.262343Z", - "shell.execute_reply": "2023-07-26T05:18:11.261799Z" - } - }, - "outputs": [], - "source": [ - "kmeans = KMeans(n_clusters=2,\n", - " random_state=2,\n", - " n_init=20).fit(X)" - ] - }, - { - "cell_type": "markdown", - "id": "df4fb0eb", - "metadata": {}, - "source": [ - "We specify `random_state` to make the results reproducible. The cluster assignments of the 50 observations are contained in `kmeans.labels_`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3e35b5d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.265599Z", - "iopub.status.busy": "2023-07-26T05:18:11.265438Z", - "iopub.status.idle": "2023-07-26T05:18:11.268983Z", - "shell.execute_reply": "2023-07-26T05:18:11.268283Z" - } - }, - "outputs": [], - "source": [ - "kmeans.labels_" - ] - }, - { - "cell_type": "markdown", - "id": "6632fa4c", - "metadata": {}, - "source": [ - "The $K$-means clustering perfectly separated the observations into two\n", - "clusters even though we did not supply any group information to\n", - "`KMeans()`. We can plot the data, with each observation\n", - "colored according to its cluster assignment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d928650a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.271491Z", - "iopub.status.busy": "2023-07-26T05:18:11.271353Z", - "iopub.status.idle": "2023-07-26T05:18:11.409914Z", - "shell.execute_reply": "2023-07-26T05:18:11.409365Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n", - "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", - "ax.set_title(\"K-Means Clustering Results with K=2\");" - ] - }, - { - "cell_type": "markdown", - "id": "1f6a6d01", - "metadata": {}, - "source": [ - "Here the observations can be easily plotted because they are\n", - "two-dimensional. If there were more than two variables then we could\n", - "instead perform PCA and plot the first two principal component score\n", - "vectors to represent the clusters.\n", - "\n", - "In this example, we knew that there really\n", - "were two clusters because we generated the data. However, for real\n", - "data, we do not know the true number of clusters, nor whether they exist in any precise way. We could\n", - "instead have performed $K$-means clustering on this example with\n", - "$K=3$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92e5175c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.412487Z", - "iopub.status.busy": "2023-07-26T05:18:11.412250Z", - "iopub.status.idle": "2023-07-26T05:18:11.548124Z", - "shell.execute_reply": "2023-07-26T05:18:11.547642Z" - } - }, - "outputs": [], - "source": [ - "kmeans = KMeans(n_clusters=3,\n", - " random_state=3,\n", - " n_init=20).fit(X)\n", - "fig, ax = plt.subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n", - "ax.set_title(\"K-Means Clustering Results with K=3\");" - ] - }, - { - "cell_type": "markdown", - "id": "82317a30", - "metadata": {}, - "source": [ - "When $K=3$, $K$-means clustering splits up the two clusters.\n", - "We have used the `n_init` argument to run the $K$-means with 20 \n", - "initial cluster assignments (the default is 10). If a\n", - "value of `n_init` greater than one is used, then $K$-means\n", - "clustering will be performed using multiple random assignments in\n", - "Step 1 of Algorithm 12.2, and the `KMeans()` \n", - "function will report only the best results. Here we compare using\n", - "`n_init=1` to `n_init=20`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4911ecc7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.551298Z", - "iopub.status.busy": "2023-07-26T05:18:11.551122Z", - "iopub.status.idle": "2023-07-26T05:18:11.570392Z", - "shell.execute_reply": "2023-07-26T05:18:11.569843Z" - } - }, - "outputs": [], - "source": [ - "kmeans1 = KMeans(n_clusters=3,\n", - " random_state=3,\n", - " n_init=1).fit(X)\n", - "kmeans20 = KMeans(n_clusters=3,\n", - " random_state=3,\n", - " n_init=20).fit(X);\n", - "kmeans1.inertia_, kmeans20.inertia_" - ] - }, - { - "cell_type": "markdown", - "id": "96e6df1f", - "metadata": {}, - "source": [ - "Note that `kmeans.inertia_` is the total within-cluster sum\n", - "of squares, which we seek to minimize by performing $K$-means\n", - "clustering (12.17). \n", - "\n", - "We *strongly* recommend always running $K$-means clustering with\n", - "a large value of `n_init`, such as 20 or 50, since otherwise an\n", - "undesirable local optimum may be obtained.\n", - "\n", - "When performing $K$-means clustering, in addition to using multiple\n", - "initial cluster assignments, it is also important to set a random seed\n", - "using the `random_state` argument to `KMeans()`. This way, the initial\n", - "cluster assignments in Step 1 can be replicated, and the $K$-means\n", - "output will be fully reproducible." - ] - }, - { - "cell_type": "markdown", - "id": "904ea858", - "metadata": {}, - "source": [ - "### Hierarchical Clustering\n", - "\n", - "The `AgglomerativeClustering()` class from\n", - "the `sklearn.clustering` package implements hierarchical clustering.\n", - "As its\n", - "name is long, we use the short hand `HClust` for *hierarchical clustering*. Note that this will not change the return type\n", - "when using this method, so instances will still be of class `AgglomerativeClustering`.\n", - "In the following example we use the data from the previous lab to plot the hierarchical clustering\n", - "dendrogram using complete, single, and average linkage clustering\n", - "with Euclidean distance as the dissimilarity measure. We begin by\n", - "clustering observations using complete linkage." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b42a700", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.573065Z", - "iopub.status.busy": "2023-07-26T05:18:11.572885Z", - "iopub.status.idle": "2023-07-26T05:18:11.577747Z", - "shell.execute_reply": "2023-07-26T05:18:11.577317Z" - } - }, - "outputs": [], - "source": [ - "HClust = AgglomerativeClustering\n", - "hc_comp = HClust(distance_threshold=0,\n", - " n_clusters=None,\n", - " linkage='complete')\n", - "hc_comp.fit(X)" - ] - }, - { - "cell_type": "markdown", - "id": "22199f4d", - "metadata": {}, - "source": [ - "This computes the entire dendrogram.\n", - "We could just as easily perform hierarchical clustering with average or single linkage instead:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50ef7eea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.580226Z", - "iopub.status.busy": "2023-07-26T05:18:11.580003Z", - "iopub.status.idle": "2023-07-26T05:18:11.583835Z", - "shell.execute_reply": "2023-07-26T05:18:11.583381Z" - } - }, - "outputs": [], - "source": [ - "hc_avg = HClust(distance_threshold=0,\n", - " n_clusters=None,\n", - " linkage='average');\n", - "hc_avg.fit(X)\n", - "hc_sing = HClust(distance_threshold=0,\n", - " n_clusters=None,\n", - " linkage='single');\n", - "hc_sing.fit(X);" - ] - }, - { - "cell_type": "markdown", - "id": "7e8db256", - "metadata": {}, - "source": [ - "To use a precomputed distance matrix, we provide an additional\n", - "argument `metric=\"precomputed\"`. In the code below, the first four lines computes the $50\\times 50$ pairwise-distance matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf7a2408", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.586413Z", - "iopub.status.busy": "2023-07-26T05:18:11.586258Z", - "iopub.status.idle": "2023-07-26T05:18:11.591990Z", - "shell.execute_reply": "2023-07-26T05:18:11.591553Z" - } - }, - "outputs": [], - "source": [ - "D = np.zeros((X.shape[0], X.shape[0]));\n", - "for i in range(X.shape[0]):\n", - " x_ = np.multiply.outer(np.ones(X.shape[0]), X[i])\n", - " D[i] = np.sqrt(np.sum((X - x_)**2, 1));\n", - "hc_sing_pre = HClust(distance_threshold=0,\n", - " n_clusters=None,\n", - " metric='precomputed',\n", - " linkage='single')\n", - "hc_sing_pre.fit(D)" - ] - }, - { - "cell_type": "markdown", - "id": "8cd7fb7c", - "metadata": {}, - "source": [ - "We use\n", - "`dendrogram()` from `scipy.cluster.hierarchy` to plot the dendrogram. However,\n", - "`dendrogram()` expects a so-called *linkage-matrix representation*\n", - "of the clustering, which is not provided by `AgglomerativeClustering()`,\n", - "but can be computed. The function `compute_linkage()` in the\n", - "`ISLP.cluster` package is provided for this purpose.\n", - "\n", - "We can now plot the dendrograms. The numbers at the bottom of the plot\n", - "identify each observation. The `dendrogram()` function has a default method to\n", - "color different branches of the tree that suggests a pre-defined cut of the tree at a particular depth.\n", - "We prefer to overwrite this default by setting this threshold to be infinite. Since we want this behavior for many dendrograms, we store these values in a dictionary `cargs` and pass this as keyword arguments using the notation `**cargs`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a118c0ab", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.595666Z", - "iopub.status.busy": "2023-07-26T05:18:11.595400Z", - "iopub.status.idle": "2023-07-26T05:18:11.876074Z", - "shell.execute_reply": "2023-07-26T05:18:11.875555Z" - } - }, - "outputs": [], - "source": [ - "cargs = {'color_threshold':-np.inf,\n", - " 'above_threshold_color':'black'}\n", - "linkage_comp = compute_linkage(hc_comp)\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "dendrogram(linkage_comp,\n", - " ax=ax,\n", - " **cargs);" - ] - }, - { - "cell_type": "markdown", - "id": "a325a001", - "metadata": {}, - "source": [ - "We may want to color branches of the tree above\n", - "and below a cut-threshold differently. This can be achieved\n", - "by changing the `color_threshold`. Let’s cut the tree at a height of 4,\n", - "coloring links that merge above 4 in black." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1ff41c0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:11.878711Z", - "iopub.status.busy": "2023-07-26T05:18:11.878497Z", - "iopub.status.idle": "2023-07-26T05:18:12.156685Z", - "shell.execute_reply": "2023-07-26T05:18:12.156217Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "dendrogram(linkage_comp,\n", - " ax=ax,\n", - " color_threshold=4,\n", - " above_threshold_color='black');" - ] - }, - { - "cell_type": "markdown", - "id": "e774708c", - "metadata": {}, - "source": [ - "To determine the cluster labels for each observation associated with a\n", - "given cut of the dendrogram, we can use the `cut_tree()` \n", - "function from `scipy.cluster.hierarchy`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2752a96", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.159239Z", - "iopub.status.busy": "2023-07-26T05:18:12.159059Z", - "iopub.status.idle": "2023-07-26T05:18:12.163542Z", - "shell.execute_reply": "2023-07-26T05:18:12.163127Z" - } - }, - "outputs": [], - "source": [ - "cut_tree(linkage_comp, n_clusters=4).T" - ] - }, - { - "cell_type": "markdown", - "id": "d501d220", - "metadata": {}, - "source": [ - "This can also be achieved by providing an argument `n_clusters`\n", - "to `HClust()`; however each cut would require recomputing\n", - "the clustering. Similarly, trees may be cut by distance threshold\n", - "with an argument of `distance_threshold` to `HClust()`\n", - "or `height` to `cut_tree()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1407f7a4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.165971Z", - "iopub.status.busy": "2023-07-26T05:18:12.165813Z", - "iopub.status.idle": "2023-07-26T05:18:12.170809Z", - "shell.execute_reply": "2023-07-26T05:18:12.170332Z" - } - }, - "outputs": [], - "source": [ - "cut_tree(linkage_comp, height=5)" - ] - }, - { - "cell_type": "markdown", - "id": "469bbad6", - "metadata": {}, - "source": [ - "To scale the variables before performing hierarchical clustering of\n", - "the observations, we use `StandardScaler()` as in our PCA example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d74f224", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.173359Z", - "iopub.status.busy": "2023-07-26T05:18:12.173198Z", - "iopub.status.idle": "2023-07-26T05:18:12.457460Z", - "shell.execute_reply": "2023-07-26T05:18:12.457026Z" - } - }, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "X_scale = scaler.fit_transform(X)\n", - "hc_comp_scale = HClust(distance_threshold=0,\n", - " n_clusters=None,\n", - " linkage='complete').fit(X_scale)\n", - "linkage_comp_scale = compute_linkage(hc_comp_scale)\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "dendrogram(linkage_comp_scale, ax=ax, **cargs)\n", - "ax.set_title(\"Hierarchical Clustering with Scaled Features\");" - ] - }, - { - "cell_type": "markdown", - "id": "3caaa1a5", - "metadata": {}, - "source": [ - "Correlation-based distances between observations can be used for\n", - "clustering. The correlation between two observations measures the\n", - "similarity of their feature values. {Suppose each observation has\n", - " $p$ features, each a single numerical value. We measure the\n", - " similarity of two such observations by computing the\n", - " correlation of these $p$ pairs of numbers.}\n", - "With $n$ observations, the $n\\times n$ correlation matrix can then be used as a similarity (or affinity) matrix, i.e. so that one minus the correlation matrix is the dissimilarity matrix used for clustering.\n", - "\n", - "Note that using correlation only makes sense for\n", - "data with at least three features since the absolute correlation\n", - "between any two observations with measurements on two features is\n", - "always one. Hence, we will cluster a three-dimensional data set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7f7da12", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.461207Z", - "iopub.status.busy": "2023-07-26T05:18:12.460949Z", - "iopub.status.idle": "2023-07-26T05:18:12.680696Z", - "shell.execute_reply": "2023-07-26T05:18:12.680192Z" - } - }, - "outputs": [], - "source": [ - "X = np.random.standard_normal((30, 3))\n", - "corD = 1 - np.corrcoef(X)\n", - "hc_cor = HClust(linkage='complete',\n", - " distance_threshold=0,\n", - " n_clusters=None,\n", - " metric='precomputed')\n", - "hc_cor.fit(corD)\n", - "linkage_cor = compute_linkage(hc_cor)\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n", - "dendrogram(linkage_cor, ax=ax, **cargs)\n", - "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");" - ] - }, - { - "cell_type": "markdown", - "id": "5b00804e", - "metadata": {}, - "source": [ - "## NCI60 Data Example\n", - "Unsupervised techniques are often used in the analysis of genomic\n", - "data. In particular, PCA and hierarchical clustering are popular\n", - "tools. We illustrate these techniques on the `NCI60` cancer cell line\n", - "microarray data, which consists of 6830 gene expression\n", - "measurements on 64 cancer cell lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b94424fc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.683099Z", - "iopub.status.busy": "2023-07-26T05:18:12.682918Z", - "iopub.status.idle": "2023-07-26T05:18:12.688821Z", - "shell.execute_reply": "2023-07-26T05:18:12.688474Z" - } - }, - "outputs": [], - "source": [ - "NCI60 = load_data('NCI60')\n", - "nci_labs = NCI60['labels']\n", - "nci_data = NCI60['data']" - ] - }, - { - "cell_type": "markdown", - "id": "7313fea6", - "metadata": {}, - "source": [ - "Each cell line is labeled with a cancer type. We do not make use of\n", - "the cancer types in performing PCA and clustering, as these are\n", - "unsupervised techniques. But after performing PCA and clustering, we\n", - "will check to see the extent to which these cancer types agree with\n", - "the results of these unsupervised techniques.\n", - "\n", - "The data has 64 rows and 6830 columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cea54566", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.691075Z", - "iopub.status.busy": "2023-07-26T05:18:12.690934Z", - "iopub.status.idle": "2023-07-26T05:18:12.693705Z", - "shell.execute_reply": "2023-07-26T05:18:12.693319Z" - } - }, - "outputs": [], - "source": [ - "nci_data.shape" - ] - }, - { - "cell_type": "markdown", - "id": "6ebf27f1", - "metadata": {}, - "source": [ - "We begin by examining the cancer types for the cell lines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4dac41bb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.695954Z", - "iopub.status.busy": "2023-07-26T05:18:12.695815Z", - "iopub.status.idle": "2023-07-26T05:18:12.700521Z", - "shell.execute_reply": "2023-07-26T05:18:12.700074Z" - } - }, - "outputs": [], - "source": [ - "nci_labs.value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "8f16f96a", - "metadata": {}, - "source": [ - "### PCA on the NCI60 Data\n", - "\n", - "We first perform PCA on the data after scaling the variables (genes)\n", - "to have standard deviation one, although here one could reasonably argue\n", - "that it is better not to scale the genes as they are measured in the same units." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8ebadd6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.702785Z", - "iopub.status.busy": "2023-07-26T05:18:12.702646Z", - "iopub.status.idle": "2023-07-26T05:18:12.866311Z", - "shell.execute_reply": "2023-07-26T05:18:12.856469Z" - } - }, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "nci_scaled = scaler.fit_transform(nci_data)\n", - "nci_pca = PCA()\n", - "nci_scores = nci_pca.fit_transform(nci_scaled)" - ] - }, - { - "cell_type": "markdown", - "id": "8a8c9932", - "metadata": {}, - "source": [ - "We now plot the first few principal component score vectors, in order\n", - "to visualize the data. The observations (cell lines) corresponding to\n", - "a given cancer type will be plotted in the same color, so that we can\n", - "see to what extent the observations within a cancer type are similar\n", - "to each other." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63b5efe3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:12.882679Z", - "iopub.status.busy": "2023-07-26T05:18:12.878318Z", - "iopub.status.idle": "2023-07-26T05:18:13.099177Z", - "shell.execute_reply": "2023-07-26T05:18:13.098712Z" - } - }, - "outputs": [], - "source": [ - "cancer_types = list(np.unique(nci_labs))\n", - "nci_groups = np.array([cancer_types.index(lab)\n", - " for lab in nci_labs.values])\n", - "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", - "ax = axes[0]\n", - "ax.scatter(nci_scores[:,0],\n", - " nci_scores[:,1],\n", - " c=nci_groups,\n", - " marker='o',\n", - " s=50)\n", - "ax.set_xlabel('PC1'); ax.set_ylabel('PC2')\n", - "ax = axes[1]\n", - "ax.scatter(nci_scores[:,0],\n", - " nci_scores[:,2],\n", - " c=nci_groups,\n", - " marker='o',\n", - " s=50)\n", - "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');" - ] - }, - { - "cell_type": "markdown", - "id": "87b33a9c", - "metadata": {}, - "source": [ - "On the whole, cell lines corresponding to a single cancer type do tend to\n", - "have similar values on the first few principal component score\n", - "vectors. This indicates that cell lines from the same cancer type tend\n", - "to have pretty similar gene expression levels." - ] - }, - { - "cell_type": "markdown", - "id": "890fdb54", - "metadata": {}, - "source": [ - "We can also plot the percent variance\n", - "explained by the principal components as well as the cumulative percent variance explained.\n", - "This is similar to the plots we made earlier for the `USArrests` data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e20c3cc1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:13.101683Z", - "iopub.status.busy": "2023-07-26T05:18:13.101534Z", - "iopub.status.idle": "2023-07-26T05:18:13.327522Z", - "shell.execute_reply": "2023-07-26T05:18:13.327041Z" - } - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(15,6))\n", - "ax = axes[0]\n", - "ticks = np.arange(nci_pca.n_components_)+1\n", - "ax.plot(ticks,\n", - " nci_pca.explained_variance_ratio_,\n", - " marker='o')\n", - "ax.set_xlabel('Principal Component');\n", - "ax.set_ylabel('PVE')\n", - "ax = axes[1]\n", - "ax.plot(ticks,\n", - " nci_pca.explained_variance_ratio_.cumsum(),\n", - " marker='o');\n", - "ax.set_xlabel('Principal Component')\n", - "ax.set_ylabel('Cumulative PVE');" - ] - }, - { - "cell_type": "markdown", - "id": "cb6eb4f8", - "metadata": {}, - "source": [ - "We see that together, the first seven principal components explain\n", - "around 40% of the variance in the data. This is not a huge amount\n", - "of the variance. However, looking at the scree plot, we see that while\n", - "each of the first seven principal components explain a substantial\n", - "amount of variance, there is a marked decrease in the variance\n", - "explained by further principal components. That is, there is an\n", - "*elbow* in the plot after approximately the seventh\n", - "principal component. This suggests that there may be little benefit\n", - "to examining more than seven or so principal components (though even\n", - "examining seven principal components may be difficult)." - ] - }, - { - "cell_type": "markdown", - "id": "8962791f", - "metadata": {}, - "source": [ - "### Clustering the Observations of the NCI60 Data\n", - "\n", - "We now perform hierarchical clustering of the cell lines in the `NCI60` data using\n", - "complete, single, and average linkage. Once again, the goal is to find out whether or not the observations cluster into distinct types of cancer. Euclidean\n", - "distance is used as the dissimilarity measure. We first write a short\n", - "function to produce\n", - "the three dendrograms." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "622de805", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:13.330190Z", - "iopub.status.busy": "2023-07-26T05:18:13.330013Z", - "iopub.status.idle": "2023-07-26T05:18:13.332964Z", - "shell.execute_reply": "2023-07-26T05:18:13.332533Z" - } - }, - "outputs": [], - "source": [ - "def plot_nci(linkage, ax, cut=-np.inf):\n", - " cargs = {'above_threshold_color':'black',\n", - " 'color_threshold':cut}\n", - " hc = HClust(n_clusters=None,\n", - " distance_threshold=0,\n", - " linkage=linkage.lower()).fit(nci_scaled)\n", - " linkage_ = compute_linkage(hc)\n", - " dendrogram(linkage_,\n", - " ax=ax,\n", - " labels=np.asarray(nci_labs),\n", - " leaf_font_size=10,\n", - " **cargs)\n", - " ax.set_title('%s Linkage' % linkage)\n", - " return hc" - ] - }, - { - "cell_type": "markdown", - "id": "9cf5f836", - "metadata": {}, - "source": [ - "Let’s plot our results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54d40449", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:13.336193Z", - "iopub.status.busy": "2023-07-26T05:18:13.335972Z", - "iopub.status.idle": "2023-07-26T05:18:14.886877Z", - "shell.execute_reply": "2023-07-26T05:18:14.886190Z" - } - }, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(3, 1, figsize=(15,30)) \n", - "ax = axes[0]; hc_comp = plot_nci('Complete', ax)\n", - "ax = axes[1]; hc_avg = plot_nci('Average', ax)\n", - "ax = axes[2]; hc_sing = plot_nci('Single', ax)" - ] - }, - { - "cell_type": "markdown", - "id": "1703afbb", - "metadata": {}, - "source": [ - "We see that the\n", - "choice of linkage certainly does affect the results\n", - "obtained. Typically, single linkage will tend to yield *trailing*\n", - "clusters: very large clusters onto which individual observations\n", - "attach one-by-one. On the other hand, complete and average linkage\n", - "tend to yield more balanced, attractive clusters. For this reason,\n", - "complete and average linkage are generally preferred to single\n", - "linkage. Clearly cell lines within a single cancer type do tend to\n", - "cluster together, although the clustering is not perfect. We will use\n", - "complete linkage hierarchical clustering for the analysis that\n", - "follows.\n", - "\n", - "We can cut the dendrogram at the height that will yield a particular\n", - "number of clusters, say four:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc80afc8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:14.891531Z", - "iopub.status.busy": "2023-07-26T05:18:14.890910Z", - "iopub.status.idle": "2023-07-26T05:18:14.903469Z", - "shell.execute_reply": "2023-07-26T05:18:14.903077Z" - } - }, - "outputs": [], - "source": [ - "linkage_comp = compute_linkage(hc_comp)\n", - "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n", - "pd.crosstab(nci_labs['label'],\n", - " pd.Series(comp_cut.reshape(-1), name='Complete'))" - ] - }, - { - "cell_type": "markdown", - "id": "d8ecae69", - "metadata": {}, - "source": [ - "There are some clear patterns. All the leukemia cell lines fall in\n", - "one cluster, while the breast cancer cell lines are spread out over\n", - "three different clusters.\n", - "\n", - "We can plot a cut on the dendrogram that produces these four clusters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40ff59f9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:14.905992Z", - "iopub.status.busy": "2023-07-26T05:18:14.905760Z", - "iopub.status.idle": "2023-07-26T05:18:15.399751Z", - "shell.execute_reply": "2023-07-26T05:18:15.399237Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(10,10))\n", - "plot_nci('Complete', ax, cut=140)\n", - "ax.axhline(140, c='r', linewidth=4);" - ] - }, - { - "cell_type": "markdown", - "id": "1c03368c", - "metadata": {}, - "source": [ - "The `axhline()` function draws a horizontal line line on top of any\n", - "existing set of axes. The argument `140` plots a horizontal\n", - "line at height 140 on the dendrogram; this is a height that\n", - "results in four distinct clusters. It is easy to verify that the\n", - "resulting clusters are the same as the ones we obtained in\n", - "`comp_cut`.\n", - "\n", - "We claimed earlier in Section 12.4.2 that\n", - "$K$-means clustering and hierarchical clustering with the dendrogram\n", - "cut to obtain the same number of clusters can yield very different\n", - "results. How do these `NCI60` hierarchical clustering results compare\n", - "to what we get if we perform $K$-means clustering with $K=4$?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1587e83b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:15.401922Z", - "iopub.status.busy": "2023-07-26T05:18:15.401768Z", - "iopub.status.idle": "2023-07-26T05:18:17.931480Z", - "shell.execute_reply": "2023-07-26T05:18:17.924183Z" - } - }, - "outputs": [], - "source": [ - "nci_kmeans = KMeans(n_clusters=4, \n", - " random_state=0,\n", - " n_init=20).fit(nci_scaled)\n", - "pd.crosstab(pd.Series(comp_cut, name='HClust'),\n", - " pd.Series(nci_kmeans.labels_, name='K-means'))" - ] - }, - { - "cell_type": "markdown", - "id": "b49c4a73", - "metadata": {}, - "source": [ - "We see that the four clusters obtained using hierarchical clustering\n", - "and $K$-means clustering are somewhat different. First we note\n", - "that the labels in the two clusterings are arbitrary. That is, swapping\n", - "the identifier of the cluster does not\n", - "change the clustering. We see here Cluster 3 in\n", - "$K$-means clustering is identical to cluster 2 in hierarchical\n", - "clustering. However, the other clusters differ: for instance,\n", - "cluster 0 in $K$-means clustering contains a portion of the\n", - "observations assigned to cluster 0 by hierarchical clustering, as well\n", - "as all of the observations assigned to cluster 1 by hierarchical\n", - "clustering.\n", - "\n", - "Rather than performing hierarchical clustering on the entire data\n", - "matrix, we can also perform hierarchical clustering on the first few\n", - "principal component score vectors, regarding these first few components\n", - "as a less noisy version of the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b09ceeab", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:17.955945Z", - "iopub.status.busy": "2023-07-26T05:18:17.954718Z", - "iopub.status.idle": "2023-07-26T05:18:18.488264Z", - "shell.execute_reply": "2023-07-26T05:18:18.487809Z" - } - }, - "outputs": [], - "source": [ - "hc_pca = HClust(n_clusters=None,\n", - " distance_threshold=0,\n", - " linkage='complete'\n", - " ).fit(nci_scores[:,:5])\n", - "linkage_pca = compute_linkage(hc_pca)\n", - "fig, ax = plt.subplots(figsize=(8,8))\n", - "dendrogram(linkage_pca,\n", - " labels=np.asarray(nci_labs),\n", - " leaf_font_size=10,\n", - " ax=ax,\n", - " **cargs)\n", - "ax.set_title(\"Hier. Clust. on First Five Score Vectors\")\n", - "pca_labels = pd.Series(cut_tree(linkage_pca,\n", - " n_clusters=4).reshape(-1),\n", - " name='Complete-PCA')\n", - "pd.crosstab(nci_labs['label'], pca_labels)" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch13-multiple-lab.ipynb b/docs/source/labs/Ch13-multiple-lab.ipynb deleted file mode 100644 index f86882c..0000000 --- a/docs/source/labs/Ch13-multiple-lab.ipynb +++ /dev/null @@ -1,1150 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "75b2d75c", - "metadata": {}, - "source": [ - "# Multiple Testing\n", - "\n", - "\n", - " \"Open\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "34e410a6", - "metadata": {}, - "source": [ - "We include our usual imports seen in earlier labs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f928b2d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:21.791802Z", - "iopub.status.busy": "2023-07-26T10:52:21.791660Z", - "iopub.status.idle": "2023-07-26T10:52:23.360588Z", - "shell.execute_reply": "2023-07-26T10:52:23.359991Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data" - ] - }, - { - "cell_type": "markdown", - "id": "12319e0a", - "metadata": {}, - "source": [ - "We also collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb4b32aa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.363356Z", - "iopub.status.busy": "2023-07-26T10:52:23.362843Z", - "iopub.status.idle": "2023-07-26T10:52:23.365980Z", - "shell.execute_reply": "2023-07-26T10:52:23.365438Z" - } - }, - "outputs": [], - "source": [ - "from scipy.stats import \\\n", - " (ttest_1samp,\n", - " ttest_rel,\n", - " ttest_ind,\n", - " t as t_dbn)\n", - "from statsmodels.stats.multicomp import \\\n", - " pairwise_tukeyhsd\n", - "from statsmodels.stats.multitest import \\\n", - " multipletests as mult_test" - ] - }, - { - "cell_type": "markdown", - "id": "a2747e58", - "metadata": {}, - "source": [ - "## Review of Hypothesis Tests\n", - "We begin by performing some one-sample $t$-tests.\n", - "\n", - "First we create 100 variables, each consisting of 10 observations. The\n", - "first 50 variables have mean $0.5$ and variance $1$, while the others\n", - "have mean $0$ and variance $1$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e12ac0cd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.368340Z", - "iopub.status.busy": "2023-07-26T10:52:23.368164Z", - "iopub.status.idle": "2023-07-26T10:52:23.371202Z", - "shell.execute_reply": "2023-07-26T10:52:23.370779Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(12)\n", - "X = rng.standard_normal((10, 100))\n", - "true_mean = np.array([0.5]*50 + [0]*50)\n", - "X += true_mean[None,:]" - ] - }, - { - "cell_type": "markdown", - "id": "70d37233", - "metadata": {}, - "source": [ - "To begin, we use `ttest_1samp()` from the\n", - "`scipy.stats` module to test $H_{0}: \\mu_1=0$, the null\n", - "hypothesis that the first variable has mean zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04d0f49e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.373512Z", - "iopub.status.busy": "2023-07-26T10:52:23.373319Z", - "iopub.status.idle": "2023-07-26T10:52:23.378383Z", - "shell.execute_reply": "2023-07-26T10:52:23.377960Z" - } - }, - "outputs": [], - "source": [ - "result = ttest_1samp(X[:,0], 0)\n", - "result.pvalue" - ] - }, - { - "cell_type": "markdown", - "id": "cf83426f", - "metadata": {}, - "source": [ - "The $p$-value comes out to 0.931, which is not low enough to\n", - "reject the null hypothesis at level $\\alpha=0.05$. In this case,\n", - "$\\mu_1=0.5$, so the null hypothesis is false. Therefore, we have made\n", - "a Type II error by failing to reject the null hypothesis when the null\n", - "hypothesis is false. \n", - "\n", - "We now test $H_{0,j}: \\mu_j=0$ for $j=1,\\ldots,100$. We compute the\n", - "100 $p$-values, and then construct a vector recording whether the\n", - "$j$th $p$-value is less than or equal to 0.05, in which case we reject\n", - "$H_{0j}$, or greater than 0.05, in which case we do not reject\n", - "$H_{0j}$, for $j=1,\\ldots,100$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1f0c695", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.380819Z", - "iopub.status.busy": "2023-07-26T10:52:23.380641Z", - "iopub.status.idle": "2023-07-26T10:52:23.408797Z", - "shell.execute_reply": "2023-07-26T10:52:23.408278Z" - } - }, - "outputs": [], - "source": [ - "p_values = np.empty(100)\n", - "for i in range(100):\n", - " p_values[i] = ttest_1samp(X[:,i], 0).pvalue\n", - "decision = pd.cut(p_values,\n", - " [0, 0.05, 1],\n", - " labels=['Reject H0',\n", - " 'Do not reject H0'])\n", - "truth = pd.Categorical(true_mean == 0,\n", - " categories=[True, False],\n", - " ordered=True)" - ] - }, - { - "cell_type": "markdown", - "id": "3d8e0d96", - "metadata": {}, - "source": [ - "Since this is a simulated data set, we can create a $2 \\times 2$ table\n", - "similar to Table 13.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a9594a0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.411256Z", - "iopub.status.busy": "2023-07-26T10:52:23.411053Z", - "iopub.status.idle": "2023-07-26T10:52:23.422652Z", - "shell.execute_reply": "2023-07-26T10:52:23.422286Z" - } - }, - "outputs": [], - "source": [ - "pd.crosstab(decision,\n", - " truth,\n", - " rownames=['Decision'],\n", - " colnames=['H0'])" - ] - }, - { - "cell_type": "markdown", - "id": "9610c817", - "metadata": {}, - "source": [ - "Therefore, at level $\\alpha=0.05$, we reject 15 of the 50 false\n", - "null hypotheses, and we incorrectly reject 5 of the true null\n", - "hypotheses. Using the notation from Section 13.3, we have\n", - "$V=5$, $S=15$, $U=45$ and $W=35$.\n", - "We have set $\\alpha=0.05$, which means that we expect to reject around\n", - "5% of the true null hypotheses. This is in line with the $2 \\times 2$\n", - "table above, which indicates that we rejected $V=5$ of the $50$ true\n", - "null hypotheses.\n", - "\n", - "In the simulation above, for the false null hypotheses, the ratio of\n", - "the mean to the standard deviation was only $0.5/1 = 0.5$. This\n", - "amounts to quite a weak signal, and it resulted in a high number of\n", - "Type II errors. Let’s instead simulate data with a stronger signal,\n", - "so that the ratio of the mean to the standard deviation for the false\n", - "null hypotheses equals $1$. We make only 10 Type II errors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25f7fc5d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.425157Z", - "iopub.status.busy": "2023-07-26T10:52:23.424798Z", - "iopub.status.idle": "2023-07-26T10:52:23.455752Z", - "shell.execute_reply": "2023-07-26T10:52:23.455052Z" - } - }, - "outputs": [], - "source": [ - "true_mean = np.array([1]*50 + [0]*50)\n", - "X = rng.standard_normal((10, 100))\n", - "X += true_mean[None,:]\n", - "for i in range(100):\n", - " p_values[i] = ttest_1samp(X[:,i], 0).pvalue\n", - "decision = pd.cut(p_values,\n", - " [0, 0.05, 1],\n", - " labels=['Reject H0',\n", - " 'Do not reject H0'])\n", - "truth = pd.Categorical(true_mean == 0,\n", - " categories=[True, False],\n", - " ordered=True)\n", - "pd.crosstab(decision,\n", - " truth,\n", - " rownames=['Decision'],\n", - " colnames=['H0'])" - ] - }, - { - "cell_type": "markdown", - "id": "f6953d33", - "metadata": {}, - "source": [ - "## Family-Wise Error Rate\n", - "Recall from (13.5) that if the null hypothesis is true\n", - "for each of $m$ independent hypothesis tests, then the FWER is equal\n", - "to $1-(1-\\alpha)^m$. We can use this expression to compute the FWER\n", - "for $m=1,\\ldots, 500$ and $\\alpha=0.05$, $0.01$, and $0.001$.\n", - "We plot the FWER for these values of $\\alpha$ in order to\n", - "reproduce Figure 13.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "369b5bd3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.458631Z", - "iopub.status.busy": "2023-07-26T10:52:23.458434Z", - "iopub.status.idle": "2023-07-26T10:52:23.710141Z", - "shell.execute_reply": "2023-07-26T10:52:23.709635Z" - } - }, - "outputs": [], - "source": [ - "m = np.linspace(1, 501)\n", - "fig, ax = plt.subplots()\n", - "[ax.plot(m,\n", - " 1 - (1 - alpha)**m,\n", - " label=r'$\\alpha=%s$' % str(alpha))\n", - " for alpha in [0.05, 0.01, 0.001]]\n", - "ax.set_xscale('log')\n", - "ax.set_xlabel('Number of Hypotheses')\n", - "ax.set_ylabel('Family-Wise Error Rate')\n", - "ax.legend()\n", - "ax.axhline(0.05, c='k', ls='--');" - ] - }, - { - "cell_type": "markdown", - "id": "3a81479e", - "metadata": {}, - "source": [ - "As discussed previously, even for moderate values of $m$ such as $50$,\n", - "the FWER exceeds $0.05$ unless $\\alpha$ is set to a very low value,\n", - "such as $0.001$. Of course, the problem with setting $\\alpha$ to such\n", - "a low value is that we are likely to make a number of Type II errors:\n", - "in other words, our power is very low.\n", - "\n", - "We now conduct a one-sample $t$-test for each of the first five\n", - "managers in the \n", - "`Fund` dataset, in order to test the null\n", - "hypothesis that the $j$th fund manager’s mean return equals zero,\n", - "$H_{0,j}: \\mu_j=0$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ce7a19f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.713438Z", - "iopub.status.busy": "2023-07-26T10:52:23.713214Z", - "iopub.status.idle": "2023-07-26T10:52:23.754484Z", - "shell.execute_reply": "2023-07-26T10:52:23.754038Z" - } - }, - "outputs": [], - "source": [ - "Fund = load_data('Fund')\n", - "fund_mini = Fund.iloc[:,:5]\n", - "fund_mini_pvals = np.empty(5)\n", - "for i in range(5):\n", - " fund_mini_pvals[i] = ttest_1samp(fund_mini.iloc[:,i], 0).pvalue\n", - "fund_mini_pvals" - ] - }, - { - "cell_type": "markdown", - "id": "7561e3a3", - "metadata": {}, - "source": [ - "The $p$-values are low for Managers One and Three, and high for the\n", - "other three managers. However, we cannot simply reject $H_{0,1}$ and\n", - "$H_{0,3}$, since this would fail to account for the multiple testing\n", - "that we have performed. Instead, we will conduct Bonferroni’s method\n", - "and Holm’s method to control the FWER.\n", - "\n", - "To do this, we use the `multipletests()` function from the\n", - "`statsmodels` module (abbreviated to `mult_test()`). Given the $p$-values,\n", - "for methods like Holm and Bonferroni the function outputs\n", - "adjusted $p$-values, which\n", - "can be thought of as a new set of $p$-values that have been corrected\n", - "for multiple testing. If the adjusted $p$-value for a given hypothesis\n", - "is less than or equal to $\\alpha$, then that hypothesis can be\n", - "rejected while maintaining a FWER of no more than $\\alpha$. In other\n", - "words, for such methods, the adjusted $p$-values resulting from the `multipletests()`\n", - "function can simply be compared to the desired FWER in order to\n", - "determine whether or not to reject each hypothesis. We will later\n", - "see that we can use the same function to control FDR as well." - ] - }, - { - "cell_type": "markdown", - "id": "5b608e46", - "metadata": {}, - "source": [ - "The `mult_test()` function takes $p$-values and a `method` argument, as well as an optional\n", - "`alpha` argument. It returns the decisions (`reject` below)\n", - "as well as the adjusted $p$-values (`bonf`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de6cffed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.756665Z", - "iopub.status.busy": "2023-07-26T10:52:23.756493Z", - "iopub.status.idle": "2023-07-26T10:52:23.759438Z", - "shell.execute_reply": "2023-07-26T10:52:23.759028Z" - } - }, - "outputs": [], - "source": [ - "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n", - "reject" - ] - }, - { - "cell_type": "markdown", - "id": "5135c6b9", - "metadata": {}, - "source": [ - "The $p$-values `bonf` are simply the `fund_mini_pvalues` multiplied by 5 and truncated to be less than\n", - "or equal to 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0de71500", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.761459Z", - "iopub.status.busy": "2023-07-26T10:52:23.761287Z", - "iopub.status.idle": "2023-07-26T10:52:23.764317Z", - "shell.execute_reply": "2023-07-26T10:52:23.763952Z" - } - }, - "outputs": [], - "source": [ - "bonf, np.minimum(fund_mini_pvals * 5, 1)" - ] - }, - { - "cell_type": "markdown", - "id": "1f0bc112", - "metadata": {}, - "source": [ - "Therefore, using Bonferroni’s method, we are able to reject the null hypothesis only for Manager\n", - "One while controlling FWER at $0.05$.\n", - "\n", - "By contrast, using Holm’s method, the adjusted $p$-values indicate\n", - "that we can reject the null\n", - "hypotheses for Managers One and Three at a FWER of $0.05$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7e87bdb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.766793Z", - "iopub.status.busy": "2023-07-26T10:52:23.766606Z", - "iopub.status.idle": "2023-07-26T10:52:23.810152Z", - "shell.execute_reply": "2023-07-26T10:52:23.809736Z" - } - }, - "outputs": [], - "source": [ - "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]" - ] - }, - { - "cell_type": "markdown", - "id": "f762fecd", - "metadata": {}, - "source": [ - "As discussed previously, Manager One seems to perform particularly\n", - "well, whereas Manager Two has poor performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e88be376", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.812384Z", - "iopub.status.busy": "2023-07-26T10:52:23.812232Z", - "iopub.status.idle": "2023-07-26T10:52:23.815971Z", - "shell.execute_reply": "2023-07-26T10:52:23.815576Z" - } - }, - "outputs": [], - "source": [ - "fund_mini.mean()" - ] - }, - { - "cell_type": "markdown", - "id": "88dbf0a6", - "metadata": {}, - "source": [ - "Is there evidence of a meaningful difference in performance between\n", - "these two managers? We can check this by performing a paired $t$-test using the `ttest_rel()` function\n", - "from `scipy.stats`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41149af6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.817997Z", - "iopub.status.busy": "2023-07-26T10:52:23.817852Z", - "iopub.status.idle": "2023-07-26T10:52:23.821301Z", - "shell.execute_reply": "2023-07-26T10:52:23.820976Z" - } - }, - "outputs": [], - "source": [ - "ttest_rel(fund_mini['Manager1'],\n", - " fund_mini['Manager2']).pvalue" - ] - }, - { - "cell_type": "markdown", - "id": "1aca6122", - "metadata": {}, - "source": [ - "The test results in a $p$-value of 0.038,\n", - "suggesting a statistically significant difference.\n", - "\n", - "However, we decided to perform this test only after examining the data\n", - "and noting that Managers One and Two had the highest and lowest mean\n", - "performances. In a sense, this means that we have implicitly\n", - "performed ${5 \\choose 2} = 5(5-1)/2=10$ hypothesis tests, rather than\n", - "just one, as discussed in Section 13.3.2. Hence, we use the\n", - "`pairwise_tukeyhsd()` function from\n", - "`statsmodels.stats.multicomp` to apply Tukey’s method\n", - " in order to adjust for multiple testing. This function takes\n", - "as input a fitted *ANOVA* regression model, which is\n", - "essentially just a linear regression in which all of the predictors\n", - "are qualitative. In this case, the response consists of the monthly\n", - "excess returns achieved by each manager, and the predictor indicates\n", - "the manager to which each return corresponds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61aabda7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:23.823367Z", - "iopub.status.busy": "2023-07-26T10:52:23.823190Z", - "iopub.status.idle": "2023-07-26T10:52:24.308715Z", - "shell.execute_reply": "2023-07-26T10:52:24.308222Z" - } - }, - "outputs": [], - "source": [ - "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n", - "managers = np.hstack([[i+1]*50 for i in range(5)])\n", - "tukey = pairwise_tukeyhsd(returns, managers)\n", - "print(tukey.summary())" - ] - }, - { - "cell_type": "markdown", - "id": "e0084fc5", - "metadata": {}, - "source": [ - "The `pairwise_tukeyhsd()` function provides confidence intervals\n", - "for the difference between each pair of managers (`lower` and\n", - "`upper`), as well as a $p$-value. All of these quantities have\n", - "been adjusted for multiple testing. Notice that the $p$-value for the\n", - "difference between Managers One and Two has increased from $0.038$ to\n", - "$0.186$, so there is no longer clear evidence of a difference between\n", - "the managers’ performances. We can plot the confidence intervals for\n", - "the pairwise comparisons using the `plot_simultaneous()` method\n", - "of `tukey`. Any pair of intervals that don’t overlap indicates a significant difference at the nominal level of 0.05. In this case,\n", - "no differences are considered significant as reported in the table above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cbcad4de", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.311170Z", - "iopub.status.busy": "2023-07-26T10:52:24.310960Z", - "iopub.status.idle": "2023-07-26T10:52:24.410742Z", - "shell.execute_reply": "2023-07-26T10:52:24.410212Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(8,8))\n", - "tukey.plot_simultaneous(ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "6278d13c", - "metadata": {}, - "source": [ - "## False Discovery Rate\n", - "Now we perform hypothesis tests for all 2,000 fund managers in the\n", - "`Fund` dataset. We perform a one-sample $t$-test\n", - "of $H_{0,j}: \\mu_j=0$, which states that the\n", - "$j$th fund manager’s mean return is zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5842190", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.413638Z", - "iopub.status.busy": "2023-07-26T10:52:24.413455Z", - "iopub.status.idle": "2023-07-26T10:52:24.827343Z", - "shell.execute_reply": "2023-07-26T10:52:24.826609Z" - } - }, - "outputs": [], - "source": [ - "fund_pvalues = np.empty(2000)\n", - "for i, manager in enumerate(Fund.columns):\n", - " fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue" - ] - }, - { - "cell_type": "markdown", - "id": "80fc2fcc", - "metadata": {}, - "source": [ - "There are far too many managers to consider trying to control the FWER.\n", - "Instead, we focus on controlling the FDR: that is, the expected fraction of rejected null hypotheses that are actually false positives.\n", - "The `multipletests()` function (abbreviated `mult_test()`) can be used to carry out the Benjamini--Hochberg procedure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c9d8bed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.830214Z", - "iopub.status.busy": "2023-07-26T10:52:24.830015Z", - "iopub.status.idle": "2023-07-26T10:52:24.834953Z", - "shell.execute_reply": "2023-07-26T10:52:24.834464Z" - } - }, - "outputs": [], - "source": [ - "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n", - "fund_qvalues[:10]" - ] - }, - { - "cell_type": "markdown", - "id": "4f73096d", - "metadata": {}, - "source": [ - "The *q-values* output by the\n", - "Benjamini--Hochberg procedure can be interpreted as the smallest FDR\n", - "threshold at which we would reject a particular null hypothesis. For\n", - "instance, a $q$-value of $0.1$ indicates that we can reject the\n", - "corresponding null hypothesis at an FDR of 10% or greater, but that\n", - "we cannot reject the null hypothesis at an FDR below 10%.\n", - "\n", - "If we control the FDR at 10%, then for how many of the fund managers can we reject $H_{0,j}: \\mu_j=0$?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfa39f7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.837627Z", - "iopub.status.busy": "2023-07-26T10:52:24.837419Z", - "iopub.status.idle": "2023-07-26T10:52:24.840834Z", - "shell.execute_reply": "2023-07-26T10:52:24.840316Z" - } - }, - "outputs": [], - "source": [ - "(fund_qvalues <= 0.1).sum()" - ] - }, - { - "cell_type": "markdown", - "id": "ccb44c8d", - "metadata": {}, - "source": [ - "We find that 146 of the 2,000 fund managers have a $q$-value below\n", - "0.1; therefore, we are able to conclude that 146 of the fund managers\n", - "beat the market at an FDR of 10%. Only about 15 (10% of 146) of\n", - "these fund managers are likely to be false discoveries.\n", - "\n", - "By contrast, if we had instead used Bonferroni’s method to control the\n", - "FWER at level $\\alpha=0.1$, then we would have failed to reject any\n", - "null hypotheses!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70b69b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.843470Z", - "iopub.status.busy": "2023-07-26T10:52:24.843237Z", - "iopub.status.idle": "2023-07-26T10:52:24.846889Z", - "shell.execute_reply": "2023-07-26T10:52:24.846276Z" - } - }, - "outputs": [], - "source": [ - "(fund_pvalues <= 0.1 / 2000).sum()" - ] - }, - { - "cell_type": "markdown", - "id": "c8a969f4", - "metadata": {}, - "source": [ - "Figure 13.6 displays the ordered\n", - "$p$-values, $p_{(1)} \\leq p_{(2)} \\leq \\cdots \\leq p_{(2000)}$, for\n", - "the `Fund` dataset, as well as the threshold for rejection by the\n", - "Benjamini--Hochberg procedure. Recall that the Benjamini--Hochberg\n", - "procedure identifies the largest $p$-value such that $p_{(j)} 0:\n", - " selected_ = fund_pvalues < sorted_[sorted_set_].max()\n", - " sorted_set_ = np.arange(sorted_set_.max())\n", - "else:\n", - " selected_ = []\n", - " sorted_set_ = []" - ] - }, - { - "cell_type": "markdown", - "id": "ddeb3900", - "metadata": {}, - "source": [ - "We now reproduce the middle panel of Figure 13.6." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0314eac9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:24.855721Z", - "iopub.status.busy": "2023-07-26T10:52:24.855334Z", - "iopub.status.idle": "2023-07-26T10:52:25.156072Z", - "shell.execute_reply": "2023-07-26T10:52:25.155438Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.scatter(np.arange(0, sorted_.shape[0]) + 1,\n", - " sorted_, s=10)\n", - "ax.set_yscale('log')\n", - "ax.set_xscale('log')\n", - "ax.set_ylabel('P-Value')\n", - "ax.set_xlabel('Index')\n", - "ax.scatter(sorted_set_+1, sorted_[sorted_set_], c='r', s=20)\n", - "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);" - ] - }, - { - "cell_type": "markdown", - "id": "83416f4a", - "metadata": {}, - "source": [ - "## A Re-Sampling Approach\n", - "Here, we implement the re-sampling approach to hypothesis testing\n", - "using the `Khan` dataset, which we investigated in\n", - "Section 13.5. First, we merge the training and\n", - "testing data, which results in observations on 83 patients for\n", - "2,308 genes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b59b8137", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:25.158998Z", - "iopub.status.busy": "2023-07-26T10:52:25.158769Z", - "iopub.status.idle": "2023-07-26T10:52:25.243020Z", - "shell.execute_reply": "2023-07-26T10:52:25.242512Z" - } - }, - "outputs": [], - "source": [ - "Khan = load_data('Khan') \n", - "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n", - "D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n", - "D['Y'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "5534c8d4", - "metadata": {}, - "source": [ - "There are four classes of cancer. For each gene, we compare the mean\n", - "expression in the second class (rhabdomyosarcoma) to the mean\n", - "expression in the fourth class (Burkitt’s lymphoma). Performing a\n", - "standard two-sample $t$-test \n", - "using `ttest_ind()` from `scipy.stats` on the $11$th\n", - "gene produces a test-statistic of -2.09 and an associated $p$-value\n", - "of 0.0412, suggesting modest evidence of a difference in mean\n", - "expression levels between the two cancer types." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96fb2f61", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:25.245359Z", - "iopub.status.busy": "2023-07-26T10:52:25.245172Z", - "iopub.status.idle": "2023-07-26T10:52:25.250139Z", - "shell.execute_reply": "2023-07-26T10:52:25.249644Z" - } - }, - "outputs": [], - "source": [ - "D2 = D[lambda df:df['Y'] == 2]\n", - "D4 = D[lambda df:df['Y'] == 4]\n", - "gene_11 = 'G0011'\n", - "observedT, pvalue = ttest_ind(D2[gene_11],\n", - " D4[gene_11],\n", - " equal_var=True)\n", - "observedT, pvalue" - ] - }, - { - "cell_type": "markdown", - "id": "3131124e", - "metadata": {}, - "source": [ - "However, this $p$-value relies on the assumption that under the null\n", - "hypothesis of no difference between the two groups, the test statistic\n", - "follows a $t$-distribution with $29+25-2=52$ degrees of freedom.\n", - "Instead of using this theoretical null distribution, we can randomly\n", - "split the 54 patients into two groups of 29 and 25, and compute a new\n", - "test statistic. Under the null hypothesis of no difference between\n", - "the groups, this new test statistic should have the same distribution\n", - "as our original one. Repeating this process 10,000 times allows us to\n", - "approximate the null distribution of the test statistic. We compute\n", - "the fraction of the time that our observed test statistic exceeds the\n", - "test statistics obtained via re-sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdc229fa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:25.252510Z", - "iopub.status.busy": "2023-07-26T10:52:25.252319Z", - "iopub.status.idle": "2023-07-26T10:52:26.208474Z", - "shell.execute_reply": "2023-07-26T10:52:26.207894Z" - } - }, - "outputs": [], - "source": [ - "B = 10000\n", - "Tnull = np.empty(B)\n", - "D_ = np.hstack([D2[gene_11], D4[gene_11]])\n", - "n_ = D2[gene_11].shape[0]\n", - "D_null = D_.copy()\n", - "for b in range(B):\n", - " rng.shuffle(D_null)\n", - " ttest_ = ttest_ind(D_null[:n_],\n", - " D_null[n_:],\n", - " equal_var=True)\n", - " Tnull[b] = ttest_.statistic\n", - "(np.abs(Tnull) > np.abs(observedT)).mean()" - ] - }, - { - "cell_type": "markdown", - "id": "c7fc4557", - "metadata": {}, - "source": [ - "This fraction, 0.0398,\n", - "is our re-sampling-based $p$-value.\n", - "It is almost identical to the $p$-value of 0.0412 obtained using the theoretical null distribution.\n", - "We can plot a histogram of the re-sampling-based test statistics in order to reproduce Figure 13.7." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3894695", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:26.210995Z", - "iopub.status.busy": "2023-07-26T10:52:26.210826Z", - "iopub.status.idle": "2023-07-26T10:52:26.458716Z", - "shell.execute_reply": "2023-07-26T10:52:26.458144Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(8,8))\n", - "ax.hist(Tnull,\n", - " bins=100,\n", - " density=True,\n", - " facecolor='y',\n", - " label='Null')\n", - "xval = np.linspace(-4.2, 4.2, 1001)\n", - "ax.plot(xval,\n", - " t_dbn.pdf(xval, D_.shape[0]-2),\n", - " c='r')\n", - "ax.axvline(observedT,\n", - " c='b',\n", - " label='Observed')\n", - "ax.legend()\n", - "ax.set_xlabel(\"Null Distribution of Test Statistic\");" - ] - }, - { - "cell_type": "markdown", - "id": "3bd21158", - "metadata": {}, - "source": [ - "The re-sampling-based null distribution is almost identical to the theoretical null distribution, which is displayed in red.\n", - "\n", - "Finally, we implement the plug-in re-sampling FDR approach outlined in\n", - "Algorithm 13.4. Depending on the speed of your\n", - "computer, calculating the FDR for all 2,308 genes in the `Khan`\n", - "dataset may take a while. Hence, we will illustrate the approach on a\n", - "random subset of 100 genes. For each gene, we first compute the\n", - "observed test statistic, and then produce 10,000 re-sampled test\n", - "statistics. This may take a few minutes to run. If you are in a rush,\n", - "then you could set `B` equal to a smaller value (e.g. `B=500`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b7392cb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T10:52:26.461754Z", - "iopub.status.busy": "2023-07-26T10:52:26.461349Z", - "iopub.status.idle": "2023-07-26T12:21:43.650925Z", - "shell.execute_reply": "2023-07-26T12:21:43.650584Z" - } - }, - "outputs": [], - "source": [ - "m, B = 100, 10000\n", - "idx = rng.choice(Khan['xtest'].columns, m, replace=False)\n", - "T_vals = np.empty(m)\n", - "Tnull_vals = np.empty((m, B))\n", - "\n", - "for j in range(m):\n", - " col = idx[j]\n", - " T_vals[j] = ttest_ind(D2[col],\n", - " D4[col],\n", - " equal_var=True).statistic\n", - " D_ = np.hstack([D2[col], D4[col]])\n", - " D_null = D_.copy()\n", - " for b in range(B):\n", - " rng.shuffle(D_null)\n", - " ttest_ = ttest_ind(D_null[:n_],\n", - " D_null[n_:],\n", - " equal_var=True)\n", - " Tnull_vals[j,b] = ttest_.statistic" - ] - }, - { - "cell_type": "markdown", - "id": "1b92df1b", - "metadata": {}, - "source": [ - "Next, we compute the number of rejected null hypotheses $R$, the\n", - "estimated number of false positives $\\widehat{V}$, and the estimated\n", - "FDR, for a range of threshold values $c$ in\n", - "Algorithm 13.4. The threshold values are chosen\n", - "using the absolute values of the test statistics from the 100 genes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cac15616", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T12:21:43.653105Z", - "iopub.status.busy": "2023-07-26T12:21:43.652856Z", - "iopub.status.idle": "2023-07-26T12:21:43.895651Z", - "shell.execute_reply": "2023-07-26T12:21:43.895292Z" - } - }, - "outputs": [], - "source": [ - "cutoffs = np.sort(np.abs(T_vals))\n", - "FDRs, Rs, Vs = np.empty((3, m))\n", - "for j in range(m):\n", - " R = np.sum(np.abs(T_vals) >= cutoffs[j])\n", - " V = np.sum(np.abs(Tnull_vals) >= cutoffs[j]) / B\n", - " Rs[j] = R\n", - " Vs[j] = V\n", - " FDRs[j] = V / R" - ] - }, - { - "cell_type": "markdown", - "id": "f6779ea0", - "metadata": {}, - "source": [ - "Now, for any given FDR, we can find the genes that will be\n", - "rejected. For example, with FDR controlled at 0.1, we reject 15 of the\n", - "100 null hypotheses. On average, we would expect about one or two of\n", - "these genes (i.e. 10% of 15) to be false discoveries. At an FDR of\n", - "0.2, we can reject the null hypothesis for 28 genes, of which we\n", - "expect around six to be false discoveries.\n", - "\n", - "The variable `idx` stores which\n", - "genes were included in our 100 randomly-selected genes. Let’s look at\n", - "the genes whose estimated FDR is less than 0.1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9661eb10", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T12:21:43.897943Z", - "iopub.status.busy": "2023-07-26T12:21:43.897797Z", - "iopub.status.idle": "2023-07-26T12:21:43.901002Z", - "shell.execute_reply": "2023-07-26T12:21:43.900637Z" - } - }, - "outputs": [], - "source": [ - "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])" - ] - }, - { - "cell_type": "markdown", - "id": "001e3fc1", - "metadata": {}, - "source": [ - "At an FDR threshold of 0.2, more genes are selected, at the cost of having a higher expected\n", - "proportion of false discoveries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18ad4900", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T12:21:43.902778Z", - "iopub.status.busy": "2023-07-26T12:21:43.902684Z", - "iopub.status.idle": "2023-07-26T12:21:43.905535Z", - "shell.execute_reply": "2023-07-26T12:21:43.905228Z" - } - }, - "outputs": [], - "source": [ - "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])" - ] - }, - { - "cell_type": "markdown", - "id": "8767f70c", - "metadata": {}, - "source": [ - "The next line generates Figure 13.11, which is similar\n", - "to Figure 13.9,\n", - "except that it is based on only a subset of the genes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28c276b6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T12:21:43.907278Z", - "iopub.status.busy": "2023-07-26T12:21:43.907150Z", - "iopub.status.idle": "2023-07-26T12:21:43.988689Z", - "shell.execute_reply": "2023-07-26T12:21:43.988352Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(Rs, FDRs, 'b', linewidth=3)\n", - "ax.set_xlabel(\"Number of Rejections\")\n", - "ax.set_ylabel(\"False Discovery Rate\");" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch14-surv-lab.ipynb b/docs/source/labs/Ch14-surv-lab.ipynb deleted file mode 100644 index 031562d..0000000 --- a/docs/source/labs/Ch14-surv-lab.ipynb +++ /dev/null @@ -1,995 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c1af8696", - "metadata": {}, - "source": [ - "# Dummy\n", - "\n", - "## Survival Analysis\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "\n", - "### Some preliminary details\n", - "\n", - "We will want to be able to see plots in the notebook. We\n", - "will therefore include the following as introduced in\n", - "Chapter 2:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bce7ad7", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "98e95e67", - "metadata": {}, - "source": [ - "#### New imports\n", - "\n", - "We again collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3741ca31", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "from lifelines import \\\n", - " (KaplanMeierFitter,\n", - " CoxPHFitter)\n", - "from lifelines.statistics import \\\n", - " (logrank_test,\n", - " multivariate_logrank_test)\n", - "from ISLP.survival import sim_time" - ] - }, - { - "cell_type": "markdown", - "id": "c1dff6d8", - "metadata": {}, - "source": [ - "In this lab, we perform survival analyses on three separate data\n", - "sets. In the first section we analyze the `BrainCancer`\n", - "data .
\n", - "Next, we examine the `Publication`\n", - "data . Finally, we explore explores\n", - "a simulated call center data set.\n", - "\n", - "We begin by importing some of our libraries at this top\n", - "level. This makes the code more readable, as scanning the first few\n", - "lines of the notebook tell us what libraries are used in this\n", - "notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6402fd76", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from ISLP.models import ModelSpec as MS\n", - "from ISLP import load_data" - ] - }, - { - "cell_type": "markdown", - "id": "90eb295f", - "metadata": {}, - "source": [ - "### Brain Cancer Data" - ] - }, - { - "cell_type": "markdown", - "id": "26338f8e", - "metadata": {}, - "source": [ - "We begin with the `BrainCancer` data set, contained in the `ISLP` package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b43f8c6f", - "metadata": {}, - "outputs": [], - "source": [ - "BrainCancer = load_data('BrainCancer')\n", - "BrainCancer.columns" - ] - }, - { - "cell_type": "markdown", - "id": "61eed2f0", - "metadata": {}, - "source": [ - "The rows index the 88 patients, while the columns contain the 8 predictors.\n", - "We first briefly examine the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4307ef61", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "BrainCancer['sex'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19c25475", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "BrainCancer['diagnosis'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9fa6491", - "metadata": {}, - "outputs": [], - "source": [ - "BrainCancer['status'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "bd1e89cb", - "metadata": {}, - "source": [ - "Before beginning an analysis, it is important to know how the\n", - "`status` variable has been coded. Most software\n", - "uses the convention that a `status` of 1 indicates an\n", - "uncensored observation, and a `status` of 0 indicates a censored\n", - "observation. But some scientists might use the opposite coding. For\n", - "the `BrainCancer` data set 35 patients died before the end of\n", - "the study.\n", - "\n", - "To begin the analysis, we re-create a Kaplan-Meier survival curve shown in the book. The main\n", - "package we will use for survival analysis\n", - "is `lifelines`.\n", - "The variable `time` corresponds to $y_i$, the time to the $i$th event (either censoring or\n", - "death). The first argument to `km.fit` are the event times with the\n", - "second argument being the censoring variable with a 1 indicating an observed\n", - "failure time. The `plot` method plots a curve with pointwise confidence\n", - "intervals. By default, they produce 90% confidence intervals. This can be changed\n", - "by setting the `alpha` argument to 1 minus the desired\n", - "confidence level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16b708ff", - "metadata": {}, - "outputs": [], - "source": [ - "km = KaplanMeierFitter()\n", - "km_brain = km.fit(BrainCancer['time'],\n", - " BrainCancer['status'])\n", - "km_brain.plot(label='Kaplan Meier estimate')" - ] - }, - { - "cell_type": "markdown", - "id": "71d6ef47", - "metadata": {}, - "source": [ - "Next we create Kaplan-Meier survival curves that are stratified by\n", - "`sex`, in order to reproduce a second figure from the book.\n", - "We do this by using the `groupby` method of a DataFrame.\n", - "This method returns a generator that can\n", - "be iterated over in the `for` loop. In this case,\n", - "the items in the `for` loop are 2-tuples representing\n", - "the groups: the first entry is the value\n", - "of the grouping column `sex` while the 2nd value\n", - "is the DataFrame consisting of all rows in the\n", - "DataFrame matching that value of `sex`.\n", - "We will want to use this data below\n", - "in the log-rank test, hence we store this\n", - "information in the dictionary `by_sex`. Finally,\n", - "we have also used the notion of\n", - " to automatically\n", - "label the different lines in the plot. String\n", - "interpolation is a powerful technique to format strings –\n", - "`python` has many ways to facilitate such operations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd07dfd6", - "metadata": {}, - "outputs": [], - "source": [ - "by_sex = {}\n", - "for sex, df in BrainCancer.groupby('sex'):\n", - " by_sex[sex] = df\n", - " km_sex = km.fit(df['time'],\n", - " df['status'])\n", - " km_sex.plot(label='Sex=%s' % sex)" - ] - }, - { - "cell_type": "markdown", - "id": "8d40bd2f", - "metadata": {}, - "source": [ - "As discussed in the book, we can perform a\n", - "log-rank test to compare the survival of males to females. We use\n", - "the `logrank_test` function from the `lifelines.statistics` module.\n", - "The first two arguments are the event times, with the second\n", - "denoting the corresponding (optional) censoring indicators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4e80bc9", - "metadata": {}, - "outputs": [], - "source": [ - "logrank_test(by_sex['Male']['time'],\n", - " by_sex['Female']['time'],\n", - " by_sex['Male']['status'],\n", - " by_sex['Female']['status'])" - ] - }, - { - "cell_type": "markdown", - "id": "3e025210", - "metadata": {}, - "source": [ - "The resulting $p$-value is $0.23$, indicating no evidence of a\n", - "difference in survival between the two sexes.\n", - "\n", - "Next, we fit Cox proportional hazards models using the `CoxPHFitter` estimator\n", - "from `lifelines`.\n", - "To begin, we consider a model that uses `sex` as the only predictor." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b57ac5fa", - "metadata": {}, - "outputs": [], - "source": [ - "coxph = CoxPHFitter # shorthand\n", - "sex_df = BrainCancer[['time', 'status', 'sex']]\n", - "model_df = MS(['time', 'status', 'sex'],\n", - " intercept=False).fit_transform(sex_df)\n", - "cox_fit = coxph().fit(model_df,\n", - " 'time',\n", - " 'status')\n", - "cox_fit.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "da8b9eff", - "metadata": {}, - "source": [ - "The first argument to `fit` should be a data frame containing\n", - "at least the event time (the second argument `time` in this case)\n", - "as well as an optional censoring variable (the argument `status` in this case).\n", - "Note also that the Cox model does not include an intercept, which is why\n", - "we used the `intercept=False` argument to `ModelSpec` above.\n", - "It is possible to obtain the likelihood ratio test comparing this model to the one\n", - "with no features as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9991b523", - "metadata": {}, - "outputs": [], - "source": [ - "cox_fit.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "fc76fd07", - "metadata": {}, - "source": [ - "Regardless of which test we use, we see that there is no clear\n", - "evidence for a difference in survival between males and females. As\n", - "we learned in this chapter, the score test from the Cox model is\n", - "exactly equal to the log rank test statistic!\n", - "\n", - "Now we fit a model that makes use of additional predictors. We first note\n", - "that one of our `diagnosis` values is missing, hence\n", - "we drop that observation before continuing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "391157a9", - "metadata": {}, - "outputs": [], - "source": [ - "cleaned = BrainCancer.dropna()\n", - "all_MS = MS(cleaned.columns,\n", - " intercept=False)\n", - "all_df = all_MS.fit_transform(cleaned)\n", - "fit_all = coxph().fit(all_df, \n", - " 'time', \n", - " 'status')\n", - "fit_all.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "18659bcd", - "metadata": {}, - "source": [ - "The `diagnosis` variable has been coded so that the baseline\n", - "corresponds to HG glioma. The results indicate that the risk associated with HG glioma\n", - "is more than eight times (i.e. $e^{2.15}=8.62$) the risk associated\n", - "with meningioma. In other words, after adjusting for the other\n", - "predictors, patients with HG glioma have much worse survival compared\n", - "to those with meningioma. In addition, larger values of the Karnofsky\n", - "index, `ki`, are associated with lower risk, i.e. longer survival.\n", - "\n", - "Finally, we plot estimated survival curves for each diagnosis category,\n", - "adjusting for the other predictors. To make these plots, we set the\n", - "values of the other predictors equal to the mean for quantitative variables\n", - "and equal to the mode for categorical. To do this, we use the\n", - "`apply()` method across rows (i.e. `axis=0`) with a function\n", - "`representative` that checks if a column is categorical\n", - "or not." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2c32bdf", - "metadata": {}, - "outputs": [], - "source": [ - "levels = cleaned['diagnosis'].unique()\n", - "def representative(series):\n", - " if hasattr(series.dtype, 'categories'):\n", - " return pd.Series.mode(series)\n", - " else:\n", - " return series.mean()\n", - "modal_data = cleaned.apply(representative, \n", - " axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "2f5e9767", - "metadata": {}, - "source": [ - "We make four\n", - "copies of the column means and assign the `diagnosis` column to be the four different\n", - "diagnoses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a6fc4b9", - "metadata": {}, - "outputs": [], - "source": [ - "modal_df = pd.DataFrame([modal_data.iloc[0] for _ in range(len(levels))])\n", - "modal_df['diagnosis'] = levels\n", - "modal_df" - ] - }, - { - "cell_type": "markdown", - "id": "dd013465", - "metadata": {}, - "source": [ - "We then construct the design matrix based on the model specification `all_MS` used to fit\n", - "the model, dropping the `time` and `status` variables as these are not\n", - "used to predict the survival functioin." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1e6cb80", - "metadata": {}, - "outputs": [], - "source": [ - "modal_X = all_MS.transform(modal_df)#.drop(columns=['time', 'status'])\n", - "modal_X.index = levels\n", - "modal_X" - ] - }, - { - "cell_type": "markdown", - "id": "d98548ee", - "metadata": {}, - "source": [ - "The estimated survival\n", - "function can be found using the `predict_survival_function()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8753eaa2", - "metadata": {}, - "outputs": [], - "source": [ - "predicted_survival = fit_all.predict_survival_function(modal_X)\n", - "predicted_survival" - ] - }, - { - "cell_type": "markdown", - "id": "a814ddee", - "metadata": {}, - "source": [ - "This returns a data frame,\n", - "whose plot methods yields the different survival curves. To avoid clutter in\n", - "the plots, we do not display confidence intervals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddccc3ac", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", - "predicted_survival.plot(ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "53997de5", - "metadata": {}, - "source": [ - "### Publication Data" - ] - }, - { - "cell_type": "markdown", - "id": "47103217", - "metadata": {}, - "source": [ - "The `Publication` data can be\n", - "found in the `ISLP` package.\n", - "We begin by plotting the Kaplan-Meier curves\n", - "stratified on the `posres` variable, which records whether the\n", - "study had a positive or negative result." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01217472", - "metadata": {}, - "outputs": [], - "source": [ - "Publication = load_data('Publication')\n", - "by_result = {}\n", - "for result, df in Publication.groupby('posres'):\n", - " by_result[result] = df\n", - " km_result = km.fit(df['time'], df['status'])\n", - " km_result.plot(label='Result=%d' % result)" - ] - }, - { - "cell_type": "markdown", - "id": "7929b6e9", - "metadata": {}, - "source": [ - "As discussed previously, the $p$-values from fitting Cox’s\n", - "proportional hazards model to the `posres` variable are quite\n", - "large, providing no evidence of a difference in time-to-publication\n", - "between studies with positive versus negative results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e966bd6", - "metadata": {}, - "outputs": [], - "source": [ - "posres_df = MS(['posres',\n", - " 'time',\n", - " 'status'], intercept=False).fit_transform(Publication)\n", - "posres_fit = coxph().fit(posres_df,\n", - " 'time',\n", - " 'status')\n", - "posres_fit.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "1020fe19", - "metadata": {}, - "source": [ - "As expected, the log-rank test provides an identical conclusion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42572496", - "metadata": {}, - "outputs": [], - "source": [ - "logrank_test(by_result[0]['time'],\n", - " by_result[1]['time'],\n", - " by_result[0]['status'],\n", - " by_result[1]['status'])" - ] - }, - { - "cell_type": "markdown", - "id": "c06a3106", - "metadata": {}, - "source": [ - "However, the results change dramatically when we include other\n", - "predictors in the model. Here we have excluded the funding mechanism\n", - "variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c22a546", - "metadata": {}, - "outputs": [], - "source": [ - "model = MS(Publication.columns.drop('mech'),\n", - " intercept=False)\n", - "coxph().fit(model.fit_transform(Publication),\n", - " 'time',\n", - " 'status').summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "2efd14bf", - "metadata": {}, - "source": [ - "We see that there are a number of statistically significant variables,\n", - "including whether the trial focused on a clinical endpoint, the impact\n", - "of the study, and whether the study had positive or negative results.\n", - "\n", - "### Call Center Data" - ] - }, - { - "cell_type": "markdown", - "id": "9dbf298f", - "metadata": {}, - "source": [ - "In this section, we will simulate survival data using the relationship\n", - "between cumulative hazard and\n", - "the survival function explored in an exercise.\n", - "Our simulated data will represent the observed\n", - "wait times (in seconds) for 2,000 customers who have phoned a call\n", - "center. In this context, censoring occurs if a customer hangs up\n", - "before his or her call is answered.\n", - "\n", - "There are three covariates: `Operators` (the number of call\n", - "center operators available at the time of the call, which can range\n", - "from $5$ to $15$), `Center` (either A, B, or C), and\n", - "`Time` of day (Morning, Afternoon, or Evening). We generate data\n", - "for these covariates so that all possibilities are equally likely: for\n", - "instance, morning, afternoon and evening calls are equally likely, and\n", - "any number of operators from $5$ to $15$ is equally likely. We use the\n", - "`ModelSpec` function from the `ISLP.models` package introduced\n", - "in Chapter 3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72e4c0c1", - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(10)\n", - "N = 2000\n", - "Operators = np.random.choice(np.arange(5, 16),\n", - " N,\n", - " replace=True)\n", - "Center = np.random.choice(['A', 'B', 'C'], \n", - " N,\n", - " replace=True)\n", - "Time = np.random.choice(['Morn.', 'After.', 'Even.'], \n", - " N,\n", - " replace=True)\n", - "D = pd.DataFrame({'Operators': Operators,\n", - " 'Center': pd.Categorical(Center),\n", - " 'Time': pd.Categorical(Time)}) \n", - "model = MS(['Operators',\n", - " 'Center',\n", - " 'Time'],\n", - " intercept=False)\n", - "X = model.fit_transform(D)" - ] - }, - { - "cell_type": "markdown", - "id": "a3f7c728", - "metadata": {}, - "source": [ - "It is worthwhile to take a peek at the design matrix `X`, so\n", - "that we can be sure that we understand how the variables have been coded. By default,\n", - "the levels of categorical variables are sorted and the first column of the usual one-hot encoding\n", - "of the variable is dropped." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b8f4b9f", - "metadata": {}, - "outputs": [], - "source": [ - "X[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "06cd05ca", - "metadata": {}, - "source": [ - "Next, we specify the coefficients and the hazard function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "982ae944", - "metadata": {}, - "outputs": [], - "source": [ - "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n", - "true_linpred = X.dot(true_beta)\n", - "hazard = lambda t: 1e-5 * t" - ] - }, - { - "cell_type": "markdown", - "id": "5054aa25", - "metadata": {}, - "source": [ - "We use the function\n", - "`sim_time` from the `ISLP.survival` package. This function\n", - "uses the relationship between the survival function\n", - "and cumulative hazard $S(t) = \\exp(-H(t))$ and the specific\n", - "form of the cumulative hazard function in the Cox model\n", - "to simulate data based on values of the linear predictor\n", - "`true_linpred` and the cumulative hazard. We can generate\n", - "if we know the inverse of the cumulative hazard function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8939727", - "metadata": {}, - "outputs": [], - "source": [ - "cum_hazard = lambda t: 1e-5 * t**2 / 2" - ] - }, - { - "cell_type": "markdown", - "id": "c0ed581d", - "metadata": {}, - "source": [ - "Here, we have set the coefficient associated with `Operators` to\n", - "equal $0.04$; in other words, each additional operator leads to a\n", - "$e^{0.04}=1.041$-fold increase in the “risk” that the call will be\n", - "answered, given the `Center` and `Time` covariates. This\n", - "makes sense: the greater the number of operators at hand, the shorter\n", - "the wait time! The coefficient associated with `Center == B` is\n", - "$-0.3$, and `Center == A` is treated as the baseline. This means\n", - "that the risk of a call being answered at Center B is 0.74 times the\n", - "risk that it will be answered at Center A; in other words, the wait\n", - "times are a bit longer at Center B.\n", - "\n", - "We are now ready to generate data under the Cox proportional hazards\n", - "model. We’ll truncate the maximum time to 1000 seconds to keep\n", - "simulated wait times reasonable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a850809e", - "metadata": {}, - "outputs": [], - "source": [ - "W = np.array([sim_time(l, cum_hazard)\n", - " for l in true_linpred])\n", - "D['Wait time'] = np.clip(W, 0, 1000)" - ] - }, - { - "cell_type": "markdown", - "id": "3f481113", - "metadata": {}, - "source": [ - "We now simulate our censoring variable, for which we assume\n", - "90% of calls were answered (`Failed==1`) before the\n", - "customer hungup (`Failed==0`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c93cd8c3", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "D['Failed'] = np.random.choice([1, 0],\n", - " N,\n", - " p=[0.9, 0.1])\n", - "D[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bac8ff69", - "metadata": {}, - "outputs": [], - "source": [ - "D['Failed'].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "75699d8c", - "metadata": {}, - "source": [ - "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cafb21e9", - "metadata": {}, - "outputs": [], - "source": [ - "by_center = {}\n", - "for center, df in D.groupby('Center'):\n", - " by_center[center] = df\n", - " km_center = km.fit(df['Wait time'], df['Failed'])\n", - " km_center.plot(label='Center=%s' % center)\n", - "ax = plt.gca()\n", - "ax.set_title(\"Probability of Still Being on Hold\")" - ] - }, - { - "cell_type": "markdown", - "id": "6c477316", - "metadata": {}, - "source": [ - "Next, we stratify by `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d404c129", - "metadata": {}, - "outputs": [], - "source": [ - "by_time = {}\n", - "for time, df in D.groupby('Time'):\n", - " by_time[time] = df\n", - " km_time = km.fit(df['Wait time'], df['Failed'])\n", - " km_time.plot(label='Time=%s' % time)\n", - "ax = plt.gca()\n", - "ax.set_title(\"Probability of Still Being on Hold\")" - ] - }, - { - "cell_type": "markdown", - "id": "dcca1dbd", - "metadata": {}, - "source": [ - "It seems that calls at Call Center B take longer to be answered than\n", - "calls at Centers A and C. Similarly, it appears that wait times are\n", - "longest in the morning and shortest in the evening hours. We can use a\n", - "log-rank test to determine whether these differences are statistically\n", - "significant using `multivariate_logrank_test`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b928758d", - "metadata": {}, - "outputs": [], - "source": [ - "multivariate_logrank_test(D['Wait time'],\n", - " D['Center'],\n", - " D['Failed'])" - ] - }, - { - "cell_type": "markdown", - "id": "285f5644", - "metadata": {}, - "source": [ - "Next, we consider the effect of `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "976558e5", - "metadata": {}, - "outputs": [], - "source": [ - "multivariate_logrank_test(D['Wait time'],\n", - " D['Time'],\n", - " D['Failed'])" - ] - }, - { - "cell_type": "markdown", - "id": "e56b7df7", - "metadata": {}, - "source": [ - "As in the case of a categorical variable with 2 outcomes, these\n", - "results are similar to the likelihood ratio test\n", - "from the Cox proportional hazards model. First, let’s\n", - "look at the results for `Center`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36f7fa13", - "metadata": {}, - "outputs": [], - "source": [ - "X = MS(['Wait time',\n", - " 'Failed',\n", - " 'Center'],\n", - " intercept=False).fit_transform(D)\n", - "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "68ab32c8", - "metadata": {}, - "source": [ - "Next, the results for `Time`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e03b31e6", - "metadata": {}, - "outputs": [], - "source": [ - "X = MS(['Wait time',\n", - " 'Failed',\n", - " 'Time'],\n", - " intercept=False).fit_transform(D)\n", - "F = coxph().fit(X, 'Wait time', 'Failed')\n", - "F.log_likelihood_ratio_test()" - ] - }, - { - "cell_type": "markdown", - "id": "fda76031", - "metadata": {}, - "source": [ - "We find that differences between centers are highly significant, as\n", - "are differences between times of day.\n", - "\n", - "Finally, we fit a complete Cox’s proportional hazards model to the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5022c12", - "metadata": {}, - "outputs": [], - "source": [ - "X = MS(D.columns,\n", - " intercept=False).fit_transform(D)\n", - "fit_queuing = coxph().fit(\n", - " X,\n", - " 'Wait time',\n", - " 'Failed')\n", - "fit_queuing.summary[['coef', 'se(coef)', 'p']]" - ] - }, - { - "cell_type": "markdown", - "id": "847ebaa7", - "metadata": {}, - "source": [ - "The $p$-values for Center B and evening time\n", - "are very small. It is also clear that the\n", - "hazard — that is, the instantaneous risk that a call will be\n", - "answered — increases with the number of operators. Since we\n", - "generated the data ourselves, we know that the true coefficients for\n", - "Center B, Center C, Operators\n", - "evening time, and morning time are $-0.3$,\n", - "$0$, $0.04$, and $0.2$, $-0.2$, respectively. The coefficient estimates\n", - "resulting from the Cox model are fairly accurate." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "all", - "formats": "ipynb,md:myst", - "notebook_metadata_filter": "all" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch2-statlearn-lab.ipynb b/docs/source/labs/Ch2-statlearn-lab.ipynb deleted file mode 100644 index 1247790..0000000 --- a/docs/source/labs/Ch2-statlearn-lab.ipynb +++ /dev/null @@ -1,3586 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "46456e09", - "metadata": {}, - "source": [ - "# Introduction to Python\n", - "\n", - "\n", - " \"Open\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "e9222139", - "metadata": {}, - "source": [ - "## Getting Started" - ] - }, - { - "cell_type": "markdown", - "id": "a84726ab", - "metadata": {}, - "source": [ - "To run the labs in this book, you will need two things:\n", - "\n", - "* An installation of `Python3`, which is the specific version of `Python` used in the labs. \n", - "* Access to `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*." - ] - }, - { - "cell_type": "markdown", - "id": "f922cc9e", - "metadata": {}, - "source": [ - "You can download and install `Python3` by following the instructions available at [anaconda.com](http://anaconda.com)." - ] - }, - { - "cell_type": "markdown", - "id": "baaeada6", - "metadata": {}, - "source": [ - " There are a number of ways to get access to `Jupyter`. Here are just a few:\n", - " \n", - " * Using Google's `Colaboratory` service: [colab.research.google.com/](https://colab.research.google.com/). \n", - " * Using `JupyterHub`, available at [jupyter.org/hub](https://jupyter.org/hub). \n", - " * Using your own `jupyter` installation. Installation instructions are available at [jupyter.org/install](https://jupyter.org/install). \n", - " \n", - "Please see the `Python` resources page on the book website [statlearning.com](https://www.statlearning.com) for up-to-date information about getting `Python` and `Jupyter` working on your computer. \n", - "\n", - "You will need to install the `ISLP` package, which provides access to the datasets and custom-built functions that we provide.\n", - "Inside a macOS or Linux terminal type `pip install ISLP`; this also installs most other packages needed in the labs. The `Python` resources page has a link to the `ISLP` documentation website.\n", - "\n", - "To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. \n", - "Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.\n", - "\n", - "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell." - ] - }, - { - "cell_type": "markdown", - "id": "7a44fbd9", - "metadata": {}, - "source": [ - "## Basic Commands" - ] - }, - { - "cell_type": "markdown", - "id": "3831ff0d", - "metadata": {}, - "source": [ - "In this lab, we will introduce some simple `Python` commands. \n", - " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/)." - ] - }, - { - "cell_type": "markdown", - "id": "b5460488", - "metadata": {}, - "source": [ - "Like most programming languages, `Python` uses *functions*\n", - "to perform operations. To run a\n", - "function called `fun`, we type\n", - "`fun(input1,input2)`, where the inputs (or *arguments*)\n", - "`input1` and `input2` tell\n", - "`Python` how to run the function. A function can have any number of\n", - "inputs. For example, the\n", - "`print()` function outputs a text representation of all of its arguments to the console." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88d0bfff", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.653253Z", - "iopub.status.busy": "2023-07-26T05:29:13.653107Z", - "iopub.status.idle": "2023-07-26T05:29:13.659825Z", - "shell.execute_reply": "2023-07-26T05:29:13.659469Z" - } - }, - "outputs": [], - "source": [ - "print('fit a model with', 11, 'variables')" - ] - }, - { - "cell_type": "markdown", - "id": "9c07629a", - "metadata": {}, - "source": [ - " The following command will provide information about the `print()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebe7aca8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.662377Z", - "iopub.status.busy": "2023-07-26T05:29:13.662207Z", - "iopub.status.idle": "2023-07-26T05:29:13.664797Z", - "shell.execute_reply": "2023-07-26T05:29:13.664422Z" - } - }, - "outputs": [], - "source": [ - "print?" - ] - }, - { - "cell_type": "markdown", - "id": "b0c41d18", - "metadata": {}, - "source": [ - "Adding two integers in `Python` is pretty intuitive." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df2f8168", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.666990Z", - "iopub.status.busy": "2023-07-26T05:29:13.666828Z", - "iopub.status.idle": "2023-07-26T05:29:13.670697Z", - "shell.execute_reply": "2023-07-26T05:29:13.670233Z" - } - }, - "outputs": [], - "source": [ - "3 + 5" - ] - }, - { - "cell_type": "markdown", - "id": "7e7b4195", - "metadata": {}, - "source": [ - "In `Python`, textual data is handled using\n", - "*strings*. For instance, `\"hello\"` and\n", - "`'hello'`\n", - "are strings. \n", - "We can concatenate them using the addition `+` symbol." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cc5c2c1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.673136Z", - "iopub.status.busy": "2023-07-26T05:29:13.672979Z", - "iopub.status.idle": "2023-07-26T05:29:13.675685Z", - "shell.execute_reply": "2023-07-26T05:29:13.675286Z" - } - }, - "outputs": [], - "source": [ - "\"hello\" + \" \" + \"world\"" - ] - }, - { - "cell_type": "markdown", - "id": "cfda314a", - "metadata": {}, - "source": [ - " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n", - " The three most important types of sequences are lists, tuples, and strings. \n", - "We introduce lists now." - ] - }, - { - "cell_type": "markdown", - "id": "764ca7ea", - "metadata": {}, - "source": [ - "The following command instructs `Python` to join together\n", - "the numbers 3, 4, and 5, and to save them as a\n", - "*list* named `x`. When we\n", - "type `x`, it gives us back the list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5d81180", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.677943Z", - "iopub.status.busy": "2023-07-26T05:29:13.677800Z", - "iopub.status.idle": "2023-07-26T05:29:13.680585Z", - "shell.execute_reply": "2023-07-26T05:29:13.680100Z" - } - }, - "outputs": [], - "source": [ - "x = [3, 4, 5]\n", - "x" - ] - }, - { - "cell_type": "markdown", - "id": "a2e75dce", - "metadata": {}, - "source": [ - "Note that we used the brackets\n", - "`[]` to construct this list. \n", - "\n", - "We will often want to add two sets of numbers together. It is reasonable to try the following code,\n", - "though it will not produce the desired results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "425a714c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.682740Z", - "iopub.status.busy": "2023-07-26T05:29:13.682602Z", - "iopub.status.idle": "2023-07-26T05:29:13.685374Z", - "shell.execute_reply": "2023-07-26T05:29:13.684966Z" - } - }, - "outputs": [], - "source": [ - "y = [4, 9, 7]\n", - "x + y" - ] - }, - { - "cell_type": "markdown", - "id": "245f069e", - "metadata": {}, - "source": [ - "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n", - "element-by-element? \n", - " In `Python`, lists hold *arbitrary* objects, and are added using *concatenation*. \n", - " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`." - ] - }, - { - "cell_type": "markdown", - "id": "071caddd", - "metadata": {}, - "source": [ - "This example reflects the fact that \n", - " `Python` is a general-purpose programming language. Much of `Python`'s data-specific\n", - "functionality comes from other packages, notably `numpy`\n", - "and `pandas`. \n", - "In the next section, we will introduce the `numpy` package. More information about `numpy` can be found at \n", - "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html)." - ] - }, - { - "cell_type": "markdown", - "id": "25581a8e", - "metadata": {}, - "source": [ - "## Introduction to Numerical Python\n", - "\n", - "As mentioned earlier, this book makes use of functionality that is contained in the `numpy` \n", - " *library*, or *package*. A package is a collection of modules that are not necessarily included in \n", - " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*." - ] - }, - { - "cell_type": "markdown", - "id": "851a40d6", - "metadata": {}, - "source": [ - " To access `numpy`, we must first `import` it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f88669a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.688017Z", - "iopub.status.busy": "2023-07-26T05:29:13.687702Z", - "iopub.status.idle": "2023-07-26T05:29:13.902613Z", - "shell.execute_reply": "2023-07-26T05:29:13.901721Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np " - ] - }, - { - "cell_type": "markdown", - "id": "5bdc55d1", - "metadata": {}, - "source": [ - "In the previous line, we named the `numpy` *module* `np`; an abbreviation for easier referencing." - ] - }, - { - "cell_type": "markdown", - "id": "7b2cbe8b", - "metadata": {}, - "source": [ - "In `numpy`, an *array* is a generic term for a multidimensional\n", - "set of numbers.\n", - "We use the `np.array()` function to define `x` and `y`, which are one-dimensional arrays, i.e. vectors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8ec382a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.908393Z", - "iopub.status.busy": "2023-07-26T05:29:13.907922Z", - "iopub.status.idle": "2023-07-26T05:29:13.914563Z", - "shell.execute_reply": "2023-07-26T05:29:13.912356Z" - } - }, - "outputs": [], - "source": [ - "x = np.array([3, 4, 5])\n", - "y = np.array([4, 9, 7])" - ] - }, - { - "cell_type": "markdown", - "id": "3a5ce193", - "metadata": {}, - "source": [ - "Note that if you forgot to run the `import numpy as np` command earlier, then\n", - "you will encounter an error in calling the `np.array()` function in the previous line. \n", - " The syntax `np.array()` indicates that the function being called\n", - "is part of the `numpy` package, which we have abbreviated as `np`." - ] - }, - { - "cell_type": "markdown", - "id": "b21e6ebd", - "metadata": {}, - "source": [ - "Since `x` and `y` have been defined using `np.array()`, we get a sensible result when we add them together. Compare this to our results in the previous section,\n", - " when we tried to add two lists without using `numpy`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04de39ad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.919758Z", - "iopub.status.busy": "2023-07-26T05:29:13.919275Z", - "iopub.status.idle": "2023-07-26T05:29:13.929618Z", - "shell.execute_reply": "2023-07-26T05:29:13.928417Z" - } - }, - "outputs": [], - "source": [ - "x + y" - ] - }, - { - "cell_type": "markdown", - "id": "33df3775", - "metadata": {}, - "source": [ - "In `numpy`, matrices are typically represented as two-dimensional arrays, and vectors as one-dimensional arrays. {While it is also possible to create matrices using `np.matrix()`, we will use `np.array()` throughout the labs in this book.}\n", - "We can create a two-dimensional array as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fee5d798", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.934509Z", - "iopub.status.busy": "2023-07-26T05:29:13.934172Z", - "iopub.status.idle": "2023-07-26T05:29:13.941331Z", - "shell.execute_reply": "2023-07-26T05:29:13.939888Z" - } - }, - "outputs": [], - "source": [ - "x = np.array([[1, 2], [3, 4]])\n", - "x" - ] - }, - { - "cell_type": "markdown", - "id": "86de84a5", - "metadata": {}, - "source": [ - "The object `x` has several \n", - "*attributes*, or associated objects. To access an attribute of `x`, we type `x.attribute`, where we replace `attribute`\n", - "with the name of the attribute. \n", - "For instance, we can access the `ndim` attribute of `x` as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5871ca00", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.946610Z", - "iopub.status.busy": "2023-07-26T05:29:13.945948Z", - "iopub.status.idle": "2023-07-26T05:29:13.952982Z", - "shell.execute_reply": "2023-07-26T05:29:13.951422Z" - } - }, - "outputs": [], - "source": [ - "x.ndim" - ] - }, - { - "cell_type": "markdown", - "id": "71a44f8e", - "metadata": {}, - "source": [ - "The output indicates that `x` is a two-dimensional array. \n", - "Similarly, `x.dtype` is the *data type* attribute of the object `x`. This indicates that `x` is \n", - "comprised of 64-bit integers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a32759c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.958221Z", - "iopub.status.busy": "2023-07-26T05:29:13.957674Z", - "iopub.status.idle": "2023-07-26T05:29:13.965373Z", - "shell.execute_reply": "2023-07-26T05:29:13.964551Z" - } - }, - "outputs": [], - "source": [ - "x.dtype" - ] - }, - { - "cell_type": "markdown", - "id": "e770da51", - "metadata": {}, - "source": [ - "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n", - " If\n", - "we had passed in any decimals, then we would have obtained an array of\n", - "*floating point numbers* (i.e. real-valued numbers)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55f01b16", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.970795Z", - "iopub.status.busy": "2023-07-26T05:29:13.970020Z", - "iopub.status.idle": "2023-07-26T05:29:13.977698Z", - "shell.execute_reply": "2023-07-26T05:29:13.976971Z" - } - }, - "outputs": [], - "source": [ - "np.array([[1, 2], [3.0, 4]]).dtype" - ] - }, - { - "cell_type": "markdown", - "id": "4595300f", - "metadata": {}, - "source": [ - "Typing `fun?` will cause `Python` to display \n", - "documentation associated with the function `fun`, if it exists.\n", - "We can try this for `np.array()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a9de618", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.983003Z", - "iopub.status.busy": "2023-07-26T05:29:13.982433Z", - "iopub.status.idle": "2023-07-26T05:29:13.987748Z", - "shell.execute_reply": "2023-07-26T05:29:13.986808Z" - } - }, - "outputs": [], - "source": [ - "np.array?" - ] - }, - { - "cell_type": "markdown", - "id": "9640f3fe", - "metadata": {}, - "source": [ - "This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33e3e6ea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:13.997637Z", - "iopub.status.busy": "2023-07-26T05:29:13.996561Z", - "iopub.status.idle": "2023-07-26T05:29:14.002488Z", - "shell.execute_reply": "2023-07-26T05:29:14.001948Z" - } - }, - "outputs": [], - "source": [ - "np.array([[1, 2], [3, 4]], float).dtype" - ] - }, - { - "cell_type": "markdown", - "id": "fbf59c0e", - "metadata": {}, - "source": [ - "The array `x` is two-dimensional. We can find out the number of rows and columns by looking\n", - "at its `shape` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "623f8434", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.004972Z", - "iopub.status.busy": "2023-07-26T05:29:14.004832Z", - "iopub.status.idle": "2023-07-26T05:29:14.007701Z", - "shell.execute_reply": "2023-07-26T05:29:14.007253Z" - } - }, - "outputs": [], - "source": [ - "x.shape" - ] - }, - { - "cell_type": "markdown", - "id": "81f0bbae", - "metadata": {}, - "source": [ - "A *method* is a function that is associated with an\n", - "object. \n", - "For instance, given an array `x`, the expression\n", - "`x.sum()` sums all of its elements, using the `sum()`\n", - "method for arrays. \n", - "The call `x.sum()` automatically provides `x` as the\n", - "first argument to its `sum()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "235738a7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.010160Z", - "iopub.status.busy": "2023-07-26T05:29:14.009950Z", - "iopub.status.idle": "2023-07-26T05:29:14.013139Z", - "shell.execute_reply": "2023-07-26T05:29:14.012702Z" - } - }, - "outputs": [], - "source": [ - "x = np.array([1, 2, 3, 4])\n", - "x.sum()" - ] - }, - { - "cell_type": "markdown", - "id": "72373ccf", - "metadata": {}, - "source": [ - "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f3494e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.015464Z", - "iopub.status.busy": "2023-07-26T05:29:14.015347Z", - "iopub.status.idle": "2023-07-26T05:29:14.018540Z", - "shell.execute_reply": "2023-07-26T05:29:14.017992Z" - } - }, - "outputs": [], - "source": [ - "x = np.array([1, 2, 3, 4])\n", - "np.sum(x)" - ] - }, - { - "cell_type": "markdown", - "id": "69967e23", - "metadata": {}, - "source": [ - " As another example, the\n", - "`reshape()` method returns a new array with the same elements as\n", - "`x`, but a different shape.\n", - " We do this by passing in a `tuple` in our call to\n", - " `reshape()`, in this case `(2, 3)`. This tuple specifies that we would like to create a two-dimensional array with \n", - "$2$ rows and $3$ columns. {Like lists, tuples represent a sequence of objects. Why do we need more than one way to create a sequence? There are a few differences between tuples and lists, but perhaps the most important is that elements of a tuple cannot be modified, whereas elements of a list can be.}\n", - " \n", - "In what follows, the\n", - "`\\n` character creates a *new line*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ca8f936", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.020924Z", - "iopub.status.busy": "2023-07-26T05:29:14.020756Z", - "iopub.status.idle": "2023-07-26T05:29:14.023635Z", - "shell.execute_reply": "2023-07-26T05:29:14.023118Z" - } - }, - "outputs": [], - "source": [ - "x = np.array([1, 2, 3, 4, 5, 6])\n", - "print('beginning x:\\n', x)\n", - "x_reshape = x.reshape((2, 3))\n", - "print('reshaped x:\\n', x_reshape)" - ] - }, - { - "cell_type": "markdown", - "id": "0c1e36ab", - "metadata": {}, - "source": [ - "The previous output reveals that `numpy` arrays are specified as a sequence\n", - "of *rows*. This is called *row-major ordering*, as opposed to *column-major ordering*." - ] - }, - { - "cell_type": "markdown", - "id": "45263db2", - "metadata": {}, - "source": [ - "`Python` (and hence `numpy`) uses 0-based\n", - "indexing. This means that to access the top left element of `x_reshape`, \n", - "we type in `x_reshape[0,0]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23fba624", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.026491Z", - "iopub.status.busy": "2023-07-26T05:29:14.026156Z", - "iopub.status.idle": "2023-07-26T05:29:14.029404Z", - "shell.execute_reply": "2023-07-26T05:29:14.028872Z" - } - }, - "outputs": [], - "source": [ - "x_reshape[0, 0] " - ] - }, - { - "cell_type": "markdown", - "id": "26f1717c", - "metadata": {}, - "source": [ - "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n", - "of `x_reshape`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a1df926", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.031676Z", - "iopub.status.busy": "2023-07-26T05:29:14.031560Z", - "iopub.status.idle": "2023-07-26T05:29:14.034395Z", - "shell.execute_reply": "2023-07-26T05:29:14.033993Z" - } - }, - "outputs": [], - "source": [ - "x_reshape[1, 2] " - ] - }, - { - "cell_type": "markdown", - "id": "c6d6d359", - "metadata": {}, - "source": [ - "Similarly, `x[2]` yields the\n", - "third entry of `x`. \n", - "\n", - "Now, let's modify the top left element of `x_reshape`. To our surprise, we discover that the first element of `x` has been modified as well!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4bfcc1e1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.036755Z", - "iopub.status.busy": "2023-07-26T05:29:14.036603Z", - "iopub.status.idle": "2023-07-26T05:29:14.039645Z", - "shell.execute_reply": "2023-07-26T05:29:14.039150Z" - } - }, - "outputs": [], - "source": [ - "print('x before we modify x_reshape:\\n', x)\n", - "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n", - "x_reshape[0, 0] = 5\n", - "print('x_reshape after we modify its top left element:\\n', x_reshape)\n", - "print('x after we modify top left element of x_reshape:\\n', x)" - ] - }, - { - "cell_type": "markdown", - "id": "10ff8c21", - "metadata": {}, - "source": [ - "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory." - ] - }, - { - "cell_type": "markdown", - "id": "ff267ecb", - "metadata": {}, - "source": [ - "We just saw that we can modify an element of an array. Can we also modify a tuple? It turns out that we cannot --- and trying to do so introduces\n", - "an *exception*, or error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "973c562a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.042240Z", - "iopub.status.busy": "2023-07-26T05:29:14.042111Z", - "iopub.status.idle": "2023-07-26T05:29:14.226949Z", - "shell.execute_reply": "2023-07-26T05:29:14.226329Z" - } - }, - "outputs": [], - "source": [ - "my_tuple = (3, 4, 5)\n", - "my_tuple[0] = 2" - ] - }, - { - "cell_type": "markdown", - "id": "56393fe3", - "metadata": {}, - "source": [ - "We now briefly mention some attributes of arrays that will come in handy. An array's `shape` attribute contains its dimension; this is always a tuple.\n", - "The `ndim` attribute yields the number of dimensions, and `T` provides its transpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56b5c382", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.229928Z", - "iopub.status.busy": "2023-07-26T05:29:14.229597Z", - "iopub.status.idle": "2023-07-26T05:29:14.233213Z", - "shell.execute_reply": "2023-07-26T05:29:14.232504Z" - } - }, - "outputs": [], - "source": [ - "x_reshape.shape, x_reshape.ndim, x_reshape.T" - ] - }, - { - "cell_type": "markdown", - "id": "22408350", - "metadata": {}, - "source": [ - "Notice that the three individual outputs `(2,3)`, `2`, and `array([[5, 4],[2, 5], [3,6]])` are themselves output as a tuple. \n", - " \n", - "We will often want to apply functions to arrays. \n", - "For instance, we can compute the\n", - "square root of the entries using the `np.sqrt()` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e624622", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.235702Z", - "iopub.status.busy": "2023-07-26T05:29:14.235564Z", - "iopub.status.idle": "2023-07-26T05:29:14.238916Z", - "shell.execute_reply": "2023-07-26T05:29:14.238262Z" - } - }, - "outputs": [], - "source": [ - "np.sqrt(x)" - ] - }, - { - "cell_type": "markdown", - "id": "6c3e074d", - "metadata": {}, - "source": [ - "We can also square the elements:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f45bd25", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.241171Z", - "iopub.status.busy": "2023-07-26T05:29:14.241044Z", - "iopub.status.idle": "2023-07-26T05:29:14.244021Z", - "shell.execute_reply": "2023-07-26T05:29:14.243599Z" - } - }, - "outputs": [], - "source": [ - "x**2" - ] - }, - { - "cell_type": "markdown", - "id": "481ca03c", - "metadata": {}, - "source": [ - "We can compute the square roots using the same notation, raising to the power of $1/2$ instead of 2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6980f628", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.246394Z", - "iopub.status.busy": "2023-07-26T05:29:14.246080Z", - "iopub.status.idle": "2023-07-26T05:29:14.249174Z", - "shell.execute_reply": "2023-07-26T05:29:14.248827Z" - } - }, - "outputs": [], - "source": [ - "x**0.5" - ] - }, - { - "cell_type": "markdown", - "id": "bc357745", - "metadata": {}, - "source": [ - "Throughout this book, we will often want to generate random data. \n", - "The `np.random.normal()` function generates a vector of random\n", - "normal variables. We can learn more about this function by looking at the help page, via a call to `np.random.normal?`.\n", - "The first line of the help page reads `normal(loc=0.0, scale=1.0, size=None)`. \n", - " This *signature* line tells us that the function's arguments are `loc`, `scale`, and `size`. These are *keyword* arguments, which means that when they are passed into\n", - " the function, they can be referred to by name (in any order). {`Python` also uses *positional* arguments. Positional arguments do not need to use a keyword. To see an example, type in `np.sum?`. We see that `a` is a positional argument, i.e. this function assumes that the first unnamed argument that it receives is the array to be summed. By contrast, `axis` and `dtype` are keyword arguments: the position in which these arguments are entered into `np.sum()` does not matter.}\n", - " By default, this function will generate random normal variable(s) with mean (`loc`) $0$ and standard deviation (`scale`) $1$; furthermore, \n", - " a single random variable will be generated unless the argument to `size` is changed. \n", - "\n", - "We now generate 50 independent random variables from a $N(0,1)$ distribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89e32100", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.251585Z", - "iopub.status.busy": "2023-07-26T05:29:14.251451Z", - "iopub.status.idle": "2023-07-26T05:29:14.254814Z", - "shell.execute_reply": "2023-07-26T05:29:14.254443Z" - } - }, - "outputs": [], - "source": [ - "x = np.random.normal(size=50)\n", - "x" - ] - }, - { - "cell_type": "markdown", - "id": "80939b41", - "metadata": {}, - "source": [ - "We create an array `y` by adding an independent $N(50,1)$ random variable to each element of `x`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3428155", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.257670Z", - "iopub.status.busy": "2023-07-26T05:29:14.257502Z", - "iopub.status.idle": "2023-07-26T05:29:14.259844Z", - "shell.execute_reply": "2023-07-26T05:29:14.259473Z" - } - }, - "outputs": [], - "source": [ - "y = x + np.random.normal(loc=50, scale=1, size=50)" - ] - }, - { - "cell_type": "markdown", - "id": "17f93000", - "metadata": {}, - "source": [ - "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n", - "correlation between `x` and `y`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df70172e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.262323Z", - "iopub.status.busy": "2023-07-26T05:29:14.262139Z", - "iopub.status.idle": "2023-07-26T05:29:14.266530Z", - "shell.execute_reply": "2023-07-26T05:29:14.266141Z" - } - }, - "outputs": [], - "source": [ - "np.corrcoef(x, y)" - ] - }, - { - "cell_type": "markdown", - "id": "8fb44ec4", - "metadata": {}, - "source": [ - "If you're following along in your own `Jupyter` notebook, then you probably noticed that you got a different set of results when you ran the past few \n", - "commands. In particular, \n", - " each\n", - "time we call `np.random.normal()`, we will get a different answer, as shown in the following example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d996956", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.269539Z", - "iopub.status.busy": "2023-07-26T05:29:14.269386Z", - "iopub.status.idle": "2023-07-26T05:29:14.272569Z", - "shell.execute_reply": "2023-07-26T05:29:14.271780Z" - } - }, - "outputs": [], - "source": [ - "print(np.random.normal(scale=5, size=2))\n", - "print(np.random.normal(scale=5, size=2)) " - ] - }, - { - "cell_type": "markdown", - "id": "988ca34a", - "metadata": {}, - "source": [ - "In order to ensure that our code provides exactly the same results\n", - "each time it is run, we can set a *random seed* \n", - "using the \n", - "`np.random.default_rng()` function.\n", - "This function takes an arbitrary, user-specified integer argument. If we set a random seed before \n", - "generating random data, then re-running our code will yield the same results. The\n", - "object `rng` has essentially all the random number generating methods found in `np.random`. Hence, to\n", - "generate normal data we use `rng.normal()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37b3a30b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.275409Z", - "iopub.status.busy": "2023-07-26T05:29:14.275284Z", - "iopub.status.idle": "2023-07-26T05:29:14.279259Z", - "shell.execute_reply": "2023-07-26T05:29:14.278925Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(1303)\n", - "print(rng.normal(scale=5, size=2))\n", - "rng2 = np.random.default_rng(1303)\n", - "print(rng2.normal(scale=5, size=2)) " - ] - }, - { - "cell_type": "markdown", - "id": "039e7f65", - "metadata": {}, - "source": [ - "Throughout the labs in this book, we use `np.random.default_rng()` whenever we\n", - "perform calculations involving random quantities within `numpy`. In principle, this\n", - "should enable the reader to exactly reproduce the stated results. However, as new versions of `numpy` become available, it is possible\n", - "that some small discrepancies may occur between the output\n", - "in the labs and the output\n", - "from `numpy`.\n", - "\n", - "The `np.mean()`, `np.var()`, and `np.std()` functions can be used\n", - "to compute the mean, variance, and standard deviation of arrays. These functions are also\n", - "available as methods on the arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4f99e0e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.282826Z", - "iopub.status.busy": "2023-07-26T05:29:14.282508Z", - "iopub.status.idle": "2023-07-26T05:29:14.285616Z", - "shell.execute_reply": "2023-07-26T05:29:14.285268Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(3)\n", - "y = rng.standard_normal(10)\n", - "np.mean(y), y.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbd70319", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.287760Z", - "iopub.status.busy": "2023-07-26T05:29:14.287646Z", - "iopub.status.idle": "2023-07-26T05:29:14.290801Z", - "shell.execute_reply": "2023-07-26T05:29:14.290395Z" - } - }, - "outputs": [], - "source": [ - "np.var(y), y.var(), np.mean((y - y.mean())**2)" - ] - }, - { - "cell_type": "markdown", - "id": "ba51f245", - "metadata": {}, - "source": [ - "Notice that by default `np.var()` divides by the sample size $n$ rather\n", - "than $n-1$; see the `ddof` argument in `np.var?`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf281844", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.292985Z", - "iopub.status.busy": "2023-07-26T05:29:14.292843Z", - "iopub.status.idle": "2023-07-26T05:29:14.295775Z", - "shell.execute_reply": "2023-07-26T05:29:14.295412Z" - } - }, - "outputs": [], - "source": [ - "np.sqrt(np.var(y)), np.std(y)" - ] - }, - { - "cell_type": "markdown", - "id": "877ca8e0", - "metadata": {}, - "source": [ - "The `np.mean()`, `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n", - "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f751b1dd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.297946Z", - "iopub.status.busy": "2023-07-26T05:29:14.297809Z", - "iopub.status.idle": "2023-07-26T05:29:14.300890Z", - "shell.execute_reply": "2023-07-26T05:29:14.300481Z" - } - }, - "outputs": [], - "source": [ - "X = rng.standard_normal((10, 3))\n", - "X" - ] - }, - { - "cell_type": "markdown", - "id": "f9028e1a", - "metadata": {}, - "source": [ - "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cbf92de5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.303121Z", - "iopub.status.busy": "2023-07-26T05:29:14.303004Z", - "iopub.status.idle": "2023-07-26T05:29:14.306153Z", - "shell.execute_reply": "2023-07-26T05:29:14.305697Z" - } - }, - "outputs": [], - "source": [ - "X.mean(axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "009195d4", - "metadata": {}, - "source": [ - "The following yields the same result." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efacc213", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.308317Z", - "iopub.status.busy": "2023-07-26T05:29:14.308171Z", - "iopub.status.idle": "2023-07-26T05:29:14.311023Z", - "shell.execute_reply": "2023-07-26T05:29:14.310704Z" - } - }, - "outputs": [], - "source": [ - "X.mean(0)" - ] - }, - { - "cell_type": "markdown", - "id": "e33fa3fc", - "metadata": {}, - "source": [ - "## Graphics\n", - "In `Python`, common practice is to use the library\n", - "`matplotlib` for graphics.\n", - "However, since `Python` was not written with data analysis in mind,\n", - " the notion of plotting is not intrinsic to the language. \n", - "We will use the `subplots()` function\n", - "from `matplotlib.pyplot` to create a figure and the\n", - "axes onto which we plot our data.\n", - "For many more examples of how to make plots in `Python`,\n", - "readers are encouraged to visit [matplotlib.org/stable/gallery/](https://matplotlib.org/stable/gallery/index.html).\n", - "\n", - "In `matplotlib`, a plot consists of a *figure* and one or more *axes*. You can think of the figure as the blank canvas upon which \n", - "one or more plots will be displayed: it is the entire plotting window. \n", - "The *axes* contain important information about each plot, such as its $x$- and $y$-axis labels,\n", - "title, and more. (Note that in `matplotlib`, the word *axes* is not the plural of *axis*: a plot's *axes* contains much more information \n", - "than just the $x$-axis and the $y$-axis.)\n", - "\n", - "We begin by importing the `subplots()` function\n", - "from `matplotlib`. We use this function\n", - "throughout when creating figures.\n", - "The function returns a tuple of length two: a figure\n", - "object as well as the relevant axes object. We will typically\n", - "pass `figsize` as a keyword argument.\n", - "Having created our axes, we attempt our first plot using its `plot()` method.\n", - "To learn more about it, \n", - "type `ax.plot?`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "637fb67d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.313130Z", - "iopub.status.busy": "2023-07-26T05:29:14.312981Z", - "iopub.status.idle": "2023-07-26T05:29:14.818758Z", - "shell.execute_reply": "2023-07-26T05:29:14.818230Z" - } - }, - "outputs": [], - "source": [ - "from matplotlib.pyplot import subplots\n", - "fig, ax = subplots(figsize=(8, 8))\n", - "x = rng.standard_normal(100)\n", - "y = rng.standard_normal(100)\n", - "ax.plot(x, y);" - ] - }, - { - "cell_type": "markdown", - "id": "ca04f15f", - "metadata": {}, - "source": [ - "We pause here to note that we have *unpacked* the tuple of length two returned by `subplots()` into the two distinct\n", - "variables `fig` and `ax`. Unpacking\n", - "is typically preferred to the following equivalent but slightly more verbose code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "109d681d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.821437Z", - "iopub.status.busy": "2023-07-26T05:29:14.821210Z", - "iopub.status.idle": "2023-07-26T05:29:14.906268Z", - "shell.execute_reply": "2023-07-26T05:29:14.905878Z" - } - }, - "outputs": [], - "source": [ - "output = subplots(figsize=(8, 8))\n", - "fig = output[0]\n", - "ax = output[1]" - ] - }, - { - "cell_type": "markdown", - "id": "5509508c", - "metadata": {}, - "source": [ - "We see that our earlier cell produced a line plot, which is the default. To create a scatterplot, we provide an additional argument to `ax.plot()`, indicating that circles should be displayed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e905982c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.908936Z", - "iopub.status.busy": "2023-07-26T05:29:14.908699Z", - "iopub.status.idle": "2023-07-26T05:29:14.995469Z", - "shell.execute_reply": "2023-07-26T05:29:14.994686Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.plot(x, y, 'o');" - ] - }, - { - "cell_type": "markdown", - "id": "42166016", - "metadata": {}, - "source": [ - "Different values\n", - "of this additional argument can be used to produce different colored lines\n", - "as well as different linestyles." - ] - }, - { - "cell_type": "markdown", - "id": "a99dac78", - "metadata": {}, - "source": [ - "As an alternative, we could use the `ax.scatter()` function to create a scatterplot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27133a4e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:14.997995Z", - "iopub.status.busy": "2023-07-26T05:29:14.997801Z", - "iopub.status.idle": "2023-07-26T05:29:15.089963Z", - "shell.execute_reply": "2023-07-26T05:29:15.089604Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(x, y, marker='o');" - ] - }, - { - "cell_type": "markdown", - "id": "000c125e", - "metadata": {}, - "source": [ - "Notice that in the code blocks above, we have ended\n", - "the last line with a semicolon. This prevents `ax.plot(x, y)` from printing\n", - "text to the notebook. However, it does not prevent a plot from being produced. \n", - " If we omit the trailing semi-colon, then we obtain the following output:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef41f90d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.092622Z", - "iopub.status.busy": "2023-07-26T05:29:15.092487Z", - "iopub.status.idle": "2023-07-26T05:29:15.212584Z", - "shell.execute_reply": "2023-07-26T05:29:15.211918Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(x, y, marker='o')" - ] - }, - { - "cell_type": "markdown", - "id": "014f66e5", - "metadata": {}, - "source": [ - "In what follows, we will use\n", - " trailing semicolons whenever the text that would be output is not\n", - "germane to the discussion at hand." - ] - }, - { - "cell_type": "markdown", - "id": "98a1db4a", - "metadata": {}, - "source": [ - "To label our plot, we make use of the `set_xlabel()`, `set_ylabel()`, and `set_title()` methods\n", - "of `ax`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16a9cb99", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.215419Z", - "iopub.status.busy": "2023-07-26T05:29:15.215027Z", - "iopub.status.idle": "2023-07-26T05:29:15.330192Z", - "shell.execute_reply": "2023-07-26T05:29:15.328644Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(x, y, marker='o')\n", - "ax.set_xlabel(\"this is the x-axis\")\n", - "ax.set_ylabel(\"this is the y-axis\")\n", - "ax.set_title(\"Plot of X vs Y\");" - ] - }, - { - "cell_type": "markdown", - "id": "7f0af2d9", - "metadata": {}, - "source": [ - " Having access to the figure object `fig` itself means that we can go in and change some aspects and then redisplay it. Here, we change\n", - " the size from `(8, 8)` to `(12, 3)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6707420e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.334119Z", - "iopub.status.busy": "2023-07-26T05:29:15.333963Z", - "iopub.status.idle": "2023-07-26T05:29:15.405814Z", - "shell.execute_reply": "2023-07-26T05:29:15.405400Z" - } - }, - "outputs": [], - "source": [ - "fig.set_size_inches(12,3)\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "3cf4ed3f", - "metadata": {}, - "source": [ - "Occasionally we will want to create several plots within a figure. This can be\n", - "achieved by passing additional arguments to `subplots()`. \n", - "Below, we create a $2 \\times 3$ grid of plots\n", - "in a figure of size determined by the `figsize` argument. In such\n", - "situations, there is often a relationship between the axes in the plots. For example,\n", - "all plots may have a common $x$-axis. The `subplots()` function can automatically handle\n", - "this situation when passed the keyword argument `sharex=True`.\n", - "The `axes` object below is an array pointing to different plots in the figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c78ef8b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.408333Z", - "iopub.status.busy": "2023-07-26T05:29:15.408192Z", - "iopub.status.idle": "2023-07-26T05:29:15.732292Z", - "shell.execute_reply": "2023-07-26T05:29:15.731605Z" - } - }, - "outputs": [], - "source": [ - "fig, axes = subplots(nrows=2,\n", - " ncols=3,\n", - " figsize=(15, 5))" - ] - }, - { - "cell_type": "markdown", - "id": "40d89b7c", - "metadata": {}, - "source": [ - "We now produce a scatter plot with `'o'` in the second column of the first row and\n", - "a scatter plot with `'+'` in the third column of the second row." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a18402fb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.734889Z", - "iopub.status.busy": "2023-07-26T05:29:15.734697Z", - "iopub.status.idle": "2023-07-26T05:29:15.959530Z", - "shell.execute_reply": "2023-07-26T05:29:15.959066Z" - } - }, - "outputs": [], - "source": [ - "axes[0,1].plot(x, y, 'o')\n", - "axes[1,2].scatter(x, y, marker='+')\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "d29648ec", - "metadata": {}, - "source": [ - "Type `subplots?` to learn more about \n", - "`subplots()`." - ] - }, - { - "cell_type": "markdown", - "id": "2a6a0c9e", - "metadata": {}, - "source": [ - "To save the output of `fig`, we call its `savefig()`\n", - "method. The argument `dpi` is the dots per inch, used\n", - "to determine how large the figure will be in pixels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80e18950", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:15.962416Z", - "iopub.status.busy": "2023-07-26T05:29:15.962231Z", - "iopub.status.idle": "2023-07-26T05:29:17.048215Z", - "shell.execute_reply": "2023-07-26T05:29:17.047676Z" - } - }, - "outputs": [], - "source": [ - "fig.savefig(\"Figure.png\", dpi=400)\n", - "fig.savefig(\"Figure.pdf\", dpi=200);" - ] - }, - { - "cell_type": "markdown", - "id": "b8e17a71", - "metadata": {}, - "source": [ - "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe8c404d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.051216Z", - "iopub.status.busy": "2023-07-26T05:29:17.051042Z", - "iopub.status.idle": "2023-07-26T05:29:17.330983Z", - "shell.execute_reply": "2023-07-26T05:29:17.330378Z" - } - }, - "outputs": [], - "source": [ - "axes[0,1].set_xlim([-1,1])\n", - "fig.savefig(\"Figure_updated.jpg\")\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "55bff84e", - "metadata": {}, - "source": [ - "We now create some more sophisticated plots. The \n", - "`ax.contour()` method produces a *contour plot* \n", - "in order to represent three-dimensional data, similar to a\n", - "topographical map. It takes three arguments:\n", - "\n", - "* A vector of `x` values (the first dimension),\n", - "* A vector of `y` values (the second dimension), and\n", - "* A matrix whose elements correspond to the `z` value (the third\n", - "dimension) for each pair of `(x,y)` coordinates.\n", - "\n", - "To create `x` and `y`, we’ll use the function `np.linspace()`. The command `np.linspace(a, b, n)` \n", - "returns a vector of `n` numbers starting at `a` and ending at `b`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a99034cb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.333539Z", - "iopub.status.busy": "2023-07-26T05:29:17.333339Z", - "iopub.status.idle": "2023-07-26T05:29:17.452013Z", - "shell.execute_reply": "2023-07-26T05:29:17.451505Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "x = np.linspace(-np.pi, np.pi, 50)\n", - "y = x\n", - "f = np.multiply.outer(np.cos(y), 1 / (1 + x**2))\n", - "ax.contour(x, y, f);" - ] - }, - { - "cell_type": "markdown", - "id": "fd6d00e6", - "metadata": {}, - "source": [ - "We can increase the resolution by adding more levels to the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c613a5df", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.454682Z", - "iopub.status.busy": "2023-07-26T05:29:17.454485Z", - "iopub.status.idle": "2023-07-26T05:29:17.604571Z", - "shell.execute_reply": "2023-07-26T05:29:17.604143Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.contour(x, y, f, levels=45);" - ] - }, - { - "cell_type": "markdown", - "id": "c1f2c4bd", - "metadata": {}, - "source": [ - "To fine-tune the output of the\n", - "`ax.contour()` function, take a\n", - "look at the help file by typing `?plt.contour`.\n", - " \n", - "The `ax.imshow()` method is similar to \n", - "`ax.contour()`, except that it produces a color-coded plot\n", - "whose colors depend on the `z` value. This is known as a\n", - "*heatmap*, and is sometimes used to plot temperature in\n", - "weather forecasts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bef21527", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.607805Z", - "iopub.status.busy": "2023-07-26T05:29:17.607474Z", - "iopub.status.idle": "2023-07-26T05:29:17.731227Z", - "shell.execute_reply": "2023-07-26T05:29:17.730768Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.imshow(f);" - ] - }, - { - "cell_type": "markdown", - "id": "35ed7b29", - "metadata": {}, - "source": [ - "## Sequences and Slice Notation" - ] - }, - { - "cell_type": "markdown", - "id": "bb0cee76", - "metadata": {}, - "source": [ - "As seen above, the\n", - "function `np.linspace()` can be used to create a sequence\n", - "of numbers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4435ed50", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.733591Z", - "iopub.status.busy": "2023-07-26T05:29:17.733437Z", - "iopub.status.idle": "2023-07-26T05:29:17.736896Z", - "shell.execute_reply": "2023-07-26T05:29:17.736428Z" - } - }, - "outputs": [], - "source": [ - "seq1 = np.linspace(0, 10, 11)\n", - "seq1" - ] - }, - { - "cell_type": "markdown", - "id": "f1495e1b", - "metadata": {}, - "source": [ - "The function `np.arange()`\n", - " returns a sequence of numbers spaced out by `step`. If `step` is not specified, then a default value of $1$ is used. Let's create a sequence\n", - " that starts at $0$ and ends at $10$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a2c664a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.739070Z", - "iopub.status.busy": "2023-07-26T05:29:17.738902Z", - "iopub.status.idle": "2023-07-26T05:29:17.742002Z", - "shell.execute_reply": "2023-07-26T05:29:17.741608Z" - } - }, - "outputs": [], - "source": [ - "seq2 = np.arange(0, 10)\n", - "seq2" - ] - }, - { - "cell_type": "markdown", - "id": "7b0e1b2f", - "metadata": {}, - "source": [ - "Why isn't $10$ output above? This has to do with *slice* notation in `Python`. \n", - "Slice notation \n", - "is used to index sequences such as lists, tuples and arrays.\n", - "Suppose we want to retrieve the fourth through sixth (inclusive) entries\n", - "of a string. We obtain a slice of the string using the indexing notation `[3:6]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "810eafdf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.744281Z", - "iopub.status.busy": "2023-07-26T05:29:17.744099Z", - "iopub.status.idle": "2023-07-26T05:29:17.747188Z", - "shell.execute_reply": "2023-07-26T05:29:17.746807Z" - } - }, - "outputs": [], - "source": [ - "\"hello world\"[3:6]" - ] - }, - { - "cell_type": "markdown", - "id": "d73d0b54", - "metadata": {}, - "source": [ - "In the code block above, the notation `3:6` is shorthand for `slice(3,6)` when used inside\n", - "`[]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7a9f652", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.749618Z", - "iopub.status.busy": "2023-07-26T05:29:17.749428Z", - "iopub.status.idle": "2023-07-26T05:29:17.752320Z", - "shell.execute_reply": "2023-07-26T05:29:17.751846Z" - } - }, - "outputs": [], - "source": [ - "\"hello world\"[slice(3,6)]" - ] - }, - { - "cell_type": "markdown", - "id": "be578cf4", - "metadata": {}, - "source": [ - "You might have expected `slice(3,6)` to output the fourth through seventh characters in the text string (recalling that `Python` begins its indexing at zero), but instead it output the fourth through sixth. \n", - " This also explains why the earlier `np.arange(0, 10)` command output only the integers from $0$ to $9$. \n", - "See the documentation `slice?` for useful options in creating slices." - ] - }, - { - "cell_type": "markdown", - "id": "7a9c183a", - "metadata": {}, - "source": [ - "## Indexing Data\n", - "To begin, we create a two-dimensional `numpy` array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24427538", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.754673Z", - "iopub.status.busy": "2023-07-26T05:29:17.754528Z", - "iopub.status.idle": "2023-07-26T05:29:17.758000Z", - "shell.execute_reply": "2023-07-26T05:29:17.757315Z" - } - }, - "outputs": [], - "source": [ - "A = np.array(np.arange(16)).reshape((4, 4))\n", - "A" - ] - }, - { - "cell_type": "markdown", - "id": "d2c583a0", - "metadata": {}, - "source": [ - "Typing `A[1,2]` retrieves the element corresponding to the second row and third\n", - "column. (As usual, `Python` indexes from $0.$)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18684ed5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.760251Z", - "iopub.status.busy": "2023-07-26T05:29:17.760111Z", - "iopub.status.idle": "2023-07-26T05:29:17.763156Z", - "shell.execute_reply": "2023-07-26T05:29:17.762782Z" - } - }, - "outputs": [], - "source": [ - "A[1,2]" - ] - }, - { - "cell_type": "markdown", - "id": "b25734ec", - "metadata": {}, - "source": [ - "The first number after the open-bracket symbol `[`\n", - " refers to the row, and the second number refers to the column. \n", - "\n", - "### Indexing Rows, Columns, and Submatrices\n", - " To select multiple rows at a time, we can pass in a list\n", - " specifying our selection. For instance, `[1,3]` will retrieve the second and fourth rows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34d27b73", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.765326Z", - "iopub.status.busy": "2023-07-26T05:29:17.765144Z", - "iopub.status.idle": "2023-07-26T05:29:17.768118Z", - "shell.execute_reply": "2023-07-26T05:29:17.767768Z" - } - }, - "outputs": [], - "source": [ - "A[[1,3]]" - ] - }, - { - "cell_type": "markdown", - "id": "97149703", - "metadata": {}, - "source": [ - "To select the first and third columns, we pass in `[0,2]` as the second argument in the square brackets.\n", - "In this case we need to supply the first argument `:` \n", - "which selects all rows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1bb799c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.770122Z", - "iopub.status.busy": "2023-07-26T05:29:17.769970Z", - "iopub.status.idle": "2023-07-26T05:29:17.773095Z", - "shell.execute_reply": "2023-07-26T05:29:17.772652Z" - } - }, - "outputs": [], - "source": [ - "A[:,[0,2]]" - ] - }, - { - "cell_type": "markdown", - "id": "2b9ce4a3", - "metadata": {}, - "source": [ - "Now, suppose that we want to select the submatrix made up of the second and fourth \n", - "rows as well as the first and third columns. This is where\n", - "indexing gets slightly tricky. It is natural to try to use lists to retrieve the rows and columns:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de853e7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.775350Z", - "iopub.status.busy": "2023-07-26T05:29:17.775182Z", - "iopub.status.idle": "2023-07-26T05:29:17.778160Z", - "shell.execute_reply": "2023-07-26T05:29:17.777807Z" - } - }, - "outputs": [], - "source": [ - "A[[1,3],[0,2]]" - ] - }, - { - "cell_type": "markdown", - "id": "dd57a0bf", - "metadata": {}, - "source": [ - " Oops --- what happened? We got a one-dimensional array of length 2 identical to" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e71bf672", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.780228Z", - "iopub.status.busy": "2023-07-26T05:29:17.780099Z", - "iopub.status.idle": "2023-07-26T05:29:17.783205Z", - "shell.execute_reply": "2023-07-26T05:29:17.782588Z" - } - }, - "outputs": [], - "source": [ - "np.array([A[1,0],A[3,2]])" - ] - }, - { - "cell_type": "markdown", - "id": "9cd50d44", - "metadata": {}, - "source": [ - " Similarly, the following code fails to extract the submatrix comprised of the second and fourth rows and the first, third, and fourth columns:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "458fe5c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.785646Z", - "iopub.status.busy": "2023-07-26T05:29:17.785525Z", - "iopub.status.idle": "2023-07-26T05:29:17.812781Z", - "shell.execute_reply": "2023-07-26T05:29:17.812287Z" - } - }, - "outputs": [], - "source": [ - "A[[1,3],[0,2,3]]" - ] - }, - { - "cell_type": "markdown", - "id": "67657309", - "metadata": {}, - "source": [ - "We can see what has gone wrong here. When supplied with two indexing lists, the `numpy` interpretation is that these provide pairs of $i,j$ indices for a series of entries. That is why the pair of lists must have the same length. However, that was not our intent, since we are looking for a submatrix.\n", - "\n", - "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "703832ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.815878Z", - "iopub.status.busy": "2023-07-26T05:29:17.815556Z", - "iopub.status.idle": "2023-07-26T05:29:17.819079Z", - "shell.execute_reply": "2023-07-26T05:29:17.818526Z" - } - }, - "outputs": [], - "source": [ - "A[[1,3]][:,[0,2]]" - ] - }, - { - "cell_type": "markdown", - "id": "25c3a988", - "metadata": {}, - "source": [ - "There are more efficient ways of achieving the same result.\n", - "\n", - "The *convenience function* `np.ix_()` allows us to extract a submatrix\n", - "using lists, by creating an intermediate *mesh* object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbf328b7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.821663Z", - "iopub.status.busy": "2023-07-26T05:29:17.821485Z", - "iopub.status.idle": "2023-07-26T05:29:17.825110Z", - "shell.execute_reply": "2023-07-26T05:29:17.824596Z" - } - }, - "outputs": [], - "source": [ - "idx = np.ix_([1,3],[0,2,3])\n", - "A[idx]" - ] - }, - { - "cell_type": "markdown", - "id": "13f73d32", - "metadata": {}, - "source": [ - "Alternatively, we can subset matrices efficiently using slices.\n", - " \n", - "The slice\n", - "`1:4:2` captures the second and fourth items of a sequence, while the slice `0:3:2` captures\n", - "the first and third items (the third element in a slice sequence is the step size)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65784eb0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.827692Z", - "iopub.status.busy": "2023-07-26T05:29:17.827506Z", - "iopub.status.idle": "2023-07-26T05:29:17.830968Z", - "shell.execute_reply": "2023-07-26T05:29:17.830460Z" - } - }, - "outputs": [], - "source": [ - "A[1:4:2,0:3:2]" - ] - }, - { - "cell_type": "markdown", - "id": "481bc57a", - "metadata": {}, - "source": [ - "Why are we able to retrieve a submatrix directly using slices but not using lists?\n", - "Its because they are different `Python` types, and\n", - "are treated differently by `numpy`.\n", - "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited." - ] - }, - { - "cell_type": "markdown", - "id": "f1c55b8a", - "metadata": {}, - "source": [ - "### Boolean Indexing\n", - "In `numpy`, a *Boolean* is a type that equals either `True` or `False` (also represented as $1$ and $0$, respectively).\n", - "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ddb0d83", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.833837Z", - "iopub.status.busy": "2023-07-26T05:29:17.833652Z", - "iopub.status.idle": "2023-07-26T05:29:17.836642Z", - "shell.execute_reply": "2023-07-26T05:29:17.836240Z" - } - }, - "outputs": [], - "source": [ - "keep_rows = np.zeros(A.shape[0], bool)\n", - "keep_rows" - ] - }, - { - "cell_type": "markdown", - "id": "f3007c8d", - "metadata": {}, - "source": [ - "We now set two of the elements to `True`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90e72009", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.839230Z", - "iopub.status.busy": "2023-07-26T05:29:17.839048Z", - "iopub.status.idle": "2023-07-26T05:29:17.844524Z", - "shell.execute_reply": "2023-07-26T05:29:17.841903Z" - } - }, - "outputs": [], - "source": [ - "keep_rows[[1,3]] = True\n", - "keep_rows" - ] - }, - { - "cell_type": "markdown", - "id": "932dac22", - "metadata": {}, - "source": [ - "Note that the elements of `keep_rows`, when viewed as integers, are the same as the\n", - "values of `np.array([0,1,0,1])`. Below, we use `==` to verify their equality. When\n", - "applied to two arrays, the `==` operation is applied elementwise." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb3b6534", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.847591Z", - "iopub.status.busy": "2023-07-26T05:29:17.847413Z", - "iopub.status.idle": "2023-07-26T05:29:17.851043Z", - "shell.execute_reply": "2023-07-26T05:29:17.850650Z" - } - }, - "outputs": [], - "source": [ - "np.all(keep_rows == np.array([0,1,0,1]))" - ] - }, - { - "cell_type": "markdown", - "id": "a8cb48c0", - "metadata": {}, - "source": [ - "(Here, the function `np.all()` has checked whether\n", - "all entries of an array are `True`. A similar function, `np.any()`, can be used to check whether any entries of an array are `True`.)" - ] - }, - { - "cell_type": "markdown", - "id": "ac824821", - "metadata": {}, - "source": [ - " However, even though `np.array([0,1,0,1])` and `keep_rows` are equal according to `==`, they index different sets of rows!\n", - "The former retrieves the first, second, first, and second rows of `A`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b146db22", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.854162Z", - "iopub.status.busy": "2023-07-26T05:29:17.853950Z", - "iopub.status.idle": "2023-07-26T05:29:17.860504Z", - "shell.execute_reply": "2023-07-26T05:29:17.860073Z" - } - }, - "outputs": [], - "source": [ - "A[np.array([0,1,0,1])]" - ] - }, - { - "cell_type": "markdown", - "id": "c0b18644", - "metadata": {}, - "source": [ - " By contrast, `keep_rows` retrieves only the second and fourth rows of `A` --- i.e. the rows for which the Boolean equals `TRUE`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bf7b4a3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.864709Z", - "iopub.status.busy": "2023-07-26T05:29:17.864481Z", - "iopub.status.idle": "2023-07-26T05:29:17.869279Z", - "shell.execute_reply": "2023-07-26T05:29:17.868135Z" - } - }, - "outputs": [], - "source": [ - "A[keep_rows]" - ] - }, - { - "cell_type": "markdown", - "id": "80686b74", - "metadata": {}, - "source": [ - "This example shows that Booleans and integers are treated differently by `numpy`." - ] - }, - { - "cell_type": "markdown", - "id": "d7905166", - "metadata": {}, - "source": [ - "We again make use of the `np.ix_()` function\n", - " to create a mesh containing the second and fourth rows, and the first, third, and fourth columns. This time, we apply the function to Booleans,\n", - " rather than lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "288011d9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.875531Z", - "iopub.status.busy": "2023-07-26T05:29:17.874719Z", - "iopub.status.idle": "2023-07-26T05:29:17.880224Z", - "shell.execute_reply": "2023-07-26T05:29:17.879831Z" - } - }, - "outputs": [], - "source": [ - "keep_cols = np.zeros(A.shape[1], bool)\n", - "keep_cols[[0, 2, 3]] = True\n", - "idx_bool = np.ix_(keep_rows, keep_cols)\n", - "A[idx_bool]" - ] - }, - { - "cell_type": "markdown", - "id": "37900313", - "metadata": {}, - "source": [ - "We can also mix a list with an array of Booleans in the arguments to `np.ix_()`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e3b1954", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.885549Z", - "iopub.status.busy": "2023-07-26T05:29:17.885336Z", - "iopub.status.idle": "2023-07-26T05:29:17.890989Z", - "shell.execute_reply": "2023-07-26T05:29:17.890528Z" - } - }, - "outputs": [], - "source": [ - "idx_mixed = np.ix_([1,3], keep_cols)\n", - "A[idx_mixed]" - ] - }, - { - "cell_type": "markdown", - "id": "2f4d70f3", - "metadata": {}, - "source": [ - "For more details on indexing in `numpy`, readers are referred\n", - "to the `numpy` tutorial mentioned earlier." - ] - }, - { - "cell_type": "markdown", - "id": "3c59c8fb", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "Data sets often contain different types of data, and may have names associated with the rows or columns. \n", - "For these reasons, they typically are best accommodated using a\n", - " *data frame*. \n", - " We can think of a data frame as a sequence\n", - "of arrays of identical length; these are the columns. Entries in the\n", - "different arrays can be combined to form a row.\n", - " The `pandas`\n", - "library can be used to create and work with data frame objects." - ] - }, - { - "cell_type": "markdown", - "id": "95297c29", - "metadata": {}, - "source": [ - "### Reading in a Data Set\n", - "\n", - "The first step of most analyses involves importing a data set into\n", - "`Python`. \n", - " Before attempting to load\n", - "a data set, we must make sure that `Python` knows where to find the file containing it. \n", - "If the\n", - "file is in the same location\n", - "as this notebook file, then we are all set. \n", - "Otherwise, \n", - "the command\n", - "`os.chdir()` can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.)" - ] - }, - { - "cell_type": "markdown", - "id": "42760180", - "metadata": {}, - "source": [ - "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d02bfb28", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:17.893962Z", - "iopub.status.busy": "2023-07-26T05:29:17.893772Z", - "iopub.status.idle": "2023-07-26T05:29:18.126793Z", - "shell.execute_reply": "2023-07-26T05:29:18.126353Z" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "Auto = pd.read_csv('Auto.csv')\n", - "Auto" - ] - }, - { - "cell_type": "markdown", - "id": "5acc3596", - "metadata": {}, - "source": [ - "The book website also has a whitespace-delimited version of this data, called `Auto.data`. This can be read in as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca6090af", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.129214Z", - "iopub.status.busy": "2023-07-26T05:29:18.129036Z", - "iopub.status.idle": "2023-07-26T05:29:18.133861Z", - "shell.execute_reply": "2023-07-26T05:29:18.133327Z" - } - }, - "outputs": [], - "source": [ - "Auto = pd.read_csv('Auto.data', delim_whitespace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "350f1f42", - "metadata": {}, - "source": [ - " Both `Auto.csv` and `Auto.data` are simply text\n", - "files. Before loading data into `Python`, it is a good idea to view it using\n", - "a text editor or other software, such as Microsoft Excel." - ] - }, - { - "cell_type": "markdown", - "id": "1cae3ad2", - "metadata": {}, - "source": [ - "We now take a look at the column of `Auto` corresponding to the variable `horsepower`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7220aa87", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.136921Z", - "iopub.status.busy": "2023-07-26T05:29:18.136464Z", - "iopub.status.idle": "2023-07-26T05:29:18.140452Z", - "shell.execute_reply": "2023-07-26T05:29:18.139936Z" - } - }, - "outputs": [], - "source": [ - "Auto['horsepower']" - ] - }, - { - "cell_type": "markdown", - "id": "c0adabf1", - "metadata": {}, - "source": [ - "We see that the `dtype` of this column is `object`. \n", - "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n", - "in the data. \n", - "We can find out why by looking at the unique values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca1939f0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.142925Z", - "iopub.status.busy": "2023-07-26T05:29:18.142803Z", - "iopub.status.idle": "2023-07-26T05:29:18.146461Z", - "shell.execute_reply": "2023-07-26T05:29:18.145934Z" - } - }, - "outputs": [], - "source": [ - "np.unique(Auto['horsepower'])" - ] - }, - { - "cell_type": "markdown", - "id": "c35e48dc", - "metadata": {}, - "source": [ - "We see the culprit is the value `?`, which is being used to encode missing values." - ] - }, - { - "cell_type": "markdown", - "id": "ccf8e5de", - "metadata": {}, - "source": [ - "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n", - "Now, each instance of `?` in the file is replaced with the\n", - "value `np.nan`, which means *not a number*:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfb084e7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.149045Z", - "iopub.status.busy": "2023-07-26T05:29:18.148875Z", - "iopub.status.idle": "2023-07-26T05:29:18.153953Z", - "shell.execute_reply": "2023-07-26T05:29:18.153625Z" - } - }, - "outputs": [], - "source": [ - "Auto = pd.read_csv('Auto.data',\n", - " na_values=['?'],\n", - " delim_whitespace=True)\n", - "Auto['horsepower'].sum()" - ] - }, - { - "cell_type": "markdown", - "id": "2e8e504f", - "metadata": {}, - "source": [ - "The `Auto.shape` attribute tells us that the data has 397\n", - "observations, or rows, and nine variables, or columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e02b6a5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.156574Z", - "iopub.status.busy": "2023-07-26T05:29:18.155920Z", - "iopub.status.idle": "2023-07-26T05:29:18.159130Z", - "shell.execute_reply": "2023-07-26T05:29:18.158724Z" - } - }, - "outputs": [], - "source": [ - "Auto.shape" - ] - }, - { - "cell_type": "markdown", - "id": "df109192", - "metadata": {}, - "source": [ - "There are\n", - "various ways to deal with missing data. \n", - "In this case, since only five of the rows contain missing\n", - "observations, we choose to use the `Auto.dropna()` method to simply remove these rows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d391f8d0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.161446Z", - "iopub.status.busy": "2023-07-26T05:29:18.161286Z", - "iopub.status.idle": "2023-07-26T05:29:18.165230Z", - "shell.execute_reply": "2023-07-26T05:29:18.164876Z" - } - }, - "outputs": [], - "source": [ - "Auto_new = Auto.dropna()\n", - "Auto_new.shape" - ] - }, - { - "cell_type": "markdown", - "id": "e1ddfb53", - "metadata": {}, - "source": [ - "### Basics of Selecting Rows and Columns\n", - " \n", - "We can use `Auto.columns` to check the variable names." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a222ca60", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.167288Z", - "iopub.status.busy": "2023-07-26T05:29:18.167156Z", - "iopub.status.idle": "2023-07-26T05:29:18.171499Z", - "shell.execute_reply": "2023-07-26T05:29:18.171112Z" - } - }, - "outputs": [], - "source": [ - "Auto = Auto_new # overwrite the previous value\n", - "Auto.columns" - ] - }, - { - "cell_type": "markdown", - "id": "be6f2daf", - "metadata": {}, - "source": [ - "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n", - "Recall that the first argument to the `[]` method\n", - "is always applied to the rows of the array. \n", - "Similarly, \n", - "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a75921ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.173985Z", - "iopub.status.busy": "2023-07-26T05:29:18.173779Z", - "iopub.status.idle": "2023-07-26T05:29:18.180116Z", - "shell.execute_reply": "2023-07-26T05:29:18.179681Z" - } - }, - "outputs": [], - "source": [ - "Auto[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "17c02ad7", - "metadata": {}, - "source": [ - "Similarly, an array of Booleans can be used to subset the rows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7469ff2b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.183321Z", - "iopub.status.busy": "2023-07-26T05:29:18.183157Z", - "iopub.status.idle": "2023-07-26T05:29:18.201363Z", - "shell.execute_reply": "2023-07-26T05:29:18.200939Z" - } - }, - "outputs": [], - "source": [ - "idx_80 = Auto['year'] > 80\n", - "Auto[idx_80]" - ] - }, - { - "cell_type": "markdown", - "id": "27eb1780", - "metadata": {}, - "source": [ - "However, if we pass in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac455404", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.203476Z", - "iopub.status.busy": "2023-07-26T05:29:18.203352Z", - "iopub.status.idle": "2023-07-26T05:29:18.209504Z", - "shell.execute_reply": "2023-07-26T05:29:18.209134Z" - } - }, - "outputs": [], - "source": [ - "Auto[['mpg', 'horsepower']]" - ] - }, - { - "cell_type": "markdown", - "id": "1629d302", - "metadata": {}, - "source": [ - "Since we did not specify an *index* column when we loaded our data frame, the rows are labeled using integers\n", - "0 to 396." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f41235d4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.211995Z", - "iopub.status.busy": "2023-07-26T05:29:18.211814Z", - "iopub.status.idle": "2023-07-26T05:29:18.215209Z", - "shell.execute_reply": "2023-07-26T05:29:18.214581Z" - } - }, - "outputs": [], - "source": [ - "Auto.index" - ] - }, - { - "cell_type": "markdown", - "id": "79d3cebb", - "metadata": {}, - "source": [ - "We can use the\n", - "`set_index()` method to re-name the rows using the contents of `Auto['name']`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31caba24", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.217421Z", - "iopub.status.busy": "2023-07-26T05:29:18.217292Z", - "iopub.status.idle": "2023-07-26T05:29:18.225169Z", - "shell.execute_reply": "2023-07-26T05:29:18.224781Z" - } - }, - "outputs": [], - "source": [ - "Auto_re = Auto.set_index('name')\n", - "Auto_re" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "594bedfa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.227488Z", - "iopub.status.busy": "2023-07-26T05:29:18.227297Z", - "iopub.status.idle": "2023-07-26T05:29:18.230280Z", - "shell.execute_reply": "2023-07-26T05:29:18.229861Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.columns" - ] - }, - { - "cell_type": "markdown", - "id": "184977f2", - "metadata": {}, - "source": [ - "We see that the column `'name'` is no longer there.\n", - " \n", - "Now that the index has been set to `name`, we can access rows of the data \n", - "frame by `name` using the `{loc[]`} method of\n", - "`Auto`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a59b083", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.232510Z", - "iopub.status.busy": "2023-07-26T05:29:18.232342Z", - "iopub.status.idle": "2023-07-26T05:29:18.238242Z", - "shell.execute_reply": "2023-07-26T05:29:18.237748Z" - } - }, - "outputs": [], - "source": [ - "rows = ['amc rebel sst', 'ford torino']\n", - "Auto_re.loc[rows]" - ] - }, - { - "cell_type": "markdown", - "id": "066af73b", - "metadata": {}, - "source": [ - "As an alternative to using the index name, we could retrieve the 4th and 5th rows of `Auto` using the `{iloc[]`} method:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54457a11", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.240468Z", - "iopub.status.busy": "2023-07-26T05:29:18.240293Z", - "iopub.status.idle": "2023-07-26T05:29:18.245745Z", - "shell.execute_reply": "2023-07-26T05:29:18.245311Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.iloc[[3,4]]" - ] - }, - { - "cell_type": "markdown", - "id": "069ca71e", - "metadata": {}, - "source": [ - "We can also use it to retrieve the 1st, 3rd and and 4th columns of `Auto_re`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a4f3faf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.247984Z", - "iopub.status.busy": "2023-07-26T05:29:18.247834Z", - "iopub.status.idle": "2023-07-26T05:29:18.254533Z", - "shell.execute_reply": "2023-07-26T05:29:18.254012Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.iloc[:,[0,2,3]]" - ] - }, - { - "cell_type": "markdown", - "id": "c6f4f9a2", - "metadata": {}, - "source": [ - "We can extract the 4th and 5th rows, as well as the 1st, 3rd and 4th columns, using\n", - "a single call to `iloc[]`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f1e6e56", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.256972Z", - "iopub.status.busy": "2023-07-26T05:29:18.256754Z", - "iopub.status.idle": "2023-07-26T05:29:18.261662Z", - "shell.execute_reply": "2023-07-26T05:29:18.261232Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.iloc[[3,4],[0,2,3]]" - ] - }, - { - "cell_type": "markdown", - "id": "6e4b778c", - "metadata": {}, - "source": [ - "Index entries need not be unique: there are several cars in the data frame named `ford galaxie 500`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1d0e795", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.263825Z", - "iopub.status.busy": "2023-07-26T05:29:18.263676Z", - "iopub.status.idle": "2023-07-26T05:29:18.268126Z", - "shell.execute_reply": "2023-07-26T05:29:18.267724Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]" - ] - }, - { - "cell_type": "markdown", - "id": "b10eabb7", - "metadata": {}, - "source": [ - "### More on Selecting Rows and Columns\n", - "Suppose now that we want to create a data frame consisting of the `weight` and `origin` of the subset of cars with \n", - "`year` greater than 80 --- i.e. those built after 1980.\n", - "To do this, we first create a Boolean array that indexes the rows.\n", - "The `loc[]` method allows for Boolean entries as well as strings:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "392f10a1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.270321Z", - "iopub.status.busy": "2023-07-26T05:29:18.270149Z", - "iopub.status.idle": "2023-07-26T05:29:18.277370Z", - "shell.execute_reply": "2023-07-26T05:29:18.276917Z" - } - }, - "outputs": [], - "source": [ - "idx_80 = Auto_re['year'] > 80\n", - "Auto_re.loc[idx_80, ['weight', 'origin']]" - ] - }, - { - "cell_type": "markdown", - "id": "d3f85975", - "metadata": {}, - "source": [ - "To do this more concisely, we can use an anonymous function called a `lambda`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f5b7906", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.279435Z", - "iopub.status.busy": "2023-07-26T05:29:18.279296Z", - "iopub.status.idle": "2023-07-26T05:29:18.287423Z", - "shell.execute_reply": "2023-07-26T05:29:18.286943Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]" - ] - }, - { - "cell_type": "markdown", - "id": "cc67849e", - "metadata": {}, - "source": [ - "The `lambda` call creates a function that takes a single\n", - "argument, here `df`, and returns `df['year']>80`.\n", - "Since it is created inside the `loc[]` method for the\n", - "dataframe `Auto_re`, that dataframe will be the argument supplied.\n", - "As another example of using a `lambda`, suppose that\n", - "we want all cars built after 1980 that achieve greater than 30 miles per gallon:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b13c5728", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.289828Z", - "iopub.status.busy": "2023-07-26T05:29:18.289661Z", - "iopub.status.idle": "2023-07-26T05:29:18.296360Z", - "shell.execute_reply": "2023-07-26T05:29:18.295846Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.loc[lambda df: (df['year'] > 80) & (df['mpg'] > 30),\n", - " ['weight', 'origin']\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "id": "7885bba9", - "metadata": {}, - "source": [ - "The symbol `&` computes an element-wise *and* operation.\n", - "As another example, suppose that we want to retrieve all `Ford` and `Datsun`\n", - "cars with `displacement` less than 300. We check whether each `name` entry contains either the string `ford` or `datsun` using the `str.contains()` method of the `index` attribute of \n", - "of the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5a2c3be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.298956Z", - "iopub.status.busy": "2023-07-26T05:29:18.298779Z", - "iopub.status.idle": "2023-07-26T05:29:18.306530Z", - "shell.execute_reply": "2023-07-26T05:29:18.305978Z" - } - }, - "outputs": [], - "source": [ - "Auto_re.loc[lambda df: (df['displacement'] < 300)\n", - " & (df.index.str.contains('ford')\n", - " | df.index.str.contains('datsun')),\n", - " ['weight', 'origin']\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "id": "8a7a6c62", - "metadata": {}, - "source": [ - "Here, the symbol `|` computes an element-wise *or* operation.\n", - " \n", - "In summary, a powerful set of operations is available to index the rows and columns of data frames. For integer based queries, use the `iloc[]` method. For string and Boolean\n", - "selections, use the `loc[]` method. For functional queries that filter rows, use the `loc[]` method\n", - "with a function (typically a `lambda`) in the rows argument.\n", - "\n", - "## For Loops\n", - "A `for` loop is a standard tool in many languages that\n", - "repeatedly evaluates some chunk of code while\n", - "varying different values inside the code.\n", - "For example, suppose we loop over elements of a list and compute their sum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6278441", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.308697Z", - "iopub.status.busy": "2023-07-26T05:29:18.308549Z", - "iopub.status.idle": "2023-07-26T05:29:18.311058Z", - "shell.execute_reply": "2023-07-26T05:29:18.310729Z" - } - }, - "outputs": [], - "source": [ - "total = 0\n", - "for value in [3,2,19]:\n", - " total += value\n", - "print('Total is: {0}'.format(total))" - ] - }, - { - "cell_type": "markdown", - "id": "92948288", - "metadata": {}, - "source": [ - "The indented code beneath the line with the `for` statement is run\n", - "for each value in the sequence\n", - "specified in the `for` statement. The loop ends either\n", - "when the cell ends or when code is indented at the same level\n", - "as the original `for` statement.\n", - "We see that the final line above which prints the total is executed\n", - "only once after the for loop has terminated. Loops\n", - "can be nested by additional indentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2aa065a8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.313814Z", - "iopub.status.busy": "2023-07-26T05:29:18.313627Z", - "iopub.status.idle": "2023-07-26T05:29:18.316245Z", - "shell.execute_reply": "2023-07-26T05:29:18.315821Z" - } - }, - "outputs": [], - "source": [ - "total = 0\n", - "for value in [2,3,19]:\n", - " for weight in [3, 2, 1]:\n", - " total += value * weight\n", - "print('Total is: {0}'.format(total))" - ] - }, - { - "cell_type": "markdown", - "id": "aaeb60c5", - "metadata": {}, - "source": [ - "Above, we summed over each combination of `value` and `weight`.\n", - "We also took advantage of the *increment* notation\n", - "in `Python`: the expression `a += b` is equivalent\n", - "to `a = a + b`. Besides\n", - "being a convenient notation, this can save time in computationally\n", - "heavy tasks in which the intermediate value of `a+b` need not\n", - "be explicitly created.\n", - "\n", - "Perhaps a more\n", - "common task would be to sum over `(value, weight)` pairs. For instance,\n", - "to compute the average value of a random variable that takes on\n", - "possible values 2, 3 or 19 with probability 0.2, 0.3, 0.5 respectively\n", - "we would compute the weighted sum. Tasks such as this\n", - "can often be accomplished using the `zip()` function that\n", - "loops over a sequence of tuples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "832c1b7d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.318251Z", - "iopub.status.busy": "2023-07-26T05:29:18.318060Z", - "iopub.status.idle": "2023-07-26T05:29:18.321951Z", - "shell.execute_reply": "2023-07-26T05:29:18.321594Z" - } - }, - "outputs": [], - "source": [ - "total = 0\n", - "for value, weight in zip([2,3,19],\n", - " [0.2,0.3,0.5]):\n", - " total += weight * value\n", - "print('Weighted average is: {0}'.format(total))" - ] - }, - { - "cell_type": "markdown", - "id": "ac163d59", - "metadata": {}, - "source": [ - "### String Formatting\n", - "In the code chunk above we also printed a string\n", - "displaying the total. However, the object `total`\n", - "is an integer and not a string.\n", - "Inserting the value of something into\n", - "a string is a common task, made\n", - "simple using\n", - "some of the powerful string formatting\n", - "tools in `Python`.\n", - "Many data cleaning tasks involve\n", - "manipulating and programmatically\n", - "producing strings.\n", - "\n", - "For example we may want to loop over the columns of a data frame and\n", - "print the percent missing in each column.\n", - "Let’s create a data frame `D` with columns in which 20% of the entries are missing i.e. set\n", - "to `np.nan`. We’ll create the\n", - "values in `D` from a normal distribution with mean 0 and variance 1 using `rng.standard_normal()`\n", - "and then overwrite some random entries using `rng.choice()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca2e7cd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.324105Z", - "iopub.status.busy": "2023-07-26T05:29:18.323988Z", - "iopub.status.idle": "2023-07-26T05:29:18.330121Z", - "shell.execute_reply": "2023-07-26T05:29:18.329787Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(1)\n", - "A = rng.standard_normal((127, 5))\n", - "M = rng.choice([0, np.nan], p=[0.8,0.2], size=A.shape)\n", - "A += M\n", - "D = pd.DataFrame(A, columns=['food',\n", - " 'bar',\n", - " 'pickle',\n", - " 'snack',\n", - " 'popcorn'])\n", - "D[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8ae1dcc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.332238Z", - "iopub.status.busy": "2023-07-26T05:29:18.331972Z", - "iopub.status.idle": "2023-07-26T05:29:18.335469Z", - "shell.execute_reply": "2023-07-26T05:29:18.335131Z" - } - }, - "outputs": [], - "source": [ - "for col in D.columns:\n", - " template = 'Column \"{0}\" has {1:.2%} missing values'\n", - " print(template.format(col,\n", - " np.isnan(D[col]).mean()))" - ] - }, - { - "cell_type": "markdown", - "id": "e4687a4a", - "metadata": {}, - "source": [ - "We see that the `template.format()` method expects two arguments `{0}`\n", - "and `{1:.2%}`, and the latter includes some formatting\n", - "information. In particular, it specifies that the second argument should be expressed as a percent with two decimal digits.\n", - "\n", - "The reference\n", - "[docs.python.org/3/library/string.html](https://docs.python.org/3/library/string.html)\n", - "includes many helpful and more complex examples." - ] - }, - { - "cell_type": "markdown", - "id": "22226024", - "metadata": {}, - "source": [ - "## Additional Graphical and Numerical Summaries\n", - "We can use the `ax.plot()` or `ax.scatter()` functions to display the quantitative variables. However, simply typing the variable names will produce an error message,\n", - "because `Python` does not know to look in the `Auto` data set for those variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30a8a663", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.338919Z", - "iopub.status.busy": "2023-07-26T05:29:18.338751Z", - "iopub.status.idle": "2023-07-26T05:29:18.449864Z", - "shell.execute_reply": "2023-07-26T05:29:18.449132Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.plot(horsepower, mpg, 'o');" - ] - }, - { - "cell_type": "markdown", - "id": "f09f4e10", - "metadata": {}, - "source": [ - "We can address this by accessing the columns directly:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81fdedb1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.452711Z", - "iopub.status.busy": "2023-07-26T05:29:18.452267Z", - "iopub.status.idle": "2023-07-26T05:29:18.556826Z", - "shell.execute_reply": "2023-07-26T05:29:18.556309Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');" - ] - }, - { - "cell_type": "markdown", - "id": "a64455fa", - "metadata": {}, - "source": [ - "Alternatively, we can use the `plot()` method with the call `Auto.plot()`.\n", - "Using this method,\n", - "the variables can be accessed by name.\n", - "The plot methods of a data frame return a familiar object:\n", - "an axes. We can use it to update the plot as we did previously:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61850515", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.559255Z", - "iopub.status.busy": "2023-07-26T05:29:18.559034Z", - "iopub.status.idle": "2023-07-26T05:29:18.680294Z", - "shell.execute_reply": "2023-07-26T05:29:18.679833Z" - } - }, - "outputs": [], - "source": [ - "ax = Auto.plot.scatter('horsepower', 'mpg');\n", - "ax.set_title('Horsepower vs. MPG')" - ] - }, - { - "cell_type": "markdown", - "id": "7c73790f", - "metadata": {}, - "source": [ - "If we want to save\n", - "the figure that contains a given axes, we can find the relevant figure\n", - "by accessing the `figure` attribute:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2e0c165", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.682678Z", - "iopub.status.busy": "2023-07-26T05:29:18.682519Z", - "iopub.status.idle": "2023-07-26T05:29:18.726894Z", - "shell.execute_reply": "2023-07-26T05:29:18.726384Z" - } - }, - "outputs": [], - "source": [ - "fig = ax.figure\n", - "fig.savefig('horsepower_mpg.png');" - ] - }, - { - "cell_type": "markdown", - "id": "2378a5bb", - "metadata": {}, - "source": [ - "We can further instruct the data frame to plot to a particular axes object. In this\n", - "case the corresponding `plot()` method will return the\n", - "modified axes we passed in as an argument. Note that\n", - "when we request a one-dimensional grid of plots, the object `axes` is similarly\n", - "one-dimensional. We place our scatter plot in the middle plot of a row of three plots\n", - "within a figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35c72e77", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.729555Z", - "iopub.status.busy": "2023-07-26T05:29:18.729403Z", - "iopub.status.idle": "2023-07-26T05:29:18.935439Z", - "shell.execute_reply": "2023-07-26T05:29:18.934984Z" - } - }, - "outputs": [], - "source": [ - "fig, axes = subplots(ncols=3, figsize=(15, 5))\n", - "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);" - ] - }, - { - "cell_type": "markdown", - "id": "95b0f3de", - "metadata": {}, - "source": [ - "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`." - ] - }, - { - "cell_type": "markdown", - "id": "5c6c4fa2", - "metadata": {}, - "source": [ - "We now consider the `cylinders` variable. Typing in `Auto.cylinders.dtype` reveals that it is being treated as a quantitative variable. \n", - "However, since there is only a small number of possible values for this variable, we may wish to treat it as \n", - " qualitative. Below, we replace\n", - "the `cylinders` column with a categorical version of `Auto.cylinders`. The function `pd.Series()` owes its name to the fact that `pandas` is often used in time series applications." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b6fb1ad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.937842Z", - "iopub.status.busy": "2023-07-26T05:29:18.937680Z", - "iopub.status.idle": "2023-07-26T05:29:18.941712Z", - "shell.execute_reply": "2023-07-26T05:29:18.941344Z" - } - }, - "outputs": [], - "source": [ - "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n", - "Auto.cylinders.dtype" - ] - }, - { - "cell_type": "markdown", - "id": "2038c16d", - "metadata": {}, - "source": [ - " Now that `cylinders` is qualitative, we can display it using\n", - " the `boxplot()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f19f48c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:18.944261Z", - "iopub.status.busy": "2023-07-26T05:29:18.944131Z", - "iopub.status.idle": "2023-07-26T05:29:19.068255Z", - "shell.execute_reply": "2023-07-26T05:29:19.067715Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.boxplot('mpg', by='cylinders', ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "4ae72e1b", - "metadata": {}, - "source": [ - "The `hist()` method can be used to plot a *histogram*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "929b6901", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:19.071259Z", - "iopub.status.busy": "2023-07-26T05:29:19.071061Z", - "iopub.status.idle": "2023-07-26T05:29:19.195022Z", - "shell.execute_reply": "2023-07-26T05:29:19.194542Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.hist('mpg', ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "db7f6e7c", - "metadata": {}, - "source": [ - "The color of the bars and the number of bins can be changed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d48f1e9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:19.197491Z", - "iopub.status.busy": "2023-07-26T05:29:19.197276Z", - "iopub.status.idle": "2023-07-26T05:29:19.318988Z", - "shell.execute_reply": "2023-07-26T05:29:19.318622Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "Auto.hist('mpg', color='red', bins=12, ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "0e53b8b6", - "metadata": {}, - "source": [ - " See `Auto.hist?` for more plotting\n", - "options.\n", - " \n", - "We can use the `pd.plotting.scatter_matrix()` function to create a *scatterplot matrix* to visualize all of the pairwise relationships between the columns in\n", - "a data frame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "400690e6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:19.321439Z", - "iopub.status.busy": "2023-07-26T05:29:19.321240Z", - "iopub.status.idle": "2023-07-26T05:29:20.519159Z", - "shell.execute_reply": "2023-07-26T05:29:20.518700Z" - } - }, - "outputs": [], - "source": [ - "pd.plotting.scatter_matrix(Auto);" - ] - }, - { - "cell_type": "markdown", - "id": "48a9a4f5", - "metadata": {}, - "source": [ - " We can also produce scatterplots\n", - "for a subset of the variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bbeca8b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:20.521548Z", - "iopub.status.busy": "2023-07-26T05:29:20.521429Z", - "iopub.status.idle": "2023-07-26T05:29:20.804202Z", - "shell.execute_reply": "2023-07-26T05:29:20.803825Z" - } - }, - "outputs": [], - "source": [ - "pd.plotting.scatter_matrix(Auto[['mpg',\n", - " 'displacement',\n", - " 'weight']]);" - ] - }, - { - "cell_type": "markdown", - "id": "d3bb4736", - "metadata": {}, - "source": [ - "The `describe()` method produces a numerical summary of each column in a data frame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1727f604", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:20.809656Z", - "iopub.status.busy": "2023-07-26T05:29:20.809455Z", - "iopub.status.idle": "2023-07-26T05:29:20.816572Z", - "shell.execute_reply": "2023-07-26T05:29:20.816157Z" - } - }, - "outputs": [], - "source": [ - "Auto[['mpg', 'weight']].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "1b508635", - "metadata": {}, - "source": [ - "We can also produce a summary of just a single column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39bf4091", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:29:20.819204Z", - "iopub.status.busy": "2023-07-26T05:29:20.818991Z", - "iopub.status.idle": "2023-07-26T05:29:20.824216Z", - "shell.execute_reply": "2023-07-26T05:29:20.823857Z" - } - }, - "outputs": [], - "source": [ - "Auto['cylinders'].describe()\n", - "Auto['mpg'].describe()" - ] - }, - { - "cell_type": "markdown", - "id": "22ecf5c3", - "metadata": {}, - "source": [ - "To exit `Jupyter`, select `File / Close and Halt`." - ] - } - ], - "metadata": { - "execution": { - "allow_errors": true - }, - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch3-linreg-lab.ipynb b/docs/source/labs/Ch3-linreg-lab.ipynb deleted file mode 100644 index faefdc7..0000000 --- a/docs/source/labs/Ch3-linreg-lab.ipynb +++ /dev/null @@ -1,1321 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "82bce88a", - "metadata": {}, - "source": [ - "# Linear Regression\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "\n", - "## Importing packages\n", - "We import our standard libraries at this top\n", - "level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca5277a6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:48.562599Z", - "iopub.status.busy": "2023-07-26T05:16:48.562472Z", - "iopub.status.idle": "2023-07-26T05:16:49.062186Z", - "shell.execute_reply": "2023-07-26T05:16:49.061609Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots" - ] - }, - { - "cell_type": "markdown", - "id": "95b0ae8c", - "metadata": {}, - "source": [ - "### New imports\n", - "Throughout this lab we will introduce new functions and libraries. However,\n", - "we will import them here to emphasize these are the new\n", - "code objects in this lab. Keeping imports near the top\n", - "of a notebook makes the code more readable, since scanning the first few\n", - "lines tells us what libraries are used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "675f24e6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.064676Z", - "iopub.status.busy": "2023-07-26T05:16:49.064483Z", - "iopub.status.idle": "2023-07-26T05:16:49.739307Z", - "shell.execute_reply": "2023-07-26T05:16:49.738853Z" - } - }, - "outputs": [], - "source": [ - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "markdown", - "id": "1fb0799f", - "metadata": {}, - "source": [ - " We will provide relevant details about the\n", - "functions below as they are needed.\n", - "\n", - "Besides importing whole modules, it is also possible\n", - "to import only a few items from a given module. This\n", - "will help keep the *namespace* clean.\n", - "We will use a few specific objects from the `statsmodels` package\n", - "which we import here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0ee23c2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.741758Z", - "iopub.status.busy": "2023-07-26T05:16:49.741609Z", - "iopub.status.idle": "2023-07-26T05:16:49.744969Z", - "shell.execute_reply": "2023-07-26T05:16:49.744551Z" - } - }, - "outputs": [], - "source": [ - "from statsmodels.stats.outliers_influence \\\n", - " import variance_inflation_factor as VIF\n", - "from statsmodels.stats.anova import anova_lm" - ] - }, - { - "cell_type": "markdown", - "id": "dbfd346e", - "metadata": {}, - "source": [ - "As one of the import statements above is quite a long line, we inserted a line break `\\` to\n", - "ease readability.\n", - "\n", - "We will also use some functions written for the labs in this book in the `ISLP`\n", - "package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b35eb887", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.747166Z", - "iopub.status.busy": "2023-07-26T05:16:49.746991Z", - "iopub.status.idle": "2023-07-26T05:16:49.867818Z", - "shell.execute_reply": "2023-07-26T05:16:49.867356Z" - } - }, - "outputs": [], - "source": [ - "from ISLP import load_data\n", - "from ISLP.models import (ModelSpec as MS,\n", - " summarize,\n", - " poly)" - ] - }, - { - "cell_type": "markdown", - "id": "84a8177e", - "metadata": {}, - "source": [ - "### Inspecting Objects and Namespaces\n", - "The\n", - "function `dir()`\n", - "provides a list of\n", - "objects in a namespace." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "961908f7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.870378Z", - "iopub.status.busy": "2023-07-26T05:16:49.870231Z", - "iopub.status.idle": "2023-07-26T05:16:49.874355Z", - "shell.execute_reply": "2023-07-26T05:16:49.873851Z" - } - }, - "outputs": [], - "source": [ - "dir()" - ] - }, - { - "cell_type": "markdown", - "id": "3efc6d99", - "metadata": {}, - "source": [ - " This shows you everything that `Python` can find at the top level.\n", - "There are certain objects like `__builtins__` that contain references to built-in\n", - "functions like `print()`.\n", - "\n", - "Every python object has its own notion of\n", - "namespace, also accessible with `dir()`. This will include\n", - "both the attributes of the object\n", - "as well as any methods associated with it. For instance, we see `'sum'` in the listing for an\n", - "array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "662caa15", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.876620Z", - "iopub.status.busy": "2023-07-26T05:16:49.876476Z", - "iopub.status.idle": "2023-07-26T05:16:49.880485Z", - "shell.execute_reply": "2023-07-26T05:16:49.880074Z" - } - }, - "outputs": [], - "source": [ - "A = np.array([3,5,11])\n", - "dir(A)" - ] - }, - { - "cell_type": "markdown", - "id": "49bdd416", - "metadata": {}, - "source": [ - " This indicates that the object `A.sum` exists. In this case it is a method\n", - "that can be used to compute the sum of the array `A` as can be seen by typing `A.sum?`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebb7d126", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.882618Z", - "iopub.status.busy": "2023-07-26T05:16:49.882477Z", - "iopub.status.idle": "2023-07-26T05:16:49.885324Z", - "shell.execute_reply": "2023-07-26T05:16:49.884886Z" - } - }, - "outputs": [], - "source": [ - "A.sum()" - ] - }, - { - "cell_type": "markdown", - "id": "9fbed3f3", - "metadata": {}, - "source": [ - "## Simple Linear Regression\n", - "In this section we will construct model \n", - "matrices (also called design matrices) using the `ModelSpec()` transform from `ISLP.models`.\n", - "\n", - "We will use the `Boston` housing data set, which is contained in the `ISLP` package. The `Boston` dataset records `medv` (median house value) for $506$ neighborhoods\n", - "around Boston. We will build a regression model to predict `medv` using $13$\n", - "predictors such as `rmvar` (average number of rooms per house),\n", - " `age` (proportion of owner-occupied units built prior to 1940), and `lstat` (percent of\n", - "households with low socioeconomic status). We will use `statsmodels` for this\n", - "task, a `Python` package that implements several commonly used\n", - "regression methods.\n", - "\n", - "We have included a simple loading function `load_data()` in the\n", - "`ISLP` package:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ea46cee", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.887855Z", - "iopub.status.busy": "2023-07-26T05:16:49.887580Z", - "iopub.status.idle": "2023-07-26T05:16:49.892800Z", - "shell.execute_reply": "2023-07-26T05:16:49.892442Z" - } - }, - "outputs": [], - "source": [ - "Boston = load_data(\"Boston\")\n", - "Boston.columns" - ] - }, - { - "cell_type": "markdown", - "id": "a8cceee6", - "metadata": {}, - "source": [ - "Type `Boston?` to find out more about these data.\n", - "\n", - "We start by using the `sm.OLS()` function to fit a\n", - "simple linear regression model. Our response will be\n", - " `medv` and `lstat` will be the single predictor.\n", - "For this model, we can create the model matrix by hand." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26c0ba88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.895108Z", - "iopub.status.busy": "2023-07-26T05:16:49.894938Z", - "iopub.status.idle": "2023-07-26T05:16:49.901385Z", - "shell.execute_reply": "2023-07-26T05:16:49.900996Z" - } - }, - "outputs": [], - "source": [ - "X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),\n", - " 'lstat': Boston['lstat']})\n", - "X[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "5ac7f183", - "metadata": {}, - "source": [ - "We extract the response, and fit the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4dd511b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.903600Z", - "iopub.status.busy": "2023-07-26T05:16:49.903426Z", - "iopub.status.idle": "2023-07-26T05:16:49.906235Z", - "shell.execute_reply": "2023-07-26T05:16:49.905900Z" - } - }, - "outputs": [], - "source": [ - "y = Boston['medv']\n", - "model = sm.OLS(y, X)\n", - "results = model.fit()" - ] - }, - { - "cell_type": "markdown", - "id": "ddf44723", - "metadata": {}, - "source": [ - "Note that `sm.OLS()` does\n", - "not fit the model; it specifies the model, and then `model.fit()` does the actual fitting. \n", - "\n", - "Our `ISLP` function `summarize()` produces a simple table of the parameter estimates,\n", - "their standard errors, t-statistics and p-values.\n", - "The function takes a single argument, such as the object `results` \n", - "returned here by the `fit`\n", - "method, and returns such a summary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eef9f8e3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.908149Z", - "iopub.status.busy": "2023-07-26T05:16:49.908028Z", - "iopub.status.idle": "2023-07-26T05:16:49.973872Z", - "shell.execute_reply": "2023-07-26T05:16:49.973443Z" - } - }, - "outputs": [], - "source": [ - "summarize(results)" - ] - }, - { - "cell_type": "markdown", - "id": "db916e19", - "metadata": {}, - "source": [ - "Before we describe other methods for working with fitted models, we outline a more useful and general framework for constructing a model matrix~`X`.\n", - "### Using Transformations: Fit and Transform\n", - "Our model above has a single predictor, and constructing `X` was straightforward. \n", - "In practice we often fit models with more than one predictor, typically selected from an array or data frame.\n", - "We may wish to introduce transformations to the variables before fitting the model, specify interactions between variables, and expand some particular variables into sets of variables (e.g. polynomials).\n", - "The `sklearn` package has a particular notion\n", - "for this type of task: a *transform*. A transform is an object\n", - "that is created with some parameters as arguments. The\n", - "object has two main methods: `fit()` and `transform()`.\n", - "\n", - "We provide a general approach for specifying models and constructing\n", - "the model matrix through the transform `ModelSpec()` in the `ISLP` library.\n", - "`ModelSpec()`\n", - "(renamed `MS()` in the preamble) creates a\n", - "transform object, and then a pair of methods\n", - "`transform()` and `fit()` are used to construct a\n", - "corresponding model matrix.\n", - "\n", - "We first describe this process for our simple regression model using a single predictor `lstat` in\n", - "the `Boston` data frame, but will use it repeatedly in more\n", - "complex tasks in this and other labs in this book.\n", - "In our case the transform is created by the expression\n", - "`design = MS(['lstat'])`.\n", - "\n", - "The `fit()` method takes the original array and may do some\n", - "initial computations on it, as specified in the transform object.\n", - "For example, it may compute means and standard deviations for centering and scaling.\n", - "The `transform()` \n", - "method applies the fitted transformation to the array of data, and produces the model matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "557170d4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.976562Z", - "iopub.status.busy": "2023-07-26T05:16:49.976352Z", - "iopub.status.idle": "2023-07-26T05:16:49.982733Z", - "shell.execute_reply": "2023-07-26T05:16:49.982315Z" - } - }, - "outputs": [], - "source": [ - "design = MS(['lstat'])\n", - "design = design.fit(Boston)\n", - "X = design.transform(Boston)\n", - "X[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "86615813", - "metadata": {}, - "source": [ - "In this simple case, the `fit()` method does very little; it simply checks that the variable `'lstat'` specified in `design` exists in `Boston`. Then `transform()` constructs the model matrix with two columns: an `intercept` and the variable `lstat`.\n", - "\n", - "These two operations can be combined with the\n", - "`fit_transform()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b83ec097", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.985396Z", - "iopub.status.busy": "2023-07-26T05:16:49.985228Z", - "iopub.status.idle": "2023-07-26T05:16:49.992390Z", - "shell.execute_reply": "2023-07-26T05:16:49.991020Z" - } - }, - "outputs": [], - "source": [ - "design = MS(['lstat'])\n", - "X = design.fit_transform(Boston)\n", - "X[:4]" - ] - }, - { - "cell_type": "markdown", - "id": "c6becbcb", - "metadata": {}, - "source": [ - "Note that, as in the previous code chunk when the two steps were done separately, the `design` object is changed as a result of the `fit()` operation. The power of this pipeline will become clearer when we fit more complex models that involve interactions and transformations." - ] - }, - { - "cell_type": "markdown", - "id": "e097120f", - "metadata": {}, - "source": [ - "Let's return to our fitted regression model.\n", - "The object\n", - "`results` has several methods that can be used for inference.\n", - "We already presented a function `summarize()` for showing the essentials of the fit.\n", - "For a full and somewhat exhaustive summary of the fit, we can use the `summary()` \n", - "method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4dce5f6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:49.995458Z", - "iopub.status.busy": "2023-07-26T05:16:49.995307Z", - "iopub.status.idle": "2023-07-26T05:16:50.003927Z", - "shell.execute_reply": "2023-07-26T05:16:50.003569Z" - } - }, - "outputs": [], - "source": [ - "results.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "9a367738", - "metadata": {}, - "source": [ - "The fitted coefficients can also be retrieved as the\n", - "`params` attribute of `results`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0edf555", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.006397Z", - "iopub.status.busy": "2023-07-26T05:16:50.006253Z", - "iopub.status.idle": "2023-07-26T05:16:50.009669Z", - "shell.execute_reply": "2023-07-26T05:16:50.009037Z" - } - }, - "outputs": [], - "source": [ - "results.params" - ] - }, - { - "cell_type": "markdown", - "id": "4472913f", - "metadata": {}, - "source": [ - "The `get_prediction()` method can be used to obtain predictions, and produce confidence intervals and\n", - "prediction intervals for the prediction of `medv` for given values of `lstat`.\n", - "\n", - "We first create a new data frame, in this case containing only the variable `lstat`, with the values for this variable at which we wish to make predictions.\n", - "We then use the `transform()` method of `design` to create the corresponding model matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdc5a3f3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.012044Z", - "iopub.status.busy": "2023-07-26T05:16:50.011931Z", - "iopub.status.idle": "2023-07-26T05:16:50.016842Z", - "shell.execute_reply": "2023-07-26T05:16:50.016427Z" - } - }, - "outputs": [], - "source": [ - "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n", - "newX = design.transform(new_df)\n", - "newX" - ] - }, - { - "cell_type": "markdown", - "id": "590d6b7d", - "metadata": {}, - "source": [ - "Next we compute the predictions at `newX`, and view them by extracting the `predicted_mean` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c6acbf0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.019127Z", - "iopub.status.busy": "2023-07-26T05:16:50.018964Z", - "iopub.status.idle": "2023-07-26T05:16:50.022350Z", - "shell.execute_reply": "2023-07-26T05:16:50.021918Z" - } - }, - "outputs": [], - "source": [ - "new_predictions = results.get_prediction(newX);\n", - "new_predictions.predicted_mean" - ] - }, - { - "cell_type": "markdown", - "id": "f2d7e037", - "metadata": {}, - "source": [ - "We can produce confidence intervals for the predicted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c472ef33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.024574Z", - "iopub.status.busy": "2023-07-26T05:16:50.024433Z", - "iopub.status.idle": "2023-07-26T05:16:50.027837Z", - "shell.execute_reply": "2023-07-26T05:16:50.027439Z" - } - }, - "outputs": [], - "source": [ - "new_predictions.conf_int(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "id": "dfabf8a7", - "metadata": {}, - "source": [ - "Prediction intervals are computing by setting `obs=True`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e2ffc7a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.029978Z", - "iopub.status.busy": "2023-07-26T05:16:50.029838Z", - "iopub.status.idle": "2023-07-26T05:16:50.032847Z", - "shell.execute_reply": "2023-07-26T05:16:50.032491Z" - } - }, - "outputs": [], - "source": [ - "new_predictions.conf_int(obs=True, alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "id": "65463c75", - "metadata": {}, - "source": [ - " For instance, the 95% confidence interval associated with an\n", - " `lstat` value of 10 is (24.47, 25.63), and the 95% prediction\n", - "interval is (12.82, 37.28). As expected, the confidence and\n", - "prediction intervals are centered around the same point (a predicted\n", - "value of 25.05 for `medv` when `lstat` equals\n", - "10), but the latter are substantially wider.\n", - "\n", - "Next we will plot `medv` and `lstat` \n", - "using `DataFrame.plot.scatter()`, \n", - "and wish to\n", - "add the regression line to the resulting plot." - ] - }, - { - "cell_type": "markdown", - "id": "f699e856", - "metadata": {}, - "source": [ - "### Defining Functions\n", - "While there is a function\n", - "within the `ISLP` package that adds a line to an existing plot, we take this opportunity\n", - "to define our first function to do so." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e56a1d3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.035185Z", - "iopub.status.busy": "2023-07-26T05:16:50.035076Z", - "iopub.status.idle": "2023-07-26T05:16:50.037782Z", - "shell.execute_reply": "2023-07-26T05:16:50.037389Z" - } - }, - "outputs": [], - "source": [ - "def abline(ax, b, m):\n", - " \"Add a line with slope m and intercept b to ax\"\n", - " xlim = ax.get_xlim()\n", - " ylim = [m * xlim[0] + b, m * xlim[1] + b]\n", - " ax.plot(xlim, ylim)" - ] - }, - { - "cell_type": "markdown", - "id": "e0a0dce5", - "metadata": {}, - "source": [ - " A few things are illustrated above. First we see the syntax for defining a function:\n", - "`def funcname(...)`. The function has arguments `ax, b, m`\n", - "where `ax` is an axis object for an exisiting plot, `b` is the intercept and\n", - "`m` is the slope of the desired line. Other plotting options can be passed on to\n", - "`ax.plot` by including additional optional arguments as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7f43ffe7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.040033Z", - "iopub.status.busy": "2023-07-26T05:16:50.039836Z", - "iopub.status.idle": "2023-07-26T05:16:50.042447Z", - "shell.execute_reply": "2023-07-26T05:16:50.042026Z" - } - }, - "outputs": [], - "source": [ - "def abline(ax, b, m, *args, **kwargs):\n", - " \"Add a line with slope m and intercept b to ax\"\n", - " xlim = ax.get_xlim()\n", - " ylim = [m * xlim[0] + b, m * xlim[1] + b]\n", - " ax.plot(xlim, ylim, *args, **kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "4f4981fa", - "metadata": {}, - "source": [ - "The addition of `*args` allows any number of\n", - "non-named arguments to `abline`, while `*kwargs` allows any\n", - "number of named arguments (such as `linewidth=3`) to `abline`.\n", - "In our function, we pass\n", - "these arguments verbatim to `ax.plot` above. Readers\n", - "interested in learning more about\n", - "functions are referred to the section on\n", - "defining functions in [docs.python.org/tutorial](https://docs.python.org/3/tutorial/controlflow.html#defining-functions).\n", - "\n", - "Let’s use our new function to add this regression line to a plot of\n", - "`medv` vs. `lstat`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f7b67c9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.044790Z", - "iopub.status.busy": "2023-07-26T05:16:50.044610Z", - "iopub.status.idle": "2023-07-26T05:16:50.159032Z", - "shell.execute_reply": "2023-07-26T05:16:50.158476Z" - } - }, - "outputs": [], - "source": [ - "ax = Boston.plot.scatter('lstat', 'medv')\n", - "abline(ax,\n", - " results.params[0],\n", - " results.params[1],\n", - " 'r--',\n", - " linewidth=3)" - ] - }, - { - "cell_type": "markdown", - "id": "50d7066f", - "metadata": {}, - "source": [ - "Thus, the final call to `ax.plot()` is `ax.plot(xlim, ylim, 'r--', linewidth=3)`.\n", - "We have used the argument `'r--'` to produce a red dashed line, and added\n", - "an argument to make it of width 3.\n", - "There is some evidence for non-linearity in the relationship between `lstat` and `medv`. We will explore this issue later in this lab.\n", - "\n", - "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays." - ] - }, - { - "cell_type": "markdown", - "id": "82ab1913", - "metadata": {}, - "source": [ - "Next we examine some diagnostic plots, several of which were discussed\n", - "in Section 3.3.3.\n", - "We can find the fitted values and residuals\n", - "of the fit as attributes of the `results` object.\n", - "Various influence measures describing the regression model\n", - "are computed with the `get_influence()` method.\n", - "As we will not use the `fig` component returned\n", - "as the first value from `subplots()`, we simply\n", - "capture the second returned value in `ax` below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b35a2fd3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.161898Z", - "iopub.status.busy": "2023-07-26T05:16:50.161592Z", - "iopub.status.idle": "2023-07-26T05:16:50.274404Z", - "shell.execute_reply": "2023-07-26T05:16:50.273842Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(8,8))[1]\n", - "ax.scatter(results.fittedvalues, results.resid)\n", - "ax.set_xlabel('Fitted value')\n", - "ax.set_ylabel('Residual')\n", - "ax.axhline(0, c='k', ls='--');" - ] - }, - { - "cell_type": "markdown", - "id": "4deb547b", - "metadata": {}, - "source": [ - " We add a horizontal line at 0 for reference using the\n", - " `ax.axhline()` method, indicating\n", - "it should be black (`c='k'`) and have a dashed linestyle (`ls='--'`).\n", - "\n", - "On the basis of the residual plot, there is some evidence of non-linearity.\n", - "Leverage statistics can be computed for any number of predictors using the\n", - "`hat_matrix_diag` attribute of the value returned by the\n", - "`get_influence()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82673b80", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.276799Z", - "iopub.status.busy": "2023-07-26T05:16:50.276637Z", - "iopub.status.idle": "2023-07-26T05:16:50.384102Z", - "shell.execute_reply": "2023-07-26T05:16:50.383325Z" - } - }, - "outputs": [], - "source": [ - "infl = results.get_influence()\n", - "ax = subplots(figsize=(8,8))[1]\n", - "ax.scatter(np.arange(X.shape[0]), infl.hat_matrix_diag)\n", - "ax.set_xlabel('Index')\n", - "ax.set_ylabel('Leverage')\n", - "np.argmax(infl.hat_matrix_diag)" - ] - }, - { - "cell_type": "markdown", - "id": "7eb69d75", - "metadata": {}, - "source": [ - " The `np.argmax()` function identifies the index of the largest element of an array, optionally computed over an axis of the array.\n", - "In this case, we maximized over the entire array\n", - "to determine which observation has the largest leverage statistic." - ] - }, - { - "cell_type": "markdown", - "id": "94c0a8c1", - "metadata": {}, - "source": [ - "## Multiple Linear Regression\n", - "In order to fit a multiple linear regression model using least squares, we again use\n", - "the `ModelSpec()` transform to construct the required\n", - "model matrix and response. The arguments\n", - "to `ModelSpec()` can be quite general, but in this case\n", - "a list of column names suffice. We consider a fit here with\n", - "the two variables `lstat` and `age`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54596dc4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.387053Z", - "iopub.status.busy": "2023-07-26T05:16:50.386874Z", - "iopub.status.idle": "2023-07-26T05:16:50.400398Z", - "shell.execute_reply": "2023-07-26T05:16:50.399938Z" - } - }, - "outputs": [], - "source": [ - "X = MS(['lstat', 'age']).fit_transform(Boston)\n", - "model1 = sm.OLS(y, X)\n", - "results1 = model1.fit()\n", - "summarize(results1)" - ] - }, - { - "cell_type": "markdown", - "id": "88450596", - "metadata": {}, - "source": [ - "Notice how we have compacted the first line into a succinct expression describing the construction of `X`.\n", - "\n", - "The `Boston` data set contains 12 variables, and so it would be cumbersome\n", - "to have to type all of these in order to perform a regression using all of the predictors.\n", - "Instead, we can use the following short-hand:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75c78238", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.402974Z", - "iopub.status.busy": "2023-07-26T05:16:50.402819Z", - "iopub.status.idle": "2023-07-26T05:16:50.406561Z", - "shell.execute_reply": "2023-07-26T05:16:50.406042Z" - } - }, - "outputs": [], - "source": [ - "terms = Boston.columns.drop('medv')\n", - "terms" - ] - }, - { - "cell_type": "markdown", - "id": "653715b5", - "metadata": {}, - "source": [ - "We can now fit the model with all the variables in `terms` using\n", - "the same model matrix builder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f14b9e1a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.409092Z", - "iopub.status.busy": "2023-07-26T05:16:50.408922Z", - "iopub.status.idle": "2023-07-26T05:16:50.427019Z", - "shell.execute_reply": "2023-07-26T05:16:50.426146Z" - } - }, - "outputs": [], - "source": [ - "X = MS(terms).fit_transform(Boston)\n", - "model = sm.OLS(y, X)\n", - "results = model.fit()\n", - "summarize(results)" - ] - }, - { - "cell_type": "markdown", - "id": "26acd5ab", - "metadata": {}, - "source": [ - "What if we would like to perform a regression using all of the variables but one? For\n", - "example, in the above regression output, `age` has a high $p$-value.\n", - "So we may wish to run a regression excluding this predictor.\n", - "The following syntax results in a regression using all predictors except `age`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a2714b1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.430858Z", - "iopub.status.busy": "2023-07-26T05:16:50.430692Z", - "iopub.status.idle": "2023-07-26T05:16:50.449487Z", - "shell.execute_reply": "2023-07-26T05:16:50.448800Z" - } - }, - "outputs": [], - "source": [ - "minus_age = Boston.columns.drop(['medv', 'age']) \n", - "Xma = MS(minus_age).fit_transform(Boston)\n", - "model1 = sm.OLS(y, Xma)\n", - "summarize(model1.fit())" - ] - }, - { - "cell_type": "markdown", - "id": "28538920", - "metadata": {}, - "source": [ - "## Multivariate Goodness of Fit\n", - "We can access the individual components of `results` by name\n", - "(`dir(results)` shows us what is available). Hence\n", - "`results.rsquared` gives us the $R^2$,\n", - "and\n", - "`np.sqrt(results.scale)` gives us the RSE.\n", - "\n", - "Variance inflation factors (section 3.3.3) are sometimes useful\n", - "to assess the effect of collinearity in the model matrix of a regression model.\n", - "We will compute the VIFs in our multiple regression fit, and use the opportunity to introduce the idea of *list comprehension*.\n", - "\n", - "### List Comprehension\n", - "Often we encounter a sequence of objects which we would like to transform\n", - "for some other task. Below, we compute the VIF for each\n", - "feature in our `X` matrix and produce a data frame\n", - "whose index agrees with the columns of `X`.\n", - "The notion of list comprehension can often make such\n", - "a task easier.\n", - "\n", - "List comprehensions are simple and powerful ways to form\n", - "lists of `Python` objects. The language also supports\n", - "dictionary and *generator* comprehension, though these are\n", - "beyond our scope here. Let's look at an example. We compute the VIF for each of the variables\n", - "in the model matrix `X`, using the function `variance_inflation_factor()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "961c9128", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.454906Z", - "iopub.status.busy": "2023-07-26T05:16:50.454582Z", - "iopub.status.idle": "2023-07-26T05:16:50.464023Z", - "shell.execute_reply": "2023-07-26T05:16:50.463522Z" - } - }, - "outputs": [], - "source": [ - "vals = [VIF(X, i)\n", - " for i in range(1, X.shape[1])]\n", - "vif = pd.DataFrame({'vif':vals},\n", - " index=X.columns[1:])\n", - "vif" - ] - }, - { - "cell_type": "markdown", - "id": "d052f0ab", - "metadata": {}, - "source": [ - "The function `VIF()` takes two arguments: a dataframe or array,\n", - "and a variable column index. In the code above we call `VIF()` on the fly for all columns in `X`. \n", - "We have excluded column 0 above (the intercept), which is not of interest. In this case the VIFs are not that exciting.\n", - "\n", - "The object `vals` above could have been constructed with the following for loop:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4886f9e9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.466225Z", - "iopub.status.busy": "2023-07-26T05:16:50.466081Z", - "iopub.status.idle": "2023-07-26T05:16:50.472696Z", - "shell.execute_reply": "2023-07-26T05:16:50.472188Z" - } - }, - "outputs": [], - "source": [ - "vals = []\n", - "for i in range(1, X.values.shape[1]):\n", - " vals.append(VIF(X.values, i))" - ] - }, - { - "cell_type": "markdown", - "id": "fd3732d5", - "metadata": {}, - "source": [ - "List comprehension allows us to perform such repetitive operations in a more straightforward way.\n", - "## Interaction Terms\n", - "It is easy to include interaction terms in a linear model using `ModelSpec()`.\n", - "Including a tuple `(\"lstat\",\"age\")` tells the model\n", - "matrix builder to include an interaction term between\n", - " `lstat` and `age`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b54d2da1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.475003Z", - "iopub.status.busy": "2023-07-26T05:16:50.474857Z", - "iopub.status.idle": "2023-07-26T05:16:50.488206Z", - "shell.execute_reply": "2023-07-26T05:16:50.487746Z" - } - }, - "outputs": [], - "source": [ - "X = MS(['lstat',\n", - " 'age',\n", - " ('lstat', 'age')]).fit_transform(Boston)\n", - "model2 = sm.OLS(y, X)\n", - "summarize(model2.fit())" - ] - }, - { - "cell_type": "markdown", - "id": "c72c4846", - "metadata": {}, - "source": [ - "## Non-linear Transformations of the Predictors\n", - "The model matrix builder can include terms beyond\n", - "just column names and interactions. For instance,\n", - "the `poly()` function supplied in `ISLP` specifies that\n", - "columns representing polynomial functions\n", - "of its first argument are added to the model matrix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b71633a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.490521Z", - "iopub.status.busy": "2023-07-26T05:16:50.490379Z", - "iopub.status.idle": "2023-07-26T05:16:50.504489Z", - "shell.execute_reply": "2023-07-26T05:16:50.504059Z" - } - }, - "outputs": [], - "source": [ - "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n", - "model3 = sm.OLS(y, X)\n", - "results3 = model3.fit()\n", - "summarize(results3)" - ] - }, - { - "cell_type": "markdown", - "id": "9f32c813", - "metadata": {}, - "source": [ - "The effectively zero *p*-value associated with the quadratic term\n", - "(i.e. the third row above) suggests that it leads to an improved model.\n", - "\n", - "By default, `poly()` creates a basis matrix for inclusion in the\n", - "model matrix whose\n", - "columns are *orthogonal polynomials*, which are designed for stable\n", - "least squares computations. {Actually, `poly()` is a wrapper for the workhorse and standalone function `Poly()` that does the work in building the model matrix.}\n", - "Alternatively, had we included an argument\n", - "`raw=True` in the above call to `poly()`, the basis matrix would consist simply of\n", - "`lstat` and `lstat**2`. Since either of these bases\n", - "represent quadratic polynomials, the fitted values would not\n", - "change in this case, just the polynomial coefficients. Also by default, the columns\n", - "created by `poly()` do not include an intercept column as\n", - "that is automatically added by `MS()`.\n", - "\n", - "We use the `anova_lm()` function to further quantify the extent to which the quadratic fit is\n", - "superior to the linear fit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d30a306", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.506891Z", - "iopub.status.busy": "2023-07-26T05:16:50.506725Z", - "iopub.status.idle": "2023-07-26T05:16:50.513731Z", - "shell.execute_reply": "2023-07-26T05:16:50.513303Z" - } - }, - "outputs": [], - "source": [ - "anova_lm(results1, results3)" - ] - }, - { - "cell_type": "markdown", - "id": "bd0155d4", - "metadata": {}, - "source": [ - "Here `results1` represents the linear submodel containing\n", - "predictors `lstat` and `age`,\n", - "while `results3` corresponds to the larger model above with a quadratic\n", - "term in `lstat`.\n", - "The `anova_lm()` function performs a hypothesis test\n", - "comparing the two models. The null hypothesis is that the quadratic\n", - "term in the bigger model is not needed, and the alternative hypothesis is that the\n", - "bigger model is superior. Here the *F*-statistic is 177.28 and\n", - "the associated *p*-value is zero.\n", - "In this case the *F*-statistic is the square of the\n", - "*t*-statistic for the quadratic term in the linear model summary\n", - "for `results3` --- a consequence of the fact that these nested\n", - "models differ by one degree of freedom.\n", - "This provides very clear evidence that the quadratic polynomial in\n", - "`lstat` improves the linear model.\n", - "This is not surprising, since earlier we saw evidence for non-linearity in the relationship between `medv`\n", - "and `lstat`.\n", - "\n", - "The function `anova_lm()` can take more than two nested models\n", - "as input, in which case it compares every successive pair of models.\n", - "That also explains why their are `NaN`s in the first row above, since\n", - "there is no previous model with which to compare the first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a5ec13f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.516035Z", - "iopub.status.busy": "2023-07-26T05:16:50.515894Z", - "iopub.status.idle": "2023-07-26T05:16:50.640835Z", - "shell.execute_reply": "2023-07-26T05:16:50.640080Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(8,8))[1]\n", - "ax.scatter(results3.fittedvalues, results3.resid)\n", - "ax.set_xlabel('Fitted value')\n", - "ax.set_ylabel('Residual')\n", - "ax.axhline(0, c='k', ls='--')" - ] - }, - { - "cell_type": "markdown", - "id": "88272f6f", - "metadata": {}, - "source": [ - "We see that when the quadratic term is included in the model,\n", - "there is little discernible pattern in the residuals.\n", - "In order to create a cubic or higher-degree polynomial fit, we can simply change the degree argument\n", - "to `poly()`." - ] - }, - { - "cell_type": "markdown", - "id": "e1a34084", - "metadata": {}, - "source": [ - "## Qualitative Predictors\n", - "Here we use the `Carseats` data, which is included in the\n", - "`ISLP` package. We will attempt to predict `Sales`\n", - "(child car seat sales) in 400 locations based on a number of\n", - "predictors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09bbc0c6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.643135Z", - "iopub.status.busy": "2023-07-26T05:16:50.643008Z", - "iopub.status.idle": "2023-07-26T05:16:50.649451Z", - "shell.execute_reply": "2023-07-26T05:16:50.649047Z" - } - }, - "outputs": [], - "source": [ - "Carseats = load_data('Carseats')\n", - "Carseats.columns" - ] - }, - { - "cell_type": "markdown", - "id": "1a882e65", - "metadata": {}, - "source": [ - "The `Carseats` \n", - " data includes qualitative predictors such as\n", - " `ShelveLoc`, an indicator of the quality of the shelving\n", - " location --- that is,\n", - "the space within a store in which the car seat is displayed. The predictor\n", - " `ShelveLoc` takes on three possible values, `Bad`, `Medium`, and `Good`.\n", - "Given a qualitative variable such as `ShelveLoc`, `ModelSpec()` generates dummy\n", - "variables automatically.\n", - "These variables are often referred to as a *one-hot encoding* of the categorical\n", - "feature. Their columns sum to one, so to avoid collinearity with an intercept, the first column is dropped. Below we see\n", - "the column `ShelveLoc[Bad]` has been dropped, since `Bad` is the first level of `ShelveLoc`.\n", - "Below we fit a multiple regression model that includes some interaction terms." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e1da1fa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:50.651966Z", - "iopub.status.busy": "2023-07-26T05:16:50.651596Z", - "iopub.status.idle": "2023-07-26T05:16:50.678716Z", - "shell.execute_reply": "2023-07-26T05:16:50.678209Z" - } - }, - "outputs": [], - "source": [ - "allvars = list(Carseats.columns.drop('Sales'))\n", - "y = Carseats['Sales']\n", - "final = allvars + [('Income', 'Advertising'),\n", - " ('Price', 'Age')]\n", - "X = MS(final).fit_transform(Carseats)\n", - "model = sm.OLS(y, X)\n", - "summarize(model.fit())" - ] - }, - { - "cell_type": "markdown", - "id": "79d9127c", - "metadata": {}, - "source": [ - "In the first line above, we made `allvars` a list, so that we\n", - "could add the interaction terms two lines down. \n", - "Our model-matrix builder has created a `ShelveLoc[Good]`\n", - "dummy variable that takes on a value of 1 if the\n", - "shelving location is good, and 0 otherwise. It has also created a `ShelveLoc[Medium]`\n", - "dummy variable that equals 1 if the shelving location is medium, and 0 otherwise.\n", - "A bad shelving location corresponds to a zero for each of the two dummy variables.\n", - "The fact that the coefficient for `ShelveLoc[Good]` in the regression output is\n", - "positive indicates that a good shelving location is associated with high sales (relative to a bad location).\n", - "And `ShelveLoc[Medium]` has a smaller positive coefficient,\n", - "indicating that a medium shelving location leads to higher sales than a bad\n", - "shelving location, but lower sales than a good shelving location." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch4-classification-lab.ipynb b/docs/source/labs/Ch4-classification-lab.ipynb deleted file mode 100644 index c5f4d47..0000000 --- a/docs/source/labs/Ch4-classification-lab.ipynb +++ /dev/null @@ -1,2633 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e6cc127e", - "metadata": {}, - "source": [ - "# Logistic Regression, LDA, QDA, and KNN\n", - "\n", - "\n", - " \"Open\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "a4e58fe1", - "metadata": {}, - "source": [ - "## The Stock Market Data\n", - "\n", - "In this lab we will examine the `Smarket` \n", - "data, which is part of the `ISLP`\n", - "library. This data set consists of percentage returns for the S&P 500\n", - "stock index over 1,250 days, from the beginning of 2001 until the end\n", - "of 2005. For each date, we have recorded the percentage returns for\n", - "each of the five previous trading days, `Lag1` through\n", - " `Lag5`. We have also recorded `Volume` (the number of\n", - "shares traded on the previous day, in billions), `Today` (the\n", - "percentage return on the date in question) and `Direction`\n", - "(whether the market was `Up` or `Down` on this date).\n", - "\n", - "We start by importing our libraries at this top level; these are all imports we have seen in previous labs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71523a7f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:52.147230Z", - "iopub.status.busy": "2023-07-26T05:16:52.147074Z", - "iopub.status.idle": "2023-07-26T05:16:53.319384Z", - "shell.execute_reply": "2023-07-26T05:16:53.318779Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data\n", - "from ISLP.models import (ModelSpec as MS,\n", - " summarize)" - ] - }, - { - "cell_type": "markdown", - "id": "4759a767", - "metadata": {}, - "source": [ - "We also collect together the new imports needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "901ed7c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.322482Z", - "iopub.status.busy": "2023-07-26T05:16:53.322239Z", - "iopub.status.idle": "2023-07-26T05:16:53.343077Z", - "shell.execute_reply": "2023-07-26T05:16:53.342596Z" - } - }, - "outputs": [], - "source": [ - "from ISLP import confusion_table\n", - "from ISLP.models import contrast\n", - "from sklearn.discriminant_analysis import \\\n", - " (LinearDiscriminantAnalysis as LDA,\n", - " QuadraticDiscriminantAnalysis as QDA)\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression" - ] - }, - { - "cell_type": "markdown", - "id": "dd5a4d61", - "metadata": {}, - "source": [ - "Now we are ready to load the `Smarket` data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b56ee0f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.345526Z", - "iopub.status.busy": "2023-07-26T05:16:53.345383Z", - "iopub.status.idle": "2023-07-26T05:16:53.358625Z", - "shell.execute_reply": "2023-07-26T05:16:53.357838Z" - } - }, - "outputs": [], - "source": [ - "Smarket = load_data('Smarket')\n", - "Smarket" - ] - }, - { - "cell_type": "markdown", - "id": "400eb0f9", - "metadata": {}, - "source": [ - "This gives a truncated listing of the data.\n", - "We can see what the variable names are." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62d49b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.361002Z", - "iopub.status.busy": "2023-07-26T05:16:53.360735Z", - "iopub.status.idle": "2023-07-26T05:16:53.364261Z", - "shell.execute_reply": "2023-07-26T05:16:53.363501Z" - } - }, - "outputs": [], - "source": [ - "Smarket.columns" - ] - }, - { - "cell_type": "markdown", - "id": "82e38285", - "metadata": {}, - "source": [ - "We compute the correlation matrix using the `corr()` method\n", - "for data frames, which produces a matrix that contains all of\n", - "the pairwise correlations among the variables.\n", - " \n", - "The `pandas` library does not report a correlation for the `Direction` variable because it is\n", - "qualitative." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc34e7b7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.366253Z", - "iopub.status.busy": "2023-07-26T05:16:53.366125Z", - "iopub.status.idle": "2023-07-26T05:16:53.372926Z", - "shell.execute_reply": "2023-07-26T05:16:53.372328Z" - } - }, - "outputs": [], - "source": [ - "Smarket.corr()" - ] - }, - { - "cell_type": "markdown", - "id": "53a52be5", - "metadata": {}, - "source": [ - "As one would expect, the correlations between the lagged return variables and\n", - "today’s return are close to zero. The only substantial correlation is between `Year` and\n", - " `Volume`. By plotting the data we see that `Volume`\n", - "is increasing over time. In other words, the average number of shares traded\n", - "daily increased from 2001 to 2005." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f075144", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.376785Z", - "iopub.status.busy": "2023-07-26T05:16:53.376551Z", - "iopub.status.idle": "2023-07-26T05:16:53.491023Z", - "shell.execute_reply": "2023-07-26T05:16:53.490615Z" - } - }, - "outputs": [], - "source": [ - "Smarket.plot(y='Volume');" - ] - }, - { - "cell_type": "markdown", - "id": "b8428c9e", - "metadata": {}, - "source": [ - "## Logistic Regression\n", - "Next, we will fit a logistic regression model in order to predict\n", - " `Direction` using `Lag1` through `Lag5` and\n", - " `Volume`. The `sm.GLM()` function fits *generalized linear models*, a class of\n", - "models that includes logistic regression. Alternatively,\n", - "the function `sm.Logit()` fits a logistic regression\n", - "model directly. The syntax of\n", - "`sm.GLM()` is similar to that of `sm.OLS()`, except\n", - "that we must pass in the argument `family=sm.families.Binomial()`\n", - "in order to tell `statsmodels` to run a logistic regression rather than some other\n", - "type of generalized linear model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ab15aa3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.493500Z", - "iopub.status.busy": "2023-07-26T05:16:53.493302Z", - "iopub.status.idle": "2023-07-26T05:16:53.566736Z", - "shell.execute_reply": "2023-07-26T05:16:53.566181Z" - } - }, - "outputs": [], - "source": [ - "allvars = Smarket.columns.drop(['Today', 'Direction', 'Year'])\n", - "design = MS(allvars)\n", - "X = design.fit_transform(Smarket)\n", - "y = Smarket.Direction == 'Up'\n", - "glm = sm.GLM(y,\n", - " X,\n", - " family=sm.families.Binomial())\n", - "results = glm.fit()\n", - "summarize(results)" - ] - }, - { - "cell_type": "markdown", - "id": "ce1f682c", - "metadata": {}, - "source": [ - "The smallest *p*-value here is associated with `Lag1`. The\n", - "negative coefficient for this predictor suggests that if the market\n", - "had a positive return yesterday, then it is less likely to go up\n", - "today. However, at a value of 0.15, the *p*-value is still\n", - "relatively large, and so there is no clear evidence of a real\n", - "association between `Lag1` and `Direction`.\n", - "\n", - "We use the `params` attribute of `results`\n", - "in order to access just the\n", - "coefficients for this fitted model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0faa6a6c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.569154Z", - "iopub.status.busy": "2023-07-26T05:16:53.568975Z", - "iopub.status.idle": "2023-07-26T05:16:53.572426Z", - "shell.execute_reply": "2023-07-26T05:16:53.572078Z" - } - }, - "outputs": [], - "source": [ - "results.params" - ] - }, - { - "cell_type": "markdown", - "id": "b989df1d", - "metadata": {}, - "source": [ - "Likewise we can use the\n", - "`pvalues` attribute to access the *p*-values for the coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "108aff98", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.574612Z", - "iopub.status.busy": "2023-07-26T05:16:53.574492Z", - "iopub.status.idle": "2023-07-26T05:16:53.578308Z", - "shell.execute_reply": "2023-07-26T05:16:53.577846Z" - } - }, - "outputs": [], - "source": [ - "results.pvalues" - ] - }, - { - "cell_type": "markdown", - "id": "5c5c1f8e", - "metadata": {}, - "source": [ - "The `predict()` method of `results` can be used to predict the\n", - "probability that the market will go up, given values of the\n", - "predictors. This method returns predictions\n", - "on the probability scale. If no data set is supplied to the `predict()`\n", - "function, then the probabilities are computed for the training data\n", - "that was used to fit the logistic regression model.\n", - "As with linear regression, one can pass an optional `exog` argument consistent\n", - "with a design matrix if desired. Here we have\n", - "printed only the first ten probabilities." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54b4b96c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.580578Z", - "iopub.status.busy": "2023-07-26T05:16:53.580429Z", - "iopub.status.idle": "2023-07-26T05:16:53.583750Z", - "shell.execute_reply": "2023-07-26T05:16:53.583333Z" - } - }, - "outputs": [], - "source": [ - "probs = results.predict()\n", - "probs[:10]" - ] - }, - { - "cell_type": "markdown", - "id": "3b8f3c42", - "metadata": {}, - "source": [ - "In order to make a prediction as to whether the market will go up or\n", - "down on a particular day, we must convert these predicted\n", - "probabilities into class labels, `Up` or `Down`. The\n", - "following two commands create a vector of class predictions based on\n", - "whether the predicted probability of a market increase is greater than\n", - "or less than 0.5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46c2887c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.586085Z", - "iopub.status.busy": "2023-07-26T05:16:53.585953Z", - "iopub.status.idle": "2023-07-26T05:16:53.588706Z", - "shell.execute_reply": "2023-07-26T05:16:53.588288Z" - } - }, - "outputs": [], - "source": [ - "labels = np.array(['Down']*1250)\n", - "labels[probs>0.5] = \"Up\"" - ] - }, - { - "cell_type": "markdown", - "id": "47ac8c81", - "metadata": {}, - "source": [ - "The `confusion_table()`\n", - "function from the `ISLP` package summarizes these predictions, showing how\n", - "many observations were correctly or incorrectly classified. Our function, which is adapted from a similar function\n", - "in the module `sklearn.metrics`, transposes the resulting\n", - "matrix and includes row and column labels.\n", - "The `confusion_table()` function takes as first argument the\n", - "predicted labels, and second argument the true labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f816c4d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.590957Z", - "iopub.status.busy": "2023-07-26T05:16:53.590766Z", - "iopub.status.idle": "2023-07-26T05:16:53.598268Z", - "shell.execute_reply": "2023-07-26T05:16:53.597798Z" - } - }, - "outputs": [], - "source": [ - "confusion_table(labels, Smarket.Direction)" - ] - }, - { - "cell_type": "markdown", - "id": "327a9525", - "metadata": {}, - "source": [ - "The diagonal elements of the confusion matrix indicate correct\n", - "predictions, while the off-diagonals represent incorrect\n", - "predictions. Hence our model correctly predicted that the market would\n", - "go up on 507 days and that it would go down on 145 days, for a\n", - "total of 507 + 145 = 652 correct predictions. The `np.mean()`\n", - "function can be used to compute the fraction of days for which the\n", - "prediction was correct. In this case, logistic regression correctly\n", - "predicted the movement of the market 52.2% of the time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23668517", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.600706Z", - "iopub.status.busy": "2023-07-26T05:16:53.600511Z", - "iopub.status.idle": "2023-07-26T05:16:53.603872Z", - "shell.execute_reply": "2023-07-26T05:16:53.603421Z" - } - }, - "outputs": [], - "source": [ - "(507+145)/1250, np.mean(labels == Smarket.Direction)" - ] - }, - { - "cell_type": "markdown", - "id": "1143b9c3", - "metadata": {}, - "source": [ - "At first glance, it appears that the logistic regression model is\n", - "working a little better than random guessing. However, this result is\n", - "misleading because we trained and tested the model on the same set of\n", - "1,250 observations. In other words, $100-52.2=47.8%$ is the\n", - "*training* error rate. As we have seen\n", - "previously, the training error rate is often overly optimistic --- it\n", - "tends to underestimate the test error rate. In\n", - "order to better assess the accuracy of the logistic regression model\n", - "in this setting, we can fit the model using part of the data, and\n", - "then examine how well it predicts the *held out* data. This\n", - "will yield a more realistic error rate, in the sense that in practice\n", - "we will be interested in our model’s performance not on the data that\n", - "we used to fit the model, but rather on days in the future for which\n", - "the market’s movements are unknown.\n", - "\n", - "To implement this strategy, we first create a Boolean vector\n", - "corresponding to the observations from 2001 through 2004. We then\n", - "use this vector to create a held out data set of observations from\n", - "2005." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86a02d7c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.606055Z", - "iopub.status.busy": "2023-07-26T05:16:53.605931Z", - "iopub.status.idle": "2023-07-26T05:16:53.609917Z", - "shell.execute_reply": "2023-07-26T05:16:53.609576Z" - } - }, - "outputs": [], - "source": [ - "train = (Smarket.Year < 2005)\n", - "Smarket_train = Smarket.loc[train]\n", - "Smarket_test = Smarket.loc[~train]\n", - "Smarket_test.shape" - ] - }, - { - "cell_type": "markdown", - "id": "26b3a6d2", - "metadata": {}, - "source": [ - "The object `train` is a vector of 1,250 elements, corresponding\n", - "to the observations in our data set. The elements of the vector that\n", - "correspond to observations that occurred before 2005 are set to\n", - "`True`, whereas those that correspond to observations in 2005 are\n", - "set to `False`. Hence `train` is a\n", - "*boolean* array, since its\n", - "elements are `True` and `False`. Boolean arrays can be used\n", - "to obtain a subset of the rows or columns of a data frame\n", - "using the `loc` method. For instance,\n", - "the command `Smarket.loc[train]` would pick out a submatrix of the\n", - "stock market data set, corresponding only to the dates before 2005,\n", - "since those are the ones for which the elements of `train` are\n", - "`True`. The `~` symbol can be used to negate all of the\n", - "elements of a Boolean vector. That is, `~train` is a vector\n", - "similar to `train`, except that the elements that are `True`\n", - "in `train` get swapped to `False` in `~train`, and vice versa.\n", - "Therefore, `Smarket.loc[~train]` yields a\n", - "subset of the rows of the data frame\n", - "of the stock market data containing only the observations for which\n", - "`train` is `False`.\n", - "The output above indicates that there are 252 such\n", - "observations.\n", - "\n", - "We now fit a logistic regression model using only the subset of the\n", - "observations that correspond to dates before 2005. We then obtain predicted probabilities of the\n", - "stock market going up for each of the days in our test set --- that is,\n", - "for the days in 2005." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f23b1c7b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.612185Z", - "iopub.status.busy": "2023-07-26T05:16:53.612009Z", - "iopub.status.idle": "2023-07-26T05:16:53.618057Z", - "shell.execute_reply": "2023-07-26T05:16:53.617645Z" - } - }, - "outputs": [], - "source": [ - "X_train, X_test = X.loc[train], X.loc[~train]\n", - "y_train, y_test = y.loc[train], y.loc[~train]\n", - "glm_train = sm.GLM(y_train,\n", - " X_train,\n", - " family=sm.families.Binomial())\n", - "results = glm_train.fit()\n", - "probs = results.predict(exog=X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "6ebe1900", - "metadata": {}, - "source": [ - "Notice that we have trained and tested our model on two completely\n", - "separate data sets: training was performed using only the dates before\n", - "2005, and testing was performed using only the dates in 2005.\n", - "\n", - "Finally, we compare the predictions for 2005 to the\n", - "actual movements of the market over that time period.\n", - "We will first store the test and training labels (recall `y_test` is binary)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82f849b9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.620635Z", - "iopub.status.busy": "2023-07-26T05:16:53.620266Z", - "iopub.status.idle": "2023-07-26T05:16:53.623107Z", - "shell.execute_reply": "2023-07-26T05:16:53.622634Z" - } - }, - "outputs": [], - "source": [ - "D = Smarket.Direction\n", - "L_train, L_test = D.loc[train], D.loc[~train]" - ] - }, - { - "cell_type": "markdown", - "id": "ac89c253", - "metadata": {}, - "source": [ - "Now we threshold the\n", - "fitted probability at 50% to form\n", - "our predicted labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e216583a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.625601Z", - "iopub.status.busy": "2023-07-26T05:16:53.625081Z", - "iopub.status.idle": "2023-07-26T05:16:53.631930Z", - "shell.execute_reply": "2023-07-26T05:16:53.631398Z" - } - }, - "outputs": [], - "source": [ - "labels = np.array(['Down']*252)\n", - "labels[probs>0.5] = 'Up'\n", - "confusion_table(labels, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "f43e1451", - "metadata": {}, - "source": [ - "The test accuracy is about 48% while the error rate is about 52%" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5538dc17", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.634500Z", - "iopub.status.busy": "2023-07-26T05:16:53.634318Z", - "iopub.status.idle": "2023-07-26T05:16:53.638177Z", - "shell.execute_reply": "2023-07-26T05:16:53.637615Z" - } - }, - "outputs": [], - "source": [ - "np.mean(labels == L_test), np.mean(labels != L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "3ddf7670", - "metadata": {}, - "source": [ - "The `!=` notation means *not equal to*, and so the last command\n", - "computes the test set error rate. The results are rather\n", - "disappointing: the test error rate is 52%, which is worse than\n", - "random guessing! Of course this result is not all that surprising,\n", - "given that one would not generally expect to be able to use previous\n", - "days’ returns to predict future market performance. (After all, if it\n", - "were possible to do so, then the authors of this book would be out\n", - "striking it rich rather than writing a statistics textbook.)\n", - "\n", - "We recall that the logistic regression model had very underwhelming\n", - "*p*-values associated with all of the predictors, and that the\n", - "smallest *p*-value, though not very small, corresponded to\n", - " `Lag1`. Perhaps by removing the variables that appear not to be\n", - "helpful in predicting `Direction`, we can obtain a more\n", - "effective model. After all, using predictors that have no relationship\n", - "with the response tends to cause a deterioration in the test error\n", - "rate (since such predictors cause an increase in variance without a\n", - "corresponding decrease in bias), and so removing such predictors may\n", - "in turn yield an improvement. Below we refit the logistic\n", - "regression using just `Lag1` and `Lag2`, which seemed to\n", - "have the highest predictive power in the original logistic regression\n", - "model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "016110f9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.640626Z", - "iopub.status.busy": "2023-07-26T05:16:53.640465Z", - "iopub.status.idle": "2023-07-26T05:16:53.650498Z", - "shell.execute_reply": "2023-07-26T05:16:53.649952Z" - } - }, - "outputs": [], - "source": [ - "model = MS(['Lag1', 'Lag2']).fit(Smarket)\n", - "X = model.transform(Smarket)\n", - "X_train, X_test = X.loc[train], X.loc[~train]\n", - "glm_train = sm.GLM(y_train,\n", - " X_train,\n", - " family=sm.families.Binomial())\n", - "results = glm_train.fit()\n", - "probs = results.predict(exog=X_test)\n", - "labels = np.array(['Down']*252)\n", - "labels[probs>0.5] = 'Up'\n", - "confusion_table(labels, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "a7468a74", - "metadata": {}, - "source": [ - "Let’s evaluate the overall accuracy as well as the accuracy within the days when\n", - "logistic regression predicts an increase." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70984cd6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.653494Z", - "iopub.status.busy": "2023-07-26T05:16:53.652768Z", - "iopub.status.idle": "2023-07-26T05:16:53.656801Z", - "shell.execute_reply": "2023-07-26T05:16:53.656039Z" - } - }, - "outputs": [], - "source": [ - "(35+106)/252,106/(106+76)" - ] - }, - { - "cell_type": "markdown", - "id": "393c0a26", - "metadata": {}, - "source": [ - "Now the results appear to be a little better: 56% of the daily\n", - "movements have been correctly predicted. It is worth noting that in\n", - "this case, a much simpler strategy of predicting that the market will\n", - "increase every day will also be correct 56% of the time! Hence, in\n", - "terms of overall error rate, the logistic regression method is no\n", - "better than the naive approach. However, the confusion matrix\n", - "shows that on days when logistic regression predicts an increase in\n", - "the market, it has a 58% accuracy rate. This suggests a possible\n", - "trading strategy of buying on days when the model predicts an\n", - "increasing market, and avoiding trades on days when a decrease is\n", - "predicted. Of course one would need to investigate more carefully\n", - "whether this small improvement was real or just due to random chance.\n", - "\n", - "Suppose that we want to predict the returns associated with particular\n", - "values of `Lag1` and `Lag2`. In particular, we want to\n", - "predict `Direction` on a day when `Lag1` and\n", - " `Lag2` equal $1.2$ and $1.1$, respectively, and on a day when they\n", - "equal $1.5$ and $-0.8$. We do this using the `predict()`\n", - "function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b745fd8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.659702Z", - "iopub.status.busy": "2023-07-26T05:16:53.659530Z", - "iopub.status.idle": "2023-07-26T05:16:53.664523Z", - "shell.execute_reply": "2023-07-26T05:16:53.664140Z" - } - }, - "outputs": [], - "source": [ - "newdata = pd.DataFrame({'Lag1':[1.2, 1.5],\n", - " 'Lag2':[1.1, -0.8]});\n", - "newX = model.transform(newdata)\n", - "results.predict(newX)" - ] - }, - { - "cell_type": "markdown", - "id": "ae928a3e", - "metadata": {}, - "source": [ - "## Linear Discriminant Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "aab265c4", - "metadata": {}, - "source": [ - "We begin by performing LDA on the `Smarket` data, using the function\n", - "`LinearDiscriminantAnalysis()`, which we have abbreviated `LDA()`. We \n", - "fit the model using only the observations before 2005." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd533313", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.668435Z", - "iopub.status.busy": "2023-07-26T05:16:53.668202Z", - "iopub.status.idle": "2023-07-26T05:16:53.670675Z", - "shell.execute_reply": "2023-07-26T05:16:53.670317Z" - } - }, - "outputs": [], - "source": [ - "lda = LDA(store_covariance=True)" - ] - }, - { - "cell_type": "markdown", - "id": "c84aa056", - "metadata": {}, - "source": [ - "Since the `LDA` estimator automatically \n", - "adds an intercept, we should remove the column corresponding to the\n", - "intercept in both `X_train` and `X_test`. We can also directly\n", - "use the labels rather than the Boolean vectors `y_train`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "953dc6ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.673288Z", - "iopub.status.busy": "2023-07-26T05:16:53.673119Z", - "iopub.status.idle": "2023-07-26T05:16:53.681150Z", - "shell.execute_reply": "2023-07-26T05:16:53.680147Z" - } - }, - "outputs": [], - "source": [ - "X_train, X_test = [M.drop(columns=['intercept'])\n", - " for M in [X_train, X_test]]\n", - "lda.fit(X_train, L_train)" - ] - }, - { - "cell_type": "markdown", - "id": "0a51b5cd", - "metadata": {}, - "source": [ - "Here we have used the list comprehensions introduced\n", - "in Section 3.6.4. Looking at our first line above, we see that the right-hand side is a list\n", - "of length two. This is because the code `for M in [X_train, X_test]` iterates over a list\n", - "of length two. While here we loop over a list,\n", - "the list comprehension method works when looping over any iterable object.\n", - "We then apply the `drop()` method to each element in the iteration, collecting\n", - "the result in a list. The left-hand side tells `Python` to unpack this list\n", - "of length two, assigning its elements to the variables `X_train` and `X_test`. Of course,\n", - "this overwrites the previous values of `X_train` and `X_test`.\n", - "\n", - "Having fit the model, we can extract the means in the two classes with the `means_` attribute. These are the average of each predictor within each class, and\n", - "are used by LDA as estimates of $\\mu_k$. These suggest that there is\n", - "a tendency for the previous 2 days’ returns to be negative on days\n", - "when the market increases, and a tendency for the previous days’\n", - "returns to be positive on days when the market declines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8917085e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.683917Z", - "iopub.status.busy": "2023-07-26T05:16:53.683495Z", - "iopub.status.idle": "2023-07-26T05:16:53.686711Z", - "shell.execute_reply": "2023-07-26T05:16:53.686302Z" - } - }, - "outputs": [], - "source": [ - "lda.means_" - ] - }, - { - "cell_type": "markdown", - "id": "14bc4692", - "metadata": {}, - "source": [ - "The estimated prior probabilities are stored in the `priors_` attribute.\n", - "The package `sklearn` typically uses this trailing `_` to denote\n", - "a quantity estimated when using the `fit()` method. We can be sure of which\n", - "entry corresponds to which label by looking at the `classes_` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efb3e9bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.689572Z", - "iopub.status.busy": "2023-07-26T05:16:53.689426Z", - "iopub.status.idle": "2023-07-26T05:16:53.693007Z", - "shell.execute_reply": "2023-07-26T05:16:53.692236Z" - } - }, - "outputs": [], - "source": [ - "lda.classes_" - ] - }, - { - "cell_type": "markdown", - "id": "50697f11", - "metadata": {}, - "source": [ - "The LDA output indicates that $\\hat\\pi_{Down}=0.492$ and\n", - "$\\hat\\pi_{Up}=0.508$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efce91fb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.698435Z", - "iopub.status.busy": "2023-07-26T05:16:53.698251Z", - "iopub.status.idle": "2023-07-26T05:16:53.701054Z", - "shell.execute_reply": "2023-07-26T05:16:53.700738Z" - } - }, - "outputs": [], - "source": [ - "lda.priors_" - ] - }, - { - "cell_type": "markdown", - "id": "a27bc841", - "metadata": {}, - "source": [ - "The linear discriminant vectors can be found in the `scalings_` attribute:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e33cd6c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.703175Z", - "iopub.status.busy": "2023-07-26T05:16:53.702968Z", - "iopub.status.idle": "2023-07-26T05:16:53.706047Z", - "shell.execute_reply": "2023-07-26T05:16:53.705603Z" - } - }, - "outputs": [], - "source": [ - "lda.scalings_" - ] - }, - { - "cell_type": "markdown", - "id": "b011d741", - "metadata": {}, - "source": [ - "These values provide the linear combination of `Lag1` and `Lag2` that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (4.24).\n", - " If $-0.64\\times `Lag1` - 0.51 \\times `Lag2` $ is large, then the LDA classifier will predict a market increase, and if it is small, then the LDA classifier will predict a market decline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc8a5178", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.708404Z", - "iopub.status.busy": "2023-07-26T05:16:53.708240Z", - "iopub.status.idle": "2023-07-26T05:16:53.711274Z", - "shell.execute_reply": "2023-07-26T05:16:53.710854Z" - } - }, - "outputs": [], - "source": [ - "lda_pred = lda.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "3c8a8a75", - "metadata": {}, - "source": [ - "As we observed in our comparison of classification methods\n", - " (Section 4.5), the LDA and logistic\n", - "regression predictions are almost identical." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31a6782b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.713574Z", - "iopub.status.busy": "2023-07-26T05:16:53.713379Z", - "iopub.status.idle": "2023-07-26T05:16:53.718661Z", - "shell.execute_reply": "2023-07-26T05:16:53.718207Z" - } - }, - "outputs": [], - "source": [ - "confusion_table(lda_pred, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "c3534a8f", - "metadata": {}, - "source": [ - "We can also estimate the\n", - "probability of each class for\n", - "each point in a training set. Applying a 50% threshold to the posterior probabilities of\n", - "being in class 1 allows us to\n", - "recreate the predictions contained in `lda_pred`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da1bffa3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.720875Z", - "iopub.status.busy": "2023-07-26T05:16:53.720726Z", - "iopub.status.idle": "2023-07-26T05:16:53.724610Z", - "shell.execute_reply": "2023-07-26T05:16:53.724076Z" - } - }, - "outputs": [], - "source": [ - "lda_prob = lda.predict_proba(X_test)\n", - "np.all(\n", - " np.where(lda_prob[:,1] >= 0.5, 'Up','Down') == lda_pred\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "6ab71d7c", - "metadata": {}, - "source": [ - "Above, we used the `np.where()` function that\n", - "creates an array with value `'Up'` for indices where\n", - "the second column of `lda_prob` (the estimated\n", - "posterior probability of `'Up'`) is greater than 0.5.\n", - "For problems with more than two classes the labels are chosen as the class whose posterior probability is highest:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d7eae3c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.726893Z", - "iopub.status.busy": "2023-07-26T05:16:53.726728Z", - "iopub.status.idle": "2023-07-26T05:16:53.730223Z", - "shell.execute_reply": "2023-07-26T05:16:53.729787Z" - } - }, - "outputs": [], - "source": [ - "np.all(\n", - " [lda.classes_[i] for i in np.argmax(lda_prob, 1)] == lda_pred\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "6a67baf1", - "metadata": {}, - "source": [ - "If we wanted to use a posterior probability threshold other than\n", - "50% in order to make predictions, then we could easily do so. For\n", - "instance, suppose that we wish to predict a market decrease only if we\n", - "are very certain that the market will indeed decrease on that\n", - "day --- say, if the posterior probability is at least 90%.\n", - "We know that the first column of `lda_prob` corresponds to the\n", - "label `Down` after having checked the `classes_` attribute, hence we use\n", - "the column index 0 rather than 1 as we did above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18fa175c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.732568Z", - "iopub.status.busy": "2023-07-26T05:16:53.732400Z", - "iopub.status.idle": "2023-07-26T05:16:53.735369Z", - "shell.execute_reply": "2023-07-26T05:16:53.734923Z" - } - }, - "outputs": [], - "source": [ - "np.sum(lda_prob[:,0] > 0.9)" - ] - }, - { - "cell_type": "markdown", - "id": "527be6fe", - "metadata": {}, - "source": [ - "No days in 2005 meet that threshold! In fact, the greatest posterior\n", - "probability of decrease in all of 2005 was 52.02%.\n", - "\n", - "The LDA classifier above is the first classifier from the\n", - "`sklearn` library. We will use several other objects\n", - "from this library. The objects\n", - "follow a common structure that simplifies tasks such as cross-validation,\n", - "which we will see in Chapter 5. Specifically,\n", - "the methods first create a generic classifier without\n", - "referring to any data. This classifier is then fit\n", - "to data with the `fit()` method and predictions are\n", - "always produced with the `predict()` method. This pattern\n", - "of first instantiating the classifier, followed by fitting it, and\n", - "then producing predictions is an explicit design choice of `sklearn`. This uniformity\n", - "makes it possible to cleanly copy the classifier so that it can be fit\n", - "on different data; e.g. different training sets arising in cross-validation.\n", - "This standard pattern also allows for a predictable formation of workflows." - ] - }, - { - "cell_type": "markdown", - "id": "66ecd10f", - "metadata": {}, - "source": [ - "## Quadratic Discriminant Analysis\n", - "We will now fit a QDA model to the `Smarket` data. QDA is\n", - "implemented via\n", - "`QuadraticDiscriminantAnalysis()`\n", - "in the `sklearn` package, which we abbreviate to `QDA()`.\n", - "The syntax is very similar to `LDA()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62499aad", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.737641Z", - "iopub.status.busy": "2023-07-26T05:16:53.737376Z", - "iopub.status.idle": "2023-07-26T05:16:53.742371Z", - "shell.execute_reply": "2023-07-26T05:16:53.741972Z" - } - }, - "outputs": [], - "source": [ - "qda = QDA(store_covariance=True)\n", - "qda.fit(X_train, L_train)" - ] - }, - { - "cell_type": "markdown", - "id": "17b38989", - "metadata": {}, - "source": [ - "The `QDA()` function will again compute `means_` and `priors_`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "296fdd89", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.744511Z", - "iopub.status.busy": "2023-07-26T05:16:53.744359Z", - "iopub.status.idle": "2023-07-26T05:16:53.747331Z", - "shell.execute_reply": "2023-07-26T05:16:53.746954Z" - } - }, - "outputs": [], - "source": [ - "qda.means_, qda.priors_" - ] - }, - { - "cell_type": "markdown", - "id": "f439efa9", - "metadata": {}, - "source": [ - "The `QDA()` classifier will estimate one covariance per class. Here is the\n", - "estimated covariance in the first class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fbbefb0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.749983Z", - "iopub.status.busy": "2023-07-26T05:16:53.749838Z", - "iopub.status.idle": "2023-07-26T05:16:53.753016Z", - "shell.execute_reply": "2023-07-26T05:16:53.752436Z" - } - }, - "outputs": [], - "source": [ - "qda.covariance_[0]" - ] - }, - { - "cell_type": "markdown", - "id": "4b33a370", - "metadata": {}, - "source": [ - "The output contains the group means. But it does not contain the\n", - "coefficients of the linear discriminants, because the QDA classifier\n", - "involves a quadratic, rather than a linear, function of the\n", - "predictors. The `predict()` function works in exactly the\n", - "same fashion as for LDA." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03094dcd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.754995Z", - "iopub.status.busy": "2023-07-26T05:16:53.754855Z", - "iopub.status.idle": "2023-07-26T05:16:53.760423Z", - "shell.execute_reply": "2023-07-26T05:16:53.760086Z" - } - }, - "outputs": [], - "source": [ - "qda_pred = qda.predict(X_test)\n", - "confusion_table(qda_pred, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "2037df4b", - "metadata": {}, - "source": [ - "Interestingly, the QDA predictions are accurate almost 60% of the\n", - "time, even though the 2005 data was not used to fit the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25022d1a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.762503Z", - "iopub.status.busy": "2023-07-26T05:16:53.762361Z", - "iopub.status.idle": "2023-07-26T05:16:53.765264Z", - "shell.execute_reply": "2023-07-26T05:16:53.764918Z" - } - }, - "outputs": [], - "source": [ - "np.mean(qda_pred == L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "cf85b45e", - "metadata": {}, - "source": [ - "This level of accuracy is quite impressive for stock market data, which is\n", - "known to be quite hard to model accurately. This suggests that the\n", - "quadratic form assumed by QDA may capture the true relationship more\n", - "accurately than the linear forms assumed by LDA and logistic\n", - "regression. However, we recommend evaluating this method’s\n", - "performance on a larger test set before betting that this approach\n", - "will consistently beat the market!" - ] - }, - { - "cell_type": "markdown", - "id": "258f98b8", - "metadata": {}, - "source": [ - "## Naive Bayes\n", - "Next, we fit a naive Bayes model to the `Smarket` data. The syntax is\n", - "similar to that of `LDA()` and `QDA()`. By\n", - "default, this implementation `GaussianNB()` of the naive Bayes classifier models each\n", - "quantitative feature using a Gaussian distribution. However, a kernel\n", - "density method can also be used to estimate the distributions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f73eb202", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.767345Z", - "iopub.status.busy": "2023-07-26T05:16:53.767204Z", - "iopub.status.idle": "2023-07-26T05:16:53.771774Z", - "shell.execute_reply": "2023-07-26T05:16:53.771427Z" - } - }, - "outputs": [], - "source": [ - "NB = GaussianNB()\n", - "NB.fit(X_train, L_train)" - ] - }, - { - "cell_type": "markdown", - "id": "9911dcdc", - "metadata": {}, - "source": [ - "The classes are stored as `classes_`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e860cc3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.773771Z", - "iopub.status.busy": "2023-07-26T05:16:53.773609Z", - "iopub.status.idle": "2023-07-26T05:16:53.776397Z", - "shell.execute_reply": "2023-07-26T05:16:53.776019Z" - } - }, - "outputs": [], - "source": [ - "NB.classes_" - ] - }, - { - "cell_type": "markdown", - "id": "8d6b9b33", - "metadata": {}, - "source": [ - "The class prior probabilities are stored in the `class_prior_` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c28bf3b5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.778509Z", - "iopub.status.busy": "2023-07-26T05:16:53.778344Z", - "iopub.status.idle": "2023-07-26T05:16:53.781156Z", - "shell.execute_reply": "2023-07-26T05:16:53.780778Z" - } - }, - "outputs": [], - "source": [ - "NB.class_prior_" - ] - }, - { - "cell_type": "markdown", - "id": "fed2c4b4", - "metadata": {}, - "source": [ - "The parameters of the features can be found in the `theta_` and `var_` attributes. The number of rows\n", - "is equal to the number of classes, while the number of columns is equal to the number of features.\n", - "We see below that the mean for feature `Lag1` in the `Down` class is 0.043." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84a9f086", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.783409Z", - "iopub.status.busy": "2023-07-26T05:16:53.783220Z", - "iopub.status.idle": "2023-07-26T05:16:53.786972Z", - "shell.execute_reply": "2023-07-26T05:16:53.786412Z" - } - }, - "outputs": [], - "source": [ - "NB.theta_" - ] - }, - { - "cell_type": "markdown", - "id": "66f0a08d", - "metadata": {}, - "source": [ - "Its variance is 1.503." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "885f5165", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.789289Z", - "iopub.status.busy": "2023-07-26T05:16:53.789145Z", - "iopub.status.idle": "2023-07-26T05:16:53.792294Z", - "shell.execute_reply": "2023-07-26T05:16:53.791863Z" - } - }, - "outputs": [], - "source": [ - "NB.var_" - ] - }, - { - "cell_type": "markdown", - "id": "db0a1ce6", - "metadata": {}, - "source": [ - "How do we know the names of these attributes? We use `NB?` (or \\textR?NB}).\n", - "\n", - "We can easily verify the mean computation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61473094", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.794766Z", - "iopub.status.busy": "2023-07-26T05:16:53.794585Z", - "iopub.status.idle": "2023-07-26T05:16:53.798951Z", - "shell.execute_reply": "2023-07-26T05:16:53.798539Z" - } - }, - "outputs": [], - "source": [ - "X_train[L_train == 'Down'].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "d4ffc86b", - "metadata": {}, - "source": [ - "Similarly for the variance:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db203a23", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.801373Z", - "iopub.status.busy": "2023-07-26T05:16:53.801183Z", - "iopub.status.idle": "2023-07-26T05:16:53.805590Z", - "shell.execute_reply": "2023-07-26T05:16:53.805195Z" - } - }, - "outputs": [], - "source": [ - "X_train[L_train == 'Down'].var(ddof=0)" - ] - }, - { - "cell_type": "markdown", - "id": "7c9078c1", - "metadata": {}, - "source": [ - "Since `NB()` is a classifier in the `sklearn` library, making predictions\n", - "uses the same syntax as for `LDA()` and `QDA()` above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b387227e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.808068Z", - "iopub.status.busy": "2023-07-26T05:16:53.807902Z", - "iopub.status.idle": "2023-07-26T05:16:53.813920Z", - "shell.execute_reply": "2023-07-26T05:16:53.813575Z" - } - }, - "outputs": [], - "source": [ - "nb_labels = NB.predict(X_test)\n", - "confusion_table(nb_labels, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ab979471", - "metadata": {}, - "source": [ - "Naive Bayes performs well on these data, with accurate predictions over 59% of the time. This is slightly worse than QDA, but much better than LDA.\n", - "\n", - "As for `LDA`, the `predict_proba()` method estimates the probability that each observation belongs to a particular class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f14ebe61", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.816106Z", - "iopub.status.busy": "2023-07-26T05:16:53.815946Z", - "iopub.status.idle": "2023-07-26T05:16:53.819614Z", - "shell.execute_reply": "2023-07-26T05:16:53.819281Z" - } - }, - "outputs": [], - "source": [ - "NB.predict_proba(X_test)[:5]" - ] - }, - { - "cell_type": "markdown", - "id": "f14c520d", - "metadata": {}, - "source": [ - "## K-Nearest Neighbors\n", - "We will now perform KNN using the `KNeighborsClassifier()` function. This function works similarly\n", - "to the other model-fitting functions that we have\n", - "encountered thus far.\n", - "\n", - "As is the\n", - "case for LDA and QDA, we fit the classifier\n", - "using the `fit` method. New\n", - "predictions are formed using the `predict` method\n", - "of the object returned by `fit()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41a12f4a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.821819Z", - "iopub.status.busy": "2023-07-26T05:16:53.821654Z", - "iopub.status.idle": "2023-07-26T05:16:53.835418Z", - "shell.execute_reply": "2023-07-26T05:16:53.834963Z" - } - }, - "outputs": [], - "source": [ - "knn1 = KNeighborsClassifier(n_neighbors=1)\n", - "knn1.fit(X_train, L_train)\n", - "knn1_pred = knn1.predict(X_test)\n", - "confusion_table(knn1_pred, L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "22ee6dff", - "metadata": {}, - "source": [ - "The results using $K=1$ are not very good, since only $50%$ of the\n", - "observations are correctly predicted. Of course, it may be that $K=1$\n", - "results in an overly-flexible fit to the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dd2f3e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.840858Z", - "iopub.status.busy": "2023-07-26T05:16:53.840380Z", - "iopub.status.idle": "2023-07-26T05:16:53.844994Z", - "shell.execute_reply": "2023-07-26T05:16:53.844563Z" - } - }, - "outputs": [], - "source": [ - "(83+43)/252, np.mean(knn1_pred == L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "cda2465d", - "metadata": {}, - "source": [ - "Below, we repeat the\n", - "analysis using $K=3$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ec72efc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.848486Z", - "iopub.status.busy": "2023-07-26T05:16:53.848323Z", - "iopub.status.idle": "2023-07-26T05:16:53.859379Z", - "shell.execute_reply": "2023-07-26T05:16:53.859009Z" - } - }, - "outputs": [], - "source": [ - "knn3 = KNeighborsClassifier(n_neighbors=3)\n", - "knn3_pred = knn3.fit(X_train, L_train).predict(X_test)\n", - "np.mean(knn3_pred == L_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ff4533bd", - "metadata": {}, - "source": [ - "The results have improved slightly. But increasing *K* further\n", - "provides no further improvements. It appears that for these data, and this train/test split,\n", - "QDA gives the best results of the methods that we have examined so\n", - "far." - ] - }, - { - "cell_type": "markdown", - "id": "5810022d", - "metadata": {}, - "source": [ - "KNN does not perform well on the `Smarket` data, but it often does provide impressive results. As an example we will apply the KNN approach to the `Caravan` data set, which is part of the `ISLP` library. This data set includes 85\n", - "predictors that measure demographic characteristics for 5,822\n", - "individuals. The response variable is `Purchase`, which\n", - "indicates whether or not a given individual purchases a caravan\n", - "insurance policy. In this data set, only 6% of people purchased\n", - "caravan insurance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98fef4ed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.862002Z", - "iopub.status.busy": "2023-07-26T05:16:53.861826Z", - "iopub.status.idle": "2023-07-26T05:16:53.880744Z", - "shell.execute_reply": "2023-07-26T05:16:53.880217Z" - } - }, - "outputs": [], - "source": [ - "Caravan = load_data('Caravan')\n", - "Purchase = Caravan.Purchase\n", - "Purchase.value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "4fc1a1df", - "metadata": {}, - "source": [ - "The method `value_counts()` takes a `pd.Series` or `pd.DataFrame` and returns\n", - "a `pd.Series` with the corresponding counts\n", - "for each unique element. In this case `Purchase` has only `Yes` and `No` values\n", - "and returns how many values of each there are." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1af72c0b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.883491Z", - "iopub.status.busy": "2023-07-26T05:16:53.883258Z", - "iopub.status.idle": "2023-07-26T05:16:53.886241Z", - "shell.execute_reply": "2023-07-26T05:16:53.885825Z" - } - }, - "outputs": [], - "source": [ - "348 / 5822" - ] - }, - { - "cell_type": "markdown", - "id": "78c25fda", - "metadata": {}, - "source": [ - "Our features will include all columns except `Purchase`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ea841e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.888642Z", - "iopub.status.busy": "2023-07-26T05:16:53.888457Z", - "iopub.status.idle": "2023-07-26T05:16:53.891757Z", - "shell.execute_reply": "2023-07-26T05:16:53.891278Z" - } - }, - "outputs": [], - "source": [ - "feature_df = Caravan.drop(columns=['Purchase'])" - ] - }, - { - "cell_type": "markdown", - "id": "55efb306", - "metadata": {}, - "source": [ - "Because the KNN classifier predicts the class of a given test\n", - "observation by identifying the observations that are nearest to it,\n", - "the scale of the variables matters. Any variables that are on a large\n", - "scale will have a much larger effect on the *distance* between\n", - "the observations, and hence on the KNN classifier, than variables that\n", - "are on a small scale. For instance, imagine a data set that contains\n", - "two variables, `salary` and `age` (measured in dollars\n", - "and years, respectively). As far as KNN is concerned, a difference of\n", - "1,000 USD in salary is enormous compared to a difference of 50 years in\n", - "age. Consequently, `salary` will drive the KNN classification\n", - "results, and `age` will have almost no effect. This is contrary\n", - "to our intuition that a salary difference of 1,000 USD is quite small\n", - "compared to an age difference of 50 years. Furthermore, the\n", - "importance of scale to the KNN classifier leads to another issue: if\n", - "we measured `salary` in Japanese yen, or if we measured\n", - " `age` in minutes, then we’d get quite different classification\n", - "results from what we get if these two variables are measured in\n", - "dollars and years.\n", - "\n", - "A good way to handle this problem is to *standardize* the data so that all variables are\n", - "given a mean of zero and a standard deviation of one. Then all\n", - "variables will be on a comparable scale. This is accomplished\n", - "using\n", - "the `StandardScaler()`\n", - "transformation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e26b14f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.893952Z", - "iopub.status.busy": "2023-07-26T05:16:53.893809Z", - "iopub.status.idle": "2023-07-26T05:16:53.896350Z", - "shell.execute_reply": "2023-07-26T05:16:53.895913Z" - } - }, - "outputs": [], - "source": [ - "scaler = StandardScaler(with_mean=True,\n", - " with_std=True,\n", - " copy=True)" - ] - }, - { - "cell_type": "markdown", - "id": "0c0d1faf", - "metadata": {}, - "source": [ - "The argument `with_mean` indicates whether or not\n", - "we should subtract the mean, while `with_std` indicates\n", - "whether or not we should scale the columns to have standard\n", - "deviation of 1 or not. Finally, the argument `copy=True`\n", - "indicates that we will always copy data, rather than\n", - "trying to do calculations in place where possible.\n", - "\n", - "This transformation can be fit\n", - "and then applied to arbitrary data. In the first line\n", - "below, the parameters for the scaling are computed and\n", - "stored in `scaler`, while the second line actually\n", - "constructs the standardized set of features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd178f38", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.898581Z", - "iopub.status.busy": "2023-07-26T05:16:53.898422Z", - "iopub.status.idle": "2023-07-26T05:16:53.907100Z", - "shell.execute_reply": "2023-07-26T05:16:53.906252Z" - } - }, - "outputs": [], - "source": [ - "scaler.fit(feature_df)\n", - "X_std = scaler.transform(feature_df)" - ] - }, - { - "cell_type": "markdown", - "id": "30bfaa05", - "metadata": {}, - "source": [ - "Now every column of `feature_std` below has a standard deviation of\n", - "one and a mean of zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18929b37", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.909697Z", - "iopub.status.busy": "2023-07-26T05:16:53.909376Z", - "iopub.status.idle": "2023-07-26T05:16:53.917328Z", - "shell.execute_reply": "2023-07-26T05:16:53.916861Z" - } - }, - "outputs": [], - "source": [ - "feature_std = pd.DataFrame(\n", - " X_std,\n", - " columns=feature_df.columns);\n", - "feature_std.std()" - ] - }, - { - "cell_type": "markdown", - "id": "4f536808", - "metadata": {}, - "source": [ - "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 198.\n", - "In this case it does not matter, as long as the variables are all on the same scale.\n", - "\n", - "Using the function `train_test_split()` we now split the observations into a test set,\n", - "containing 1000 observations, and a training set containing the remaining\n", - "observations. The argument `random_state=0` ensures that we get\n", - "the same split each time we rerun the code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d439d0b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.919758Z", - "iopub.status.busy": "2023-07-26T05:16:53.919493Z", - "iopub.status.idle": "2023-07-26T05:16:53.924297Z", - "shell.execute_reply": "2023-07-26T05:16:53.923909Z" - } - }, - "outputs": [], - "source": [ - "(X_train,\n", - " X_test,\n", - " y_train,\n", - " y_test) = train_test_split(feature_std,\n", - " Purchase,\n", - " test_size=1000,\n", - " random_state=0)" - ] - }, - { - "cell_type": "markdown", - "id": "87fcf773", - "metadata": {}, - "source": [ - "`?train_test_split` reveals that the non-keyword arguments can be `lists`, `arrays`, `pandas dataframes` etc that all have the same length (`shape[0]`) and hence are *indexable*. In this case they are the dataframe `feature_std` and the response variable `Purchase`. \n", - "We fit a KNN model on the training data using $K=1$,\n", - "and evaluate its performance on the test data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b8c0171", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.926683Z", - "iopub.status.busy": "2023-07-26T05:16:53.926533Z", - "iopub.status.idle": "2023-07-26T05:16:53.979689Z", - "shell.execute_reply": "2023-07-26T05:16:53.979266Z" - } - }, - "outputs": [], - "source": [ - "knn1 = KNeighborsClassifier(n_neighbors=1)\n", - "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n", - "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")" - ] - }, - { - "cell_type": "markdown", - "id": "3aa030b6", - "metadata": {}, - "source": [ - "The KNN error rate on the 1,000 test observations is about $11%$.\n", - "At first glance, this may appear to be fairly good. However, since\n", - "just over 6% of customers purchased insurance, we could get the error\n", - "rate down to almost 6% by always predicting `No` regardless of the\n", - "values of the predictors! This is known as the *null rate*.}\n", - "\n", - "Suppose that there is some non-trivial cost to trying to sell\n", - "insurance to a given individual. For instance, perhaps a salesperson\n", - "must visit each potential customer. If the company tries to sell\n", - "insurance to a random selection of customers, then the success rate\n", - "will be only 6%, which may be far too low given the costs\n", - "involved. Instead, the company would like to try to sell insurance\n", - "only to customers who are likely to buy it. So the overall error rate\n", - "is not of interest. Instead, the fraction of individuals that are\n", - "correctly predicted to buy insurance is of interest." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f31b6e26", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.982811Z", - "iopub.status.busy": "2023-07-26T05:16:53.982601Z", - "iopub.status.idle": "2023-07-26T05:16:53.989512Z", - "shell.execute_reply": "2023-07-26T05:16:53.989071Z" - } - }, - "outputs": [], - "source": [ - "confusion_table(knn1_pred, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "de5fb529", - "metadata": {}, - "source": [ - "It turns out that KNN with $K=1$ does far better than random guessing\n", - "among the customers that are predicted to buy insurance. Among 62\n", - "such customers, 9, or 14.5%, actually do purchase insurance.\n", - "This is double the rate that one would obtain from random guessing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a8c37aa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.992418Z", - "iopub.status.busy": "2023-07-26T05:16:53.992103Z", - "iopub.status.idle": "2023-07-26T05:16:53.995535Z", - "shell.execute_reply": "2023-07-26T05:16:53.995078Z" - } - }, - "outputs": [], - "source": [ - "9/(53+9)" - ] - }, - { - "cell_type": "markdown", - "id": "a898b7d0", - "metadata": {}, - "source": [ - "### Tuning Parameters\n", - "\n", - "The number of neighbors in KNN is referred to as a *tuning parameter*, also referred to as a *hyperparameter*.\n", - "We do not know *a priori* what value to use. It is therefore of interest\n", - "to see how the classifier performs on test data as we vary these\n", - "parameters. This can be achieved with a `for` loop, described in Section 2.3.8.\n", - "Here we use a for loop to look at the accuracy of our classifier in the group predicted to purchase\n", - "insurance as we vary the number of neighbors from 1 to 5:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "153662f8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:53.999510Z", - "iopub.status.busy": "2023-07-26T05:16:53.999082Z", - "iopub.status.idle": "2023-07-26T05:16:54.130823Z", - "shell.execute_reply": "2023-07-26T05:16:54.130318Z" - } - }, - "outputs": [], - "source": [ - "for K in range(1,6):\n", - " knn = KNeighborsClassifier(n_neighbors=K)\n", - " knn_pred = knn.fit(X_train, y_train).predict(X_test)\n", - " C = confusion_table(knn_pred, y_test)\n", - " templ = ('K={0:d}: # predicted to rent: {1:>2},' +\n", - " ' # who did rent {2:d}, accuracy {3:.1%}')\n", - " pred = C.loc['Yes'].sum()\n", - " did_rent = C.loc['Yes','Yes']\n", - " print(templ.format(\n", - " K,\n", - " pred,\n", - " did_rent,\n", - " did_rent / pred))" - ] - }, - { - "cell_type": "markdown", - "id": "39ba18b3", - "metadata": {}, - "source": [ - "We see some variability --- the numbers for `K=4` are very different from the rest.\n", - "\n", - "### Comparison to Logistic Regression\n", - "As a comparison, we can also fit a logistic regression model to the\n", - "data. This can also be done\n", - "with `sklearn`, though by default it fits\n", - "something like the *ridge regression* version\n", - "of logistic regression, which we introduce in Chapter 6. This can\n", - "be modified by appropriately setting the argument `C` below. Its default\n", - "value is 1 but by setting it to a very large number, the algorithm converges to the same solution as the usual (unregularized)\n", - "logistic regression estimator discussed above.\n", - "\n", - "Unlike the\n", - "`statsmodels` package, `sklearn` focuses less on\n", - "inference and more on classification. Hence,\n", - "the `summary` methods seen in `statsmodels`\n", - "and our simplified version seen with `summarize` are not\n", - "generally available for the classifiers in `sklearn`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55b99a3f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:54.134280Z", - "iopub.status.busy": "2023-07-26T05:16:54.133924Z", - "iopub.status.idle": "2023-07-26T05:16:54.790553Z", - "shell.execute_reply": "2023-07-26T05:16:54.788632Z" - } - }, - "outputs": [], - "source": [ - "logit = LogisticRegression(C=1e10, solver='liblinear')\n", - "logit.fit(X_train, y_train)\n", - "logit_pred = logit.predict_proba(X_test)\n", - "logit_labels = np.where(logit_pred[:,1] > 5, 'Yes', 'No')\n", - "confusion_table(logit_labels, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "4e0ebc83", - "metadata": {}, - "source": [ - "We used the argument `solver='liblinear'` above to\n", - "avoid a warning with the default solver which would indicate that\n", - "the algorithm does not converge.\n", - "\n", - "If we use $0.5$ as the predicted probability cut-off for the\n", - "classifier, then we have a problem: none of the test\n", - "observations are predicted to purchase insurance. However, we are not required to use a\n", - "cut-off of $0.5$. If we instead predict a purchase any time the\n", - "predicted probability of purchase exceeds $0.25$, we get much better\n", - "results: we predict that 29 people will purchase insurance, and we are\n", - "correct for about 31% of these people. This is almost five times\n", - "better than random guessing!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c2ea9f5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:54.800869Z", - "iopub.status.busy": "2023-07-26T05:16:54.800067Z", - "iopub.status.idle": "2023-07-26T05:16:54.901396Z", - "shell.execute_reply": "2023-07-26T05:16:54.824967Z" - } - }, - "outputs": [], - "source": [ - "logit_labels = np.where(logit_pred[:,1]>0.25, 'Yes', 'No')\n", - "confusion_table(logit_labels, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a6bf0a4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.014712Z", - "iopub.status.busy": "2023-07-26T05:16:55.014300Z", - "iopub.status.idle": "2023-07-26T05:16:55.022432Z", - "shell.execute_reply": "2023-07-26T05:16:55.021283Z" - } - }, - "outputs": [], - "source": [ - "9/(20+9)" - ] - }, - { - "cell_type": "markdown", - "id": "4d04bbc7", - "metadata": {}, - "source": [ - "## Linear and Poisson Regression on the Bikeshare Data\n", - "Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6.\n", - "The response `bikers` measures the number of bike rentals per hour\n", - "in Washington, DC in the period 2010--2012." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "769300df", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.026479Z", - "iopub.status.busy": "2023-07-26T05:16:55.026122Z", - "iopub.status.idle": "2023-07-26T05:16:55.040298Z", - "shell.execute_reply": "2023-07-26T05:16:55.039107Z" - } - }, - "outputs": [], - "source": [ - "Bike = load_data('Bikeshare')" - ] - }, - { - "cell_type": "markdown", - "id": "8cc82fa9", - "metadata": {}, - "source": [ - "Let's have a peek at the dimensions and names of the variables in this dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02b351f5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.043729Z", - "iopub.status.busy": "2023-07-26T05:16:55.043547Z", - "iopub.status.idle": "2023-07-26T05:16:55.047018Z", - "shell.execute_reply": "2023-07-26T05:16:55.046428Z" - } - }, - "outputs": [], - "source": [ - "Bike.shape, Bike.columns" - ] - }, - { - "cell_type": "markdown", - "id": "7784e3a1", - "metadata": {}, - "source": [ - "### Linear Regression\n", - "\n", - "We begin by fitting a linear regression model to the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "926c908d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.049121Z", - "iopub.status.busy": "2023-07-26T05:16:55.049011Z", - "iopub.status.idle": "2023-07-26T05:16:55.569728Z", - "shell.execute_reply": "2023-07-26T05:16:55.567494Z" - } - }, - "outputs": [], - "source": [ - "X = MS(['mnth',\n", - " 'hr',\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit']).fit_transform(Bike)\n", - "Y = Bike['bikers']\n", - "M_lm = sm.OLS(Y, X).fit()\n", - "summarize(M_lm)" - ] - }, - { - "cell_type": "markdown", - "id": "b29a0708", - "metadata": {}, - "source": [ - "There are 24 levels in `hr` and 40 rows in all.\n", - "In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated\n", - "as the baseline values, and so no coefficient estimates are provided\n", - "for them: implicitly, their coefficient estimates are zero, and all\n", - "other levels are measured relative to these baselines. For example,\n", - "the Feb coefficient of $6.845$ signifies that, holding all other\n", - "variables constant, there are on average about 7 more riders in\n", - "February than in January. Similarly there are about 16.5 more riders\n", - "in March than in January.\n", - "\n", - "The results seen in Section 4.6.1\n", - "used a slightly different coding of the variables `hr` and `mnth`, as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "064b24d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.575305Z", - "iopub.status.busy": "2023-07-26T05:16:55.575124Z", - "iopub.status.idle": "2023-07-26T05:16:55.582612Z", - "shell.execute_reply": "2023-07-26T05:16:55.580612Z" - } - }, - "outputs": [], - "source": [ - "hr_encode = contrast('hr', 'sum')\n", - "mnth_encode = contrast('mnth', 'sum')" - ] - }, - { - "cell_type": "markdown", - "id": "fba11e8e", - "metadata": {}, - "source": [ - "Refitting again:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27c1aa54", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:55.591223Z", - "iopub.status.busy": "2023-07-26T05:16:55.589141Z", - "iopub.status.idle": "2023-07-26T05:16:56.075120Z", - "shell.execute_reply": "2023-07-26T05:16:56.073529Z" - } - }, - "outputs": [], - "source": [ - "X2 = MS([mnth_encode,\n", - " hr_encode,\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit']).fit_transform(Bike)\n", - "M2_lm = sm.OLS(Y, X2).fit()\n", - "S2 = summarize(M2_lm)\n", - "S2" - ] - }, - { - "cell_type": "markdown", - "id": "d057b162", - "metadata": {}, - "source": [ - "What is the difference between the two codings? In `M2_lm`, a\n", - "coefficient estimate is reported for all but level `23` of `hr`\n", - "and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate\n", - "for the last level of `mnth` is not zero: instead, it equals the\n", - "negative of the sum of the coefficient estimates for all of the\n", - "other levels. Similarly, in `M2_lm`, the coefficient estimate\n", - "for the last level of `hr` is the negative of the sum of the\n", - "coefficient estimates for all of the other levels. This means that the\n", - "coefficients of `hr` and `mnth` in `M2_lm` will always sum\n", - "to zero, and can be interpreted as the difference from the mean\n", - "level. For example, the coefficient for January of $-46.087$ indicates\n", - "that, holding all other variables constant, there are typically 46\n", - "fewer riders in January relative to the yearly average.\n", - "\n", - "It is important to realize that the choice of coding really does not\n", - "matter, provided that we interpret the model output correctly in light\n", - "of the coding used. For example, we see that the predictions from the\n", - "linear model are the same regardless of coding:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd0114c8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.092324Z", - "iopub.status.busy": "2023-07-26T05:16:56.087118Z", - "iopub.status.idle": "2023-07-26T05:16:56.137844Z", - "shell.execute_reply": "2023-07-26T05:16:56.135218Z" - } - }, - "outputs": [], - "source": [ - "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "bcb6ae87", - "metadata": {}, - "source": [ - "The sum of squared differences is zero. We can also see this using the\n", - "`np.allclose()` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "292585a1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.147457Z", - "iopub.status.busy": "2023-07-26T05:16:56.146798Z", - "iopub.status.idle": "2023-07-26T05:16:56.159395Z", - "shell.execute_reply": "2023-07-26T05:16:56.157990Z" - } - }, - "outputs": [], - "source": [ - "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)" - ] - }, - { - "cell_type": "markdown", - "id": "5bffbdd1", - "metadata": {}, - "source": [ - "To reproduce the left-hand side of Figure 4.13\n", - "we must first obtain the coefficient estimates associated with\n", - "`mnth`. The coefficients for January through November can be obtained\n", - "directly from the `M2_lm` object. The coefficient for December\n", - "must be explicitly computed as the negative sum of all the other\n", - "months. We first extract all the coefficients for month from\n", - "the coefficients of `M2_lm`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e05e0575", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.169355Z", - "iopub.status.busy": "2023-07-26T05:16:56.168352Z", - "iopub.status.idle": "2023-07-26T05:16:56.190269Z", - "shell.execute_reply": "2023-07-26T05:16:56.186463Z" - } - }, - "outputs": [], - "source": [ - "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", - "coef_month" - ] - }, - { - "cell_type": "markdown", - "id": "d542caac", - "metadata": {}, - "source": [ - "Next, we append `Dec` as the negative of the sum of all other months." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f04a25e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.213429Z", - "iopub.status.busy": "2023-07-26T05:16:56.210143Z", - "iopub.status.idle": "2023-07-26T05:16:56.249114Z", - "shell.execute_reply": "2023-07-26T05:16:56.246382Z" - } - }, - "outputs": [], - "source": [ - "months = Bike['mnth'].dtype.categories\n", - "coef_month = pd.concat([\n", - " coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'\n", - " ])\n", - " ])\n", - "coef_month" - ] - }, - { - "cell_type": "markdown", - "id": "6e3ef375", - "metadata": {}, - "source": [ - "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n", - "the labels in the index." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20c7c054", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.310254Z", - "iopub.status.busy": "2023-07-26T05:16:56.306244Z", - "iopub.status.idle": "2023-07-26T05:16:56.486315Z", - "shell.execute_reply": "2023-07-26T05:16:56.485755Z" - } - }, - "outputs": [], - "source": [ - "fig_month, ax_month = subplots(figsize=(8,8))\n", - "x_month = np.arange(coef_month.shape[0])\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "6032f6a9", - "metadata": {}, - "source": [ - "Reproducing the right-hand plot in Figure 4.13 follows a similar process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f83f4bf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.489411Z", - "iopub.status.busy": "2023-07-26T05:16:56.489143Z", - "iopub.status.idle": "2023-07-26T05:16:56.493292Z", - "shell.execute_reply": "2023-07-26T05:16:56.492665Z" - } - }, - "outputs": [], - "source": [ - "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", - "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()], index=['hr[23]'])\n", - " ])" - ] - }, - { - "cell_type": "markdown", - "id": "58200be9", - "metadata": {}, - "source": [ - "We now make the hour plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86e9a741", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.496145Z", - "iopub.status.busy": "2023-07-26T05:16:56.495966Z", - "iopub.status.idle": "2023-07-26T05:16:56.609790Z", - "shell.execute_reply": "2023-07-26T05:16:56.609280Z" - } - }, - "outputs": [], - "source": [ - "fig_hr, ax_hr = subplots(figsize=(8,8))\n", - "x_hr = np.arange(coef_hr.shape[0])\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticks(x_hr[::2])\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "5db06a3e", - "metadata": {}, - "source": [ - "### Poisson Regression\n", - "\n", - "Now we fit instead a Poisson regression model to the\n", - "`Bikeshare` data. Very little changes, except that we now use the\n", - "function `sm.GLM()` with the Poisson family specified:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9448278", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:56.612317Z", - "iopub.status.busy": "2023-07-26T05:16:56.612179Z", - "iopub.status.idle": "2023-07-26T05:16:57.099663Z", - "shell.execute_reply": "2023-07-26T05:16:57.097888Z" - } - }, - "outputs": [], - "source": [ - "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()" - ] - }, - { - "cell_type": "markdown", - "id": "e7b3bc38", - "metadata": {}, - "source": [ - "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec8505be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:57.113476Z", - "iopub.status.busy": "2023-07-26T05:16:57.110476Z", - "iopub.status.idle": "2023-07-26T05:16:57.156419Z", - "shell.execute_reply": "2023-07-26T05:16:57.148075Z" - } - }, - "outputs": [], - "source": [ - "S_pois = summarize(M_pois)\n", - "coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef']\n", - "coef_month = pd.concat([coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'])])\n", - "coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef']\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()],\n", - " index=['hr[23]'])])" - ] - }, - { - "cell_type": "markdown", - "id": "f6fb37ef", - "metadata": {}, - "source": [ - "The plotting is as before." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b8a5edf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:57.169051Z", - "iopub.status.busy": "2023-07-26T05:16:57.167599Z", - "iopub.status.idle": "2023-07-26T05:16:57.482769Z", - "shell.execute_reply": "2023-07-26T05:16:57.482137Z" - } - }, - "outputs": [], - "source": [ - "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20)\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "3c256bf2", - "metadata": {}, - "source": [ - "We compare the fitted values of the two models.\n", - "The fitted values are stored in the `fittedvalues` attribute\n", - "returned by the `fit()` method for both the linear regression and the Poisson\n", - "fits. The linear predictors are stored as the attribute `lin_pred`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d487c7ed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:57.485339Z", - "iopub.status.busy": "2023-07-26T05:16:57.484975Z", - "iopub.status.idle": "2023-07-26T05:16:57.605612Z", - "shell.execute_reply": "2023-07-26T05:16:57.605058Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(M2_lm.fittedvalues,\n", - " M_pois.fittedvalues,\n", - " s=20)\n", - "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n", - "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n", - "ax.axline([0,0], c='black', linewidth=3,\n", - " linestyle='--', slope=1);" - ] - }, - { - "cell_type": "markdown", - "id": "17ccc1bf", - "metadata": {}, - "source": [ - "The predictions from the Poisson regression model are correlated with\n", - "those from the linear model; however, the former are non-negative. As\n", - "a result the Poisson regression predictions tend to be larger than\n", - "those from the linear model for either very low or very high levels of\n", - "ridership.\n", - "\n", - "In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument\n", - "`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function\n", - "with `family=sm.families.Binomial()` to perform logistic regression. Other\n", - "choices for the `family` argument can be used to fit other types\n", - "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n", - "model." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch5-resample-lab.ipynb b/docs/source/labs/Ch5-resample-lab.ipynb deleted file mode 100644 index df0614f..0000000 --- a/docs/source/labs/Ch5-resample-lab.ipynb +++ /dev/null @@ -1,1064 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "3a3f2f85", - "metadata": {}, - "source": [ - "# Cross-Validation and the Bootstrap\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this lab, we explore the resampling techniques covered in this\n", - "chapter. Some of the commands in this lab may take a while to run on\n", - "your computer.\n", - "\n", - "We again begin by placing most of our imports at this top level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60fad148", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:58.904948Z", - "iopub.status.busy": "2023-07-26T05:16:58.904833Z", - "iopub.status.idle": "2023-07-26T05:16:59.817459Z", - "shell.execute_reply": "2023-07-26T05:16:59.816977Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data\n", - "from ISLP.models import (ModelSpec as MS,\n", - " summarize,\n", - " poly)\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "78fcfe7a", - "metadata": {}, - "source": [ - "There are several new imports needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2478aeb4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.820190Z", - "iopub.status.busy": "2023-07-26T05:16:59.819944Z", - "iopub.status.idle": "2023-07-26T05:16:59.822616Z", - "shell.execute_reply": "2023-07-26T05:16:59.822236Z" - } - }, - "outputs": [], - "source": [ - "from functools import partial\n", - "from sklearn.model_selection import \\\n", - " (cross_validate,\n", - " KFold,\n", - " ShuffleSplit)\n", - "from sklearn.base import clone\n", - "from ISLP.models import sklearn_sm" - ] - }, - { - "cell_type": "markdown", - "id": "713d30db", - "metadata": {}, - "source": [ - "## The Validation Set Approach\n", - "We explore the use of the validation set approach in order to estimate\n", - "the test error rates that result from fitting various linear models on\n", - "the `Auto` data set.\n", - "\n", - "We use the function `train_test_split()` to split\n", - "the data into training and validation sets. As there are 392 observations,\n", - "we split into two equal sets of size 196 using the\n", - "argument `test_size=196`. It is generally a good idea to set a random seed\n", - "when performing operations like this that contain an\n", - "element of randomness, so that the results obtained can be reproduced\n", - "precisely at a later time. We set the random seed of the splitter\n", - "with the argument `random_state=0`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99c95faf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.824651Z", - "iopub.status.busy": "2023-07-26T05:16:59.824498Z", - "iopub.status.idle": "2023-07-26T05:16:59.830661Z", - "shell.execute_reply": "2023-07-26T05:16:59.830199Z" - } - }, - "outputs": [], - "source": [ - "Auto = load_data('Auto')\n", - "Auto_train, Auto_valid = train_test_split(Auto,\n", - " test_size=196,\n", - " random_state=0)" - ] - }, - { - "cell_type": "markdown", - "id": "57be35df", - "metadata": {}, - "source": [ - "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41b0717d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.832904Z", - "iopub.status.busy": "2023-07-26T05:16:59.832740Z", - "iopub.status.idle": "2023-07-26T05:16:59.837926Z", - "shell.execute_reply": "2023-07-26T05:16:59.837164Z" - } - }, - "outputs": [], - "source": [ - "hp_mm = MS(['horsepower'])\n", - "X_train = hp_mm.fit_transform(Auto_train)\n", - "y_train = Auto_train['mpg']\n", - "model = sm.OLS(y_train, X_train)\n", - "results = model.fit()" - ] - }, - { - "cell_type": "markdown", - "id": "7f1bef95", - "metadata": {}, - "source": [ - "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n", - "created using the validation data set. We also calculate the validation MSE of our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7ea3c0d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.840693Z", - "iopub.status.busy": "2023-07-26T05:16:59.840426Z", - "iopub.status.idle": "2023-07-26T05:16:59.847201Z", - "shell.execute_reply": "2023-07-26T05:16:59.846618Z" - } - }, - "outputs": [], - "source": [ - "X_valid = hp_mm.transform(Auto_valid)\n", - "y_valid = Auto_valid['mpg']\n", - "valid_pred = results.predict(X_valid)\n", - "np.mean((y_valid - valid_pred)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "6dba5d55", - "metadata": {}, - "source": [ - "Hence our estimate for the validation MSE of the linear regression\n", - "fit is $23.62$.\n", - "\n", - "We can also estimate the validation error for\n", - "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n", - "as a training and test set and returns the MSE on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a02a2d05", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.849828Z", - "iopub.status.busy": "2023-07-26T05:16:59.849638Z", - "iopub.status.idle": "2023-07-26T05:16:59.852938Z", - "shell.execute_reply": "2023-07-26T05:16:59.852364Z" - } - }, - "outputs": [], - "source": [ - "def evalMSE(terms,\n", - " response,\n", - " train,\n", - " test):\n", - "\n", - " mm = MS(terms)\n", - " X_train = mm.fit_transform(train)\n", - " y_train = train[response]\n", - "\n", - " X_test = mm.transform(test)\n", - " y_test = test[response]\n", - "\n", - " results = sm.OLS(y_train, X_train).fit()\n", - " test_pred = results.predict(X_test)\n", - "\n", - " return np.mean((y_test - test_pred)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "39ab59b1", - "metadata": {}, - "source": [ - "Let’s use this function to estimate the validation MSE\n", - "using linear, quadratic and cubic fits. We use the `enumerate()` function\n", - "here, which gives both the values and indices of objects as one iterates\n", - "over a for loop." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51d93dea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.855432Z", - "iopub.status.busy": "2023-07-26T05:16:59.855267Z", - "iopub.status.idle": "2023-07-26T05:16:59.867911Z", - "shell.execute_reply": "2023-07-26T05:16:59.867517Z" - } - }, - "outputs": [], - "source": [ - "MSE = np.zeros(3)\n", - "for idx, degree in enumerate(range(1, 4)):\n", - " MSE[idx] = evalMSE([poly('horsepower', degree)],\n", - " 'mpg',\n", - " Auto_train,\n", - " Auto_valid)\n", - "MSE" - ] - }, - { - "cell_type": "markdown", - "id": "936e168a", - "metadata": {}, - "source": [ - "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n", - "choose a different training/validation split instead, then we\n", - "can expect somewhat different errors on the validation set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83432f06", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.870663Z", - "iopub.status.busy": "2023-07-26T05:16:59.870321Z", - "iopub.status.idle": "2023-07-26T05:16:59.884091Z", - "shell.execute_reply": "2023-07-26T05:16:59.883749Z" - } - }, - "outputs": [], - "source": [ - "Auto_train, Auto_valid = train_test_split(Auto,\n", - " test_size=196,\n", - " random_state=3)\n", - "MSE = np.zeros(3)\n", - "for idx, degree in enumerate(range(1, 4)):\n", - " MSE[idx] = evalMSE([poly('horsepower', degree)],\n", - " 'mpg',\n", - " Auto_train,\n", - " Auto_valid)\n", - "MSE" - ] - }, - { - "cell_type": "markdown", - "id": "f5ceb357", - "metadata": {}, - "source": [ - "Using this split of the observations into a training set and a validation set,\n", - "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n", - "\n", - "These results are consistent with our previous findings: a model that\n", - "predicts `mpg` using a quadratic function of `horsepower`\n", - "performs better than a model that involves only a linear function of\n", - "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`." - ] - }, - { - "cell_type": "markdown", - "id": "6d624a5c", - "metadata": {}, - "source": [ - "## Cross-Validation\n", - "In theory, the cross-validation estimate can be computed for any generalized\n", - "linear model. {}\n", - "In practice, however, the simplest way to cross-validate in\n", - "Python is to use `sklearn`, which has a different interface or API\n", - "than `statsmodels`, the code we have been using to fit GLMs.\n", - "\n", - "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n", - "requires the use of a *wrapper*.\n", - "In the `ISLP` package,\n", - "we provide \n", - "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n", - "models fit by `statsmodels`.\n", - "\n", - "The class `sklearn_sm()` \n", - "has as its first argument\n", - "a model from `statsmodels`. It can take two additional\n", - "optional arguments: `model_str` which can be\n", - "used to specify a formula, and `model_args` which should\n", - "be a dictionary of additional arguments used when fitting\n", - "the model. For example, to fit a logistic regression model\n", - "we have to specify a `family` argument. This\n", - "is passed as `model_args={'family':sm.families.Binomial()}`.\n", - "\n", - "Here is our wrapper in action:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bcfc433f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:16:59.886618Z", - "iopub.status.busy": "2023-07-26T05:16:59.886459Z", - "iopub.status.idle": "2023-07-26T05:17:00.662796Z", - "shell.execute_reply": "2023-07-26T05:17:00.662228Z" - } - }, - "outputs": [], - "source": [ - "hp_model = sklearn_sm(sm.OLS,\n", - " MS(['horsepower']))\n", - "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n", - "cv_results = cross_validate(hp_model,\n", - " X,\n", - " Y,\n", - " cv=Auto.shape[0])\n", - "cv_err = np.mean(cv_results['test_score'])\n", - "cv_err" - ] - }, - { - "cell_type": "markdown", - "id": "5b0f6f30", - "metadata": {}, - "source": [ - "The arguments to `cross_validate()` are as follows: an\n", - "object with the appropriate `fit()`, `predict()`,\n", - "and `score()` methods, an\n", - "array of features `X` and a response `Y`. \n", - "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n", - "$K$ results in $K$-fold cross-validation. We have provided a value \n", - "corresponding to the total number of observations, which results in\n", - "leave-one-out cross-validation (LOOCV). The `cross_validate()` function produces a dictionary with several components;\n", - "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23." - ] - }, - { - "cell_type": "markdown", - "id": "b527f67f", - "metadata": {}, - "source": [ - "We can repeat this procedure for increasingly complex polynomial fits.\n", - "To automate the process, we again\n", - "use a for loop which iteratively fits polynomial\n", - "regressions of degree 1 to 5, computes the\n", - "associated cross-validation error, and stores it in the $i$th element\n", - "of the vector `cv_error`. The variable `d` in the for loop\n", - "corresponds to the degree of the polynomial. We begin by initializing the\n", - "vector. This command may take a couple of seconds to run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f951ffc8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:00.665287Z", - "iopub.status.busy": "2023-07-26T05:17:00.665106Z", - "iopub.status.idle": "2023-07-26T05:17:01.422477Z", - "shell.execute_reply": "2023-07-26T05:17:01.422022Z" - } - }, - "outputs": [], - "source": [ - "cv_error = np.zeros(5)\n", - "H = np.array(Auto['horsepower'])\n", - "M = sklearn_sm(sm.OLS)\n", - "for i, d in enumerate(range(1,6)):\n", - " X = np.power.outer(H, np.arange(d+1))\n", - " M_CV = cross_validate(M,\n", - " X,\n", - " Y,\n", - " cv=Auto.shape[0])\n", - " cv_error[i] = np.mean(M_CV['test_score'])\n", - "cv_error" - ] - }, - { - "cell_type": "markdown", - "id": "792f1304", - "metadata": {}, - "source": [ - "As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n", - "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n", - "\n", - "Above we introduced the `outer()` method of the `np.power()`\n", - "function. The `outer()` method is applied to an operation\n", - "that has two arguments, such as `add()`, `min()`, or\n", - "`power()`.\n", - "It has two arrays as\n", - "arguments, and then forms a larger\n", - "array where the operation is applied to each pair of elements of the\n", - "two arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3610b5a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:01.424821Z", - "iopub.status.busy": "2023-07-26T05:17:01.424682Z", - "iopub.status.idle": "2023-07-26T05:17:01.427733Z", - "shell.execute_reply": "2023-07-26T05:17:01.427314Z" - } - }, - "outputs": [], - "source": [ - "A = np.array([3, 5, 9])\n", - "B = np.array([2, 4])\n", - "np.add.outer(A, B)" - ] - }, - { - "cell_type": "markdown", - "id": "983625b2", - "metadata": {}, - "source": [ - "In the CV example above, we used $K=n$, but of course we can also use $K\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this lab we implement many of the techniques discussed in this chapter.\n", - "We import some of our libraries at this top\n", - "level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "564883b2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:07.048830Z", - "iopub.status.busy": "2023-07-26T05:17:07.048718Z", - "iopub.status.idle": "2023-07-26T05:17:08.239907Z", - "shell.execute_reply": "2023-07-26T05:17:08.239413Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "from statsmodels.api import OLS\n", - "import sklearn.model_selection as skm\n", - "import sklearn.linear_model as skl\n", - "from sklearn.preprocessing import StandardScaler\n", - "from ISLP import load_data\n", - "from ISLP.models import ModelSpec as MS\n", - "from functools import partial" - ] - }, - { - "cell_type": "markdown", - "id": "0594e13e", - "metadata": {}, - "source": [ - "We again collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9aa8d3ee", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:08.242431Z", - "iopub.status.busy": "2023-07-26T05:17:08.242231Z", - "iopub.status.idle": "2023-07-26T05:17:09.939443Z", - "shell.execute_reply": "2023-07-26T05:17:09.938977Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.pipeline import Pipeline\n", - "from sklearn.decomposition import PCA\n", - "from sklearn.cross_decomposition import PLSRegression\n", - "from ISLP.models import \\\n", - " (Stepwise,\n", - " sklearn_selected,\n", - " sklearn_selection_path)\n", - "!pip install l0bnb\n", - "from l0bnb import fit_path" - ] - }, - { - "cell_type": "markdown", - "id": "06a58c0b", - "metadata": {}, - "source": [ - "We have installed the package `l0bnb` on the fly. Note the escaped `!pip install` --- this is run as a separate system command. \n", - "## Subset Selection Methods\n", - "Here we implement methods that reduce the number of parameters in a\n", - "model by restricting the model to a subset of the input variables.\n", - "\n", - "### Forward Selection\n", - " \n", - "We will apply the forward-selection approach to the `Hitters` \n", - "data. We wish to predict a baseball player’s `Salary` on the\n", - "basis of various statistics associated with performance in the\n", - "previous year.\n", - "\n", - "First of all, we note that the `Salary` variable is missing for\n", - "some of the players. The `np.isnan()` function can be used to\n", - "identify the missing observations. It returns an array\n", - "of the same shape as the input vector, with a `True` for any elements that\n", - "are missing, and a `False` for non-missing elements. The\n", - "`sum()` method can then be used to count all of the\n", - "missing elements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05371e33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.942405Z", - "iopub.status.busy": "2023-07-26T05:17:09.942257Z", - "iopub.status.idle": "2023-07-26T05:17:09.952925Z", - "shell.execute_reply": "2023-07-26T05:17:09.952485Z" - } - }, - "outputs": [], - "source": [ - "Hitters = load_data('Hitters')\n", - "np.isnan(Hitters['Salary']).sum()" - ] - }, - { - "cell_type": "markdown", - "id": "8071b084", - "metadata": {}, - "source": [ - " We see that `Salary` is missing for 59 players. The\n", - "`dropna()` method of data frames removes all of the rows that have missing\n", - "values in any variable (by default --- see `Hitters.dropna?`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa5bc5b8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.955373Z", - "iopub.status.busy": "2023-07-26T05:17:09.955215Z", - "iopub.status.idle": "2023-07-26T05:17:09.959055Z", - "shell.execute_reply": "2023-07-26T05:17:09.958533Z" - } - }, - "outputs": [], - "source": [ - "Hitters = Hitters.dropna();\n", - "Hitters.shape" - ] - }, - { - "cell_type": "markdown", - "id": "d5017407", - "metadata": {}, - "source": [ - "We first choose the best model using forward selection based on $C_p$ (6.2). This score\n", - "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n", - "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n", - " our scoring function computes the negative $C_p$ statistic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10494837", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.961940Z", - "iopub.status.busy": "2023-07-26T05:17:09.961736Z", - "iopub.status.idle": "2023-07-26T05:17:09.964700Z", - "shell.execute_reply": "2023-07-26T05:17:09.964271Z" - } - }, - "outputs": [], - "source": [ - "def nCp(sigma2, estimator, X, Y):\n", - " \"Negative Cp statistic\"\n", - " n, p = X.shape\n", - " Yhat = estimator.predict(X)\n", - " RSS = np.sum((Y - Yhat)**2)\n", - " return -(RSS + 2 * p * sigma2) / n " - ] - }, - { - "cell_type": "markdown", - "id": "126e9322", - "metadata": {}, - "source": [ - "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n", - "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8173cef", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.967518Z", - "iopub.status.busy": "2023-07-26T05:17:09.967341Z", - "iopub.status.idle": "2023-07-26T05:17:09.983184Z", - "shell.execute_reply": "2023-07-26T05:17:09.982693Z" - } - }, - "outputs": [], - "source": [ - "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n", - "Y = np.array(Hitters['Salary'])\n", - "X = design.transform(Hitters)\n", - "sigma2 = OLS(Y,X).fit().scale" - ] - }, - { - "cell_type": "markdown", - "id": "f873c04f", - "metadata": {}, - "source": [ - "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section 5.3.3 to freeze the first argument with our estimate of $\\sigma^2$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cc8d7bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.985554Z", - "iopub.status.busy": "2023-07-26T05:17:09.985389Z", - "iopub.status.idle": "2023-07-26T05:17:09.987653Z", - "shell.execute_reply": "2023-07-26T05:17:09.987280Z" - } - }, - "outputs": [], - "source": [ - "neg_Cp = partial(nCp, sigma2)" - ] - }, - { - "cell_type": "markdown", - "id": "3ff5a859", - "metadata": {}, - "source": [ - "We can now use `neg_Cp()` as a scorer for model selection." - ] - }, - { - "cell_type": "markdown", - "id": "2b30b7bd", - "metadata": {}, - "source": [ - "Along with a score we need to specify the search strategy. This is done through the object\n", - "`Stepwise()` in the `ISLP.models` package. The method `Stepwise.first_peak()`\n", - "runs forward stepwise until any further additions to the model do not result\n", - "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n", - "runs a fixed number of steps of stepwise search." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "440b4c50", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.989896Z", - "iopub.status.busy": "2023-07-26T05:17:09.989765Z", - "iopub.status.idle": "2023-07-26T05:17:09.992061Z", - "shell.execute_reply": "2023-07-26T05:17:09.991502Z" - } - }, - "outputs": [], - "source": [ - "strategy = Stepwise.first_peak(design,\n", - " direction='forward',\n", - " max_terms=len(design.terms))" - ] - }, - { - "cell_type": "markdown", - "id": "77079484", - "metadata": {}, - "source": [ - "We now fit a linear regression model with `Salary` as outcome using forward\n", - "selection. To do so, we use the function `sklearn_selected()` from the `ISLP.models` package. This takes\n", - "a model from `statsmodels` along with a search strategy and selects a model with its\n", - "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n", - "selected." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b33a8617", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:09.994597Z", - "iopub.status.busy": "2023-07-26T05:17:09.994397Z", - "iopub.status.idle": "2023-07-26T05:17:10.882860Z", - "shell.execute_reply": "2023-07-26T05:17:10.882342Z" - } - }, - "outputs": [], - "source": [ - "hitters_MSE = sklearn_selected(OLS,\n", - " strategy)\n", - "hitters_MSE.fit(Hitters, Y)\n", - "hitters_MSE.selected_state_" - ] - }, - { - "cell_type": "markdown", - "id": "233e45a1", - "metadata": {}, - "source": [ - "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7f554cf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:10.885149Z", - "iopub.status.busy": "2023-07-26T05:17:10.884996Z", - "iopub.status.idle": "2023-07-26T05:17:11.423862Z", - "shell.execute_reply": "2023-07-26T05:17:11.423408Z" - } - }, - "outputs": [], - "source": [ - "hitters_Cp = sklearn_selected(OLS,\n", - " strategy,\n", - " scoring=neg_Cp)\n", - "hitters_Cp.fit(Hitters, Y)\n", - "hitters_Cp.selected_state_" - ] - }, - { - "cell_type": "markdown", - "id": "75e8008b", - "metadata": {}, - "source": [ - "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n", - " \n", - "As an alternative to using $C_p$, we might try cross-validation to select a model in forward selection. For this, we need a\n", - "method that stores the full path of models found in forward selection, and allows predictions for each of these. This can be done with the `sklearn_selection_path()` \n", - "estimator from `ISLP.models`. The function `cross_val_predict()` from `ISLP.models`\n", - "computes the cross-validated predictions for each of the models\n", - "along the path, which we can use to evaluate the cross-validated MSE\n", - "along the path." - ] - }, - { - "cell_type": "markdown", - "id": "fc753395", - "metadata": {}, - "source": [ - "Here we define a strategy that fits the full forward selection path.\n", - "While there are various parameter choices for `sklearn_selection_path()`,\n", - "we use the defaults here, which selects the model at each step based on the biggest reduction in RSS." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb7091b8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:11.426460Z", - "iopub.status.busy": "2023-07-26T05:17:11.426313Z", - "iopub.status.idle": "2023-07-26T05:17:11.428837Z", - "shell.execute_reply": "2023-07-26T05:17:11.428385Z" - } - }, - "outputs": [], - "source": [ - "strategy = Stepwise.fixed_steps(design,\n", - " len(design.terms),\n", - " direction='forward')\n", - "full_path = sklearn_selection_path(OLS, strategy)" - ] - }, - { - "cell_type": "markdown", - "id": "7f5f599f", - "metadata": {}, - "source": [ - "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56adc728", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:11.431149Z", - "iopub.status.busy": "2023-07-26T05:17:11.431007Z", - "iopub.status.idle": "2023-07-26T05:17:11.925588Z", - "shell.execute_reply": "2023-07-26T05:17:11.925166Z" - } - }, - "outputs": [], - "source": [ - "full_path.fit(Hitters, Y)\n", - "Yhat_in = full_path.predict(Hitters)\n", - "Yhat_in.shape" - ] - }, - { - "cell_type": "markdown", - "id": "0b771d23", - "metadata": {}, - "source": [ - "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n", - "in-sample MSE. As expected, the in-sample MSE improves each step we take,\n", - "indicating we must use either the validation or cross-validation\n", - "approach to select the number of steps. We fix the y-axis to range from\n", - "50,000 to 250,000 to compare to the cross-validation and validation\n", - "set MSE below, as well as other methods such as ridge regression, lasso and\n", - "principal components regression." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "387267d9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:11.928605Z", - "iopub.status.busy": "2023-07-26T05:17:11.928329Z", - "iopub.status.idle": "2023-07-26T05:17:12.168674Z", - "shell.execute_reply": "2023-07-26T05:17:12.165742Z" - } - }, - "outputs": [], - "source": [ - "mse_fig, ax = subplots(figsize=(8,8))\n", - "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n", - "n_steps = insample_mse.shape[0]\n", - "ax.plot(np.arange(n_steps),\n", - " insample_mse,\n", - " 'k', # color black\n", - " label='In-sample')\n", - "ax.set_ylabel('MSE',\n", - " fontsize=20)\n", - "ax.set_xlabel('# steps of forward stepwise',\n", - " fontsize=20)\n", - "ax.set_xticks(np.arange(n_steps)[::2])\n", - "ax.legend()\n", - "ax.set_ylim([50000,250000]);" - ] - }, - { - "cell_type": "markdown", - "id": "fc09acdc", - "metadata": {}, - "source": [ - "Notice the expression `None` in `Y[:,None]` above.\n", - "This adds an axis (dimension) to the one-dimensional array `Y`,\n", - "which allows it to be recycled when subtracted from the two-dimensional `Yhat_in`." - ] - }, - { - "cell_type": "markdown", - "id": "2a1511cd", - "metadata": {}, - "source": [ - "We are now ready to use cross-validation to estimate test error along\n", - "the model path. We must use *only the training observations* to perform all aspects of model-fitting --- including\n", - "variable selection. Therefore, the determination of which model of a\n", - "given size is best must be made using \\emph{only the training\n", - " observations} in each training fold. This point is subtle but important. If the full data\n", - "set is used to select the best subset at each step, then the validation\n", - "set errors and cross-validation errors that we obtain will not be\n", - "accurate estimates of the test error.\n", - "\n", - "We now compute the cross-validated predicted values using 5-fold cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0047ba88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:12.180601Z", - "iopub.status.busy": "2023-07-26T05:17:12.179903Z", - "iopub.status.idle": "2023-07-26T05:17:14.657028Z", - "shell.execute_reply": "2023-07-26T05:17:14.656647Z" - } - }, - "outputs": [], - "source": [ - "K = 5\n", - "kfold = skm.KFold(K,\n", - " random_state=0,\n", - " shuffle=True)\n", - "Yhat_cv = skm.cross_val_predict(full_path,\n", - " Hitters,\n", - " Y,\n", - " cv=kfold)\n", - "Yhat_cv.shape" - ] - }, - { - "cell_type": "markdown", - "id": "c6df5d31", - "metadata": {}, - "source": [ - "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n", - "\n", - "At each model along the path, we compute the MSE in each of the cross-validation folds.\n", - "These we will average to get the mean MSE, and can also use the individual values to compute a crude estimate of the standard error of the mean. {The estimate is crude because the five error estimates are based on overlapping training sets, and hence are not independent.}\n", - "Hence we must know the test indices for each cross-validation\n", - "split. This can be found by using the `split()` method of `kfold`. Because\n", - "we fixed the random state above, whenever we split any array with the same\n", - "number of rows as $Y$ we recover the same training and test indices, though we simply\n", - "ignore the training indices below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27b0eb63", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:14.659368Z", - "iopub.status.busy": "2023-07-26T05:17:14.659240Z", - "iopub.status.idle": "2023-07-26T05:17:14.663478Z", - "shell.execute_reply": "2023-07-26T05:17:14.663025Z" - } - }, - "outputs": [], - "source": [ - "cv_mse = []\n", - "for train_idx, test_idx in kfold.split(Y):\n", - " errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n", - " cv_mse.append(errors.mean(0)) # column means\n", - "cv_mse = np.array(cv_mse).T\n", - "cv_mse.shape" - ] - }, - { - "cell_type": "markdown", - "id": "223556f8", - "metadata": {}, - "source": [ - "We now add the cross-validation error estimates to our MSE plot.\n", - "We include the mean error across the five folds, and the estimate of the standard error of the mean." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a45fe587", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:14.665854Z", - "iopub.status.busy": "2023-07-26T05:17:14.665694Z", - "iopub.status.idle": "2023-07-26T05:17:14.764103Z", - "shell.execute_reply": "2023-07-26T05:17:14.763688Z" - } - }, - "outputs": [], - "source": [ - "ax.errorbar(np.arange(n_steps), \n", - " cv_mse.mean(1),\n", - " cv_mse.std(1) / np.sqrt(K),\n", - " label='Cross-validated',\n", - " c='r') # color red\n", - "ax.set_ylim([50000,250000])\n", - "ax.legend()\n", - "mse_fig" - ] - }, - { - "cell_type": "markdown", - "id": "fb4fa850", - "metadata": {}, - "source": [ - "To repeat the above using the validation set approach, we simply change our\n", - "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n", - "of 20%, similar to the size of each test set in 5-fold cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c71c2079", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:14.766668Z", - "iopub.status.busy": "2023-07-26T05:17:14.766176Z", - "iopub.status.idle": "2023-07-26T05:17:15.254311Z", - "shell.execute_reply": "2023-07-26T05:17:15.253748Z" - } - }, - "outputs": [], - "source": [ - "validation = skm.ShuffleSplit(n_splits=1, \n", - " test_size=0.2,\n", - " random_state=0)\n", - "for train_idx, test_idx in validation.split(Y):\n", - " full_path.fit(Hitters.iloc[train_idx],\n", - " Y[train_idx])\n", - " Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n", - " errors = (Yhat_val - Y[test_idx,None])**2\n", - " validation_mse = errors.mean(0)" - ] - }, - { - "cell_type": "markdown", - "id": "d34bb7ab", - "metadata": {}, - "source": [ - " As for the in-sample MSE case, the validation set approach does not provide standard errors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ff34edf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:15.257176Z", - "iopub.status.busy": "2023-07-26T05:17:15.256855Z", - "iopub.status.idle": "2023-07-26T05:17:15.361521Z", - "shell.execute_reply": "2023-07-26T05:17:15.360872Z" - } - }, - "outputs": [], - "source": [ - "ax.plot(np.arange(n_steps), \n", - " validation_mse,\n", - " 'b--', # color blue, broken line\n", - " label='Validation')\n", - "ax.set_xticks(np.arange(n_steps)[::2])\n", - "ax.set_ylim([50000,250000])\n", - "ax.legend()\n", - "mse_fig" - ] - }, - { - "cell_type": "markdown", - "id": "e5517334", - "metadata": {}, - "source": [ - "### Best Subset Selection\n", - "Forward stepwise is a *greedy* selection procedure; at each step it augments the current set by including one additional variable. We now apply best subset selection to the `Hitters` \n", - "data, which for every subset size, searches for the best set of predictors. \n", - "\n", - "We will use a package called `l0bnb` to perform\n", - "best subset selection.\n", - "Instead of constraining the subset to be a given size,\n", - "this package produces a path of solutions using the subset size as a\n", - "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "845f3fa0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:15.364952Z", - "iopub.status.busy": "2023-07-26T05:17:15.364689Z", - "iopub.status.idle": "2023-07-26T05:17:15.379444Z", - "shell.execute_reply": "2023-07-26T05:17:15.379028Z" - } - }, - "outputs": [], - "source": [ - "D = design.fit_transform(Hitters)\n", - "D = D.drop('intercept', axis=1)\n", - "X = np.asarray(D)" - ] - }, - { - "cell_type": "markdown", - "id": "4c0d0e19", - "metadata": {}, - "source": [ - "Here we excluded the first column corresponding to the intercept, as\n", - "`l0bnb` will fit the intercept separately. We can find a path using the `fit_path()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9f943e7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:15.381427Z", - "iopub.status.busy": "2023-07-26T05:17:15.381284Z", - "iopub.status.idle": "2023-07-26T05:17:18.000267Z", - "shell.execute_reply": "2023-07-26T05:17:17.999357Z" - } - }, - "outputs": [], - "source": [ - "path = fit_path(X, \n", - " Y,\n", - " max_nonzeros=X.shape[1])" - ] - }, - { - "cell_type": "markdown", - "id": "67ef90b5", - "metadata": {}, - "source": [ - "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "657ac171", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.003862Z", - "iopub.status.busy": "2023-07-26T05:17:18.003705Z", - "iopub.status.idle": "2023-07-26T05:17:18.006563Z", - "shell.execute_reply": "2023-07-26T05:17:18.006222Z" - } - }, - "outputs": [], - "source": [ - "path[3]" - ] - }, - { - "cell_type": "markdown", - "id": "2c9e040a", - "metadata": {}, - "source": [ - "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n", - "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n", - "\n", - "## Ridge Regression and the Lasso\n", - "We will use the `sklearn.linear_model` package (for which\n", - "we use `skl` as shorthand below)\n", - "to fit ridge and lasso regularized linear models on the `Hitters` data.\n", - "We start with the model matrix `X` (without an intercept) that we computed in the previous section on best subset regression.\n", - "\n", - "### Ridge Regression\n", - "We will use the function `skl.ElasticNet()` to fit both ridge and the lasso.\n", - "To fit a *path* of ridge regressions models, we use\n", - "`skl.ElasticNet.path()`, which can fit both ridge and lasso, as well as a hybrid mixture; ridge regression\n", - "corresponds to `l1_ratio=0`.\n", - "It is good practice to standardize the columns of `X` in these applications, if the variables are measured in different units. Since `skl.ElasticNet()` does no normalization, we have to take care of that ourselves.\n", - "Since we\n", - "standardize first, in order to find coefficient\n", - "estimates on the original scale, we must *unstandardize*\n", - "the coefficient estimates. The parameter\n", - "$\\lambda$ in (6.5) and (6.7) is called `alphas` in `sklearn`. In order to\n", - "be consistent with the rest of this chapter, we use `lambdas`\n", - "rather than `alphas` in what follows. {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e576ad54", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.008980Z", - "iopub.status.busy": "2023-07-26T05:17:18.008783Z", - "iopub.status.idle": "2023-07-26T05:17:18.078116Z", - "shell.execute_reply": "2023-07-26T05:17:18.077656Z" - } - }, - "outputs": [], - "source": [ - "Xs = X - X.mean(0)[None,:]\n", - "X_scale = X.std(0)\n", - "Xs = Xs / X_scale[None,:]\n", - "lambdas = 10**np.linspace(8, -2, 100) / Y.std()\n", - "soln_array = skl.ElasticNet.path(Xs,\n", - " Y,\n", - " l1_ratio=0.,\n", - " alphas=lambdas)[1]\n", - "soln_array.shape" - ] - }, - { - "cell_type": "markdown", - "id": "5ba706cd", - "metadata": {}, - "source": [ - "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n", - "By default the `skl.ElasticNet.path` method fits a path along\n", - "an automatically selected range of $\\lambda$ values, except for the case when\n", - "`l1_ratio=0`, which results in ridge regression (as is the case here). {The reason is rather technical; for all models except ridge, we can find the smallest value of $\\lambda$ for which all coefficients are zero. For ridge this value is $\\infty$.} So here\n", - "we have chosen to implement the function over a grid of values ranging\n", - "from $\\lambda=10^{8}$ to $\\lambda=10^{-2}$ scaled by the standard\n", - "deviation of $y$, essentially covering the full range of scenarios\n", - "from the null model containing only the intercept, to the least\n", - "squares fit.\n", - "\n", - "Associated with each value of $\\lambda$ is a vector of ridge\n", - "regression coefficients, that can be accessed by\n", - "a column of `soln_array`. In this case, `soln_array` is a $19 \\times 100$ matrix, with\n", - "19 rows (one for each predictor) and 100\n", - "columns (one for each value of $\\lambda$).\n", - "\n", - "We transpose this matrix and turn it into a data frame to facilitate viewing and plotting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ef15192", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.080752Z", - "iopub.status.busy": "2023-07-26T05:17:18.080552Z", - "iopub.status.idle": "2023-07-26T05:17:18.094265Z", - "shell.execute_reply": "2023-07-26T05:17:18.093783Z" - } - }, - "outputs": [], - "source": [ - "soln_path = pd.DataFrame(soln_array.T,\n", - " columns=D.columns,\n", - " index=-np.log(lambdas))\n", - "soln_path.index.name = 'negative log(lambda)'\n", - "soln_path" - ] - }, - { - "cell_type": "markdown", - "id": "f73f5191", - "metadata": {}, - "source": [ - "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n", - "To control the location of the legend we first set `legend` to `False` in the\n", - "plot method, adding it afterward with the `legend()` method of `ax`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40b67c85", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.097009Z", - "iopub.status.busy": "2023-07-26T05:17:18.096807Z", - "iopub.status.idle": "2023-07-26T05:17:18.336481Z", - "shell.execute_reply": "2023-07-26T05:17:18.335993Z" - } - }, - "outputs": [], - "source": [ - "path_fig, ax = subplots(figsize=(8,8))\n", - "soln_path.plot(ax=ax, legend=False)\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Standardized coefficients', fontsize=20)\n", - "ax.legend(loc='upper left');" - ] - }, - { - "cell_type": "markdown", - "id": "84e52006", - "metadata": {}, - "source": [ - "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.) \n", - "We expect the coefficient estimates to be much smaller, in terms of\n", - "$\\ell_2$ norm, when a large value of $\\lambda$ is used, as compared to\n", - "when a small value of $\\lambda$ is used. (Recall that the $\\ell_2$ norm is the square root of the sum of squared coefficient values.) We display the coefficients at the $40$th step,\n", - "where $\\lambda$ is 25.535." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d14bd86c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.338709Z", - "iopub.status.busy": "2023-07-26T05:17:18.338591Z", - "iopub.status.idle": "2023-07-26T05:17:18.342588Z", - "shell.execute_reply": "2023-07-26T05:17:18.342104Z" - } - }, - "outputs": [], - "source": [ - "beta_hat = soln_path.loc[soln_path.index[39]]\n", - "lambdas[39], beta_hat" - ] - }, - { - "cell_type": "markdown", - "id": "98ba8e99", - "metadata": {}, - "source": [ - "Let’s compute the $\\ell_2$ norm of the standardized coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "759e215a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.344904Z", - "iopub.status.busy": "2023-07-26T05:17:18.344761Z", - "iopub.status.idle": "2023-07-26T05:17:18.347862Z", - "shell.execute_reply": "2023-07-26T05:17:18.347417Z" - } - }, - "outputs": [], - "source": [ - "np.linalg.norm(beta_hat)" - ] - }, - { - "cell_type": "markdown", - "id": "ce79e7cb", - "metadata": {}, - "source": [ - "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n", - "Note the much larger $\\ell_2$ norm of the\n", - "coefficients associated with this smaller value of $\\lambda$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b58d5ccb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.350171Z", - "iopub.status.busy": "2023-07-26T05:17:18.350022Z", - "iopub.status.idle": "2023-07-26T05:17:18.353142Z", - "shell.execute_reply": "2023-07-26T05:17:18.352734Z" - } - }, - "outputs": [], - "source": [ - "beta_hat = soln_path.loc[soln_path.index[59]]\n", - "lambdas[59], np.linalg.norm(beta_hat)" - ] - }, - { - "cell_type": "markdown", - "id": "cc9a1f70", - "metadata": {}, - "source": [ - "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n", - "The `Pipeline()` object\n", - "in `sklearn` provides a clear way to separate feature\n", - "normalization from the fitting of the ridge model itself." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74b5cae2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.355548Z", - "iopub.status.busy": "2023-07-26T05:17:18.355367Z", - "iopub.status.idle": "2023-07-26T05:17:18.370341Z", - "shell.execute_reply": "2023-07-26T05:17:18.369949Z" - } - }, - "outputs": [], - "source": [ - "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n", - "scaler = StandardScaler(with_mean=True, with_std=True)\n", - "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n", - "pipe.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "7a69991b", - "metadata": {}, - "source": [ - "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a89989c3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.372736Z", - "iopub.status.busy": "2023-07-26T05:17:18.372583Z", - "iopub.status.idle": "2023-07-26T05:17:18.375751Z", - "shell.execute_reply": "2023-07-26T05:17:18.375223Z" - } - }, - "outputs": [], - "source": [ - "np.linalg.norm(ridge.coef_)" - ] - }, - { - "cell_type": "markdown", - "id": "c86254e4", - "metadata": {}, - "source": [ - " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before. \n", - "### Estimating Test Error of Ridge Regression\n", - "Choosing an *a priori* value of $\\lambda$ for ridge regression is\n", - "difficult if not impossible. We will want to use the validation method\n", - "or cross-validation to select the tuning parameter. The reader may not\n", - "be surprised that the `Pipeline()` approach can be used in\n", - "`skm.cross_validate()` with either a validation method\n", - "(i.e. `validation`) or $k$-fold cross-validation.\n", - "\n", - "We fix the random state of the splitter\n", - "so that the results obtained will be reproducible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3eb41945", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.377750Z", - "iopub.status.busy": "2023-07-26T05:17:18.377627Z", - "iopub.status.idle": "2023-07-26T05:17:18.385518Z", - "shell.execute_reply": "2023-07-26T05:17:18.385025Z" - } - }, - "outputs": [], - "source": [ - "validation = skm.ShuffleSplit(n_splits=1,\n", - " test_size=0.5,\n", - " random_state=0)\n", - "ridge.alpha = 0.01\n", - "results = skm.cross_validate(ridge,\n", - " X,\n", - " Y,\n", - " scoring='neg_mean_squared_error',\n", - " cv=validation)\n", - "-results['test_score']" - ] - }, - { - "cell_type": "markdown", - "id": "660f7c63", - "metadata": {}, - "source": [ - "The test MSE is 1.342e+05. Note\n", - "that if we had instead simply fit a model with just an intercept, we\n", - "would have predicted each test observation using the mean of the\n", - "training observations. We can get the same result by fitting a ridge regression model\n", - "with a *very* large value of $\\lambda$. Note that `1e10`\n", - "means $10^{10}$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "635bc363", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.387843Z", - "iopub.status.busy": "2023-07-26T05:17:18.387682Z", - "iopub.status.idle": "2023-07-26T05:17:18.396692Z", - "shell.execute_reply": "2023-07-26T05:17:18.396288Z" - } - }, - "outputs": [], - "source": [ - "ridge.alpha = 1e10\n", - "results = skm.cross_validate(ridge,\n", - " X,\n", - " Y,\n", - " scoring='neg_mean_squared_error',\n", - " cv=validation)\n", - "-results['test_score']" - ] - }, - { - "cell_type": "markdown", - "id": "c14ad9a1", - "metadata": {}, - "source": [ - "Obviously choosing $\\lambda=0.01$ is arbitrary, so we will use cross-validation or the validation-set\n", - "approach to choose the tuning parameter $\\lambda$.\n", - "The object `GridSearchCV()` allows exhaustive\n", - "grid search to choose such a parameter.\n", - "\n", - "We first use the validation set method\n", - "to choose $\\lambda$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e117267f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.399267Z", - "iopub.status.busy": "2023-07-26T05:17:18.399145Z", - "iopub.status.idle": "2023-07-26T05:17:18.894315Z", - "shell.execute_reply": "2023-07-26T05:17:18.893964Z" - } - }, - "outputs": [], - "source": [ - "param_grid = {'ridge__alpha': lambdas}\n", - "grid = skm.GridSearchCV(pipe,\n", - " param_grid,\n", - " cv=validation,\n", - " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)\n", - "grid.best_params_['ridge__alpha']\n", - "grid.best_estimator_" - ] - }, - { - "cell_type": "markdown", - "id": "bc09d074", - "metadata": {}, - "source": [ - "Alternatively, we can use 5-fold cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0037e9e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:18.896714Z", - "iopub.status.busy": "2023-07-26T05:17:18.896538Z", - "iopub.status.idle": "2023-07-26T05:17:22.201859Z", - "shell.execute_reply": "2023-07-26T05:17:22.201489Z" - } - }, - "outputs": [], - "source": [ - "grid = skm.GridSearchCV(pipe, \n", - " param_grid,\n", - " cv=kfold,\n", - " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)\n", - "grid.best_params_['ridge__alpha']\n", - "grid.best_estimator_" - ] - }, - { - "cell_type": "markdown", - "id": "9690407d", - "metadata": {}, - "source": [ - "Recall we set up the `kfold` object for 5-fold cross-validation on page 296. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n", - "to right." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71f0f5c5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:22.203851Z", - "iopub.status.busy": "2023-07-26T05:17:22.203701Z", - "iopub.status.idle": "2023-07-26T05:17:22.319890Z", - "shell.execute_reply": "2023-07-26T05:17:22.318803Z" - } - }, - "outputs": [], - "source": [ - "ridge_fig, ax = subplots(figsize=(8,8))\n", - "ax.errorbar(-np.log(lambdas),\n", - " -grid.cv_results_['mean_test_score'],\n", - " yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n", - "ax.set_ylim([50000,250000])\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "11087e26", - "metadata": {}, - "source": [ - "One can cross-validate different metrics to choose a parameter. The default\n", - "metric for `skl.ElasticNet()` is test $R^2$.\n", - "Let’s compare $R^2$ to MSE for cross-validation here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb265379", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:22.322704Z", - "iopub.status.busy": "2023-07-26T05:17:22.322434Z", - "iopub.status.idle": "2023-07-26T05:17:25.602424Z", - "shell.execute_reply": "2023-07-26T05:17:25.601831Z" - } - }, - "outputs": [], - "source": [ - "grid_r2 = skm.GridSearchCV(pipe, \n", - " param_grid,\n", - " cv=kfold)\n", - "grid_r2.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "9881fe49", - "metadata": {}, - "source": [ - "Finally, let’s plot the results for cross-validated $R^2$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79616f48", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:25.604857Z", - "iopub.status.busy": "2023-07-26T05:17:25.604672Z", - "iopub.status.idle": "2023-07-26T05:17:25.718682Z", - "shell.execute_reply": "2023-07-26T05:17:25.718164Z" - } - }, - "outputs": [], - "source": [ - "r2_fig, ax = subplots(figsize=(8,8))\n", - "ax.errorbar(-np.log(lambdas),\n", - " grid_r2.cv_results_['mean_test_score'],\n", - " yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "5e700bf5", - "metadata": {}, - "source": [ - "### Fast Cross-Validation for Solution Paths\n", - "The ridge, lasso, and elastic net can be efficiently fit along a sequence of $\\lambda$ values, creating what is known as a *solution path* or *regularization path*. Hence there is specialized code to fit\n", - "such paths, and to choose a suitable value of $\\lambda$ using cross-validation. Even with\n", - "identical splits the results will not agree *exactly* with our `grid`\n", - "above because the standardization of each feature in `grid` is carried out on each fold,\n", - "while in `pipeCV` below it is carried out only once.\n", - "Nevertheless, the results are similar as the normalization\n", - "is relatively stable across folds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11e55883", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:25.721504Z", - "iopub.status.busy": "2023-07-26T05:17:25.721335Z", - "iopub.status.idle": "2023-07-26T05:17:26.086495Z", - "shell.execute_reply": "2023-07-26T05:17:26.085843Z" - } - }, - "outputs": [], - "source": [ - "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n", - " l1_ratio=0,\n", - " cv=kfold)\n", - "pipeCV = Pipeline(steps=[('scaler', scaler),\n", - " ('ridge', ridgeCV)])\n", - "pipeCV.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "75eb9d33", - "metadata": {}, - "source": [ - "Let’s produce a plot again of the cross-validation error to see that\n", - "it is similar to using `skm.GridSearchCV`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d107f961", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.090198Z", - "iopub.status.busy": "2023-07-26T05:17:26.090011Z", - "iopub.status.idle": "2023-07-26T05:17:26.211437Z", - "shell.execute_reply": "2023-07-26T05:17:26.210939Z" - } - }, - "outputs": [], - "source": [ - "tuned_ridge = pipeCV.named_steps['ridge']\n", - "ridgeCV_fig, ax = subplots(figsize=(8,8))\n", - "ax.errorbar(-np.log(lambdas),\n", - " tuned_ridge.mse_path_.mean(1),\n", - " yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))\n", - "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n", - "ax.set_ylim([50000,250000])\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "23a89187", - "metadata": {}, - "source": [ - "We see that the value of $\\lambda$ that results in the\n", - "smallest cross-validation error is 1.19e-02, available\n", - "as the value `tuned_ridge.alpha_`. What is the test MSE\n", - "associated with this value of $\\lambda$?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21d65d1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.214203Z", - "iopub.status.busy": "2023-07-26T05:17:26.213975Z", - "iopub.status.idle": "2023-07-26T05:17:26.217529Z", - "shell.execute_reply": "2023-07-26T05:17:26.216950Z" - } - }, - "outputs": [], - "source": [ - "np.min(tuned_ridge.mse_path_.mean(1))" - ] - }, - { - "cell_type": "markdown", - "id": "e8bb2108", - "metadata": {}, - "source": [ - "This represents a further improvement over the test MSE that we got\n", - "using $\\lambda=4$. Finally, `tuned_ridge.coef_`\n", - "has the coefficients fit on the entire data set\n", - "at this value of $\\lambda$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11373de9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.219927Z", - "iopub.status.busy": "2023-07-26T05:17:26.219801Z", - "iopub.status.idle": "2023-07-26T05:17:26.222854Z", - "shell.execute_reply": "2023-07-26T05:17:26.222347Z" - } - }, - "outputs": [], - "source": [ - "tuned_ridge.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "8e7ab31e", - "metadata": {}, - "source": [ - "As expected, none of the coefficients are zero—ridge regression does\n", - "not perform variable selection!" - ] - }, - { - "cell_type": "markdown", - "id": "6e2cb7c4", - "metadata": {}, - "source": [ - "### Evaluating Test Error of Cross-Validated Ridge\n", - "Choosing $\\lambda$ using cross-validation provides a single regression\n", - "estimator, similar to fitting a linear regression model as we saw in\n", - "Chapter 3. It is therefore reasonable to estimate what its test error\n", - "is. We run into a problem here in that cross-validation will have\n", - "*touched* all of its data in choosing $\\lambda$, hence we have no\n", - "further data to estimate test error. A compromise is to do an initial\n", - "split of the data into two disjoint sets: a training set and a test set.\n", - "We then fit a cross-validation\n", - "tuned ridge regression on the training set, and evaluate its performance on the test set.\n", - "We might call this cross-validation nested\n", - "within the validation set approach. A priori there is no reason to use\n", - "half of the data for each of the two sets in validation. Below, we use\n", - "75% for training and 25% for test, with the estimator being ridge\n", - "regression tuned using 5-fold cross-validation. This can be achieved\n", - "in code as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72181964", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.225411Z", - "iopub.status.busy": "2023-07-26T05:17:26.225192Z", - "iopub.status.idle": "2023-07-26T05:17:26.228812Z", - "shell.execute_reply": "2023-07-26T05:17:26.228380Z" - } - }, - "outputs": [], - "source": [ - "outer_valid = skm.ShuffleSplit(n_splits=1, \n", - " test_size=0.25,\n", - " random_state=1)\n", - "inner_cv = skm.KFold(n_splits=5,\n", - " shuffle=True,\n", - " random_state=2)\n", - "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n", - " l1_ratio=0,\n", - " cv=inner_cv)\n", - "pipeCV = Pipeline(steps=[('scaler', scaler),\n", - " ('ridge', ridgeCV)]);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c3054b5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.231904Z", - "iopub.status.busy": "2023-07-26T05:17:26.231669Z", - "iopub.status.idle": "2023-07-26T05:17:26.556426Z", - "shell.execute_reply": "2023-07-26T05:17:26.556096Z" - } - }, - "outputs": [], - "source": [ - "results = skm.cross_validate(pipeCV, \n", - " X,\n", - " Y,\n", - " cv=outer_valid,\n", - " scoring='neg_mean_squared_error')\n", - "-results['test_score']" - ] - }, - { - "cell_type": "markdown", - "id": "f2f899f2", - "metadata": {}, - "source": [ - "### The Lasso\n", - "We saw that ridge regression with a wise choice of $\\lambda$ can\n", - "outperform least squares as well as the null model on the\n", - " `Hitters` data set. We now ask whether the lasso can yield\n", - "either a more accurate or a more interpretable model than ridge\n", - "regression. In order to fit a lasso model, we once again use the\n", - "`ElasticNetCV()` function; however, this time we use the argument\n", - "`l1_ratio=1`. Other than that change, we proceed just as we did in\n", - "fitting a ridge model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72bb0c1d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.560895Z", - "iopub.status.busy": "2023-07-26T05:17:26.560717Z", - "iopub.status.idle": "2023-07-26T05:17:26.616139Z", - "shell.execute_reply": "2023-07-26T05:17:26.615605Z" - } - }, - "outputs": [], - "source": [ - "lassoCV = skl.ElasticNetCV(n_alphas=100, \n", - " l1_ratio=1,\n", - " cv=kfold)\n", - "pipeCV = Pipeline(steps=[('scaler', scaler),\n", - " ('lasso', lassoCV)])\n", - "pipeCV.fit(X, Y)\n", - "tuned_lasso = pipeCV.named_steps['lasso']\n", - "tuned_lasso.alpha_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69adc7ac", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.618605Z", - "iopub.status.busy": "2023-07-26T05:17:26.618409Z", - "iopub.status.idle": "2023-07-26T05:17:26.629654Z", - "shell.execute_reply": "2023-07-26T05:17:26.629177Z" - } - }, - "outputs": [], - "source": [ - "lambdas, soln_array = skl.Lasso.path(Xs, \n", - " Y,\n", - " l1_ratio=1,\n", - " n_alphas=100)[:2]\n", - "soln_path = pd.DataFrame(soln_array.T,\n", - " columns=D.columns,\n", - " index=-np.log(lambdas))" - ] - }, - { - "cell_type": "markdown", - "id": "cc6d84d3", - "metadata": {}, - "source": [ - "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n", - "tuning parameter, some of the coefficients will be exactly equal to\n", - "zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2aa75789", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.632157Z", - "iopub.status.busy": "2023-07-26T05:17:26.631965Z", - "iopub.status.idle": "2023-07-26T05:17:26.833444Z", - "shell.execute_reply": "2023-07-26T05:17:26.832719Z" - } - }, - "outputs": [], - "source": [ - "path_fig, ax = subplots(figsize=(8,8))\n", - "soln_path.plot(ax=ax, legend=False)\n", - "ax.legend(loc='upper left')\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Standardized coefficiients', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "87d43d52", - "metadata": {}, - "source": [ - "The smallest cross-validated error is lower than the test set MSE of the null model\n", - "and of least squares, and very similar to the test MSE of 115526.71 of ridge\n", - "regression (page 303) with $\\lambda$ chosen by cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e38c9bed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.836589Z", - "iopub.status.busy": "2023-07-26T05:17:26.836233Z", - "iopub.status.idle": "2023-07-26T05:17:26.841023Z", - "shell.execute_reply": "2023-07-26T05:17:26.840686Z" - } - }, - "outputs": [], - "source": [ - "np.min(tuned_lasso.mse_path_.mean(1))" - ] - }, - { - "cell_type": "markdown", - "id": "d091d74c", - "metadata": {}, - "source": [ - "Let’s again produce a plot of the cross-validation error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53c42724", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.843812Z", - "iopub.status.busy": "2023-07-26T05:17:26.843647Z", - "iopub.status.idle": "2023-07-26T05:17:26.968792Z", - "shell.execute_reply": "2023-07-26T05:17:26.968349Z" - } - }, - "outputs": [], - "source": [ - "lassoCV_fig, ax = subplots(figsize=(8,8))\n", - "ax.errorbar(-np.log(tuned_lasso.alphas_),\n", - " tuned_lasso.mse_path_.mean(1),\n", - " yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))\n", - "ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')\n", - "ax.set_ylim([50000,250000])\n", - "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "fad5c750", - "metadata": {}, - "source": [ - "However, the lasso has a substantial advantage over ridge regression\n", - "in that the resulting coefficient estimates are sparse. Here we see\n", - "that 6 of the 19 coefficient estimates are exactly zero. So the lasso\n", - "model with $\\lambda$ chosen by cross-validation contains only 13\n", - "variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f4942ba", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.971271Z", - "iopub.status.busy": "2023-07-26T05:17:26.971065Z", - "iopub.status.idle": "2023-07-26T05:17:26.974897Z", - "shell.execute_reply": "2023-07-26T05:17:26.973908Z" - } - }, - "outputs": [], - "source": [ - "tuned_lasso.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "af600bbc", - "metadata": {}, - "source": [ - "As in ridge regression, we could evaluate the test error\n", - "of cross-validated lasso by first splitting into\n", - "test and training sets and internally running\n", - "cross-validation on the training set. We leave\n", - "this as an exercise." - ] - }, - { - "cell_type": "markdown", - "id": "8d9aeb7b", - "metadata": {}, - "source": [ - "## PCR and PLS Regression\n", - "\n", - "### Principal Components Regression" - ] - }, - { - "cell_type": "markdown", - "id": "1ef4790c", - "metadata": {}, - "source": [ - "Principal components regression (PCR) can be performed using\n", - "`PCA()` from the `sklearn.decomposition`\n", - "module. We now apply PCR to the `Hitters` data, in order to\n", - "predict `Salary`. Again, ensure that the missing values have\n", - "been removed from the data, as described in Section 6.5.1.\n", - "\n", - "We use `LinearRegression()` to fit the regression model\n", - "here. Note that it fits an intercept by default, unlike\n", - "the `OLS()` function seen earlier in Section 6.5.1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72a876bb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.977635Z", - "iopub.status.busy": "2023-07-26T05:17:26.977456Z", - "iopub.status.idle": "2023-07-26T05:17:26.983252Z", - "shell.execute_reply": "2023-07-26T05:17:26.982747Z" - } - }, - "outputs": [], - "source": [ - "pca = PCA(n_components=2)\n", - "linreg = skl.LinearRegression()\n", - "pipe = Pipeline([('pca', pca),\n", - " ('linreg', linreg)])\n", - "pipe.fit(X, Y)\n", - "pipe.named_steps['linreg'].coef_" - ] - }, - { - "cell_type": "markdown", - "id": "77d5a225", - "metadata": {}, - "source": [ - "When performing PCA, the results vary depending\n", - "on whether the data has been *standardized* or not.\n", - "As in the earlier examples, this can be accomplished\n", - "by including an additional step in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0e821c6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.985960Z", - "iopub.status.busy": "2023-07-26T05:17:26.985773Z", - "iopub.status.idle": "2023-07-26T05:17:26.991831Z", - "shell.execute_reply": "2023-07-26T05:17:26.991247Z" - } - }, - "outputs": [], - "source": [ - "pipe = Pipeline([('scaler', scaler), \n", - " ('pca', pca),\n", - " ('linreg', linreg)])\n", - "pipe.fit(X, Y)\n", - "pipe.named_steps['linreg'].coef_" - ] - }, - { - "cell_type": "markdown", - "id": "0c5ebec9", - "metadata": {}, - "source": [ - "We can of course use CV to choose the number of components, by\n", - "using `skm.GridSearchCV`, in this\n", - "case fixing the parameters to vary the\n", - "`n_components`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ac6886c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:26.994661Z", - "iopub.status.busy": "2023-07-26T05:17:26.994482Z", - "iopub.status.idle": "2023-07-26T05:17:27.138949Z", - "shell.execute_reply": "2023-07-26T05:17:27.138448Z" - } - }, - "outputs": [], - "source": [ - "param_grid = {'pca__n_components': range(1, 20)}\n", - "grid = skm.GridSearchCV(pipe,\n", - " param_grid,\n", - " cv=kfold,\n", - " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "dc21d28d", - "metadata": {}, - "source": [ - "Let’s plot the results as we have for other methods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e0c5a96", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.141752Z", - "iopub.status.busy": "2023-07-26T05:17:27.141555Z", - "iopub.status.idle": "2023-07-26T05:17:27.257460Z", - "shell.execute_reply": "2023-07-26T05:17:27.256920Z" - } - }, - "outputs": [], - "source": [ - "pcr_fig, ax = subplots(figsize=(8,8))\n", - "n_comp = param_grid['pca__n_components']\n", - "ax.errorbar(n_comp,\n", - " -grid.cv_results_['mean_test_score'],\n", - " grid.cv_results_['std_test_score'] / np.sqrt(K))\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n", - "ax.set_xlabel('# principal components', fontsize=20)\n", - "ax.set_xticks(n_comp[::2])\n", - "ax.set_ylim([50000,250000]);" - ] - }, - { - "cell_type": "markdown", - "id": "0c0aadc3", - "metadata": {}, - "source": [ - "We see that the smallest cross-validation error occurs when\n", - "17\n", - "components are used. However, from the plot we also see that the\n", - "cross-validation error is roughly the same when only one component is\n", - "included in the model. This suggests that a model that uses just a\n", - "small number of components might suffice.\n", - "\n", - "The CV score is provided for each possible number of components from\n", - "1 to 19 inclusive. The `PCA()` method complains\n", - "if we try to fit an intercept only with `n_components=0`\n", - "so we also compute the MSE for just the null model with\n", - "these splits." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9df0fb7f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.260143Z", - "iopub.status.busy": "2023-07-26T05:17:27.259963Z", - "iopub.status.idle": "2023-07-26T05:17:27.267101Z", - "shell.execute_reply": "2023-07-26T05:17:27.266627Z" - } - }, - "outputs": [], - "source": [ - "Xn = np.zeros((X.shape[0], 1))\n", - "cv_null = skm.cross_validate(linreg,\n", - " Xn,\n", - " Y,\n", - " cv=kfold,\n", - " scoring='neg_mean_squared_error')\n", - "-cv_null['test_score'].mean()" - ] - }, - { - "cell_type": "markdown", - "id": "89f0e0bb", - "metadata": {}, - "source": [ - "The `explained_variance_ratio_`\n", - "attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using\n", - "different numbers of components. This concept is discussed in greater\n", - "detail in Section 12.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "458c21a4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.269230Z", - "iopub.status.busy": "2023-07-26T05:17:27.269087Z", - "iopub.status.idle": "2023-07-26T05:17:27.272035Z", - "shell.execute_reply": "2023-07-26T05:17:27.271586Z" - } - }, - "outputs": [], - "source": [ - "pipe.named_steps['pca'].explained_variance_ratio_" - ] - }, - { - "cell_type": "markdown", - "id": "bdd994d2", - "metadata": {}, - "source": [ - "Briefly, we can think of\n", - "this as the amount of information about the predictors\n", - "that is captured using $M$ principal components. For example, setting\n", - "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n", - "By $M=6$ it increases to\n", - "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all 100% of the variance." - ] - }, - { - "cell_type": "markdown", - "id": "af5147dc", - "metadata": {}, - "source": [ - "### Partial Least Squares\n", - "Partial least squares (PLS) is implemented in the\n", - "`PLSRegression()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f49d464e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.274642Z", - "iopub.status.busy": "2023-07-26T05:17:27.274472Z", - "iopub.status.idle": "2023-07-26T05:17:27.279195Z", - "shell.execute_reply": "2023-07-26T05:17:27.278730Z" - } - }, - "outputs": [], - "source": [ - "pls = PLSRegression(n_components=2, \n", - " scale=True)\n", - "pls.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "7a07b988", - "metadata": {}, - "source": [ - "As was the case in PCR, we will want to\n", - "use CV to choose the number of components." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e118b5b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.281479Z", - "iopub.status.busy": "2023-07-26T05:17:27.281289Z", - "iopub.status.idle": "2023-07-26T05:17:27.395617Z", - "shell.execute_reply": "2023-07-26T05:17:27.395127Z" - } - }, - "outputs": [], - "source": [ - "param_grid = {'n_components':range(1, 20)}\n", - "grid = skm.GridSearchCV(pls,\n", - " param_grid,\n", - " cv=kfold,\n", - " scoring='neg_mean_squared_error')\n", - "grid.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "99dec910", - "metadata": {}, - "source": [ - "As for our other methods, we plot the MSE." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f98407c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:27.398116Z", - "iopub.status.busy": "2023-07-26T05:17:27.397950Z", - "iopub.status.idle": "2023-07-26T05:17:27.513229Z", - "shell.execute_reply": "2023-07-26T05:17:27.512778Z" - } - }, - "outputs": [], - "source": [ - "pls_fig, ax = subplots(figsize=(8,8))\n", - "n_comp = param_grid['n_components']\n", - "ax.errorbar(n_comp,\n", - " -grid.cv_results_['mean_test_score'],\n", - " grid.cv_results_['std_test_score'] / np.sqrt(K))\n", - "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n", - "ax.set_xlabel('# principal components', fontsize=20)\n", - "ax.set_xticks(n_comp[::2])\n", - "ax.set_ylim([50000,250000]);" - ] - }, - { - "cell_type": "markdown", - "id": "59ef9b7f", - "metadata": {}, - "source": [ - "CV error is minimized at 12,\n", - "though there is little noticable difference between this point and a much lower number like 2 or 3 components." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch7-nonlin-lab.ipynb b/docs/source/labs/Ch7-nonlin-lab.ipynb deleted file mode 100644 index de4c75b..0000000 --- a/docs/source/labs/Ch7-nonlin-lab.ipynb +++ /dev/null @@ -1,1789 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8d006b12", - "metadata": {}, - "source": [ - "# Non-Linear Modeling\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this lab, we demonstrate some of the nonlinear models discussed in\n", - "this chapter. We use the `Wage` data as a running example, and show that many of the complex non-linear fitting procedures discussed can easily be implemented in \\Python.\n", - "\n", - "As usual, we start with some of our standard imports." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77af2dc4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:29.006535Z", - "iopub.status.busy": "2023-07-26T05:17:29.006365Z", - "iopub.status.idle": "2023-07-26T05:17:30.236882Z", - "shell.execute_reply": "2023-07-26T05:17:30.236365Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np, pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data\n", - "from ISLP.models import (summarize,\n", - " poly,\n", - " ModelSpec as MS)\n", - "from statsmodels.stats.anova import anova_lm" - ] - }, - { - "cell_type": "markdown", - "id": "39ee2f1e", - "metadata": {}, - "source": [ - "We again collect the new imports\n", - "needed for this lab. Many of these are developed specifically for the\n", - "`ISLP` package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b57d1d51", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.240347Z", - "iopub.status.busy": "2023-07-26T05:17:30.240127Z", - "iopub.status.idle": "2023-07-26T05:17:30.254879Z", - "shell.execute_reply": "2023-07-26T05:17:30.254420Z" - } - }, - "outputs": [], - "source": [ - "from pygam import (s as s_gam,\n", - " l as l_gam,\n", - " f as f_gam,\n", - " LinearGAM,\n", - " LogisticGAM)\n", - "\n", - "from ISLP.transforms import (BSpline,\n", - " NaturalSpline)\n", - "from ISLP.models import bs, ns\n", - "from ISLP.pygam import (approx_lam,\n", - " degrees_of_freedom,\n", - " plot as plot_gam,\n", - " anova as anova_gam)" - ] - }, - { - "cell_type": "markdown", - "id": "6fe472df", - "metadata": {}, - "source": [ - "## Polynomial Regression and Step Functions\n", - "We start by demonstrating how Figure 7.1 can be reproduced.\n", - "Let's begin by loading the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65f0e562", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.257519Z", - "iopub.status.busy": "2023-07-26T05:17:30.257098Z", - "iopub.status.idle": "2023-07-26T05:17:30.265239Z", - "shell.execute_reply": "2023-07-26T05:17:30.264877Z" - } - }, - "outputs": [], - "source": [ - "Wage = load_data('Wage')\n", - "y = Wage['wage']\n", - "age = Wage['age']" - ] - }, - { - "cell_type": "markdown", - "id": "36e5e181", - "metadata": {}, - "source": [ - "Throughout most of this lab, our response is `Wage['wage']`, which\n", - "we have stored as `y` above. \n", - "As in Section 3.6.6, we will use the `poly()` function to create a model matrix\n", - "that will fit a $4$th degree polynomial in `age`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a391fae6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.267640Z", - "iopub.status.busy": "2023-07-26T05:17:30.267444Z", - "iopub.status.idle": "2023-07-26T05:17:30.380537Z", - "shell.execute_reply": "2023-07-26T05:17:30.378736Z" - } - }, - "outputs": [], - "source": [ - "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n", - "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n", - "summarize(M)" - ] - }, - { - "cell_type": "markdown", - "id": "5ffdb585", - "metadata": {}, - "source": [ - "This polynomial is constructed using the function `poly()`,\n", - "which creates\n", - "a special *transformer* `Poly()` (using `sklearn` terminology\n", - "for feature transformations such as `PCA()` seen in Section 6.5.3) which\n", - "allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also \n", - "the \n", - "discussion of transformations on\n", - "page 127. \n", - "\n", - "In the code above, the first line executes the `fit()` method\n", - "using the dataframe\n", - "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n", - "on the training data, and these will be used on all subsequent\n", - "evaluations of the `transform()` method. For example, it is used\n", - "on the second line, as well as in the plotting function developed below." - ] - }, - { - "cell_type": "markdown", - "id": "45d9130a", - "metadata": {}, - "source": [ - "We now create a grid of values for `age` at which we want\n", - "predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d672f40e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.388859Z", - "iopub.status.busy": "2023-07-26T05:17:30.387288Z", - "iopub.status.idle": "2023-07-26T05:17:30.401783Z", - "shell.execute_reply": "2023-07-26T05:17:30.398614Z" - } - }, - "outputs": [], - "source": [ - "age_grid = np.linspace(age.min(),\n", - " age.max(),\n", - " 100)\n", - "age_df = pd.DataFrame({'age': age_grid})" - ] - }, - { - "cell_type": "markdown", - "id": "b6e9a066", - "metadata": {}, - "source": [ - "Finally, we wish to plot the data and add the fit from the fourth-degree polynomial. As we will make\n", - "several similar plots below, we first write a function\n", - "to create all the ingredients and produce the plot. Our function\n", - "takes in a model specification (here a basis specified by a\n", - "transform), as well as a grid of `age` values. The function\n", - "produces a fitted curve as well as 95% confidence bands. By using\n", - "an argument for `basis` we can produce and plot the results with several different\n", - "transforms, such as the splines we will see shortly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56174265", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.414085Z", - "iopub.status.busy": "2023-07-26T05:17:30.411883Z", - "iopub.status.idle": "2023-07-26T05:17:30.434639Z", - "shell.execute_reply": "2023-07-26T05:17:30.432466Z" - } - }, - "outputs": [], - "source": [ - "def plot_wage_fit(age_df, \n", - " basis,\n", - " title):\n", - "\n", - " X = basis.transform(Wage)\n", - " Xnew = basis.transform(age_df)\n", - " M = sm.OLS(y, X).fit()\n", - " preds = M.get_prediction(Xnew)\n", - " bands = preds.conf_int(alpha=0.05)\n", - " fig, ax = subplots(figsize=(8,8))\n", - " ax.scatter(age,\n", - " y,\n", - " facecolor='gray',\n", - " alpha=0.5)\n", - " for val, ls in zip([preds.predicted_mean,\n", - " bands[:,0],\n", - " bands[:,1]],\n", - " ['b','r--','r--']):\n", - " ax.plot(age_df.values, val, ls, linewidth=3)\n", - " ax.set_title(title, fontsize=20)\n", - " ax.set_xlabel('Age', fontsize=20)\n", - " ax.set_ylabel('Wage', fontsize=20);\n", - " return ax" - ] - }, - { - "cell_type": "markdown", - "id": "cf21e69b", - "metadata": {}, - "source": [ - "We include an argument `alpha` to `ax.scatter()`\n", - "to add some transparency to the points. This provides a visual indication\n", - "of density. Notice the use of the `zip()` function in the\n", - "`for` loop above (see Section 2.3.8).\n", - "We have three lines to plot, each with different colors and line\n", - "types. Here `zip()` conveniently bundles these together as\n", - "iterators in the loop. {In `Python` speak, an \"iterator\" is an object with a finite number of values, that can be iterated on, as in a loop.}\n", - "\n", - "We now plot the fit of the fourth-degree polynomial using this\n", - "function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a10422f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.447621Z", - "iopub.status.busy": "2023-07-26T05:17:30.446550Z", - "iopub.status.idle": "2023-07-26T05:17:30.730675Z", - "shell.execute_reply": "2023-07-26T05:17:30.730133Z" - } - }, - "outputs": [], - "source": [ - "plot_wage_fit(age_df, \n", - " poly_age,\n", - " 'Degree-4 Polynomial');" - ] - }, - { - "cell_type": "markdown", - "id": "8b5c9ab5", - "metadata": {}, - "source": [ - "With polynomial regression we must decide on the degree of\n", - "the polynomial to use. Sometimes we just wing it, and decide to use\n", - "second or third degree polynomials, simply to obtain a nonlinear fit. But we can\n", - "make such a decision in a more systematic way. One way to do this is through hypothesis\n", - "tests, which we demonstrate here. We now fit a series of models ranging from\n", - "linear (degree-one) to degree-five polynomials,\n", - "and look to determine the simplest model that is sufficient to\n", - "explain the relationship between `wage` and `age`. We use the\n", - "`anova_lm()` function, which performs a series of ANOVA\n", - "tests.\n", - "An \\emph{analysis of\n", - " variance} or ANOVA tests the null\n", - "hypothesis that a model $\\mathcal{M}_1$ is sufficient to explain the\n", - "data against the alternative hypothesis that a more complex model\n", - "$\\mathcal{M}_2$ is required. The determination is based on an F-test.\n", - "To perform the test, the models $\\mathcal{M}_1$ and $\\mathcal{M}_2$ must be *nested*:\n", - "the space spanned by the predictors in $\\mathcal{M}_1$ must be a subspace of the\n", - "space spanned by the predictors in $\\mathcal{M}_2$. In this case, we\n", - "fit five different polynomial\n", - "models and sequentially compare the simpler model to the more complex\n", - "model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8105e672", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.732769Z", - "iopub.status.busy": "2023-07-26T05:17:30.732623Z", - "iopub.status.idle": "2023-07-26T05:17:30.781558Z", - "shell.execute_reply": "2023-07-26T05:17:30.779955Z" - } - }, - "outputs": [], - "source": [ - "models = [MS([poly('age', degree=d)]) \n", - " for d in range(1, 6)]\n", - "Xs = [model.fit_transform(Wage) for model in models]\n", - "anova_lm(*[sm.OLS(y, X_).fit()\n", - " for X_ in Xs])" - ] - }, - { - "cell_type": "markdown", - "id": "7bfb9873", - "metadata": {}, - "source": [ - "Notice the `*` in the `anova_lm()` line above. This\n", - "function takes a variable number of non-keyword arguments, in this case fitted models.\n", - "When these models are provided as a list (as is done here), it must be \n", - "prefixed by `*`.\n", - "\n", - "The p-value comparing the linear `models[0]` to the quadratic\n", - "`models[1]` is essentially zero, indicating that a linear\n", - "fit is not sufficient. {Indexing starting at zero is confusing for the polynomial degree example, since `models[1]` is quadratic rather than linear!} Similarly the p-value comparing the quadratic\n", - "`models[1]` to the cubic `models[2]` is very low (0.0017), so the\n", - "quadratic fit is also insufficient. The p-value comparing the cubic\n", - "and degree-four polynomials, `models[2]` and `models[3]`, is\n", - "approximately 5%, while the degree-five polynomial `models[4]` seems\n", - "unnecessary because its p-value is 0.37. Hence, either a cubic or a\n", - "quartic polynomial appear to provide a reasonable fit to the data, but\n", - "lower- or higher-order models are not justified.\n", - "\n", - "In this case, instead of using the `anova()` function, we could\n", - "have obtained these p-values more succinctly by exploiting the fact\n", - "that `poly()` creates orthogonal polynomials." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "095fca7f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.790773Z", - "iopub.status.busy": "2023-07-26T05:17:30.789916Z", - "iopub.status.idle": "2023-07-26T05:17:30.829956Z", - "shell.execute_reply": "2023-07-26T05:17:30.827755Z" - } - }, - "outputs": [], - "source": [ - "summarize(M)" - ] - }, - { - "cell_type": "markdown", - "id": "65394c3e", - "metadata": {}, - "source": [ - "Notice that the p-values are the same, and in fact the square of\n", - "the t-statistics are equal to the F-statistics from the\n", - "`anova_lm()` function; for example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b576607", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.839811Z", - "iopub.status.busy": "2023-07-26T05:17:30.837704Z", - "iopub.status.idle": "2023-07-26T05:17:30.856244Z", - "shell.execute_reply": "2023-07-26T05:17:30.854399Z" - } - }, - "outputs": [], - "source": [ - "(-11.983)**2" - ] - }, - { - "cell_type": "markdown", - "id": "44af9855", - "metadata": {}, - "source": [ - "However, the ANOVA method works whether or not we used orthogonal\n", - "polynomials, provided the models are nested. For example, we can use\n", - "`anova_lm()` to compare the following three\n", - "models, which all have a linear term in `education` and a\n", - "polynomial in `age` of different degrees:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7242df77", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.866948Z", - "iopub.status.busy": "2023-07-26T05:17:30.865892Z", - "iopub.status.idle": "2023-07-26T05:17:30.922189Z", - "shell.execute_reply": "2023-07-26T05:17:30.920195Z" - } - }, - "outputs": [], - "source": [ - "models = [MS(['education', poly('age', degree=d)])\n", - " for d in range(1, 4)]\n", - "XEs = [model.fit_transform(Wage)\n", - " for model in models]\n", - "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])" - ] - }, - { - "cell_type": "markdown", - "id": "4887dbee", - "metadata": {}, - "source": [ - "As an alternative to using hypothesis tests and ANOVA, we could choose\n", - "the polynomial degree using cross-validation, as discussed in Chapter 5.\n", - "\n", - "Next we consider the task of predicting whether an individual earns\n", - "more than $250,000 per year. We proceed much as before, except\n", - "that first we create the appropriate response vector, and then apply\n", - "the `glm()` function using the binomial family in order\n", - "to fit a polynomial logistic regression model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7972aea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:30.932685Z", - "iopub.status.busy": "2023-07-26T05:17:30.932050Z", - "iopub.status.idle": "2023-07-26T05:17:31.040926Z", - "shell.execute_reply": "2023-07-26T05:17:31.003239Z" - } - }, - "outputs": [], - "source": [ - "X = poly_age.transform(Wage)\n", - "high_earn = Wage['high_earn'] = y > 250 # shorthand\n", - "glm = sm.GLM(y > 250,\n", - " X,\n", - " family=sm.families.Binomial())\n", - "B = glm.fit()\n", - "summarize(B)" - ] - }, - { - "cell_type": "markdown", - "id": "cd7f2a1d", - "metadata": {}, - "source": [ - "Once again, we make predictions using the `get_prediction()` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84f172f2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.067292Z", - "iopub.status.busy": "2023-07-26T05:17:31.066686Z", - "iopub.status.idle": "2023-07-26T05:17:31.149946Z", - "shell.execute_reply": "2023-07-26T05:17:31.109686Z" - } - }, - "outputs": [], - "source": [ - "newX = poly_age.transform(age_df)\n", - "preds = B.get_prediction(newX)\n", - "bands = preds.conf_int(alpha=0.05)" - ] - }, - { - "cell_type": "markdown", - "id": "96a82fa2", - "metadata": {}, - "source": [ - "We now plot the estimated relationship." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fcf376bd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.174590Z", - "iopub.status.busy": "2023-07-26T05:17:31.158361Z", - "iopub.status.idle": "2023-07-26T05:17:31.302984Z", - "shell.execute_reply": "2023-07-26T05:17:31.302329Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "rng = np.random.default_rng(0)\n", - "ax.scatter(age +\n", - " 0.2 * rng.uniform(size=y.shape[0]),\n", - " np.where(high_earn, 0.198, 0.002),\n", - " fc='gray',\n", - " marker='|')\n", - "for val, ls in zip([preds.predicted_mean,\n", - " bands[:,0],\n", - " bands[:,1]],\n", - " ['b','r--','r--']):\n", - " ax.plot(age_df.values, val, ls, linewidth=3)\n", - "ax.set_title('Degree-4 Polynomial', fontsize=20)\n", - "ax.set_xlabel('Age', fontsize=20)\n", - "ax.set_ylim([0,0.2])\n", - "ax.set_ylabel('P(Wage > 250)', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "ece6ba93", - "metadata": {}, - "source": [ - "We have drawn the `age` values corresponding to the observations with\n", - "`wage` values above 250 as gray marks on the top of the plot, and\n", - "those with `wage` values below 250 are shown as gray marks on the\n", - "bottom of the plot. We added a small amount of noise to jitter\n", - "the `age` values a bit so that observations with the same `age`\n", - "value do not cover each other up. This type of plot is often called a\n", - "*rug plot*.\n", - "\n", - "In order to fit a step function, as discussed in\n", - "Section 7.2, we first use the `pd.qcut()`\n", - "function to discretize `age` based on quantiles. Then we use `pd.get_dummies()` to create the\n", - "columns of the model matrix for this categorical variable. Note that this function will\n", - "include *all* columns for a given categorical, rather than the usual approach which drops one\n", - "of the levels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c4d00900", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.305474Z", - "iopub.status.busy": "2023-07-26T05:17:31.305329Z", - "iopub.status.idle": "2023-07-26T05:17:31.336068Z", - "shell.execute_reply": "2023-07-26T05:17:31.334266Z" - } - }, - "outputs": [], - "source": [ - "cut_age = pd.qcut(age, 4)\n", - "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())" - ] - }, - { - "cell_type": "markdown", - "id": "68f44046", - "metadata": {}, - "source": [ - "Here `pd.qcut()` automatically picked the cutpoints based on the quantiles 25%, 50% and 75%, which results in four regions. We could also have specified our own\n", - "quantiles directly instead of the argument `4`. For cuts not based\n", - "on quantiles we would use the `pd.cut()` function.\n", - "The function `pd.qcut()` (and `pd.cut()`) returns an ordered categorical variable.\n", - " The regression model then creates a set of\n", - "dummy variables for use in the regression. Since `age` is the only variable in the model, the value $94,158.40 is the average salary for those under 33.75 years of\n", - "age, and the other coefficients are the average\n", - "salary for those in the other age groups. We can produce\n", - "predictions and plots just as we did in the case of the polynomial\n", - "fit." - ] - }, - { - "cell_type": "markdown", - "id": "4e157a87", - "metadata": {}, - "source": [ - "## Splines\n", - "In order to fit regression splines, we use transforms\n", - "from the `ISLP` package. The actual spline\n", - "evaluation functions are in the `scipy.interpolate` package;\n", - "we have simply wrapped them as transforms\n", - "similar to `Poly()` and `PCA()`.\n", - "\n", - "In Section 7.4, we saw\n", - "that regression splines can be fit by constructing an appropriate\n", - "matrix of basis functions. The `BSpline()` function generates the\n", - "entire matrix of basis functions for splines with the specified set of\n", - "knots. By default, the B-splines produced are cubic. To change the degree, use\n", - "the argument `degree`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3817102", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.345711Z", - "iopub.status.busy": "2023-07-26T05:17:31.343152Z", - "iopub.status.idle": "2023-07-26T05:17:31.444719Z", - "shell.execute_reply": "2023-07-26T05:17:31.422743Z" - } - }, - "outputs": [], - "source": [ - "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n", - "bs_age = bs_.transform(age)\n", - "bs_age.shape" - ] - }, - { - "cell_type": "markdown", - "id": "711c9413", - "metadata": {}, - "source": [ - "This results in a seven-column matrix, which is what is expected for a cubic-spline basis with 3 interior knots. \n", - "We can form this same matrix using the `bs()` object,\n", - "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.\n", - "\n", - "We now fit a cubic spline model to the `Wage` data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c06c0f27", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.504387Z", - "iopub.status.busy": "2023-07-26T05:17:31.502827Z", - "iopub.status.idle": "2023-07-26T05:17:31.545517Z", - "shell.execute_reply": "2023-07-26T05:17:31.543597Z" - } - }, - "outputs": [], - "source": [ - "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n", - "Xbs = bs_age.fit_transform(Wage)\n", - "M = sm.OLS(y, Xbs).fit()\n", - "summarize(M)" - ] - }, - { - "cell_type": "markdown", - "id": "a5698ae9", - "metadata": {}, - "source": [ - "The column names are a little cumbersome, and have caused us to truncate the printed summary. They can be set on construction using the `name` argument as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cb32db0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.554111Z", - "iopub.status.busy": "2023-07-26T05:17:31.553059Z", - "iopub.status.idle": "2023-07-26T05:17:31.605969Z", - "shell.execute_reply": "2023-07-26T05:17:31.601942Z" - } - }, - "outputs": [], - "source": [ - "bs_age = MS([bs('age',\n", - " internal_knots=[25,40,60],\n", - " name='bs(age)')])\n", - "Xbs = bs_age.fit_transform(Wage)\n", - "M = sm.OLS(y, Xbs).fit()\n", - "summarize(M)" - ] - }, - { - "cell_type": "markdown", - "id": "4d709fec", - "metadata": {}, - "source": [ - "Notice that there are 6 spline coefficients rather than 7. This is because, by default,\n", - "`bs()` assumes `intercept=False`, since we typically have an overall intercept in the model.\n", - "So it generates the spline basis with the given knots, and then discards one of the basis functions to account for the intercept. \n", - "\n", - "We could also use the `df` (degrees of freedom) option to\n", - "specify the complexity of the spline. We see above that with 3 knots,\n", - "the spline basis has 6 columns or degrees of freedom. When we specify\n", - "`df=6` rather than the actual knots, `bs()` will produce a\n", - "spline with 3 knots chosen at uniform quantiles of the training data.\n", - "We can see these chosen knots most easily using `Bspline()` directly:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f26b715f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.613882Z", - "iopub.status.busy": "2023-07-26T05:17:31.613189Z", - "iopub.status.idle": "2023-07-26T05:17:31.632577Z", - "shell.execute_reply": "2023-07-26T05:17:31.631063Z" - } - }, - "outputs": [], - "source": [ - "BSpline(df=6).fit(age).internal_knots_" - ] - }, - { - "cell_type": "markdown", - "id": "e606523e", - "metadata": {}, - "source": [ - " When asking for six degrees of freedom,\n", - "the transform chooses knots at ages 33.75, 42.0, and 51.0,\n", - "which correspond to the 25th, 50th, and 75th percentiles of\n", - "`age`.\n", - "\n", - "When using B-splines we need not limit ourselves to cubic polynomials\n", - "(i.e. `degree=3`). For instance, using `degree=0` results\n", - "in piecewise constant functions, as in our example with\n", - "`pd.qcut()` above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6109816", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.640827Z", - "iopub.status.busy": "2023-07-26T05:17:31.640229Z", - "iopub.status.idle": "2023-07-26T05:17:31.697215Z", - "shell.execute_reply": "2023-07-26T05:17:31.695639Z" - } - }, - "outputs": [], - "source": [ - "bs_age0 = MS([bs('age',\n", - " df=3, \n", - " degree=0)]).fit(Wage)\n", - "Xbs0 = bs_age0.transform(Wage)\n", - "summarize(sm.OLS(y, Xbs0).fit())" - ] - }, - { - "cell_type": "markdown", - "id": "53ce3ef2", - "metadata": {}, - "source": [ - "This fit should be compared with cell [15] where we use `qcut()`\n", - "to create four bins by cutting at the 25%, 50% and 75% quantiles of\n", - "`age`. Since we specified `df=3` for degree-zero splines here, there will also be\n", - "knots at the same three quantiles. Although the coefficients appear different, we\n", - "see that this is a result of the different coding. For example, the\n", - "first coefficient is identical in both cases, and is the mean response\n", - "in the first bin. For the second coefficient, we have\n", - "$94.158 + 22.349 = 116.507 \\approx 116.611$, the latter being the mean\n", - "in the second bin in cell [15]. Here the intercept is coded by a column\n", - "of ones, so the second, third and fourth coefficients are increments\n", - "for those bins. Why is the sum not exactly the same? It turns out that\n", - "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n", - "deciding bin membership." - ] - }, - { - "cell_type": "markdown", - "id": "59807efd", - "metadata": {}, - "source": [ - "In order to fit a natural spline, we use the `NaturalSpline()` \n", - "transform with the corresponding helper `ns()`. Here we fit a natural spline with five\n", - "degrees of freedom (excluding the intercept) and plot the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c73a3da", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.706944Z", - "iopub.status.busy": "2023-07-26T05:17:31.705664Z", - "iopub.status.idle": "2023-07-26T05:17:31.757111Z", - "shell.execute_reply": "2023-07-26T05:17:31.754514Z" - } - }, - "outputs": [], - "source": [ - "ns_age = MS([ns('age', df=5)]).fit(Wage)\n", - "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n", - "summarize(M_ns)" - ] - }, - { - "cell_type": "markdown", - "id": "d819f968", - "metadata": {}, - "source": [ - "We now plot the natural spline using our plotting function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d73789b4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.766336Z", - "iopub.status.busy": "2023-07-26T05:17:31.764642Z", - "iopub.status.idle": "2023-07-26T05:17:31.949222Z", - "shell.execute_reply": "2023-07-26T05:17:31.947680Z" - } - }, - "outputs": [], - "source": [ - "plot_wage_fit(age_df,\n", - " ns_age,\n", - " 'Natural spline, df=5');" - ] - }, - { - "cell_type": "markdown", - "id": "09616af2", - "metadata": {}, - "source": [ - "## Smoothing Splines and GAMs\n", - "A smoothing spline is a special case of a GAM with squared-error loss\n", - "and a single feature. To fit GAMs in `Python` we will use the\n", - "`pygam` package which can be installed via `pip install pygam`. The\n", - "estimator `LinearGAM()` uses squared-error loss.\n", - "The GAM is specified by associating each column\n", - "of a model matrix with a particular smoothing operation:\n", - "`s` for smoothing spline; `l` for linear, and `f` for factor or categorical variables.\n", - "The argument `0` passed to `s` below indicates that this smoother will\n", - "apply to the first column of a feature matrix. Below, we pass it a\n", - "matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\\lambda$ as discussed in Section 7.5.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e70b87d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:31.962335Z", - "iopub.status.busy": "2023-07-26T05:17:31.958770Z", - "iopub.status.idle": "2023-07-26T05:17:32.190885Z", - "shell.execute_reply": "2023-07-26T05:17:32.188959Z" - } - }, - "outputs": [], - "source": [ - "X_age = np.asarray(age).reshape((-1,1))\n", - "gam = LinearGAM(s_gam(0, lam=0.6))\n", - "gam.fit(X_age, y)" - ] - }, - { - "cell_type": "markdown", - "id": "f95cf22c", - "metadata": {}, - "source": [ - "The `pygam` library generally expects a matrix of features so we reshape `age` to be a matrix (a two-dimensional array) instead\n", - "of a vector (i.e. a one-dimensional array). The `-1` in the call to the `reshape()` method tells `numpy` to impute the\n", - "size of that dimension based on the remaining entries of the shape tuple.\n", - " \n", - "Let’s investigate how the fit changes with the smoothing parameter `lam`.\n", - "The function `np.logspace()` is similar to `np.linspace()` but spaces points\n", - "evenly on the log-scale. Below we vary `lam` from $10^{-2}$ to $10^6$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efe03b12", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:32.212724Z", - "iopub.status.busy": "2023-07-26T05:17:32.212362Z", - "iopub.status.idle": "2023-07-26T05:17:33.141120Z", - "shell.execute_reply": "2023-07-26T05:17:33.140497Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", - "for lam in np.logspace(-2, 6, 5):\n", - " gam = LinearGAM(s_gam(0, lam=lam)).fit(X_age, y)\n", - " ax.plot(age_grid,\n", - " gam.predict(age_grid),\n", - " label='{:.1e}'.format(lam),\n", - " linewidth=3)\n", - "ax.set_xlabel('Age', fontsize=20)\n", - "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='$\\lambda$');" - ] - }, - { - "cell_type": "markdown", - "id": "6590a835", - "metadata": {}, - "source": [ - "The `pygam` package can perform a search for an optimal smoothing parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acff2af2", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:33.143621Z", - "iopub.status.busy": "2023-07-26T05:17:33.143494Z", - "iopub.status.idle": "2023-07-26T05:17:34.781067Z", - "shell.execute_reply": "2023-07-26T05:17:34.758430Z" - } - }, - "outputs": [], - "source": [ - "gam_opt = gam.gridsearch(X_age, y)\n", - "ax.plot(age_grid,\n", - " gam_opt.predict(age_grid),\n", - " label='Grid search',\n", - " linewidth=4)\n", - "ax.legend()\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "c979dbf1", - "metadata": {}, - "source": [ - "Alternatively, we can fix the degrees of freedom of the smoothing\n", - "spline using a function included in the `ISLP.pygam` package. Below we\n", - "find a value of $\\lambda$ that gives us roughly four degrees of\n", - "freedom. We note here that these degrees of freedom include the\n", - "unpenalized intercept and linear term of the smoothing spline, hence there are at least two\n", - "degrees of freedom." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2d25550", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:34.825629Z", - "iopub.status.busy": "2023-07-26T05:17:34.825425Z", - "iopub.status.idle": "2023-07-26T05:17:35.011756Z", - "shell.execute_reply": "2023-07-26T05:17:35.006694Z" - } - }, - "outputs": [], - "source": [ - "age_term = gam.terms[0]\n", - "lam_4 = approx_lam(X_age, age_term, 4)\n", - "age_term.lam = lam_4\n", - "degrees_of_freedom(X_age, age_term)" - ] - }, - { - "cell_type": "markdown", - "id": "ce629653", - "metadata": {}, - "source": [ - "Let’s vary the degrees of freedom in a similar plot to above. We choose the degrees of freedom\n", - "as the desired degrees of freedom plus one to account for the fact that these smoothing\n", - "splines always have an intercept term. Hence, a value of one for `df` is just a linear fit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc6322de", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:35.024537Z", - "iopub.status.busy": "2023-07-26T05:17:35.022461Z", - "iopub.status.idle": "2023-07-26T05:17:35.863008Z", - "shell.execute_reply": "2023-07-26T05:17:35.862422Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X_age,\n", - " y,\n", - " facecolor='gray',\n", - " alpha=0.3)\n", - "for df in [1,3,4,8,15]:\n", - " lam = approx_lam(X_age, age_term, df+1)\n", - " age_term.lam = lam\n", - " gam.fit(X_age, y)\n", - " ax.plot(age_grid,\n", - " gam.predict(age_grid),\n", - " label='{:d}'.format(df),\n", - " linewidth=4)\n", - "ax.set_xlabel('Age', fontsize=20)\n", - "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='Degrees of freedom');" - ] - }, - { - "cell_type": "markdown", - "id": "2cb73ff7", - "metadata": {}, - "source": [ - "### Additive Models with Several Terms\n", - "The strength of generalized additive models lies in their ability to fit multivariate regression models with more flexibility than linear models. We demonstrate two approaches: the first in a more manual fashion using natural splines and piecewise constant functions, and the second using the `pygam` package and smoothing splines.\n", - "\n", - "We now fit a GAM by hand to predict\n", - "`wage` using natural spline functions of `year` and `age`,\n", - "treating `education` as a qualitative predictor, as in (7.16).\n", - "Since this is just a big linear regression model\n", - "using an appropriate choice of basis functions, we can simply do this\n", - "using the `sm.OLS()` function.\n", - "\n", - "We will build the model matrix in a more manual fashion here, since we wish\n", - "to access the pieces separately when constructing partial dependence plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "703d2570", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:35.865454Z", - "iopub.status.busy": "2023-07-26T05:17:35.865140Z", - "iopub.status.idle": "2023-07-26T05:17:35.892627Z", - "shell.execute_reply": "2023-07-26T05:17:35.889738Z" - } - }, - "outputs": [], - "source": [ - "ns_age = NaturalSpline(df=4).fit(age)\n", - "ns_year = NaturalSpline(df=5).fit(Wage['year'])\n", - "Xs = [ns_age.transform(age),\n", - " ns_year.transform(Wage['year']),\n", - " pd.get_dummies(Wage['education']).values]\n", - "X_bh = np.hstack(Xs)\n", - "gam_bh = sm.OLS(y, X_bh).fit()" - ] - }, - { - "cell_type": "markdown", - "id": "34cae11a", - "metadata": {}, - "source": [ - "Here the function `NaturalSpline()` is the workhorse supporting\n", - "the `ns()` helper function. We chose to use all columns of the\n", - "indicator matrix for the categorical variable `education`, making an intercept redundant.\n", - "Finally, we stacked the three component matrices horizontally to form the model matrix `X_bh`. \n", - "\n", - "We now show how to construct partial dependence plots for each of the terms in our rudimentary GAM. We can do this by hand,\n", - "given grids for `age` and `year`.\n", - " We simply predict with new $X$ matrices, fixing all but one of the features at a time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "766a7b1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:35.908556Z", - "iopub.status.busy": "2023-07-26T05:17:35.902581Z", - "iopub.status.idle": "2023-07-26T05:17:36.057026Z", - "shell.execute_reply": "2023-07-26T05:17:36.055296Z" - } - }, - "outputs": [], - "source": [ - "age_grid = np.linspace(age.min(),\n", - " age.max(),\n", - " 100)\n", - "X_age_bh = X_bh.copy()[:100]\n", - "X_age_bh[:] = X_bh[:].mean(0)[None,:]\n", - "X_age_bh[:,:4] = ns_age.transform(age_grid)\n", - "preds = gam_bh.get_prediction(X_age_bh)\n", - "bounds_age = preds.conf_int(alpha=0.05)\n", - "partial_age = preds.predicted_mean\n", - "center = partial_age.mean()\n", - "partial_age -= center\n", - "bounds_age -= center\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.plot(age_grid, partial_age, 'b', linewidth=3)\n", - "ax.plot(age_grid, bounds_age[:,0], 'r--', linewidth=3)\n", - "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n", - "ax.set_xlabel('Age')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of age on wage', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "83e636f3", - "metadata": {}, - "source": [ - "Let's explain in some detail what we did above. The idea is to create a new prediction matrix, where all but the columns belonging to `age` are constant (and set to their training-data means). The four columns for `age` are filled in with the natural spline basis evaluated at the 100 values in `age_grid`.\n", - "\n", - "* We made a grid of length 100 in `age`, and created a matrix `X_age_bh` with 100 rows and the same number of columns as `X_bh`.\n", - "* We replaced every row of this matrix with the column means of the original.\n", - "* We then replace just the first four columns representing `age` with the natural spline basis computed at the values in `age_grid`. \n", - "\n", - "The remaining steps should by now be familiar.\n", - "\n", - "We also look at the effect of `year` on `wage`; the process is the same." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a9ed841", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.068318Z", - "iopub.status.busy": "2023-07-26T05:17:36.067032Z", - "iopub.status.idle": "2023-07-26T05:17:36.214624Z", - "shell.execute_reply": "2023-07-26T05:17:36.214052Z" - } - }, - "outputs": [], - "source": [ - "year_grid = np.linspace(2003, 2009, 100)\n", - "year_grid = np.linspace(Wage['year'].min(),\n", - " Wage['year'].max(),\n", - " 100)\n", - "X_year_bh = X_bh.copy()[:100]\n", - "X_year_bh[:] = X_bh[:].mean(0)[None,:]\n", - "X_year_bh[:,4:9] = ns_year.transform(year_grid)\n", - "preds = gam_bh.get_prediction(X_year_bh)\n", - "bounds_year = preds.conf_int(alpha=0.05)\n", - "partial_year = preds.predicted_mean\n", - "center = partial_year.mean()\n", - "partial_year -= center\n", - "bounds_year -= center\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.plot(year_grid, partial_year, 'b', linewidth=3)\n", - "ax.plot(year_grid, bounds_year[:,0], 'r--', linewidth=3)\n", - "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n", - "ax.set_xlabel('Year')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of year on wage', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "4aca2254", - "metadata": {}, - "source": [ - "We now fit the model (7.16) using smoothing splines rather\n", - "than natural splines. All of the\n", - "terms in (7.16) are fit simultaneously, taking each other\n", - "into account to explain the response. The `pygam` package only works with matrices, so we must convert\n", - "the categorical series `education` to its array representation, which can be found\n", - "with the `cat.codes` attribute of `education`. As `year` only has 7 unique values, we\n", - "use only seven basis functions for it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccf068b1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.216725Z", - "iopub.status.busy": "2023-07-26T05:17:36.216587Z", - "iopub.status.idle": "2023-07-26T05:17:36.383722Z", - "shell.execute_reply": "2023-07-26T05:17:36.379206Z" - } - }, - "outputs": [], - "source": [ - "gam_full = LinearGAM(s_gam(0) +\n", - " s_gam(1, n_splines=7) +\n", - " f_gam(2, lam=0))\n", - "Xgam = np.column_stack([age,\n", - " Wage['year'],\n", - " Wage['education'].cat.codes])\n", - "gam_full = gam_full.fit(Xgam, y)" - ] - }, - { - "cell_type": "markdown", - "id": "a7e035d3", - "metadata": {}, - "source": [ - "The two `s_gam()` terms result in smoothing spline fits, and use a default value for $\\lambda$ (`lam=0.6`), which is somewhat arbitrary. For the categorical term `education`, specified using a `f_gam()` term, we specify `lam=0` to avoid any shrinkage.\n", - "We produce the partial dependence plot in `age` to see the effect of these choices.\n", - "\n", - "The values for the plot\n", - "are generated by the `pygam` package. We provide a `plot_gam()`\n", - "function for partial-dependence plots in `ISLP.pygam`, which makes this job easier than in our last example with natural splines." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38b719f1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.404487Z", - "iopub.status.busy": "2023-07-26T05:17:36.401365Z", - "iopub.status.idle": "2023-07-26T05:17:36.620141Z", - "shell.execute_reply": "2023-07-26T05:17:36.619756Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_gam(gam_full, 0, ax=ax)\n", - "ax.set_xlabel('Age')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "c7be9454", - "metadata": {}, - "source": [ - "We see that the function is somewhat wiggly. It is more natural to specify the `df` than a value for `lam`. \n", - "We refit a GAM using four degrees of freedom each for\n", - "`age` and `year`. Recall that the addition of one below takes into account the intercept\n", - "of the smoothing spline." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02142f6e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.622262Z", - "iopub.status.busy": "2023-07-26T05:17:36.622071Z", - "iopub.status.idle": "2023-07-26T05:17:36.728386Z", - "shell.execute_reply": "2023-07-26T05:17:36.725088Z" - } - }, - "outputs": [], - "source": [ - "age_term = gam_full.terms[0]\n", - "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n", - "year_term = gam_full.terms[1]\n", - "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n", - "gam_full = gam_full.fit(Xgam, y)" - ] - }, - { - "cell_type": "markdown", - "id": "08085085", - "metadata": {}, - "source": [ - "Note that updating `age_term.lam` above updates it in `gam_full.terms[0]` as well! Likewise for `year_term.lam`.\n", - "\n", - "Repeating the plot for `age`, we see that it is much smoother.\n", - "We also produce the plot for `year`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94587b05", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.738456Z", - "iopub.status.busy": "2023-07-26T05:17:36.737408Z", - "iopub.status.idle": "2023-07-26T05:17:36.927722Z", - "shell.execute_reply": "2023-07-26T05:17:36.917522Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_gam(gam_full,\n", - " 1,\n", - " ax=ax)\n", - "ax.set_xlabel('Year')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of year on wage', fontsize=20)" - ] - }, - { - "cell_type": "markdown", - "id": "5b373d5c", - "metadata": {}, - "source": [ - "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bba4c757", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:36.975410Z", - "iopub.status.busy": "2023-07-26T05:17:36.975007Z", - "iopub.status.idle": "2023-07-26T05:17:37.089224Z", - "shell.execute_reply": "2023-07-26T05:17:37.088696Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax = plot_gam(gam_full, 2)\n", - "ax.set_xlabel('Education')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of wage on education',\n", - " fontsize=20);\n", - "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);" - ] - }, - { - "cell_type": "markdown", - "id": "38ff8ddb", - "metadata": {}, - "source": [ - "### ANOVA Tests for Additive Models\n", - "In all of our models, the function of `year` looks rather linear. We can\n", - "perform a series of ANOVA tests in order to determine which of these\n", - "three models is best: a GAM that excludes `year` ($\\mathcal{M}_1$),\n", - "a GAM that uses a linear function of `year` ($\\mathcal{M}_2$), or a\n", - "GAM that uses a spline function of `year` ($\\mathcal{M}_3$)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32368085", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.091963Z", - "iopub.status.busy": "2023-07-26T05:17:37.091763Z", - "iopub.status.idle": "2023-07-26T05:17:37.181347Z", - "shell.execute_reply": "2023-07-26T05:17:37.177854Z" - } - }, - "outputs": [], - "source": [ - "gam_0 = LinearGAM(age_term + f_gam(2, lam=0))\n", - "gam_0.fit(Xgam, y)\n", - "gam_linear = LinearGAM(age_term +\n", - " l_gam(1, lam=0) +\n", - " f_gam(2, lam=0))\n", - "gam_linear.fit(Xgam, y)" - ] - }, - { - "cell_type": "markdown", - "id": "c95b177b", - "metadata": {}, - "source": [ - "Notice our use of `age_term` in the expressions above. We do this because\n", - "earlier we set the value for `lam` in this term to achieve four degrees of freedom.\n", - "\n", - "To directly assess the effect of `year` we run an ANOVA on the\n", - "three models fit above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7ba9957", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.190026Z", - "iopub.status.busy": "2023-07-26T05:17:37.188398Z", - "iopub.status.idle": "2023-07-26T05:17:37.218825Z", - "shell.execute_reply": "2023-07-26T05:17:37.206670Z" - } - }, - "outputs": [], - "source": [ - "anova_gam(gam_0, gam_linear, gam_full)" - ] - }, - { - "cell_type": "markdown", - "id": "825ea833", - "metadata": {}, - "source": [ - " We find that there is compelling evidence that a GAM with a linear\n", - "function in `year` is better than a GAM that does not include\n", - "`year` at all ($p$-value= 0.002). However, there is no\n", - "evidence that a non-linear function of `year` is needed\n", - "($p$-value=0.435). In other words, based on the results of this\n", - "ANOVA, $\\mathcal{M}_2$ is preferred.\n", - "\n", - "We can repeat the same process for `age` as well. We see there is very clear evidence that\n", - "a non-linear term is required for `age`.\n", - "\\newpage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffc0099a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.237108Z", - "iopub.status.busy": "2023-07-26T05:17:37.233509Z", - "iopub.status.idle": "2023-07-26T05:17:37.451340Z", - "shell.execute_reply": "2023-07-26T05:17:37.449175Z" - } - }, - "outputs": [], - "source": [ - "gam_0 = LinearGAM(year_term +\n", - " f_gam(2, lam=0))\n", - "gam_linear = LinearGAM(l_gam(0, lam=0) +\n", - " year_term +\n", - " f_gam(2, lam=0))\n", - "gam_0.fit(Xgam, y)\n", - "gam_linear.fit(Xgam, y)\n", - "anova_gam(gam_0, gam_linear, gam_full)" - ] - }, - { - "cell_type": "markdown", - "id": "3a243d63", - "metadata": {}, - "source": [ - "There is a (verbose) `summary()` method for the GAM fit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08026a6b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.465361Z", - "iopub.status.busy": "2023-07-26T05:17:37.464408Z", - "iopub.status.idle": "2023-07-26T05:17:37.476379Z", - "shell.execute_reply": "2023-07-26T05:17:37.473563Z" - } - }, - "outputs": [], - "source": [ - "gam_full.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "839acb14", - "metadata": {}, - "source": [ - "We can make predictions from `gam` objects, just like from\n", - "`lm` objects, using the `predict()` method for the class\n", - "`gam`. Here we make predictions on the training set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9191d615", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.488339Z", - "iopub.status.busy": "2023-07-26T05:17:37.487573Z", - "iopub.status.idle": "2023-07-26T05:17:37.520050Z", - "shell.execute_reply": "2023-07-26T05:17:37.516172Z" - } - }, - "outputs": [], - "source": [ - "Yhat = gam_full.predict(Xgam)" - ] - }, - { - "cell_type": "markdown", - "id": "a98a10a7", - "metadata": {}, - "source": [ - "In order to fit a logistic regression GAM, we use `LogisticGAM()` \n", - "from `pygam`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92007a5f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:37.533259Z", - "iopub.status.busy": "2023-07-26T05:17:37.531878Z", - "iopub.status.idle": "2023-07-26T05:17:38.910096Z", - "shell.execute_reply": "2023-07-26T05:17:38.906703Z" - } - }, - "outputs": [], - "source": [ - "gam_logit = LogisticGAM(age_term + \n", - " l_gam(1, lam=0) +\n", - " f_gam(2, lam=0))\n", - "gam_logit.fit(Xgam, high_earn)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4dd6aa2f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:38.924790Z", - "iopub.status.busy": "2023-07-26T05:17:38.922174Z", - "iopub.status.idle": "2023-07-26T05:17:39.053438Z", - "shell.execute_reply": "2023-07-26T05:17:39.047514Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax = plot_gam(gam_logit, 2)\n", - "ax.set_xlabel('Education')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of wage on education',\n", - " fontsize=20);\n", - "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);" - ] - }, - { - "cell_type": "markdown", - "id": "e4e9e093", - "metadata": {}, - "source": [ - "The model seems to be very flat, with especially high error bars for the first category.\n", - "Let's look at the data a bit more closely." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a6e8754", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.066631Z", - "iopub.status.busy": "2023-07-26T05:17:39.064606Z", - "iopub.status.idle": "2023-07-26T05:17:39.115103Z", - "shell.execute_reply": "2023-07-26T05:17:39.111772Z" - } - }, - "outputs": [], - "source": [ - "pd.crosstab(Wage['high_earn'], Wage['education'])" - ] - }, - { - "cell_type": "markdown", - "id": "06d2d2fd", - "metadata": {}, - "source": [ - "We see that there are no high earners in the first category of\n", - "education, meaning that the model will have a hard time fitting. We\n", - "will fit a logistic regression GAM excluding all observations falling into this\n", - "category. This provides more sensible results.\n", - "\n", - "To do so,\n", - "we could subset the model matrix, though this will not remove the\n", - "column from `Xgam`. While we can deduce which column corresponds\n", - "to this feature, for reproducibility’s sake we reform the model matrix\n", - "on this smaller subset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c92b60be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.164808Z", - "iopub.status.busy": "2023-07-26T05:17:39.164544Z", - "iopub.status.idle": "2023-07-26T05:17:39.168909Z", - "shell.execute_reply": "2023-07-26T05:17:39.168184Z" - } - }, - "outputs": [], - "source": [ - "only_hs = Wage['education'] == '1. < HS Grad'\n", - "Wage_ = Wage.loc[~only_hs]\n", - "Xgam_ = np.column_stack([Wage_['age'],\n", - " Wage_['year'],\n", - " Wage_['education'].cat.codes-1])\n", - "high_earn_ = Wage_['high_earn']" - ] - }, - { - "cell_type": "markdown", - "id": "02dc4908", - "metadata": {}, - "source": [ - "In the second-to-last line above, we subtract one from the codes of the category, due to a bug in `pygam`. It just relabels\n", - "the education values and hence has no effect on the fit.\n", - " \n", - "We now fit the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e525e8d0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.173520Z", - "iopub.status.busy": "2023-07-26T05:17:39.173179Z", - "iopub.status.idle": "2023-07-26T05:17:39.334235Z", - "shell.execute_reply": "2023-07-26T05:17:39.329778Z" - } - }, - "outputs": [], - "source": [ - "gam_logit_ = LogisticGAM(age_term +\n", - " year_term +\n", - " f_gam(2, lam=0))\n", - "gam_logit_.fit(Xgam_, high_earn_)" - ] - }, - { - "cell_type": "markdown", - "id": "9601ab00", - "metadata": {}, - "source": [ - "Let’s look at the effect of `education`, `year` and `age` on high earner status now that we’ve\n", - "removed those observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3e66cf9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.345416Z", - "iopub.status.busy": "2023-07-26T05:17:39.344660Z", - "iopub.status.idle": "2023-07-26T05:17:39.468357Z", - "shell.execute_reply": "2023-07-26T05:17:39.465567Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax = plot_gam(gam_logit_, 2)\n", - "ax.set_xlabel('Education')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n", - "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n", - " fontsize=8);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd924348", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.479166Z", - "iopub.status.busy": "2023-07-26T05:17:39.477744Z", - "iopub.status.idle": "2023-07-26T05:17:39.605116Z", - "shell.execute_reply": "2023-07-26T05:17:39.604574Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax = plot_gam(gam_logit_, 1)\n", - "ax.set_xlabel('Year')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of high earner status on year',\n", - " fontsize=20);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d3ec90a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.607454Z", - "iopub.status.busy": "2023-07-26T05:17:39.607274Z", - "iopub.status.idle": "2023-07-26T05:17:39.737422Z", - "shell.execute_reply": "2023-07-26T05:17:39.736830Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax = plot_gam(gam_logit_, 0)\n", - "ax.set_xlabel('Age')\n", - "ax.set_ylabel('Effect on wage')\n", - "ax.set_title('Partial dependence of high earner status on age', fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "id": "ea4a7d79", - "metadata": {}, - "source": [ - "## Local Regression\n", - "We illustrate the use of local regression using the `lowess()` \n", - "function from `sm.nonparametric`. Some implementations of\n", - "GAMs allow terms to be local regression operators; this is not the case in `pygam`.\n", - "\n", - "Here we fit local linear regression models using spans of 0.2\n", - "and 0.5; that is, each neighborhood consists of 20% or 50% of\n", - "the observations. As expected, using a span of 0.5 is smoother than 0.2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f2bc0eb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:39.740507Z", - "iopub.status.busy": "2023-07-26T05:17:39.740171Z", - "iopub.status.idle": "2023-07-26T05:17:39.902495Z", - "shell.execute_reply": "2023-07-26T05:17:39.902127Z" - } - }, - "outputs": [], - "source": [ - "lowess = sm.nonparametric.lowess\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n", - "for span in [0.2, 0.5]:\n", - " fitted = lowess(y,\n", - " age,\n", - " frac=span,\n", - " xvals=age_grid)\n", - " ax.plot(age_grid,\n", - " fitted,\n", - " label='{:.1f}'.format(span),\n", - " linewidth=4)\n", - "ax.set_xlabel('Age', fontsize=20)\n", - "ax.set_ylabel('Wage', fontsize=20);\n", - "ax.legend(title='span', fontsize=15);" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch8-baggboost-lab.ipynb b/docs/source/labs/Ch8-baggboost-lab.ipynb deleted file mode 100644 index 43349ee..0000000 --- a/docs/source/labs/Ch8-baggboost-lab.ipynb +++ /dev/null @@ -1,1237 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7b0242c6", - "metadata": {}, - "source": [ - "# Tree-Based Methods\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "We import some of our usual libraries at this top\n", - "level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cc7120f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:41.271445Z", - "iopub.status.busy": "2023-07-26T05:17:41.271226Z", - "iopub.status.idle": "2023-07-26T05:17:42.756473Z", - "shell.execute_reply": "2023-07-26T05:17:42.756027Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "from statsmodels.datasets import get_rdataset\n", - "import sklearn.model_selection as skm\n", - "from ISLP import load_data, confusion_table\n", - "from ISLP.models import ModelSpec as MS" - ] - }, - { - "cell_type": "markdown", - "id": "75645461", - "metadata": {}, - "source": [ - "We also collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "864fa15e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.759051Z", - "iopub.status.busy": "2023-07-26T05:17:42.758860Z", - "iopub.status.idle": "2023-07-26T05:17:42.832228Z", - "shell.execute_reply": "2023-07-26T05:17:42.831597Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.tree import (DecisionTreeClassifier as DTC,\n", - " DecisionTreeRegressor as DTR,\n", - " plot_tree,\n", - " export_text)\n", - "from sklearn.metrics import (accuracy_score,\n", - " log_loss)\n", - "from sklearn.ensemble import \\\n", - " (RandomForestRegressor as RF,\n", - " GradientBoostingRegressor as GBR)\n", - "from ISLP.bart import BART" - ] - }, - { - "cell_type": "markdown", - "id": "7b68e1e9", - "metadata": {}, - "source": [ - "## Fitting Classification Trees" - ] - }, - { - "cell_type": "markdown", - "id": "85b1d9af", - "metadata": {}, - "source": [ - "We first use classification trees to analyze the `Carseats` data set.\n", - "In these data, `Sales` is a continuous variable, and so we begin\n", - "by recoding it as a binary variable. We use the `where()` \n", - "function to create a variable, called `High`, which takes on a\n", - "value of `Yes` if the `Sales` variable exceeds 8, and takes\n", - "on a value of `No` otherwise." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfb4c83b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.836883Z", - "iopub.status.busy": "2023-07-26T05:17:42.836681Z", - "iopub.status.idle": "2023-07-26T05:17:42.843170Z", - "shell.execute_reply": "2023-07-26T05:17:42.842647Z" - } - }, - "outputs": [], - "source": [ - "Carseats = load_data('Carseats')\n", - "High = np.where(Carseats.Sales > 8,\n", - " \"Yes\",\n", - " \"No\")" - ] - }, - { - "cell_type": "markdown", - "id": "8e50828c", - "metadata": {}, - "source": [ - "We now use `DecisionTreeClassifier()` to fit a classification tree in\n", - "order to predict `High` using all variables but `Sales`.\n", - "To do so, we must form a model matrix as we did when fitting regression\n", - "models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d9e7a14", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.845711Z", - "iopub.status.busy": "2023-07-26T05:17:42.845521Z", - "iopub.status.idle": "2023-07-26T05:17:42.856897Z", - "shell.execute_reply": "2023-07-26T05:17:42.856518Z" - } - }, - "outputs": [], - "source": [ - "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n", - "D = model.fit_transform(Carseats)\n", - "feature_names = list(D.columns)\n", - "X = np.asarray(D)" - ] - }, - { - "cell_type": "markdown", - "id": "b42d6270", - "metadata": {}, - "source": [ - "We have converted `D` from a data frame to an array `X`, which is needed in some of the analysis below. We also need the `feature_names` for annotating our plots later.\n", - "\n", - "There are several options needed to specify the classifier,\n", - "such as `max_depth` (how deep to grow the tree), `min_samples_split`\n", - "(minimum number of observations in a node to be eligible for splitting)\n", - "and `criterion` (whether to use Gini or cross-entropy as the split criterion).\n", - "We also set `random_state` for reproducibility; ties in the split criterion are broken at random." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "970c4515", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.859418Z", - "iopub.status.busy": "2023-07-26T05:17:42.859055Z", - "iopub.status.idle": "2023-07-26T05:17:42.866816Z", - "shell.execute_reply": "2023-07-26T05:17:42.866395Z" - } - }, - "outputs": [], - "source": [ - "clf = DTC(criterion='entropy',\n", - " max_depth=3,\n", - " random_state=0) \n", - "clf.fit(X, High)" - ] - }, - { - "cell_type": "markdown", - "id": "5ccdc9e7", - "metadata": {}, - "source": [ - "In our discussion of qualitative features in Section 3.3,\n", - "we noted that for a linear regression model such a feature could be\n", - "represented by including a matrix of dummy variables (one-hot-encoding) in the model\n", - "matrix, using the formula notation of `statsmodels`.\n", - "As mentioned in Section 8.1, there is a more\n", - "natural way to handle qualitative features when building a decision\n", - "tree, that does not require such dummy variables; each split amounts to partitioning the levels into two groups.\n", - "However, \n", - "the `sklearn` implementation of decision trees does not take\n", - "advantage of this approach; instead it simply treats the one-hot-encoded levels as separate variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d9a51dc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.868805Z", - "iopub.status.busy": "2023-07-26T05:17:42.868636Z", - "iopub.status.idle": "2023-07-26T05:17:42.872257Z", - "shell.execute_reply": "2023-07-26T05:17:42.871848Z" - } - }, - "outputs": [], - "source": [ - "accuracy_score(High, clf.predict(X))" - ] - }, - { - "cell_type": "markdown", - "id": "7fd4219f", - "metadata": {}, - "source": [ - "With only the default arguments, the training error rate is\n", - "21%.\n", - "For classification trees, we can\n", - "access the value of the deviance using `log_loss()`,\n", - "\\begin{equation*}\n", - "\\begin{split}\n", - "-2 \\sum_m \\sum_k n_{mk} \\log \\hat{p}_{mk},\n", - "\\end{split}\n", - "\\end{equation*}\n", - "where $n_{mk}$ is the number of observations in the $m$th terminal\n", - "node that belong to the $k$th class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cbc04b67", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.874181Z", - "iopub.status.busy": "2023-07-26T05:17:42.874073Z", - "iopub.status.idle": "2023-07-26T05:17:42.878254Z", - "shell.execute_reply": "2023-07-26T05:17:42.877678Z" - } - }, - "outputs": [], - "source": [ - "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n", - "resid_dev" - ] - }, - { - "cell_type": "markdown", - "id": "bca81c07", - "metadata": {}, - "source": [ - "This is closely related to the *entropy*, defined in (8.7).\n", - "A small deviance indicates a\n", - "tree that provides a good fit to the (training) data.\n", - " \n", - "One of the most attractive properties of trees is that they can\n", - "be graphically displayed. Here we use the `plot()` function\n", - "to display the tree structure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "809830c7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:42.880423Z", - "iopub.status.busy": "2023-07-26T05:17:42.880256Z", - "iopub.status.idle": "2023-07-26T05:17:43.233297Z", - "shell.execute_reply": "2023-07-26T05:17:43.232754Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(12,12))[1]\n", - "plot_tree(clf,\n", - " feature_names=feature_names,\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "c7cf2e12", - "metadata": {}, - "source": [ - "The most important indicator of `Sales` appears to be `ShelveLoc`.\n", - "\n", - "We can see a text representation of the tree using\n", - "`export_text()`, which displays the split\n", - "criterion (e.g. `Price <= 92.5`) for each branch.\n", - "For leaf nodes it shows the overall prediction \n", - "(`Yes` or `No`). \n", - " We can also see the number of observations in that\n", - "leaf that take on values of `Yes` and `No` by specifying `show_weights=True`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abe8c7fc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.235787Z", - "iopub.status.busy": "2023-07-26T05:17:43.235629Z", - "iopub.status.idle": "2023-07-26T05:17:43.238645Z", - "shell.execute_reply": "2023-07-26T05:17:43.238197Z" - } - }, - "outputs": [], - "source": [ - "print(export_text(clf,\n", - " feature_names=feature_names,\n", - " show_weights=True))" - ] - }, - { - "cell_type": "markdown", - "id": "f06c2f4c", - "metadata": {}, - "source": [ - "In order to properly evaluate the performance of a classification tree\n", - "on these data, we must estimate the test error rather than simply\n", - "computing the training error. We split the observations into a\n", - "training set and a test set, build the tree using the training set,\n", - "and evaluate its performance on the test data. This pattern is\n", - "similar to that in Chapter 6, with the linear models\n", - "replaced here by decision trees --- the code for validation\n", - "is almost identical. This approach leads to correct predictions\n", - "for 68.5% of the locations in the test data set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7a1f736", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.241246Z", - "iopub.status.busy": "2023-07-26T05:17:43.241128Z", - "iopub.status.idle": "2023-07-26T05:17:43.248339Z", - "shell.execute_reply": "2023-07-26T05:17:43.247882Z" - } - }, - "outputs": [], - "source": [ - "validation = skm.ShuffleSplit(n_splits=1,\n", - " test_size=200,\n", - " random_state=0)\n", - "results = skm.cross_validate(clf,\n", - " D,\n", - " High,\n", - " cv=validation)\n", - "results['test_score']" - ] - }, - { - "cell_type": "markdown", - "id": "533af069", - "metadata": {}, - "source": [ - "Next, we consider whether pruning the tree might lead to improved\n", - "classification performance. We first split the data into a training and\n", - "test set. We will use cross-validation to prune the tree on the training\n", - "set, and then evaluate the performance of the pruned tree on the test\n", - "set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c663a623", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.250854Z", - "iopub.status.busy": "2023-07-26T05:17:43.250675Z", - "iopub.status.idle": "2023-07-26T05:17:43.253625Z", - "shell.execute_reply": "2023-07-26T05:17:43.253117Z" - } - }, - "outputs": [], - "source": [ - "(X_train,\n", - " X_test,\n", - " High_train,\n", - " High_test) = skm.train_test_split(X,\n", - " High,\n", - " test_size=0.5,\n", - " random_state=0)\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "9e618049", - "metadata": {}, - "source": [ - "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2f2a403", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.256072Z", - "iopub.status.busy": "2023-07-26T05:17:43.255891Z", - "iopub.status.idle": "2023-07-26T05:17:43.261392Z", - "shell.execute_reply": "2023-07-26T05:17:43.260822Z" - } - }, - "outputs": [], - "source": [ - "clf = DTC(criterion='entropy', random_state=0)\n", - "clf.fit(X_train, High_train)\n", - "accuracy_score(High_test, clf.predict(X_test))" - ] - }, - { - "cell_type": "markdown", - "id": "bb59eecd", - "metadata": {}, - "source": [ - "Next we use the `cost_complexity_pruning_path()` method of\n", - "`clf` to extract cost-complexity values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2505658", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.264289Z", - "iopub.status.busy": "2023-07-26T05:17:43.264075Z", - "iopub.status.idle": "2023-07-26T05:17:43.268404Z", - "shell.execute_reply": "2023-07-26T05:17:43.267912Z" - } - }, - "outputs": [], - "source": [ - "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n", - "kfold = skm.KFold(10,\n", - " random_state=1,\n", - " shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "id": "0fdf1106", - "metadata": {}, - "source": [ - "This yields a set of impurities and $\\alpha$ values\n", - "from which we can extract an optimal one by cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09a7bd33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.270876Z", - "iopub.status.busy": "2023-07-26T05:17:43.270706Z", - "iopub.status.idle": "2023-07-26T05:17:43.553527Z", - "shell.execute_reply": "2023-07-26T05:17:43.553080Z" - } - }, - "outputs": [], - "source": [ - "grid = skm.GridSearchCV(clf,\n", - " {'ccp_alpha': ccp_path.ccp_alphas},\n", - " refit=True,\n", - " cv=kfold,\n", - " scoring='accuracy')\n", - "grid.fit(X_train, High_train)\n", - "grid.best_score_" - ] - }, - { - "cell_type": "markdown", - "id": "ff585d2c", - "metadata": {}, - "source": [ - "Let’s take a look at the pruned true." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a75dea32", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:43.556004Z", - "iopub.status.busy": "2023-07-26T05:17:43.555851Z", - "iopub.status.idle": "2023-07-26T05:17:44.479893Z", - "shell.execute_reply": "2023-07-26T05:17:44.479062Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(12, 12))[1]\n", - "best_ = grid.best_estimator_\n", - "plot_tree(best_,\n", - " feature_names=feature_names,\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "c81a06b3", - "metadata": {}, - "source": [ - "This is quite a bushy tree. We could count the leaves, or query\n", - "`best_` instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f1e3d19", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.483391Z", - "iopub.status.busy": "2023-07-26T05:17:44.483211Z", - "iopub.status.idle": "2023-07-26T05:17:44.486537Z", - "shell.execute_reply": "2023-07-26T05:17:44.486111Z" - } - }, - "outputs": [], - "source": [ - "best_.tree_.n_leaves" - ] - }, - { - "cell_type": "markdown", - "id": "7a32f63a", - "metadata": {}, - "source": [ - "The tree with 30 terminal\n", - "nodes results in the lowest cross-validation error rate, with an accuracy of\n", - "68.5%. How well does this pruned tree perform on the test data set? Once\n", - "again, we apply the `predict()` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30f5c2f9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.489160Z", - "iopub.status.busy": "2023-07-26T05:17:44.489048Z", - "iopub.status.idle": "2023-07-26T05:17:44.498333Z", - "shell.execute_reply": "2023-07-26T05:17:44.497705Z" - } - }, - "outputs": [], - "source": [ - "print(accuracy_score(High_test,\n", - " best_.predict(X_test)))\n", - "confusion = confusion_table(best_.predict(X_test),\n", - " High_test)\n", - "confusion" - ] - }, - { - "cell_type": "markdown", - "id": "adbe5709", - "metadata": {}, - "source": [ - "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance." - ] - }, - { - "cell_type": "markdown", - "id": "ad50114a", - "metadata": {}, - "source": [ - "## Fitting Regression Trees\n", - "Here we fit a regression tree to the `Boston` data set. The\n", - "steps are similar to those for classification trees." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ca6bc7a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.500404Z", - "iopub.status.busy": "2023-07-26T05:17:44.500198Z", - "iopub.status.idle": "2023-07-26T05:17:44.510037Z", - "shell.execute_reply": "2023-07-26T05:17:44.509664Z" - } - }, - "outputs": [], - "source": [ - "Boston = load_data(\"Boston\")\n", - "model = MS(Boston.columns.drop('medv'), intercept=False)\n", - "D = model.fit_transform(Boston)\n", - "feature_names = list(D.columns)\n", - "X = np.asarray(D)" - ] - }, - { - "cell_type": "markdown", - "id": "ebebb4a7", - "metadata": {}, - "source": [ - "First, we split the data into training and test sets, and fit the tree\n", - "to the training data. Here we use 30% of the data for the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15cfb553", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.512140Z", - "iopub.status.busy": "2023-07-26T05:17:44.511989Z", - "iopub.status.idle": "2023-07-26T05:17:44.515238Z", - "shell.execute_reply": "2023-07-26T05:17:44.514701Z" - } - }, - "outputs": [], - "source": [ - "(X_train,\n", - " X_test,\n", - " y_train,\n", - " y_test) = skm.train_test_split(X,\n", - " Boston['medv'],\n", - " test_size=0.3,\n", - " random_state=0)" - ] - }, - { - "cell_type": "markdown", - "id": "ae21c490", - "metadata": {}, - "source": [ - "Having formed our training and test data sets, we fit the regression tree." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d18820a3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.517521Z", - "iopub.status.busy": "2023-07-26T05:17:44.517330Z", - "iopub.status.idle": "2023-07-26T05:17:44.858666Z", - "shell.execute_reply": "2023-07-26T05:17:44.858062Z" - } - }, - "outputs": [], - "source": [ - "reg = DTR(max_depth=3)\n", - "reg.fit(X_train, y_train)\n", - "ax = subplots(figsize=(12,12))[1]\n", - "plot_tree(reg,\n", - " feature_names=feature_names,\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "5a4deec7", - "metadata": {}, - "source": [ - "The variable `lstat` measures the percentage of individuals with\n", - "lower socioeconomic status. The tree indicates that lower\n", - "values of `lstat` correspond to more expensive houses.\n", - "The tree predicts a median house price of $12,042 for small-sized homes (`rm < 6.8`), in\n", - "suburbs in which residents have low socioeconomic status (`lstat > 14.4`) and the crime-rate is moderate (`crim > 5.8`)." - ] - }, - { - "cell_type": "markdown", - "id": "f5cad860", - "metadata": {}, - "source": [ - "Now we use the cross-validation function to see whether pruning\n", - "the tree will improve performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "619f8d94", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.861023Z", - "iopub.status.busy": "2023-07-26T05:17:44.860905Z", - "iopub.status.idle": "2023-07-26T05:17:44.914455Z", - "shell.execute_reply": "2023-07-26T05:17:44.913988Z" - } - }, - "outputs": [], - "source": [ - "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n", - "kfold = skm.KFold(5,\n", - " shuffle=True,\n", - " random_state=10)\n", - "grid = skm.GridSearchCV(reg,\n", - " {'ccp_alpha': ccp_path.ccp_alphas},\n", - " refit=True,\n", - " cv=kfold,\n", - " scoring='neg_mean_squared_error')\n", - "G = grid.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "b971e775", - "metadata": {}, - "source": [ - "In keeping with the cross-validation results, we use the pruned tree\n", - "to make predictions on the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "779a14b6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.917435Z", - "iopub.status.busy": "2023-07-26T05:17:44.917262Z", - "iopub.status.idle": "2023-07-26T05:17:44.920852Z", - "shell.execute_reply": "2023-07-26T05:17:44.920436Z" - } - }, - "outputs": [], - "source": [ - "best_ = grid.best_estimator_\n", - "np.mean((y_test - best_.predict(X_test))**2)" - ] - }, - { - "cell_type": "markdown", - "id": "507d174f", - "metadata": {}, - "source": [ - "In other words, the test set MSE associated with the regression tree\n", - "is 28.07. The square root of\n", - "the MSE is therefore around\n", - "5.30,\n", - "indicating that this model leads to test predictions that are within around\n", - "$5300\n", - "of the true median home value for the suburb.\n", - "\n", - "Let’s plot the best tree to see how interpretable it is." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b04030b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:44.923340Z", - "iopub.status.busy": "2023-07-26T05:17:44.923189Z", - "iopub.status.idle": "2023-07-26T05:17:45.244796Z", - "shell.execute_reply": "2023-07-26T05:17:45.244381Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(12,12))[1]\n", - "plot_tree(G.best_estimator_,\n", - " feature_names=feature_names,\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "8e9aba94", - "metadata": {}, - "source": [ - "## Bagging and Random Forests" - ] - }, - { - "cell_type": "markdown", - "id": "81afb557", - "metadata": {}, - "source": [ - "Here we apply bagging and random forests to the `Boston` data, using\n", - "the `RandomForestRegressor()` from the `sklearn.ensemble` package. Recall\n", - "that bagging is simply a special case of a random forest with\n", - "$m=p$. Therefore, the `RandomForestRegressor()` function can be used to\n", - "perform both bagging and random forests. We start with bagging." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7dedce31", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:45.248119Z", - "iopub.status.busy": "2023-07-26T05:17:45.247930Z", - "iopub.status.idle": "2023-07-26T05:17:45.416858Z", - "shell.execute_reply": "2023-07-26T05:17:45.416407Z" - } - }, - "outputs": [], - "source": [ - "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n", - "bag_boston.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "b020aef3", - "metadata": {}, - "source": [ - "The argument `max_features` indicates that all 12 predictors should\n", - "be considered for each split of the tree --- in other words, that\n", - "bagging should be done. How well does this bagged model perform on\n", - "the test set?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f2c7336", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:45.418831Z", - "iopub.status.busy": "2023-07-26T05:17:45.418689Z", - "iopub.status.idle": "2023-07-26T05:17:45.525786Z", - "shell.execute_reply": "2023-07-26T05:17:45.525224Z" - } - }, - "outputs": [], - "source": [ - "ax = subplots(figsize=(8,8))[1]\n", - "y_hat_bag = bag_boston.predict(X_test)\n", - "ax.scatter(y_hat_bag, y_test)\n", - "np.mean((y_test - y_hat_bag)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "6e6db784", - "metadata": {}, - "source": [ - "The test set MSE associated with the bagged regression tree is\n", - "14.63, about half that obtained using an optimally-pruned single\n", - "tree. We could change the number of trees grown from the default of\n", - "100 by\n", - "using the `n_estimators` argument:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bba2439", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:45.528244Z", - "iopub.status.busy": "2023-07-26T05:17:45.528120Z", - "iopub.status.idle": "2023-07-26T05:17:46.372586Z", - "shell.execute_reply": "2023-07-26T05:17:46.372196Z" - } - }, - "outputs": [], - "source": [ - "bag_boston = RF(max_features=X_train.shape[1],\n", - " n_estimators=500,\n", - " random_state=0).fit(X_train, y_train)\n", - "y_hat_bag = bag_boston.predict(X_test)\n", - "np.mean((y_test - y_hat_bag)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "7fa447de", - "metadata": {}, - "source": [ - "There is not much change. Bagging and random forests cannot overfit by\n", - "increasing the number of trees, but can underfit if the number is too small.\n", - "\n", - "Growing a random forest proceeds in exactly the same way, except that\n", - "we use a smaller value of the `max_features` argument. By default,\n", - "`RandomForestRegressor()` uses $p$ variables when building a random\n", - "forest of regression trees (i.e. it defaults to bagging), and `RandomForestClassifier()` uses\n", - "$\\sqrt{p}$ variables when building a\n", - "random forest of classification trees. Here we use `max_features=6`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90457fbe", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:46.375077Z", - "iopub.status.busy": "2023-07-26T05:17:46.374893Z", - "iopub.status.idle": "2023-07-26T05:17:46.498984Z", - "shell.execute_reply": "2023-07-26T05:17:46.498519Z" - } - }, - "outputs": [], - "source": [ - "RF_boston = RF(max_features=6,\n", - " random_state=0).fit(X_train, y_train)\n", - "y_hat_RF = RF_boston.predict(X_test)\n", - "np.mean((y_test - y_hat_RF)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "f9ddb8fa", - "metadata": {}, - "source": [ - "The test set MSE is 20.04;\n", - "this indicates that random forests did somewhat worse than bagging\n", - "in this case. Extracting the `feature_importances_` values from the fitted model, we can view the\n", - "importance of each variable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8aae1857", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:46.501498Z", - "iopub.status.busy": "2023-07-26T05:17:46.501332Z", - "iopub.status.idle": "2023-07-26T05:17:46.511203Z", - "shell.execute_reply": "2023-07-26T05:17:46.510809Z" - } - }, - "outputs": [], - "source": [ - "feature_imp = pd.DataFrame(\n", - " {'importance':RF_boston.feature_importances_},\n", - " index=feature_names)\n", - "feature_imp.sort_values(by='importance', ascending=False)" - ] - }, - { - "cell_type": "markdown", - "id": "74b9ffb6", - "metadata": {}, - "source": [ - " This\n", - "is a relative measure of the total decrease in node impurity that results from\n", - "splits over that variable, averaged over all trees (this was plotted in Figure 8.9 for a model fit to the `Heart` data). \n", - "\n", - "The results indicate that across all of the trees considered in the\n", - "random forest, the wealth level of the community (`lstat`) and the\n", - "house size (`rm`) are by far the two most important variables." - ] - }, - { - "cell_type": "markdown", - "id": "82ed6937", - "metadata": {}, - "source": [ - "## Boosting" - ] - }, - { - "cell_type": "markdown", - "id": "d420075c", - "metadata": {}, - "source": [ - "Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`\n", - "to fit boosted regression trees to the `Boston` data\n", - "set. For classification we would use `GradientBoostingClassifier()`.\n", - "The argument `n_estimators=5000`\n", - "indicates that we want 5000 trees, and the option\n", - "`max_depth=3` limits the depth of each tree. The\n", - "argument `learning_rate` is the $\\lambda$\n", - "mentioned earlier in the description of boosting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "887b4fa9", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:46.513662Z", - "iopub.status.busy": "2023-07-26T05:17:46.513485Z", - "iopub.status.idle": "2023-07-26T05:17:50.160797Z", - "shell.execute_reply": "2023-07-26T05:17:50.160288Z" - } - }, - "outputs": [], - "source": [ - "boost_boston = GBR(n_estimators=5000,\n", - " learning_rate=0.001,\n", - " max_depth=3,\n", - " random_state=0)\n", - "boost_boston.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "97019e71", - "metadata": {}, - "source": [ - "We can see how the training error decreases with the `train_score_` attribute.\n", - "To get an idea of how the test error decreases we can use the\n", - "`staged_predict()` method to get the predicted values along the path." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c56696c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:50.163556Z", - "iopub.status.busy": "2023-07-26T05:17:50.163389Z", - "iopub.status.idle": "2023-07-26T05:17:50.751622Z", - "shell.execute_reply": "2023-07-26T05:17:50.751113Z" - } - }, - "outputs": [], - "source": [ - "test_error = np.zeros_like(boost_boston.train_score_)\n", - "for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):\n", - " test_error[idx] = np.mean((y_test - y_)**2)\n", - "\n", - "plot_idx = np.arange(boost_boston.train_score_.shape[0])\n", - "ax = subplots(figsize=(8,8))[1]\n", - "ax.plot(plot_idx,\n", - " boost_boston.train_score_,\n", - " 'b',\n", - " label='Training')\n", - "ax.plot(plot_idx,\n", - " test_error,\n", - " 'r',\n", - " label='Test')\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "id": "f1a851f2", - "metadata": {}, - "source": [ - "We now use the boosted model to predict `medv` on the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a636f63", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:50.753880Z", - "iopub.status.busy": "2023-07-26T05:17:50.753719Z", - "iopub.status.idle": "2023-07-26T05:17:50.765303Z", - "shell.execute_reply": "2023-07-26T05:17:50.764814Z" - } - }, - "outputs": [], - "source": [ - "y_hat_boost = boost_boston.predict(X_test);\n", - "np.mean((y_test - y_hat_boost)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "fb7f53af", - "metadata": {}, - "source": [ - " The test MSE obtained is 14.48,\n", - "similar to the test MSE for bagging. If we want to, we can\n", - "perform boosting with a different value of the shrinkage parameter\n", - "$\\lambda$ in (8.10). The default value is 0.001, but\n", - "this is easily modified. Here we take $\\lambda=0.2$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e2ab3c7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:50.767698Z", - "iopub.status.busy": "2023-07-26T05:17:50.767534Z", - "iopub.status.idle": "2023-07-26T05:17:53.090781Z", - "shell.execute_reply": "2023-07-26T05:17:53.090355Z" - } - }, - "outputs": [], - "source": [ - "boost_boston = GBR(n_estimators=5000,\n", - " learning_rate=0.2,\n", - " max_depth=3,\n", - " random_state=0)\n", - "boost_boston.fit(X_train,\n", - " y_train)\n", - "y_hat_boost = boost_boston.predict(X_test);\n", - "np.mean((y_test - y_hat_boost)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "9360b5b0", - "metadata": {}, - "source": [ - "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n", - "as $\\lambda=0.001$." - ] - }, - { - "cell_type": "markdown", - "id": "1693f5da", - "metadata": {}, - "source": [ - "## Bayesian Additive Regression Trees" - ] - }, - { - "cell_type": "markdown", - "id": "47c83ab9", - "metadata": {}, - "source": [ - "In this section we demonstrate a `Python` implementation of BART found in the\n", - "`ISLP.bart` package. We fit a model\n", - "to the `Boston` housing data set. This `BART()` estimator is\n", - "designed for quantitative outcome variables, though other implementations are available for\n", - "fitting logistic and probit models to categorical outcomes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0b41c04", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:53.093602Z", - "iopub.status.busy": "2023-07-26T05:17:53.093377Z", - "iopub.status.idle": "2023-07-26T05:17:54.986360Z", - "shell.execute_reply": "2023-07-26T05:17:54.985944Z" - } - }, - "outputs": [], - "source": [ - "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n", - "bart_boston.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "af48867c", - "metadata": {}, - "source": [ - "On this data set, with this split into test and training, we see that the test error of BART is similar to that of random forest." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fda8fffc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:54.988616Z", - "iopub.status.busy": "2023-07-26T05:17:54.988463Z", - "iopub.status.idle": "2023-07-26T05:17:55.919353Z", - "shell.execute_reply": "2023-07-26T05:17:55.918371Z" - } - }, - "outputs": [], - "source": [ - "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n", - "np.mean((y_test - yhat_test)**2)" - ] - }, - { - "cell_type": "markdown", - "id": "bad852b7", - "metadata": {}, - "source": [ - "We can check how many times each variable appeared in the collection of trees.\n", - "This gives a summary similar to the variable importance plot for boosting and random forests." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be77f0e4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:55.922114Z", - "iopub.status.busy": "2023-07-26T05:17:55.921834Z", - "iopub.status.idle": "2023-07-26T05:17:55.926261Z", - "shell.execute_reply": "2023-07-26T05:17:55.925458Z" - } - }, - "outputs": [], - "source": [ - "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n", - " index=D.columns)\n", - "var_inclusion" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/labs/Ch9-svm-lab.ipynb b/docs/source/labs/Ch9-svm-lab.ipynb deleted file mode 100644 index 7d8ebe5..0000000 --- a/docs/source/labs/Ch9-svm-lab.ipynb +++ /dev/null @@ -1,1200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d45c6d2b", - "metadata": {}, - "source": [ - "# Support Vector Machines\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "\n", - "In this lab, we use the `sklearn.svm` library to demonstrate the support\n", - "vector classifier and the support vector machine.\n", - "\n", - "We import some of our usual libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eeaa5be0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:57.286672Z", - "iopub.status.busy": "2023-07-26T05:17:57.286340Z", - "iopub.status.idle": "2023-07-26T05:17:58.454461Z", - "shell.execute_reply": "2023-07-26T05:17:58.453915Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib.pyplot import subplots, cm\n", - "import sklearn.model_selection as skm\n", - "from ISLP import load_data, confusion_table" - ] - }, - { - "cell_type": "markdown", - "id": "26ebd377", - "metadata": {}, - "source": [ - "We also collect the new imports\n", - "needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41a59634", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.457424Z", - "iopub.status.busy": "2023-07-26T05:17:58.457095Z", - "iopub.status.idle": "2023-07-26T05:17:58.490405Z", - "shell.execute_reply": "2023-07-26T05:17:58.490061Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.svm import SVC\n", - "from ISLP.svm import plot as plot_svm\n", - "from sklearn.metrics import RocCurveDisplay" - ] - }, - { - "cell_type": "markdown", - "id": "f197b846", - "metadata": {}, - "source": [ - "We will use the function `RocCurveDisplay.from_estimator()` to\n", - "produce several ROC plots, using a shorthand `roc_curve`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9a175d7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.493151Z", - "iopub.status.busy": "2023-07-26T05:17:58.492879Z", - "iopub.status.idle": "2023-07-26T05:17:58.496409Z", - "shell.execute_reply": "2023-07-26T05:17:58.495164Z" - } - }, - "outputs": [], - "source": [ - "roc_curve = RocCurveDisplay.from_estimator # shorthand" - ] - }, - { - "cell_type": "markdown", - "id": "f666c212", - "metadata": {}, - "source": [ - "## Support Vector Classifier\n", - "\n", - "We now use the `SupportVectorClassifier()` function (abbreviated `SVC()`) from `sklearn` to fit the support vector\n", - "classifier for a given value of the parameter `C`. The\n", - "`C` argument allows us to specify the cost of a violation to\n", - "the margin. When the `cost` argument is small, then the margins\n", - "will be wide and many support vectors will be on the margin or will\n", - "violate the margin. When the `C` argument is large, then the\n", - "margins will be narrow and there will be few support vectors on the\n", - "margin or violating the margin.\n", - "\n", - "Here we demonstrate\n", - "the use of `SVC()` on a two-dimensional example, so that we can\n", - "plot the resulting decision boundary. We begin by generating the\n", - "observations, which belong to two classes, and checking whether the\n", - "classes are linearly separable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7216b47", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.499025Z", - "iopub.status.busy": "2023-07-26T05:17:58.498850Z", - "iopub.status.idle": "2023-07-26T05:17:58.613732Z", - "shell.execute_reply": "2023-07-26T05:17:58.613136Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(1)\n", - "X = rng.standard_normal((50, 2))\n", - "y = np.array([-1]*25+[1]*25)\n", - "X[y==1] += 1\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0],\n", - " X[:,1],\n", - " c=y,\n", - " cmap=cm.coolwarm);" - ] - }, - { - "cell_type": "markdown", - "id": "7b4aff06", - "metadata": {}, - "source": [ - "They are not. We now fit the classifier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed329198", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.616777Z", - "iopub.status.busy": "2023-07-26T05:17:58.616601Z", - "iopub.status.idle": "2023-07-26T05:17:58.621468Z", - "shell.execute_reply": "2023-07-26T05:17:58.621124Z" - } - }, - "outputs": [], - "source": [ - "svm_linear = SVC(C=10, kernel='linear')\n", - "svm_linear.fit(X, y)" - ] - }, - { - "cell_type": "markdown", - "id": "5e6b4c79", - "metadata": {}, - "source": [ - "The support vector classifier with two features can\n", - "be visualized by plotting values of its *decision function*.\n", - "We have included a function for this in the `ISLP` package (inspired by a similar\n", - "example in the `sklearn` docs)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95494b8b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.623538Z", - "iopub.status.busy": "2023-07-26T05:17:58.623369Z", - "iopub.status.idle": "2023-07-26T05:17:58.810058Z", - "shell.execute_reply": "2023-07-26T05:17:58.809447Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X,\n", - " y,\n", - " svm_linear,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "f6ce1246", - "metadata": {}, - "source": [ - "The decision\n", - "boundary between the two classes is linear (because we used the\n", - "argument `kernel='linear'`). The support vectors are marked with `+`\n", - "and the remaining observations are plotted as circles.\n", - "\n", - "What if we instead used a smaller value of the cost parameter?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98c2236f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.812870Z", - "iopub.status.busy": "2023-07-26T05:17:58.812658Z", - "iopub.status.idle": "2023-07-26T05:17:58.967044Z", - "shell.execute_reply": "2023-07-26T05:17:58.966462Z" - } - }, - "outputs": [], - "source": [ - "svm_linear_small = SVC(C=0.1, kernel='linear')\n", - "svm_linear_small.fit(X, y)\n", - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X,\n", - " y,\n", - " svm_linear_small,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "906f4bb8", - "metadata": {}, - "source": [ - "With a smaller value of the cost parameter, we\n", - "obtain a larger number of support vectors, because the margin is now\n", - "wider. For linear kernels, we can extract the\n", - "coefficients of the linear decision boundary as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b498f594", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.969920Z", - "iopub.status.busy": "2023-07-26T05:17:58.969735Z", - "iopub.status.idle": "2023-07-26T05:17:58.973409Z", - "shell.execute_reply": "2023-07-26T05:17:58.972682Z" - } - }, - "outputs": [], - "source": [ - "svm_linear.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "90a0ee53", - "metadata": {}, - "source": [ - "Since the support vector machine is an estimator in `sklearn`, we\n", - "can use the usual machinery to tune it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b65e80d6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:58.975865Z", - "iopub.status.busy": "2023-07-26T05:17:58.975747Z", - "iopub.status.idle": "2023-07-26T05:17:59.006899Z", - "shell.execute_reply": "2023-07-26T05:17:59.006545Z" - } - }, - "outputs": [], - "source": [ - "kfold = skm.KFold(5, \n", - " random_state=0,\n", - " shuffle=True)\n", - "grid = skm.GridSearchCV(svm_linear,\n", - " {'C':[0.001,0.01,0.1,1,5,10,100]},\n", - " refit=True,\n", - " cv=kfold,\n", - " scoring='accuracy')\n", - "grid.fit(X, y)\n", - "grid.best_params_" - ] - }, - { - "cell_type": "markdown", - "id": "d390528c", - "metadata": {}, - "source": [ - "We can easily access the cross-validation errors for each of these models\n", - "in `grid.cv_results_`. This prints out a lot of detail, so we\n", - "extract the accuracy results only." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bba8fad7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.010681Z", - "iopub.status.busy": "2023-07-26T05:17:59.010231Z", - "iopub.status.idle": "2023-07-26T05:17:59.014165Z", - "shell.execute_reply": "2023-07-26T05:17:59.013759Z" - } - }, - "outputs": [], - "source": [ - "grid.cv_results_[('mean_test_score')]" - ] - }, - { - "cell_type": "markdown", - "id": "703e2d43", - "metadata": {}, - "source": [ - "We see that `C=1` results in the highest cross-validation\n", - "accuracy of 0.74, though\n", - "the accuracy is the same for several values of `C`.\n", - "The classifier `grid.best_estimator_` can be used to predict the class\n", - "label on a set of test observations. Let’s generate a test data set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad64269d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.017638Z", - "iopub.status.busy": "2023-07-26T05:17:59.017453Z", - "iopub.status.idle": "2023-07-26T05:17:59.020397Z", - "shell.execute_reply": "2023-07-26T05:17:59.019642Z" - } - }, - "outputs": [], - "source": [ - "X_test = rng.standard_normal((20, 2))\n", - "y_test = np.array([-1]*10+[1]*10)\n", - "X_test[y_test==1] += 1" - ] - }, - { - "cell_type": "markdown", - "id": "db41f5e2", - "metadata": {}, - "source": [ - "Now we predict the class labels of these test observations. Here we\n", - "use the best model selected by cross-validation in order to make the\n", - "predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5107fca1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.026576Z", - "iopub.status.busy": "2023-07-26T05:17:59.026365Z", - "iopub.status.idle": "2023-07-26T05:17:59.033664Z", - "shell.execute_reply": "2023-07-26T05:17:59.033173Z" - } - }, - "outputs": [], - "source": [ - "best_ = grid.best_estimator_\n", - "y_test_hat = best_.predict(X_test)\n", - "confusion_table(y_test_hat, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "bbfc8005", - "metadata": {}, - "source": [ - "Thus, with this value of `C`,\n", - "70% of the test\n", - "observations are correctly classified. What if we had instead used\n", - "`C=0.001`?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0320d9e0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.035936Z", - "iopub.status.busy": "2023-07-26T05:17:59.035645Z", - "iopub.status.idle": "2023-07-26T05:17:59.041475Z", - "shell.execute_reply": "2023-07-26T05:17:59.041054Z" - } - }, - "outputs": [], - "source": [ - "svm_ = SVC(C=0.001,\n", - " kernel='linear').fit(X, y)\n", - "y_test_hat = svm_.predict(X_test)\n", - "confusion_table(y_test_hat, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "427d775f", - "metadata": {}, - "source": [ - "In this case 60% of test observations are correctly classified.\n", - "\n", - "We now consider a situation in which the two classes are linearly\n", - "separable. Then we can find an optimal separating hyperplane using the\n", - "`SVC()` estimator. We first\n", - "further separate the two classes in our simulated data so that they\n", - "are linearly separable:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84d7e778", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.043762Z", - "iopub.status.busy": "2023-07-26T05:17:59.043557Z", - "iopub.status.idle": "2023-07-26T05:17:59.144334Z", - "shell.execute_reply": "2023-07-26T05:17:59.143881Z" - } - }, - "outputs": [], - "source": [ - "X[y==1] += 1.9;\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);" - ] - }, - { - "cell_type": "markdown", - "id": "ff7bdad1", - "metadata": {}, - "source": [ - "Now the observations are just barely linearly separable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abb1f8be", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.147029Z", - "iopub.status.busy": "2023-07-26T05:17:59.146868Z", - "iopub.status.idle": "2023-07-26T05:17:59.154787Z", - "shell.execute_reply": "2023-07-26T05:17:59.153508Z" - } - }, - "outputs": [], - "source": [ - "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n", - "y_hat = svm_.predict(X)\n", - "confusion_table(y_hat, y)" - ] - }, - { - "cell_type": "markdown", - "id": "c44297cc", - "metadata": {}, - "source": [ - "We fit the\n", - "support vector classifier and plot the resulting hyperplane, using a\n", - "very large value of `C` so that no observations are\n", - "misclassified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e4ed2f5", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.157293Z", - "iopub.status.busy": "2023-07-26T05:17:59.157100Z", - "iopub.status.idle": "2023-07-26T05:17:59.287805Z", - "shell.execute_reply": "2023-07-26T05:17:59.287264Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X,\n", - " y,\n", - " svm_,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "2836d70d", - "metadata": {}, - "source": [ - "Indeed no training errors were made and only three support vectors were used.\n", - "In fact, the large value of `C` also means that these three support points are *on the margin*, and define it.\n", - "One may wonder how good the classifier could be on test data that depends on only three data points!\n", - " We now try a smaller\n", - "value of `C`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "164a611c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.291112Z", - "iopub.status.busy": "2023-07-26T05:17:59.290933Z", - "iopub.status.idle": "2023-07-26T05:17:59.296989Z", - "shell.execute_reply": "2023-07-26T05:17:59.296618Z" - } - }, - "outputs": [], - "source": [ - "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n", - "y_hat = svm_.predict(X)\n", - "confusion_table(y_hat, y)" - ] - }, - { - "cell_type": "markdown", - "id": "39a432d1", - "metadata": {}, - "source": [ - "Using `C=0.1`, we again do not misclassify any training observations, but we\n", - "also obtain a much wider margin and make use of twelve support\n", - "vectors. These jointly define the orientation of the decision boundary, and since there are more of them, it is more stable. It seems possible that this model will perform better on test\n", - "data than the model with `C=1e5` (and indeed, a simple experiment with a large test set would bear this out)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c67591a1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.299723Z", - "iopub.status.busy": "2023-07-26T05:17:59.299515Z", - "iopub.status.idle": "2023-07-26T05:17:59.442236Z", - "shell.execute_reply": "2023-07-26T05:17:59.441618Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X,\n", - " y,\n", - " svm_,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "25e61f65", - "metadata": {}, - "source": [ - "## Support Vector Machine\n", - "In order to fit an SVM using a non-linear kernel, we once again use\n", - "the `SVC()` estimator. However, now we use a different value\n", - "of the parameter `kernel`. To fit an SVM with a polynomial\n", - "kernel we use `kernel=\"poly\"`, and to fit an SVM with a\n", - "radial kernel we use\n", - "`kernel=\"rbf\"`. In the former case we also use the\n", - "`degree` argument to specify a degree for the polynomial kernel\n", - "(this is $d$ in (9.22)), and in the latter case we use\n", - "`gamma` to specify a value of $\\gamma$ for the radial basis\n", - "kernel (9.24).\n", - "\n", - "We first generate some data with a non-linear class boundary, as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "322be574", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.445260Z", - "iopub.status.busy": "2023-07-26T05:17:59.445043Z", - "iopub.status.idle": "2023-07-26T05:17:59.448424Z", - "shell.execute_reply": "2023-07-26T05:17:59.447969Z" - } - }, - "outputs": [], - "source": [ - "X = rng.standard_normal((200, 2))\n", - "X[:100] += 2\n", - "X[100:150] -= 2\n", - "y = np.array([1]*150+[2]*50)" - ] - }, - { - "cell_type": "markdown", - "id": "22fe2182", - "metadata": {}, - "source": [ - "Plotting the data makes it clear that the class boundary is indeed non-linear." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04fda182", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.451843Z", - "iopub.status.busy": "2023-07-26T05:17:59.451105Z", - "iopub.status.idle": "2023-07-26T05:17:59.552259Z", - "shell.execute_reply": "2023-07-26T05:17:59.551787Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0],\n", - " X[:,1],\n", - " c=y,\n", - " cmap=cm.coolwarm)" - ] - }, - { - "cell_type": "markdown", - "id": "64913fe3", - "metadata": {}, - "source": [ - "The data is randomly split into training and testing groups. We then\n", - "fit the training data using the `SVC()` estimator with a\n", - "radial kernel and $\\gamma=1$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c2690d1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.554419Z", - "iopub.status.busy": "2023-07-26T05:17:59.554263Z", - "iopub.status.idle": "2023-07-26T05:17:59.559300Z", - "shell.execute_reply": "2023-07-26T05:17:59.558839Z" - } - }, - "outputs": [], - "source": [ - "(X_train, \n", - " X_test,\n", - " y_train,\n", - " y_test) = skm.train_test_split(X,\n", - " y,\n", - " test_size=0.5,\n", - " random_state=0)\n", - "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1)\n", - "svm_rbf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "5da9efdb", - "metadata": {}, - "source": [ - "The plot shows that the resulting SVM has a decidedly non-linear\n", - "boundary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3eb171e8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.561548Z", - "iopub.status.busy": "2023-07-26T05:17:59.561354Z", - "iopub.status.idle": "2023-07-26T05:17:59.836831Z", - "shell.execute_reply": "2023-07-26T05:17:59.836408Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X_train,\n", - " y_train,\n", - " svm_rbf,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "ab5b1446", - "metadata": {}, - "source": [ - "We can see from the figure that there are a fair number of training\n", - "errors in this SVM fit. If we increase the value of `C`, we\n", - "can reduce the number of training errors. However, this comes at the\n", - "price of a more irregular decision boundary that seems to be at risk\n", - "of overfitting the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9a6b905b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:17:59.839191Z", - "iopub.status.busy": "2023-07-26T05:17:59.838970Z", - "iopub.status.idle": "2023-07-26T05:18:00.004302Z", - "shell.execute_reply": "2023-07-26T05:18:00.003731Z" - } - }, - "outputs": [], - "source": [ - "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1e5)\n", - "svm_rbf.fit(X_train, y_train)\n", - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X_train,\n", - " y_train,\n", - " svm_rbf,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "300c1b8b", - "metadata": {}, - "source": [ - "We can perform cross-validation using `skm.GridSearchCV()` to select the\n", - "best choice of $\\gamma$ and `C` for an SVM with a radial\n", - "kernel:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ab01d6c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.007020Z", - "iopub.status.busy": "2023-07-26T05:18:00.006845Z", - "iopub.status.idle": "2023-07-26T05:18:00.113455Z", - "shell.execute_reply": "2023-07-26T05:18:00.112946Z" - } - }, - "outputs": [], - "source": [ - "kfold = skm.KFold(5, \n", - " random_state=0,\n", - " shuffle=True)\n", - "grid = skm.GridSearchCV(svm_rbf,\n", - " {'C':[0.1,1,10,100,1000],\n", - " 'gamma':[0.5,1,2,3,4]},\n", - " refit=True,\n", - " cv=kfold,\n", - " scoring='accuracy');\n", - "grid.fit(X_train, y_train)\n", - "grid.best_params_" - ] - }, - { - "cell_type": "markdown", - "id": "1bb987ae", - "metadata": {}, - "source": [ - "The best choice of parameters under five-fold CV is achieved at `C=1`\n", - "and `gamma=0.5`, though several other values also achieve the same\n", - "value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "166a6acb", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.116122Z", - "iopub.status.busy": "2023-07-26T05:18:00.115942Z", - "iopub.status.idle": "2023-07-26T05:18:00.360989Z", - "shell.execute_reply": "2023-07-26T05:18:00.360361Z" - } - }, - "outputs": [], - "source": [ - "best_svm = grid.best_estimator_\n", - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X_train,\n", - " y_train,\n", - " best_svm,\n", - " ax=ax)\n", - "\n", - "y_hat_test = best_svm.predict(X_test)\n", - "confusion_table(y_hat_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "39ee6f32", - "metadata": {}, - "source": [ - "With these parameters, 12% of test\n", - "observations are misclassified by this SVM." - ] - }, - { - "cell_type": "markdown", - "id": "f0ea699d", - "metadata": {}, - "source": [ - "## ROC Curves\n", - "\n", - "SVMs and support vector classifiers output class labels for each\n", - "observation. However, it is also possible to obtain *fitted values*\n", - "for each observation, which are the numerical scores used to\n", - "obtain the class labels. For instance, in the case of a support vector\n", - "classifier, the fitted value for an observation $X= (X_1, X_2, \\ldots,\n", - "X_p)^T$ takes the form $\\hat{\\beta}_0 + \\hat{\\beta}_1 X_1 +\n", - "\\hat{\\beta}_2 X_2 + \\ldots + \\hat{\\beta}_p X_p$. For an SVM with a\n", - "non-linear kernel, the equation that yields the fitted value is given\n", - "in (9.23). The sign of the fitted value\n", - "determines on which side of the decision boundary the observation\n", - "lies. Therefore, the relationship between the fitted value and the\n", - "class prediction for a given observation is simple: if the fitted\n", - "value exceeds zero then the observation is assigned to one class, and\n", - "if it is less than zero then it is assigned to the other.\n", - "By changing this threshold from zero to some positive value,\n", - "we skew the classifications in favor of one class versus the other.\n", - "By considering a range of these thresholds, positive and negative, we produce the ingredients for a ROC plot.\n", - "We can access these values by calling the `decision_function()`\n", - "method of a fitted SVM estimator.\n", - "\n", - "The function `ROCCurveDisplay.from_estimator()` (which we have abbreviated to `roc_curve()`) will produce a plot of a ROC curve. It takes a fitted estimator as its first argument, followed\n", - "by a model matrix $X$ and labels $y$. The argument `name` is used in the legend,\n", - "while `color` is used for the color of the line. Results are plotted\n", - "on our axis object `ax`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0607fc41", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.363471Z", - "iopub.status.busy": "2023-07-26T05:18:00.363318Z", - "iopub.status.idle": "2023-07-26T05:18:00.469265Z", - "shell.execute_reply": "2023-07-26T05:18:00.468690Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "roc_curve(best_svm,\n", - " X_train,\n", - " y_train,\n", - " name='Training',\n", - " color='r',\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "54446e71", - "metadata": {}, - "source": [ - " In this example, the SVM appears to provide accurate predictions. By increasing\n", - "$\\gamma$ we can produce a more flexible fit and generate further\n", - "improvements in accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5211a882", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.471742Z", - "iopub.status.busy": "2023-07-26T05:18:00.471575Z", - "iopub.status.idle": "2023-07-26T05:18:00.629647Z", - "shell.execute_reply": "2023-07-26T05:18:00.629119Z" - } - }, - "outputs": [], - "source": [ - "svm_flex = SVC(kernel=\"rbf\", \n", - " gamma=50,\n", - " C=1)\n", - "svm_flex.fit(X_train, y_train)\n", - "fig, ax = subplots(figsize=(8,8))\n", - "roc_curve(svm_flex,\n", - " X_train,\n", - " y_train,\n", - " name='Training $\\gamma=50$',\n", - " color='r',\n", - " ax=ax);" - ] - }, - { - "cell_type": "markdown", - "id": "de7e4be8", - "metadata": {}, - "source": [ - "However, these ROC curves are all on the training data. We are really\n", - "more interested in the level of prediction accuracy on the test\n", - "data. When we compute the ROC curves on the test data, the model with\n", - "$\\gamma=0.5$ appears to provide the most accurate results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12acc4ff", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.632175Z", - "iopub.status.busy": "2023-07-26T05:18:00.631994Z", - "iopub.status.idle": "2023-07-26T05:18:00.636710Z", - "shell.execute_reply": "2023-07-26T05:18:00.636238Z" - } - }, - "outputs": [], - "source": [ - "roc_curve(svm_flex,\n", - " X_test,\n", - " y_test,\n", - " name='Test $\\gamma=50$',\n", - " color='b',\n", - " ax=ax)\n", - "fig;" - ] - }, - { - "cell_type": "markdown", - "id": "eb5c8aeb", - "metadata": {}, - "source": [ - "Let’s look at our tuned SVM." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21c81913", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.639004Z", - "iopub.status.busy": "2023-07-26T05:18:00.638813Z", - "iopub.status.idle": "2023-07-26T05:18:00.753454Z", - "shell.execute_reply": "2023-07-26T05:18:00.752986Z" - } - }, - "outputs": [], - "source": [ - "fig, ax = subplots(figsize=(8,8))\n", - "for (X_, y_, c, name) in zip(\n", - " (X_train, X_test),\n", - " (y_train, y_test),\n", - " ('r', 'b'),\n", - " ('CV tuned on training',\n", - " 'CV tuned on test')):\n", - " roc_curve(best_svm,\n", - " X_,\n", - " y_,\n", - " name=name,\n", - " ax=ax,\n", - " color=c)" - ] - }, - { - "cell_type": "markdown", - "id": "b9fefe9f", - "metadata": {}, - "source": [ - "## SVM with Multiple Classes\n", - "\n", - "If the response is a factor containing more than two levels, then the\n", - "`SVC()` function will perform multi-class classification using\n", - "either the one-versus-one approach (when `decision_function_shape=='ovo'`)\n", - "or one-versus-rest {One-versus-rest is also known as one-versus-all.} (when `decision_function_shape=='ovr'`).\n", - "We explore that setting briefly here by\n", - "generating a third class of observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fff4fa8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.756116Z", - "iopub.status.busy": "2023-07-26T05:18:00.755939Z", - "iopub.status.idle": "2023-07-26T05:18:00.855998Z", - "shell.execute_reply": "2023-07-26T05:18:00.855206Z" - } - }, - "outputs": [], - "source": [ - "rng = np.random.default_rng(123)\n", - "X = np.vstack([X, rng.standard_normal((50, 2))])\n", - "y = np.hstack([y, [0]*50])\n", - "X[y==0,1] += 2\n", - "fig, ax = subplots(figsize=(8,8))\n", - "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);" - ] - }, - { - "cell_type": "markdown", - "id": "b7adc87d", - "metadata": {}, - "source": [ - "We now fit an SVM to the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5396f2df", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:00.861579Z", - "iopub.status.busy": "2023-07-26T05:18:00.861401Z", - "iopub.status.idle": "2023-07-26T05:18:01.455936Z", - "shell.execute_reply": "2023-07-26T05:18:01.455227Z" - } - }, - "outputs": [], - "source": [ - "svm_rbf_3 = SVC(kernel=\"rbf\",\n", - " C=10,\n", - " gamma=1,\n", - " decision_function_shape='ovo');\n", - "svm_rbf_3.fit(X, y)\n", - "fig, ax = subplots(figsize=(8,8))\n", - "plot_svm(X,\n", - " y,\n", - " svm_rbf_3,\n", - " scatter_cmap=cm.tab10,\n", - " ax=ax)" - ] - }, - { - "cell_type": "markdown", - "id": "837644f5", - "metadata": {}, - "source": [ - "The `sklearn.svm` library can also be used to perform support vector\n", - "regression with a numerical response using the estimator `SupportVectorRegression()`." - ] - }, - { - "cell_type": "markdown", - "id": "a6bc0cbc", - "metadata": {}, - "source": [ - "## Application to Gene Expression Data\n", - "\n", - "We now examine the `Khan` data set, which consists of a number of\n", - "tissue samples corresponding to four distinct types of small round\n", - "blue cell tumors. For each tissue sample, gene expression measurements\n", - "are available. The data set consists of training data, `xtrain`\n", - "and `ytrain`, and testing data, `xtest` and `ytest`.\n", - "\n", - "We examine the dimension of the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f63c575e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:01.458214Z", - "iopub.status.busy": "2023-07-26T05:18:01.458015Z", - "iopub.status.idle": "2023-07-26T05:18:01.539063Z", - "shell.execute_reply": "2023-07-26T05:18:01.538275Z" - } - }, - "outputs": [], - "source": [ - "Khan = load_data('Khan')\n", - "Khan['xtrain'].shape, Khan['xtest'].shape" - ] - }, - { - "cell_type": "markdown", - "id": "bfd6492c", - "metadata": {}, - "source": [ - "This data set consists of expression measurements for 2,308\n", - "genes. The training and test sets consist of 63 and 20\n", - "observations, respectively.\n", - "\n", - "We will use a support vector approach to predict cancer subtype using\n", - "gene expression measurements. In this data set, there is a very\n", - "large number of features relative to the number of observations. This\n", - "suggests that we should use a linear kernel, because the additional\n", - "flexibility that will result from using a polynomial or radial kernel \n", - "is unnecessary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32091338", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:01.541953Z", - "iopub.status.busy": "2023-07-26T05:18:01.541758Z", - "iopub.status.idle": "2023-07-26T05:18:01.578259Z", - "shell.execute_reply": "2023-07-26T05:18:01.577863Z" - } - }, - "outputs": [], - "source": [ - "khan_linear = SVC(kernel='linear', C=10)\n", - "khan_linear.fit(Khan['xtrain'], Khan['ytrain'])\n", - "confusion_table(khan_linear.predict(Khan['xtrain']),\n", - " Khan['ytrain'])" - ] - }, - { - "cell_type": "markdown", - "id": "23043ab0", - "metadata": {}, - "source": [ - "We see that there are *no* training\n", - "errors. In fact, this is not surprising, because the large number of\n", - "variables relative to the number of observations implies that it is\n", - "easy to find hyperplanes that fully separate the classes. We are more\n", - "interested in the support vector classifier’s performance on the\n", - "test observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9058023", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-26T05:18:01.582842Z", - "iopub.status.busy": "2023-07-26T05:18:01.582631Z", - "iopub.status.idle": "2023-07-26T05:18:01.598298Z", - "shell.execute_reply": "2023-07-26T05:18:01.597510Z" - } - }, - "outputs": [], - "source": [ - "confusion_table(khan_linear.predict(Khan['xtest']),\n", - " Khan['ytest'])" - ] - }, - { - "cell_type": "markdown", - "id": "d0d5aba4", - "metadata": {}, - "source": [ - "We see that using `C=10` yields two test set errors on these data." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md:myst", - "main_language": "python" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c978d67c101866759599fa560a48c5fa39fe383e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 26 Jul 2023 10:41:40 -0400 Subject: [PATCH 054/195] updating ISLP_labs; removing notebooks dir --- docs/ISLP_labs | 2 +- docs/notebooks/requirements.txt | 160 -------------------------------- 2 files changed, 1 insertion(+), 161 deletions(-) delete mode 100644 docs/notebooks/requirements.txt diff --git a/docs/ISLP_labs b/docs/ISLP_labs index 322b7ec..58b8849 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit 322b7ec7ac3906157f808da84c23b9388996f23c +Subproject commit 58b88498ea92347721099e8c7b0c8248829b5ac7 diff --git a/docs/notebooks/requirements.txt b/docs/notebooks/requirements.txt deleted file mode 100644 index 8da6115..0000000 --- a/docs/notebooks/requirements.txt +++ /dev/null @@ -1,160 +0,0 @@ -anyio==3.6.2 -appnope==0.1.3 -argon2-cffi==21.3.0 -argon2-cffi-bindings==21.2.0 -arrow==1.2.3 -astor==0.8.1 -asttokens==2.2.1 -attrs==22.2.0 -autograd==1.5 -autograd-gamma==0.5.0 -backcall==0.2.0 -beautifulsoup4==4.12.1 -bleach==6.0.0 -build==0.10.0 -CacheControl==0.12.11 -certifi==2023.5.7 -cffi==1.15.1 -charset-normalizer==3.1.0 -cleo==2.0.1 -comm==0.1.3 -contourpy==1.0.7 -crashtest==0.4.1 -cycler==0.11.0 -debugpy==1.6.7 -decorator==5.1.1 -defusedxml==0.7.1 -distlib==0.3.6 -dulwich==0.21.5 -executing==1.2.0 -fastjsonschema==2.16.3 -filelock==3.11.0 -fonttools==4.39.4 -formulaic==0.6.1 -fqdn==1.5.1 -future==0.18.3 -html5lib==1.1 -idna==3.4 -importlib-metadata==6.1.0 -importlib-resources==5.12.0 -installer==0.7.0 -interface-meta==1.3.0 -ipykernel==6.22.0 -ipython==8.12.0 -ipython-genutils==0.2.0 -ipywidgets==8.0.6 -ISLP==0.3.16 -isoduration==20.11.0 -jaraco.classes==3.2.3 -jedi==0.18.2 -Jinja2==3.1.2 -joblib==1.2.0 -jsonpointer==2.3 -jsonschema==4.17.3 -jupyter==1.0.0 -jupyter-console==6.6.3 -jupyter-events==0.6.3 -jupyter_client==8.1.0 -jupyter_core==5.3.0 -jupyter_server==2.5.0 -jupyter_server_terminals==0.4.4 -jupyterlab-pygments==0.2.2 -jupyterlab-widgets==3.0.7 -keyring==23.13.1 -kiwisolver==1.4.4 -lab==7.3 -lifelines==0.27.7 -lockfile==0.12.2 -lxml==4.9.2 -markdown-it-py==2.2.0 -MarkupSafe==2.1.2 -matplotlib==3.7.1 -matplotlib-inline==0.1.6 -mdurl==0.1.2 -mistune==2.0.5 -more-itertools==9.1.0 -msgpack==1.0.5 -nbclassic==0.5.5 -nbclient==0.7.3 -nbconvert==7.3.0 -nbformat==5.8.0 -nest-asyncio==1.5.6 -notebook==6.5.4 -notebook_shim==0.2.2 -numpy==1.24.2 -packaging==23.0 -pandas==2.0.1 -pandocfilters==1.5.0 -parso==0.8.3 -patsy==0.5.3 -pexpect==4.8.0 -pickleshare==0.7.5 -Pillow==9.5.0 -pkginfo==1.9.6 -platformdirs==3.2.0 -poetry==1.5.0 -poetry-core==1.6.0 -poetry-plugin-export==1.3.1 -progressbar2==4.2.0 -prometheus-client==0.16.0 -prompt-toolkit==3.0.38 -psutil==5.9.4 -ptyprocess==0.7.0 -pure-eval==0.2.2 -pycparser==2.21 -pygam==0.9.0 -Pygments==2.14.0 -pyparsing==3.0.9 -pyproject_hooks==1.0.0 -pyrsistent==0.19.3 -python-dateutil==2.8.2 -python-json-logger==2.0.7 -python-utils==3.5.2 -pytz==2023.3 -pytz-deprecation-shim==0.1.0.post0 -PyYAML==6.0 -pyzmq==25.0.2 -qtconsole==5.4.3 -QtPy==2.3.1 -rapidfuzz==2.15.1 -requests==2.30.0 -requests-toolbelt==1.0.0 -rfc3339-validator==0.1.4 -rfc3986-validator==0.1.1 -scikit-learn==1.2.2 -scipy==1.10.1 -Send2Trash==1.8.0 -shellingham==1.5.0.post1 -simplejson==3.19.1 -six==1.16.0 -sniffio==1.3.0 -soupsieve==2.4 -stack-data==0.6.2 -statsmodels==0.14.0 -terminado==0.17.1 -threadpoolctl==3.1.0 -tinycss2==1.2.1 -tomli==2.0.1 -tomlkit==0.11.8 -tornado==6.2 -traitlets==5.9.0 -trove-classifiers==2023.5.2 -txt2tags==3.8 -typing_extensions==4.5.0 -tzdata==2023.3 -uri-template==1.2.0 -urllib3==1.26.15 -virtualenv==20.23.0 -wcwidth==0.2.6 -webcolors==1.13 -webencodings==0.5.1 -websocket-client==1.5.1 -widgetsnbextension==4.0.7 -wrapt==1.15.0 -xattr==0.10.1 -zipp==3.15.0 -torch==2.0.0 -torchvision==0.15.1 -pytorch_lightning==2.0.2 -torchinfo==1.7.2 -torchmetrics==0.11.4 From 1657d3ecc2bfa965c843effe7cda7a9b5931f95e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 11:57:45 -0400 Subject: [PATCH 055/195] BF: fix errors in calls to pytorch_lightning methods since PDF --- ISLP/torch/lightning.py | 67 +++++++++++++++++++++++++++++------------ 1 file changed, 47 insertions(+), 20 deletions(-) diff --git a/ISLP/torch/lightning.py b/ISLP/torch/lightning.py index 82c45db..b7a338b 100644 --- a/ISLP/torch/lightning.py +++ b/ISLP/torch/lightning.py @@ -14,7 +14,7 @@ from pytorch_lightning import (LightningModule, LightningDataModule) -from pytorch_lightning.utilities.distributed import rank_zero_only +from pytorch_lightning.utilities import rank_zero_only from pytorch_lightning.callbacks import Callback class SimpleDataModule(LightningDataModule): @@ -132,14 +132,15 @@ def __init__(self, model, loss, optimizer=None, - metrics={}, + metrics=None, on_epoch=True, pre_process_y_for_metrics=lambda y: y): super(SimpleModule, self).__init__() self.model = model - self.loss = loss or nn.MSELoss() + self.loss = loss + optimizer = optimizer or RMSprop(model.parameters()) self._optimizer = optimizer self.metrics = metrics @@ -160,8 +161,10 @@ def training_step(self, batch, batch_idx): y_ = self.pre_process_y_for_metrics(y) for _metric in self.metrics.keys(): + pl_metric = self.metrics[_metric] self.log(f"train_{_metric}", - self.metrics[_metric](preds, y_), + pl_metric(preds.to(pl_metric.device), + y_.to(pl_metric.device)), on_epoch=self.on_epoch) return loss @@ -181,22 +184,36 @@ def configure_optimizers(self): @staticmethod def regression(model, + metrics=None, + device='cpu', **kwargs): - loss = nn.MSELoss() + + if metrics is None: + metrics = {} + + loss = nn.MSELoss().to(device) + if device is not None: + for key, metric in metrics.items(): + metrics[key] = metric.to(device) return SimpleModule(model, loss, + metrics=metrics, **kwargs) @staticmethod def binary_classification(model, - metrics={}, - device=None, + metrics=None, + device='cpu', **kwargs): + + if metrics is None: + metrics = {} + loss = nn.BCEWithLogitsLoss() if 'accuracy' not in metrics: - metrics['accuracy'] = Accuracy() + metrics['accuracy'] = Accuracy('binary') if device is not None: - for key, metric in metrics: + for key, metric in metrics.items(): metrics[key] = metric.to(device) return SimpleModule(model, loss, @@ -205,15 +222,21 @@ def binary_classification(model, **kwargs) @staticmethod - def classification(model, - metrics={}, - device=None, - **kwargs): - loss = nn.CrossEntropyLoss() + def multiclass(model, + num_classes, + metrics=None, + device='cpu', + **kwargs): + + if metrics is None: + metrics = {} + + loss = nn.CrossEntropyLoss().to(device) if 'accuracy' not in metrics: - metrics['accuracy'] = Accuracy() + metrics['accuracy'] = Accuracy('multiclass', + num_classes=num_classes) if device is not None: - for key, metric in metrics: + for key, metric in metrics.items(): metrics[key] = metric.to(device) return SimpleModule(model, loss, @@ -233,7 +256,7 @@ def on_validation_batch_start(self, pl_module, batch, batch_idx, - dataloader_idx): + dataloader_idx=0): x, y = batch self.val_preds.append(pl_module.forward(x)) self.val_targets.append(y) @@ -252,8 +275,10 @@ def on_validation_epoch_end(self, on_epoch=pl_module.on_epoch) for _metric in pl_module.metrics.keys(): + pl_metric = pl_module.metrics[_metric] pl_module.log(f"valid_{_metric}", - pl_module.metrics[_metric](preds, targets_), + pl_metric(preds.to(pl_metric.device), + targets_.to(pl_metric.device)), on_epoch=pl_module.on_epoch) def on_test_epoch_start(self, @@ -267,7 +292,7 @@ def on_test_batch_start(self, pl_module, batch, batch_idx, - dataloader_idx): + dataloader_idx=0): x, y = batch self.test_preds.append(pl_module.forward(x)) self.test_targets.append(y) @@ -286,7 +311,9 @@ def on_test_epoch_end(self, on_epoch=pl_module.on_epoch) for _metric in pl_module.metrics.keys(): + pl_metric = pl_module.metrics[_metric] pl_module.log(f"test_{_metric}", - pl_module.metrics[_metric](preds, targets_), + pl_metric(preds.to(pl_metric.device), + targets_.to(pl_metric.device)), on_epoch=pl_module.on_epoch) From a2867fa77f6e31f0237fbd1576fb5321c05371d8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 12:01:43 -0400 Subject: [PATCH 056/195] adding jupytext for readthedocs build --- docs/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/requirements.txt b/docs/requirements.txt index 1e093a5..10bce0e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -4,3 +4,4 @@ myst_nb sphinx-book-theme rpy2 sphinx_rtd_theme +jupytext From 66650ffcf693e4f7ad81e186936a3399f9dc1122 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 12:14:34 -0400 Subject: [PATCH 057/195] revert to the original name --- ISLP/torch/lightning.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ISLP/torch/lightning.py b/ISLP/torch/lightning.py index b7a338b..e3b4088 100644 --- a/ISLP/torch/lightning.py +++ b/ISLP/torch/lightning.py @@ -222,11 +222,11 @@ def binary_classification(model, **kwargs) @staticmethod - def multiclass(model, - num_classes, - metrics=None, - device='cpu', - **kwargs): + def classification(model, + num_classes, + metrics=None, + device='cpu', + **kwargs): if metrics is None: metrics = {} From 81cfa6dec3cb10c1d07cac4855ada92104aa645b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 13:33:07 -0400 Subject: [PATCH 058/195] update submodule, v2 in install instructions --- docs/ISLP_labs | 2 +- docs/source/installation.myst | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index 58b8849..46f3711 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit 58b88498ea92347721099e8c7b0c8248829b5ac7 +Subproject commit 46f3711a7ee6883d66ba4fc61c6700a2da02b4e7 diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 054cee6..ce360f7 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,9 +61,11 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/frozen_requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/frozen_requirements.txt ``` +For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2_devel). + ## Torch requirements The `ISLP` labs use `torch` and various related packages for the lab From e5b66c876dad471a9c562cd47b87b6251d6391f0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 13:44:59 -0400 Subject: [PATCH 059/195] requirements in library are not frozen --- frozen_requirements.txt | 10 ---------- torch_requirements.txt | 10 +++++----- 2 files changed, 5 insertions(+), 15 deletions(-) delete mode 100644 frozen_requirements.txt diff --git a/frozen_requirements.txt b/frozen_requirements.txt deleted file mode 100644 index 73b5e2e..0000000 --- a/frozen_requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -numpy==1.21.6 -scipy==1.10.1 -pandas==1.4.1 -lxml==4.8.0 -scikit-learn==1.2.2 -joblib==1.2.0 -statsmodels==0.13.2 -lifelines==0.27.6 -pygam==0.8.0 -l0bnb==1.0.0 diff --git a/torch_requirements.txt b/torch_requirements.txt index 374ee22..d7656a8 100644 --- a/torch_requirements.txt +++ b/torch_requirements.txt @@ -1,5 +1,5 @@ -torch==2.0.0 -torchvision==0.15.1 -pytorch_lightning==1.7.7 -torchinfo==1.7.2 -torchmetrics==0.9.3 +torch +torchvision +pytorch_lightning +torchinfo +torchmetrics From 99700bc1d409bd172f0667502c9df3050f2b6be8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 11:00:25 -0700 Subject: [PATCH 060/195] Create pylint.yml --- .github/workflows/pylint.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/pylint.yml diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml new file mode 100644 index 0000000..383e65c --- /dev/null +++ b/.github/workflows/pylint.yml @@ -0,0 +1,23 @@ +name: Pylint + +on: [push] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.8", "3.9", "3.10"] + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pylint + - name: Analysing the code with pylint + run: | + pylint $(git ls-files '*.py') From ea1965c98e5c63324a47d2024bcf4f55b798df07 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 11:03:27 -0700 Subject: [PATCH 061/195] Create python-package-conda.yml --- .github/workflows/python-package-conda.yml | 34 ++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 .github/workflows/python-package-conda.yml diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml new file mode 100644 index 0000000..384f9b7 --- /dev/null +++ b/.github/workflows/python-package-conda.yml @@ -0,0 +1,34 @@ +name: Python Package using Conda + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + conda env update --file environment.yml --name base + - name: Lint with flake8 + run: | + conda install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + conda install pytest + pytest From a5fed84d1dcc19b3a6d569207b22ab007915d03d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 14:27:02 -0400 Subject: [PATCH 062/195] back to v1 for docs/ISLP_labs so that readthedocs shows v1 labs --- docs/ISLP_labs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index 46f3711..ca12db6 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit 46f3711a7ee6883d66ba4fc61c6700a2da02b4e7 +Subproject commit ca12db6546e69d0a3c6785fa34fc77ebbf4fdbd1 From 399cde6d2114fb1066d788d9dfc97cdecd23bade Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 14:31:10 -0400 Subject: [PATCH 063/195] fix links to frozen environments --- .readthedocs.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index bf4c424..121d88a 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -31,9 +31,9 @@ sphinx: # Optionally declare the Python requirements required to build your docs python: install: - - requirements: frozen_requirements.txt + - requirements: https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/frozen_requirements.txt; - requirements: docs/requirements.txt - - requirements: torch_requirements.txt + - requirements: https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/torch_requirements.txt; - requirements: keras_requirements.txt - method: pip path: . From d088c9ad18994f3d28a5a9503c3abb2da832eef1 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 14:35:30 -0400 Subject: [PATCH 064/195] get frozen reqs from docs/ISLP_labs --- .readthedocs.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 121d88a..f0c90dd 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -31,9 +31,9 @@ sphinx: # Optionally declare the Python requirements required to build your docs python: install: - - requirements: https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/frozen_requirements.txt; + - requirements: docs/ISLP_labs/frozen_requirements.txt - requirements: docs/requirements.txt - - requirements: https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/torch_requirements.txt; + - requirements: docs/ISLP_labs/torch_requirements.txt - requirements: keras_requirements.txt - method: pip path: . From 972281001ca50533c9bcdf94d1f99b5194639aef Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 14:45:50 -0400 Subject: [PATCH 065/195] executed version of IMDB --- docs/source/imdb.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/source/imdb.ipynb b/docs/source/imdb.ipynb index ae0d7dd..d9ba5cb 100644 --- a/docs/source/imdb.ipynb +++ b/docs/source/imdb.ipynb @@ -345,11 +345,10 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "source///ipynb,jupyterbook///md:myst,jupyterbook///ipynb", - "main_language": "python" + "formats": "md,ipynb" }, "kernelspec": { - "display_name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -363,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.17" } }, "nbformat": 4, From 8328bb0fa059480df91b899673a010605bb2b96d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 15:07:14 -0400 Subject: [PATCH 066/195] remove keras requirement --- .readthedocs.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index f0c90dd..368691f 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -34,6 +34,5 @@ python: - requirements: docs/ISLP_labs/frozen_requirements.txt - requirements: docs/requirements.txt - requirements: docs/ISLP_labs/torch_requirements.txt - - requirements: keras_requirements.txt - method: pip path: . From c38ce521b27866e7e2fe0d8293ea7269c88d66fb Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 12:22:58 -0700 Subject: [PATCH 067/195] Update python-package-conda.yml --- .github/workflows/python-package-conda.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 384f9b7..4a1c295 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -20,7 +20,9 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - conda env update --file environment.yml --name base + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt + - name: Lint with flake8 run: | conda install flake8 From 3015be944aa3274e980d7450d3cfa95fca7e723b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 18:15:58 -0400 Subject: [PATCH 068/195] updating docs --- docs/README.rst | 5 +++++ docs/source/conf.py | 2 +- docs/source/labs.rst | 3 ++- 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/README.rst b/docs/README.rst index cee991e..709ce7d 100644 --- a/docs/README.rst +++ b/docs/README.rst @@ -7,3 +7,8 @@ Ridge regression ================ There is a snippet that should be inserted to remove the many warnings raised. + +Frozen reqs +=========== + +The versions of the labs are referred to in `source/installation.myst`, `source/labs.rst` diff --git a/docs/source/conf.py b/docs/source/conf.py index f4521cd..6007ef5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -29,7 +29,7 @@ numpydoc_class_members_toctree = False nb_execution_mode = "auto" nb_execution_timeout = 60*20 #*100 -nb_execution_excludepatterns = ['Ch10*'] +nb_execution_excludepatterns = ['Ch10*', 'imdb.ipynb'] nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} diff --git a/docs/source/labs.rst b/docs/source/labs.rst index a70370e..b562cb9 100644 --- a/docs/source/labs.rst +++ b/docs/source/labs.rst @@ -15,7 +15,8 @@ Package versions To ensure you have the same package versions as those built here, run: - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/frozen_requirements.txt + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/frozen_requirements.txt; + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/torch_requirements.txt .. toctree:: :maxdepth: 1 From 0c5dc9e642e8ad5400fa7fe82ca1f5ad1c0c6cdc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 00:05:54 -0400 Subject: [PATCH 069/195] fixing flake errors --- ISLP/bart/likelihood.py | 2 +- ISLP/models/model_spec.py | 2 +- docs/fix_and_run_notebooks.py | 1 - setup.py | 5 +---- 4 files changed, 3 insertions(+), 7 deletions(-) diff --git a/ISLP/bart/likelihood.py b/ISLP/bart/likelihood.py index 28f341d..cfa3ce6 100644 --- a/ISLP/bart/likelihood.py +++ b/ISLP/bart/likelihood.py @@ -82,7 +82,7 @@ def marginal_loglikelihood(response, if not incremental: if responsesq_sum is None: responsesq_sum = (response**2).sum() - response_moments = (n, response_sum, responseseq_sum) + response_moments = (n, response_sum, responsesq_sum) logL -= n * 0.5 * np.log(sigmasq) logL -= 0.5 * responsesq_sum / sigmasq diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index dda82b2..07a8b88 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -715,7 +715,7 @@ def contrast(col, is_categorical=True, encoder=encoder) -def ordinal(col, *args, **kwargs): +def ordinal(col, name=None, *args, **kwargs): """ Create ordinal encoding of categorical feature. diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index de76437..1545aec 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -8,7 +8,6 @@ for f in glob(os.path.join(dirname, 'source', 'labs', 'Ch14*')): os.remove(f) print(f) - stop version = 'v1' main = 'main' diff --git a/setup.py b/setup.py index 568200d..ff4a13b 100755 --- a/setup.py +++ b/setup.py @@ -213,10 +213,7 @@ def version_error_msg(pkg_name, found_ver, min_ver): # get long_description -if sys.version_info[0] > 2: - long_description = open('README.md', 'rt', encoding='utf-8').read() -else: - long_description = unicode(file('README.md').read(), 'utf-8') +long_description = open('README.md', 'rt', encoding='utf-8').read() def main(**extra_args): setup(name=info.NAME, From 81b934624fdd866cb912c145dafe4d997ad907c0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 21:11:01 -0700 Subject: [PATCH 070/195] Update python-package-conda.yml --- .github/workflows/python-package-conda.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 4a1c295..27b6ba3 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -22,6 +22,7 @@ jobs: run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt + pip install git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | From fab27dd2fbce7d547520cf4893730c0787c607de Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 00:33:50 -0400 Subject: [PATCH 071/195] test the hitters part of deep learning lab --- tests/deeplearning/test_hitters.py | 481 +++++++++++++++++++++++++++++ 1 file changed, 481 insertions(+) create mode 100644 tests/deeplearning/test_hitters.py diff --git a/tests/deeplearning/test_hitters.py b/tests/deeplearning/test_hitters.py new file mode 100644 index 0000000..bf609b9 --- /dev/null +++ b/tests/deeplearning/test_hitters.py @@ -0,0 +1,481 @@ +import numpy as np +import pandas as pd +from matplotlib.pyplot import subplots +from sklearn.linear_model import \ + (LinearRegression, + Lasso) +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import KFold +from sklearn.pipeline import Pipeline +from ISLP import load_data +from ISLP.models import ModelSpec as MS +from sklearn.model_selection import \ + (train_test_split, + GridSearchCV) + +# torch + +import torch +from torch import nn +from torch.utils.data import TensorDataset + +# torch helpers + +from torchmetrics import MeanAbsoluteError +from torchinfo import summary + +# pytorch lightning + +from pytorch_lightning import Trainer +from pytorch_lightning.loggers import CSVLogger + +# setting seed + +from pytorch_lightning import seed_everything +seed_everything(0, workers=True) +torch.use_deterministic_algorithms(True, warn_only=True) + +# ISLP.torch + +from ISLP.torch import (SimpleDataModule, + SimpleModule, + ErrorTracker, + rec_num_workers) + + +def test_hitters(max_epochs=2, + num_lam=5): + + Hitters = load_data('Hitters').dropna() + n = Hitters.shape[0] + + # We will fit two linear models (least squares and lasso) and compare their performance + # to that of a neural network. For this comparison we will use mean absolute error on a validation dataset. + # \begin{equation*} + # \begin{split} + # \mbox{MAE}(y,\hat{y}) = \frac{1}{n} \sum_{i=1}^n |y_i-\hat{y}_i|. + # \end{split} + # \end{equation*} + # We set up the model matrix and the response. + + # In[11]: + + + model = MS(Hitters.columns.drop('Salary'), intercept=False) + X = model.fit_transform(Hitters).to_numpy() + Y = Hitters['Salary'].to_numpy() + + + # The `to_numpy()` method above converts `pandas` + # data frames or series to `numpy` arrays. + # We do this because we will need to use `sklearn` to fit the lasso model, + # and it requires this conversion. + # We also use a linear regression method from `sklearn`, rather than the method + # in Chapter~3 from `statsmodels`, to facilitate the comparisons. + + # We now split the data into test and training, fixing the random + # state used by `sklearn` to do the split. + + # In[12]: + + + (X_train, + X_test, + Y_train, + Y_test) = train_test_split(X, + Y, + test_size=1/3, + random_state=1) + + + # ### Linear Models + # We fit the linear model and evaluate the test error directly. + + # In[13]: + + + hit_lm = LinearRegression().fit(X_train, Y_train) + Yhat_test = hit_lm.predict(X_test) + np.abs(Yhat_test - Y_test).mean() + + + # Next we fit the lasso using `sklearn`. We are using + # mean absolute error to select and evaluate a model, rather than mean squared error. + # The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work, we create a cross-validation grid and perform the cross-validation directly. + # + # We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform, + # and then fit the lasso without further normalization. + + # In[14]: + + + scaler = StandardScaler(with_mean=True, with_std=True) + lasso = Lasso(warm_start=True, max_iter=30000) + standard_lasso = Pipeline(steps=[('scaler', scaler), + ('lasso', lasso)]) + + + # We need to create a grid of values for $\lambda$. As is common practice, + # we choose a grid of 100 values of $\lambda$, uniform on the log scale from `lam_max` down to `0.01*lam_max`. Here `lam_max` is the smallest value of + # $\lambda$ with an all-zero solution. This value equals the largest absolute inner-product between any predictor and the (centered) response. {The derivation of this result is beyond the scope of this book.} + + # In[15]: + + + X_s = scaler.fit_transform(X_train) + n = X_s.shape[0] + lam_max = np.fabs(X_s.T.dot(Y_train - Y_train.mean())).max() / n + param_grid = {'alpha': np.exp(np.linspace(0, np.log(0.01), num_lam)) + * lam_max} + + + # Note that we had to transform the data first, since the scale of the variables impacts the choice of $\lambda$. + # We now perform cross-validation using this sequence of $\lambda$ values. + + # In[16]: + + + cv = KFold(10, + shuffle=True, + random_state=1) + grid = GridSearchCV(lasso, + param_grid, + cv=cv, + scoring='neg_mean_absolute_error') + grid.fit(X_train, Y_train); + + + # We extract the lasso model with best cross-validated mean absolute error, and evaluate its + # performance on `X_test` and `Y_test`, which were not used in + # cross-validation. + + # In[17]: + + + trained_lasso = grid.best_estimator_ + Yhat_test = trained_lasso.predict(X_test) + np.fabs(Yhat_test - Y_test).mean() + + + # This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point. + # + # ### Specifying a Network: Classes and Inheritance + # To fit the neural network, we first set up a model structure + # that describes the network. + # Doing so requires us to define new classes specific to the model we wish to fit. + # Typically this is done in `pytorch` by sub-classing a generic + # representation of a network, which is the approach we take here. + # Although this example is simple, we will go through the steps in some detail, since it will serve us well + # for the more complex examples to follow. + + # In[18]: + + + class HittersModel(nn.Module): + + def __init__(self, input_size): + super(HittersModel, self).__init__() + self.flatten = nn.Flatten() + self.sequential = nn.Sequential( + nn.Linear(input_size, 50), + nn.ReLU(), + nn.Dropout(0.4), + nn.Linear(50, 1)) + + def forward(self, x): + x = self.flatten(x) + return torch.flatten(self.sequential(x)) + + + # The `class` statement identifies the code chunk as a + # declaration for a class `HittersModel` + # that inherits from the base class `nn.Module`. This base + # class is ubiquitous in `torch` and represents the + # mappings in the neural networks. + # + # Indented beneath the `class` statement are the methods of this class: + # in this case `__init__` and `forward`. The `__init__` method is + # called when an instance of the class is created as in the cell + # below. In the methods, `self` always refers to an instance of the + # class. In the `__init__` method, we have attached two objects to + # `self` as attributes: `flatten` and `sequential`. These are used in + # the `forward` method to describe the map that this module implements. + # + # There is one additional line in the `__init__` method, which + # is a call to + # `super()`. This function allows subclasses (i.e. `HittersModel`) + # to access methods of the class they inherit from. For example, + # the class `nn.Module` has its own `__init__` method, which is different from + # the `HittersModel.__init__()` method we’ve written above. + # Using `super()` allows us to call the method of the base class. For + # `torch` models, we will always be making this `super()` call as it is necessary + # for the model to be properly interpreted by `torch`. + # + # The object `nn.Module` has more methods than simply `__init__` and `forward`. These + # methods are directly accessible to `HittersModel` instances because of this inheritance. + # One such method we will see shortly is the `eval()` method, used + # to disable dropout for when we want to evaluate the model on test data. + + # In[19]: + + + hit_model = HittersModel(X.shape[1]) + + + # The object `self.sequential` is a composition of four maps. The + # first maps the 19 features of `Hitters` to 50 dimensions, introducing $50\times 19+50$ parameters + # for the weights and *intercept* of the map (often called the *bias*). This layer + # is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a + # linear map down to 1 dimension, again with a bias. The total number of + # trainable parameters is therefore $50\times 19+50+50+1=1051$. + + # The package `torchinfo` provides a `summary()` function that neatly summarizes + # this information. We specify the size of the input and see the size + # of each tensor as it passes through layers of the network. + + # In[20]: + + + summary(hit_model, + input_size=X_train.shape, + col_names=['input_size', + 'output_size', + 'num_params']) + + + # We have truncated the end of the output slightly, here and in subsequent uses. + # + # We now need to transform our training data into a form accessible to `torch`. + # The basic + # datatype in `torch` is a `tensor`, which is very similar + # to an `ndarray` from early chapters. + # We also note here that `torch` typically + # works with 32-bit (*single precision*) + # rather than 64-bit (*double precision*) floating point numbers. + # We therefore convert our data to `np.float32` before + # forming the tensor. + # The $X$ and $Y$ tensors are then arranged into a `Dataset` + # recognized by `torch` + # using `TensorDataset()`. + + # In[21]: + + + X_train_t = torch.tensor(X_train.astype(np.float32)) + Y_train_t = torch.tensor(Y_train.astype(np.float32)) + hit_train = TensorDataset(X_train_t, Y_train_t) + + + # We do the same for the test data. + + # In[22]: + + + X_test_t = torch.tensor(X_test.astype(np.float32)) + Y_test_t = torch.tensor(Y_test.astype(np.float32)) + hit_test = TensorDataset(X_test_t, Y_test_t) + + + # Finally, this dataset is passed to a `DataLoader()` which ultimately + # passes data into our network. While this may seem + # like a lot of overhead, this structure is helpful for more + # complex tasks where data may live on different machines, + # or where data must be passed to a GPU. + # We provide a helper function `SimpleDataModule()` in `ISLP` to make this task easier for + # standard usage. + # One of its arguments is `num_workers`, which indicates + # how many processes we will use + # for loading the data. For small + # data like `Hitters` this will have little effect, but + # it does provide an advantage for the `MNIST` and `CIFAR100` examples below. + # The `torch` package will inspect the process running and determine a + # maximum number of workers. {This depends on the computing hardware and the number of cores available.} We’ve included a function + # `rec_num_workers()` to compute this so we know how many + # workers might be reasonable (here the max was 16). + + # In[23]: + + + max_num_workers = rec_num_workers() + + + # The general training setup in `pytorch_lightning` involves + # training, validation and test data. These are each + # represented by different data loaders. During each epoch, + # we run a training step to learn the model and a validation + # step to track the error. The test data is typically + # used at the end of training to evaluate the model. + # + # In this case, as we had split only into test and training, + # we’ll use the test data as validation data with the + # argument `validation=hit_test`. The + # `validation` argument can be a float between 0 and 1, an + # integer, or a + # `Dataset`. If a float (respectively, integer), it is interpreted + # as a percentage (respectively number) of the *training* observations to be used for validation. + # If it is a `Dataset`, it is passed directly to a data loader. + + # In[24]: + + + hit_dm = SimpleDataModule(hit_train, + hit_test, + batch_size=32, + num_workers=min(4, max_num_workers), + validation=hit_test) + + + # Next we must provide a `pytorch_lightning` module that controls + # the steps performed during the training process. We provide methods for our + # `SimpleModule()` that simply record the value + # of the loss function and any additional + # metrics at the end of each epoch. These operations + # are controlled by the methods `SimpleModule.[training/test/validation]_step()`, though + # we will not be modifying these in our examples. + + # In[25]: + + + hit_module = SimpleModule.regression(hit_model, + metrics={'mae':MeanAbsoluteError()}) + + + # By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in + # (10.23). + # We have also asked for mean absolute error to be tracked as well + # in the metrics that are logged. + # + # We log our results via `CSVLogger()`, which in this case stores the results in a CSV file within a directory `logs/hitters`. After the fitting is complete, this allows us to load the + # results as a `pd.DataFrame()` and visualize them below. There are + # several ways to log the results within `pytorch_lightning`, though + # we will not cover those here in detail. + + # In[26]: + + + hit_logger = CSVLogger('logs', name='hitters') + + + # Finally we are ready to train our model and log the results. We + # use the `Trainer()` object from `pytorch_lightning` + # to do this work. The argument `datamodule=hit_dm` tells the trainer + # how training/validation/test logs are produced, + # while the first argument `hit_module` + # specifies the network architecture + # as well as the training/validation/test steps. + # The `callbacks` argument allows for + # several tasks to be carried out at various + # points while training a model. Here + # our `ErrorTracker()` callback will enable + # us to compute validation error while training + # and, finally, the test error. + # We now fit the model for 50 epochs. + + # In[27]: + + + hit_trainer = Trainer(deterministic=True, + max_epochs=max_epochs, + log_every_n_steps=5, + logger=hit_logger, + callbacks=[ErrorTracker()]) + hit_trainer.fit(hit_module, datamodule=hit_dm) + + + # At each step of SGD, the algorithm randomly selects 32 training observations for + # the computation of the gradient. Recall from Section 10.7 + # that an epoch amounts to the number of SGD steps required to process $n$ + # observations. Since the training set has + # $n=175$, and we specified a `batch_size` of 32 in the construction of `hit_dm`, an epoch is $175/32=5.5$ SGD steps. + # + # After having fit the model, we can evaluate performance on our test + # data using the `test()` method of our trainer. + + # In[28]: + + + hit_trainer.test(hit_module, datamodule=hit_dm) + + + # The results of the fit have been logged into a CSV file. We can find the + # results specific to this run in the `experiment.metrics_file_path` + # attribute of our logger. Note that each time the model is fit, the logger will output + # results into a new subdirectory of our directory `logs/hitters`. + # + # We now create a plot of the MAE (mean absolute error) as a function of + # the number of epochs. + # First we retrieve the logged summaries. + + # In[29]: + + + hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path) + + + # Since we will produce similar plots in later examples, we write a + # simple generic function to produce this plot. + + # In[30]: + + + def summary_plot(results, + ax, + col='loss', + valid_legend='Validation', + training_legend='Training', + ylabel='Loss', + fontsize=20): + for (column, + color, + label) in zip([f'train_{col}_epoch', + f'valid_{col}'], + ['black', + 'red'], + [training_legend, + valid_legend]): + results.plot(x='epoch', + y=column, + label=label, + marker='o', + color=color, + ax=ax) + ax.set_xlabel('Epoch') + ax.set_ylabel(ylabel) + return ax + + + # We now set up our axes, and use our function to produce the MAE plot. + + # In[31]: + + + fig, ax = subplots(1, 1, figsize=(6, 6)) + ax = summary_plot(hit_results, + ax, + col='mae', + ylabel='MAE', + valid_legend='Validation (=Test)') + ax.set_ylim([0, 400]) + ax.set_xticks(np.linspace(0, 50, 11).astype(int)); + + + # We can predict directly from the final model, and + # evaluate its performance on the test data. + # Before fitting, we call the `eval()` method + # of `hit_model`. + # This tells + # `torch` to effectively consider this model to be fitted, so that + # we can use it to predict on new data. For our model here, + # the biggest change is that the dropout layers will + # be turned off, i.e. no weights will be randomly + # dropped in predicting on new data. + + # In[32]: + + + hit_model.eval() + preds = hit_module(X_test_t) + torch.abs(Y_test_t - preds).mean() + + + From 226e40f727d114798eb1767195869ad08ce4274d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 00:47:15 -0400 Subject: [PATCH 072/195] deep learning test --- tests/deeplearning/__init__.py | 0 tests/deeplearning/test_mnist.py | 258 +++++++++++++++++++++++++++++++ 2 files changed, 258 insertions(+) create mode 100644 tests/deeplearning/__init__.py create mode 100644 tests/deeplearning/test_mnist.py diff --git a/tests/deeplearning/__init__.py b/tests/deeplearning/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/deeplearning/test_mnist.py b/tests/deeplearning/test_mnist.py new file mode 100644 index 0000000..c6d39d9 --- /dev/null +++ b/tests/deeplearning/test_mnist.py @@ -0,0 +1,258 @@ + +# torch + +import torch +from torch import nn + +# torch helpers + +from torchinfo import summary + +# pytorch lightning + +from pytorch_lightning import Trainer +from pytorch_lightning.loggers import CSVLogger + +# setting seed + +from pytorch_lightning import seed_everything +seed_everything(0, workers=True) +torch.use_deterministic_algorithms(True, warn_only=True) + +# ISLP.torch + +from ISLP.torch import (SimpleDataModule, + SimpleModule, + ErrorTracker) + +from torchvision.datasets import MNIST +from torchvision.transforms import ToTensor + +def test_mnist(max_epochs=2): + + + # ## Multilayer Network on the MNIST Digit Data + # The `torchvision` package comes with a number of example datasets, + # including the `MNIST` digit data. Our first step is to retrieve + # the training and test data sets; the `MNIST()` function within + # `torchvision.datasets` is provided for this purpose. The + # data will be downloaded the first time this function is executed, and stored in the directory `data/MNIST`. + + # In[34]: + + + (mnist_train, + mnist_test) = [MNIST(root='data', + train=train, + download=True, + transform=ToTensor()) + for train in [True, False]] + mnist_train + + + # There are 60,000 images in the training data and 10,000 in the test + # data. The images are $28\times 28$, and stored as a matrix of pixels. We + # need to transform each one into a vector. + # + # Neural networks are somewhat sensitive to the scale of the inputs, much as ridge and + # lasso regularization are affected by scaling. Here the inputs are eight-bit + # grayscale values between 0 and 255, so we rescale to the unit + # interval. {Note: eight bits means $2^8$, which equals 256. Since the convention + # is to start at $0$, the possible values range from $0$ to $255$.} + # This transformation, along with some reordering + # of the axes, is performed by the `ToTensor()` transform + # from the `torchvision.transforms` package. + # + # As in our `Hitters` example, we form a data module + # from the training and test datasets, setting aside 20% + # of the training images for validation. + + # In[35]: + + + mnist_dm = SimpleDataModule(mnist_train, + mnist_test, + validation=0.2, + num_workers=2, + batch_size=256) + + + # Let’s take a look at the data that will get fed into our network. We loop through the first few + # chunks of the test dataset, breaking after 2 batches: + + # In[36]: + + + for idx, (X_ ,Y_) in enumerate(mnist_dm.train_dataloader()): + print('X: ', X_.shape) + print('Y: ', Y_.shape) + if idx >= 1: + break + + + # We see that the $X$ for each batch consists of 256 images of size `1x28x28`. + # Here the `1` indicates a single channel (greyscale). For RGB images such as `CIFAR100` below, + # we will see that the `1` in the size will be replaced by `3` for the three RGB channels. + # + # Now we are ready to specify our neural network. + + # In[37]: + + + class MNISTModel(nn.Module): + def __init__(self): + super(MNISTModel, self).__init__() + self.layer1 = nn.Sequential( + nn.Flatten(), + nn.Linear(28*28, 256), + nn.ReLU(), + nn.Dropout(0.4)) + self.layer2 = nn.Sequential( + nn.Linear(256, 128), + nn.ReLU(), + nn.Dropout(0.3)) + self._forward = nn.Sequential( + self.layer1, + self.layer2, + nn.Linear(128, 10)) + def forward(self, x): + return self._forward(x) + + + # We see that in the first layer, each `1x28x28` image is flattened, then mapped to + # 256 dimensions where we apply a ReLU activation with 40% dropout. + # A second layer maps the first layer’s output down to + # 128 dimensions, applying a ReLU activation with 30% dropout. Finally, + # the 128 dimensions are mapped down to 10, the number of classes in the + # `MNIST` data. + + # In[38]: + + + mnist_model = MNISTModel() + + + # We can check that the model produces output of expected size based + # on our existing batch `X_` above. + + # In[39]: + + + mnist_model(X_).size() + + + # Let’s take a look at the summary of the model. Instead of an `input_size` we can pass + # a tensor of correct shape. In this case, we pass through the final + # batched `X_` from above. + + # In[40]: + + + summary(mnist_model, + input_data=X_, + col_names=['input_size', + 'output_size', + 'num_params']) + + + # Having set up both the model and the data module, fitting this model is + # now almost identical to the `Hitters` example. In contrast to our regression model, here we will use the + # `SimpleModule.classification()` method which + # uses the cross-entropy loss function instead of mean squared error. It must be supplied with the number of classes in the problem. + + # In[41]: + + + mnist_module = SimpleModule.classification(mnist_model, + num_classes=10) + mnist_logger = CSVLogger('logs', name='MNIST') + + + # Now we are ready to go. The final step is to supply training data, and fit the model. + + # In[42]: + + + mnist_trainer = Trainer(deterministic=True, + max_epochs=max_epochs, + logger=mnist_logger, + callbacks=[ErrorTracker()]) + mnist_trainer.fit(mnist_module, + datamodule=mnist_dm) + + + # We have suppressed the output here, which is a progress report on the + # fitting of the model, grouped by epoch. This is very useful, since on + # large datasets fitting can take time. Fitting this model took 245 + # seconds on a MacBook Pro with an Apple M1 Pro chip with 10 cores and 16 GB of RAM. + # Here we specified a + # validation split of 20%, so training is actually performed on + # 80% of the 60,000 observations in the training set. This is an + # alternative to actually supplying validation data, like we did for the `Hitters` data. + # SGD uses batches + # of 256 observations in computing the gradient, and doing the + # arithmetic, we see that an epoch corresponds to 188 gradient steps. + + # `SimpleModule.classification()` includes + # an accuracy metric by default. Other + # classification metrics can be added from `torchmetrics`. + # We will use our `summary_plot()` function to display + # accuracy across epochs. + + + mnist_trainer.test(mnist_module, + datamodule=mnist_dm) + + + # Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic + # regression. For LDA we refer the reader to Section 4.7.3. + # Although we could use the `sklearn` function `LogisticRegression()` to fit + # multiclass logistic regression, we are set up here to fit such a model + # with `torch`. + # We just have an input layer and an output layer, and omit the hidden layers! + + # In[45]: + + + class MNIST_MLR(nn.Module): + def __init__(self): + super(MNIST_MLR, self).__init__() + self.linear = nn.Sequential(nn.Flatten(), + nn.Linear(784, 10)) + def forward(self, x): + return self.linear(x) + + mlr_model = MNIST_MLR() + mlr_module = SimpleModule.classification(mlr_model, + num_classes=10) + mlr_logger = CSVLogger('logs', name='MNIST_MLR') + + + # In[46]: + + + mlr_trainer = Trainer(deterministic=True, + max_epochs=30, + callbacks=[ErrorTracker()]) + mlr_trainer.fit(mlr_module, datamodule=mnist_dm) + + + # We fit the model just as before and compute the test results. + + # In[47]: + + + mlr_trainer.test(mlr_module, + datamodule=mnist_dm) + + + # The accuracy is above 90% even for this pretty simple model. + # + # As in the `Hitters` example, we delete some of + # the objects we created above. + + # In[48]: + + + + From cce66604d4b46d66fc6e83bbc021d13af3512f14 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 00:59:41 -0400 Subject: [PATCH 073/195] use importlib.resources instead of pkg_resources --- ISLP/__init__.py | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/ISLP/__init__.py b/ISLP/__init__.py index ae230d3..01b189e 100644 --- a/ISLP/__init__.py +++ b/ISLP/__init__.py @@ -7,27 +7,28 @@ from os.path import join as pjoin import pandas as pd, numpy as np -from pkg_resources import resource_filename +from importlib.resources import (as_file, + files) # data originally saved via: [sm.datasets.get_rdataset(n, 'ISLR').data.to_csv('../ISLP/data/%s.csv' % n, index=False) for n in ['Carseats', 'College', 'Credit', 'Default', 'Hitters', 'Auto', 'OJ', 'Portfolio', 'Smarket', 'Wage', 'Weekly', 'Caravan']] def load_data(dataset): if dataset == 'NCI60': - features = resource_filename('ISLP', pjoin('data', 'NCI60data.npy')) + features = as_file(files('ISLP').joinpath('data', 'NCI60data.npy')) X = np.load(features) - labels = resource_filename('ISLP', pjoin('data', 'NCI60labs.csv')) + labels = as_file(files('ISLP').joinpath('data', 'NCI60labs.csv')) Y = pd.read_csv(labels) return {'data':X, 'labels':Y} elif dataset == 'Khan': - xtest = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Khan_xtest.csv'))) + xtest = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_xtest.csv'))) xtest = xtest.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) - ytest = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Khan_ytest.csv'))) + ytest = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_ytest.csv'))) ytest = ytest.rename(columns={'x':'Y'}) ytest = ytest['Y'] - xtrain = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Khan_xtrain.csv'))) + xtrain = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_xtrain.csv'))) xtrain = xtrain.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) - ytrain = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Khan_ytrain.csv'))) + ytrain = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_ytrain.csv'))) ytrain = ytrain.rename(columns={'x':'Y'}) ytrain = ytrain['Y'] @@ -36,33 +37,33 @@ def load_data(dataset): 'ytest':ytest, 'ytrain':ytrain} elif dataset == 'Hitters': - filename = resource_filename('ISLP', pjoin('data', '%s.csv' % dataset)) + filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) df = pd.read_csv(filename) for col in ['League', 'Division', 'NewLeague']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'Carseats': - filename = resource_filename('ISLP', pjoin('data', '%s.csv' % dataset)) + filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) df = pd.read_csv(filename) for col in ['ShelveLoc', 'Urban', 'US']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'NYSE': - filename = resource_filename('ISLP', pjoin('data', '%s.csv' % dataset)) + filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) df = pd.read_csv(filename).set_index('date') return df elif dataset == 'Publication': - df = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Publication.csv'))) + df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Publication.csv'))) for col in ['mech']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'BrainCancer': - df = pd.read_csv(resource_filename('ISLP', pjoin('data', 'BrainCancer.csv'))) + df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'BrainCancer.csv'))) for col in ['sex', 'diagnosis', 'loc', 'stereo']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'Bikeshare': - filename = resource_filename('ISLP', pjoin('data', '%s.csv' % dataset)) + filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) df = pd.read_csv(filename) df['weathersit'] = pd.Categorical(df['weathersit'], ordered=False) # setting order to avoid alphabetical @@ -79,11 +80,11 @@ def load_data(dataset): categories=range(24)) return df elif dataset == 'Wage': - df = pd.read_csv(resource_filename('ISLP', pjoin('data', 'Wage.csv'))) + df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Wage.csv'))) df['education'] = pd.Categorical(df['education'], ordered=True) return df else: - filename = resource_filename('ISLP', pjoin('data', '%s.csv' % dataset)) + filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) return pd.read_csv(filename) from sklearn.metrics import confusion_matrix as _confusion_matrix From 5d085d301c7e58ecc78dd42dc878b773f9bc5b73 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:21:32 -0400 Subject: [PATCH 074/195] using context manager --- ISLP/__init__.py | 55 ++++++++++++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 23 deletions(-) diff --git a/ISLP/__init__.py b/ISLP/__init__.py index 01b189e..aba332b 100644 --- a/ISLP/__init__.py +++ b/ISLP/__init__.py @@ -14,21 +14,26 @@ def load_data(dataset): if dataset == 'NCI60': - features = as_file(files('ISLP').joinpath('data', 'NCI60data.npy')) - X = np.load(features) - labels = as_file(files('ISLP').joinpath('data', 'NCI60labs.csv')) - Y = pd.read_csv(labels) + with as_file(files('ISLP').joinpath('data', 'NCI60data.npy')) as features: + X = np.load(features) + labels = as_file(files('ISLP').joinpath('data', 'NCI60labs.csv')) + Y = pd.read_csv(labels) return {'data':X, 'labels':Y} elif dataset == 'Khan': - xtest = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_xtest.csv'))) + with as_file(files('ISLP').joinpath('data', 'Khan_xtest.csv')) as xtest: + xtest = pd.read_csv(xtest) xtest = xtest.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) - ytest = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_ytest.csv'))) + with as_file(files('ISLP').joinpath('data', 'Khan_ytest.csv')) as ytest: + ytest = pd.read_csv(ytest) ytest = ytest.rename(columns={'x':'Y'}) ytest = ytest['Y'] - xtrain = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_xtrain.csv'))) - xtrain = xtrain.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) - ytrain = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Khan_ytrain.csv'))) + with as_file(files('ISLP').joinpath('data', 'Khan_xtrain.csv')) as xtrain: + xtrain = pd.read_csv(xtrain) + xtrain = xtrain.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) + + with as_file(files('ISLP').joinpath('data', 'Khan_ytrain.csv')) as ytrain: + ytrain = pd.read_csv(ytrain) ytrain = ytrain.rename(columns={'x':'Y'}) ytrain = ytrain['Y'] @@ -36,35 +41,38 @@ def load_data(dataset): 'xtrain':xtrain, 'ytest':ytest, 'ytrain':ytrain} + elif dataset == 'Hitters': - filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) - df = pd.read_csv(filename) + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + df = pd.read_csv(filename) for col in ['League', 'Division', 'NewLeague']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'Carseats': - filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) - df = pd.read_csv(filename) + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + df = pd.read_csv(filename) for col in ['ShelveLoc', 'Urban', 'US']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'NYSE': - filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) - df = pd.read_csv(filename).set_index('date') + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + df = pd.read_csv(filename).set_index('date') return df elif dataset == 'Publication': - df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Publication.csv'))) + with as_file(files('ISLP').joinpath('data', 'Publication.csv')) as f: + df = pd.read_csv(f) for col in ['mech']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'BrainCancer': - df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'BrainCancer.csv'))) + with as_file(files('ISLP').joinpath('data', 'BrainCancer.csv')) as f: + df = pd.read_csv(f) for col in ['sex', 'diagnosis', 'loc', 'stereo']: df[col] = pd.Categorical(df[col]) return df elif dataset == 'Bikeshare': - filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) - df = pd.read_csv(filename) + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + df = pd.read_csv(filename) df['weathersit'] = pd.Categorical(df['weathersit'], ordered=False) # setting order to avoid alphabetical df['mnth'] = pd.Categorical(df['mnth'], @@ -80,12 +88,13 @@ def load_data(dataset): categories=range(24)) return df elif dataset == 'Wage': - df = pd.read_csv(as_file(files('ISLP').joinpath('data', 'Wage.csv'))) - df['education'] = pd.Categorical(df['education'], ordered=True) + with as_file(files('ISLP').joinpath('data', 'Wage.csv')) as f: + df = pd.read_csv(f) + df['education'] = pd.Categorical(df['education'], ordered=True) return df else: - filename = as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) - return pd.read_csv(filename) + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + return pd.read_csv(filename) from sklearn.metrics import confusion_matrix as _confusion_matrix From 305e0f290d15aee0f88622eaf4e6fb9664809438 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:31:20 -0400 Subject: [PATCH 075/195] BF: a few indents, missed context manager --- ISLP/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ISLP/__init__.py b/ISLP/__init__.py index aba332b..0cd8d3e 100644 --- a/ISLP/__init__.py +++ b/ISLP/__init__.py @@ -16,7 +16,7 @@ def load_data(dataset): if dataset == 'NCI60': with as_file(files('ISLP').joinpath('data', 'NCI60data.npy')) as features: X = np.load(features) - labels = as_file(files('ISLP').joinpath('data', 'NCI60labs.csv')) + with as_file(files('ISLP').joinpath('data', 'NCI60labs.csv')) as labels: Y = pd.read_csv(labels) return {'data':X, 'labels':Y} elif dataset == 'Khan': @@ -33,7 +33,7 @@ def load_data(dataset): xtrain = xtrain.rename(columns=dict([('V%d' % d, 'G%04d' % d) for d in range(1, len(xtest.columns)+0)])) with as_file(files('ISLP').joinpath('data', 'Khan_ytrain.csv')) as ytrain: - ytrain = pd.read_csv(ytrain) + ytrain = pd.read_csv(ytrain) ytrain = ytrain.rename(columns={'x':'Y'}) ytrain = ytrain['Y'] From 30956063a63fa2a941950d2e961e95650d2587d1 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:46:11 -0400 Subject: [PATCH 076/195] added labs bit to workflow --- .github/workflows/python-package-conda.yml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 27b6ba3..a54705f 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -23,7 +23,6 @@ jobs: pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - - name: Lint with flake8 run: | conda install flake8 @@ -35,3 +34,9 @@ jobs: run: | conda install pytest pytest + - name: Test labs (except Ch2, Ch10) + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb + From 6ff4a02be8f76a8db9f37a4536d87184d31be816 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:47:14 -0400 Subject: [PATCH 077/195] update workflows --- .github/workflows/build-labs.yml | 38 ++++++++++++++++++++++ .github/workflows/python-package-conda.yml | 5 --- 2 files changed, 38 insertions(+), 5 deletions(-) create mode 100644 .github/workflows/build-labs.yml diff --git a/.github/workflows/build-labs.yml b/.github/workflows/build-labs.yml new file mode 100644 index 0000000..29097bc --- /dev/null +++ b/.github/workflows/build-labs.yml @@ -0,0 +1,38 @@ +name: Build labs + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt + pip install git+https://github.com/intro-stat-learning/ISLP.git + - name: Lint with flake8 + run: | + conda install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test labs (except Ch2, Ch10) + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb + diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index a54705f..3024b7f 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -34,9 +34,4 @@ jobs: run: | conda install pytest pytest - - name: Test labs (except Ch2, Ch10) - run: | - git clone https://github.com/intro-stat-learning/ISLP_labs.git - cd ISLP_labs - jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb From 46e219522aaf8445fe569a35514b39666902875c Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 30 Jul 2023 22:49:54 -0700 Subject: [PATCH 078/195] Create test-labs.yml --- .github/workflows/test-labs.yml | 38 +++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 .github/workflows/test-labs.yml diff --git a/.github/workflows/test-labs.yml b/.github/workflows/test-labs.yml new file mode 100644 index 0000000..29097bc --- /dev/null +++ b/.github/workflows/test-labs.yml @@ -0,0 +1,38 @@ +name: Build labs + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt + pip install git+https://github.com/intro-stat-learning/ISLP.git + - name: Lint with flake8 + run: | + conda install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test labs (except Ch2, Ch10) + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb + From 5f16131bfa2eaadd57daa829e5dfda719d849622 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:52:16 -0400 Subject: [PATCH 079/195] test labs workflow --- .github/workflows/build-labs.yml | 38 -------------------------------- .github/workflows/test-labs.yml | 2 +- 2 files changed, 1 insertion(+), 39 deletions(-) delete mode 100644 .github/workflows/build-labs.yml diff --git a/.github/workflows/build-labs.yml b/.github/workflows/build-labs.yml deleted file mode 100644 index 29097bc..0000000 --- a/.github/workflows/build-labs.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Build labs - -on: [push] - -jobs: - build-linux: - runs-on: ubuntu-latest - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: '3.10' - - name: Add conda to system path - run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - - name: Install dependencies - run: | - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt - pip install git+https://github.com/intro-stat-learning/ISLP.git - - name: Lint with flake8 - run: | - conda install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test labs (except Ch2, Ch10) - run: | - git clone https://github.com/intro-stat-learning/ISLP_labs.git - cd ISLP_labs - jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb - diff --git a/.github/workflows/test-labs.yml b/.github/workflows/test-labs.yml index 29097bc..d4b0e17 100644 --- a/.github/workflows/test-labs.yml +++ b/.github/workflows/test-labs.yml @@ -30,7 +30,7 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test labs (except Ch2, Ch10) + - name: Test labs run: | git clone https://github.com/intro-stat-learning/ISLP_labs.git cd ISLP_labs From d6d08ace0a30bff74fb40210ed85f75de4e924ad Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:55:12 -0400 Subject: [PATCH 080/195] removing workflow --- .github/workflows/test-labs.yml | 38 --------------------------------- 1 file changed, 38 deletions(-) delete mode 100644 .github/workflows/test-labs.yml diff --git a/.github/workflows/test-labs.yml b/.github/workflows/test-labs.yml deleted file mode 100644 index d4b0e17..0000000 --- a/.github/workflows/test-labs.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Build labs - -on: [push] - -jobs: - build-linux: - runs-on: ubuntu-latest - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: '3.10' - - name: Add conda to system path - run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - - name: Install dependencies - run: | - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt - pip install git+https://github.com/intro-stat-learning/ISLP.git - - name: Lint with flake8 - run: | - conda install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test labs - run: | - git clone https://github.com/intro-stat-learning/ISLP_labs.git - cd ISLP_labs - jupyter nbconvert --execute --inplace Ch{3,4,5,6,7,8,9,11,12,13}*nb - From 97dfb56cb977d2257c8e610419dd89757dfc2cd5 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 31 Jul 2023 01:55:42 -0400 Subject: [PATCH 081/195] removing pylint workflow --- .github/workflows/pylint.yml | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 .github/workflows/pylint.yml diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml deleted file mode 100644 index 383e65c..0000000 --- a/.github/workflows/pylint.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: Pylint - -on: [push] - -jobs: - build: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.8", "3.9", "3.10"] - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pylint - - name: Analysing the code with pylint - run: | - pylint $(git ls-files '*.py') From bd0af3474fb4b6306c749a194b350199cd332a49 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Fri, 4 Aug 2023 10:08:24 -0700 Subject: [PATCH 082/195] update to info.py, fixing github link and short description --- ISLP/info.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ISLP/info.py b/ISLP/info.py index 9dba591..2d41024 100644 --- a/ISLP/info.py +++ b/ISLP/info.py @@ -25,7 +25,7 @@ "Programming Language :: Python", "Topic :: Scientific/Engineering"] -description = 'Testing a fixed value of lambda' +description = 'Library for ISLP labs' # Note: this long_description is actually a copy/paste from the top-level # README.txt, so that it shows up nicely on PyPI. So please remember to edit @@ -54,7 +54,7 @@ MAINTAINER_EMAIL = "" DESCRIPTION = description LONG_DESCRIPTION = long_description -URL = "http://github.org/jonathan.taylor/ISLP" +URL = "http://github.org/intro-stat-learning/ISLP" DOWNLOAD_URL = "" LICENSE = "BSD license" CLASSIFIERS = CLASSIFIERS From 55ee527e87a99238a52680470212abf309e45b77 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:34:38 -0700 Subject: [PATCH 083/195] updating info.py with torch requirements; updating requirements.txt, install instructions --- ISLP/info.py | 5 ++++- docs/source/installation.myst | 16 +++------------- requirements.txt | 6 +++++- 3 files changed, 12 insertions(+), 15 deletions(-) diff --git a/ISLP/info.py b/ISLP/info.py index 2d41024..870c1e9 100644 --- a/ISLP/info.py +++ b/ISLP/info.py @@ -76,5 +76,8 @@ "sklearn (>=%s)" % SKLEARN_MIN_VERSION, "lifelines", "joblib", - "pygam" + "pygam", + "torch", + "torchmetrics", + "pytorch_lightning" ] diff --git a/docs/source/installation.myst b/docs/source/installation.myst index ce360f7..48d2365 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,7 +61,7 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/frozen_requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/requirements.txt ``` For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2_devel). @@ -69,11 +69,8 @@ For more specific install instructions go to [ISLP_labs](https://github.com/intr ## Torch requirements The `ISLP` labs use `torch` and various related packages for the lab -on deep learning. The requirements can be found -[here](https://github.com/intro-stat-learning/ISLP/blob/main/torch_requirements.txt). Alternatively, you can install them -directly using `pip` within a terminal. As above, ensure that you have -activated that conda environment (Mac OS or Linux) or started a -terminal within that environment from the Anaconda app (Windows). +on deep learning. Most of the requirements are included in the requirements for `ISLP` though the labs +also use `torchinfo` and `torchvision`. These will be installed by the `requirements.txt` above. ```{attention} Because @@ -82,13 +79,6 @@ have pinned the versions at specific versions that were used to make current verisons of the labs. ``` -```{code-cell} ipython3 ---- -tags: [skip-execution] ---- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/torch_requirements.txt -``` - ## Jupyter ### Mac OS X diff --git a/requirements.txt b/requirements.txt index 1503cf7..134b920 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,5 +7,9 @@ scikit-learn>=1.2 joblib statsmodels>=0.13 lifelines +pygam # for GAM in Ch7 +torch +pytorch_lightning +torchmetrics #l0bnb # for bestsubsets -#pygam # for GAM in Ch7 + From d2efc5adf5e5f83ba86f918cb38eca6b584f12e9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:46:16 -0700 Subject: [PATCH 084/195] updating ISLP_labs --- docs/ISLP_labs | 2 +- docs/source/installation.myst | 4 ++-- docs/source/labs.rst | 3 +-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index ca12db6..dc35f72 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit ca12db6546e69d0a3c6785fa34fc77ebbf4fdbd1 +Subproject commit dc35f729e16344bc2105f2a3532bd84baa1162f4 diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 48d2365..542644e 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,10 +61,10 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt ``` -For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2_devel). +For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2). ## Torch requirements diff --git a/docs/source/labs.rst b/docs/source/labs.rst index b562cb9..5828324 100644 --- a/docs/source/labs.rst +++ b/docs/source/labs.rst @@ -15,8 +15,7 @@ Package versions To ensure you have the same package versions as those built here, run: - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/frozen_requirements.txt; - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v1/torch_requirements.txt + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt; .. toctree:: :maxdepth: 1 From 7d2a6b104d45c990838d3fc05cfb3d1578b371ee Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:51:34 -0700 Subject: [PATCH 085/195] moved requirements --- .readthedocs.yaml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 368691f..7da8629 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -31,8 +31,7 @@ sphinx: # Optionally declare the Python requirements required to build your docs python: install: - - requirements: docs/ISLP_labs/frozen_requirements.txt + - requirements: docs/ISLP_labs/requirements.txt - requirements: docs/requirements.txt - - requirements: docs/ISLP_labs/torch_requirements.txt - method: pip path: . From 4b563046485ec37a7974aa48829335923d5f6f8b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:56:34 -0700 Subject: [PATCH 086/195] updated python version for rtd --- .readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 7da8629..46a954e 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.9" + python: "3.11" apt_packages: - r-base jobs: From 9c52700aeae3fe2737466a28d46a66c40339c426 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:57:09 -0700 Subject: [PATCH 087/195] jupytext already in docs requirements --- .readthedocs.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 46a954e..a8f6297 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -14,7 +14,6 @@ build: - r-base jobs: pre_build: - - pip install nbformat jupytext - python docs/fix_and_run_notebooks.py submodules: From 5fcfaad2ea7d449a06ede6f096c96cab9e8272e9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 11:59:39 -0700 Subject: [PATCH 088/195] added execute to nbconvert --- docs/fix_and_run_notebooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 1545aec..0dbc12f 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -55,7 +55,7 @@ if labname[:4] != 'Ch10': # clear outputs for all but Ch10 - os.system(f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') + os.system(f'jupyter nbconvert --execute --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') From 5726b67156aa839fc14ed91f9e4fde316d4ae1de Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 12:18:16 -0700 Subject: [PATCH 089/195] update to version v2 for docs --- docs/fix_and_run_notebooks.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 0dbc12f..aa1642a 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -9,7 +9,7 @@ os.remove(f) print(f) -version = 'v1' +version = 'v2' main = 'main' os.system(f''' cd {dirname}/ISLP_labs; @@ -17,7 +17,6 @@ mkdir -p {dirname}/source/labs; cp -r * {dirname}/source/labs; git checkout {main}; -pip install -r {dirname}/source/labs/frozen_requirements.txt; ''') for nbfile in glob(os.path.join(dirname, 'source', 'labs', '*nb')): From 11355e2f398de592537de224495fa3cb15726734 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 12:25:17 -0700 Subject: [PATCH 090/195] unused commit; update to rtd --- ISLP/torch/lightning.py | 2 +- docs/README.rst | 3 ++- docs/fix_and_run_notebooks.py | 7 +++++++ 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/ISLP/torch/lightning.py b/ISLP/torch/lightning.py index e3b4088..d7056ec 100644 --- a/ISLP/torch/lightning.py +++ b/ISLP/torch/lightning.py @@ -7,7 +7,7 @@ DataLoader, Dataset) from torch import tensor, Generator, concat -from torchvision import transforms + from torch.utils.data import TensorDataset from torchmetrics import Accuracy diff --git a/docs/README.rst b/docs/README.rst index 709ce7d..41df584 100644 --- a/docs/README.rst +++ b/docs/README.rst @@ -11,4 +11,5 @@ There is a snippet that should be inserted to remove the many warnings raised. Frozen reqs =========== -The versions of the labs are referred to in `source/installation.myst`, `source/labs.rst` +The versions of the labs are referred to in `source/installation.myst`, `source/labs.rst`. Version built +on `readthedocs` is references in `fix_and_run_notebooks.py` diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index aa1642a..0df8f01 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -11,6 +11,10 @@ version = 'v2' main = 'main' + +print(f'checking out version {version} of the labs') +print(f'checking out version {main} of ISLP') + os.system(f''' cd {dirname}/ISLP_labs; git checkout {version}; @@ -28,6 +32,9 @@ Open In Colab + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{version}?labpath={labname}.ipynb) + ''' # allow errors for lab2 From 75c343f56ed91934fd483dfa08d03a1819294cf0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 13:21:53 -0700 Subject: [PATCH 091/195] remove execute, let sphinx do it --- docs/fix_and_run_notebooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 0df8f01..65e1227 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -61,7 +61,7 @@ if labname[:4] != 'Ch10': # clear outputs for all but Ch10 - os.system(f'jupyter nbconvert --execute --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') + os.system(f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') From df4347abf446b42224bcfe9fa25565988f58dadd Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 14:06:39 -0700 Subject: [PATCH 092/195] Update README.md --- README.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index a5aac69..9970d13 100644 --- a/README.md +++ b/README.md @@ -37,13 +37,8 @@ pip install ISLP ### Torch requirements The `ISLP` labs use `torch` and various related packages for the lab on deep learning. The requirements -can be found [here](torch_requirements.txt). Alternatively, you can install them directly using `pip` within a terminal. As above, ensure -that you have activated that conda environment (Mac OS or Linux) or -started a terminal within that environment from the Anaconda app (Windows). - -```{python} -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt -``` +are included in the requirements for `ISLP` with the exception of those needed +for the labs which are included in the [requirements for the labs](https://github.com/intro-stat-learning/ISLP_labs/blob/main/requirements.txt). ## Jupyter From 8ff350c569857a19ca7e99564fba72f107ae21ef Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 14:04:22 -0700 Subject: [PATCH 093/195] unused torch requirements; they are in main requirements except those needed for lab --- torch_requirements.txt | 5 ----- 1 file changed, 5 deletions(-) delete mode 100644 torch_requirements.txt diff --git a/torch_requirements.txt b/torch_requirements.txt deleted file mode 100644 index d7656a8..0000000 --- a/torch_requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -torch -torchvision -pytorch_lightning -torchinfo -torchmetrics From 0a0c7f29ab6be0795c9211b183543da4f2f55fa0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 15:15:14 -0700 Subject: [PATCH 094/195] removing unused pip requirements --- .github/workflows/python-package-conda.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 3024b7f..109d1d7 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -21,7 +21,6 @@ jobs: - name: Install dependencies run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/torch_requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | From b62d819d8775b270784ae22b4742fc6a39b56188 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 15:25:21 -0700 Subject: [PATCH 095/195] two missing torch dependencies --- .github/workflows/python-package-conda.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 109d1d7..d33ac80 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -22,6 +22,7 @@ jobs: run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git + pip install torchvision torchinfo - name: Lint with flake8 run: | conda install flake8 From 10a9b299101cdf55a209a2189df04ca7ebe528a2 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 6 Aug 2023 15:26:37 -0700 Subject: [PATCH 096/195] tab / whitespace --- .github/workflows/python-package-conda.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index d33ac80..3ce43cc 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -22,7 +22,7 @@ jobs: run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - pip install torchvision torchinfo + pip install torchvision torchinfo - name: Lint with flake8 run: | conda install flake8 From e7e57cb3b96775f36ee283314220bbd2c8a7b4e3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 8 Aug 2023 14:49:38 -0700 Subject: [PATCH 097/195] testing v2_devel with renamed notebooks --- docs/fix_and_run_notebooks.py | 11 ++++------- docs/source/installation.myst | 4 ++-- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index 65e1227..af57293 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -9,18 +9,15 @@ os.remove(f) print(f) -version = 'v2' -main = 'main' +version = 'v2_devel' print(f'checking out version {version} of the labs') -print(f'checking out version {main} of ISLP') os.system(f''' cd {dirname}/ISLP_labs; git checkout {version}; mkdir -p {dirname}/source/labs; cp -r * {dirname}/source/labs; -git checkout {main}; ''') for nbfile in glob(os.path.join(dirname, 'source', 'labs', '*nb')): @@ -37,8 +34,8 @@ ''' - # allow errors for lab2 - if labname[:3] == 'Ch6': + # allow errors for Ch02, suppress warnings for Ch06 + if labname[:3] == 'Ch06': colab_code = (''' ```{code-cell} :tags: [hide-cell] @@ -53,7 +50,7 @@ ``` ''' + colab_code) - if labname[:3] == 'Ch2': + if labname[:3] == 'Ch02': nb = nbformat.read(open(nbfile), 4) nb.metadata.setdefault('execution', {})['allow_errors'] = True nbformat.write(nb, open(nbfile, 'w')) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 542644e..48d2365 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,10 +61,10 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/requirements.txt ``` -For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2). +For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2_devel). ## Torch requirements From 2be34bd491341ca28573cd450c5462620e521e43 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 8 Aug 2023 15:16:42 -0700 Subject: [PATCH 098/195] back to v2 --- docs/fix_and_run_notebooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_run_notebooks.py index af57293..a6aefe8 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_run_notebooks.py @@ -9,7 +9,7 @@ os.remove(f) print(f) -version = 'v2_devel' +version = 'v2' print(f'checking out version {version} of the labs') From 732e04d5a1ed89b863827c61642b3323853490f4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 8 Aug 2023 15:18:19 -0700 Subject: [PATCH 099/195] BF labs.rst --- docs/source/labs.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/labs.rst b/docs/source/labs.rst index 5828324..653020d 100644 --- a/docs/source/labs.rst +++ b/docs/source/labs.rst @@ -20,14 +20,14 @@ To ensure you have the same package versions as those built here, run: .. toctree:: :maxdepth: 1 - labs/Ch2-statlearn-lab - labs/Ch3-linreg-lab - labs/Ch4-classification-lab - labs/Ch5-resample-lab - labs/Ch6-varselect-lab - labs/Ch7-nonlin-lab - labs/Ch8-baggboost-lab - labs/Ch9-svm-lab + labs/Ch02-statlearn-lab + labs/Ch03-linreg-lab + labs/Ch04-classification-lab + labs/Ch05-resample-lab + labs/Ch06-varselect-lab + labs/Ch07-nonlin-lab + labs/Ch08-baggboost-lab + labs/Ch09-svm-lab labs/Ch10-deeplearning-lab labs/Ch11-surv-lab labs/Ch12-unsup-lab From ed8ab7e9d870f57fa57df8c2082b958e5b3fa2d1 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 8 Aug 2023 16:13:19 -0700 Subject: [PATCH 100/195] v2_devel->v2 in install doc --- docs/source/installation.myst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 48d2365..542644e 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -61,10 +61,10 @@ To ensure you have the same package versions as those built here, run: --- tags: [skip-execution] --- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2_devel/requirements.txt +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt ``` -For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2_devel). +For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2). ## Torch requirements From 2425f9bf29b424b4e72304208948ccb3d7d38ce2 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 8 Aug 2023 23:18:36 -0700 Subject: [PATCH 101/195] Developing actions (#3) * test building Ch02 lab * removing one input * put env first? * trying to store lab artifact * whitespace * fix whitespace * trying to find path of built lab * whitespace * action for Ch03 * rename * BF: name of lab * adding input for which lab to build * rename, allowing errors * fix name on github * action to build all labs * name of task in action * add all labs except deeplearning in action * omitting Ch13 from build * doc building action * whitespace * put labs into where sphinx expects * make missing lab directory * make sure to install r-base before building rpy2 * retain the docs * trying a mac test build * adding windows, using pip for pytest, flake8 * rename script, running script in github action * don't strip Ch13 * BF: sequence of when notebooks are fixed * trying deploy docs * move up one directory * BF: directory level * adding --version to clear script * write permission * BF: arg string * adding pages: write * save notebooks to inspect * trying to debug clearing notebooks * the cp was overwriting cleared notebooks * don't store markdown * BF: wrong dir * increasing timeout for pytest' * remove windows for now --- .github/workflows/build_docs.yml | 86 +++++++++++++++ .github/workflows/build_notebook.yml | 61 ++++++++++ .github/workflows/build_notebook_errors.yml | 61 ++++++++++ .github/workflows/build_save_labs.yml | 104 ++++++++++++++++++ .github/workflows/build_test.yml | 72 ++++++++++++ .github/workflows/python-package-conda.yml | 37 ------- .readthedocs.yaml | 2 +- ...otebooks.py => fix_and_clear_notebooks.py} | 28 +++-- docs/source/conf.py | 2 +- 9 files changed, 406 insertions(+), 47 deletions(-) create mode 100644 .github/workflows/build_docs.yml create mode 100644 .github/workflows/build_notebook.yml create mode 100644 .github/workflows/build_notebook_errors.yml create mode 100644 .github/workflows/build_save_labs.yml create mode 100644 .github/workflows/build_test.yml delete mode 100644 .github/workflows/python-package-conda.yml rename docs/{fix_and_run_notebooks.py => fix_and_clear_notebooks.py} (72%) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml new file mode 100644 index 0000000..7328c69 --- /dev/null +++ b/.github/workflows/build_docs.yml @@ -0,0 +1,86 @@ +# This builds and deploys ISLP docs + +name: Build docs + +# Controls when the workflow will run +on: + workflow_dispatch: + inputs: + LABS: + description: 'Labs version' + required: true + default: 'v2' + type: string + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + sudo apt-get install r-base + pip install -r requirements.txt + pip install -r docs/requirements.txt + pip install . + + # Checkout labs + - name: Checkout version of labs + env: + LABS: ${{ inputs.LABS }} + run: | + git submodule update --init --force docs/ISLP_labs + cd docs + python fix_and_clear_notebooks.py --version $LABS + rm source/labs/Ch*md + + - name: Make docs + run: | + cd docs + make html + + # Store the output + - name: Upload docs + uses: actions/upload-artifact@v3 + with: + name: ISLP_docs + path: docs/build/html + retention-days: 5 + + deploy: + runs-on: ubuntu-latest + needs: build + + # Grant GITHUB_TOKEN the permissions required to make a Pages deployment + permissions: + pages: write # to deploy to Pages + id-token: write # to verify the deployment originates from an appropriate source + + environment: + name: github-pages + url: ${{steps.deployment.outputs.page_url}} + + steps: + - uses: actions/download-artifact@master + with: + name: ISLP_docs + path: . + - uses: actions/configure-pages@v1 + - uses: actions/upload-pages-artifact@v1 + with: + path: . + - id: deployment + uses: actions/deploy-pages@main \ No newline at end of file diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml new file mode 100644 index 0000000..4a6c9f4 --- /dev/null +++ b/.github/workflows/build_notebook.yml @@ -0,0 +1,61 @@ +# This is a basic workflow to help you get started with Actions + +name: Build a notebook + +# Controls when the workflow will run +on: + workflow_dispatch: + inputs: + LABS: + description: 'Labs version' + required: true + default: 'v2' + type: string + ID: + description: 'Which lab to build' + required: true + default: '02' + type: string + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + pip install . + + # Runs a set of commands using the runners shell + - name: Build notebook + env: + LABS: ${{ inputs.LABS }} + ID: ${{ inputs.ID }} + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + git checkout $LABS + jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb + jupyter nbconvert --to html Ch*$ID*lab.ipynb + + # Store the output + - name: Upload labs + env: + ID: ${{ inputs.ID }} + uses: actions/upload-artifact@v3 + with: + name: ISLP_labs + path: ISLP_labs/Ch*$ID* + retention-days: 1 \ No newline at end of file diff --git a/.github/workflows/build_notebook_errors.yml b/.github/workflows/build_notebook_errors.yml new file mode 100644 index 0000000..4419557 --- /dev/null +++ b/.github/workflows/build_notebook_errors.yml @@ -0,0 +1,61 @@ +# This is a basic workflow to help you get started with Actions + +name: Build a notebook (allow errors, capture result) + +# Controls when the workflow will run +on: + workflow_dispatch: + inputs: + LABS: + description: 'Labs version' + required: true + default: 'v2' + type: string + ID: + description: 'Which lab to build' + required: true + default: '02' + type: string + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + pip install . + + # Runs a set of commands using the runners shell + - name: Build notebook, allowing errors + env: + LABS: ${{ inputs.LABS }} + ID: ${{ inputs.ID }} + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + git checkout $LABS + jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb --allow-errors + + # Store the output + - name: Upload labs + env: + ID: ${{ inputs.ID }} + uses: actions/upload-artifact@v3 + with: + name: ISLP_labs + path: ISLP_labs/Ch*$ID*nb + retention-days: 1 + diff --git a/.github/workflows/build_save_labs.yml b/.github/workflows/build_save_labs.yml new file mode 100644 index 0000000..abdf3d5 --- /dev/null +++ b/.github/workflows/build_save_labs.yml @@ -0,0 +1,104 @@ +# This is a basic workflow to help you get started with Actions + +name: Build + save notebooks (not 10,13) + +# Controls when the workflow will run +on: + workflow_dispatch: + inputs: + LABS: + description: 'Labs version' + required: true + default: 'v2' + type: string + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + pip install . + + # Runs a set of commands using the runners shell + - name: Build Ch02 notebook (allow errors) + env: + LABS: ${{ inputs.LABS }} + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + git checkout $LABS + rm Ch10* + rm Ch13* + jupyter nbconvert --execute --inplace --allow-errors Ch02*lab.ipynb + + - name: Build Ch03 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch03*lab.ipynb + + - name: Build Ch04 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch04*lab.ipynb + + - name: Build Ch05 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch05*lab.ipynb + + - name: Build Ch06 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch06*lab.ipynb + + - name: Build Ch07 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch07*lab.ipynb + + - name: Build Ch08 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch08*lab.ipynb + + - name: Build Ch09 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch09*lab.ipynb + + - name: Build Ch11 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch11*lab.ipynb + + - name: Build Ch12 notebook + run: | + cd ISLP_labs + jupyter nbconvert --execute --inplace Ch12*lab.ipynb + + - name: Build HTML + run: | + cd ISLP_labs + jupyter nbconvert --to html Ch*ipynb + + # Store the output + - name: Upload labs + uses: actions/upload-artifact@v3 + with: + name: ISLP_labs + path: ISLP_labs/Ch* + retention-days: 1 \ No newline at end of file diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml new file mode 100644 index 0000000..bca136a --- /dev/null +++ b/.github/workflows/build_test.yml @@ -0,0 +1,72 @@ +name: Build and test + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + pip install git+https://github.com/intro-stat-learning/ISLP.git + pip install torchvision torchinfo + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + timeout-minutes: 12 + run: | + pip install pytest + pytest + + build-mac: + runs-on: macos-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + pip install git+https://github.com/intro-stat-learning/ISLP.git + pip install torchvision torchinfo + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + timeout-minutes: 12 + run: | + pip install pytest + pytest + + diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml deleted file mode 100644 index 3ce43cc..0000000 --- a/.github/workflows/python-package-conda.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: Python Package using Conda - -on: [push] - -jobs: - build-linux: - runs-on: ubuntu-latest - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: '3.10' - - name: Add conda to system path - run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - - name: Install dependencies - run: | - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install git+https://github.com/intro-stat-learning/ISLP.git - pip install torchvision torchinfo - - name: Lint with flake8 - run: | - conda install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - conda install pytest - pytest - diff --git a/.readthedocs.yaml b/.readthedocs.yaml index a8f6297..44bfa25 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -14,7 +14,7 @@ build: - r-base jobs: pre_build: - - python docs/fix_and_run_notebooks.py + - python docs/fix_and_clear_notebooks.py submodules: include: all diff --git a/docs/fix_and_run_notebooks.py b/docs/fix_and_clear_notebooks.py similarity index 72% rename from docs/fix_and_run_notebooks.py rename to docs/fix_and_clear_notebooks.py index a6aefe8..aac28ee 100644 --- a/docs/fix_and_run_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -1,6 +1,13 @@ -import os, nbformat +import os +import nbformat +from argparse import ArgumentParser from glob import glob +parser = ArgumentParser() +parser.add_argument('--version', default='v2') +args = parser.parse_args() +version = args.version + import __main__ dirname = os.path.split(__main__.__file__)[0] print(dirname) @@ -9,8 +16,6 @@ os.remove(f) print(f) -version = 'v2' - print(f'checking out version {version} of the labs') os.system(f''' @@ -55,12 +60,16 @@ nb.metadata.setdefault('execution', {})['allow_errors'] = True nbformat.write(nb, open(nbfile, 'w')) - if labname[:4] != 'Ch10': + if labname[:4] not in ['Ch10', 'Ch13']: - # clear outputs for all but Ch10 - os.system(f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}') + # clear outputs for all but Ch10,Ch13 + cmd = f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}' + print(f'Running: {cmd}') + os.system(cmd) - os.system(f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}') + cmd = f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}' + print(f'Running: {cmd}') + os.system(cmd) myst = open(f'{base}.md').read().strip() @@ -75,5 +84,8 @@ open(f'{base}.md', 'w').write(myst) - os.system(f'jupytext --sync {base}.ipynb; rm {base}.md') +# cmd = f'jupytext --sync {base}.ipynb; rm {base}.md' + cmd = f'jupytext --sync {base}.ipynb; ' + print(f'Running: {cmd}') + os.system(cmd) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6007ef5..dac1a2d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -29,7 +29,7 @@ numpydoc_class_members_toctree = False nb_execution_mode = "auto" nb_execution_timeout = 60*20 #*100 -nb_execution_excludepatterns = ['Ch10*', 'imdb.ipynb'] +nb_execution_excludepatterns = ['Ch10*', 'Ch13*', 'imdb.ipynb'] nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} From bdbec379c859c9b5bbb2cb482ec1411b1b74858f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 15 Aug 2023 21:34:05 -0700 Subject: [PATCH 102/195] always return a two-dimensional column with _get_clumn (#4) --- ISLP/models/columns.py | 34 +++++++++++++--------------------- 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/ISLP/models/columns.py b/ISLP/models/columns.py index c15ace2..e3c2106 100644 --- a/ISLP/models/columns.py +++ b/ISLP/models/columns.py @@ -52,7 +52,7 @@ def get_columns(self, X, fit=False): Column names """ - cols = _get_column(self.idx, X, ndarray=False) + cols = _get_column(self.idx, X) if fit: self.fit_encoder(X) @@ -88,7 +88,7 @@ def fit_encoder(self, X): ------- None """ - cols = _get_column(self.idx, X, ndarray=False) + cols = _get_column(self.idx, X) if self.encoder is not None: try: check_is_fitted(self.encoder) @@ -102,31 +102,23 @@ def fit_encoder(self, X): def _get_column(idx, X, - twodim=False, - loc=True, - ndarray=True): + loc=True): """ - Extract column `idx` from `X`, - optionally making it two-dimensional - as many sklearn encoders assume - two-dimensional input + Extract column `idx` from `X` + as a two-dimensional ndarray or a pd.DataFrame """ if isinstance(X, np.ndarray): - col = X[:, idx] + col = X[:, [idx]] elif hasattr(X, 'loc'): if loc: - col = X.loc[:, idx] + col = X.loc[:, [idx]] else: # use iloc instead - col = X.iloc[:, idx] + col = X.iloc[:, [idx]] else: raise ValueError('expecting an ndarray or a ' + '"loc/iloc" methods, got %s' % str(X)) - if ndarray: - if twodim and np.asarray(col).ndim == 1: - return np.asarray(col).reshape((-1, 1)) - return np.asarray(col) - else: - return col + + return col def _get_column_info(X, @@ -158,12 +150,12 @@ def _get_column_info(X, name = str(col) if is_categorical[i]: if is_ordinal[i]: - Xcol = _get_column(col, X, twodim=True) + Xcol = _get_column(col, X) encoder = clone(default_encoders['ordinal']) encoder.fit(Xcol) columns = ['{0}'.format(col)] else: - Xcol = _get_column(col, X, twodim=True, ndarray=True) + Xcol = _get_column(col, X) encoder = clone(default_encoders['categorical']) cols = encoder.fit_transform(Xcol) if hasattr(encoder, 'columns_'): @@ -179,7 +171,7 @@ def _get_column_info(X, tuple(columns), encoder) else: - Xcol = _get_column(col, X, twodim=True) + Xcol = _get_column(col, X) column_info[col] = Column(col, name, columns=(name,)) From 7780420195fe9a145d605c8f5378d684b6d0050a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 11:37:58 -0700 Subject: [PATCH 103/195] building notebooks on linux and macos --- .github/workflows/build_notebook.yml | 44 ++++++++++++++++++++- .github/workflows/build_notebook_errors.yml | 43 +++++++++++++++++++- 2 files changed, 83 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 4a6c9f4..8d50f07 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -14,16 +14,56 @@ on: ID: description: 'Which lab to build' required: true - default: '02' + default: '03' type: string # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: # This workflow contains a single job called "build" - build: + build-linux: # The type of runner that the job will run on runs-on: ubuntu-latest + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + pip install . + + # Runs a set of commands using the runners shell + - name: Build notebook + env: + LABS: ${{ inputs.LABS }} + ID: ${{ inputs.ID }} + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + git checkout $LABS + jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb + jupyter nbconvert --to html Ch*$ID*lab.ipynb + + # Store the output + - name: Upload labs + env: + ID: ${{ inputs.ID }} + uses: actions/upload-artifact@v3 + with: + name: ISLP_labs + path: ISLP_labs/Ch*$ID* + retention-days: 1 + + build-mac: + # The type of runner that the job will run on + runs-on: macos-latest + # Steps represent a sequence of tasks that will be executed as part of the job steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it diff --git a/.github/workflows/build_notebook_errors.yml b/.github/workflows/build_notebook_errors.yml index 4419557..28b4b5e 100644 --- a/.github/workflows/build_notebook_errors.yml +++ b/.github/workflows/build_notebook_errors.yml @@ -19,8 +19,8 @@ on: # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: - # This workflow contains a single job called "build" - build: + + build-linux: # The type of runner that the job will run on runs-on: ubuntu-latest @@ -59,3 +59,42 @@ jobs: path: ISLP_labs/Ch*$ID*nb retention-days: 1 + build-mac: + # The type of runner that the job will run on + runs-on: macos-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v2 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + pip install . + + # Runs a set of commands using the runners shell + - name: Build notebook, allowing errors + env: + LABS: ${{ inputs.LABS }} + ID: ${{ inputs.ID }} + run: | + git clone https://github.com/intro-stat-learning/ISLP_labs.git + cd ISLP_labs + git checkout $LABS + jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb --allow-errors + + # Store the output + - name: Upload labs + env: + ID: ${{ inputs.ID }} + uses: actions/upload-artifact@v3 + with: + name: ISLP_labs + path: ISLP_labs/Ch*$ID*nb + retention-days: 1 + From 8b16f04df6b817c42d63aa2284b28eef46627dfa Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:04:22 -0700 Subject: [PATCH 104/195] max numpy requirement for numba --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 134b920..4fe0974 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy>=1.7.1 +numpy>=1.7.1,<1.25 # max version for numba scipy>=0.9 pandas>=0.20 pandas<=1.9 From 4298d880d56ce03a0b20b2f6dfc7bcc90e4c7046 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:06:47 -0700 Subject: [PATCH 105/195] use requirements.txt --- setup.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/setup.py b/setup.py index ff4a13b..9ad4db8 100755 --- a/setup.py +++ b/setup.py @@ -187,18 +187,20 @@ def version_error_msg(pkg_name, found_ver, min_ver): req_type='install_requires', heavy=True).check_fill(extra_setuptools_args) -#requirements = open('requirements.txt').read().strip().split('\n') - -requirements = '''numpy -scipy -jupyter -pandas -lxml # pandas needs this for html -scikit-learn -joblib -pygam # for GAM in Ch7 -lifelines'''.split('\n') -#l0bnb # for bestsubsets +dirname = os.path.dirname(__file__) +requirements = open(os.path.join(dirname, + 'requirements.txt')).read().strip().split('\n') + +# requirements = '''numpy +# scipy +# jupyter +# pandas +# lxml # pandas needs this for html +# scikit-learn +# joblib +# pygam # for GAM in Ch7 +# lifelines'''.split('\n') +# #l0bnb # for bestsubsets From a06fc825c0eac37ca6d39be7c06a1f41927f769f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:08:53 -0700 Subject: [PATCH 106/195] remove whitespace --- requirements.txt | 2 -- 1 file changed, 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 4fe0974..4c1827e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,5 +11,3 @@ pygam # for GAM in Ch7 torch pytorch_lightning torchmetrics -#l0bnb # for bestsubsets - From d65db3a437eff4581361e2f34967a8901d18be21 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:27:08 -0700 Subject: [PATCH 107/195] switching to pyproject.toml --- pyproject.toml | 17 +++++++++++++++ setup.py | 56 +++++++++++++++++++++++++------------------------- 2 files changed, 45 insertions(+), 28 deletions(-) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..df053e4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,17 @@ +[build-system] +requires = ["setuptools>=42", + "wheel", + "numpy>=1.7.1,<1.25", # max version for numba + "scipy>=0.9", + "pandas>=0.20,<=1.9", + "lxml", # pandas needs this for html + "scikit-learn>=1.2", + "joblib", + "statsmodels>=0.13", + "lifelines", + "pygam", # for GAM in Ch7 + "torch", + "pytorch_lightning", + "torchmetrics" + ] +build-backend = "setuptools.build_meta" diff --git a/setup.py b/setup.py index 9ad4db8..4a0d908 100755 --- a/setup.py +++ b/setup.py @@ -168,28 +168,28 @@ def version_error_msg(pkg_name, found_ver, min_ver): # Define extensions EXTS = [] -SetupDependency('numpy', info.NUMPY_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) -SetupDependency('scipy', info.SCIPY_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) -SetupDependency('matplotlib', info.MATPLOTLIB_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) -SetupDependency('pandas', info.PANDAS_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) -SetupDependency('statsmodels', info.STATSMODELS_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) -SetupDependency('scikit-learn', info.SKLEARN_MIN_VERSION, - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) - -dirname = os.path.dirname(__file__) -requirements = open(os.path.join(dirname, - 'requirements.txt')).read().strip().split('\n') +# SetupDependency('numpy', info.NUMPY_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) +# SetupDependency('scipy', info.SCIPY_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) +# SetupDependency('matplotlib', info.MATPLOTLIB_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) +# SetupDependency('pandas', info.PANDAS_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) +# SetupDependency('statsmodels', info.STATSMODELS_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) +# SetupDependency('scikit-learn', info.SKLEARN_MIN_VERSION, +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) + +# dirname = os.path.dirname(__file__) +# requirements = open(os.path.join(dirname, +# 'requirements.txt')).read().strip().split('\n') # requirements = '''numpy # scipy @@ -204,12 +204,12 @@ def version_error_msg(pkg_name, found_ver, min_ver): -for req in requirements: - req = req.split('#')[0] - import sys; sys.stderr.write(req+'\n') - SetupDependency(req, "0.0", - req_type='install_requires', - heavy=True).check_fill(extra_setuptools_args) +# for req in requirements: +# req = req.split('#')[0] +# import sys; sys.stderr.write(req+'\n') +# SetupDependency(req, "0.0", +# req_type='install_requires', +# heavy=True).check_fill(extra_setuptools_args) cmdclass=versioneer.get_cmdclass() From c75e501dd6bd0faf8196de6f27cbe5cecba97873 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:30:54 -0700 Subject: [PATCH 108/195] install torch deps only at test step --- .github/workflows/build_test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index bca136a..f316693 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -22,7 +22,6 @@ jobs: run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - pip install torchvision torchinfo - name: Lint with flake8 run: | pip install flake8 @@ -33,6 +32,7 @@ jobs: - name: Test with pytest timeout-minutes: 12 run: | + pip install torchvision torchinfo pip install pytest pytest @@ -55,7 +55,6 @@ jobs: run: | pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - pip install torchvision torchinfo - name: Lint with flake8 run: | pip install flake8 @@ -66,6 +65,7 @@ jobs: - name: Test with pytest timeout-minutes: 12 run: | + pip install torchvision torchinfo pip install pytest pytest From c3d9d7394b96716e770d7a1291c95b88e756e049 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:39:26 -0700 Subject: [PATCH 109/195] versioneer package --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index df053e4..faed5d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ requires = ["setuptools>=42", "pygam", # for GAM in Ch7 "torch", "pytorch_lightning", - "torchmetrics" + "torchmetrics", + "versioneer" ] build-backend = "setuptools.build_meta" From 2e15e4a889d145d8a0c210a6b8e1281ec8fa6af8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:48:56 -0700 Subject: [PATCH 110/195] removed redundant requirements.txt --- .github/workflows/build_test.yml | 4 +- setup.py | 144 ------------------------------- 2 files changed, 2 insertions(+), 146 deletions(-) diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index f316693..feebbd6 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -20,7 +20,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + # pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | @@ -53,7 +53,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt + # pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt pip install git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | diff --git a/setup.py b/setup.py index 4a0d908..c30a6d7 100755 --- a/setup.py +++ b/setup.py @@ -51,107 +51,6 @@ def read_vars_from(ver_file): info = read_vars_from(pjoin('ISLP', 'info.py')) -class SetupDependency(object): - """ SetupDependency class - - Parameters - ---------- - import_name : str - Name with which required package should be ``import``ed. - min_ver : str - Distutils version string giving minimum version for package. - req_type : {'install_requires', 'setup_requires'}, optional - Setuptools dependency type. - heavy : {False, True}, optional - If True, and package is already installed (importable), then do not add - to the setuptools dependency lists. This prevents setuptools - reinstalling big packages when the package was installed without using - setuptools, or this is an upgrade, and we want to avoid the pip default - behavior of upgrading all dependencies. - install_name : str, optional - Name identifying package to install from pypi etc, if different from - `import_name`. - """ - - def __init__(self, import_name, - min_ver, - req_type='install_requires', - heavy=False, - install_name=None): - self.import_name = import_name - self.min_ver = min_ver - self.req_type = req_type - self.heavy = heavy - self.install_name = (import_name if install_name is None - else install_name) - - def check_fill(self, setuptools_kwargs): - """ Process this dependency, maybe filling `setuptools_kwargs` - - Run checks on this dependency. If not using setuptools, then raise - error for unmet dependencies. If using setuptools, add missing or - not-heavy dependencies to `setuptools_kwargs`. - - A heavy dependency is one that is inconvenient to install - automatically, such as numpy or (particularly) scipy, matplotlib. - - Parameters - ---------- - setuptools_kwargs : dict - Dictionary of setuptools keyword arguments that may be modified - in-place while checking dependencies. - """ - found_ver = get_pkg_version(self.import_name) - ver_err_msg = version_error_msg(self.import_name, - found_ver, - self.min_ver) - if not 'setuptools' in sys.modules: - # Not using setuptools; raise error for any unmet dependencies - if ver_err_msg is not None: - raise RuntimeError(ver_err_msg) - return - # Using setuptools; add packages to given section of - # setup/install_requires, unless it's a heavy dependency for which we - # already have an acceptable importable version. - if self.heavy and ver_err_msg is None: - return - new_req = '{0}>={1}'.format(self.import_name, self.min_ver) - old_reqs = setuptools_kwargs.get(self.req_type, []) - setuptools_kwargs[self.req_type] = old_reqs + [new_req] - -def get_pkg_version(pkg_name): - """ Return package version for `pkg_name` if installed - - Returns - ------- - pkg_version : str or None - Return None if package not importable. Return 'unknown' if standard - ``__version__`` string not present. Otherwise return version string. - """ - try: - pkg = __import__(pkg_name) - except ImportError: - return None - try: - return pkg.__version__ - except AttributeError: - return 'unknown' - -def version_error_msg(pkg_name, found_ver, min_ver): - """ Return informative error message for version or None - """ - if found_ver is None: - return 'We need package {0}, but not importable'.format(pkg_name) - if found_ver == 'unknown': - return 'We need {0} version {1}, but cannot get version'.format( - pkg_name, min_ver) - if LooseVersion(found_ver) >= LooseVersion(min_ver): - return None - return 'We need {0} version {1}, but found version {2}'.format( - pkg_name, found_ver, min_ver) - - - # Try to preempt setuptools monkeypatching of Extension handling when Pyrex # is missing. Otherwise the monkeypatched Extension will change .pyx # filenames to .c filenames, and we probably don't have the .c files. @@ -168,49 +67,6 @@ def version_error_msg(pkg_name, found_ver, min_ver): # Define extensions EXTS = [] -# SetupDependency('numpy', info.NUMPY_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) -# SetupDependency('scipy', info.SCIPY_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) -# SetupDependency('matplotlib', info.MATPLOTLIB_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) -# SetupDependency('pandas', info.PANDAS_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) -# SetupDependency('statsmodels', info.STATSMODELS_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) -# SetupDependency('scikit-learn', info.SKLEARN_MIN_VERSION, -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) - -# dirname = os.path.dirname(__file__) -# requirements = open(os.path.join(dirname, -# 'requirements.txt')).read().strip().split('\n') - -# requirements = '''numpy -# scipy -# jupyter -# pandas -# lxml # pandas needs this for html -# scikit-learn -# joblib -# pygam # for GAM in Ch7 -# lifelines'''.split('\n') -# #l0bnb # for bestsubsets - - - -# for req in requirements: -# req = req.split('#')[0] -# import sys; sys.stderr.write(req+'\n') -# SetupDependency(req, "0.0", -# req_type='install_requires', -# heavy=True).check_fill(extra_setuptools_args) - cmdclass=versioneer.get_cmdclass() # get long_description From 8e4aecf3b90f5f87c15064f5f47b3bd8dfab2102 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:50:57 -0700 Subject: [PATCH 111/195] installing from checked out git repo --- .github/workflows/build_test.yml | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index feebbd6..c2d228b 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -20,8 +20,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - # pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install git+https://github.com/intro-stat-learning/ISLP.git + pip install . # git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | pip install flake8 @@ -53,8 +52,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - # pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP/main/requirements.txt - pip install git+https://github.com/intro-stat-learning/ISLP.git + pip install . # git+https://github.com/intro-stat-learning/ISLP.git - name: Lint with flake8 run: | pip install flake8 From 08c279ebb8c459eb66099c1ac6f841e3fe6c3c2f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 12:59:47 -0700 Subject: [PATCH 112/195] moving requirements --- pyproject.toml | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index faed5d2..5fc53d9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,18 +1,23 @@ +[project] +name = "ISLP" +version = "0.3.19" +dependencies = ["numpy>=1.7.1,<1.25", # max version for numba + "scipy>=0.9", + "pandas>=0.20,<=1.9", + "lxml", # pandas needs this for html + "scikit-learn>=1.2", + "joblib", + "statsmodels>=0.13", + "lifelines", + "pygam", # for GAM in Ch7 + "torch", + "pytorch_lightning", + "torchmetrics", + ] + [build-system] requires = ["setuptools>=42", "wheel", - "numpy>=1.7.1,<1.25", # max version for numba - "scipy>=0.9", - "pandas>=0.20,<=1.9", - "lxml", # pandas needs this for html - "scikit-learn>=1.2", - "joblib", - "statsmodels>=0.13", - "lifelines", - "pygam", # for GAM in Ch7 - "torch", - "pytorch_lightning", - "torchmetrics", "versioneer" ] build-backend = "setuptools.build_meta" From 8fae3b6603cb3d8ca511bcbfe2454d438b25d846 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 13:24:12 -0700 Subject: [PATCH 113/195] install jupyter for lab --- .github/workflows/build_notebook.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 8d50f07..358f568 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -37,6 +37,7 @@ jobs: - name: Install dependencies run: | pip install . + pip install jupyterlab # Runs a set of commands using the runners shell - name: Build notebook @@ -77,6 +78,7 @@ jobs: - name: Install dependencies run: | pip install . + pip install jupyterlab # Runs a set of commands using the runners shell - name: Build notebook From 5852a8f8d39d23158c43017a95b9416067866637 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 13:25:15 -0700 Subject: [PATCH 114/195] syntax error --- .github/workflows/build_notebook.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 358f568..ca1b38d 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -37,7 +37,7 @@ jobs: - name: Install dependencies run: | pip install . - pip install jupyterlab + pip install jupyterlab # Runs a set of commands using the runners shell - name: Build notebook From 36dbddb5b45cbefdefddae0c1a00b56b364d5124 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 14:53:37 -0700 Subject: [PATCH 115/195] updating to setup-python action --- .github/workflows/build_docs.yml | 2 +- .github/workflows/build_notebook.yml | 4 ++-- .github/workflows/build_notebook_errors.yml | 4 ++-- .github/workflows/build_save_labs.yml | 2 +- .github/workflows/build_test.yml | 8 ++++---- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 7328c69..ee88d2c 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -24,7 +24,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index ca1b38d..3fcb6f1 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -28,7 +28,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' @@ -69,7 +69,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' diff --git a/.github/workflows/build_notebook_errors.yml b/.github/workflows/build_notebook_errors.yml index 28b4b5e..d6c0f21 100644 --- a/.github/workflows/build_notebook_errors.yml +++ b/.github/workflows/build_notebook_errors.yml @@ -28,7 +28,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' @@ -67,7 +67,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' diff --git a/.github/workflows/build_save_labs.yml b/.github/workflows/build_save_labs.yml index abdf3d5..7d2f694 100644 --- a/.github/workflows/build_save_labs.yml +++ b/.github/workflows/build_save_labs.yml @@ -23,7 +23,7 @@ jobs: steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: '3.10' cache: 'pip' diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index c2d228b..495663c 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -11,7 +11,7 @@ jobs: steps: - uses: actions/checkout@v3 - name: Set up Python 3.10 - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: '3.10' - name: Add conda to system path @@ -20,7 +20,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - pip install . # git+https://github.com/intro-stat-learning/ISLP.git + pip install . - name: Lint with flake8 run: | pip install flake8 @@ -43,7 +43,7 @@ jobs: steps: - uses: actions/checkout@v3 - name: Set up Python 3.10 - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: '3.10' - name: Add conda to system path @@ -52,7 +52,7 @@ jobs: echo $CONDA/bin >> $GITHUB_PATH - name: Install dependencies run: | - pip install . # git+https://github.com/intro-stat-learning/ISLP.git + pip install . - name: Lint with flake8 run: | pip install flake8 From 08a0823aa22c0c6b4a893a7d155c8f6e8afe1dff Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 15:01:18 -0700 Subject: [PATCH 116/195] changing path of artifact --- .github/workflows/build_notebook.yml | 6 +++--- .github/workflows/build_notebook_errors.yml | 4 ++-- .github/workflows/build_save_labs.yml | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 3fcb6f1..d8a5664 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -50,7 +50,7 @@ jobs: git checkout $LABS jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb jupyter nbconvert --to html Ch*$ID*lab.ipynb - + # Store the output - name: Upload labs env: @@ -58,7 +58,7 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch*$ID* + path: Ch*$ID* retention-days: 1 build-mac: @@ -99,5 +99,5 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch*$ID* + path: Ch*$ID* retention-days: 1 \ No newline at end of file diff --git a/.github/workflows/build_notebook_errors.yml b/.github/workflows/build_notebook_errors.yml index d6c0f21..747d930 100644 --- a/.github/workflows/build_notebook_errors.yml +++ b/.github/workflows/build_notebook_errors.yml @@ -56,7 +56,7 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch*$ID*nb + path: Ch*$ID*nb retention-days: 1 build-mac: @@ -95,6 +95,6 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch*$ID*nb + path: Ch*$ID*nb retention-days: 1 diff --git a/.github/workflows/build_save_labs.yml b/.github/workflows/build_save_labs.yml index 7d2f694..57ebf78 100644 --- a/.github/workflows/build_save_labs.yml +++ b/.github/workflows/build_save_labs.yml @@ -100,5 +100,5 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch* + path: Ch* retention-days: 1 \ No newline at end of file From bcc9ef18a2e1fae7b808a18793e664cce48c1d71 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 15:01:59 -0700 Subject: [PATCH 117/195] whitespace --- .github/workflows/build_notebook.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index d8a5664..9258034 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -50,7 +50,7 @@ jobs: git checkout $LABS jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb jupyter nbconvert --to html Ch*$ID*lab.ipynb - + # Store the output - name: Upload labs env: From 0868b12962e1f55da5258b4b498d3c8f5bc5b161 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 15:07:34 -0700 Subject: [PATCH 118/195] trying to fix path of lab --- .github/workflows/build_notebook.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 9258034..f72b6ee 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -58,7 +58,7 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: Ch*$ID* + path: ISLP_labs/Ch*nb retention-days: 1 build-mac: From 8447c029828be6447d4a0aaf1608209b19b13eba Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 15:13:04 -0700 Subject: [PATCH 119/195] only want build lab --- .github/workflows/build_notebook.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index f72b6ee..8e97a82 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -48,8 +48,9 @@ jobs: git clone https://github.com/intro-stat-learning/ISLP_labs.git cd ISLP_labs git checkout $LABS - jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb - jupyter nbconvert --to html Ch*$ID*lab.ipynb + cp Ch*$ID*lab.ipynb .. + jupyter nbconvert --execute --inplace ../Ch*$ID*lab.ipynb + jupyter nbconvert --to html ../Ch*$ID*lab.ipynb # Store the output - name: Upload labs @@ -58,7 +59,7 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: ISLP_labs/Ch*nb + path: Ch* retention-days: 1 build-mac: From 630174eacead99e3f53bc930639acd121204ad45 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 15:19:17 -0700 Subject: [PATCH 120/195] path updates to workflows --- .github/workflows/build_notebook.yml | 7 ++++--- .github/workflows/build_notebook_errors.yml | 12 ++++++++---- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build_notebook.yml b/.github/workflows/build_notebook.yml index 8e97a82..dbf97e8 100644 --- a/.github/workflows/build_notebook.yml +++ b/.github/workflows/build_notebook.yml @@ -90,8 +90,9 @@ jobs: git clone https://github.com/intro-stat-learning/ISLP_labs.git cd ISLP_labs git checkout $LABS - jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb - jupyter nbconvert --to html Ch*$ID*lab.ipynb + cp Ch*$ID*lab.ipynb .. + jupyter nbconvert --execute --inplace ../Ch*$ID*lab.ipynb + jupyter nbconvert --to html ../Ch*$ID*lab.ipynb # Store the output - name: Upload labs @@ -100,5 +101,5 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: Ch*$ID* + path: Ch* retention-days: 1 \ No newline at end of file diff --git a/.github/workflows/build_notebook_errors.yml b/.github/workflows/build_notebook_errors.yml index 747d930..d5fabee 100644 --- a/.github/workflows/build_notebook_errors.yml +++ b/.github/workflows/build_notebook_errors.yml @@ -47,7 +47,9 @@ jobs: git clone https://github.com/intro-stat-learning/ISLP_labs.git cd ISLP_labs git checkout $LABS - jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb --allow-errors + cp Ch*$ID*lab.ipynb .. + jupyter nbconvert --execute --inplace ../Ch*$ID*lab.ipynb --allow-errors + jupyter nbconvert --to html ../Ch*$ID*lab.ipynb # Store the output - name: Upload labs @@ -56,7 +58,7 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: Ch*$ID*nb + path: Ch* retention-days: 1 build-mac: @@ -86,7 +88,9 @@ jobs: git clone https://github.com/intro-stat-learning/ISLP_labs.git cd ISLP_labs git checkout $LABS - jupyter nbconvert --execute --inplace Ch*$ID*lab.ipynb --allow-errors + cp Ch*$ID*lab.ipynb .. + jupyter nbconvert --execute --inplace ../Ch*$ID*lab.ipynb --allow-errors + jupyter nbconvert --to html ../Ch*$ID*lab.ipynb # Store the output - name: Upload labs @@ -95,6 +99,6 @@ jobs: uses: actions/upload-artifact@v3 with: name: ISLP_labs - path: Ch*$ID*nb + path: Ch* retention-days: 1 From 5f5b0d3160fe5055a8c61fa890115d34147c1248 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 16:05:22 -0700 Subject: [PATCH 121/195] Devel docs (#5) * LAB_version * setting a __lab_version__ for building docs * not clearing lab outputs * including built version of labs * BF: changed name of docs version * BF: punctuation fix * BF: syntax errors * docs version JSON * link for library version * BF: wrong repo * update message about binder --- .github/workflows/build_docs.yml | 10 +----- ISLP/__init__.py | 1 + docs/fix_and_clear_notebooks.py | 25 +++++++++------ docs/source/conf.py | 25 ++++++++++++++- docs/source/docs_version.json | 4 +++ docs/source/installation.myst | 14 ++------- docs/source/labs.myst | 54 ++++++++++++++++++++++++++++++++ docs/source/labs.rst | 36 --------------------- 8 files changed, 102 insertions(+), 67 deletions(-) create mode 100644 docs/source/docs_version.json create mode 100644 docs/source/labs.myst delete mode 100644 docs/source/labs.rst diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index ee88d2c..507063d 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -5,12 +5,6 @@ name: Build docs # Controls when the workflow will run on: workflow_dispatch: - inputs: - LABS: - description: 'Labs version' - required: true - default: 'v2' - type: string # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: @@ -39,12 +33,10 @@ jobs: # Checkout labs - name: Checkout version of labs - env: - LABS: ${{ inputs.LABS }} run: | git submodule update --init --force docs/ISLP_labs cd docs - python fix_and_clear_notebooks.py --version $LABS + python fix_and_clear_notebooks.py --noclear rm source/labs/Ch*md - name: Make docs diff --git a/ISLP/__init__.py b/ISLP/__init__.py index 0cd8d3e..4e7e3f4 100644 --- a/ISLP/__init__.py +++ b/ISLP/__init__.py @@ -119,3 +119,4 @@ def confusion_table(predicted_labels, from . import _version __version__ = _version.get_versions()['version'] + diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index aac28ee..2e42f03 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -1,16 +1,23 @@ import os +import sys +import json import nbformat from argparse import ArgumentParser from glob import glob -parser = ArgumentParser() -parser.add_argument('--version', default='v2') -args = parser.parse_args() -version = args.version - import __main__ dirname = os.path.split(__main__.__file__)[0] print(dirname) +sys.path.append(os.path.join(dirname, 'source')) +from conf import docs_version +#docs_version = json.loads(open(os.path.join(dirname, 'source', 'docs_version.json')).read()) + +parser = ArgumentParser() +parser.add_argument('--version', default=docs_version['labs']) +parser.add_argument('--clear', dest='clear', action='store_true', default=False) +parser.add_argument('--noclear', dest='clear', action='store_false') +args = parser.parse_args() +version = args.version for f in glob(os.path.join(dirname, 'source', 'labs', 'Ch14*')): os.remove(f) @@ -63,9 +70,10 @@ if labname[:4] not in ['Ch10', 'Ch13']: # clear outputs for all but Ch10,Ch13 - cmd = f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}' - print(f'Running: {cmd}') - os.system(cmd) + if args.clear: + cmd = f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}' + print(f'Running the clearing command: {cmd}') + os.system(cmd) cmd = f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}' print(f'Running: {cmd}') @@ -84,7 +92,6 @@ open(f'{base}.md', 'w').write(myst) -# cmd = f'jupytext --sync {base}.ipynb; rm {base}.md' cmd = f'jupytext --sync {base}.ipynb; ' print(f'Running: {cmd}') os.system(cmd) diff --git a/docs/source/conf.py b/docs/source/conf.py index dac1a2d..b81dcbf 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -2,12 +2,35 @@ # -- Project information +import json +import os + project = 'ISLP' copyright = '2023, ISLP authors' author = 'Jonathan Taylor' release = '0.1' -version = '0.1.0' +import ISLP +version = ISLP.__version__ + +import __main__ +dirname = os.path.split(__main__.__file__)[0] +print(dirname) + +docs_version = {"labs": "v2", + "library": "v0.3.18"} + +#docs_version = json.loads(open(os.path.join(dirname, 'docs_version.json')).read()) +lab_version = docs_version['labs'] + +myst_enable_extensions = ['substitution'] + +myst_substitutions = { + "ISLP_lab_link": f"[ISLP_labs/{lab_version}](https://github.com/intro-stat-learning/ISLP_labs/tree/{lab_version})", + "ISLP_binder_code": f"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{lab_version})", + "ISLP_lab_version": "[ISLP/{0}](https://github.com/intro-stat-learning/ISLP/tree/{0})".format(docs_version['library']) + } +myst_number_code_blocks = ['python', 'ipython3'] # -- General configuration diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json new file mode 100644 index 0000000..1a334d8 --- /dev/null +++ b/docs/source/docs_version.json @@ -0,0 +1,4 @@ +{"labs": "v2", + "library": "v0.3.18", + "comment":"library should be version of ISLP pointed to in ISLP/labs" +} diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 542644e..cb42d7f 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -52,20 +52,10 @@ pip install ISLP ```{attention} Python packages change frequently. The labs here are built -with specific versions of the various packages. +with {{ ISLP_lab_link }}. Visit the lab git repo for specific instructions +to install the frozen environment. ``` -To ensure you have the same package versions as those built here, run: - -```{code-cell} ipython3 ---- -tags: [skip-execution] ---- -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt -``` - -For more specific install instructions go to [ISLP_labs](https://github.com/intro-stat-learning/ISLP_labs/tree/v2). - ## Torch requirements The `ISLP` labs use `torch` and various related packages for the lab diff --git a/docs/source/labs.myst b/docs/source/labs.myst new file mode 100644 index 0000000..373c500 --- /dev/null +++ b/docs/source/labs.myst @@ -0,0 +1,54 @@ +--- +file_format: mystnb +kernelspec: + name: python3 + display_name: python3 +myst_number_code_blocks: python +--- + +# Labs + +{{ ISLP_binder_code }} + +The current version of the labs for `ISLP` are included here. + +## Package versions + + +```{attention} + +Python packages change frequently. The labs here are built +with {{ ISLP_lab_link }}. Visit the lab git repo for specific instructions +to install the frozen environment. + + +``` + +```{warning} +The version of the `ISLP` library used to build these labs +may differ slightly from the one documented here. +The labs are built with {{ ISLP_lab_version }}. + +The [Binder](http://mybinder.org) link above will run {{ ISLP_lab_link }} with +library version {{ ISLP_lab_version }}. + +``` + + +```{toctree} +maxdepth: 1 + +labs/Ch02-statlearn-lab +labs/Ch03-linreg-lab +labs/Ch04-classification-lab +labs/Ch05-resample-lab +labs/Ch06-varselect-lab +labs/Ch07-nonlin-lab +labs/Ch08-baggboost-lab +labs/Ch09-svm-lab +labs/Ch10-deeplearning-lab +labs/Ch11-surv-lab +labs/Ch12-unsup-lab +labs/Ch13-multiple-lab +``` + diff --git a/docs/source/labs.rst b/docs/source/labs.rst deleted file mode 100644 index 653020d..0000000 --- a/docs/source/labs.rst +++ /dev/null @@ -1,36 +0,0 @@ -Labs -==== - -The current version of the labs for `ISLP` are included here. - - -Package versions ----------------- - -.. attention:: - - Python packages change frequently. The labs here are built - with specific versions of the various packages. - - -To ensure you have the same package versions as those built here, run: - - pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt; - -.. toctree:: - :maxdepth: 1 - - labs/Ch02-statlearn-lab - labs/Ch03-linreg-lab - labs/Ch04-classification-lab - labs/Ch05-resample-lab - labs/Ch06-varselect-lab - labs/Ch07-nonlin-lab - labs/Ch08-baggboost-lab - labs/Ch09-svm-lab - labs/Ch10-deeplearning-lab - labs/Ch11-surv-lab - labs/Ch12-unsup-lab - labs/Ch13-multiple-lab - - From 01e63d7d93d4ebf53ebc4fc52dda3b4c2dd506ee Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 18:02:31 -0700 Subject: [PATCH 122/195] V0.3.19rc (#6) * adding more description to pyproject.toml * bumping docs and labs to v0.3.19rc/v2.1rc * fixing doc link in README * moved labs to v22.1rc * fixing build_docs.yml --- .github/workflows/build_docs.yml | 1 - README.md | 2 +- docs/ISLP_labs | 2 +- docs/source/docs_version.json | 4 ++-- pyproject.toml | 28 +++++++++++++++++++++++++++- 5 files changed, 31 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 507063d..351712e 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -27,7 +27,6 @@ jobs: - name: Install dependencies run: | sudo apt-get install r-base - pip install -r requirements.txt pip install -r docs/requirements.txt pip install . diff --git a/README.md b/README.md index 9970d13..6cd576f 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ is your `islp` environment. This information appears near the top left in the An ## Documentation -See the [read the docs page](https://islp.readthedocs.io/en/latest) for the latest documentation. +See the [docs](https://intro-stat-learning.github.io/ISLP/labs.html) for the latest documentation. diff --git a/docs/ISLP_labs b/docs/ISLP_labs index dc35f72..5c29f1c 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit dc35f729e16344bc2105f2a3532bd84baa1162f4 +Subproject commit 5c29f1c9e4fa723f09c7c779a3edc7753bb808b1 diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index 1a334d8..46447a1 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2", - "library": "v0.3.18", +{"labs": "v2.1rc", + "library": "v0.3.19rc", "comment":"library should be version of ISLP pointed to in ISLP/labs" } diff --git a/pyproject.toml b/pyproject.toml index 5fc53d9..ea0c3f8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,33 @@ dependencies = ["numpy>=1.7.1,<1.25", # max version for numba "pytorch_lightning", "torchmetrics", ] - +description = "Library for ISLP labs" +readme = "README.md" +requires-python = ">=3.9" +license = {file = "LICENSE.txt"} +keywords = [] +authors = [ + {name = "Jonathan Taylor", email="jonathan.taylor@stanford.edu" }, + {name = "Trevor Hastie", email="hastie@stanford.edu" } + ] +maintainers = [ + {name = "Jonathan Taylor", email="jonathan.taylor@stanford.edu" }, + ] +classifiers = ["Development Status :: 3 - Alpha", + "Environment :: Console", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: BSD License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Topic :: Scientific/Engineering" + ] +[project.urls] # Optional +"Homepage" = "https://github.com/intro-stat-learning/ISLP" +"Bug Reports" = "https://github.com/intro-stat-learning/ISLP/issues" +"Funding" = "https://donate.pypi.org" +"Say Thanks!" = "http://saythanks.io/to/example" +"Source" = "https://github.com/pypa/sampleproject/" + [build-system] requires = ["setuptools>=42", "wheel", From 431ea2d0281da03fb8f97541998d8c5f96e0a7cb Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 18:03:51 -0700 Subject: [PATCH 123/195] v0.3.19 for docs --- docs/source/docs_version.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index 46447a1..b8193c7 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ {"labs": "v2.1rc", - "library": "v0.3.19rc", + "library": "v0.3.19", "comment":"library should be version of ISLP pointed to in ISLP/labs" } From 60084b78f5a66e4c9b0f6363760cfabf2d1e9511 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 18:51:13 -0700 Subject: [PATCH 124/195] use json for version --- docs/fix_and_clear_notebooks.py | 4 ++-- docs/source/conf.py | 7 +++---- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index 2e42f03..c712df5 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -7,10 +7,10 @@ import __main__ dirname = os.path.split(__main__.__file__)[0] -print(dirname) sys.path.append(os.path.join(dirname, 'source')) from conf import docs_version -#docs_version = json.loads(open(os.path.join(dirname, 'source', 'docs_version.json')).read()) + +print('building docs:', docs_version) parser = ArgumentParser() parser.add_argument('--version', default=docs_version['labs']) diff --git a/docs/source/conf.py b/docs/source/conf.py index b81dcbf..5c08bd8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -9,18 +9,17 @@ copyright = '2023, ISLP authors' author = 'Jonathan Taylor' -release = '0.1' import ISLP version = ISLP.__version__ import __main__ -dirname = os.path.split(__main__.__file__)[0] -print(dirname) +dirname = os.path.split(__file__)[0] +print(dirname, 'dirname') docs_version = {"labs": "v2", "library": "v0.3.18"} -#docs_version = json.loads(open(os.path.join(dirname, 'docs_version.json')).read()) +docs_version = json.loads(open(os.path.join(dirname, 'docs_version.json')).read()) lab_version = docs_version['labs'] myst_enable_extensions = ['substitution'] From 0206c2c1e2e6d180eb0d89c0603fd2f53298038a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 19:10:12 -0700 Subject: [PATCH 125/195] whitespace in toc --- docs/source/labs.myst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/labs.myst b/docs/source/labs.myst index 373c500..57499aa 100644 --- a/docs/source/labs.myst +++ b/docs/source/labs.myst @@ -41,7 +41,7 @@ maxdepth: 1 labs/Ch02-statlearn-lab labs/Ch03-linreg-lab labs/Ch04-classification-lab -labs/Ch05-resample-lab +labs/Ch05-resample-lab labs/Ch06-varselect-lab labs/Ch07-nonlin-lab labs/Ch08-baggboost-lab @@ -49,6 +49,6 @@ labs/Ch09-svm-lab labs/Ch10-deeplearning-lab labs/Ch11-surv-lab labs/Ch12-unsup-lab -labs/Ch13-multiple-lab +labs/Ch13-multiple-lab ``` From 8a3f16d52813c103c50f1d477bf364a6218e1c67 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 19:21:34 -0700 Subject: [PATCH 126/195] dont build labs on doc build --- docs/source/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 5c08bd8..41ec91b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -51,7 +51,8 @@ numpydoc_class_members_toctree = False nb_execution_mode = "auto" nb_execution_timeout = 60*20 #*100 -nb_execution_excludepatterns = ['Ch10*', 'Ch13*', 'imdb.ipynb'] +# labs will be built with specific commits of ISLP/ISLP_labs +nb_execution_excludepatterns = ['Ch*', 'imdb.ipynb'] nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} From ffbc0979eafc75da136159b1fdd85e4622ab1fd6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:04:34 -0700 Subject: [PATCH 127/195] trying to upload actions due to deprecation warning --- .github/workflows/build_docs.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 351712e..df604f3 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -69,8 +69,8 @@ jobs: with: name: ISLP_docs path: . - - uses: actions/configure-pages@v1 - - uses: actions/upload-pages-artifact@v1 + - uses: actions/configure-pages@v3 + - uses: actions/upload-pages-artifact@v2 with: path: . - id: deployment From 3b140844055fd3bac1e9a54ba0a67069e7fa3428 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:12:43 -0700 Subject: [PATCH 128/195] run Ch06 --- docs/source/conf.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 41ec91b..df2738b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -52,7 +52,9 @@ nb_execution_mode = "auto" nb_execution_timeout = 60*20 #*100 # labs will be built with specific commits of ISLP/ISLP_labs -nb_execution_excludepatterns = ['Ch*', 'imdb.ipynb'] +# we want Ch06 run to exlucde the warnings +nb_execution_excludepatterns = (['imdb.ipynb'] + + ['Ch{i:02d}*' for i in range(2, 14) if i != 6]) nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} From 0251c658686da0bb4e5a8ce0e9b69167d7c601cc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:22:21 -0700 Subject: [PATCH 129/195] updating ISLP_labs --- docs/ISLP_labs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index 5c29f1c..ddec59a 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit 5c29f1c9e4fa723f09c7c779a3edc7753bb808b1 +Subproject commit ddec59a839e6d47232aea0a3cb226658dec7ac83 From bfe4f2baec510c7b7ca9a2cc3841d975ba7a951d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:23:21 -0700 Subject: [PATCH 130/195] need to use f-strings --- docs/source/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index df2738b..49f3e34 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -54,7 +54,8 @@ # labs will be built with specific commits of ISLP/ISLP_labs # we want Ch06 run to exlucde the warnings nb_execution_excludepatterns = (['imdb.ipynb'] + - ['Ch{i:02d}*' for i in range(2, 14) if i != 6]) + [f'Ch{i:02d}*' for i in range(2, 14) if i != 6]) +print('exclude patterns', nb_execution_excludepatterns) nb_execution_allow_errors = True #nb_kernel_rgx_aliases = {'python3': "islp_test"} From 070e688296959ceef807c5f841fa4282e3799f8e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:26:24 -0700 Subject: [PATCH 131/195] BF: wasn't catching Ch06 correctly --- docs/fix_and_clear_notebooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index c712df5..5a908f3 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -47,7 +47,7 @@ ''' # allow errors for Ch02, suppress warnings for Ch06 - if labname[:3] == 'Ch06': + if labname[:4] == 'Ch06': colab_code = (''' ```{code-cell} :tags: [hide-cell] From 8a0a69b5c16ec786a07574bad686a8d7b08f9f04 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:42:28 -0700 Subject: [PATCH 132/195] moving docs to v2.1 from v2.1rc --- docs/source/docs_version.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index b8193c7..1f8632a 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2.1rc", +{"labs": "v2.1", "library": "v0.3.19", "comment":"library should be version of ISLP pointed to in ISLP/labs" } From 01a4d54221bd06e22907e2e391dd52daf56232fb Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 00:24:11 -0700 Subject: [PATCH 133/195] small edit to workflow --- .github/workflows/build_docs.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index df604f3..7af9e99 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -6,9 +6,11 @@ name: Build docs on: workflow_dispatch: -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" +# A workflow run is made up of one or more jobs that can run +# sequentially or in parallel + +jobs: # This workflow contains a single + # job called "build" build: # The type of runner that the job will run on From 98d98ff2b50afe89c644442cf63f11f5685b9fa0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 22:14:28 -0700 Subject: [PATCH 134/195] script to make a sequence of notebooks with a given set of requirements, optionally inplace --- docs/make_notebooks.py | 102 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 docs/make_notebooks.py diff --git a/docs/make_notebooks.py b/docs/make_notebooks.py new file mode 100644 index 0000000..956762c --- /dev/null +++ b/docs/make_notebooks.py @@ -0,0 +1,102 @@ +''' +Run notebooks in an isolated environment specified by a requirements.txt file +''' + +from hashlib import md5 +import tempfile +import os +from argparse import ArgumentParser + + +parser = ArgumentParser() +parser.add_argument('--requirements', + default='requirements.txt') +parser.add_argument('labs', + metavar='N', + type=str, + nargs='+') +parser.add_argument('--python', + default='3.10') +parser.add_argument('--tarball', + default=None, + dest='tarball') +parser.add_argument('--inplace', + default=False, + action='store_true', + help='run notebooks in place?') +parser.add_argument('--timeout', + default=5000, + help='preprocessor timeout') + +def make_notebooks(requirements='requirements.txt', + srcs=[], + dests=[], + tarball='', + inplace=False, + tmpdir='', + python='3.10', + timeout=5000 # should be enough for Ch10 + ): + + if tarball and inplace: + raise ValueError('tarball option expects notebooks in a tmpdir, while inplace does not copy to a tmpdir') + + md5_ = md5() + md5_.update(open(requirements, 'rb').read()); + hash_ = md5_.hexdigest()[:8] + + env_name = f'isolated_env_{hash_}' + + setup_cmd = f''' + conda create -n {env_name} python={python} -y; + conda run -n {env_name} pip install -r {requirements} jupyter jupytext; + ''' + + print(setup_cmd) + os.system(setup_cmd) + + archive_files = [] + for src_, dest_ in zip(srcs, dests): + if src_ != dest_: + os.system(f'cp {src_} {dest_}') + name = os.path.split(dest_)[1] + build_cmd = f'''conda run -n {env_name} jupyter nbconvert --inplace --execute --ExecutePreprocessor.timeout={timeout} {dest_} ''' + if '02' in name: + build_cmd += ' --allow-errors ' + + print(build_cmd) + os.system(build_cmd) + archive_files.append(name) + + archive_files = ' '.join(archive_files) + + if tarball: + tarball = os.path.abspath(tarball) + tarball_cmd = f''' + cd {tmpdir}; tar -cvzf {tarball} {archive_files} + ''' + print(tarball_cmd) + os.system(tarball_cmd) + + os.system(f'conda env remove -n {env_name}') + +if __name__ == '__main__': + + args = parser.parse_args() + srcs = [os.path.abspath(l) for l in args.labs] + + tmpdir = tempfile.mkdtemp() + + if args.inplace: + dests = srcs + else: + dests = [os.path.join(tmpdir, os.path.split(l)[1]) for l in args.labs] + + make_notebooks(requirements=os.path.abspath(args.requirements), + srcs=srcs, + dests=dests, + inplace=args.inplace, + tmpdir=tmpdir, + python=args.python, + tarball=args.tarball, + timeout=args.timeout) From 60bdbd93b72a04ecbc46dfd78ef638ad47dc5b8a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:35:00 -0700 Subject: [PATCH 135/195] updating lab version --- docs/source/docs_version.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index 1f8632a..fd97959 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2.1", +{"labs": "v2.1.2", "library": "v0.3.19", "comment":"library should be version of ISLP pointed to in ISLP/labs" } From 416e03c276b5a0b40fe34520d4f543a51825b0b5 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:43:32 -0700 Subject: [PATCH 136/195] updating ISLP_labs --- docs/ISLP_labs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index ddec59a..f4ecee2 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit ddec59a839e6d47232aea0a3cb226658dec7ac83 +Subproject commit f4ecee2e614dcd34c13e573568b02dcfc5da99b4 From dfc0148ad2929914941a114de6f9b08d7864e53b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:50:04 -0700 Subject: [PATCH 137/195] making sure Ch06 runs in proper requirements --- .github/workflows/build_docs.yml | 4 +++- docs/fix_and_clear_notebooks.py | 2 +- docs/make_notebooks.py | 11 ++++++++--- docs/source/conf.py | 2 +- 4 files changed, 13 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 7af9e99..c5fc0e5 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -5,7 +5,8 @@ name: Build docs # Controls when the workflow will run on: workflow_dispatch: - + inputs: null + # A workflow run is made up of one or more jobs that can run # sequentially or in parallel @@ -38,6 +39,7 @@ jobs: # This workflow contains a single git submodule update --init --force docs/ISLP_labs cd docs python fix_and_clear_notebooks.py --noclear + python make_notebooks.py --inplace --requirements=ISLP_labs/requirements.txt source/labs/Ch06-varselect-lab.ipynb rm source/labs/Ch*md - name: Make docs diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index 5a908f3..b63540d 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -62,7 +62,7 @@ ``` ''' + colab_code) - if labname[:3] == 'Ch02': + if labname[:4] == 'Ch02': nb = nbformat.read(open(nbfile), 4) nb.metadata.setdefault('execution', {})['allow_errors'] = True nbformat.write(nb, open(nbfile, 'w')) diff --git a/docs/make_notebooks.py b/docs/make_notebooks.py index 956762c..cfea244 100644 --- a/docs/make_notebooks.py +++ b/docs/make_notebooks.py @@ -27,6 +27,8 @@ parser.add_argument('--timeout', default=5000, help='preprocessor timeout') +parser.add_argument('--env_tag', + default='') def make_notebooks(requirements='requirements.txt', srcs=[], @@ -35,7 +37,8 @@ def make_notebooks(requirements='requirements.txt', inplace=False, tmpdir='', python='3.10', - timeout=5000 # should be enough for Ch10 + timeout=5000, # should be enough for Ch10 + env_tag='', ): if tarball and inplace: @@ -45,7 +48,7 @@ def make_notebooks(requirements='requirements.txt', md5_.update(open(requirements, 'rb').read()); hash_ = md5_.hexdigest()[:8] - env_name = f'isolated_env_{hash_}' + env_name = f'isolated_env_{hash_}' + env_tag setup_cmd = f''' conda create -n {env_name} python={python} -y; @@ -55,6 +58,7 @@ def make_notebooks(requirements='requirements.txt', print(setup_cmd) os.system(setup_cmd) + # may need to up "ulimit -n 4096" archive_files = [] for src_, dest_ in zip(srcs, dests): if src_ != dest_: @@ -99,4 +103,5 @@ def make_notebooks(requirements='requirements.txt', tmpdir=tmpdir, python=args.python, tarball=args.tarball, - timeout=args.timeout) + timeout=args.timeout, + env_tag=args.env_tag) diff --git a/docs/source/conf.py b/docs/source/conf.py index 49f3e34..aa6e9b4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -54,7 +54,7 @@ # labs will be built with specific commits of ISLP/ISLP_labs # we want Ch06 run to exlucde the warnings nb_execution_excludepatterns = (['imdb.ipynb'] + - [f'Ch{i:02d}*' for i in range(2, 14) if i != 6]) + [f'Ch{i:02d}*' for i in range(2, 14)]) print('exclude patterns', nb_execution_excludepatterns) nb_execution_allow_errors = True From 7462cb1322ab297e4add9891b3c465b692a4d7cd Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 01:14:38 -0700 Subject: [PATCH 138/195] cleanup the fix script --- .github/workflows/build_docs.yml | 3 +- docs/fix_and_clear_notebooks.py | 152 +++++++++++++++++-------------- 2 files changed, 88 insertions(+), 67 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index c5fc0e5..a10bc0e 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -38,7 +38,8 @@ jobs: # This workflow contains a single run: | git submodule update --init --force docs/ISLP_labs cd docs - python fix_and_clear_notebooks.py --noclear + cp ISLP_labs/Ch*nb source/labs + python fix_and_clear_notebooks.py source/labs/Ch*nb python make_notebooks.py --inplace --requirements=ISLP_labs/requirements.txt source/labs/Ch06-varselect-lab.ipynb rm source/labs/Ch*md diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index b63540d..7a59894 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -1,98 +1,118 @@ + +from dataclasses import dataclass +from copy import copy + +import shlex +import subprocess import os import sys import json import nbformat from argparse import ArgumentParser -from glob import glob -import __main__ -dirname = os.path.split(__main__.__file__)[0] -sys.path.append(os.path.join(dirname, 'source')) -from conf import docs_version +def get_version(): + import __main__ + dirname = os.path.split(__main__.__file__)[0] + sys.path.append(os.path.join(dirname, 'source')) + from conf import docs_version + sys.path = sys.path[:-1] + return docs_version -print('building docs:', docs_version) -parser = ArgumentParser() -parser.add_argument('--version', default=docs_version['labs']) -parser.add_argument('--clear', dest='clear', action='store_true', default=False) -parser.add_argument('--noclear', dest='clear', action='store_false') -args = parser.parse_args() -version = args.version +@dataclass +class Lab(object): -for f in glob(os.path.join(dirname, 'source', 'labs', 'Ch14*')): - os.remove(f) - print(f) - -print(f'checking out version {version} of the labs') + labfile: str + version: str = 'v2' -os.system(f''' -cd {dirname}/ISLP_labs; -git checkout {version}; -mkdir -p {dirname}/source/labs; -cp -r * {dirname}/source/labs; -''') + def __post_init__(self): + self.labfile = os.path.abspath(self.labfile) -for nbfile in glob(os.path.join(dirname, 'source', 'labs', '*nb')): - base = os.path.splitext(nbfile)[0] - labname = os.path.split(base)[1] + def fix_header(self): + labname = os.path.split(self.labfile)[1] + base = os.path.splitext(self.labfile)[0] + args = shlex.split(f'jupytext --set-formats ipynb,md:myst {self.labfile}') + subprocess.run(args) - colab_code = f''' - - Open In Colab + # successful run of jupytext + myst = open(f'{base}.md').read().strip() + split_myst = myst.split('\n') + new_myst = [] + colab_code = f''' + +Open In Colab -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{version}?labpath={labname}.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{self.version}?labpath={labname}.ipynb) ''' - # allow errors for Ch02, suppress warnings for Ch06 - if labname[:4] == 'Ch06': - colab_code = (''' -```{code-cell} -:tags: [hide-cell] - -import warnings -warnings.simplefilter('ignore') -``` + chapter_buffer = 200 # should use a regex... + for l in split_myst[:chapter_buffer]: # assumes Chapter appears in first 200 linesmyst.split('\n') + if l.strip()[:9] != '# Chapter': # exclude the line with "# Chapter" + if 'Lab:' in l: + l = l.replace('Lab:', '') + '\n' + colab_code + new_myst.append(l) + + myst = '\n'.join(new_myst + split_myst[chapter_buffer:]) + open(f'{base}.md', 'w').write(myst) + + args = shlex.split(f'jupytext --set-formats Rmd,ipynb {base}.ipynb') + subprocess.run(args) + + args = shlex.split(f'jupytext --sync {base}.ipynb') + subprocess.run(args) + + + subprocess.run(['rm', f'{base}.md']) + +def fix_Ch06(Ch06_nbfile): + + nb = nbformat.read(open(Ch06_nbfile), 4) + + md_cell = copy(nb.cells[0]) + md_cell['id'] = md_cell['id'] + '_duplicate' + + src = ''' ```{attention} Using `skl.ElasticNet` to fit ridge regression -throws up many warnings. We have suppressed them below. +throws up many warnings. We have suppressed them below by a call to `warnings.simplefilter()`. ``` -''' + colab_code) - if labname[:4] == 'Ch02': - nb = nbformat.read(open(nbfile), 4) - nb.metadata.setdefault('execution', {})['allow_errors'] = True - nbformat.write(nb, open(nbfile, 'w')) +''' + + md_cell['source'] = [l +'\n' for l in src.split('\n')] - if labname[:4] not in ['Ch10', 'Ch13']: + for i, cell in enumerate(nb.cells): + if cell['cell_type'] == 'code': + code_cell = copy(cell) + code_cell['id'] = code_cell['id'] + '_duplicate' + code_cell['source'] = ['import warnings\n', 'warnings.simplefilter("ignore")\n'] + break - # clear outputs for all but Ch10,Ch13 - if args.clear: - cmd = f'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {nbfile}' - print(f'Running the clearing command: {cmd}') - os.system(cmd) + nb.cells.insert(i, md_cell) + nb.cells.insert(i+1, code_cell) - cmd = f'jupytext --set-formats ipynb,md:myst {nbfile}; jupytext --sync {nbfile}' - print(f'Running: {cmd}') - os.system(cmd) + nbformat.write(nb, open(Ch06_nbfile, 'w')) + subprocess.run(shlex.split(f'jupytext --sync {Ch06_nbfile}')) - myst = open(f'{base}.md').read().strip() +if __name__ == "__main__": - new_myst = [] - for l in myst.split('\n'): - if l.strip()[:9] != '# Chapter': - if 'Lab:' in l: - l = l.replace('Lab:', '') + '\n' + colab_code - new_myst.append(l) + docs_version = get_version() - myst = '\n'.join(new_myst) # remove the "Chapter %d + parser = ArgumentParser() + parser.add_argument('labs', + metavar='N', + type=str, + nargs='+') - open(f'{base}.md', 'w').write(myst) + args = parser.parse_args() - cmd = f'jupytext --sync {base}.ipynb; ' - print(f'Running: {cmd}') - os.system(cmd) + for labfile in args.labs: + l = Lab(labfile=labfile, version=docs_version['labs']) + l.fix_header() + if '06' in labfile: + fix_Ch06(labfile) From 3ee60347b04c13aac4a990eee9cccbe1a5c90c94 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 01:19:54 -0700 Subject: [PATCH 139/195] missing dir --- .github/workflows/build_docs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index a10bc0e..bed6b03 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -38,6 +38,7 @@ jobs: # This workflow contains a single run: | git submodule update --init --force docs/ISLP_labs cd docs + mkdir -p source/labs cp ISLP_labs/Ch*nb source/labs python fix_and_clear_notebooks.py source/labs/Ch*nb python make_notebooks.py --inplace --requirements=ISLP_labs/requirements.txt source/labs/Ch06-varselect-lab.ipynb From 18efaa39abefc0ae79a581d9ed58da82415467c4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 11:58:00 -0700 Subject: [PATCH 140/195] comment on labs/library version --- docs/source/docs_version.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index fd97959..2a2e035 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ {"labs": "v2.1.2", "library": "v0.3.19", - "comment":"library should be version of ISLP pointed to in ISLP/labs" + "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt" } From f2f6cbae8cafba8e0cb92d1befb8cad2002c59f8 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 14:46:19 -0700 Subject: [PATCH 141/195] BF: fix of notebooks seems to have not synced --- .github/workflows/build_docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index bed6b03..ab7cca5 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -40,7 +40,7 @@ jobs: # This workflow contains a single cd docs mkdir -p source/labs cp ISLP_labs/Ch*nb source/labs - python fix_and_clear_notebooks.py source/labs/Ch*nb + python fix_and_clear_notebooks.py source/labs/Ch*nb --rm_md python make_notebooks.py --inplace --requirements=ISLP_labs/requirements.txt source/labs/Ch06-varselect-lab.ipynb rm source/labs/Ch*md From a2b1cbb26bf48f062346972da2f861afeb05a068 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 15:33:24 -0700 Subject: [PATCH 142/195] rm_md flag --- docs/fix_and_clear_notebooks.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index 7a59894..2543c5a 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -24,7 +24,8 @@ class Lab(object): labfile: str version: str = 'v2' - + rm_md: bool = True + def __post_init__(self): self.labfile = os.path.abspath(self.labfile) @@ -56,16 +57,20 @@ def fix_header(self): new_myst.append(l) myst = '\n'.join(new_myst + split_myst[chapter_buffer:]) + open(f'{base}.md', 'w').write(myst) + args = shlex.split(f'jupytext --sync {base}.ipynb') + subprocess.run(args) + args = shlex.split(f'jupytext --set-formats Rmd,ipynb {base}.ipynb') subprocess.run(args) args = shlex.split(f'jupytext --sync {base}.ipynb') subprocess.run(args) - - subprocess.run(['rm', f'{base}.md']) + if self.rm_md: + subprocess.run(['rm', f'{base}.md']) def fix_Ch06(Ch06_nbfile): @@ -107,6 +112,10 @@ def fix_Ch06(Ch06_nbfile): metavar='N', type=str, nargs='+') + parser.add_argument('--rm_md', + dest='rm_md', + action='store_true', + default=False) args = parser.parse_args() From e5928fda25913d7d928c256f7573e72d80c7d416 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 16:51:41 -0700 Subject: [PATCH 143/195] BF: redundant extension --- docs/fix_and_clear_notebooks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/fix_and_clear_notebooks.py b/docs/fix_and_clear_notebooks.py index 2543c5a..50eebe2 100644 --- a/docs/fix_and_clear_notebooks.py +++ b/docs/fix_and_clear_notebooks.py @@ -41,11 +41,11 @@ def fix_header(self): new_myst = [] colab_code = f''' - + Open In Colab -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{self.version}?labpath={labname}.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{self.version}?labpath={labname}) ''' From a7a1d727163d039311ee66c67b4cd25a56b04759 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 30 Aug 2023 12:05:43 -0700 Subject: [PATCH 144/195] smaller int to fit under int32 --- ISLP/bart/bart.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ISLP/bart/bart.py b/ISLP/bart/bart.py index 27a7f01..099c52e 100644 --- a/ISLP/bart/bart.py +++ b/ISLP/bart/bart.py @@ -141,7 +141,7 @@ def fit(self, if self.n_jobs <= 0: n_jobs = 1 - random_idx = random_state.randint(0,2**32-1,size=(n_jobs,)) + random_idx = random_state.randint(0,2**30-1,size=(n_jobs,)) # 2**31-1 should be OK for int32 parallel = Parallel(n_jobs=len(random_idx)) From 96b8cc99469581988a7cc0ef8650a6690b9d9415 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 30 Aug 2023 12:07:21 -0700 Subject: [PATCH 145/195] windows test --- .github/workflows/build_test_windows.yml | 40 ++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 .github/workflows/build_test_windows.yml diff --git a/.github/workflows/build_test_windows.yml b/.github/workflows/build_test_windows.yml new file mode 100644 index 0000000..8496ac4 --- /dev/null +++ b/.github/workflows/build_test_windows.yml @@ -0,0 +1,40 @@ +name: Build and test + +on: [push] + +jobs: + + build-windows: + runs-on: windows-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install . + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + timeout-minutes: 12 + run: | + pip install torchvision torchinfo + pip install pytest + pytest + + + From 78b81f9c5d72729596d50e7db10dd4487114316d Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 30 Aug 2023 13:15:45 -0700 Subject: [PATCH 146/195] instructions fix; added windows test --- .github/workflows/build_test.yml | 32 +++++++++++++++++++ .github/workflows/build_test_windows.yml | 40 ------------------------ docs/source/installation.myst | 7 +++++ 3 files changed, 39 insertions(+), 40 deletions(-) delete mode 100644 .github/workflows/build_test_windows.yml diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index 495663c..a4bd1aa 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -35,6 +35,38 @@ jobs: pip install pytest pytest + build-windows: + runs-on: windows-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v4 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + pip install . + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + timeout-minutes: 12 + run: | + pip install torchvision torchinfo + pip install pytest + pytest + build-mac: runs-on: macos-latest strategy: diff --git a/.github/workflows/build_test_windows.yml b/.github/workflows/build_test_windows.yml deleted file mode 100644 index 8496ac4..0000000 --- a/.github/workflows/build_test_windows.yml +++ /dev/null @@ -1,40 +0,0 @@ -name: Build and test - -on: [push] - -jobs: - - build-windows: - runs-on: windows-latest - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Add conda to system path - run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - - name: Install dependencies - run: | - pip install . - - name: Lint with flake8 - run: | - pip install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - timeout-minutes: 12 - run: | - pip install torchvision torchinfo - pip install pytest - pytest - - - diff --git a/docs/source/installation.myst b/docs/source/installation.myst index cb42d7f..7f6aa7f 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -73,6 +73,13 @@ current verisons of the labs. ### Mac OS X +```{attention} + +If you are using the Anaconda GUI, it is recommended that you install JupyterLab through the GUI +and skip the step below. Installing both through the GUI and `pip` may result in conflicts and +a broken JupyterLab. +``` + If JupyterLab is not already installed, run the following after having activated your `islp` environment: ```{code-cell} ipython3 From 95db9ef72931c8adccac6f6cd816c65fe6eb98f3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 30 Aug 2023 14:06:27 -0700 Subject: [PATCH 147/195] clarify install --- docs/source/installation.myst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 7f6aa7f..97589bd 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -78,6 +78,9 @@ current verisons of the labs. If you are using the Anaconda GUI, it is recommended that you install JupyterLab through the GUI and skip the step below. Installing both through the GUI and `pip` may result in conflicts and a broken JupyterLab. + +If you have installed JupyterLab in your environment via the GUI, the above call `pip install ISLP` may be made within +any running notebook within that environment. ``` If JupyterLab is not already installed, run the following after having activated your `islp` environment: From f523248b79a356166df11eab0bc900d812f24bb9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 7 Oct 2023 07:53:40 -0700 Subject: [PATCH 148/195] clarifying python version --- docs/source/installation.myst | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 97589bd..ec06b03 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -14,16 +14,18 @@ good practice. ## Mac OS X / Linux -To create a conda environment in a Mac OS X or Linux environment run: +To create a Python conda environment in a Mac OS X or Linux environment run: ```{code-cell} ipython3 --- tags: [skip-execution] --- -conda create --name islp +conda create --name islp python ``` -To run python code in this environment, you must activate it: +Current conda should have this at least 3.9. If not, replace `python` +with `python=3.10` or some other more recent version. To run python +code in this environment, you must activate it: ```{code-cell} ipython3 --- From 78b66041d4ecbb0f61bb4c781ee3d355809d0b8f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 7 Oct 2023 08:09:30 -0700 Subject: [PATCH 149/195] adding link to zip archive, update version --- docs/source/conf.py | 4 +--- docs/source/docs_version.json | 2 +- docs/source/labs.myst | 4 ++++ 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index aa6e9b4..546d74f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,9 +16,6 @@ dirname = os.path.split(__file__)[0] print(dirname, 'dirname') -docs_version = {"labs": "v2", - "library": "v0.3.18"} - docs_version = json.loads(open(os.path.join(dirname, 'docs_version.json')).read()) lab_version = docs_version['labs'] @@ -26,6 +23,7 @@ myst_substitutions = { "ISLP_lab_link": f"[ISLP_labs/{lab_version}](https://github.com/intro-stat-learning/ISLP_labs/tree/{lab_version})", + "ISLP_zip_link": f"[ISLP_labs/{lab_version}.zip](https://github.com/intro-stat-learning/ISLP_labs/archive/refs/tags/{lab_version}.zip)", "ISLP_binder_code": f"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{lab_version})", "ISLP_lab_version": "[ISLP/{0}](https://github.com/intro-stat-learning/ISLP/tree/{0})".format(docs_version['library']) } diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index 2a2e035..34e55c7 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ {"labs": "v2.1.2", - "library": "v0.3.19", + "library": "v0.3.20", "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt" } diff --git a/docs/source/labs.myst b/docs/source/labs.myst index 57499aa..b33bd3d 100644 --- a/docs/source/labs.myst +++ b/docs/source/labs.myst @@ -12,6 +12,7 @@ myst_number_code_blocks: python The current version of the labs for `ISLP` are included here. + ## Package versions @@ -22,6 +23,9 @@ with {{ ISLP_lab_link }}. Visit the lab git repo for specific instructions to install the frozen environment. +A zip file containig all the labs and data files can be downloaded +here {{ ISLP_zip_link }}. + ``` ```{warning} From 2c45489869c69a8aa76304c9e2e189b5a96986dc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 7 Oct 2023 08:27:19 -0700 Subject: [PATCH 150/195] V0.3.20rc (#8) * clarifying python version * adding link to zip archive, update version * updating labs version --- docs/source/conf.py | 4 +--- docs/source/docs_version.json | 4 ++-- docs/source/installation.myst | 8 +++++--- docs/source/labs.myst | 4 ++++ 4 files changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index aa6e9b4..546d74f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,9 +16,6 @@ dirname = os.path.split(__file__)[0] print(dirname, 'dirname') -docs_version = {"labs": "v2", - "library": "v0.3.18"} - docs_version = json.loads(open(os.path.join(dirname, 'docs_version.json')).read()) lab_version = docs_version['labs'] @@ -26,6 +23,7 @@ myst_substitutions = { "ISLP_lab_link": f"[ISLP_labs/{lab_version}](https://github.com/intro-stat-learning/ISLP_labs/tree/{lab_version})", + "ISLP_zip_link": f"[ISLP_labs/{lab_version}.zip](https://github.com/intro-stat-learning/ISLP_labs/archive/refs/tags/{lab_version}.zip)", "ISLP_binder_code": f"[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/{lab_version})", "ISLP_lab_version": "[ISLP/{0}](https://github.com/intro-stat-learning/ISLP/tree/{0})".format(docs_version['library']) } diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index 2a2e035..d58fc64 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2.1.2", - "library": "v0.3.19", +{"labs": "v2.1.3", + "library": "v0.3.20", "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt" } diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 97589bd..ec06b03 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -14,16 +14,18 @@ good practice. ## Mac OS X / Linux -To create a conda environment in a Mac OS X or Linux environment run: +To create a Python conda environment in a Mac OS X or Linux environment run: ```{code-cell} ipython3 --- tags: [skip-execution] --- -conda create --name islp +conda create --name islp python ``` -To run python code in this environment, you must activate it: +Current conda should have this at least 3.9. If not, replace `python` +with `python=3.10` or some other more recent version. To run python +code in this environment, you must activate it: ```{code-cell} ipython3 --- diff --git a/docs/source/labs.myst b/docs/source/labs.myst index 57499aa..b33bd3d 100644 --- a/docs/source/labs.myst +++ b/docs/source/labs.myst @@ -12,6 +12,7 @@ myst_number_code_blocks: python The current version of the labs for `ISLP` are included here. + ## Package versions @@ -22,6 +23,9 @@ with {{ ISLP_lab_link }}. Visit the lab git repo for specific instructions to install the frozen environment. +A zip file containig all the labs and data files can be downloaded +here {{ ISLP_zip_link }}. + ``` ```{warning} From ed7c99f96bd1cadee547f6792707a8cdd59aa061 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 7 Oct 2023 08:34:50 -0700 Subject: [PATCH 151/195] incrementing version in pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ea0c3f8..3f998a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ISLP" -version = "0.3.19" +version = "0.3.20" dependencies = ["numpy>=1.7.1,<1.25", # max version for numba "scipy>=0.9", "pandas>=0.20,<=1.9", From 5897f5de6015d58d406e3828c9e631843825e579 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 7 Oct 2023 08:56:35 -0700 Subject: [PATCH 152/195] minor doc tweak --- docs/source/installation.myst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index ec06b03..2373910 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -107,7 +107,7 @@ The notebooks for the labs can be run in [Google Colab](https://colab.research.google.com) with a few caveats: - Labs that use files in the filesystem will require one to mount your - Google Drive. See [help](https://colab.research.google.com/notebooks/io.ipynb) + Google Drive. See Google's [help](https://colab.research.google.com/notebooks/io.ipynb). - The packages will have to be reinstalled each time a new runtime is started. For most labs, inserting `pip install ISLP` at the top of the notebook will suffice, though Colab will ask you to restart after installation. From 8f66bf346cebecef2d88a83f0dec5199c185184b Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 10 Oct 2023 19:55:42 -0700 Subject: [PATCH 153/195] adding license --- LICENSE | 27 +++++++++++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 28 insertions(+), 1 deletion(-) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..dd8ced0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,27 @@ +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + (1) Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + (2) Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + + (3)The name of the author may not be used to + endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR +IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING +IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 3f998a7..5ea100d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,7 @@ dependencies = ["numpy>=1.7.1,<1.25", # max version for numba description = "Library for ISLP labs" readme = "README.md" requires-python = ">=3.9" -license = {file = "LICENSE.txt"} +license = {file = "LICENSE"} keywords = [] authors = [ {name = "Jonathan Taylor", email="jonathan.taylor@stanford.edu" }, From 579166baae49f80bb751915b329783111cf9edaf Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 10 Oct 2023 19:56:47 -0700 Subject: [PATCH 154/195] update pyproject --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5ea100d..ab3acfa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ISLP" -version = "0.3.20" +version = "0.3.21" dependencies = ["numpy>=1.7.1,<1.25", # max version for numba "scipy>=0.9", "pandas>=0.20,<=1.9", From c7f3ed921ed53879b6ff8609ce275682a6b16c12 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 17 Oct 2023 11:45:01 -0700 Subject: [PATCH 155/195] adding authors --- pyproject.toml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ab3acfa..b94fdf7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,8 +20,11 @@ requires-python = ">=3.9" license = {file = "LICENSE"} keywords = [] authors = [ + {name = "Trevor Hastie", email="hastie@stanford.edu" }, + {name = "Gareth James", email="gareth@emory.edu"}, {name = "Jonathan Taylor", email="jonathan.taylor@stanford.edu" }, - {name = "Trevor Hastie", email="hastie@stanford.edu" } + {name = "Rob Tibshirani", email="tibs@stanford.edu" }, + {name = "Daniela Witten", email="dwitten@uw.edu" }, ] maintainers = [ {name = "Jonathan Taylor", email="jonathan.taylor@stanford.edu" }, From 02bbd0f10fb21b2d21be5060994705ca58d52c0b Mon Sep 17 00:00:00 2001 From: danielawitten Date: Mon, 6 Nov 2023 09:46:50 -0800 Subject: [PATCH 156/195] Update README.md added authors --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index 6cd576f..ec76826 100644 --- a/README.md +++ b/README.md @@ -60,5 +60,13 @@ is your `islp` environment. This information appears near the top left in the An See the [docs](https://intro-stat-learning.github.io/ISLP/labs.html) for the latest documentation. +## Authors + +- Jonathan Taylor +- Trevor Hastie +- Gareth James +- Robert Tibshirani +- Daniela Witten + From 19e7fb2c73e0a4b8839b77188dd546765c9012b5 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 8 Nov 2023 16:53:31 -0800 Subject: [PATCH 157/195] docs: add danielawitten as a contributor for code, and content (#11) * docs: update README.md [skip ci] * docs: create .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 27 +++++++++++++++++++++++++++ README.md | 25 +++++++++++++++++++++++++ 2 files changed, 52 insertions(+) create mode 100644 .all-contributorsrc diff --git a/.all-contributorsrc b/.all-contributorsrc new file mode 100644 index 0000000..60f314c --- /dev/null +++ b/.all-contributorsrc @@ -0,0 +1,27 @@ +{ + "files": [ + "README.md" + ], + "imageSize": 100, + "commit": false, + "commitType": "docs", + "commitConvention": "angular", + "contributors": [ + { + "login": "danielawitten", + "name": "danielawitten", + "avatar_url": "https://avatars.githubusercontent.com/u/12654191?v=4", + "profile": "https://github.com/danielawitten", + "contributions": [ + "code", + "content" + ] + } + ], + "contributorsPerLine": 7, + "skipCi": true, + "repoType": "github", + "repoHost": "https://github.com", + "projectName": "ISLP", + "projectOwner": "intro-stat-learning" +} diff --git a/README.md b/README.md index ec76826..379ca73 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ # ISLP + +[![All Contributors](https://img.shields.io/badge/all_contributors-1-orange.svg?style=flat-square)](#contributors-) + This package collects data sets and various helper functions for ISLP. @@ -70,3 +73,25 @@ See the [docs](https://intro-stat-learning.github.io/ISLP/labs.html) for the lat + +## Contributors ✨ + +Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): + + + + + + + + + + +
danielawitten
danielawitten
💻 🖋
+ + + + + + +This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! \ No newline at end of file From eb177251ef21a706c5de4f3b8cb417580e00e027 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 8 Nov 2023 16:55:06 -0800 Subject: [PATCH 158/195] docs: add trevorhastie as a contributor for code, and content (#12) * docs: update README.md [skip ci] * docs: update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 10 ++++++++++ README.md | 3 ++- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 60f314c..4f2cb92 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -16,6 +16,16 @@ "code", "content" ] + }, + { + "login": "trevorhastie", + "name": "trevorhastie", + "avatar_url": "https://avatars.githubusercontent.com/u/13293253?v=4", + "profile": "https://web.stanford.edu/~hastie/", + "contributions": [ + "code", + "content" + ] } ], "contributorsPerLine": 7, diff --git a/README.md b/README.md index 379ca73..98b7089 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ISLP -[![All Contributors](https://img.shields.io/badge/all_contributors-1-orange.svg?style=flat-square)](#contributors-) +[![All Contributors](https://img.shields.io/badge/all_contributors-2-orange.svg?style=flat-square)](#contributors-) This package collects data sets and various helper functions @@ -85,6 +85,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d danielawitten
danielawitten
💻 🖋 + trevorhastie
trevorhastie
💻 🖋 From f6b64e253504ea38e215f15c09175055f4200706 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Wed, 8 Nov 2023 16:56:54 -0800 Subject: [PATCH 159/195] docs: add tibshirani as a contributor for code, and content (#14) * docs: update README.md [skip ci] * docs: update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 10 ++++++++++ README.md | 3 ++- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 4f2cb92..585a78c 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -26,6 +26,16 @@ "code", "content" ] + }, + { + "login": "tibshirani", + "name": "tibshirani", + "avatar_url": "https://avatars.githubusercontent.com/u/2848609?v=4", + "profile": "https://github.com/tibshirani", + "contributions": [ + "code", + "content" + ] } ], "contributorsPerLine": 7, diff --git a/README.md b/README.md index 98b7089..546ddba 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ISLP -[![All Contributors](https://img.shields.io/badge/all_contributors-2-orange.svg?style=flat-square)](#contributors-) +[![All Contributors](https://img.shields.io/badge/all_contributors-3-orange.svg?style=flat-square)](#contributors-) This package collects data sets and various helper functions @@ -86,6 +86,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d danielawitten
danielawitten
💻 🖋 trevorhastie
trevorhastie
💻 🖋 + tibshirani
tibshirani
💻 🖋 From c4c3e3dd089757388021de1aba09edb1912d04e2 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 14 Nov 2023 11:43:20 -0800 Subject: [PATCH 160/195] gui install note --- docs/source/installation.myst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index 2373910..cd3ea7c 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -73,6 +73,11 @@ current verisons of the labs. ## Jupyter +```{attention} +If using the Anaconda App, `jupyter` can be installed with a GUI. Use +the GUI install instead of the `pip` install below. +``` + ### Mac OS X ```{attention} From fc829b8bc1a5d15af53d396c0b98b9a151ea5a6f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 14 Nov 2023 11:59:38 -0800 Subject: [PATCH 161/195] updating setup.py for distutils deprecation --- setup.py | 6 +- versioneer.py | 2109 ------------------------------------------------- 2 files changed, 1 insertion(+), 2114 deletions(-) delete mode 100644 versioneer.py diff --git a/setup.py b/setup.py index c30a6d7..688c9c0 100755 --- a/setup.py +++ b/setup.py @@ -4,7 +4,6 @@ import os import sys from os.path import join as pjoin, dirname, exists -from distutils.version import LooseVersion # BEFORE importing distutils, remove MANIFEST. distutils doesn't properly # update it when the contents of directories change. if exists('MANIFEST'): os.remove('MANIFEST') @@ -15,10 +14,7 @@ # Package for getting versions from git tags import versioneer -# Import distutils _after_ setuptools import, and after removing -# MANIFEST -from distutils.core import setup -from distutils.extension import Extension +from setuptools import setup # Get various parameters for this version, stored in ISLP/info.py diff --git a/versioneer.py b/versioneer.py deleted file mode 100644 index b4cd1d6..0000000 --- a/versioneer.py +++ /dev/null @@ -1,2109 +0,0 @@ - -# Version: 0.21 - -"""The Versioneer - like a rocketeer, but for versions. - -The Versioneer -============== - -* like a rocketeer, but for versions! -* https://github.com/python-versioneer/python-versioneer -* Brian Warner -* License: Public Domain -* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3 -* [![Latest Version][pypi-image]][pypi-url] -* [![Build Status][travis-image]][travis-url] - -This is a tool for managing a recorded version number in distutils-based -python projects. The goal is to remove the tedious and error-prone "update -the embedded version string" step from your release process. Making a new -release should be as easy as recording a new tag in your version-control -system, and maybe making new tarballs. - - -## Quick Install - -* `pip install versioneer` to somewhere in your $PATH -* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md)) -* run `versioneer install` in your source tree, commit the results -* Verify version information with `python setup.py version` - -## Version Identifiers - -Source trees come from a variety of places: - -* a version-control system checkout (mostly used by developers) -* a nightly tarball, produced by build automation -* a snapshot tarball, produced by a web-based VCS browser, like github's - "tarball from tag" feature -* a release tarball, produced by "setup.py sdist", distributed through PyPI - -Within each source tree, the version identifier (either a string or a number, -this tool is format-agnostic) can come from a variety of places: - -* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows - about recent "tags" and an absolute revision-id -* the name of the directory into which the tarball was unpacked -* an expanded VCS keyword ($Id$, etc) -* a `_version.py` created by some earlier build step - -For released software, the version identifier is closely related to a VCS -tag. Some projects use tag names that include more than just the version -string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool -needs to strip the tag prefix to extract the version identifier. For -unreleased software (between tags), the version identifier should provide -enough information to help developers recreate the same tree, while also -giving them an idea of roughly how old the tree is (after version 1.2, before -version 1.3). Many VCS systems can report a description that captures this, -for example `git describe --tags --dirty --always` reports things like -"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the -0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has -uncommitted changes). - -The version identifier is used for multiple purposes: - -* to allow the module to self-identify its version: `myproject.__version__` -* to choose a name and prefix for a 'setup.py sdist' tarball - -## Theory of Operation - -Versioneer works by adding a special `_version.py` file into your source -tree, where your `__init__.py` can import it. This `_version.py` knows how to -dynamically ask the VCS tool for version information at import time. - -`_version.py` also contains `$Revision$` markers, and the installation -process marks `_version.py` to have this marker rewritten with a tag name -during the `git archive` command. As a result, generated tarballs will -contain enough information to get the proper version. - -To allow `setup.py` to compute a version too, a `versioneer.py` is added to -the top level of your source tree, next to `setup.py` and the `setup.cfg` -that configures it. This overrides several distutils/setuptools commands to -compute the version when invoked, and changes `setup.py build` and `setup.py -sdist` to replace `_version.py` with a small static file that contains just -the generated version data. - -## Installation - -See [INSTALL.md](./INSTALL.md) for detailed installation instructions. - -## Version-String Flavors - -Code which uses Versioneer can learn about its version string at runtime by -importing `_version` from your main `__init__.py` file and running the -`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can -import the top-level `versioneer.py` and run `get_versions()`. - -Both functions return a dictionary with different flavors of version -information: - -* `['version']`: A condensed version string, rendered using the selected - style. This is the most commonly used value for the project's version - string. The default "pep440" style yields strings like `0.11`, - `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section - below for alternative styles. - -* `['full-revisionid']`: detailed revision identifier. For Git, this is the - full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". - -* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the - commit date in ISO 8601 format. This will be None if the date is not - available. - -* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that - this is only accurate if run in a VCS checkout, otherwise it is likely to - be False or None - -* `['error']`: if the version string could not be computed, this will be set - to a string describing the problem, otherwise it will be None. It may be - useful to throw an exception in setup.py if this is set, to avoid e.g. - creating tarballs with a version string of "unknown". - -Some variants are more useful than others. Including `full-revisionid` in a -bug report should allow developers to reconstruct the exact code being tested -(or indicate the presence of local changes that should be shared with the -developers). `version` is suitable for display in an "about" box or a CLI -`--version` output: it can be easily compared against release notes and lists -of bugs fixed in various releases. - -The installer adds the following text to your `__init__.py` to place a basic -version in `YOURPROJECT.__version__`: - - from ._version import get_versions - __version__ = get_versions()['version'] - del get_versions - -## Styles - -The setup.cfg `style=` configuration controls how the VCS information is -rendered into a version string. - -The default style, "pep440", produces a PEP440-compliant string, equal to the -un-prefixed tag name for actual releases, and containing an additional "local -version" section with more detail for in-between builds. For Git, this is -TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags ---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the -tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and -that this commit is two revisions ("+2") beyond the "0.11" tag. For released -software (exactly equal to a known tag), the identifier will only contain the -stripped tag, e.g. "0.11". - -Other styles are available. See [details.md](details.md) in the Versioneer -source tree for descriptions. - -## Debugging - -Versioneer tries to avoid fatal errors: if something goes wrong, it will tend -to return a version of "0+unknown". To investigate the problem, run `setup.py -version`, which will run the version-lookup code in a verbose mode, and will -display the full contents of `get_versions()` (including the `error` string, -which may help identify what went wrong). - -## Known Limitations - -Some situations are known to cause problems for Versioneer. This details the -most significant ones. More can be found on Github -[issues page](https://github.com/python-versioneer/python-versioneer/issues). - -### Subprojects - -Versioneer has limited support for source trees in which `setup.py` is not in -the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are -two common reasons why `setup.py` might not be in the root: - -* Source trees which contain multiple subprojects, such as - [Buildbot](https://github.com/buildbot/buildbot), which contains both - "master" and "slave" subprojects, each with their own `setup.py`, - `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI - distributions (and upload multiple independently-installable tarballs). -* Source trees whose main purpose is to contain a C library, but which also - provide bindings to Python (and perhaps other languages) in subdirectories. - -Versioneer will look for `.git` in parent directories, and most operations -should get the right version string. However `pip` and `setuptools` have bugs -and implementation details which frequently cause `pip install .` from a -subproject directory to fail to find a correct version string (so it usually -defaults to `0+unknown`). - -`pip install --editable .` should work correctly. `setup.py install` might -work too. - -Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in -some later version. - -[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking -this issue. The discussion in -[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the -issue from the Versioneer side in more detail. -[pip PR#3176](https://github.com/pypa/pip/pull/3176) and -[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve -pip to let Versioneer work correctly. - -Versioneer-0.16 and earlier only looked for a `.git` directory next to the -`setup.cfg`, so subprojects were completely unsupported with those releases. - -### Editable installs with setuptools <= 18.5 - -`setup.py develop` and `pip install --editable .` allow you to install a -project into a virtualenv once, then continue editing the source code (and -test) without re-installing after every change. - -"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a -convenient way to specify executable scripts that should be installed along -with the python package. - -These both work as expected when using modern setuptools. When using -setuptools-18.5 or earlier, however, certain operations will cause -`pkg_resources.DistributionNotFound` errors when running the entrypoint -script, which must be resolved by re-installing the package. This happens -when the install happens with one version, then the egg_info data is -regenerated while a different version is checked out. Many setup.py commands -cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into -a different virtualenv), so this can be surprising. - -[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes -this one, but upgrading to a newer version of setuptools should probably -resolve it. - - -## Updating Versioneer - -To upgrade your project to a new release of Versioneer, do the following: - -* install the new Versioneer (`pip install -U versioneer` or equivalent) -* edit `setup.cfg`, if necessary, to include any new configuration settings - indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details. -* re-run `versioneer install` in your source tree, to replace - `SRC/_version.py` -* commit any changed files - -## Future Directions - -This tool is designed to make it easily extended to other version-control -systems: all VCS-specific components are in separate directories like -src/git/ . The top-level `versioneer.py` script is assembled from these -components by running make-versioneer.py . In the future, make-versioneer.py -will take a VCS name as an argument, and will construct a version of -`versioneer.py` that is specific to the given VCS. It might also take the -configuration arguments that are currently provided manually during -installation by editing setup.py . Alternatively, it might go the other -direction and include code from all supported VCS systems, reducing the -number of intermediate scripts. - -## Similar projects - -* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time - dependency -* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of - versioneer -* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools - plugin - -## License - -To make Versioneer easier to embed, all its code is dedicated to the public -domain. The `_version.py` that it creates is also in the public domain. -Specifically, both are released under the Creative Commons "Public Domain -Dedication" license (CC0-1.0), as described in -https://creativecommons.org/publicdomain/zero/1.0/ . - -[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg -[pypi-url]: https://pypi.python.org/pypi/versioneer/ -[travis-image]: -https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg -[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer - -""" -# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring -# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements -# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error -# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with -# pylint:disable=attribute-defined-outside-init,too-many-arguments - -import configparser -import errno -import json -import os -import re -import subprocess -import sys -from typing import Callable, Dict - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_root(): - """Get the project root directory. - - We require that all commands are run from the project root, i.e. the - directory that contains setup.py, setup.cfg, and versioneer.py . - """ - root = os.path.realpath(os.path.abspath(os.getcwd())) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - # allow 'python path/to/setup.py COMMAND' - root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - err = ("Versioneer was unable to run the project root directory. " - "Versioneer requires setup.py to be executed from " - "its immediate directory (like 'python setup.py COMMAND'), " - "or in a way that lets it use sys.argv[0] to find the root " - "(like 'python path/to/setup.py COMMAND').") - raise VersioneerBadRootError(err) - try: - # Certain runtime workflows (setup.py install/develop in a setuptools - # tree) execute all dependencies in a single python process, so - # "versioneer" may be imported multiple times, and python's shared - # module-import table will cache the first one. So we can't use - # os.path.dirname(__file__), as that will find whichever - # versioneer.py was first imported, even in later projects. - my_path = os.path.realpath(os.path.abspath(__file__)) - me_dir = os.path.normcase(os.path.splitext(my_path)[0]) - vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) - if me_dir != vsr_dir: - print("Warning: build in %s is using versioneer.py from %s" - % (os.path.dirname(my_path), versioneer_py)) - except NameError: - pass - return root - - -def get_config_from_root(root): - """Read the project setup.cfg file to determine Versioneer config.""" - # This might raise OSError (if setup.cfg is missing), or - # configparser.NoSectionError (if it lacks a [versioneer] section), or - # configparser.NoOptionError (if it lacks "VCS="). See the docstring at - # the top of versioneer.py for instructions on writing your setup.cfg . - setup_cfg = os.path.join(root, "setup.cfg") - parser = configparser.ConfigParser() - with open(setup_cfg, "r") as cfg_file: - parser.read_file(cfg_file) - VCS = parser.get("versioneer", "VCS") # mandatory - - # Dict-like interface for non-mandatory entries - section = parser["versioneer"] - - cfg = VersioneerConfig() - cfg.VCS = VCS - cfg.style = section.get("style", "") - cfg.versionfile_source = section.get("versionfile_source") - cfg.versionfile_build = section.get("versionfile_build") - cfg.tag_prefix = section.get("tag_prefix") - if cfg.tag_prefix in ("''", '""'): - cfg.tag_prefix = "" - cfg.parentdir_prefix = section.get("parentdir_prefix") - cfg.verbose = section.get("verbose") - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -# these dictionaries contain VCS-specific tools -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - HANDLERS.setdefault(vcs, {})[method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -LONG_VERSION_PY['git'] = r''' -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. Generated by -# versioneer-0.21 (https://github.com/python-versioneer/python-versioneer) - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Callable, Dict - - -def get_keywords(): - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" - git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" - git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_config(): - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "%(STYLE)s" - cfg.tag_prefix = "%(TAG_PREFIX)s" - cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" - cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %%s" %% dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %%s" %% (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %%s (error)" %% dispcmd) - print("stdout was %%s" %% stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %%s but none started with prefix %%s" %% - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %%d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%%s', no digits" %% ",".join(refs - tags)) - if verbose: - print("likely tags: %%s" %% ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %%s" %% r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - TAG_PREFIX_REGEX = "*" - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - TAG_PREFIX_REGEX = r"\*" - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=True) - if rc != 0: - if verbose: - print("Directory %%s not under git control" %% root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", - "--always", "--long", - "--match", - "%%s%%s" %% (tag_prefix, TAG_PREFIX_REGEX)], - cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%%s'" - %% describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%%s' doesn't start with prefix '%%s'" - print(fmt %% (full_tag, tag_prefix)) - pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" - %% (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) - pieces["distance"] = int(count_out) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%%d.dev%%d" %% (post_version+1, pieces["distance"]) - else: - rendered += ".post0.dev%%d" %% (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%%d" %% pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%%s'" %% style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions(): - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} -''' - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - TAG_PREFIX_REGEX = "*" - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - TAG_PREFIX_REGEX = r"\*" - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=True) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", - "--always", "--long", - "--match", - "%s%s" % (tag_prefix, TAG_PREFIX_REGEX)], - cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) - pieces["distance"] = int(count_out) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def do_vcs_install(manifest_in, versionfile_source, ipy): - """Git-specific installation logic for Versioneer. - - For Git, this means creating/changing .gitattributes to mark _version.py - for export-subst keyword substitution. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - files = [manifest_in, versionfile_source] - if ipy: - files.append(ipy) - try: - my_path = __file__ - if my_path.endswith(".pyc") or my_path.endswith(".pyo"): - my_path = os.path.splitext(my_path)[0] + ".py" - versioneer_file = os.path.relpath(my_path) - except NameError: - versioneer_file = "versioneer.py" - files.append(versioneer_file) - present = False - try: - with open(".gitattributes", "r") as fobj: - for line in fobj: - if line.strip().startswith(versionfile_source): - if "export-subst" in line.strip().split()[1:]: - present = True - break - except OSError: - pass - if not present: - with open(".gitattributes", "a+") as fobj: - fobj.write(f"{versionfile_source} export-subst\n") - files.append(".gitattributes") - run_command(GITS, ["add", "--"] + files) - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -SHORT_VERSION_PY = """ -# This file was generated by 'versioneer.py' (0.21) from -# revision-control system data, or from the parent directory name of an -# unpacked source archive. Distribution tarballs contain a pre-generated copy -# of this file. - -import json - -version_json = ''' -%s -''' # END VERSION_JSON - - -def get_versions(): - return json.loads(version_json) -""" - - -def versions_from_file(filename): - """Try to determine the version from _version.py if present.""" - try: - with open(filename) as f: - contents = f.read() - except OSError: - raise NotThisMethod("unable to read _version.py") - mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - raise NotThisMethod("no version_json in _version.py") - return json.loads(mo.group(1)) - - -def write_to_version_file(filename, versions): - """Write the given version number to the given _version.py file.""" - os.unlink(filename) - contents = json.dumps(versions, sort_keys=True, - indent=1, separators=(",", ": ")) - with open(filename, "w") as f: - f.write(SHORT_VERSION_PY % contents) - - print("set %s to '%s'" % (filename, versions["version"])) - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version+1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -class VersioneerBadRootError(Exception): - """The project root directory is unknown or missing key files.""" - - -def get_versions(verbose=False): - """Get the project version from whatever source is available. - - Returns dict with two keys: 'version' and 'full'. - """ - if "versioneer" in sys.modules: - # see the discussion in cmdclass.py:get_cmdclass() - del sys.modules["versioneer"] - - root = get_root() - cfg = get_config_from_root(root) - - assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" - handlers = HANDLERS.get(cfg.VCS) - assert handlers, "unrecognized VCS '%s'" % cfg.VCS - verbose = verbose or cfg.verbose - assert cfg.versionfile_source is not None, \ - "please set versioneer.versionfile_source" - assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" - - versionfile_abs = os.path.join(root, cfg.versionfile_source) - - # extract version from first of: _version.py, VCS command (e.g. 'git - # describe'), parentdir. This is meant to work for developers using a - # source checkout, for users of a tarball created by 'setup.py sdist', - # and for users of a tarball/zipball created by 'git archive' or github's - # download-from-tag feature or the equivalent in other VCSes. - - get_keywords_f = handlers.get("get_keywords") - from_keywords_f = handlers.get("keywords") - if get_keywords_f and from_keywords_f: - try: - keywords = get_keywords_f(versionfile_abs) - ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) - if verbose: - print("got version from expanded keyword %s" % ver) - return ver - except NotThisMethod: - pass - - try: - ver = versions_from_file(versionfile_abs) - if verbose: - print("got version from file %s %s" % (versionfile_abs, ver)) - return ver - except NotThisMethod: - pass - - from_vcs_f = handlers.get("pieces_from_vcs") - if from_vcs_f: - try: - pieces = from_vcs_f(cfg.tag_prefix, root, verbose) - ver = render(pieces, cfg.style) - if verbose: - print("got version from VCS %s" % ver) - return ver - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - if verbose: - print("got version from parentdir %s" % ver) - return ver - except NotThisMethod: - pass - - if verbose: - print("unable to compute version") - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, "error": "unable to compute version", - "date": None} - - -def get_version(): - """Get the short version string for this project.""" - return get_versions()["version"] - - -def get_cmdclass(cmdclass=None): - """Get the custom setuptools/distutils subclasses used by Versioneer. - - If the package uses a different cmdclass (e.g. one from numpy), it - should be provide as an argument. - """ - if "versioneer" in sys.modules: - del sys.modules["versioneer"] - # this fixes the "python setup.py develop" case (also 'install' and - # 'easy_install .'), in which subdependencies of the main project are - # built (using setup.py bdist_egg) in the same python process. Assume - # a main project A and a dependency B, which use different versions - # of Versioneer. A's setup.py imports A's Versioneer, leaving it in - # sys.modules by the time B's setup.py is executed, causing B to run - # with the wrong versioneer. Setuptools wraps the sub-dep builds in a - # sandbox that restores sys.modules to it's pre-build state, so the - # parent is protected against the child's "import versioneer". By - # removing ourselves from sys.modules here, before the child build - # happens, we protect the child from the parent's versioneer too. - # Also see https://github.com/python-versioneer/python-versioneer/issues/52 - - cmds = {} if cmdclass is None else cmdclass.copy() - - # we add "version" to both distutils and setuptools - from distutils.core import Command - - class cmd_version(Command): - description = "report generated version string" - user_options = [] - boolean_options = [] - - def initialize_options(self): - pass - - def finalize_options(self): - pass - - def run(self): - vers = get_versions(verbose=True) - print("Version: %s" % vers["version"]) - print(" full-revisionid: %s" % vers.get("full-revisionid")) - print(" dirty: %s" % vers.get("dirty")) - print(" date: %s" % vers.get("date")) - if vers["error"]: - print(" error: %s" % vers["error"]) - cmds["version"] = cmd_version - - # we override "build_py" in both distutils and setuptools - # - # most invocation pathways end up running build_py: - # distutils/build -> build_py - # distutils/install -> distutils/build ->.. - # setuptools/bdist_wheel -> distutils/install ->.. - # setuptools/bdist_egg -> distutils/install_lib -> build_py - # setuptools/install -> bdist_egg ->.. - # setuptools/develop -> ? - # pip install: - # copies source tree to a tempdir before running egg_info/etc - # if .git isn't copied too, 'git describe' will fail - # then does setup.py bdist_wheel, or sometimes setup.py install - # setup.py egg_info -> ? - - # we override different "build_py" commands for both environments - if 'build_py' in cmds: - _build_py = cmds['build_py'] - elif "setuptools" in sys.modules: - from setuptools.command.build_py import build_py as _build_py - else: - from distutils.command.build_py import build_py as _build_py - - class cmd_build_py(_build_py): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_py.run(self) - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if cfg.versionfile_build: - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_py"] = cmd_build_py - - if 'build_ext' in cmds: - _build_ext = cmds['build_ext'] - elif "setuptools" in sys.modules: - from setuptools.command.build_ext import build_ext as _build_ext - else: - from distutils.command.build_ext import build_ext as _build_ext - - class cmd_build_ext(_build_ext): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_ext.run(self) - if self.inplace: - # build_ext --inplace will only build extensions in - # build/lib<..> dir with no _version.py to write to. - # As in place builds will already have a _version.py - # in the module dir, we do not need to write one. - return - # now locate _version.py in the new build/ directory and replace - # it with an updated value - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_ext"] = cmd_build_ext - - if "cx_Freeze" in sys.modules: # cx_freeze enabled? - from cx_Freeze.dist import build_exe as _build_exe - # nczeczulin reports that py2exe won't like the pep440-style string - # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. - # setup(console=[{ - # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION - # "product_version": versioneer.get_version(), - # ... - - class cmd_build_exe(_build_exe): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _build_exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["build_exe"] = cmd_build_exe - del cmds["build_py"] - - if 'py2exe' in sys.modules: # py2exe enabled? - from py2exe.distutils_buildexe import py2exe as _py2exe - - class cmd_py2exe(_py2exe): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _py2exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["py2exe"] = cmd_py2exe - - # we override different "sdist" commands for both environments - if 'sdist' in cmds: - _sdist = cmds['sdist'] - elif "setuptools" in sys.modules: - from setuptools.command.sdist import sdist as _sdist - else: - from distutils.command.sdist import sdist as _sdist - - class cmd_sdist(_sdist): - def run(self): - versions = get_versions() - self._versioneer_generated_versions = versions - # unless we update this, the command will keep using the old - # version - self.distribution.metadata.version = versions["version"] - return _sdist.run(self) - - def make_release_tree(self, base_dir, files): - root = get_root() - cfg = get_config_from_root(root) - _sdist.make_release_tree(self, base_dir, files) - # now locate _version.py in the new base_dir directory - # (remembering that it may be a hardlink) and replace it with an - # updated value - target_versionfile = os.path.join(base_dir, cfg.versionfile_source) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, - self._versioneer_generated_versions) - cmds["sdist"] = cmd_sdist - - return cmds - - -CONFIG_ERROR = """ -setup.cfg is missing the necessary Versioneer configuration. You need -a section like: - - [versioneer] - VCS = git - style = pep440 - versionfile_source = src/myproject/_version.py - versionfile_build = myproject/_version.py - tag_prefix = - parentdir_prefix = myproject- - -You will also need to edit your setup.py to use the results: - - import versioneer - setup(version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), ...) - -Please read the docstring in ./versioneer.py for configuration instructions, -edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. -""" - -SAMPLE_CONFIG = """ -# See the docstring in versioneer.py for instructions. Note that you must -# re-run 'versioneer.py setup' after changing this section, and commit the -# resulting files. - -[versioneer] -#VCS = git -#style = pep440 -#versionfile_source = -#versionfile_build = -#tag_prefix = -#parentdir_prefix = - -""" - -OLD_SNIPPET = """ -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions -""" - -INIT_PY_SNIPPET = """ -from . import {0} -__version__ = {0}.get_versions()['version'] -""" - - -def do_setup(): - """Do main VCS-independent setup function for installing Versioneer.""" - root = get_root() - try: - cfg = get_config_from_root(root) - except (OSError, configparser.NoSectionError, - configparser.NoOptionError) as e: - if isinstance(e, (OSError, configparser.NoSectionError)): - print("Adding sample versioneer config to setup.cfg", - file=sys.stderr) - with open(os.path.join(root, "setup.cfg"), "a") as f: - f.write(SAMPLE_CONFIG) - print(CONFIG_ERROR, file=sys.stderr) - return 1 - - print(" creating %s" % cfg.versionfile_source) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - - ipy = os.path.join(os.path.dirname(cfg.versionfile_source), - "__init__.py") - if os.path.exists(ipy): - try: - with open(ipy, "r") as f: - old = f.read() - except OSError: - old = "" - module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] - snippet = INIT_PY_SNIPPET.format(module) - if OLD_SNIPPET in old: - print(" replacing boilerplate in %s" % ipy) - with open(ipy, "w") as f: - f.write(old.replace(OLD_SNIPPET, snippet)) - elif snippet not in old: - print(" appending to %s" % ipy) - with open(ipy, "a") as f: - f.write(snippet) - else: - print(" %s unmodified" % ipy) - else: - print(" %s doesn't exist, ok" % ipy) - ipy = None - - # Make sure both the top-level "versioneer.py" and versionfile_source - # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so - # they'll be copied into source distributions. Pip won't be able to - # install the package without this. - manifest_in = os.path.join(root, "MANIFEST.in") - simple_includes = set() - try: - with open(manifest_in, "r") as f: - for line in f: - if line.startswith("include "): - for include in line.split()[1:]: - simple_includes.add(include) - except OSError: - pass - # That doesn't cover everything MANIFEST.in can do - # (http://docs.python.org/2/distutils/sourcedist.html#commands), so - # it might give some false negatives. Appending redundant 'include' - # lines is safe, though. - if "versioneer.py" not in simple_includes: - print(" appending 'versioneer.py' to MANIFEST.in") - with open(manifest_in, "a") as f: - f.write("include versioneer.py\n") - else: - print(" 'versioneer.py' already in MANIFEST.in") - if cfg.versionfile_source not in simple_includes: - print(" appending versionfile_source ('%s') to MANIFEST.in" % - cfg.versionfile_source) - with open(manifest_in, "a") as f: - f.write("include %s\n" % cfg.versionfile_source) - else: - print(" versionfile_source already in MANIFEST.in") - - # Make VCS-specific changes. For git, this means creating/changing - # .gitattributes to mark _version.py for export-subst keyword - # substitution. - do_vcs_install(manifest_in, cfg.versionfile_source, ipy) - return 0 - - -def scan_setup_py(): - """Validate the contents of setup.py against Versioneer's expectations.""" - found = set() - setters = False - errors = 0 - with open("setup.py", "r") as f: - for line in f.readlines(): - if "import versioneer" in line: - found.add("import") - if "versioneer.get_cmdclass()" in line: - found.add("cmdclass") - if "versioneer.get_version()" in line: - found.add("get_version") - if "versioneer.VCS" in line: - setters = True - if "versioneer.versionfile_source" in line: - setters = True - if len(found) != 3: - print("") - print("Your setup.py appears to be missing some important items") - print("(but I might be wrong). Please make sure it has something") - print("roughly like the following:") - print("") - print(" import versioneer") - print(" setup( version=versioneer.get_version(),") - print(" cmdclass=versioneer.get_cmdclass(), ...)") - print("") - errors += 1 - if setters: - print("You should remove lines like 'versioneer.VCS = ' and") - print("'versioneer.versionfile_source = ' . This configuration") - print("now lives in setup.cfg, and should be removed from setup.py") - print("") - errors += 1 - return errors - - -if __name__ == "__main__": - cmd = sys.argv[1] - if cmd == "setup": - errors = do_setup() - errors += scan_setup_py() - if errors: - sys.exit(1) From 18e95bc47ad1f0fe7c55034db84a71698a0ef37a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 14 Nov 2023 12:01:16 -0800 Subject: [PATCH 162/195] update install instructions --- docs/source/installation.myst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index cd3ea7c..b82bebf 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -24,7 +24,9 @@ conda create --name islp python ``` Current conda should have this at least 3.9. If not, replace `python` -with `python=3.10` or some other more recent version. To run python +with `python=3.10` or `python=3.11`. The newest version `python=3.12` is not +currently supported as some packages, such as `torch` are not installable. +To run python code in this environment, you must activate it: ```{code-cell} ipython3 From 3ebd2394d1352e6eae4ec8dd42be7979ee6354c7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 10:44:46 -0800 Subject: [PATCH 163/195] ensuring boolean columns get cast to float for statsmodels --- ISLP/models/model_spec.py | 12 ++++++++---- tests/models/test_boolean_columns.py | 23 +++++++++++++++++++++++ 2 files changed, 31 insertions(+), 4 deletions(-) create mode 100644 tests/models/test_boolean_columns.py diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 07a8b88..983a85d 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -107,7 +107,6 @@ def fit(self, X, y=None): cats = self.encoder_.categories_[0] column_names = [str(n) for n in cats] - if isinstance(X, pd.DataFrame): # expecting a column, we take .iloc[:,0] X = X.iloc[:,0] @@ -635,18 +634,23 @@ def build_model(column_info, if isinstance(X, (pd.Series, pd.DataFrame)): df = pd.concat(dfs, axis=1) df.index = X.index - return df else: - return np.column_stack(dfs) + return np.column_stack(dfs).astype(float) else: # return a 0 design zero = np.zeros(X.shape[0]) if isinstance(X, (pd.Series, pd.DataFrame)): df = pd.DataFrame({'zero': zero}) df.index = X.index - return df else: return zero + # if we reach here, we will be returning a DataFrame + + for col in df.columns: + if df[col].dtype == bool: + df[col] = df[col].astype(float) + return df + def derived_feature(variables, encoder=None, name=None, use_transform=True): """ Create a Feature, optionally diff --git a/tests/models/test_boolean_columns.py b/tests/models/test_boolean_columns.py new file mode 100644 index 0000000..7b5a429 --- /dev/null +++ b/tests/models/test_boolean_columns.py @@ -0,0 +1,23 @@ +import pandas as pd +import statsmodels.api as sm +import numpy as np +from itertools import combinations + +from ISLP.models import ModelSpec as MS + +rng = np.random.default_rng(0) + +df = pd.DataFrame({'A':rng.standard_normal(10), + 'B':np.array([1,2,3,2,1,1,1,3,2,1], int), + 'C':np.array([True,False,False,True,True]*2, bool), + 'D':rng.standard_normal(10)}) +Y = rng.standard_normal(10) + +def test_all(): + + for i in range(1, 5): + for comb in combinations(['A','B','C','D'], i): + + X = MS(comb).fit_transform(df) + sm.OLS(Y, X).fit() + From f265fc758ecefdcb42a9413ad124372a1613a8fd Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 10:48:07 -0800 Subject: [PATCH 164/195] updating version --- docs/source/docs_version.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index d58fc64..e4d5788 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ {"labs": "v2.1.3", - "library": "v0.3.20", - "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt" + "library": "v0.3.22", + "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt; don't forget to strip warnings!!!!!!!!" } From 97fb1fe91d46fe57294b7f0fc1bf7eb817a96446 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 11:49:35 -0800 Subject: [PATCH 165/195] Setup work (#18) * ensuring boolean columns get cast to float for statsmodels * editing setup files, cleanup * commenting out info * cleanup of setup.py * use iloc for float conversion in case there are duplicate columnes --- ISLP/info.py | 83 --------------------------------------- ISLP/models/model_spec.py | 11 +++--- pyproject.toml | 6 ++- setup.cfg | 3 ++ setup.py | 62 ++--------------------------- 5 files changed, 17 insertions(+), 148 deletions(-) delete mode 100644 ISLP/info.py diff --git a/ISLP/info.py b/ISLP/info.py deleted file mode 100644 index 870c1e9..0000000 --- a/ISLP/info.py +++ /dev/null @@ -1,83 +0,0 @@ -""" This file contains defines parameters for regreg that we use to fill -settings in setup.py, the regreg top-level docstring, and for building the docs. -In setup.py in particular, we exec this file, so it cannot import regreg -""" - -# regreg version information. An empty _version_extra corresponds to a -# full release. '.dev' as a _version_extra string means this is a development -# version -_version_major = 0 -_version_minor = 2 -_version_micro = 0 -_version_extra = '' - -# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z" -__version__ = "%s.%s.%s%s" % (_version_major, - _version_minor, - _version_micro, - _version_extra) - -CLASSIFIERS = ["Development Status :: 3 - Alpha", - "Environment :: Console", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: BSD License", - "Operating System :: OS Independent", - "Programming Language :: Python", - "Topic :: Scientific/Engineering"] - -description = 'Library for ISLP labs' - -# Note: this long_description is actually a copy/paste from the top-level -# README.txt, so that it shows up nicely on PyPI. So please remember to edit -# it only in one place and sync it correctly. -long_description = \ -""" -============ -Fixed lambda -============ - -This mini-package contains a module to perform -a fixed lambda test for the LASSO. -""" - -# versions -NUMPY_MIN_VERSION='1.7.1' -SCIPY_MIN_VERSION = '0.9' -PANDAS_MIN_VERSION = "0.20" -PANDAS_MAX_VERSION = "1.9" -SKLEARN_MIN_VERSION = '1.2' -STATSMODELS_MIN_VERSION = '0.13' -MATPLOTLIB_MIN_VERSION = '3.3.3' - -NAME = 'ISLP' -MAINTAINER = "Jonathan Taylor" -MAINTAINER_EMAIL = "" -DESCRIPTION = description -LONG_DESCRIPTION = long_description -URL = "http://github.org/intro-stat-learning/ISLP" -DOWNLOAD_URL = "" -LICENSE = "BSD license" -CLASSIFIERS = CLASSIFIERS -AUTHOR = "ISLP authors" -AUTHOR_EMAIL = "" -PLATFORMS = "OS Independent" -MAJOR = _version_major -MINOR = _version_minor -MICRO = _version_micro -ISRELEASE = _version_extra == '' -VERSION = __version__ -STATUS = 'alpha' -PROVIDES = [] -REQUIRES = ["numpy (>=%s)" % NUMPY_MIN_VERSION, - "scipy (>=%s)" % SCIPY_MIN_VERSION, - "statsmodels (>=%s)" % STATSMODELS_MIN_VERSION, - "pandas (>=%s)" % PANDAS_MIN_VERSION, - "pandas (<=%s)" % PANDAS_MAX_VERSION, - "sklearn (>=%s)" % SKLEARN_MIN_VERSION, - "lifelines", - "joblib", - "pygam", - "torch", - "torchmetrics", - "pytorch_lightning" - ] diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 983a85d..ce09e01 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -632,7 +632,7 @@ def build_model(column_info, if len(dfs): if isinstance(X, (pd.Series, pd.DataFrame)): - df = pd.concat(dfs, axis=1) + df = pd.concat(dfs, axis='columns') df.index = X.index else: return np.column_stack(dfs).astype(float) @@ -645,10 +645,11 @@ def build_model(column_info, return zero # if we reach here, we will be returning a DataFrame - - for col in df.columns: - if df[col].dtype == bool: - df[col] = df[col].astype(float) + # make sure all columns are floats + + for i, _ in enumerate(df.columns): + if df.iloc[:,i].dtype == bool: + df.iloc[:,i] = df.iloc[:,i].astype(float) return df def derived_feature(variables, encoder=None, name=None, use_transform=True): diff --git a/pyproject.toml b/pyproject.toml index b94fdf7..e0359ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ISLP" -version = "0.3.21" +version = "0.3.22" dependencies = ["numpy>=1.7.1,<1.25", # max version for numba "scipy>=0.9", "pandas>=0.20,<=1.9", @@ -37,6 +37,7 @@ classifiers = ["Development Status :: 3 - Alpha", "Programming Language :: Python", "Topic :: Scientific/Engineering" ] + [project.urls] # Optional "Homepage" = "https://github.com/intro-stat-learning/ISLP" "Bug Reports" = "https://github.com/intro-stat-learning/ISLP/issues" @@ -44,6 +45,9 @@ classifiers = ["Development Status :: 3 - Alpha", "Say Thanks!" = "http://saythanks.io/to/example" "Source" = "https://github.com/pypa/sampleproject/" +[project.optional-dependencies] +doc = ['Sphinx>=3.0'] + [build-system] requires = ["setuptools>=42", "wheel", diff --git a/setup.cfg b/setup.cfg index 14d7ccd..4a6f58e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -4,3 +4,6 @@ style = pep440 versionfile_source = ISLP/_version.py tag_prefix = parentdir_prefix = ISLP- + +[metadata] +license_files = LICENSE.txt \ No newline at end of file diff --git a/setup.py b/setup.py index 688c9c0..bf51c2e 100755 --- a/setup.py +++ b/setup.py @@ -16,74 +16,18 @@ from setuptools import setup -# Get various parameters for this version, stored in ISLP/info.py - -class Bunch(object): - def __init__(self, vars): - for key, name in vars.items(): - if key.startswith('__'): - continue - self.__dict__[key] = name - -def read_vars_from(ver_file): - """ Read variables from Python text file - - Parameters - ---------- - ver_file : str - Filename of file to read - - Returns - ------- - info_vars : Bunch instance - Bunch object where variables read from `ver_file` appear as - attributes - """ - # Use exec for compabibility with Python 3 - ns = {} - with open(ver_file, 'rt') as fobj: - exec(fobj.read(), ns) - return Bunch(ns) - -info = read_vars_from(pjoin('ISLP', 'info.py')) - -# Try to preempt setuptools monkeypatching of Extension handling when Pyrex -# is missing. Otherwise the monkeypatched Extension will change .pyx -# filenames to .c filenames, and we probably don't have the .c files. -sys.path.insert(0, pjoin(dirname(__file__), 'fake_pyrex')) -# Set setuptools extra arguments -extra_setuptools_args = dict( - tests_require=['nose'], - test_suite='nose.collector', - zip_safe=False, - extras_require = dict( - doc=['Sphinx>=1.0'], - test=['nose>=0.10.1'])) # Define extensions EXTS = [] -cmdclass=versioneer.get_cmdclass() +cmdclass = versioneer.get_cmdclass() # get long_description long_description = open('README.md', 'rt', encoding='utf-8').read() def main(**extra_args): - setup(name=info.NAME, - maintainer=info.MAINTAINER, - maintainer_email=info.MAINTAINER_EMAIL, - description=info.DESCRIPTION, - url=info.URL, - download_url=info.DOWNLOAD_URL, - license=info.LICENSE, - classifiers=info.CLASSIFIERS, - author=info.AUTHOR, - author_email=info.AUTHOR_EMAIL, - platforms=info.PLATFORMS, - version=versioneer.get_version(), - requires=info.REQUIRES, - provides=info.PROVIDES, + setup(version=versioneer.get_version(), packages = ['ISLP', 'ISLP.models', 'ISLP.models', @@ -104,4 +48,4 @@ def main(**extra_args): #simple way to test what setup will do #python setup.py install --prefix=/tmp if __name__ == "__main__": - main(**extra_setuptools_args) + main() From ee2298651576de0b97e15f5d4c446580d426fd58 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 11:53:11 -0800 Subject: [PATCH 166/195] incrementing lab version --- docs/source/docs_version.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index e4d5788..f91228e 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2.1.3", +{"labs": "v2.1.4", "library": "v0.3.22", "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt; don't forget to strip warnings!!!!!!!!" } From 8fbadc70eeada7d3aa43183a029558bc7bdaed6a Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 12:13:19 -0800 Subject: [PATCH 167/195] fix versioneer --- ISLP/_version.py | 121 +++++++++++++++++++++++++++++++---------------- pyproject.toml | 10 +++- setup.cfg | 6 --- 3 files changed, 89 insertions(+), 48 deletions(-) diff --git a/ISLP/_version.py b/ISLP/_version.py index 9b01ea2..c2d7406 100644 --- a/ISLP/_version.py +++ b/ISLP/_version.py @@ -5,8 +5,9 @@ # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. -# This file is released into the public domain. Generated by -# versioneer-0.21 (https://github.com/python-versioneer/python-versioneer) +# This file is released into the public domain. +# Generated by versioneer-0.29 +# https://github.com/python-versioneer/python-versioneer """Git implementation of _version.py.""" @@ -15,10 +16,11 @@ import re import subprocess import sys -from typing import Callable, Dict +from typing import Any, Callable, Dict, List, Optional, Tuple +import functools -def get_keywords(): +def get_keywords() -> Dict[str, str]: """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must @@ -34,8 +36,15 @@ def get_keywords(): class VersioneerConfig: """Container for Versioneer configuration parameters.""" + VCS: str + style: str + tag_prefix: str + parentdir_prefix: str + versionfile_source: str + verbose: bool -def get_config(): + +def get_config() -> VersioneerConfig: """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py @@ -57,9 +66,9 @@ class NotThisMethod(Exception): HANDLERS: Dict[str, Dict[str, Callable]] = {} -def register_vcs_handler(vcs, method): # decorator +def register_vcs_handler(vcs: str, method: str) -> Callable: # decorator """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): + def decorate(f: Callable) -> Callable: """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} @@ -68,11 +77,25 @@ def decorate(f): return decorate -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): +def run_command( + commands: List[str], + args: List[str], + cwd: Optional[str] = None, + verbose: bool = False, + hide_stderr: bool = False, + env: Optional[Dict[str, str]] = None, +) -> Tuple[Optional[str], Optional[int]]: """Call the given command(s).""" assert isinstance(commands, list) process = None + + popen_kwargs: Dict[str, Any] = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + for command in commands: try: dispcmd = str([command] + args) @@ -80,10 +103,9 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, process = subprocess.Popen([command] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr - else None)) + else None), **popen_kwargs) break - except OSError: - e = sys.exc_info()[1] + except OSError as e: if e.errno == errno.ENOENT: continue if verbose: @@ -103,7 +125,11 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, return stdout, process.returncode -def versions_from_parentdir(parentdir_prefix, root, verbose): +def versions_from_parentdir( + parentdir_prefix: str, + root: str, + verbose: bool, +) -> Dict[str, Any]: """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both @@ -128,13 +154,13 @@ def versions_from_parentdir(parentdir_prefix, root, verbose): @register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): +def git_get_keywords(versionfile_abs: str) -> Dict[str, str]: """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. - keywords = {} + keywords: Dict[str, str] = {} try: with open(versionfile_abs, "r") as fobj: for line in fobj: @@ -156,7 +182,11 @@ def git_get_keywords(versionfile_abs): @register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): +def git_versions_from_keywords( + keywords: Dict[str, str], + tag_prefix: str, + verbose: bool, +) -> Dict[str, Any]: """Get version information from git keywords.""" if "refnames" not in keywords: raise NotThisMethod("Short version file found") @@ -220,7 +250,12 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): @register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): +def git_pieces_from_vcs( + tag_prefix: str, + root: str, + verbose: bool, + runner: Callable = run_command +) -> Dict[str, Any]: """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* @@ -228,13 +263,18 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): version string, meaning we're inside a checked out source tree. """ GITS = ["git"] - TAG_PREFIX_REGEX = "*" if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] - TAG_PREFIX_REGEX = r"\*" + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=True) + hide_stderr=not verbose) if rc != 0: if verbose: print("Directory %s not under git control" % root) @@ -242,11 +282,10 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty", - "--always", "--long", - "--match", - "%s%s" % (tag_prefix, TAG_PREFIX_REGEX)], - cwd=root) + describe_out, rc = runner(GITS, [ + "describe", "--tags", "--dirty", "--always", "--long", + "--match", f"{tag_prefix}[[:digit:]]*" + ], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") @@ -256,7 +295,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() - pieces = {} + pieces: Dict[str, Any] = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None @@ -335,8 +374,8 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): else: # HEX: no tags pieces["closest-tag"] = None - count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root) - pieces["distance"] = int(count_out) # total number of commits + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() @@ -348,14 +387,14 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): return pieces -def plus_or_dot(pieces): +def plus_or_dot(pieces: Dict[str, Any]) -> str: """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" -def render_pep440(pieces): +def render_pep440(pieces: Dict[str, Any]) -> str: """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you @@ -380,7 +419,7 @@ def render_pep440(pieces): return rendered -def render_pep440_branch(pieces): +def render_pep440_branch(pieces: Dict[str, Any]) -> str: """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . The ".dev0" means not master branch. Note that .dev0 sorts backwards @@ -410,7 +449,7 @@ def render_pep440_branch(pieces): return rendered -def pep440_split_post(ver): +def pep440_split_post(ver: str) -> Tuple[str, Optional[int]]: """Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the @@ -420,7 +459,7 @@ def pep440_split_post(ver): return vc[0], int(vc[1] or 0) if len(vc) == 2 else None -def render_pep440_pre(pieces): +def render_pep440_pre(pieces: Dict[str, Any]) -> str: """TAG[.postN.devDISTANCE] -- No -dirty. Exceptions: @@ -432,7 +471,7 @@ def render_pep440_pre(pieces): tag_version, post_version = pep440_split_post(pieces["closest-tag"]) rendered = tag_version if post_version is not None: - rendered += ".post%d.dev%d" % (post_version+1, pieces["distance"]) + rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) else: rendered += ".post0.dev%d" % (pieces["distance"]) else: @@ -444,7 +483,7 @@ def render_pep440_pre(pieces): return rendered -def render_pep440_post(pieces): +def render_pep440_post(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards @@ -471,7 +510,7 @@ def render_pep440_post(pieces): return rendered -def render_pep440_post_branch(pieces): +def render_pep440_post_branch(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . The ".dev0" means not master branch. @@ -500,7 +539,7 @@ def render_pep440_post_branch(pieces): return rendered -def render_pep440_old(pieces): +def render_pep440_old(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. @@ -522,7 +561,7 @@ def render_pep440_old(pieces): return rendered -def render_git_describe(pieces): +def render_git_describe(pieces: Dict[str, Any]) -> str: """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. @@ -542,7 +581,7 @@ def render_git_describe(pieces): return rendered -def render_git_describe_long(pieces): +def render_git_describe_long(pieces: Dict[str, Any]) -> str: """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. @@ -562,7 +601,7 @@ def render_git_describe_long(pieces): return rendered -def render(pieces, style): +def render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]: """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", @@ -598,7 +637,7 @@ def render(pieces, style): "date": pieces.get("date")} -def get_versions(): +def get_versions() -> Dict[str, Any]: """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some diff --git a/pyproject.toml b/pyproject.toml index e0359ad..94a197f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,14 @@ doc = ['Sphinx>=3.0'] [build-system] requires = ["setuptools>=42", "wheel", - "versioneer" + "versioneer[toml]" ] build-backend = "setuptools.build_meta" + +[tool.versioneer] +VCS = "git" +style = "pep440" +versionfile_source = "ISLP/_version.py" +versionfile_build = "ISLP/_version.py" +tag_prefix = "v" +parentdir_prefix = "ISLP-" diff --git a/setup.cfg b/setup.cfg index 4a6f58e..c59c035 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,9 +1,3 @@ -[versioneer] -VCS = git -style = pep440 -versionfile_source = ISLP/_version.py -tag_prefix = -parentdir_prefix = ISLP- [metadata] license_files = LICENSE.txt \ No newline at end of file From 630680531b1e20a5ccaf7ddc71fc9557e4a419d4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 18 Jan 2024 12:17:21 -0800 Subject: [PATCH 168/195] dynamic version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 94a197f..f4211b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,5 @@ [project] name = "ISLP" -version = "0.3.22" dependencies = ["numpy>=1.7.1,<1.25", # max version for numba "scipy>=0.9", "pandas>=0.20,<=1.9", @@ -37,6 +36,7 @@ classifiers = ["Development Status :: 3 - Alpha", "Programming Language :: Python", "Topic :: Scientific/Engineering" ] +dynamic = ["version"] [project.urls] # Optional "Homepage" = "https://github.com/intro-stat-learning/ISLP" From 6beb4191e8df701dc8e11e57629eff6c6015ff26 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 19 Mar 2024 09:08:35 -0700 Subject: [PATCH 169/195] casting categorical to array --- ISLP/models/model_spec.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index ce09e01..624999e 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -259,8 +259,8 @@ def fit(self, X, y=None): self.column_info_ = _get_column_info(X, self.columns_, - self.is_categorical_, - self.is_ordinal_, + np.asarray(self.is_categorical_), + np.asarray(self.is_ordinal_), default_encoders=self.default_encoders) # include each column as a Feature # so that their columns are built if needed From c50b45b5cbaed6f5bc001ebc7cb5733eb303fb22 Mon Sep 17 00:00:00 2001 From: architkulkarni Date: Tue, 4 Jun 2024 12:18:31 -0700 Subject: [PATCH 170/195] Remove upper bound version constraint on `numpy` dependency (#27) Co-authored-by: Jonathan Taylor --- pyproject.toml | 3 ++- requirements.txt | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f4211b4..03b3b45 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,7 @@ [project] name = "ISLP" -dependencies = ["numpy>=1.7.1,<1.25", # max version for numba +version = "0.3.22" +dependencies = ["numpy>=1.7.1", "scipy>=0.9", "pandas>=0.20,<=1.9", "lxml", # pandas needs this for html diff --git a/requirements.txt b/requirements.txt index 4c1827e..10bff6a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy>=1.7.1,<1.25 # max version for numba +numpy>=1.7.1 scipy>=0.9 pandas>=0.20 pandas<=1.9 From 7bb8fa463f53e2f909844ab16f7d725b6832fd93 Mon Sep 17 00:00:00 2001 From: "D. Debnath" Date: Wed, 5 Jun 2024 00:49:21 +0530 Subject: [PATCH 171/195] Fix warning: Passing html literal strings is deprecated. (#24) See: https://github.com/pandas-dev/pandas/pull/53805 Passing html literal strings is deprecated. Wrap literal string/bytes input in ``io.StringIO``/``io.BytesIO`` instead. --- ISLP/models/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ISLP/models/__init__.py b/ISLP/models/__init__.py index ee124d3..df06e70 100644 --- a/ISLP/models/__init__.py +++ b/ISLP/models/__init__.py @@ -4,6 +4,7 @@ """ import numpy as np, pandas as pd +from io import StringIO from .model_spec import (ModelSpec, Column, @@ -46,7 +47,7 @@ def summarize(results, """ tab = results.summary().tables[1] - results_table = pd.read_html(tab.as_html(), + results_table = pd.read_html(StringIO(tab.as_html()), index_col=0, header=0)[0] if not conf_int: From a956573e9e8604e97863ef355b47201210704c2d Mon Sep 17 00:00:00 2001 From: allispaul <69171104+allispaul@users.noreply.github.com> Date: Tue, 4 Jun 2024 14:27:08 -0500 Subject: [PATCH 172/195] cast to np.array before prediction (#20) --- ISLP/bart/bart.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ISLP/bart/bart.py b/ISLP/bart/bart.py index 099c52e..3c933ca 100644 --- a/ISLP/bart/bart.py +++ b/ISLP/bart/bart.py @@ -105,7 +105,7 @@ def predict(self, for trees in self.trees_sample_: for tree in trees: - tree_fit = np.array([tree.predict_out_of_sample(x) for x in X]) + tree_fit = np.array([tree.predict_out_of_sample(x) for x in np.asarray(X)]) output += tree_fit output = output / nsample return self._inverse(output) @@ -122,7 +122,7 @@ def staged_predict(self, for nstep, trees in enumerate(trees_sample_): for tree in trees: - tree_fit = np.array([tree.predict_out_of_sample(x) for x in X]) + tree_fit = np.array([tree.predict_out_of_sample(x) for x in np.asarray(X)]) output[nstep] += tree_fit output = np.cumsum(output, 0) / (np.arange(nsample) + 1)[:,None] From 81013626b17d37cd8556bec4c4fa773c43c66870 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:33:59 -0700 Subject: [PATCH 173/195] removing fixed version in pyproject.toml --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 03b3b45..4362f93 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,5 @@ [project] name = "ISLP" -version = "0.3.22" dependencies = ["numpy>=1.7.1", "scipy>=0.9", "pandas>=0.20,<=1.9", From a63f9b5f1a9dce328ccd417141ac7cdf083cbf09 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:35:02 -0700 Subject: [PATCH 174/195] uncapping pandas version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4362f93..b887aaa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,7 @@ name = "ISLP" dependencies = ["numpy>=1.7.1", "scipy>=0.9", - "pandas>=0.20,<=1.9", + "pandas>=0.20", "lxml", # pandas needs this for html "scikit-learn>=1.2", "joblib", From 21d1724116c39d58f19181bf283aa99b87bd98dc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:38:37 -0700 Subject: [PATCH 175/195] switch to 3.12 for tests --- .github/workflows/build_test.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index a4bd1aa..23de8b3 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -10,10 +10,10 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Set up Python 3.10 + - name: Set up Python 3.12 uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.12' - name: Add conda to system path run: | # $CONDA is an environment variable pointing to the root of the miniconda directory @@ -42,10 +42,10 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Set up Python 3.10 + - name: Set up Python 3.12 uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.12' - name: Add conda to system path run: | # $CONDA is an environment variable pointing to the root of the miniconda directory @@ -74,10 +74,10 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Set up Python 3.10 + - name: Set up Python 3.12 uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.12' - name: Add conda to system path run: | # $CONDA is an environment variable pointing to the root of the miniconda directory From 9ee31b7e9b17387086cad31b7572b704a8b66ae3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:47:17 -0700 Subject: [PATCH 176/195] ignoring torch tests for mac --- .github/workflows/build_test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_test.yml b/.github/workflows/build_test.yml index 23de8b3..767a62e 100644 --- a/.github/workflows/build_test.yml +++ b/.github/workflows/build_test.yml @@ -97,6 +97,6 @@ jobs: run: | pip install torchvision torchinfo pip install pytest - pytest + pytest --ignore tests/deeplearning/test_hitters.py --ignore tests/deeplearning/test_mnist.py From c2e55b212defb05b8d8179cca75bcb78545acead Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:56:16 -0700 Subject: [PATCH 177/195] addressing a typecast warning from pandas --- ISLP/models/model_spec.py | 4 ++-- tests/bart/test_bart.py | 2 -- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 624999e..0ee92d5 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -647,9 +647,9 @@ def build_model(column_info, # if we reach here, we will be returning a DataFrame # make sure all columns are floats - for i, _ in enumerate(df.columns): + for i, col in enumerate(df.columns): if df.iloc[:,i].dtype == bool: - df.iloc[:,i] = df.iloc[:,i].astype(float) + df[col] = df.iloc[:,i].astype(float) return df def derived_feature(variables, encoder=None, name=None, use_transform=True): diff --git a/tests/bart/test_bart.py b/tests/bart/test_bart.py index d12a0a2..903bb83 100644 --- a/tests/bart/test_bart.py +++ b/tests/bart/test_bart.py @@ -19,8 +19,6 @@ def test_bart(): clone(B) - return B - if __name__ == "__main__": test_bart() From 8f730ac44f3041f88d7f5616020ba73af65535c3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 12:59:05 -0700 Subject: [PATCH 178/195] address sklearn fit_params warning --- ISLP/models/generic_selector.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/ISLP/models/generic_selector.py b/ISLP/models/generic_selector.py index b0261e9..37b1f93 100644 --- a/ISLP/models/generic_selector.py +++ b/ISLP/models/generic_selector.py @@ -166,7 +166,7 @@ def __init__(self, # don't mess with this unless testing self._TESTING_INTERRUPT_MODE = False - def fit(self, X, y, groups=None, **fit_params): + def fit(self, X, y, groups=None, **params): """Perform feature selection and learn model from training data. Parameters @@ -183,7 +183,7 @@ def fit(self, X, y, groups=None, **fit_params): groups: array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. Passed to the fit method of the cross-validator. - fit_params: various, optional + params: various, optional Additional parameters that are being passed to the estimator. For example, `sample_weights=weights`. @@ -218,7 +218,7 @@ def fit(self, X, y, groups=None, **fit_params): groups=groups, cv=self.cv, pre_dispatch=self.pre_dispatch, - **fit_params) + **params) # keep a running track of the best state @@ -242,7 +242,7 @@ def fit(self, X, y, groups=None, **fit_params): X, y, groups=groups, - **fit_params) + **params) iteration += 1 cur, best_, self.finished_ = self.update_results_check(results_, self.path_, @@ -287,7 +287,7 @@ def fit_transform(self, X, y, groups=None, - **fit_params): + **params): """Fit to training data then reduce X to its most important features. Parameters @@ -304,7 +304,7 @@ def fit_transform(self, groups: array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. Passed to the fit method of the cross-validator. - fit_params: various, optional + params: various, optional Additional parameters that are being passed to the estimator. For example, `sample_weights=weights`. @@ -313,7 +313,7 @@ def fit_transform(self, Reduced feature subset of X, shape={n_samples, k_features} """ - self.fit(X, y, groups=groups, **fit_params) + self.fit(X, y, groups=groups, **params) return self.transform(X) def get_metric_dict(self, confidence_interval=0.95): @@ -368,7 +368,7 @@ def _batch(self, X, y, groups=None, - **fit_params): + **params): results = [] @@ -388,7 +388,7 @@ def _batch(self, groups=groups, cv=self.cv, pre_dispatch=self.pre_dispatch, - **fit_params) + **params) for state in candidates) for state, scores in work: @@ -484,7 +484,7 @@ def _calc_score(estimator, groups=None, cv=None, pre_dispatch='2*n_jobs', - **fit_params): + **params): X_state = build_submodel(X, state) @@ -497,11 +497,11 @@ def _calc_score(estimator, scoring=scorer, n_jobs=1, pre_dispatch=pre_dispatch, - fit_params=fit_params) + params=params) else: estimator.fit(X_state, y, - **fit_params) + **params) scores = np.array([scorer(estimator, X_state, y)]) From a96b93e8ca0304cfcc1b2abce44b639ea4cd1165 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 13:18:30 -0700 Subject: [PATCH 179/195] Cleanup data files (#29) * test of datasets * docstring fix, fix labels of confusion matrix * flexible level for CIs in summarize --- ISLP/__init__.py | 125 +++++++++++++++++++++++++++------------- ISLP/models/__init__.py | 19 +++--- tests/test_datasets.py | 30 ++++++++++ 3 files changed, 123 insertions(+), 51 deletions(-) create mode 100644 tests/test_datasets.py diff --git a/ISLP/__init__.py b/ISLP/__init__.py index 4e7e3f4..6cd1ee1 100644 --- a/ISLP/__init__.py +++ b/ISLP/__init__.py @@ -6,13 +6,53 @@ """ from os.path import join as pjoin -import pandas as pd, numpy as np from importlib.resources import (as_file, files) +import pandas as pd, numpy as np +from sklearn.metrics import confusion_matrix as _confusion_matrix +from sklearn.metrics._classification import unique_labels # data originally saved via: [sm.datasets.get_rdataset(n, 'ISLR').data.to_csv('../ISLP/data/%s.csv' % n, index=False) for n in ['Carseats', 'College', 'Credit', 'Default', 'Hitters', 'Auto', 'OJ', 'Portfolio', 'Smarket', 'Wage', 'Weekly', 'Caravan']] +def _make_categorical(dataset): + unordered = _unordered.setdefault(dataset, []) + ordered = _ordered.setdefault(dataset, []) + with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: + df = pd.read_csv(filename) + for col in unordered: + df[col] = pd.Categorical(df[col]) + for col in ordered: + df[col] = pd.Categorical(df[col], ordered=True) + if dataset in _index: + df = df.set_index(_index[dataset]) + return df + +_unordered = {'Hitters':['League', 'Division', 'NewLeague'], + 'Caravan':['Purchase'], + 'Carseats':['ShelveLoc', 'Urban', 'US'], + 'College':['Private'], + 'Publication':['mech'], + 'BrainCancer':['sex', 'diagnosis', 'loc', 'stereo'], + 'Wage':['maritl', 'race', 'region', 'jobclass', 'health', 'health_ins'], + 'Default':['default', 'student'], + 'Credit':['Gender', 'Student', 'Married', 'Ethnicity'], + 'OJ':['Purchase', 'Store7'], + 'Smarket':['Direction'], + 'Weekly':['Direction'] + } +_ordered = {'Wage':['education'], + } +_index = {'Auto':'name'} + +_datasets = sorted(list(_unordered.keys()) + + list(_ordered.keys()) + + ['NCI60', + 'Khan', + 'Bikeshare', + 'NYSE']) + def load_data(dataset): + if dataset == 'NCI60': with as_file(files('ISLP').joinpath('data', 'NCI60data.npy')) as features: X = np.load(features) @@ -42,34 +82,6 @@ def load_data(dataset): 'ytest':ytest, 'ytrain':ytrain} - elif dataset == 'Hitters': - with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: - df = pd.read_csv(filename) - for col in ['League', 'Division', 'NewLeague']: - df[col] = pd.Categorical(df[col]) - return df - elif dataset == 'Carseats': - with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: - df = pd.read_csv(filename) - for col in ['ShelveLoc', 'Urban', 'US']: - df[col] = pd.Categorical(df[col]) - return df - elif dataset == 'NYSE': - with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: - df = pd.read_csv(filename).set_index('date') - return df - elif dataset == 'Publication': - with as_file(files('ISLP').joinpath('data', 'Publication.csv')) as f: - df = pd.read_csv(f) - for col in ['mech']: - df[col] = pd.Categorical(df[col]) - return df - elif dataset == 'BrainCancer': - with as_file(files('ISLP').joinpath('data', 'BrainCancer.csv')) as f: - df = pd.read_csv(f) - for col in ['sex', 'diagnosis', 'loc', 'stereo']: - df[col] = pd.Categorical(df[col]) - return df elif dataset == 'Bikeshare': with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: df = pd.read_csv(filename) @@ -87,27 +99,60 @@ def load_data(dataset): ordered=False, categories=range(24)) return df - elif dataset == 'Wage': - with as_file(files('ISLP').joinpath('data', 'Wage.csv')) as f: - df = pd.read_csv(f) - df['education'] = pd.Categorical(df['education'], ordered=True) - return df - else: + elif dataset == 'NYSE': with as_file(files('ISLP').joinpath('data', '%s.csv' % dataset)) as filename: - return pd.read_csv(filename) + df = pd.read_csv(filename) + # setting order to avoid alphabetical + df['day_of_week'] = pd.Categorical(df['day_of_week'], + ordered=False, + categories=['mon', + 'tues', + 'wed', + 'thur', + 'fri']) + return df.set_index('date') + else: + return _make_categorical(dataset) +load_data.__doc__ = f""" +Load dataset from ISLP package. -from sklearn.metrics import confusion_matrix as _confusion_matrix +Choices are: {_datasets} + +Parameters +---------- + +dataset: str + +Returns +------- + +data: array-like or dict + Either a `pd.DataFrame` representing the dataset or a dictionary + containing different parts of the dataset. + +""" def confusion_table(predicted_labels, - true_labels): + true_labels, + labels=None): """ Return a data frame version of confusion matrix with rows given by predicted label and columns the truth. + + Parameters + ---------- + + predicted_labels: array-like + These will form rows of confusion matrix. + + true_labels: array-like + These will form columns of confusion matrix. """ - labels = sorted(np.unique(list(true_labels) + - list(predicted_labels))) + if labels is None: + labels = unique_labels(true_labels, + predicted_labels) C = _confusion_matrix(true_labels, predicted_labels, labels=labels) diff --git a/ISLP/models/__init__.py b/ISLP/models/__init__.py index df06e70..cff02f8 100644 --- a/ISLP/models/__init__.py +++ b/ISLP/models/__init__.py @@ -25,13 +25,14 @@ sklearn_selection_path) def summarize(results, - conf_int=False): + conf_int=False, + level=None): """ Take a fit statsmodels and summarize it by returning the usual coefficient estimates, their standard errors, the usual test statistics and P-values as well as - (optionally) 95% confidence intervals. + (optionally) confidence intervals. Based on: @@ -46,7 +47,11 @@ def summarize(results, Include 95% confidence intervals? """ - tab = results.summary().tables[1] + if level is not None: + conf_int = True + if level is None: + level = 0.95 + tab = results.summary(alpha=1-level).tables[1] results_table = pd.read_html(StringIO(tab.as_html()), index_col=0, header=0)[0] @@ -58,12 +63,4 @@ def summarize(results, return results_table[results_table.columns[:-2]] return results_table -# def poly(X, degree): -# """ -# Create columns of design matrix -# for orthogonal polynomial for a given series X -# """ - -# result = Poly(degree=degree).fit_transform(X) - diff --git a/tests/test_datasets.py b/tests/test_datasets.py new file mode 100644 index 0000000..39f1447 --- /dev/null +++ b/tests/test_datasets.py @@ -0,0 +1,30 @@ +# test that all datasets import + +from ISLP import load_data +import numpy as np +import pytest + +datasets = ['Auto', + 'Bikeshare', + 'Boston', + 'BrainCancer', + 'Caravan', + 'Carseats', + 'College', + 'Credit', + 'Default', + 'Fund', + 'Hitters', + 'NYSE', + 'OJ', + 'Portfolio', + 'Publication', + 'Smarket', + 'Wage', + 'Weekly'] + +@pytest.mark.parametrize('dataset', datasets) +def test_load(dataset): + df = load_data(dataset) + for col in df.columns: + assert df[col].dtype != np.dtype(object) From e4464791965976633558a5db8a3711c51d7dadf0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 13:25:07 -0700 Subject: [PATCH 180/195] changed default_encoders to categorical_encoders (#30) --- ISLP/models/columns.py | 17 ++++++++-------- ISLP/models/model_spec.py | 33 ++++++++++++++++++++----------- pyproject.toml | 3 ++- setup.py | 1 - tests/models/test_columns.py | 5 ++++- tests/models/test_model_matrix.py | 22 ++++++++++----------- 6 files changed, 47 insertions(+), 34 deletions(-) diff --git a/ISLP/models/columns.py b/ISLP/models/columns.py index e3c2106..7ea6adb 100644 --- a/ISLP/models/columns.py +++ b/ISLP/models/columns.py @@ -9,7 +9,6 @@ from sklearn.utils.validation import check_is_fitted from sklearn.exceptions import NotFittedError - class Column(NamedTuple): """ @@ -125,10 +124,7 @@ def _get_column_info(X, columns, is_categorical, is_ordinal, - default_encoders={ - 'ordinal': OrdinalEncoder(), - 'categorical': OneHotEncoder() - } + categorical_encoders={} ): @@ -151,12 +147,18 @@ def _get_column_info(X, if is_categorical[i]: if is_ordinal[i]: Xcol = _get_column(col, X) - encoder = clone(default_encoders['ordinal']) + if col not in categorical_encoders: + encoder = clone(categorical_encoders['ordinal']) + else: + encoder = categorical_encoders[col] encoder.fit(Xcol) columns = ['{0}'.format(col)] else: Xcol = _get_column(col, X) - encoder = clone(default_encoders['categorical']) + if col not in categorical_encoders: + encoder = clone(categorical_encoders['categorical']) + else: + encoder = categorical_encoders[col] cols = encoder.fit_transform(Xcol) if hasattr(encoder, 'columns_'): columns_ = encoder.columns_ @@ -181,7 +183,6 @@ def _get_column_info(X, # https://github.com/scikit-learn/scikit-learn/blob/2beed55847ee70d363bdbfe14ee4401438fba057/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py # max_bins is ignored - def _check_categories(categorical_features, X): """Check and validate categorical features in X diff --git a/ISLP/models/model_spec.py b/ISLP/models/model_spec.py index 0ee92d5..d970bb7 100644 --- a/ISLP/models/model_spec.py +++ b/ISLP/models/model_spec.py @@ -154,6 +154,7 @@ def fit(self, X, y=None): raise ValueError('method must be one of ["drop", "sum", None] or a callable' + 'that returns a contrast matrix and column names given the number' + ' of levels') + return self def transform(self, X): @@ -171,22 +172,23 @@ def transform(self, X): class ModelSpec(TransformerMixin, BaseEstimator): - ''' - - Parameters + '''Parameters ---------- terms : sequence (optional) + Sequence of sets whose elements are columns of *X* when fit. For :py:class:`pd.DataFrame` these can be column names. intercept : bool (optional) + Include a column for intercept? categorical_features : array-like of {bool, int} of shape (n_features) or shape (n_categorical_features,), default=None. + Indicates the categorical features. Will be ignored if *X* is a :py:class:`pd.DataFrame` or :py:class:`pd.Series`. @@ -195,25 +197,31 @@ class ModelSpec(TransformerMixin, BaseEstimator): - integer array-like : integer indices indicating categorical features. - default_encoders : dict - Dictionary whose keys are elements of *terms* and values - are transforms to be applied to the associate columns in the model matrix - by running the *fit_transform* method when *fit* is called and overwriting - these values in the dictionary. + categorical_encoders : dict + + Dictionary whose keys are elements of *terms* that represent + **categorical variables**. Its values are transforms to be + applied to the associate columns in the model matrix by + running the *fit_transform* method when *fit* is called and + overwriting these values in the dictionary. + ''' def __init__(self, terms=[], intercept=True, categorical_features=None, - default_encoders={'categorical': Contrast(method='drop'), - 'ordinal': OrdinalEncoder()} + categorical_encoders={} ): self.intercept = intercept self.terms = terms self.categorical_features = categorical_features - self.default_encoders = default_encoders + + self.categorical_encoders = categorical_encoders + self.categorical_encoders_ = {'ordinal': OrdinalEncoder(), + 'categorical': Contrast(method='drop')} + self.categorical_encoders_.update(**categorical_encoders) def fit(self, X, y=None): @@ -261,7 +269,8 @@ def fit(self, X, y=None): self.columns_, np.asarray(self.is_categorical_), np.asarray(self.is_ordinal_), - default_encoders=self.default_encoders) + categorical_encoders=self.categorical_encoders_) + # include each column as a Feature # so that their columns are built if needed diff --git a/pyproject.toml b/pyproject.toml index b887aaa..de708f8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,7 +51,8 @@ doc = ['Sphinx>=3.0'] [build-system] requires = ["setuptools>=42", "wheel", - "versioneer[toml]" + "versioneer[toml]", + "Sphinx>=1.0" ] build-backend = "setuptools.build_meta" diff --git a/setup.py b/setup.py index bf51c2e..e068529 100755 --- a/setup.py +++ b/setup.py @@ -16,7 +16,6 @@ from setuptools import setup - # Define extensions EXTS = [] diff --git a/tests/models/test_columns.py b/tests/models/test_columns.py index a86941b..77ba784 100644 --- a/tests/models/test_columns.py +++ b/tests/models/test_columns.py @@ -3,6 +3,7 @@ from pandas.api.types import CategoricalDtype from ISLP.models.columns import _get_column_info +from ISLP.models.model_spec import Contrast def test_column_info(): @@ -15,5 +16,7 @@ def test_column_info(): print(_get_column_info(df, df.columns, [False]*4+[True], - [False]*5)) + [False]*5, + categorical_encoders={'categorical':Contrast(method='drop')})) + diff --git a/tests/models/test_model_matrix.py b/tests/models/test_model_matrix.py index a2759f4..70b9cab 100644 --- a/tests/models/test_model_matrix.py +++ b/tests/models/test_model_matrix.py @@ -37,7 +37,7 @@ def test_ndarray(): X = rng.standard_normal((50,5)) M = ModelSpec(terms=[1, (3,2)], - default_encoders=default_encoders) + categorical_encoders=default_encoders) M.fit(X) MX = M.transform(X) @@ -51,7 +51,7 @@ def test_dataframe1(): D = pd.DataFrame(X, columns=['A','B','C','D','E']) M = ModelSpec(terms=['A','D',('D','E')], - default_encoders=default_encoders) + categorical_encoders=default_encoders) clone(M) MX = np.asarray(M.fit_transform(D)) @@ -66,7 +66,7 @@ def test_dataframe2(): D = pd.DataFrame(X, columns=['V','B','A','D','E']) M = ModelSpec(terms=['A', 'D', 'B', ('D','E'), 'V'], - default_encoders=default_encoders) + categorical_encoders=default_encoders) clone(M) MX = M.fit_transform(D) @@ -83,7 +83,7 @@ def test_dataframe3(): D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=['A', 'E', ('D','E')], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = np.asarray(M.fit_transform(D)) M2 = clone(M) @@ -105,7 +105,7 @@ def test_dataframe4(): D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=['A', 'E', ('D','E'), 'D'], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = np.asarray(M.fit_transform(D)) DE = pd.get_dummies(D['E']) @@ -129,7 +129,7 @@ def test_dataframe5(): D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=['A', 'E', ('D','E')], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = np.asarray(M.fit_transform(D)) # check they agree on copy of dataframe @@ -148,7 +148,7 @@ def test_dataframe6(): D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=['A',W,(W,'D',)], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = M.fit_transform(D) MX = np.asarray(MX) @@ -162,7 +162,7 @@ def test_dataframe7(): D['Eee'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=D.columns.drop(['Y','C']), - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = M.fit_transform(D) print(MX.columns) MX = np.asarray(MX) @@ -179,7 +179,7 @@ def test_dataframe8(): # raises a ValueError because poly will have been already fit -- need new instance of Poly W = Feature(('A',), 'poly(A)', poly) M = ModelSpec(terms=list(D.columns.drop(['Y','C'])) + [(W,'E')], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = M.fit_transform(D) print(MX.columns) @@ -198,7 +198,7 @@ def test_dataframe9(): W = Feature(('A',), 'poly(A)', poly) U = Feature(('B',), 'poly(B)', clone(poly)) M = ModelSpec(terms=list(D.columns.drop(['Y','C'])) + [W,U], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = M.fit_transform(D) print(MX.columns) @@ -215,7 +215,7 @@ def test_dataframe10(): D['E'] = pd.Categorical(rng.choice(range(4,8), 50, replace=True)) M = ModelSpec(terms=['A', 'E', 'C', W, (W, 'D',), U], - default_encoders=default_encoders) + categorical_encoders=default_encoders) MX = M.fit_transform(D) print(MX.columns) MX = np.asarray(MX) From 38dbd0842fbec87f9a7c946d84b726a6f10f04a1 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 13:27:44 -0700 Subject: [PATCH 181/195] Svm plot (#28) * improved plot for 2-class SVM * added margin as well --- ISLP/svm.py | 33 +++++++++++++++++++++++++++++---- 1 file changed, 29 insertions(+), 4 deletions(-) diff --git a/ISLP/svm.py b/ISLP/svm.py index bedf288..8afcd5a 100644 --- a/ISLP/svm.py +++ b/ISLP/svm.py @@ -28,6 +28,12 @@ def plot(X, ''' Graphical representation of fitted support vector classifier. + There are two types of support vectors: + + - Points violating the margin but correctly classified. These are marked with a black '+'. + + - Misclassified points. These are marked with a red 'x'. + Parameters ---------- @@ -89,7 +95,7 @@ def plot(X, # draw the points - ax.scatter(X0, X1, c=Y, cmap=scatter_cmap) + ax.scatter(X0, X1, c=Y, cmap=scatter_cmap, s=200) # add the contour @@ -113,8 +119,27 @@ def plot(X, cmap=decision_cmap, alpha=alpha) - # add the support vectors + decision_val = svm.decision_function(X_pred) - ax.scatter(X[svm.support_,features[0]], - X[svm.support_,features[1]], marker='+', c='k', s=200) + # add the support vectors + if svm.classes_.shape[0] == 2: # 2-class problem + + ax.contourf(xval, + yval, + decision_val.reshape(yval.shape), + levels=[-1,1], + cmap=decision_cmap, + alpha=alpha) + + D = svm.decision_function(X[svm.support_]) + Y_ = (2 * (Y[svm.support_] == svm.classes_[1]) - 1) + violate_margin = (Y_ * D) > 0 + ax.scatter(X[svm.support_,features[0]][violate_margin], + X[svm.support_,features[1]][violate_margin], marker='+', c='k', s=50) + misclassified = ~violate_margin + ax.scatter(X[svm.support_,features[0]][misclassified], + X[svm.support_,features[1]][misclassified], marker='x', c='r', s=50) + else: + ax.scatter(X[svm.support_,features[0]], + X[svm.support_,features[1]], marker='+', c='k', s=50) From 0a967238ca14cdae03cc14e675d19567d86b9bca Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 20:10:56 -0700 Subject: [PATCH 182/195] update to v4 artifacts --- .github/workflows/build_docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index ab7cca5..7a21062 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -51,7 +51,7 @@ jobs: # This workflow contains a single # Store the output - name: Upload docs - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ISLP_docs path: docs/build/html From de02742e513adae3919f35e3e1bc5b9a6cbf4930 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 20:54:50 -0700 Subject: [PATCH 183/195] trying to use Nose 20.x --- .github/workflows/build_docs.yml | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 7a21062..2c9e453 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -21,11 +21,13 @@ jobs: # This workflow contains a single steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - uses: actions/checkout@v3 + with: + node-version: 20.x - uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.12' cache: 'pip' - + node-version: 20.x # Install - name: Install dependencies run: | @@ -75,9 +77,12 @@ jobs: # This workflow contains a single with: name: ISLP_docs path: . - - uses: actions/configure-pages@v3 - - uses: actions/upload-pages-artifact@v2 + - uses: actions/configure-pages@v4 + with: + node-version: 20.x + - uses: actions/upload-pages-artifact@v3 with: + node-version: 20.x path: . - id: deployment uses: actions/deploy-pages@main \ No newline at end of file From c4284491677b582a3472be8334d9178fe0b4d5f9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 21:03:45 -0700 Subject: [PATCH 184/195] don't need node for setup-python --- .github/workflows/build_docs.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 2c9e453..9afeacc 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -27,7 +27,6 @@ jobs: # This workflow contains a single with: python-version: '3.12' cache: 'pip' - node-version: 20.x # Install - name: Install dependencies run: | From dba19c10659138de5146f3ec5d6f92b2a227aa2c Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 21:08:36 -0700 Subject: [PATCH 185/195] update labs to v2.2 --- docs/ISLP_labs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/ISLP_labs b/docs/ISLP_labs index f4ecee2..5d793a3 160000 --- a/docs/ISLP_labs +++ b/docs/ISLP_labs @@ -1 +1 @@ -Subproject commit f4ecee2e614dcd34c13e573568b02dcfc5da99b4 +Subproject commit 5d793a33a8d5025181439b8d0f193c37c69ee20a From 04e21d87ee71a7d795f73bc7020eb16b8f637f07 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 4 Jun 2024 21:25:21 -0700 Subject: [PATCH 186/195] updating docs json file --- docs/source/docs_version.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/docs_version.json b/docs/source/docs_version.json index f91228e..d6217ce 100644 --- a/docs/source/docs_version.json +++ b/docs/source/docs_version.json @@ -1,4 +1,4 @@ -{"labs": "v2.1.4", - "library": "v0.3.22", +{"labs": "v2.2", + "library": "v0.4.0", "comment":"labs should be version of ISLP pointed to in ISLP_labs/README.md, library version should be explicitly marked in ISLP_labs/requirements.txt; don't forget to strip warnings!!!!!!!!" } From 1587e591bf555592b4a444aa553288dc67b90a46 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 19 Dec 2024 13:07:45 -0800 Subject: [PATCH 187/195] commiting python3.12 change --- docs/source/installation.myst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/installation.myst b/docs/source/installation.myst index b82bebf..5fba989 100644 --- a/docs/source/installation.myst +++ b/docs/source/installation.myst @@ -24,8 +24,7 @@ conda create --name islp python ``` Current conda should have this at least 3.9. If not, replace `python` -with `python=3.10` or `python=3.11`. The newest version `python=3.12` is not -currently supported as some packages, such as `torch` are not installable. +with `python=3.10`, `python=3.11` or `python=3.12`. To run python code in this environment, you must activate it: From 707a2fe7db42c9f599bd0af8212d505bec3817f6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 19 Dec 2024 13:08:23 -0800 Subject: [PATCH 188/195] removing node for checkout --- .github/workflows/build_docs.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 9afeacc..9260015 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -20,9 +20,7 @@ jobs: # This workflow contains a single # Steps represent a sequence of tasks that will be executed as part of the job steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - with: - node-version: 20.x + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: '3.12' From e4657bfcf9227d7ccbb8794ef8c98ad885175810 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 29 May 2024 21:57:34 -0700 Subject: [PATCH 189/195] loosening numpy and pandas versions --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index de708f8..a898c17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,7 @@ name = "ISLP" dependencies = ["numpy>=1.7.1", "scipy>=0.9", - "pandas>=0.20", + "pandas>=1.5", "lxml", # pandas needs this for html "scikit-learn>=1.2", "joblib", @@ -15,7 +15,7 @@ dependencies = ["numpy>=1.7.1", ] description = "Library for ISLP labs" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" license = {file = "LICENSE"} keywords = [] authors = [ From a5d54b3c9816b0194bbee119b79bff70ca0b56e7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 31 Jan 2026 16:27:28 -0800 Subject: [PATCH 190/195] fixes to build requirements --- pyproject.toml | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a898c17..aa30d72 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,23 @@ classifiers = ["Development Status :: 3 - Alpha", ] dynamic = ["version"] +[tool.setuptools] +packages = [ + "ISLP", + "ISLP.models", + "ISLP.bart", + "ISLP.torch", + "ISLP.data" +] +include-package-data = true + +[tool.setuptools.package-data] +ISLP = ["data/*.csv", "data/*.npy", "data/*.data"] + +[tool.setuptools.dynamic] +version = {attr = "ISLP.__version__"} # Assuming ISLP.__version__ holds your version + + [project.urls] # Optional "Homepage" = "https://github.com/intro-stat-learning/ISLP" "Bug Reports" = "https://github.com/intro-stat-learning/ISLP/issues" @@ -51,8 +68,14 @@ doc = ['Sphinx>=3.0'] [build-system] requires = ["setuptools>=42", "wheel", - "versioneer[toml]", "Sphinx>=1.0" + "numpy", + "pandas", + "scipy", + "scikit-learn", + "joblib", + "statsmodels", + "versioneer[toml]" ] build-backend = "setuptools.build_meta" From 273a72765e9f875753b4de1b8fb318bd9cc6eb24 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 31 Jan 2026 16:28:31 -0800 Subject: [PATCH 191/195] simplifying setup.py, logic in pyproject.toml --- setup.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/setup.py b/setup.py index e068529..e0710d3 100755 --- a/setup.py +++ b/setup.py @@ -27,21 +27,21 @@ def main(**extra_args): setup(version=versioneer.get_version(), - packages = ['ISLP', - 'ISLP.models', - 'ISLP.models', - 'ISLP.bart', - 'ISLP.torch', - 'ISLP.data' - ], - ext_modules = EXTS, - package_data = {"ISLP":["data/*csv", "data/*npy", "data/*data"]}, - include_package_data=True, - data_files=[], - scripts=[], - long_description=long_description, - cmdclass = cmdclass, - **extra_args + # packages = ['ISLP', + # 'ISLP.models', + # 'ISLP.models', + # 'ISLP.bart', + # 'ISLP.torch', + # 'ISLP.data' + # ], + # ext_modules = EXTS, + # package_data = {"ISLP":["data/*csv", "data/*npy", "data/*data"]}, + # include_package_data=True, + # data_files=[], + # scripts=[], + # long_description=long_description, + # cmdclass = cmdclass, + # **extra_args ) #simple way to test what setup will do From 4281ada66688c196aed6a5fcb998585e13fa0688 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sat, 31 Jan 2026 16:37:11 -0800 Subject: [PATCH 192/195] fixing _estimator_type flags --- ISLP/models/generic_selector.py | 40 +++++++++++++++++++-------------- 1 file changed, 23 insertions(+), 17 deletions(-) diff --git a/ISLP/models/generic_selector.py b/ISLP/models/generic_selector.py index 37b1f93..7c9329e 100644 --- a/ISLP/models/generic_selector.py +++ b/ISLP/models/generic_selector.py @@ -28,7 +28,10 @@ import scipy as sp from sklearn.metrics import get_scorer -from sklearn.base import (clone, MetaEstimatorMixin) +from sklearn.base import (clone, + MetaEstimatorMixin, + is_classifier, + is_regressor) from sklearn.model_selection import cross_val_score from joblib import Parallel, delayed @@ -149,13 +152,13 @@ def __init__(self, self.scoring = scoring if scoring is None: - if self.est_._estimator_type == 'classifier': + if is_classifier(self.est_): scoring = 'accuracy' - elif self.est_._estimator_type == 'regressor': + elif is_regressor(self.est_): scoring = 'r2' else: - raise AttributeError('Estimator must ' - 'be a Classifier or Regressor.') + scoring = None + if isinstance(scoring, str): self.scorer = get_scorer(scoring) else: @@ -166,7 +169,7 @@ def __init__(self, # don't mess with this unless testing self._TESTING_INTERRUPT_MODE = False - def fit(self, X, y, groups=None, **params): + def fit(self, X, y, groups=None, **fit_params): """Perform feature selection and learn model from training data. Parameters @@ -183,7 +186,7 @@ def fit(self, X, y, groups=None, **params): groups: array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. Passed to the fit method of the cross-validator. - params: various, optional + fit_params: various, optional Additional parameters that are being passed to the estimator. For example, `sample_weights=weights`. @@ -218,7 +221,7 @@ def fit(self, X, y, groups=None, **params): groups=groups, cv=self.cv, pre_dispatch=self.pre_dispatch, - **params) + **fit_params) # keep a running track of the best state @@ -242,7 +245,7 @@ def fit(self, X, y, groups=None, **params): X, y, groups=groups, - **params) + **fit_params) iteration += 1 cur, best_, self.finished_ = self.update_results_check(results_, self.path_, @@ -287,7 +290,7 @@ def fit_transform(self, X, y, groups=None, - **params): + **fit_params): """Fit to training data then reduce X to its most important features. Parameters @@ -304,7 +307,7 @@ def fit_transform(self, groups: array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. Passed to the fit method of the cross-validator. - params: various, optional + fit_params: various, optional Additional parameters that are being passed to the estimator. For example, `sample_weights=weights`. @@ -313,7 +316,7 @@ def fit_transform(self, Reduced feature subset of X, shape={n_samples, k_features} """ - self.fit(X, y, groups=groups, **params) + self.fit(X, y, groups=groups, **fit_params) return self.transform(X) def get_metric_dict(self, confidence_interval=0.95): @@ -368,7 +371,7 @@ def _batch(self, X, y, groups=None, - **params): + **fit_params): results = [] @@ -388,7 +391,7 @@ def _batch(self, groups=groups, cv=self.cv, pre_dispatch=self.pre_dispatch, - **params) + **fit_params) for state in candidates) for state, scores in work: @@ -484,8 +487,11 @@ def _calc_score(estimator, groups=None, cv=None, pre_dispatch='2*n_jobs', - **params): + **fit_params): + if scorer is None: + scorer = lambda estimator, X, y: estimator.score(X, y) + X_state = build_submodel(X, state) if cv: @@ -497,11 +503,11 @@ def _calc_score(estimator, scoring=scorer, n_jobs=1, pre_dispatch=pre_dispatch, - params=params) + fit_params=fit_params) else: estimator.fit(X_state, y, - **params) + **fit_params) scores = np.array([scorer(estimator, X_state, y)]) From 4d9f0286be6c94834c9a0657a0aa7c42daf0093f Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 2 Feb 2026 12:34:49 -0800 Subject: [PATCH 193/195] feat: Add scikit-learn estimator type tags Adds the `__sklearn_tags__` method to the `sklearn_sm` and `sklearn_selected` wrappers. This allows scikit-learn to correctly identify the estimator type (regressor or classifier) based on the statsmodels model. This change enables the use of scikit-learn's cross-validation and model selection tools with these wrappers. Tests have been added to verify that OLS and GLM Binomial models are correctly identified. --- ISLP/models/sklearn_wrap.py | 15 +++++++++- pyproject.toml | 2 +- tests/models/test_sklearn_wrap.py | 46 +++++++++++++++++++++++++++++++ 3 files changed, 61 insertions(+), 2 deletions(-) create mode 100644 tests/models/test_sklearn_wrap.py diff --git a/ISLP/models/sklearn_wrap.py b/ISLP/models/sklearn_wrap.py index 123130b..121da75 100644 --- a/ISLP/models/sklearn_wrap.py +++ b/ISLP/models/sklearn_wrap.py @@ -49,7 +49,17 @@ def __init__(self, self.model_type = model_type self.model_spec = model_spec self.model_args = model_args - + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + if self.model_type == sm.OLS: + tags.estimator_type = 'regressor' + elif (issubclass(self.model_type, sm.GLM) and + 'family' in self.model_args and + isinstance(self.model_args.get('family', None), sm.families.Binomial)): + tags.estimator_type = 'classifier' + return tags + def fit(self, X, y): """ Fit a statsmodel model @@ -171,6 +181,9 @@ def __init__(self, self.cv = cv self.scoring = scoring + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + return tags def fit(self, X, y): """ diff --git a/pyproject.toml b/pyproject.toml index aa30d72..5fe63fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -68,7 +68,7 @@ doc = ['Sphinx>=3.0'] [build-system] requires = ["setuptools>=42", "wheel", - "Sphinx>=1.0" + "Sphinx>=1.0", "numpy", "pandas", "scipy", diff --git a/tests/models/test_sklearn_wrap.py b/tests/models/test_sklearn_wrap.py new file mode 100644 index 0000000..c3616bd --- /dev/null +++ b/tests/models/test_sklearn_wrap.py @@ -0,0 +1,46 @@ + +import numpy as np +import pandas as pd +import statsmodels.api as sm +from sklearn.base import is_classifier, is_regressor +import pytest + +from ISLP.models.sklearn_wrap import sklearn_sm, sklearn_selected +from ISLP.models.model_spec import ModelSpec +from ISLP.models.strategy import min_max + +@pytest.fixture +def model_setup(): + X = pd.DataFrame({'X1': np.random.rand(10), 'X2': np.random.rand(10), 'X3': np.random.rand(10)}) + y = pd.Series(np.random.randint(0, 2, 10)) # For classifier + model_spec_dummy = ModelSpec(['X1', 'X2', 'X3']).fit(X) + min_max_strategy_dummy = min_max(model_spec_dummy, min_terms=1, max_terms=2) + return X, y, model_spec_dummy, min_max_strategy_dummy + +def test_OLS_is_regressor(): + model = sklearn_sm(sm.OLS) + assert model.__sklearn_tags__().estimator_type == 'regressor' + assert is_regressor(model) + +def test_GLM_binomial_is_classifier(): + model = sklearn_sm(sm.GLM, model_args={'family': sm.families.Binomial()}) + assert model.__sklearn_tags__().estimator_type == 'classifier' + assert is_classifier(model) + +def test_GLM_binomial_probit_is_classifier(): + model = sklearn_sm(sm.GLM, model_args={'family': sm.families.Binomial(link=sm.families.links.Probit())}) + assert model.__sklearn_tags__().estimator_type == 'classifier' + assert is_classifier(model) + + +def test_selected_OLS_is_regressor(model_setup): + X, y, model_spec_dummy, min_max_strategy_dummy = model_setup + model = sklearn_selected(sm.OLS, strategy=min_max_strategy_dummy) + assert model.__sklearn_tags__().estimator_type == 'regressor' + assert is_regressor(model) + +def test_selected_GLM_binomial_is_classifier(model_setup): + X, y, model_spec_dummy, min_max_strategy_dummy = model_setup + model = sklearn_selected(sm.GLM, strategy=min_max_strategy_dummy, model_args={'family': sm.families.Binomial()}) + assert model.__sklearn_tags__().estimator_type == 'classifier' + assert is_classifier(model) From 2beabce8cc2f027cd3f44eb158f47f1b4a40a9c3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 2 Feb 2026 12:44:26 -0800 Subject: [PATCH 194/195] removing redundant setup.py --- setup.py | 50 -------------------------------------------------- 1 file changed, 50 deletions(-) delete mode 100755 setup.py diff --git a/setup.py b/setup.py deleted file mode 100755 index e0710d3..0000000 --- a/setup.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python -''' Installation script for ISLP package ''' - -import os -import sys -from os.path import join as pjoin, dirname, exists -# BEFORE importing distutils, remove MANIFEST. distutils doesn't properly -# update it when the contents of directories change. -if exists('MANIFEST'): os.remove('MANIFEST') - -# Unconditionally require setuptools -import setuptools - -# Package for getting versions from git tags -import versioneer - -from setuptools import setup - -# Define extensions -EXTS = [] - -cmdclass = versioneer.get_cmdclass() - -# get long_description - -long_description = open('README.md', 'rt', encoding='utf-8').read() - -def main(**extra_args): - setup(version=versioneer.get_version(), - # packages = ['ISLP', - # 'ISLP.models', - # 'ISLP.models', - # 'ISLP.bart', - # 'ISLP.torch', - # 'ISLP.data' - # ], - # ext_modules = EXTS, - # package_data = {"ISLP":["data/*csv", "data/*npy", "data/*data"]}, - # include_package_data=True, - # data_files=[], - # scripts=[], - # long_description=long_description, - # cmdclass = cmdclass, - # **extra_args - ) - -#simple way to test what setup will do -#python setup.py install --prefix=/tmp -if __name__ == "__main__": - main() From bcd85989111f50bca3667cc123708b571614cfc6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 2 Feb 2026 12:49:40 -0800 Subject: [PATCH 195/195] unused pkg_resources import --- ISLP/torch/imdb.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ISLP/torch/imdb.py b/ISLP/torch/imdb.py index 617489d..3dfacfe 100644 --- a/ISLP/torch/imdb.py +++ b/ISLP/torch/imdb.py @@ -12,7 +12,6 @@ import torch from torch.utils.data import TensorDataset from scipy.sparse import load_npz -from pkg_resources import resource_filename from pickle import load as load_pickle import urllib