Pull for latest version.

.github/workflows/python-pytest.yml

@@ -8,6 +8,9 @@ on:
     branches: [ main ]
   pull_request:
     branches: [ main ]
+  schedule:
+    # since * is a special character in YAML you have to quote this string
+    - cron:  '0 22 1/7 * *'
 
 jobs:
   build:
@@ -17,28 +20,41 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: [3.7, 3.8, 3.6]
-        exclude:
-          - os: windows-latest
-            python-version: 3.6
+        python-version: ["3.8", "3.9", "3.10"] # feature-tools only update to 0.23.3 on python3.6 and the latest is 1.2.0(2021/11)
+        tf-version: ["\">=2.0.0,<=2.10\"", "disable"]  # empty string indicates the latest version
+
+#        exclude:
+#          - os: windows-latest
+#            python-version: "==2.10"
+#          - os: windows-latest
+#            tf-version: ""
+
     steps:
-    - uses: conda-incubator/setup-miniconda@v2
-    - name: Set up Python ${{ matrix.python-version }}
+    - uses: actions/checkout@v2
+
+    - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        auto-update-conda: true
         python-version: ${{ matrix.python-version }}
-    - name: Conda list
-      shell: bash -l {0}
-      run: conda list
+
+# pip install "sktime<=0.17.2"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install git+https://github.com/DataCanvasIO/Hypernets
-        conda install -y -c conda-forge prophet==1.0.1
         pip install -r requirements.txt
-        pip install pytest-cov==2.4.0  python-coveralls  codacy-coverage
+        pip install pytest-cov  python-coveralls  codacy-coverage
+
+    - name: Install tensorflow dependencies
+      if:  ${{ matrix.tf-version != 'disable' }}
+      run: |
+        pip install tensorflow${{ matrix.tf-version }}
+
+    - name: pip list
+      run: |
         pip list
+        pip check
+
     - name: Test with pytest
       run: |
         pytest --cov=hyperts --durations=30

CONTRIBUTING.md

@@ -0,0 +1,34 @@
+# Contributing Guidelines
+HyperTS has been developed and used by many active community members. Everyone is more than welcomed  to make the project better and more accessible to more users. Whether it's a bug report, new feature, correction, or additional documentation, we greatly value feedback and contributions from our community.
+
+We are proud of this project and have been working to make it great since day one. We believe you will love it and we need your help and everything about HyperTS you have in your mind pushes this project forward.
+
+Join Us!
+
+## Bug Reports and Feature Requests
+The single most important contribution that you can make is to report bugs and make feature requests. The development work on HyperTS is largely driven by these, so please make your voice heard!
+
+Here are some issue templates we recommend  while you report bugs or suggest features.
+ - Bug Issue
+ - Feature Request
+ - Other Issues
+
+Ideally, you can attach some code in your issue to reproduce the bug.
+
+
+## Contributing via Pull Requests
+Code contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
+
+ 1. You are working against the latest source on the master branch.
+ 2. You check existing open, and recently merged, pull requests to make
+   sure someone else hasn't addressed the problem already.
+ 3. You open an issue to discuss any significant work - we would hate for
+   your time to be wasted.
+
+To send us a pull request, please:
+
+ 1. Fork the repository.
+ 2. Modify the source 
+ 3. Ensure local tests pass.
+ 4. Commit to your fork using clear commit messages.
+ 5. Send us a pull request.

README.md

@@ -1,2 +1,245 @@
-# HyperTS
-A AutoML&AutoDL Toolkit about Time Series.
+<h1 align="center">
+<img src="https://raw.githubusercontent.com/DataCanvasIO/HyperTS/main/docs/static/images/logo.png" width="400" align=center/>
+</h1><br>
+
+<h4 align="center">Statistics | Deep Learning | Neural Architecture Search</h4>
+
+<div align="center">
+
+[简体中文](README_zh_CN.md) • [Installation](https://hyperts.readthedocs.io/en/latest/contents/0200_installation.html) • [Documentation](https://hyperts.readthedocs.io/en/latest/) • [Examples](https://github.com/DataCanvasIO/HyperTS/tree/main/examples) 
+
+[![Python Versions](https://img.shields.io/pypi/pyversions/hyperts.svg)](https://pypi.org/project/hyperts)
+[![pypi](https://img.shields.io/pypi/v/hyperts.svg)](https://pypi.python.org/pypi/hyperts)
+[![conda](https://img.shields.io/conda/vn/conda-forge/HyperTS.svg)](https://anaconda.org/conda-forge/HyperTS)
+[![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-2.0+-blue.svg)](https://pypi.org/project/hyperts)
+[![License](https://img.shields.io/github/license/DataCanvasIO/hyperts.svg)](https://github.com/DataCanvasIO/hyperts/blob/master/LICENSE)
+</div>
+
+:dizzy: Easy-to-use, powerful, and unified full pipeline automated time series toolkit. Supports forecasting, classification, regression, and anomaly detection.
+
+
+## We Are Hiring！
+Dear folks, we are offering challenging opportunities located in Beijing for both professionals and students who are keen on AutoML/NAS. Come be a part of DataCanvas! Please send your CV to yangjian@zetyun.com. (Application deadline: TBD.) 
+
+
+## Overview
+HyperTS is a Python package that provides an end-to-end time series (TS) analysis toolkit. It covers complete and flexible AutoML workflows for TS, including data clearning, preprocessing, feature engineering, model selection, hyperparamter optimization, result evaluation, and visualization.
+
+Multi-mode drive, light-heavy combination is the highlighted features of HyperTS. Therefore, statistical models (STATS), deep learning (DL), and neural architecture search (NAS) can be switched arbitrarily to get a powerful TS estimator.
+
+As an easy-to-use and lower-threshold API, users can get a model after simply running the experiment, and then execute ```.predict()```, ```.predict_proba()```, ```.evalute()```, ```.plot()``` for various time series analysis.
+
+## Installation
+
+Note:
+
+- Prophet is required by HyperTS, install it from ``conda`` before installing HyperTS using ``pip``.
+- Tensorflow is an optional dependency for HyperTS, install it if using DL and NAS mode.
+
+HyperTS is available on Pypi and can be installed with ``pip``:
+
+```bash
+pip install hyperts
+```
+
+You can also install HyperTS from ``conda`` via the ``conda-forge`` channel:
+
+```bash
+conda install -c conda-forge hyperts
+```
+
+If you would like the most up-to-date version, you can instead install direclty from ``github``:
+
+```bash
+git clone git@github.com:DataCanvasIO/HyperTS.git
+cd HyperTS
+pip install -e . 
+pip install tensorflow #optional, recommended version: >=2.0.0,<=2.10.0
+````
+
+For more installation tips, see [installation](https://hyperts.readthedocs.io/en/latest/contents/0200_installation.html).
+
+## Tutorial
+
+|[English Docs](https://hyperts.readthedocs.io/en/latest/) / [Chinese Docs](https://hyperts.readthedocs.io/zh_CN/latest)| Discription |
+| --------------------------------- | --------------------------------- |
+[Expected Data Format](https://hyperts.readthedocs.io/en/latest/contents/0300_dataformat.html)|What data formats do HyperTS expect?|
+|[Quick Start](https://hyperts.readthedocs.io/en/latest/contents/0400_quick_start.html)| How to get started quickly with HyperTS?|
+|[Advanced Ladder](https://hyperts.readthedocs.io/en/latest/contents/0500_advanced_config.html)|How to realize the potential of HyperTS?|
+|[Custom Functions](https://hyperts.readthedocs.io/en/latest/contents/0600_custom_functions.html)|How to customize the functions of HyperTS?|
+
+## Examples
+
+Time Series Forecasting
+
+Users can quickly create and ```run()``` an experiment with ```make_experiment()```, where ```train_data```, and ```task``` are required input parameters. In the following forecast example, we define the experiment as a multivariate-forecast ```task```, and use the statistical model (stat ```mode```) . Besides, the mandatory arguments ```timestamp``` and ```covariates``` (if have) should also be defined in the experiment.
+
+```python
+from hyperts import make_experiment
+from hyperts.datasets import load_network_traffic
+
+from sklearn.model_selection import train_test_split
+
+data = load_network_traffic()
+train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False)
+
+model = make_experiment(train_data.copy(),
+                        task='multivariate-forecast',
+                        mode='stats',
+                        timestamp='TimeStamp',
+                        covariates=['HourSin', 'WeekCos', 'CBWD']).run()
+
+X_test, y_test = model.split_X_y(test_data.copy())
+
+y_pred = model.predict(X_test)
+
+scores = model.evaluate(y_test, y_pred)
+
+model.plot(forecast=y_pred, actual=test_data)
+```
+
+![Forecast_Figure](https://raw.githubusercontent.com/DataCanvasIO/HyperTS/main/docs/static/images/Actual_vs_Forecast.jpg)
+
+<details>
+  <summary>Time Series Classification (click to expand)</summary>
+
+```python
+from hyperts import make_experiment
+from hyperts.datasets import load_basic_motions
+
+from sklearn.metrics import f1_score
+from sklearn.model_selection import train_test_split
+
+data = load_basic_motions()
+train_data, test_data = train_test_split(data, test_size=0.2)
+
+model = make_experiment(train_data.copy(),
+                        task='classification',
+                        mode='dl',
+                        tf_gpu_usage_strategy=1,
+                        reward_metric='accuracy',
+                        max_trials=30,
+                        early_stopping_rounds=10).run()
+
+X_test, y_test = model.split_X_y(test_data.copy())
+
+y_pred = model.predict(X_test)
+y_proba = model.predict_proba(X_test)
+
+scores = model.evaluate(y_test, y_pred, y_proba=y_proba, metrics=['accuracy', 'auc', f1_score])
+
+print(scores)
+  ```
+</details>
+
+<details>
+  <summary>Time Series Anomaly Detection (click to expand)</summary>
+
+```python
+from hyperts import make_experiment
+from hyperts.datasets import load_real_known_cause_dataset
+
+from sklearn.model_selection import train_test_split
+
+data = load_real_known_cause_dataset()
+ground_truth = data.pop('anomaly')
+
+detection_length = 15000
+train_data, test_data = train_test_split(data, test_size=detection_length, shuffle=False)
+
+model = make_experiment(train_data.copy(),
+                        task='detection',
+                        mode='stats',
+                        reward_metric='f1',
+                        max_trials=30,
+                        early_stopping_rounds=10).run()
+
+X_test, _ = model.split_X_y(test_data.copy())
+y_test = ground_truth.iloc[-detection_length:]
+
+y_pred = model.predict(X_test)
+y_proba = model.predict_proba(X_test)
+
+scores = model.evaluate(y_test, y_pred, y_proba=y_proba)
+
+model.plot(y_pred, actual=test_data, history=train_data, interactive=False)
+  ```
+</details>
+
+<details>
+  <summary>Time Series MetaFeatures Extract (click to expand)</summary>
+
+```python
+from hyperts.toolbox import metafeatures_from_timeseries
+from hyperts.datasets import load_random_univariate_forecast_dataset
+
+data = load_random_univariate_forecast_dataset()
+
+metafeatures = metafeatures_from_timeseries(x=data, timestamp='ds', scale_ts=True)
+```
+</details>
+
+**More detailed guides:** [EXAMPLES.](https://github.com/DataCanvasIO/HyperTS/tree/main/examples)
+
+## Key Features
+
+HyperTS supports the following features:
+
+**Multi-task Support:** Time series forecasting, classification, regression, and anomaly detection.
+
+**Multi-mode Support:** A large collection of TS models, including statistical models (Prophet, ARIMA, VAR, iForest, etc.), deep learning models(DeepAR, GRU, LSTNet, NBeats, VAE, etc.), and neural architecture search.
+
+**Multi-variate Support:** From univariate to multivariate time series.
+
+**Covariates Support:** Deep learning models support covariates as input featues for time series forecasting. 
+
+**Probabilistic Intervals Support:** Time series forecasting visualization can show confidence intervals.
+
+**Diversified Preprocessing:** Outlier clipping, missing value imputing, sequence smoothing, normalization, etc. 
+
+**Abundant Metrics:** A variety of performance metrics to evaluate results and guide models optimization, including MSE, SMAPE, Accuracy, F1-Score,etc. 
+
+**Powerful Search Strategies:** Adapting Grid Search, Monte Carlo Tree Search, Evolution Algorithm combined with a meta-learner to enable a powerful and effective TS pipeline.
+
+**Ensemble:** Refinement greedy emsemble glues the most powerful models.
+
+**Cross Validation:** Multiple time series cross-validation strategies ensure generalization ability.
+
+## Communication
+- If you wish to contribute to this project, please refer to [CONTRIBUTING](CONTRIBUTING.md).
+- If you have any question or idea, you can alse participate with our [Discussions Community](https://github.com/DataCanvasIO/HyperTS/discussions).
+
+## HyperTS Related Projects
+* [Hypernets](https://github.com/DataCanvasIO/Hypernets): A general automated machine learning (AutoML) framework.
+* [HyperGBM](https://github.com/DataCanvasIO/HyperGBM): A full pipeline AutoML tool integrated various GBM models.
+* [HyperDT/DeepTables](https://github.com/DataCanvasIO/DeepTables): An AutoDL tool for tabular data.
+* [HyperKeras](https://github.com/DataCanvasIO/HyperKeras): An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras.
+* [HyperBoard](https://github.com/DataCanvasIO/HyperBoard): A visualization tool for Hypernets.
+* [Cooka](https://github.com/DataCanvasIO/Cooka): Lightweight interactive AutoML system.
+
+![DataCanvas AutoML Toolkit](https://raw.githubusercontent.com/DataCanvasIO/HyperTS/main/docs/static/images/DAT_latest.png)
+
+## Citation
+
+If you use HyperTS in your research, please cite us as follows:
+
+   Xiaojing Zhang，Haifeng Wu，Jian Yang. 
+   **HyperTS: A Full-Pipeline Automated Time Series Analysis Toolkit.** https://github.com/DataCanvasIO/HyperTS. 2022. Version 0.2.x.
+
+BibTex:
+
+```
+@misc{hyperts,
+  author={Xiaojing Zhang，Haifeng Wu，Jian Yang.},
+  title={{HyperTS}: { A Full-Pipeline Automated Time Series Analysis Toolkit}},
+  howpublished={https://github.com/DataCanvasIO/HyperTS},
+  note={Version 0.2.x},
+  year={2022}
+}
+```
+
+## DataCanvas
+
+![datacanvas](https://raw.githubusercontent.com/DataCanvasIO/HyperTS/main/docs/static/images/dc_logo_1.png)
+
+HyperTS is an open source project created by [DataCanvas](https://www.datacanvas.com/).