diff --git a/notebooks/playground-weighted.ipynb b/notebooks/playground-weighted.ipynb new file mode 100644 index 0000000..bbf0590 --- /dev/null +++ b/notebooks/playground-weighted.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# load data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/mnt/lustre01/pf/g/g260086/Python/SeasonalPred/notebooks'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDA not available\n", + "swvl1.shape (1332, 37, 42)\n", + "t2m.shape (1332, 37, 42)\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "from netCDF4 import Dataset\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "\n", + "from predictability_utils.utils import helpers, io\n", + "from predictability_utils.methods.lrlin_method import run_lrlin\n", + "from predictability_utils.methods.cca_method_Copy1 import run_cca\n", + "\n", + "import torch\n", + "torch.manual_seed(42)\n", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + " torch.set_default_tensor_type(\"torch.cuda.FloatTensor\")\n", + "else:\n", + " print(\"CUDA not available\")\n", + " device = torch.device(\"cpu\")\n", + " torch.set_default_tensor_type(\"torch.FloatTensor\")\n", + "\n", + " \n", + "#root_data = '/gpfs/work/nonnenma/data/forecast_predictability/pyrina/'\n", + "root_data = '/work/gg0304/g260086/HZG-ML-work/Data'\n", + "root_results = '/work/gg0304/g260086/HZG-ML-work/Results'\n", + "\n", + "n_latents = 5\n", + "train_months, test_months = [2,3,4], [5,6,7]\n", + "train_years = np.arange(0, 46)\n", + "test_years = np.arange(71, 111)\n", + "n_test_years = len(test_years)\n", + "n_bt = 500\n", + "\n", + "# Volumetric soil water layer 1 (EU) ANOMALIES\n", + "source_data, _ = io.data_load('swvl1', 'EU', 'anomalies', root_data)\n", + "\n", + "# Temperature at 2m (EU) ANOMALIES\n", + "target_data, _ = io.data_load('t2m', 'EU', 'anomalies', root_data)\n", + "\n", + "# training data time stamps and map shape\n", + "nc_fn = root_data + \"/t2m_ERA20c_monthly_1900-2010.EU.mv.nc\"\n", + "ts = Dataset(nc_fn, 'r').variables['time'].__array__().data\n", + "t2m_eu = Dataset(nc_fn, 'r').variables['t2m'].__array__().data\n", + "map_shape = t2m_eu.shape[1:]\n", + "\n", + "#lat lon data\n", + "lat = Dataset(nc_fn, 'r').variables['latitude'].__array__().data\n", + "lon = Dataset(nc_fn, 'r').variables['longitude'].__array__().data\n", + "\n", + "idcs = helpers.split_train_data(train_months, test_months, train_years, test_years)\n", + "idx_source_train, idx_target_train, idx_source_test, idx_target_test = idcs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# recreate CCA analysis\n", + "- Canonical correlation analysis to identify subspaces $U$, $V$ in source space $X$ and target space $Y$, respectively, such that $(UX)_i$ and $(VY)_i$ are maximally correlated.\n", + "- in a second step, establish a (linear) mapping from $VY \\approx Q UX$ to predict $VY$ from $UX$.\n", + "- predict new $Y$ from $Y \\approx V^\\dagger Q UX$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1914 - 1959\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1915 - 1960\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1916 - 1961\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1917 - 1962\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1918 - 1963\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1919 - 1964\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1920 - 1965\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1921 - 1966\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1922 - 1967\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1923 - 1968\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1924 - 1969\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "calibration period: 1925 - 1970\n", + "test period: 1971 - 2010\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# train an ensemble of models from a sliding window of training years starting in 1914\n", + "model_preds, model_targets = [], []\n", + "for calibration_offset in range(14,26):\n", + " \n", + " calibration_period = train_years + calibration_offset\n", + " \n", + " print(f'calibration period: {1900+calibration_period[0]} - {1900+calibration_period[-1]}' )\n", + " print(f'test period: {1900+test_years[0]} - {1900+test_years[-1]}' )\n", + " \n", + " idcs = helpers.split_train_data(train_months, test_months, calibration_period, test_years)\n", + " idx_source_train, idx_target_train, idx_source_test, idx_target_test = idcs\n", + " \n", + " anomaly_corrs, params = run_cca(source_data, target_data, n_latents, idcs, train_months, test_months, train_years, test_years, if_plot=True, map_shape=map_shape)\n", + " model_preds.append(params['out_pred']) # grab predictions for later averaging\n", + " model_targets.append(params['out_true']) #" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# compute an ensemble-averaged prediction\n", + "from predictability_utils.utils import viz\n", + "\n", + "# test sets should be the same\n", + "assert np.all( [np.all(out_true==model_targets[0]) for out_true in model_targets] ) \n", + "\n", + "# average prediction\n", + "mean_pred = np.mean(np.stack(model_preds), axis=0)\n", + "\n", + "anomaly_corrs = helpers.compute_anomaly_corrs(model_targets[0], mean_pred)\n", + "viz.visualize_anomaly_corrs(anomaly_corrs.reshape(*map_shape)) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(500, 1554)\n" + ] + } + ], + "source": [ + "#calculate the significance of the anomaly_corrs with bootstrapping\n", + "#build a distribution for each grid point\n", + "#the anomaly_corrs found above will be significant if they don't fall in this distribution\n", + "import random\n", + "corrs_perm = [ ]\n", + "pred = mean_pred\n", + "orig = model_targets[0]\n", + "\n", + "for _ in range(n_bt):\n", + " pred_perm = np.random.permutation(pred)\n", + " n_corr = helpers.compute_anomaly_corrs(pred_perm,orig)\n", + " corrs_perm.append(n_corr)\n", + "\n", + "print(np.asarray(corrs_perm).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1554)\n", + "(1, 1554)\n" + ] + } + ], + "source": [ + "#calculate distribution's percentiles\n", + "PrecLow = [ ]\n", + "PrecUp = [ ]\n", + "\n", + "corr = np.asarray(corrs_perm)\n", + "pl = np.percentile(corr, 2.5, axis=0)\n", + "pu = np.percentile(corr, 97.5, axis=0)\n", + "PrecLow.append(pl)\n", + "PrecUp.append(pu)\n", + "\n", + "print(np.asarray(PrecLow).shape); print(np.asarray(PrecUp).shape); " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(37, 42)\n" + ] + } + ], + "source": [ + "lp = np.asarray(PrecLow)\n", + "up = np.asarray(PrecUp)\n", + "up.shape\n", + "#Evaluate if loo_corrs are in the distribution\n", + "\n", + "mask_lp = np.less(anomaly_corrs,lp)\n", + "mask_up = np.greater(anomaly_corrs,up)\n", + "mask = np.add(mask_lp.astype(int),mask_up.astype(int))\n", + "print(mask.reshape(*map_shape).shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "viz.visualize_anomaly_corrs(mask.reshape(*map_shape)) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40, 1554)\n", + "(40, 1554)\n", + "40\n" + ] + } + ], + "source": [ + "print(mean_pred.shape); print(model_targets[0].shape); print(len(model_targets[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40, 1554)\n" + ] + } + ], + "source": [ + "#Method: leave-one-out cross-validated correlation (loo-correlation)\n", + "#correlate the time series but this time Leaving One year Out\n", + "\n", + "loo_corrs = [ ]\n", + "for i in range(0,n_test_years):\n", + " loo_pred = mean_pred[np.arange(len(mean_pred))!=i]\n", + " loo_orig = model_targets[0][np.arange(len(model_targets[0]))!=i]\n", + " \n", + " n_corr = helpers.compute_anomaly_corrs(loo_pred,loo_orig)\n", + " loo_corrs.append(n_corr)\n", + " #print(n_correl.shape); print(loo_pred.shape); print(loo_orig.shape);\n", + "print(np.asarray(loo_corrs).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loo_corrs_mean = np.mean(np.stack(loo_corrs), axis=0)\n", + "viz.visualize_anomaly_corrs(loo_corrs_mean.reshape(*map_shape)) \n", + "np.array_equal(np.asarray(loo_corrs),anomaly_corrs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "#correlate the permuted time series using loo-correlation\n", + "#build a distribution for each grid point\n", + "#the correlations loo_corrs will be significant if they don't fall in this distribution\n", + "loo_corrs_perm = [ ]\n", + "for i in range(0,n_test_years):\n", + " loo_pred = mean_pred[np.arange(len(mean_pred))!=i]\n", + " loo_orig = model_targets[0][np.arange(len(model_targets[0]))!=i]\n", + " for _ in range(n_bt):\n", + " loo_pred_perm = np.random.permutation(loo_pred)\n", + " n_corr = helpers.compute_anomaly_corrs(loo_pred_perm,loo_orig)\n", + " loo_corrs_perm.append(n_corr)\n", + " #print(n_correl.shape); print(loo_pred.shape); print(loo_orig.shape);\n", + "print(np.asarray(loo_corrs_perm).shape)\n", + "loo_corrs_p = np.asarray(loo_corrs_perm).reshape(n_test_years,n_bt,n_corr.size)\n", + "print(loo_corrs_p.shape)\n", + "\n", + "OutFile = root_results + '/Correl_Distribution_weighted_swvl1'\n", + "np.save(OutFile,loo_corrs_p)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loo_corrs_dist = np.load(root_results + '/Correl_Distribution_weighted_swvl1.npy')\n", + "loo_corrs_dist.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#calculate the percentiles\n", + "\n", + "loo_PrecLow = [ ]\n", + "loo_PrecUp = [ ]\n", + "\n", + "for i in range(0,n_test_years):\n", + " loo_corr = loo_corrs_dist[i,:,:]\n", + " #print(loo_corr.shape); print(loo_corr[0][0]);\n", + " PrecLow = np.percentile(loo_corr, 2.5, axis=0)\n", + " PrecUp = np.percentile(loo_corr, 97.5, axis=0)\n", + " loo_PrecLow.append(PrecLow)\n", + " loo_PrecUp.append(PrecUp)\n", + " \n", + " #print(PrecUp.shape); print(PrecLow.shape); print(PrecUp[0]); print(PrecLow[0])\n", + "print(np.asarray(loo_PrecLow).shape); print(np.asarray(loo_PrecUp).shape); " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loo_corrs_or = np.asarray(loo_corrs)\n", + "lp = np.asarray(loo_PrecLow)\n", + "up = np.asarray(loo_PrecUp)\n", + "up.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Evaluate if loo_corrs_or are in the distribution\n", + "mask_lp = np.zeros(loo_corrs_or.shape)\n", + "mask_up = np.zeros(loo_corrs_or.shape)\n", + "mask = np.zeros(loo_corrs_or.shape)\n", + "\n", + "for i in range(0,n_test_years):\n", + " \n", + " mask_lp[i,:] = np.less(loo_corrs_or[i,:],lp[i,:])\n", + " mask_up[i,:] = np.greater(loo_corrs_or[i,:],up[i,:])\n", + " mask[i,:] = np.add(mask_lp[i,:],mask_up[i,:])\n", + "\n", + "##show significance only for the regions which were significant in every loo_corrs_or iteration\n", + "sign_loo_corrs_prod = np.prod(mask,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "viz.visualize_anomaly_corrs(sign_loo_corrs_prod.reshape(*map_shape)) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compare against single model that learns from full training data\n", + "\n", + "calibration_period = np.arange(14, 71) # training period 1914-1970\n", + "\n", + "idcs = helpers.split_train_data(train_months, test_months, calibration_period, test_years)\n", + "idx_source_train, idx_target_train, idx_source_test, idx_target_test = idcs\n", + "anomaly_corrs, params = run_cca(source_data, target_data, n_latents, idcs, if_plot=True, map_shape=map_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "calibration_period = np.arange(14, 70) # training period 1914-1969\n", + "\n", + "idcs = helpers.split_train_data(train_months, test_months, calibration_period, test_years)\n", + "idx_source_train, idx_target_train, idx_source_test, idx_target_test = idcs\n", + "anomaly_corrs, params = run_cca(source_data, target_data, n_latents, idcs, if_plot=True, map_shape=map_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# simple low-rank linear prediction (pixel MSEs) \n", + "\n", + "- set up simple model $Y = W X$ with $W = U V$\n", + "- low-rank: if $Y \\in \\mathbb{R}^N, X \\in \\mathbb{R}^M$, then $W \\in \\mathbb{R}^{N \\times M}$, but $U \\in \\mathbb{R}^{N \\times k}, V \\in \\mathbb{R}^{k \\times M}$ with $k << M,N$ !\n", + "- low-rank structure saves us parameters: $M N$ parameters in $W$, but only $N k + k M$ in $U$ and $V$, helps prevent overfitting on low samples size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "anomaly_corrs, params = run_lrlin(source_data, target_data, n_latents, idcs, if_plot=True, map_shape=map_shape,\n", + " n_epochs=2000, lr=1e-1, batch_size=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# debug" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/predictability_utils/methods/cca_method_Copy1.py b/predictability_utils/methods/cca_method_Copy1.py new file mode 100644 index 0000000..0753927 --- /dev/null +++ b/predictability_utils/methods/cca_method_Copy1.py @@ -0,0 +1,73 @@ +import numpy as np +from sklearn.cross_decomposition import CCA + +from predictability_utils.utils import viz, helpers +import matplotlib.pyplot as plt + + +def run_cca(source_data, target_data, n_latents, idcs, train_months, test_months, train_years, test_years, if_plot=False, map_shape=None): + + T = source_data.shape[0] + assert T == target_data.shape[0] + idx_source_train, idx_target_train, idx_source_test, idx_target_test = idcs + + # predict T2ms in Summer from soil moisture levels in Spring (1900 - 1950) + X = source_data.reshape(T, -1)[idx_source_train,:].reshape(len(train_months)*len(train_years),-1) + Y1 = target_data.reshape(T, -1)[idx_target_train,:].mean(axis=0) + Y = np.stack((Y1,Y1,Y1),axis=0).reshape(len(train_months)*len(train_years),-1) + + # fit CCA-based model + ccam = CCA_method(n_latents=n_latents) + ccam.fit(X,Y) + + # predict T2ms for test data (1951 - 2010) + X_f = source_data.reshape(T, -1)[idx_source_test,:].reshape(len(test_months)*len(test_years),-1) + out_pred1 = ccam.predict(X_f) + out_pred = out_pred1.reshape(len(test_months),len(test_years),-1).mean(axis=0) + + # evaluate prediction performance + out_true = target_data.reshape(T, -1)[idx_target_test,:].mean(axis=0) + anomaly_corrs = helpers.compute_anomaly_corrs(out_true, out_pred) + + # visualize anomaly correlations + if if_plot: + viz.visualize_anomaly_corrs(anomaly_corrs.reshape(*map_shape)) + + params = {'U' : ccam._cca.y_loadings_, 'V': ccam._cca.x_rotations_, 'Q': ccam._Q, + 'out_pred' : out_pred, 'out_true' : out_true } + + return anomaly_corrs, params + +class CCA_method(): + + def __init__(self, n_latents): + + self._n_latents = n_latents + self._cca = CCA(n_components=n_latents, + scale=False, max_iter=10000, tol=1e-8) + self._Q = np.eye(self._n_latents) + + def fit(self, X, Y): + + # projections U'X, V'Y such that U'X and V'Y are maximally correlated + self._cca.fit(X, Y) + + # get time-course of projected data + UX, VY = self._cca.transform(X, Y) + + # learn linear regression VY = UX * Q + # (Q will be optimal in least-squares sense) + self._Q = np.linalg.pinv(UX).dot(VY) + + def predict(self, X): + + # transform source data into latent space + UX = self._cca.transform(X) + + # predict latent activity in target space + QUX = UX.dot(self._Q) + + # predict observed activity in target space + Ypred = QUX.dot(self._cca.y_loadings_.T) + + return Ypred diff --git a/predictability_utils/utils/helpers.py b/predictability_utils/utils/helpers.py index fdcb648..17cada4 100644 --- a/predictability_utils/utils/helpers.py +++ b/predictability_utils/utils/helpers.py @@ -11,16 +11,18 @@ def compute_anomaly_corrs(out_true, out_pred): def split_train_data(ts, y_train, train_months, test_months): y_total = len(ts)//12 m_train = y_train * 12 + + mm_train = len(ts) - (40*12) #choose the last 40 years idx_target_train = np.zeros((len(train_months), y_train), dtype=np.int) idx_source_train = np.zeros((len(train_months), y_train), dtype=np.int) - idx_target_test = np.zeros((len(test_months), y_total - y_train), dtype=np.int) - idx_source_test = np.zeros((len(test_months), y_total - y_train), dtype=np.int) + idx_target_test = np.zeros((len(test_months), y_test), dtype=np.int) + idx_source_test = np.zeros((len(test_months), y_test), dtype=np.int) for i,m in enumerate(train_months): idx_source_train[i,:] = np.arange(m, m_train, 12) - idx_source_test[i,:] = np.arange(m_train+m, len(ts), 12) + idx_source_test[i,:] = np.arange(mm_train+m, len(ts), 12) for i,m in enumerate(test_months): idx_target_train[i,:] = np.arange(m, m_train, 12) - idx_target_test[i,:] = np.arange(m_train+m, len(ts), 12) + idx_target_test[i,:] = np.arange(mm_train+m, len(ts), 12) return idx_source_train, idx_target_train, idx_source_test, idx_target_test