Pushing the docs to 0.22/ for branch: 0.22.X, commit 97b8b09416de2e8fce806192f52f3233df5782af

Author: sklearn-ci

0.22/_downloads/00a5ddd24a9ad44708f4ab3b157ef0ff/plot_stack_predictors.py

@@ -99,7 +99,7 @@ def plot_regression_results(ax, y_true, y_pred, title, scores, elapsed_time):
     score = cross_validate(est, X, y,
                            scoring=['r2', 'neg_mean_absolute_error'],
                            n_jobs=-1, verbose=0)
-    elapsed_time = time.time() - time.time()
+    elapsed_time = time.time() - start_time
 
     y_pred = cross_val_predict(est, X, y, n_jobs=-1, verbose=0)
     plot_regression_results(

0.22/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\nimport numpy as np\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import cross_validate, cross_val_predict\n\nX, y = load_boston(return_X_y=True)\n\nfig, axs = plt.subplots(2, 2, figsize=(9, 7))\naxs = np.ravel(axs)\n\nfor ax, (name, est) in zip(axs, estimators + [('Stacking Regressor',\n                                               stacking_regressor)]):\n    start_time = time.time()\n    score = cross_validate(est, X, y,\n                           scoring=['r2', 'neg_mean_absolute_error'],\n                           n_jobs=-1, verbose=0)\n    elapsed_time = time.time() - time.time()\n\n    y_pred = cross_val_predict(est, X, y, n_jobs=-1, verbose=0)\n    plot_regression_results(\n        ax, y, y_pred,\n        name,\n        (r'$R^2={:.2f} \\pm {:.2f}$' + '\\n' + r'$MAE={:.2f} \\pm {:.2f}$')\n        .format(np.mean(score['test_r2']),\n                np.std(score['test_r2']),\n                -np.mean(score['test_neg_mean_absolute_error']),\n                np.std(score['test_neg_mean_absolute_error'])),\n        elapsed_time)\n\nplt.suptitle('Single predictors versus stacked predictors')\nplt.tight_layout()\nplt.subplots_adjust(top=0.9)\nplt.show()"
+        "import time\nimport numpy as np\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import cross_validate, cross_val_predict\n\nX, y = load_boston(return_X_y=True)\n\nfig, axs = plt.subplots(2, 2, figsize=(9, 7))\naxs = np.ravel(axs)\n\nfor ax, (name, est) in zip(axs, estimators + [('Stacking Regressor',\n                                               stacking_regressor)]):\n    start_time = time.time()\n    score = cross_validate(est, X, y,\n                           scoring=['r2', 'neg_mean_absolute_error'],\n                           n_jobs=-1, verbose=0)\n    elapsed_time = time.time() - start_time\n\n    y_pred = cross_val_predict(est, X, y, n_jobs=-1, verbose=0)\n    plot_regression_results(\n        ax, y, y_pred,\n        name,\n        (r'$R^2={:.2f} \\pm {:.2f}$' + '\\n' + r'$MAE={:.2f} \\pm {:.2f}$')\n        .format(np.mean(score['test_r2']),\n                np.std(score['test_r2']),\n                -np.mean(score['test_neg_mean_absolute_error']),\n                np.std(score['test_neg_mean_absolute_error'])),\n        elapsed_time)\n\nplt.suptitle('Single predictors versus stacked predictors')\nplt.tight_layout()\nplt.subplots_adjust(top=0.9)\nplt.show()"
       ]
     },
     {