Pushing the docs to dev/ for branch: master, commit 0ab5c678bba02888b62b777b4c757e367b3458d5

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint(\"Mean squared error: %.2f\"\n      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\nplt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',\n         linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Jaques Grobler\n# License: BSD 3 clause\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# Load the diabetes dataset\ndiabetes = datasets.load_diabetes()\n\n\n# Use only one feature\ndiabetes_X = diabetes.data[:, np.newaxis, 2]\n\n# Split the data into training/testing sets\ndiabetes_X_train = diabetes_X[:-20]\ndiabetes_X_test = diabetes_X[-20:]\n\n# Split the targets into training/testing sets\ndiabetes_y_train = diabetes.target[:-20]\ndiabetes_y_test = diabetes.target[-20:]\n\n# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(diabetes_X_train, diabetes_y_train)\n\n# Make predictions using the testing set\ndiabetes_y_pred = regr.predict(diabetes_X_test)\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\n# The mean squared error\nprint(\"Mean squared error: %.2f\"\n      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n\n# Plot outputs\nplt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\nplt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -26,6 +26,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
 
 # Load the diabetes dataset
 diabetes = datasets.load_diabetes()
@@ -48,18 +49,20 @@
 # Train the model using the training sets
 regr.fit(diabetes_X_train, diabetes_y_train)
 
+# Make predictions using the testing set
+diabetes_y_pred = regr.predict(diabetes_X_test)
+
 # The coefficients
 print('Coefficients: \n', regr.coef_)
 # The mean squared error
 print("Mean squared error: %.2f"
-      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
+      % mean_squared_error(diabetes_y_test, diabetes_y_pred))
 # Explained variance score: 1 is perfect prediction
-print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
+print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
 
 # Plot outputs
 plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
-plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
-         linewidth=3)
+plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
 
 plt.xticks(())
 plt.yticks(())