Pushing the docs to dev/ for branch: master, commit 37a9d187f9eb4864fb52556e9f293f8ff4f698b7

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 57af4ad45c223405da86d36f09e97508
+config: f356e7646dfa6003e9bcdf9a6b0fd0ce
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the\nvalues of all other features (the complement features). Due to the limits\nof human perception the size of the target feature set must be small (usually,\none or two) thus the target features are usually chosen among the most\nimportant features\n(see :attr:`~sklearn.ensemble.GradientBoostingRegressor.feature_importances_`).\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the California\nhousing dataset. The example is taken from [1]_.\n\nThe plot shows four one-way and one two-way partial dependence plots.\nThe target variables for the one-way PDP are:\nmedian income (`MedInc`), avg. occupants per household (`AvgOccup`),\nmedian house age (`HouseAge`), and avg. rooms per household (`AveRooms`).\n\nWe can clearly see that the median house price shows a linear relationship\nwith the median income (top left) and that the house price drops when the\navg. occupants per household increases (top middle).\nThe top right plot shows that the house age in a district does not have\na strong influence on the (median) house price; so does the average rooms\nper household.\nThe tick marks on the x-axis represent the deciles of the feature values\nin the training data.\n\nPartial dependence plots with two target features enable us to visualize\ninteractions among them. The two-way partial dependence plot shows the\ndependence of median house price on joint values of house age and avg.\noccupants per household. We can clearly see an interaction between the\ntwo features:\nFor an avg. occupancy greater than two, the house price is nearly independent\nof the house age, whereas for values less than two there is a strong dependence\non age.\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman,\n    \"Elements of Statistical Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n\n"
+        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the\nvalues of all other features (the complement features). Due to the limits\nof human perception the size of the target feature set must be small (usually,\none or two) thus the target features are usually chosen among the most\nimportant features.\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.neural_network.MLPRegressor` and a\n:class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the\nCalifornia housing dataset. The example is taken from [1]_.\n\nThe plots show four 1-way and two 1-way partial dependence plots (ommitted for\n:class:`~sklearn.neural_network.MLPRegressor` due to computation time).\nThe target variables for the one-way PDP are: median income (`MedInc`),\naverage occupants per household (`AvgOccup`), median house age (`HouseAge`),\nand average rooms per household (`AveRooms`).\n\nWe can clearly see that the median house price shows a linear relationship\nwith the median income (top left) and that the house price drops when the\naverage occupants per household increases (top middle).\nThe top right plot shows that the house age in a district does not have\na strong influence on the (median) house price; so does the average rooms\nper household.\nThe tick marks on the x-axis represent the deciles of the feature values\nin the training data.\n\nWe also observe that :class:`~sklearn.neural_network.MLPRegressor` has much\nsmoother predictions than\n:class:`~sklearn.ensemble.GradientBoostingRegressor`. For the plots to be\ncomparable, it is necessary to subtract the average value of the target\n``y``: The 'recursion' method, used by default for\n:class:`~sklearn.ensemble.GradientBoostingRegressor`, does not account for\nthe initial predictor (in our case the average target). Setting the target\naverage to 0 avoids this bias.\n\nPartial dependence plots with two target features enable us to visualize\ninteractions among them. The two-way partial dependence plot shows the\ndependence of median house price on joint values of house age and average\noccupants per household. We can clearly see an interaction between the\ntwo features: for an average occupancy greater than two, the house price is\nnearly independent of the house age, whereas for values less than two there\nis a strong dependence on age.\n\nOn a third figure, we have plotted the same partial dependence plot, this time\nin 3 dimensions.\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman,\n    \"Elements of Statistical Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.ensemble.partial_dependence import plot_partial_dependence\nfrom sklearn.ensemble.partial_dependence import partial_dependence\nfrom sklearn.datasets.california_housing import fetch_california_housing\n\n\ndef main():\n    cal_housing = fetch_california_housing()\n\n    # split 80/20 train-test\n    X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,\n                                                        cal_housing.target,\n                                                        test_size=0.2,\n                                                        random_state=1)\n    names = cal_housing.feature_names\n\n    print(\"Training GBRT...\")\n    clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n                                    learning_rate=0.1, loss='huber',\n                                    random_state=1)\n    clf.fit(X_train, y_train)\n    print(\" done.\")\n\n    print('Convenience plot with ``partial_dependence_plots``')\n\n    features = [0, 5, 1, 2, (5, 1)]\n    fig, axs = plot_partial_dependence(clf, X_train, features,\n                                       feature_names=names,\n                                       n_jobs=3, grid_resolution=50)\n    fig.suptitle('Partial dependence of house value on nonlocation features\\n'\n                 'for the California housing dataset')\n    plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle\n\n    print('Custom 3d plot via ``partial_dependence``')\n    fig = plt.figure()\n\n    target_feature = (1, 5)\n    pdp, axes = partial_dependence(clf, target_feature,\n                                   X=X_train, grid_resolution=50)\n    XX, YY = np.meshgrid(axes[0], axes[1])\n    Z = pdp[0].reshape(list(map(np.size, axes))).T\n    ax = Axes3D(fig)\n    surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,\n                           cmap=plt.cm.BuPu, edgecolor='k')\n    ax.set_xlabel(names[target_feature[0]])\n    ax.set_ylabel(names[target_feature[1]])\n    ax.set_zlabel('Partial dependence')\n    #  pretty init view\n    ax.view_init(elev=22, azim=122)\n    plt.colorbar(surf)\n    plt.suptitle('Partial dependence of house value on median\\n'\n                 'age and average occupancy')\n    plt.subplots_adjust(top=0.9)\n\n    plt.show()\n\n\n# Needed on Windows because plot_partial_dependence uses multiprocessing\nif __name__ == '__main__':\n    main()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.inspection import partial_dependence\nfrom sklearn.inspection import plot_partial_dependence\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.datasets.california_housing import fetch_california_housing\n\n\ndef main():\n    cal_housing = fetch_california_housing()\n\n    X, y = cal_housing.data, cal_housing.target\n    names = cal_housing.feature_names\n\n    # Center target to avoid gradient boosting init bias: gradient boosting\n    # with the 'recursion' method does not account for the initial estimator\n    # (here the average target, by default)\n    y -= y.mean()\n\n    print(\"Training MLPRegressor...\")\n    est = MLPRegressor(activation='logistic')\n    est.fit(X, y)\n    print('Computing partial dependence plots...')\n    # We don't compute the 2-way PDP (5, 1) here, because it is a lot slower\n    # with the brute method.\n    features = [0, 5, 1, 2]\n    plot_partial_dependence(est, X, features, feature_names=names,\n                            n_jobs=3, grid_resolution=50)\n    fig = plt.gcf()\n    fig.suptitle('Partial dependence of house value on non-___location features\\n'\n                 'for the California housing dataset, with MLPRegressor')\n    plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle\n\n    print(\"Training GradientBoostingRegressor...\")\n    est = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n                                    learning_rate=0.1, loss='huber',\n                                    random_state=1)\n    est.fit(X, y)\n    print('Computing partial dependence plots...')\n    features = [0, 5, 1, 2, (5, 1)]\n    plot_partial_dependence(est, X, features, feature_names=names,\n                            n_jobs=3, grid_resolution=50)\n    fig = plt.gcf()\n    fig.suptitle('Partial dependence of house value on non-___location features\\n'\n                 'for the California housing dataset, with Gradient Boosting')\n    plt.subplots_adjust(top=0.9)\n\n    print('Custom 3d plot via ``partial_dependence``')\n    fig = plt.figure()\n\n    target_feature = (1, 5)\n    pdp, axes = partial_dependence(est, X, target_feature,\n                                   grid_resolution=50)\n    XX, YY = np.meshgrid(axes[0], axes[1])\n    Z = pdp[0].T\n    ax = Axes3D(fig)\n    surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,\n                           cmap=plt.cm.BuPu, edgecolor='k')\n    ax.set_xlabel(names[target_feature[0]])\n    ax.set_ylabel(names[target_feature[1]])\n    ax.set_zlabel('Partial dependence')\n    #  pretty init view\n    ax.view_init(elev=22, azim=122)\n    plt.colorbar(surf)\n    plt.suptitle('Partial dependence of house value on median\\n'\n                 'age and average occupancy, with Gradient Boosting')\n    plt.subplots_adjust(top=0.9)\n\n    plt.show()\n\n\n# Needed on Windows because plot_partial_dependence uses multiprocessing\nif __name__ == '__main__':\n    main()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -8,35 +8,47 @@
 values of all other features (the complement features). Due to the limits
 of human perception the size of the target feature set must be small (usually,
 one or two) thus the target features are usually chosen among the most
-important features
-(see :attr:`~sklearn.ensemble.GradientBoostingRegressor.feature_importances_`).
+important features.
 
 This example shows how to obtain partial dependence plots from a
-:class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the California
-housing dataset. The example is taken from [1]_.
+:class:`~sklearn.neural_network.MLPRegressor` and a
+:class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the
+California housing dataset. The example is taken from [1]_.
 
-The plot shows four one-way and one two-way partial dependence plots.
-The target variables for the one-way PDP are:
-median income (`MedInc`), avg. occupants per household (`AvgOccup`),
-median house age (`HouseAge`), and avg. rooms per household (`AveRooms`).
+The plots show four 1-way and two 1-way partial dependence plots (ommitted for
+:class:`~sklearn.neural_network.MLPRegressor` due to computation time).
+The target variables for the one-way PDP are: median income (`MedInc`),
+average occupants per household (`AvgOccup`), median house age (`HouseAge`),
+and average rooms per household (`AveRooms`).
 
 We can clearly see that the median house price shows a linear relationship
 with the median income (top left) and that the house price drops when the
-avg. occupants per household increases (top middle).
+average occupants per household increases (top middle).
 The top right plot shows that the house age in a district does not have
 a strong influence on the (median) house price; so does the average rooms
 per household.
 The tick marks on the x-axis represent the deciles of the feature values
 in the training data.
 
+We also observe that :class:`~sklearn.neural_network.MLPRegressor` has much
+smoother predictions than
+:class:`~sklearn.ensemble.GradientBoostingRegressor`. For the plots to be
+comparable, it is necessary to subtract the average value of the target
+``y``: The 'recursion' method, used by default for
+:class:`~sklearn.ensemble.GradientBoostingRegressor`, does not account for
+the initial predictor (in our case the average target). Setting the target
+average to 0 avoids this bias.
+
 Partial dependence plots with two target features enable us to visualize
 interactions among them. The two-way partial dependence plot shows the
-dependence of median house price on joint values of house age and avg.
+dependence of median house price on joint values of house age and average
 occupants per household. We can clearly see an interaction between the
-two features:
-For an avg. occupancy greater than two, the house price is nearly independent
-of the house age, whereas for values less than two there is a strong dependence
-on age.
+two features: for an average occupancy greater than two, the house price is
+nearly independent of the house age, whereas for values less than two there
+is a strong dependence on age.
+
+On a third figure, we have plotted the same partial dependence plot, this time
+in 3 dimensions.
 
 .. [1] T. Hastie, R. Tibshirani and J. Friedman,
     "Elements of Statistical Learning Ed. 2", Springer, 2009.
@@ -48,51 +60,62 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-
 from mpl_toolkits.mplot3d import Axes3D
 
-from sklearn.model_selection import train_test_split
+from sklearn.inspection import partial_dependence
+from sklearn.inspection import plot_partial_dependence
 from sklearn.ensemble import GradientBoostingRegressor
-from sklearn.ensemble.partial_dependence import plot_partial_dependence
-from sklearn.ensemble.partial_dependence import partial_dependence
+from sklearn.neural_network import MLPRegressor
 from sklearn.datasets.california_housing import fetch_california_housing
 
 
 def main():
     cal_housing = fetch_california_housing()
 
-    # split 80/20 train-test
-    X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,
-                                                        cal_housing.target,
-                                                        test_size=0.2,
-                                                        random_state=1)
+    X, y = cal_housing.data, cal_housing.target
     names = cal_housing.feature_names
 
-    print("Training GBRT...")
-    clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,
+    # Center target to avoid gradient boosting init bias: gradient boosting
+    # with the 'recursion' method does not account for the initial estimator
+    # (here the average target, by default)
+    y -= y.mean()
+
+    print("Training MLPRegressor...")
+    est = MLPRegressor(activation='logistic')
+    est.fit(X, y)
+    print('Computing partial dependence plots...')
+    # We don't compute the 2-way PDP (5, 1) here, because it is a lot slower
+    # with the brute method.
+    features = [0, 5, 1, 2]
+    plot_partial_dependence(est, X, features, feature_names=names,
+                            n_jobs=3, grid_resolution=50)
+    fig = plt.gcf()
+    fig.suptitle('Partial dependence of house value on non-___location features\n'
+                 'for the California housing dataset, with MLPRegressor')
+    plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle
+
+    print("Training GradientBoostingRegressor...")
+    est = GradientBoostingRegressor(n_estimators=100, max_depth=4,
                                     learning_rate=0.1, loss='huber',
                                     random_state=1)
-    clf.fit(X_train, y_train)
-    print(" done.")
-
-    print('Convenience plot with ``partial_dependence_plots``')
-
+    est.fit(X, y)
+    print('Computing partial dependence plots...')
     features = [0, 5, 1, 2, (5, 1)]
-    fig, axs = plot_partial_dependence(clf, X_train, features,
-                                       feature_names=names,
-                                       n_jobs=3, grid_resolution=50)
-    fig.suptitle('Partial dependence of house value on nonlocation features\n'
-                 'for the California housing dataset')
-    plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle
+    plot_partial_dependence(est, X, features, feature_names=names,
+                            n_jobs=3, grid_resolution=50)
+    fig = plt.gcf()
+    fig.suptitle('Partial dependence of house value on non-___location features\n'
+                 'for the California housing dataset, with Gradient Boosting')
+    plt.subplots_adjust(top=0.9)
 
     print('Custom 3d plot via ``partial_dependence``')
     fig = plt.figure()
 
     target_feature = (1, 5)
-    pdp, axes = partial_dependence(clf, target_feature,
-                                   X=X_train, grid_resolution=50)
+    pdp, axes = partial_dependence(est, X, target_feature,
+                                   grid_resolution=50)
     XX, YY = np.meshgrid(axes[0], axes[1])
-    Z = pdp[0].reshape(list(map(np.size, axes))).T
+    Z = pdp[0].T
     ax = Axes3D(fig)
     surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
                            cmap=plt.cm.BuPu, edgecolor='k')
@@ -103,7 +126,7 @@ def main():
     ax.view_init(elev=22, azim=122)
     plt.colorbar(surf)
     plt.suptitle('Partial dependence of house value on median\n'
-                 'age and average occupancy')
+                 'age and average occupancy, with Gradient Boosting')
     plt.subplots_adjust(top=0.9)
 
     plt.show()