Pushing the docs to dev/ for branch: master, commit 846e6a38b032c46490e3556cf9550a4563525e8d

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -123,7 +123,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "`tree_disp.axes_` is a numpy array container the axes used to draw the\npartial dependence plots. This can be passed to `mlp_disp` to have the same\naffect of drawing the plots on top of each other. Furthermore, the\n`mlp_disp.figure_` stores the figure, which allows for resizing the figure\nafter calling `plot`.\n\n"
+        "`tree_disp.axes_` is a numpy array container the axes used to draw the\npartial dependence plots. This can be passed to `mlp_disp` to have the same\naffect of drawing the plots on top of each other. Furthermore, the\n`mlp_disp.figure_` stores the figure, which allows for resizing the figure\nafter calling `plot`. In this case `tree_disp.axes_` has two dimensions, thus\n`plot` will only show the y label and y ticks on the left most plot.\n\n"
       ]
     },
     {

dev/_downloads/51a82a09a4aa0f703f69fb5d4f15104f/plot_partial_dependence_visualization_api.ipynb

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "print('Computing partial dependence plots...')\ntic = time()\n# We don't compute the 2-way PDP (5, 1) here, because it is a lot slower\n# with the brute method.\nfeatures = [0, 5, 1, 2]\nplot_partial_dependence(est, X_train, features, feature_names=names,\n                        n_jobs=3, grid_resolution=20)\nprint(\"done in {:.3f}s\".format(time() - tic))\nfig = plt.gcf()\nfig.suptitle('Partial dependence of house value on non-___location features\\n'\n             'for the California housing dataset, with MLPRegressor')\nfig.subplots_adjust(wspace=0.8, hspace=0.3)"
+        "print('Computing partial dependence plots...')\ntic = time()\n# We don't compute the 2-way PDP (5, 1) here, because it is a lot slower\n# with the brute method.\nfeatures = [0, 5, 1, 2]\nplot_partial_dependence(est, X_train, features, feature_names=names,\n                        n_jobs=3, grid_resolution=20)\nprint(\"done in {:.3f}s\".format(time() - tic))\nfig = plt.gcf()\nfig.suptitle('Partial dependence of house value on non-___location features\\n'\n             'for the California housing dataset, with MLPRegressor')\nfig.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "print('Computing partial dependence plots...')\ntic = time()\nfeatures = [0, 5, 1, 2, (5, 1)]\nplot_partial_dependence(est, X_train, features, feature_names=names,\n                        n_jobs=3, grid_resolution=20)\nprint(\"done in {:.3f}s\".format(time() - tic))\nfig = plt.gcf()\nfig.suptitle('Partial dependence of house value on non-___location features\\n'\n             'for the California housing dataset, with Gradient Boosting')\nfig.subplots_adjust(wspace=0.8, hspace=0.3)"
+        "print('Computing partial dependence plots...')\ntic = time()\nfeatures = [0, 5, 1, 2, (5, 1)]\nplot_partial_dependence(est, X_train, features, feature_names=names,\n                        n_jobs=3, grid_resolution=20)\nprint(\"done in {:.3f}s\".format(time() - tic))\nfig = plt.gcf()\nfig.suptitle('Partial dependence of house value on non-___location features\\n'\n             'for the California housing dataset, with Gradient Boosting')\nfig.subplots_adjust(wspace=0.4, hspace=0.3)"
       ]
     },
     {

dev/_downloads/5a693c97e821586539ab9d250762742c/plot_partial_dependence.ipynb

@@ -114,7 +114,8 @@
 # partial dependence plots. This can be passed to `mlp_disp` to have the same
 # affect of drawing the plots on top of each other. Furthermore, the
 # `mlp_disp.figure_` stores the figure, which allows for resizing the figure
-# after calling `plot`.
+# after calling `plot`. In this case `tree_disp.axes_` has two dimensions, thus
+# `plot` will only show the y label and y ticks on the left most plot.
 
 tree_disp.plot(line_kw={"label": "Decision Tree"})
 mlp_disp.plot(line_kw={"label": "Multi-layer Perceptron", "c": "red"},

dev/_downloads/781bb5a2dc85df6b75ee78d2eb118b0b/plot_partial_dependence_visualization_api.py

@@ -111,7 +111,7 @@
 fig = plt.gcf()
 fig.suptitle('Partial dependence of house value on non-___location features\n'
              'for the California housing dataset, with MLPRegressor')
-fig.subplots_adjust(wspace=0.8, hspace=0.3)
+fig.subplots_adjust(hspace=0.3)
 
 ##############################################################################
 # Partial Dependence computation for Gradient Boosting
@@ -150,7 +150,7 @@
 fig = plt.gcf()
 fig.suptitle('Partial dependence of house value on non-___location features\n'
              'for the California housing dataset, with Gradient Boosting')
-fig.subplots_adjust(wspace=0.8, hspace=0.3)
+fig.subplots_adjust(wspace=0.4, hspace=0.3)
 
 
 ##############################################################################