Pushing the docs to dev/ for branch: master, commit 97185ecd3ec0bc8409781f3259b2436ba2a075bb

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Advanced Plotting With Partial Dependence\n\nThe :func:`~sklearn.inspection.plot_partial_dependence` function returns a\n:class:`~sklearn.inspection.PartialDependenceDisplay` object that can be used\nfor plotting without needing to recalculate the partial dependence. In this\nexample, we show how to plot partial dependence plots and how to quickly\ncustomize the plot with the visualization API.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>See also `sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`</p></div>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_boston\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.inspection import plot_partial_dependence"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Train models on the boston housing price dataset\n================================================\n\nFirst, we train a decision tree and a multi-layer perceptron on the boston\nhousing price dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "boston = load_boston()\nX, y = boston.data, boston.target\nfeature_names = boston.feature_names\n\ntree = DecisionTreeRegressor()\nmlp = make_pipeline(StandardScaler(),\n                    MLPRegressor(hidden_layer_sizes=(100, 100),\n                                 tol=1e-2, max_iter=500, random_state=0))\ntree.fit(X, y)\nmlp.fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plotting partial dependence for two features\n============================================\n\nWe plot partial dependence curves for features \"LSTAT\" and \"RM\" for\nthe decision tree. With two features,\n:func:`~sklearn.inspection.plot_partial_dependence` expects to plot two\ncurves. Here the plot function place a grid of two plots using the space\ndefined by `ax` .\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Decision Tree\")\ntree_disp = plot_partial_dependence(tree, X, [\"LSTAT\", \"RM\"],\n                                    feature_names=feature_names, ax=ax)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The partial depdendence curves can be plotted for the multi-layer perceptron.\nIn this case, `line_kw` is passed to\n:func:`~sklearn.inspection.plot_partial_dependence` to change the color of\nthe curve.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Multi-layer Perceptron\")\nmlp_disp = plot_partial_dependence(mlp, X, [\"LSTAT\", \"RM\"],\n                                   feature_names=feature_names, ax=ax,\n                                   line_kw={\"c\": \"red\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plotting partial dependence of the two models together\n======================================================\n\nThe `tree_disp` and `mlp_disp`\n:class:`~sklearn.inspection.PartialDependenceDisplay` objects contain all the\ncomputed information needed to recreate the partial dependence curves. This\nmeans we can easily create additional plots without needing to recompute the\ncurves.\n\nOne way to plot the curves is to place them in the same figure, with the\ncurves of each model on each row. First, we create a figure with two axes\nwithin two rows and one column. The two axes are passed to the\n:func:`~sklearn.inspection.PartialDependenceDisplay.plot` functions of\n`tree_disp` and `mlp_disp`. The given axes will be used by the plotting\nfunction to draw the partial dependence. The resulting plot places the\ndecision tree partial dependence curves in the first row of the\nmulti-layer perceptron in the second row.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))\ntree_disp.plot(ax=ax1)\nax1.set_title(\"Decision Tree\")\nmlp_disp.plot(ax=ax2, line_kw={\"c\": \"red\"})\nax2.set_title(\"Multi-layer Perceptron\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Another way to compare the curves is to plot them on top of each other. Here,\nwe create a figure with one row and two columns. The axes are passed into the\n:func:`~sklearn.inspection.PartialDependenceDisplay.plot` function as a list,\nwhich will plot the partial dependence curves of each model on the same axes.\nThe length of the axes list must be equal to the number of plots drawn.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Sets this image as the thumbnail for sphinx gallery\n# sphinx_gallery_thumbnail_number = 4\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))\ntree_disp.plot(ax=[ax1, ax2], line_kw={\"label\": \"Decision Tree\"})\nmlp_disp.plot(ax=[ax1, ax2], line_kw={\"label\": \"Multi-layer Perceptron\",\n                                      \"c\": \"red\"})\nax1.legend()\nax2.legend()"
+      ]
+    },
+    {
+      "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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "tree_disp.plot(line_kw={\"label\": \"Decision Tree\"})\nmlp_disp.plot(line_kw={\"label\": \"Multi-layer Perceptron\", \"c\": \"red\"},\n              ax=tree_disp.axes_)\ntree_disp.figure_.set_size_inches(10, 6)\ntree_disp.axes_[0, 0].legend()\ntree_disp.axes_[0, 1].legend()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plotting partial dependence for one feature\n===========================================\n\nHere, we plot the partial dependence curves for a single feature, \"LSTAT\", on\nthe same axes. In this case, `tree_disp.axes_` is passed into the second\nplot function.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "tree_disp = plot_partial_dependence(tree, X, [\"LSTAT\"],\n                                    feature_names=feature_names)\nmlp_disp = plot_partial_dependence(mlp, X, [\"LSTAT\"],\n                                   feature_names=feature_names,\n                                   ax=tree_disp.axes_, line_kw={\"c\": \"red\"})"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

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.tight_layout(rect=[0, 0.03, 1, 0.95])"
+        "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)"
       ]
     },
     {
@@ -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.tight_layout(rect=[0, 0.03, 1, 0.95])"
+        "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)"
       ]
     },
     {

dev/_downloads/5a693c97e821586539ab9d250762742c/plot_partial_dependence.ipynb

@@ -0,0 +1,139 @@
+"""
+=========================================
+Advanced Plotting With Partial Dependence
+=========================================
+The :func:`~sklearn.inspection.plot_partial_dependence` function returns a
+:class:`~sklearn.inspection.PartialDependenceDisplay` object that can be used
+for plotting without needing to recalculate the partial dependence. In this
+example, we show how to plot partial dependence plots and how to quickly
+customize the plot with the visualization API.
+
+.. note::
+
+    See also :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
+
+"""
+print(__doc__)
+
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_boston
+from sklearn.neural_network import MLPRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.inspection import plot_partial_dependence
+
+
+##############################################################################
+# Train models on the boston housing price dataset
+# ================================================
+#
+# First, we train a decision tree and a multi-layer perceptron on the boston
+# housing price dataset.
+
+boston = load_boston()
+X, y = boston.data, boston.target
+feature_names = boston.feature_names
+
+tree = DecisionTreeRegressor()
+mlp = make_pipeline(StandardScaler(),
+                    MLPRegressor(hidden_layer_sizes=(100, 100),
+                                 tol=1e-2, max_iter=500, random_state=0))
+tree.fit(X, y)
+mlp.fit(X, y)
+
+
+##############################################################################
+# Plotting partial dependence for two features
+# ============================================
+#
+# We plot partial dependence curves for features "LSTAT" and "RM" for
+# the decision tree. With two features,
+# :func:`~sklearn.inspection.plot_partial_dependence` expects to plot two
+# curves. Here the plot function place a grid of two plots using the space
+# defined by `ax` .
+fig, ax = plt.subplots(figsize=(12, 6))
+ax.set_title("Decision Tree")
+tree_disp = plot_partial_dependence(tree, X, ["LSTAT", "RM"],
+                                    feature_names=feature_names, ax=ax)
+
+##############################################################################
+# The partial depdendence curves can be plotted for the multi-layer perceptron.
+# In this case, `line_kw` is passed to
+# :func:`~sklearn.inspection.plot_partial_dependence` to change the color of
+# the curve.
+fig, ax = plt.subplots(figsize=(12, 6))
+ax.set_title("Multi-layer Perceptron")
+mlp_disp = plot_partial_dependence(mlp, X, ["LSTAT", "RM"],
+                                   feature_names=feature_names, ax=ax,
+                                   line_kw={"c": "red"})
+
+##############################################################################
+# Plotting partial dependence of the two models together
+# ======================================================
+#
+# The `tree_disp` and `mlp_disp`
+# :class:`~sklearn.inspection.PartialDependenceDisplay` objects contain all the
+# computed information needed to recreate the partial dependence curves. This
+# means we can easily create additional plots without needing to recompute the
+# curves.
+#
+# One way to plot the curves is to place them in the same figure, with the
+# curves of each model on each row. First, we create a figure with two axes
+# within two rows and one column. The two axes are passed to the
+# :func:`~sklearn.inspection.PartialDependenceDisplay.plot` functions of
+# `tree_disp` and `mlp_disp`. The given axes will be used by the plotting
+# function to draw the partial dependence. The resulting plot places the
+# decision tree partial dependence curves in the first row of the
+# multi-layer perceptron in the second row.
+
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))
+tree_disp.plot(ax=ax1)
+ax1.set_title("Decision Tree")
+mlp_disp.plot(ax=ax2, line_kw={"c": "red"})
+ax2.set_title("Multi-layer Perceptron")
+
+##############################################################################
+# Another way to compare the curves is to plot them on top of each other. Here,
+# we create a figure with one row and two columns. The axes are passed into the
+# :func:`~sklearn.inspection.PartialDependenceDisplay.plot` function as a list,
+# which will plot the partial dependence curves of each model on the same axes.
+# The length of the axes list must be equal to the number of plots drawn.
+
+# Sets this image as the thumbnail for sphinx gallery
+# sphinx_gallery_thumbnail_number = 4
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
+tree_disp.plot(ax=[ax1, ax2], line_kw={"label": "Decision Tree"})
+mlp_disp.plot(ax=[ax1, ax2], line_kw={"label": "Multi-layer Perceptron",
+                                      "c": "red"})
+ax1.legend()
+ax2.legend()
+
+##############################################################################
+# `tree_disp.axes_` is a numpy array container the axes used to draw the
+# 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`.
+
+tree_disp.plot(line_kw={"label": "Decision Tree"})
+mlp_disp.plot(line_kw={"label": "Multi-layer Perceptron", "c": "red"},
+              ax=tree_disp.axes_)
+tree_disp.figure_.set_size_inches(10, 6)
+tree_disp.axes_[0, 0].legend()
+tree_disp.axes_[0, 1].legend()
+plt.show()
+
+
+##############################################################################
+# Plotting partial dependence for one feature
+# ===========================================
+#
+# Here, we plot the partial dependence curves for a single feature, "LSTAT", on
+# the same axes. In this case, `tree_disp.axes_` is passed into the second
+# plot function.
+tree_disp = plot_partial_dependence(tree, X, ["LSTAT"],
+                                    feature_names=feature_names)
+mlp_disp = plot_partial_dependence(mlp, X, ["LSTAT"],
+                                   feature_names=feature_names,
+                                   ax=tree_disp.axes_, line_kw={"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.tight_layout(rect=[0, 0.03, 1, 0.95])
+fig.subplots_adjust(wspace=0.8, hspace=0.3)
 
 ##############################################################################
 # Partial Dependence computation for Gradient Boosting
@@ -150,7 +150,8 @@
 fig = plt.gcf()
 fig.suptitle('Partial dependence of house value on non-___location features\n'
              'for the California housing dataset, with Gradient Boosting')
-fig.tight_layout(rect=[0, 0.03, 1, 0.95])
+fig.subplots_adjust(wspace=0.8, hspace=0.3)
+
 
 ##############################################################################
 # Analysis of the plots