Pushing the docs to dev/ for branch: master, commit 76de8833195e8f4ebeaefd65e29e9b429fa2a4e7

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,14 +26,14 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport pandas as pd\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"
+        "print(__doc__)\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\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"
+        "Train models on the diabetes dataset\n================================================\n\nFirst, we train a decision tree and a multi-layer perceptron on the diabetes\ndataset.\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "boston = load_boston()\nX = pd.DataFrame(boston.data, columns=boston.feature_names)\ny = boston.target\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)"
+        "diabetes = load_diabetes()\nX = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)\ny = diabetes.target\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"
+        "Plotting partial dependence for two features\n============================================\n\nWe plot partial dependence curves for features \"age\" and \"bmi\" (body mass\nindex) for the 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"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Decision Tree\")\ntree_disp = plot_partial_dependence(tree, X, [\"LSTAT\", \"RM\"], ax=ax)"
+        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Decision Tree\")\ntree_disp = plot_partial_dependence(tree, X, [\"age\", \"bmi\"], ax=ax)"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Multi-layer Perceptron\")\nmlp_disp = plot_partial_dependence(mlp, X, [\"LSTAT\", \"RM\"], ax=ax,\n                                   line_kw={\"c\": \"red\"})"
+        "fig, ax = plt.subplots(figsize=(12, 6))\nax.set_title(\"Multi-layer Perceptron\")\nmlp_disp = plot_partial_dependence(mlp, X, [\"age\", \"bmi\"], ax=ax,\n                                   line_kw={\"c\": \"red\"})"
       ]
     },
     {
@@ -141,7 +141,7 @@
       "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"
+        "Plotting partial dependence for one feature\n===========================================\n\nHere, we plot the partial dependence curves for a single feature, \"age\", on\nthe same axes. In this case, `tree_disp.axes_` is passed into the second\nplot function.\n\n"
       ]
     },
     {
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "tree_disp = plot_partial_dependence(tree, X, [\"LSTAT\"])\nmlp_disp = plot_partial_dependence(mlp, X, [\"LSTAT\"],\n                                   ax=tree_disp.axes_, line_kw={\"c\": \"red\"})"
+        "tree_disp = plot_partial_dependence(tree, X, [\"age\"])\nmlp_disp = plot_partial_dependence(mlp, X, [\"age\"],\n                                   ax=tree_disp.axes_, line_kw={\"c\": \"red\"})"
       ]
     }
   ],

dev/_downloads/51a82a09a4aa0f703f69fb5d4f15104f/plot_partial_dependence_visualization_api.ipynb

@@ -17,7 +17,7 @@
 
 import pandas as pd
 import matplotlib.pyplot as plt
-from sklearn.datasets import load_boston
+from sklearn.datasets import load_diabetes
 from sklearn.neural_network import MLPRegressor
 from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import make_pipeline
@@ -26,15 +26,15 @@
 
 
 ##############################################################################
-# Train models on the boston housing price dataset
+# Train models on the diabetes dataset
 # ================================================
 #
-# First, we train a decision tree and a multi-layer perceptron on the boston
-# housing price dataset.
+# First, we train a decision tree and a multi-layer perceptron on the diabetes
+# dataset.
 
-boston = load_boston()
-X = pd.DataFrame(boston.data, columns=boston.feature_names)
-y = boston.target
+diabetes = load_diabetes()
+X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
+y = diabetes.target
 
 tree = DecisionTreeRegressor()
 mlp = make_pipeline(StandardScaler(),
@@ -43,19 +43,18 @@
 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,
+# We plot partial dependence curves for features "age" and "bmi" (body mass
+# index) 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"], ax=ax)
+tree_disp = plot_partial_dependence(tree, X, ["age", "bmi"], ax=ax)
 
 ##############################################################################
 # The partial depdendence curves can be plotted for the multi-layer perceptron.
@@ -64,7 +63,7 @@
 # the curve.
 fig, ax = plt.subplots(figsize=(12, 6))
 ax.set_title("Multi-layer Perceptron")
-mlp_disp = plot_partial_dependence(mlp, X, ["LSTAT", "RM"], ax=ax,
+mlp_disp = plot_partial_dependence(mlp, X, ["age", "bmi"], ax=ax,
                                    line_kw={"c": "red"})
 
 ##############################################################################
@@ -124,14 +123,13 @@
 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
+# Here, we plot the partial dependence curves for a single feature, "age", 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"])
-mlp_disp = plot_partial_dependence(mlp, X, ["LSTAT"],
+tree_disp = plot_partial_dependence(tree, X, ["age"])
+mlp_disp = plot_partial_dependence(mlp, X, ["age"],
                                    ax=tree_disp.axes_, line_kw={"c": "red"})