Rebuild dev docs at master=8b78293

Author: sklearn-ci

dev/_downloads/plot_classifier_comparison.py

@@ -41,14 +41,13 @@
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
 from sklearn.naive_bayes import GaussianNB
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
 
 h = .02  # step size in the mesh
 
 names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
          "Decision Tree", "Random Forest", "AdaBoost", "Naive Bayes",
-         "Linear Discriminant Analysis", "Quadratic Discriminant Analysis"]
+         "QDA"]
 
 classifiers = [
     KNeighborsClassifier(3),
@@ -59,7 +58,6 @@
     RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
     AdaBoostClassifier(),
     GaussianNB(),
-    LinearDiscriminantAnalysis(),
     QuadraticDiscriminantAnalysis()]
 
 X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
@@ -76,7 +74,7 @@
 figure = plt.figure(figsize=(27, 9))
 i = 1
 # iterate over datasets
-for ds in datasets:
+for ds_cnt, ds in enumerate(datasets):
     # preprocess dataset, split into training and test part
     X, y = ds
     X = StandardScaler().fit_transform(X)
@@ -92,6 +90,8 @@
     cm = plt.cm.RdBu
     cm_bright = ListedColormap(['#FF0000', '#0000FF'])
     ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
+    if ds_cnt == 0:
+        ax.set_title("Input data")
     # Plot the training points
     ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
     # and testing points
@@ -129,7 +129,8 @@
         ax.set_ylim(yy.min(), yy.max())
         ax.set_xticks(())
         ax.set_yticks(())
-        ax.set_title(name)
+        if ds_cnt == 0:
+            ax.set_title(name)
         ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                 size=15, horizontalalignment='right')
         i += 1

dev/_images/plot_classifier_comparison.png

@@ -34,6 +34,6 @@ set.
 .. literalinclude:: plot_classifier_comparison.py
     :lines: 23-
 
-**Total running time of the example:**  11.36 seconds
-( 0 minutes  11.36 seconds)
+**Total running time of the example:**  11.31 seconds
+( 0 minutes  11.31 seconds)
 

dev/_images/plot_classifier_comparison1.png

@@ -266,3 +266,27 @@ features::
 .. include:: covtype.rst
 
 .. include:: rcv1.rst
+
+.. _boston_house_prices
+
+.. include:: ../../sklearn/datasets/descr/boston_house_prices.rst
+
+.. _breast_cancer
+
+.. include:: ../../sklearn/datasets/descr/breast_cancer.rst
+
+.. _diabetes
+
+.. include:: ../../sklearn/datasets/descr/diabetes.rst
+
+.. _digits
+
+.. include:: ../../sklearn/datasets/descr/digits.rst
+
+.. _iris
+
+.. include:: ../../sklearn/datasets/descr/iris.rst
+
+.. _linnerud
+
+.. include:: ../../sklearn/datasets/descr/linnerud.rst

dev/_images/plot_classifier_comparison_001.png

@@ -201,14 +201,13 @@
 <span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier"><span class="n">DecisionTreeClassifier</span></a>
 <span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier"><span class="n">RandomForestClassifier</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier"><span class="n">AdaBoostClassifier</span></a>
 <span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB"><span class="n">GaussianNB</span></a>
-<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><span class="n">LinearDiscriminantAnalysis</span></a>
 <span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><span class="n">QuadraticDiscriminantAnalysis</span></a>
 
 <span class="n">h</span> <span class="o">=</span> <span class="o">.</span><span class="mo">02</span>  <span class="c"># step size in the mesh</span>
 
 <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s">&quot;Nearest Neighbors&quot;</span><span class="p">,</span> <span class="s">&quot;Linear SVM&quot;</span><span class="p">,</span> <span class="s">&quot;RBF SVM&quot;</span><span class="p">,</span> <span class="s">&quot;Gaussian Process&quot;</span><span class="p">,</span>
          <span class="s">&quot;Decision Tree&quot;</span><span class="p">,</span> <span class="s">&quot;Random Forest&quot;</span><span class="p">,</span> <span class="s">&quot;AdaBoost&quot;</span><span class="p">,</span> <span class="s">&quot;Naive Bayes&quot;</span><span class="p">,</span>
-         <span class="s">&quot;Linear Discriminant Analysis&quot;</span><span class="p">,</span> <span class="s">&quot;Quadratic Discriminant Analysis&quot;</span><span class="p">]</span>
+         <span class="s">&quot;QDA&quot;</span><span class="p">]</span>
 
 <span class="n">classifiers</span> <span class="o">=</span> <span class="p">[</span>
     <a href="../../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier"><span class="n">KNeighborsClassifier</span></a><span class="p">(</span><span class="mi">3</span><span class="p">),</span>
@@ -219,7 +218,6 @@
     <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier"><span class="n">RandomForestClassifier</span></a><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
     <a href="../../modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier"><span class="n">AdaBoostClassifier</span></a><span class="p">(),</span>
     <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB"><span class="n">GaussianNB</span></a><span class="p">(),</span>
-    <a href="../../modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><span class="n">LinearDiscriminantAnalysis</span></a><span class="p">(),</span>
     <a href="../../modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><span class="n">QuadraticDiscriminantAnalysis</span></a><span class="p">()]</span>
 
 <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification"><span class="n">make_classification</span></a><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
@@ -236,7 +234,7 @@
 <span class="n">figure</span> <span class="o">=</span> <a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">27</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
 <span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="c"># iterate over datasets</span>
-<span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
     <span class="c"># preprocess dataset, split into training and test part</span>
     <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
     <span class="n">X</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler"><span class="n">StandardScaler</span></a><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
@@ -252,6 +250,8 @@
     <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
     <span class="n">cm_bright</span> <span class="o">=</span> <a href="http://matplotlib.org/api/colors_api.html#matplotlib.colors.ListedColormap"><span class="n">ListedColormap</span></a><span class="p">([</span><span class="s">&#39;#FF0000&#39;</span><span class="p">,</span> <span class="s">&#39;#0000FF&#39;</span><span class="p">])</span>
     <span class="n">ax</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&quot;Input data&quot;</span><span class="p">)</span>
     <span class="c"># Plot the training points</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">)</span>
     <span class="c"># and testing points</span>
@@ -289,7 +289,8 @@
         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="s">&#39;</span><span class="si">%.2f</span><span class="s">&#39;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s">&#39;0&#39;</span><span class="p">),</span>
                 <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s">&#39;right&#39;</span><span class="p">)</span>
         <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -298,8 +299,8 @@
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p><strong>Total running time of the example:</strong>  11.36 seconds
-( 0 minutes  11.36 seconds)</p>
+<p><strong>Total running time of the example:</strong>  11.31 seconds
+( 0 minutes  11.31 seconds)</p>
 </div>
 
 

dev/_images/plot_classifier_comparison_carousel.png

@@ -176,7 +176,7 @@
 (see <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor.feature_importances_" title="sklearn.ensemble.GradientBoostingRegressor.feature_importances_"><tt class="xref py py-attr docutils literal"><span class="pre">feature_importances_</span></tt></a>).</p>
 <p>This example shows how to obtain partial dependence plots from a
 <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><tt class="xref py py-class docutils literal"><span class="pre">GradientBoostingRegressor</span></tt></a> trained on the California
-housing dataset. The example is taken from <a class="reference internal" href="../../modules/ensemble.html#htf2009" id="id2">[HTF2009]</a>.</p>
+housing dataset. The example is taken from <a class="reference internal" href="#htf2009" id="id2">[HTF2009]</a>.</p>
 <p>The plot shows four one-way and one two-way partial dependence plots.
 The target variables for the one-way PDP are:
 median income (<cite>MedInc</cite>), avg. occupants per household (<cite>AvgOccup</cite>),