Rebuild dev docs at master=53bc02a

Author: sklearn-ci

dev/_sources/auto_examples/ensemble/plot_bias_variance.txt

@@ -83,6 +83,6 @@ References
 .. literalinclude:: plot_bias_variance.py
     :lines: 64-
 
-**Total running time of the example:**  2.27 seconds
-( 0 minutes  2.27 seconds)
+**Total running time of the example:**  2.33 seconds
+( 0 minutes  2.33 seconds)
 

dev/_sources/auto_examples/ensemble/plot_forest_iris.txt

@@ -70,6 +70,6 @@ samples are built sequentially and so do not use multiple cores.
 .. literalinclude:: plot_forest_iris.py
     :lines: 41-
 
-**Total running time of the example:**  12.31 seconds
-( 0 minutes  12.31 seconds)
+**Total running time of the example:**  12.08 seconds
+( 0 minutes  12.08 seconds)
 

dev/_sources/auto_examples/preprocessing/plot_function_transformer.txt

@@ -25,6 +25,6 @@ PCA transformed data.
 .. literalinclude:: plot_function_transformer.py
     :lines: 11-
 
-**Total running time of the example:**  0.00 seconds
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+**Total running time of the example:**  0.01 seconds
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dev/auto_examples/ensemble/plot_bias_variance.html

@@ -354,8 +354,8 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
 <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>  2.27 seconds
-( 0 minutes  2.27 seconds)</p>
+<p><strong>Total running time of the example:</strong>  2.33 seconds
+( 0 minutes  2.33 seconds)</p>
 </div>
 </div>
 

dev/auto_examples/ensemble/plot_forest_iris.html

@@ -327,8 +327,8 @@
 <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>  12.31 seconds
-( 0 minutes  12.31 seconds)</p>
+<p><strong>Total running time of the example:</strong>  12.08 seconds
+( 0 minutes  12.08 seconds)</p>
 </div>
 
 

dev/auto_examples/ensemble/plot_partial_dependence.html

@@ -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>),

dev/auto_examples/preprocessing/plot_function_transformer.html

@@ -236,8 +236,8 @@
     <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>  0.00 seconds
-( 0 minutes  0.00 seconds)</p>
+<p><strong>Total running time of the example:</strong>  0.01 seconds
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 </div>
 
 

dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html

@@ -237,31 +237,31 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </tbody>
 </table>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r108" rules="none">
+<table class="docutils citation" frame="void" id="r1" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R108]</a></td><td>Obtaining calibrated probability estimates from decision trees
+<tr><td class="label"><a class="fn-backref" href="#id1">[R1]</a></td><td>Obtaining calibrated probability estimates from decision trees
 and naive Bayesian classifiers, B. Zadrozny &amp; C. Elkan, ICML 2001</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r109" rules="none">
+<table class="docutils citation" frame="void" id="r2" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id2">[R109]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
+<tr><td class="label"><a class="fn-backref" href="#id2">[R2]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
 Probability Estimates, B. Zadrozny &amp; C. Elkan, (KDD 2002)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r110" rules="none">
+<table class="docutils citation" frame="void" id="r3" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id3">[R110]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
+<tr><td class="label"><a class="fn-backref" href="#id3">[R3]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
 Regularized Likelihood Methods, J. Platt, (1999)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r111" rules="none">
+<table class="docutils citation" frame="void" id="r4" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id4">[R111]</a></td><td>Predicting Good Probabilities with Supervised Learning,
+<tr><td class="label"><a class="fn-backref" href="#id4">[R4]</a></td><td>Predicting Good Probabilities with Supervised Learning,
 A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</td></tr>
 </tbody>
 </table>

dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.AgglomerativeClustering<a class="headerlink" href="#sklearn-cluster-agglomerativeclustering" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.AgglomerativeClustering">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x20281b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L583"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x19331b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L583"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerative Clustering</p>
 <p>Recursively merges the pair of clusters that minimally increases
 a given linkage distance.</p>
@@ -284,7 +284,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.AgglomerativeClustering.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x20281b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x19331b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.FeatureAgglomeration<a class="headerlink" href="#sklearn-cluster-featureagglomeration" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.FeatureAgglomeration">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x20281b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L746"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x19331b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L746"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerate features.</p>
 <p>Similar to AgglomerativeClustering, but recursively merges features
 instead of samples.</p>
@@ -293,7 +293,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.FeatureAgglomeration.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x20281b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x19331b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/53bc02a/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">