Rebuild dev docs at master=9564b94

Author: sklearn-ci

dev/stable/auto_examples/applications/topics_extraction_with_nmf.html

@@ -144,8 +144,8 @@
 <span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 <span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
 
-<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a></a></a></a>
-<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a></a></a></a>
+<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a></a></a></a></a>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a></a></a></a></a>
 <span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_20newsgroups</span>
 
 <span class="n">n_samples</span> <span class="o">=</span> <span class="mi">2000</span>
@@ -163,15 +163,15 @@
 <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                              <span class="n">remove</span><span class="o">=</span><span class="p">(</span><span class="s">&#39;headers&#39;</span><span class="p">,</span> <span class="s">&#39;footers&#39;</span><span class="p">,</span> <span class="s">&#39;quotes&#39;</span><span class="p">))</span>
 
-<span class="n">vectorizer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a></a></a></a><span class="p">(</span><span class="n">max_df</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span>
+<span class="n">vectorizer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a></a></a></a></a><span class="p">(</span><span class="n">max_df</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="n">n_features</span><span class="p">,</span>
                              <span class="n">stop_words</span><span class="o">=</span><span class="s">&#39;english&#39;</span><span class="p">)</span>
 <span class="n">tfidf</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="n">n_samples</span><span class="p">])</span>
 <span class="k">print</span><span class="p">(</span><span class="s">&quot;done in </span><span class="si">%0.3f</span><span class="s">s.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
 
 <span class="c"># Fit the NMF model</span>
 <span class="k">print</span><span class="p">(</span><span class="s">&quot;Fitting the NMF model with n_samples=</span><span class="si">%d</span><span class="s"> and n_features=</span><span class="si">%d</span><span class="s">...&quot;</span>
       <span class="o">%</span> <span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">))</span>
-<span class="n">nmf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a></a></a></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_topics</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">tfidf</span><span class="p">)</span>
+<span class="n">nmf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF"><span class="n">NMF</span></a></a></a></a></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_topics</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">tfidf</span><span class="p">)</span>
 <span class="k">print</span><span class="p">(</span><span class="s">&quot;done in </span><span class="si">%0.3f</span><span class="s">s.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
 
 <span class="n">feature_names</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">()</span>

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

@@ -132,7 +132,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></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><code class="descclassname">sklearn.cluster.</code><code class="descname">AgglomerativeClustering</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f95b18&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L607"><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><code class="descclassname">sklearn.cluster.</code><code class="descname">AgglomerativeClustering</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x28baaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L607"><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>
@@ -239,7 +239,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.AgglomerativeClustering.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f95b18&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
+<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x28baaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L690"><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/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html

@@ -132,7 +132,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></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><code class="descclassname">sklearn.cluster.</code><code class="descname">FeatureAgglomeration</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f95b18&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L779"><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><code class="descclassname">sklearn.cluster.</code><code class="descname">FeatureAgglomeration</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x28baaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L779"><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>
@@ -239,7 +239,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.FeatureAgglomeration.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f95b18&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
+<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x28baaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/cluster/hierarchical.py#L690"><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">

dev/stable/modules/generated/sklearn.feature_selection.SelectKBest.html

@@ -132,7 +132,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.feature_selection</span></code></a>.SelectKBest<a class="headerlink" href="#sklearn-feature-selection-selectkbest" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.feature_selection.SelectKBest">
-<em class="property">class </em><code class="descclassname">sklearn.feature_selection.</code><code class="descname">SelectKBest</code><span class="sig-paren">(</span><em>score_func=&lt;function f_classif at 0x68b8848&gt;</em>, <em>k=10</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/feature_selection/univariate_selection.py#L412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">sklearn.feature_selection.</code><code class="descname">SelectKBest</code><span class="sig-paren">(</span><em>score_func=&lt;function f_classif at 0x61dd7d0&gt;</em>, <em>k=10</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/feature_selection/univariate_selection.py#L412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Select features according to the k highest scores.</p>
 <p>Read more in the <a class="reference internal" href="../feature_selection.html#univariate-feature-selection"><span>User Guide</span></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -198,7 +198,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_s
 </table>
 <dl class="method">
 <dt id="sklearn.feature_selection.SelectKBest.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><em>score_func=&lt;function f_classif at 0x68b8848&gt;</em>, <em>k=10</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/feature_selection/univariate_selection.py#L452"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.__init__" title="Permalink to this definition">¶</a></dt>
+<code class="descname">__init__</code><span class="sig-paren">(</span><em>score_func=&lt;function f_classif at 0x61dd7d0&gt;</em>, <em>k=10</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9564b94/sklearn/feature_selection/univariate_selection.py#L452"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">