Rebuild dev docs at master=9fe25df

Author: sklearn-ci

dev/_downloads/plot_robust_fit.py

@@ -82,7 +82,7 @@
 
     legend_title = 'Error of Mean\nAbsolute Deviation\nto Non-corrupt Data'
     legend = plt.legend(loc='upper right', frameon=False, title=legend_title,
-                        fontsize='x-small')
+                        prop=dict(size='x-small'))
     plt.xlim(-4, 10.2)
     plt.ylim(-2, 10.2)
     plt.title(title)

dev/_sources/auto_examples/linear_model/plot_robust_fit.txt

@@ -67,6 +67,6 @@ What we can see that:
 .. literalinclude:: plot_robust_fit.py
     :lines: 28-
 
-**Total running time of the example:**  15.77 seconds
-( 0 minutes  15.77 seconds)
+**Total running time of the example:**  12.23 seconds
+( 0 minutes  12.23 seconds)
 

dev/auto_examples/linear_model/plot_robust_fit.html

@@ -253,15 +253,15 @@
 
     <span class="n">legend_title</span> <span class="o">=</span> <span class="s">&#39;Error of Mean</span><span class="se">\n</span><span class="s">Absolute Deviation</span><span class="se">\n</span><span class="s">to Non-corrupt Data&#39;</span>
     <span class="n">legend</span> <span class="o">=</span> <a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">&#39;upper right&#39;</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">legend_title</span><span class="p">,</span>
-                        <span class="n">fontsize</span><span class="o">=</span><span class="s">&#39;x-small&#39;</span><span class="p">)</span>
+                        <span class="n">prop</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="s">&#39;x-small&#39;</span><span class="p">))</span>
     <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlim"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mf">10.2</span><span class="p">)</span>
     <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylim"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mf">10.2</span><span class="p">)</span>
     <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="n">title</span><span class="p">)</span>
 <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>  15.77 seconds
-( 0 minutes  15.77 seconds)</p>
+<p><strong>Total running time of the example:</strong>  12.23 seconds
+( 0 minutes  12.23 seconds)</p>
 </div>
 
 

dev/modules/generated/sklearn.base.BaseEstimator.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></a>.BaseEstimator<a class="headerlink" href="#sklearn-base-baseestimator" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.BaseEstimator">
-<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all estimators in scikit-learn</p>
 <p class="rubric">Notes</p>
 <p>All estimators should specify all the parameters that can be set
@@ -201,7 +201,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.get_params">
-<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -226,7 +226,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.set_params">
-<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as pipelines). The former have parameters of the form

dev/modules/generated/sklearn.base.TransformerMixin.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></a>.TransformerMixin<a class="headerlink" href="#sklearn-base-transformermixin" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.TransformerMixin">
-<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L435"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L435"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mixin class for all transformers in scikit-learn.</p>
 <p class="rubric">Methods</p>
 <table border="1" class="longtable docutils">
@@ -194,7 +194,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.TransformerMixin.fit_transform">
-<tt class="descname">fit_transform</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>**fit_params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L438"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">fit_transform</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>**fit_params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L438"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to X and y with optional parameters fit_params
 and returns a transformed version of X.</p>

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

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></tt></a>.CalibratedClassifierCV<a class="headerlink" href="#sklearn-calibration-calibratedclassifiercv" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.calibration.CalibratedClassifierCV">
-<em class="property">class </em><tt class="descclassname">sklearn.calibration.</tt><tt class="descname">CalibratedClassifierCV</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L29"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.calibration.</tt><tt class="descname">CalibratedClassifierCV</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L29"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Permalink to this definition">¶</a></dt>
 <dd><p>Probability calibration with isotonic regression or sigmoid.</p>
 <p>With this class, the base_estimator is fit on the train set of the
 cross-validation generator and the test set is used for calibration.
@@ -294,12 +294,12 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </table>
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L98"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L98"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.fit">
-<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L103"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L103"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit the calibrated model</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -331,7 +331,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.get_params">
-<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get parameters for this estimator.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -356,7 +356,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict">
-<tt class="descname">predict</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L216"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">predict</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L216"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Predict the target of new samples. Can be different from the
 prediction of the uncalibrated classifier.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -381,7 +381,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict_proba">
-<tt class="descname">predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L186"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L186"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Permalink to this definition">¶</a></dt>
 <dd><p>Posterior probabilities of classification</p>
 <p>This function returns posterior probabilities of classification
 according to each class on an array of test vectors X.</p>
@@ -407,7 +407,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.score">
-<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L292"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L292"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the mean accuracy on the given test data and labels.</p>
 <p>In multi-label classification, this is the subset accuracy
 which is a harsh metric since you require for each sample that
@@ -442,7 +442,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.set_params">
-<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the parameters of this estimator.</p>
 <p>The method works on simple estimators as well as on nested objects
 (such as pipelines). The former have parameters of the form

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

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></tt></a>.calibration_curve<a class="headerlink" href="#sklearn-calibration-calibration-curve" title="Permalink to this headline">¶</a></h1>
 <dl class="function">
 <dt id="sklearn.calibration.calibration_curve">
-<tt class="descclassname">sklearn.calibration.</tt><tt class="descname">calibration_curve</tt><big>(</big><em>y_true</em>, <em>y_prob</em>, <em>normalize=False</em>, <em>n_bins=5</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/d9f3277/sklearn/calibration.py#L491"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Permalink to this definition">¶</a></dt>
+<tt class="descclassname">sklearn.calibration.</tt><tt class="descname">calibration_curve</tt><big>(</big><em>y_true</em>, <em>y_prob</em>, <em>normalize=False</em>, <em>n_bins=5</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/9fe25df/sklearn/calibration.py#L491"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute true and predicted probabilities for a calibration curve.</p>
 <p>Read more in the <a class="reference internal" href="../calibration.html#calibration"><em>User Guide</em></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">