Rebuild dev docs at master=4bd1286

Author: sklearn-ci

dev/_sources/modules/preprocessing.txt

@@ -411,6 +411,18 @@ Then we fit the estimator, and transform a data point.
 In the result, the first two numbers encode the gender, the next set of three
 numbers the continent and the last four the web browser.
 
+Note that, if there is a possibilty that the training data might have missing categorical
+features, one has to explicitly set ``n_values``. For example,
+
+    >>> enc = preprocessing.OneHotEncoder(n_values=[2, 3, 4])
+    >>> # Note that for there are missing categorical values for the 2nd and 3rd
+    >>> # feature
+    >>> enc.fit([[1, 2, 3], [0, 2, 0]])  # doctest: +ELLIPSIS
+    OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
+           handle_unknown='error', n_values=[2, 3, 4], sparse=True)
+    >>> enc.transform([[1, 0, 0]]).toarray()
+    array([[ 0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.]])
+
 See :ref:`dict_feature_extraction` for categorical features that are represented
 as a dict, not as integers.
 

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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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/ca88f91/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/4bd1286/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">