Rebuild dev docs at master=36acda2

Author: sklearn-ci

dev/_sources/whats_new.txt

@@ -170,9 +170,11 @@ Enhancements
    - Added a ``fit_predict`` method for :class:`mixture.GMM` and subclasses.
      By `Cory Lorenz`_.
 
-   - Added the :func:`metrics.label_ranking_loss` metrics.
+   - Added the :func:`metrics.label_ranking_loss` metric.
      By `Arnaud Joly`_.
 
+   - Added the :func:`metrics.cohen_kappa_score` metric.
+
    - Added a ``warm_start`` constructor parameter to the bagging ensemble
      models to increase the size of the ensemble. By
      `Tim Head`_.
@@ -227,6 +229,10 @@ Enhancements
    - RCV1 dataset loader (:func:`sklearn.datasets.fetch_rcv1`).
      By `Tom Dupre la Tour`_.
 
+   - The "Wisconsin Breast Cancer" classical two-class classification dataset
+     is now included in scikit-learn, available with
+     :fun:`sklearn.dataset.load_breast_cancer`.
+
    - Upgraded to joblib 0.9.3 to benefit from the new automatic batching of
      short tasks. This makes it possible for scikit-learn to benefit from
      parallelism when many very short tasks are executed in parallel, for
@@ -306,6 +312,27 @@ Enhancements
 
    - Added :func:`metrics.pairwise.laplacian_kernel`.  By `Clyde Fare <https://github.com/Clyde-fare>`_.
 
+   - :class:`covariance.GraphLasso` allows separate control of the convergence criterion
+     for the Elastic-Net subproblem via  the ``enet_tol`` parameter.
+
+   - Improved verbosity in :class:`decomposition.DictinaryLearning`.
+
+   - :class:`ensemble.RandomForestClassifier` and
+     :class:`ensemble.RandomForestRegressor` no longer explicitly store the
+     samples used in bagging, resulting in a much reduced memory footprint for
+     storing random forest models.
+
+   - Added ``positive`` option to :class:`linear_model.Lars` and
+     :func:`linear_model.lars_path` to force coefficients to be positive.
+     (`#5131 <https://github.com/scikit-learn/scikit-learn/pull/5131>`)
+
+   - Added the ``X_norm_squared`` parameter to :func:`metrics.pairwise.euclidean_distances`
+     to provide precomputed squared norms for ``X``.
+
+   - Added the ``fit_predict`` method to :class:`pipeline.Pipeline`.
+
+   - Added the :func:`preprocessing.min_max_scale` function.
+
 Bug fixes
 .........
     - Fixed non-determinism in :class:`dummy.DummyClassifier` with sparse
@@ -368,6 +395,33 @@ Bug fixes
       platform dependent output, and failed on `fit_transform`.
       By `Arthur Mensch`_.
 
+    - Fixes to the ``Bunch`` class used to store datasets.
+
+    - Fixed :func:`ensemble.plot_partial_dependence` ignoring the
+      ``percentiles`` parameter.
+
+    - Providing a ``dict`` as vocabulary in ``CountVectorizer`` no longer
+      leads to inconsistent results when pickling.
+
+    - Fixed the conditions on when a precomputed Gram matrix needs to
+      be recomputed in :class:`linear_model.LinearRegression`,
+      :class:`linear_model.OrthogonalMatchingPursuit`,
+      :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet`.
+
+    - Fixed inconsistent memory layout in the coordinate descent solver
+      that affected :class:`linear_model.DictionaryLearning` and
+      :class:`covariance.GraphLasso`. (`#5337 <https://github.com/scikit-learn/scikit-learn/pull/5337>`)
+      By `Oliver Grisel`_.
+
+    - :class:`manifold.LocallyLinearEmbedding` no longer ignores the ``reg``
+      parameter.
+
+    - Nearest Neighbor estimators with custom distance metrics can now be pickled.
+      (`4362 <https://github.com/scikit-learn/scikit-learn/pull/4362>`)
+
+    - Fixed a bug in :class:`pipeline.FeatureUnion` where ``transformer_weights``
+      were not properly handled when performing grid-searches.
+
     - Fixed a bug in :class:`linear_model.LogisticRegression` and
       :class:`linear_model.LogisticRegressionCV` when using
       ``class_weight='balanced'```or ``class_weight='auto'``.
@@ -414,6 +468,54 @@ API changes summary
       features (according to `coefs_` or `feature_importances_`)
       which are below a certain threshold value instead.
 
+    - :class:`cluster.KMeans` re-runs cluster-assignments in case of non-convergence,
+      to ensure consistency of ``predict(X)`` and ``labels_``. By `Vighnesh Birodkar`_.
+
+    - Classifier and Regressor models are now tagged as such using the
+      ``_estimator_type`` attribute.
+
+    - Cross-validation iterators allways provide indices into training and test set,
+      not boolean masks.
+
+    - The ``decision_function`` on all regressors was deprecated and will be
+      removed in 0.19.  Use ``predict`` instead.
+
+    - :func:`datasets.load_lfw_pairs` is deprecated and will be removed in 0.19.
+      Use :func:`datasets.fetch_lfw_pairs` instead.
+
+    - The deprecated ``hmm`` module was removed.
+
+    - The deprecated ``Bootstrap`` cross-validation iterator was removed.
+
+    - The deprecated ``Ward`` and ``WardAgglomerative`` classes have been removed.
+      Use :class:`clustering.AgglomerativeClustering` instead.
+
+    - :func:`cross_validation.check_cv` is now a public function.
+
+    - The property ``residues_`` of :class:`linear_model.LinearRegression` is deprecated
+      and will be removed in 0.19.
+
+    - The deprecated ``n_jobs`` parameter of :class:`linear_model.LinearRegression` has been moved
+      to the constructor.
+
+    - Removed deprecated ``class_weight`` parameter from :class:`linear_model.SGDClassifier`'s ``fit``
+      method. Use the construction parameter instead.
+
+   - The deprecated support for the sequence of sequences (or list of lists) multilabel
+     format was removed. To convert to and from the supported binary
+     indicator matrix format, use
+     :class:`MultiLabelBinarizer <preprocessing.MultiLabelBinarizer>`.
+
+   - The behavior of calling the ``inverse_transform`` method of ``Pipeline.pipeline`` will
+     change in 0.19. It will no longer reshape one-dimensional input to two-dimensional input.
+
+   - The deprecated attributes ``indicator_matrix_``, ``multilabel_`` and ``classes_`` of
+     :class:`preprocessing.LabelBinarizer` were removed.
+
+   - Using ``gamma=0`` in :class:`svm.SVC` and :class:`svm.SVR` to automatically set the
+     gamma to ``1. / n_features`` is deprecated and will be removed in 0.19.
+     Use ``gamma="auto"`` instead.
+
 .. _changes_0_1_16:
 
 Version 0.16.1

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/22fc441/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/36acda2/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/22fc441/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/36acda2/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/22fc441/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/36acda2/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/22fc441/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/36acda2/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/22fc441/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/36acda2/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>